topic
stringlengths 3
96
| wiki
stringlengths 33
127
| url
stringlengths 101
106
| action
stringclasses 7
values | sent
stringlengths 34
223
| annotation
stringlengths 74
227
| logic
stringlengths 207
5.45k
| logic_str
stringlengths 37
493
| interpret
stringlengths 43
471
| num_func
stringclasses 15
values | nid
stringclasses 13
values | g_ids
stringlengths 70
455
| g_ids_features
stringlengths 98
670
| g_adj
stringlengths 79
515
| table_header
stringlengths 40
458
| table_cont
large_stringlengths 135
4.41k
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
bolt thrust | https://en.wikipedia.org/wiki/Bolt_thrust | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-26967904-1.html.csv | superlative | the .454 casull chambering cartridge for revolvers has the highest amount of f_bolt in kgf . | {'scope': 'all', 'col_superlative': '5', 'row_superlative': '8', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'f bolt ( kgf )'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; f bolt ( kgf ) }'}, 'chambering'], 'result': '.454 casull', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; f bolt ( kgf ) } ; chambering }'}, '.454 casull'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; f bolt ( kgf ) } ; chambering } ; .454 casull } = true', 'tointer': 'select the row whose f bolt ( kgf ) record of all rows is maximum . the chambering record of this row is .454 casull .'} | eq { hop { argmax { all_rows ; f bolt ( kgf ) } ; chambering } ; .454 casull } = true | select the row whose f bolt ( kgf ) record of all rows is maximum . the chambering record of this row is .454 casull . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'f bolt ( kgf )_5': 5, 'chambering_6': 6, '.454 casull_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'f bolt ( kgf )_5': 'f bolt ( kgf )', 'chambering_6': 'chambering', '.454 casull_7': '.454 casull'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'f bolt ( kgf )_5': [0], 'chambering_6': [1], '.454 casull_7': [2]} | ['chambering', 'p1 diameter ( mm )', 'a external ( cm 2 )', 'p max ( bar )', 'f bolt ( kgf )', 'f bolt'] | [['.22 long rifle', '5.74', '0.2587', '1650', '435', 'n ( lbf )'], ['9x19 mm parabellum', '9.93', '0.7744', '2350', '1820', 'n ( lbf )'], ['.357 sig', '10.77', '0.9110', '3050', '2779', 'n ( lbf )'], ['.380 acp', '9.70', '0.7390', '1500', '1130', 'n ( lbf )'], ['.40 s & w', '10.77', '0.9110', '2250', '2050', 'n ( lbf )'], ['10 mm auto', '10.81', '0.9178', '2300', '2111', 'n ( lbf )'], ['.45 acp', '12.09', '1.1671', '1300', '1517', 'n ( lbf )'], ['.454 casull', '12.13', '1.1556', '3900', '4507', 'n ( lbf )']] |
1962 denver broncos season | https://en.wikipedia.org/wiki/1962_Denver_Broncos_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17778417-1.html.csv | count | there were three games in total where the attendance was over 30000 fans . | {'scope': 'all', 'criterion': 'greater_than', 'value': '30000', 'result': '3', 'col': '7', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'attendance', '30000'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose attendance record is greater than 30000 .', 'tostr': 'filter_greater { all_rows ; attendance ; 30000 }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_greater { all_rows ; attendance ; 30000 } }', 'tointer': 'select the rows whose attendance record is greater than 30000 . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_greater { all_rows ; attendance ; 30000 } } ; 3 } = true', 'tointer': 'select the rows whose attendance record is greater than 30000 . the number of such rows is 3 .'} | eq { count { filter_greater { all_rows ; attendance ; 30000 } } ; 3 } = true | select the rows whose attendance record is greater than 30000 . the number of such rows is 3 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_greater_0': 0, 'all_rows_4': 4, 'attendance_5': 5, '30000_6': 6, '3_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_greater_0': 'filter_greater', 'all_rows_4': 'all_rows', 'attendance_5': 'attendance', '30000_6': '30000', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_greater_0': [1], 'all_rows_4': [0], 'attendance_5': [0], '30000_6': [0], '3_7': [2]} | ['week', 'date', 'opponent', 'result', 'game site', 'record', 'attendance'] | [['1', 'september 7 , 1962', 'san diego chargers', 'w 30 - 21', 'university of denver stadium', '1 - 0', '28000'], ['2', 'september 15 , 1962', 'buffalo bills', 'w 23 - 20', 'war memorial stadium', '2 - 0', '30577'], ['3', 'september 21 , 1962', 'boston patriots', 'l 16 - 41', 'boston university field', '2 - 1', '21038'], ['4', 'september 30 , 1962', 'new york titans', 'w 32 - 10', 'polo grounds', '3 - 1', '17213'], ['5', 'october 5 , 1962', 'oakland raiders', 'w 44 - 7', 'bears stadium', '4 - 1', '22452'], ['6', 'october 14 , 1962', 'oakland raiders', 'w 23 - 6', 'frank youell field', '5 - 1', '7000'], ['7', 'october 21 , 1962', 'houston oilers', 'w 20 - 10', 'bears stadium', '6 - 1', '34496'], ['8', 'october 28 , 1962', 'buffalo bills', 'l 35 - 48', 'bears stadium', '6 - 2', '26051'], ['9', 'november 4 , 1962', 'san diego chargers', 'w 23 - 20', 'balboa stadium', '7 - 2', '20827'], ['10', 'november 11 , 1962', 'boston patriots', 'l 29 - 33', 'bears stadium', '7 - 3', '28187'], ['11', 'november 18 , 1962', 'dallas texans', 'l 29 - 33', 'bears stadium', '7 - 4', '23523'], ['12', 'november 22 , 1962', 'new york titans', 'l 45 - 46', 'bears stadium', '7 - 5', '15776'], ['13', 'december 2 , 1962', 'houston oilers', 'l 17 - 34', 'jeppesen stadium', '7 - 6', '30650'], ['14', 'december 9 , 1962', 'dallas texans', 'l 10 - 17', 'cotton bowl', '7 - 7', '19137']] |
pasquale di sabatino | https://en.wikipedia.org/wiki/Pasquale_Di_Sabatino | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17755575-1.html.csv | ordinal | in pasquale di sabatino 3rd season with tomcat racing he competed in 12 races . | {'scope': 'subset', 'row': '3', 'col': '1', 'order': '3', 'col_other': '4', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'subset': {'col': '3', 'criterion': 'equal', 'value': 'tomcat racing'}} | {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'nth_argmin', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'team', 'tomcat racing'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; team ; tomcat racing }', 'tointer': 'select the rows whose team record fuzzily matches to tomcat racing .'}, 'season', '3'], 'result': None, 'ind': 1, 'tostr': 'nth_argmin { filter_eq { all_rows ; team ; tomcat racing } ; season ; 3 }'}, 'races'], 'result': '12', 'ind': 2, 'tostr': 'hop { nth_argmin { filter_eq { all_rows ; team ; tomcat racing } ; season ; 3 } ; races }'}, '12'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { nth_argmin { filter_eq { all_rows ; team ; tomcat racing } ; season ; 3 } ; races } ; 12 } = true', 'tointer': 'select the rows whose team record fuzzily matches to tomcat racing . select the row whose season record of these rows is 3rd minimum . the races record of this row is 12 .'} | eq { hop { nth_argmin { filter_eq { all_rows ; team ; tomcat racing } ; season ; 3 } ; races } ; 12 } = true | select the rows whose team record fuzzily matches to tomcat racing . select the row whose season record of these rows is 3rd minimum . the races record of this row is 12 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'num_hop_2': 2, 'nth_argmin_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'team_6': 6, 'tomcat racing_7': 7, 'season_8': 8, '3_9': 9, 'races_10': 10, '12_11': 11} | {'eq_3': 'eq', 'result_4': 'true', 'num_hop_2': 'num_hop', 'nth_argmin_1': 'nth_argmin', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'team_6': 'team', 'tomcat racing_7': 'tomcat racing', 'season_8': 'season', '3_9': '3', 'races_10': 'races', '12_11': '12'} | {'eq_3': [4], 'result_4': [], 'num_hop_2': [3], 'nth_argmin_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'team_6': [0], 'tomcat racing_7': [0], 'season_8': [1], '3_9': [1], 'races_10': [2], '12_11': [3]} | ['season', 'series', 'team', 'races', 'wins', 'poles', 'podiums', 'points', 'position'] | [['2005', 'italian formula junior 1600', 'tomcat racing', '12', '5', '3', '10', '268', '1st'], ['2005', 'italian formula renault 2.0 winter series', 'tomcat racing', '4', '0', '0', '1', '12', '10th'], ['2006', 'italian formula renault 2.0', 'tomcat racing', '12', '0', '0', '0', '0', '36th'], ['2006', 'formula renault 3.5 series', 'cram competition', '10', '0', '0', '0', '0', '39th'], ['2007', 'formula renault 3.5 series', 'gd racing', '17', '0', '0', '0', '2', '26th'], ['2008', 'formula renault 3.5 series', 'comtec racing', '13', '0', '0', '1', '16', '20th'], ['2009', 'formula renault 3.5 series', 'rc motorsport', '15', '1', '0', '2', '39', '12th'], ['2010', 'italian formula three championship', 'alan racing', '4', '0', '0', '0', '0', '30th'], ['2010', 'italian formula three championship', 'rc motorsport', '4', '0', '0', '0', '0', '30th'], ['2011', 'auto gp', 'ombra racing', '14', '0', '0', '0', '38', '12th'], ['2012', 'world touring car championship', 'bamboo - engineering', '14', '0', '0', '0', '0', 'nc']] |
sports in charlotte , north carolina | https://en.wikipedia.org/wiki/Sports_in_Charlotte%2C_North_Carolina | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15720079-4.html.csv | count | four of the venues in charlotte , north carolina were built prior to 1960 . | {'scope': 'all', 'criterion': 'less_than', 'value': '1960', 'result': '4', 'col': '6', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'year built', '1960'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose year built record is less than 1960 .', 'tostr': 'filter_less { all_rows ; year built ; 1960 }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_less { all_rows ; year built ; 1960 } }', 'tointer': 'select the rows whose year built record is less than 1960 . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_less { all_rows ; year built ; 1960 } } ; 4 } = true', 'tointer': 'select the rows whose year built record is less than 1960 . the number of such rows is 4 .'} | eq { count { filter_less { all_rows ; year built ; 1960 } } ; 4 } = true | select the rows whose year built record is less than 1960 . the number of such rows is 4 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_less_0': 0, 'all_rows_4': 4, 'year built_5': 5, '1960_6': 6, '4_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_less_0': 'filter_less', 'all_rows_4': 'all_rows', 'year built_5': 'year built', '1960_6': '1960', '4_7': '4'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_less_0': [1], 'all_rows_4': [0], 'year built_5': [0], '1960_6': [0], '4_7': [2]} | ['venue', 'location', 'capacity', 'owner', 'environment', 'year built'] | [['bank of america stadium', 'uptown charlotte', '73778', 'carolina panthers', 'open air , natural grass', '1996'], ['time warner cable arena', 'uptown charlotte', '20200', 'city of charlotte', 'indoor arena', '2005'], ['american legion memorial stadium', 'elizabeth , charlotte', '16000', 'mecklenburg parks & rec', 'open air , natural grass', '1936'], ["bojangles ' coliseum", 'coliseum drive , charlotte', '9605', 'city of charlotte', 'indoor arena', '1955'], ['jerry richardson stadium', 'university city , charlotte', '15314', 'unc charlotte', 'open air , artificial turf', '2012'], ['charlotte motor speedway', 'concord , nc', '140000 +', 'speedway motorsports', 'open air , asphalt', '1960'], ['dale f halton arena', 'university city , charlotte', '9105', 'unc charlotte', 'indoor arena', '1996'], ['john m belk arena', 'davidson , nc', '5223', 'davidson college', 'indoor arena', '1989'], ['transamerica field', 'university city , charlotte', '4000', 'unc charlotte', 'open air , natural grass', '1996'], ['richardson stadium', 'davidson , nc', '6000', 'davidson college', 'open air , artificial turf', '1923'], ['irwin belk complex', 'biddleville , charlotte', '4500', 'johnson c smith university', 'open air , natural grass', '2003'], ['winthrop coliseum', 'rock hill , sc', '6100', 'winthrop university', 'indoor arena', '1982'], ['knights stadium', 'fort mill , sc', '10002', 'york county , sc', 'open air , natural grass', '1990'], ['concord speedway', 'midland , nc', '8000', 'concord speedway', 'open air , asphalt', '1956']] |
list of world records in canoeing | https://en.wikipedia.org/wiki/List_of_world_records_in_canoeing | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14884844-1.html.csv | ordinal | the second fastest 200 meter distance record in canoeing is held by a russian . | {'row': '2', 'col': '3', 'order': '2', 'col_other': '4', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'record', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; record ; 2 }'}, 'nationality'], 'result': 'russia', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; record ; 2 } ; nationality }'}, 'russia'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; record ; 2 } ; nationality } ; russia } = true', 'tointer': 'select the row whose record record of all rows is 2nd minimum . the nationality record of this row is russia .'} | eq { hop { nth_argmin { all_rows ; record ; 2 } ; nationality } ; russia } = true | select the row whose record record of all rows is 2nd minimum . the nationality record of this row is russia . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'record_5': 5, '2_6': 6, 'nationality_7': 7, 'russia_8': 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmin_0': 'nth_argmin', 'all_rows_4': 'all_rows', 'record_5': 'record', '2_6': '2', 'nationality_7': 'nationality', 'russia_8': 'russia'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'record_5': [0], '2_6': [0], 'nationality_7': [1], 'russia_8': [2]} | ['distance', 'event', 'record', 'nationality', 'year', 'location'] | [['200 m', 'k1', '33.8 s', 'canada', '2012', 'montreal , canada'], ['200 m', 'k2', '30.962 s', 'russia', '2012', 'duisburg , germany'], ['200 m', 'k4', '29.023 s', 'hungary', '1997', 'plovdiv , bulgaria'], ['500 m', 'k1', '1:35.554 s', 'canada', '2008', 'beijing , china'], ['500 m', 'k2', '1:26.873 s', 'belarus', '2008', 'poznan , poland'], ['500 m', 'k4', '1:19.650 s', 'slovakia', '2002', 'szeged , hungary'], ['1000 m', 'k1', '3:22.485 s', 'germany', '2011', 'belgrade , serbia'], ['1000 m', 'k2', '3:09.190 s', 'italy', '1996', 'atlanta , usa'], ['1000 m', 'k4', '2:47.734 s', 'germany', '2011', 'szeged , hungary']] |
list of soccer clubs in australia | https://en.wikipedia.org/wiki/List_of_soccer_clubs_in_Australia | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1742186-16.html.csv | superlative | the earliest founded soccer club in australia is the northern demon club . | {'scope': 'all', 'col_superlative': '5', 'row_superlative': '4', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '1', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'founded'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; founded }'}, 'team'], 'result': 'northern demons', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; founded } ; team }'}, 'northern demons'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; founded } ; team } ; northern demons } = true', 'tointer': 'select the row whose founded record of all rows is minimum . the team record of this row is northern demons .'} | eq { hop { argmin { all_rows ; founded } ; team } ; northern demons } = true | select the row whose founded record of all rows is minimum . the team record of this row is northern demons . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'founded_5': 5, 'team_6': 6, 'northern demons_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'founded_5': 'founded', 'team_6': 'team', 'northern demons_7': 'northern demons'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'founded_5': [0], 'team_6': [1], 'northern demons_7': [2]} | ['team', 'coach', 'home ground', 'location', 'founded'] | [['the cove sc', 'danny graystone', 'club cove', 'hallett cove', '1983'], ['gawler', 'john duthie', 'karbeethan reserve', 'evanston', '1978'], ['nab', 'unknown', 'athelstone recreation reserve', 'athelstone', '1989'], ['northern demons', 'anthony brevi', 'byrne park', 'port pirie', '1951'], ['salisbury united', 'unknown', 'steve jarvis park', 'salisbury', '1954'], ['seaford', 'ben dale', 'karingal reserve', 'seaford', '1970'], ['south adelaide', 'aldo maricic', "o ' sullivan beach sports complex", "o ' sullivan beach", '1997'], ['sturt lions fc', 'alan paice', 'a a bailey recreation ground', 'clarence gardens', '2011'], ['western toros', 'leigh mathews', 'pennington oval', 'pennington', 'unknown'], ['west adelaide', 'ross aloisi', 'kingston gardens', 'adelaide', '1962']] |
ireland in the eurovision song contest 1990 | https://en.wikipedia.org/wiki/Ireland_in_the_Eurovision_Song_Contest_1990 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16729685-1.html.csv | comparative | ann breen 's " oh , darling " scored 14 more points than fran meen 's " say that you love me " in the 1990 eurovision song contest . | {'row_1': '2', 'row_2': '3', 'col': '4', 'col_other': '2,3', 'relation': 'diff', 'record_mentioned': 'no', 'diff_result': {'diff_value': '14', 'bigger': 'row1'}} | {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'diff', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'artist', 'ann breen'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose artist record fuzzily matches to ann breen .', 'tostr': 'filter_eq { all_rows ; artist ; ann breen }'}, 'points'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; artist ; ann breen } ; points }', 'tointer': 'select the rows whose artist record fuzzily matches to ann breen . take the points record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'artist', 'fran meen'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose artist record fuzzily matches to fran meen .', 'tostr': 'filter_eq { all_rows ; artist ; fran meen }'}, 'points'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; artist ; fran meen } ; points }', 'tointer': 'select the rows whose artist record fuzzily matches to fran meen . take the points record of this row .'}], 'result': '14', 'ind': 4, 'tostr': 'diff { hop { filter_eq { all_rows ; artist ; ann breen } ; points } ; hop { filter_eq { all_rows ; artist ; fran meen } ; points } }'}, '14'], 'result': True, 'ind': 5, 'tostr': 'eq { diff { hop { filter_eq { all_rows ; artist ; ann breen } ; points } ; hop { filter_eq { all_rows ; artist ; fran meen } ; points } } ; 14 }', 'tointer': 'select the rows whose artist record fuzzily matches to ann breen . take the points record of this row . select the rows whose artist record fuzzily matches to fran meen . take the points record of this row . the first record is 14 larger than the second record .'}, {'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'artist', 'ann breen'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose artist record fuzzily matches to ann breen .', 'tostr': 'filter_eq { all_rows ; artist ; ann breen }'}, 'song'], 'result': 'oh , darling', 'ind': 6, 'tostr': 'hop { filter_eq { all_rows ; artist ; ann breen } ; song }'}, 'oh , darling'], 'result': True, 'ind': 7, 'tostr': 'eq { hop { filter_eq { all_rows ; artist ; ann breen } ; song } ; oh , darling }', 'tointer': 'the song record of the first row is oh , darling .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'artist', 'fran meen'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose artist record fuzzily matches to fran meen .', 'tostr': 'filter_eq { all_rows ; artist ; fran meen }'}, 'song'], 'result': 'say that you love me', 'ind': 8, 'tostr': 'hop { filter_eq { all_rows ; artist ; fran meen } ; song }'}, 'say that you love me'], 'result': True, 'ind': 9, 'tostr': 'eq { hop { filter_eq { all_rows ; artist ; fran meen } ; song } ; say that you love me }', 'tointer': 'the song record of the second row is say that you love me .'}], 'result': True, 'ind': 10, 'tostr': 'and { eq { hop { filter_eq { all_rows ; artist ; ann breen } ; song } ; oh , darling } ; eq { hop { filter_eq { all_rows ; artist ; fran meen } ; song } ; say that you love me } }', 'tointer': 'the song record of the first row is oh , darling . the song record of the second row is say that you love me .'}], 'result': True, 'ind': 11, 'tostr': 'and { eq { diff { hop { filter_eq { all_rows ; artist ; ann breen } ; points } ; hop { filter_eq { all_rows ; artist ; fran meen } ; points } } ; 14 } ; and { eq { hop { filter_eq { all_rows ; artist ; ann breen } ; song } ; oh , darling } ; eq { hop { filter_eq { all_rows ; artist ; fran meen } ; song } ; say that you love me } } } = true', 'tointer': 'select the rows whose artist record fuzzily matches to ann breen . take the points record of this row . select the rows whose artist record fuzzily matches to fran meen . take the points record of this row . the first record is 14 larger than the second record . the song record of the first row is oh , darling . the song record of the second row is say that you love me .'} | and { eq { diff { hop { filter_eq { all_rows ; artist ; ann breen } ; points } ; hop { filter_eq { all_rows ; artist ; fran meen } ; points } } ; 14 } ; and { eq { hop { filter_eq { all_rows ; artist ; ann breen } ; song } ; oh , darling } ; eq { hop { filter_eq { all_rows ; artist ; fran meen } ; song } ; say that you love me } } } = true | select the rows whose artist record fuzzily matches to ann breen . take the points record of this row . select the rows whose artist record fuzzily matches to fran meen . take the points record of this row . the first record is 14 larger than the second record . the song record of the first row is oh , darling . the song record of the second row is say that you love me . | 14 | 12 | {'and_11': 11, 'result_12': 12, 'eq_5': 5, 'diff_4': 4, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_13': 13, 'artist_14': 14, 'ann breen_15': 15, 'points_16': 16, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_17': 17, 'artist_18': 18, 'fran meen_19': 19, 'points_20': 20, '14_21': 21, 'and_10': 10, 'str_eq_7': 7, 'str_hop_6': 6, 'song_22': 22, 'oh , darling_23': 23, 'str_eq_9': 9, 'str_hop_8': 8, 'song_24': 24, 'say that you love me_25': 25} | {'and_11': 'and', 'result_12': 'true', 'eq_5': 'eq', 'diff_4': 'diff', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_13': 'all_rows', 'artist_14': 'artist', 'ann breen_15': 'ann breen', 'points_16': 'points', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_17': 'all_rows', 'artist_18': 'artist', 'fran meen_19': 'fran meen', 'points_20': 'points', '14_21': '14', 'and_10': 'and', 'str_eq_7': 'str_eq', 'str_hop_6': 'str_hop', 'song_22': 'song', 'oh , darling_23': 'oh , darling', 'str_eq_9': 'str_eq', 'str_hop_8': 'str_hop', 'song_24': 'song', 'say that you love me_25': 'say that you love me'} | {'and_11': [12], 'result_12': [], 'eq_5': [11], 'diff_4': [5], 'num_hop_2': [4], 'filter_str_eq_0': [2, 6], 'all_rows_13': [0], 'artist_14': [0], 'ann breen_15': [0], 'points_16': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3, 8], 'all_rows_17': [1], 'artist_18': [1], 'fran meen_19': [1], 'points_20': [3], '14_21': [5], 'and_10': [11], 'str_eq_7': [10], 'str_hop_6': [7], 'song_22': [6], 'oh , darling_23': [7], 'str_eq_9': [10], 'str_hop_8': [9], 'song_24': [8], 'say that you love me_25': [9]} | ['draw', 'artist', 'song', 'points', 'place'] | [['1', 'the memories', 'if it means losing you', '57', '8th'], ['2', 'ann breen', 'oh , darling', '80', '4th'], ['3', 'fran meen', 'say that you love me', '66', '6th'], ['4', 'dreams', "sin sin ( that 's that )", '73', '5th'], ['5', 'connor stevens', 'count on me', '88', '3rd'], ['6', 'linda martin and friends', 'all the people in the world', '105', '2nd'], ['7', 'maggie toal', 'feed him with love', '61', '7th'], ['8', 'liam reilly', 'somewhere in europe', '130', '1st']] |
tasmania cricket team list a records | https://en.wikipedia.org/wiki/Tasmania_cricket_team_List_A_records | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16351707-15.html.csv | unique | damien wright is the only tasmania cricket team player to have more than 50 wickets . | {'scope': 'all', 'row': '1', 'col': '2', 'col_other': '3', 'criterion': 'greater_than', 'value': '50', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 's wicket', '50'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose s wicket record is greater than 50 .', 'tostr': 'filter_greater { all_rows ; s wicket ; 50 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_greater { all_rows ; s wicket ; 50 } }', 'tointer': 'select the rows whose s wicket record is greater than 50 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 's wicket', '50'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose s wicket record is greater than 50 .', 'tostr': 'filter_greater { all_rows ; s wicket ; 50 }'}, 'player'], 'result': 'damien wright', 'ind': 2, 'tostr': 'hop { filter_greater { all_rows ; s wicket ; 50 } ; player }'}, 'damien wright'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_greater { all_rows ; s wicket ; 50 } ; player } ; damien wright }', 'tointer': 'the player record of this unqiue row is damien wright .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_greater { all_rows ; s wicket ; 50 } } ; eq { hop { filter_greater { all_rows ; s wicket ; 50 } ; player } ; damien wright } } = true', 'tointer': 'select the rows whose s wicket record is greater than 50 . there is only one such row in the table . the player record of this unqiue row is damien wright .'} | and { only { filter_greater { all_rows ; s wicket ; 50 } } ; eq { hop { filter_greater { all_rows ; s wicket ; 50 } ; player } ; damien wright } } = true | select the rows whose s wicket record is greater than 50 . there is only one such row in the table . the player record of this unqiue row is damien wright . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_greater_0': 0, 'all_rows_6': 6, 's wicket_7': 7, '50_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'player_9': 9, 'damien wright_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_greater_0': 'filter_greater', 'all_rows_6': 'all_rows', 's wicket_7': 's wicket', '50_8': '50', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'player_9': 'player', 'damien wright_10': 'damien wright'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_greater_0': [1, 2], 'all_rows_6': [0], 's wicket_7': [0], '50_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'player_9': [2], 'damien wright_10': [3]} | ['rank', 's wicket', 'player', 'matches', 'average'] | [['1', '63', 'damien wright', '53', '28.80'], ['2', '48', 'adam griffith', '38', '32.58'], ['3', '43', 'shaun young', '64', '33.33'], ['4', '40', 'brett geeves', '30', '28.42'], ['= 4', '40', 'daniel marsh', '71', '40.60']] |
list of town tramway systems in the netherlands | https://en.wikipedia.org/wiki/List_of_town_tramway_systems_in_the_Netherlands | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-12562214-1.html.csv | unique | in the list of town tramway systems in the netherlands , the only tramway system in nijmegen with traction type electric its date ( from ) is 4 june 1911 . | {'scope': 'subset', 'row': '9', 'col': '3', 'col_other': '4', 'criterion': 'equal', 'value': 'electric', 'subset': {'col': '2', 'criterion': 'equal', 'value': 'nijmegen'}} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'location', 'nijmegen'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; location ; nijmegen }', 'tointer': 'select the rows whose location record fuzzily matches to nijmegen .'}, 'traction type', 'electric'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose location record fuzzily matches to nijmegen . among these rows , select the rows whose traction type record fuzzily matches to electric .', 'tostr': 'filter_eq { filter_eq { all_rows ; location ; nijmegen } ; traction type ; electric }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_eq { filter_eq { all_rows ; location ; nijmegen } ; traction type ; electric } }', 'tointer': 'select the rows whose location record fuzzily matches to nijmegen . among these rows , select the rows whose traction type record fuzzily matches to electric . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'location', 'nijmegen'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; location ; nijmegen }', 'tointer': 'select the rows whose location record fuzzily matches to nijmegen .'}, 'traction type', 'electric'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose location record fuzzily matches to nijmegen . among these rows , select the rows whose traction type record fuzzily matches to electric .', 'tostr': 'filter_eq { filter_eq { all_rows ; location ; nijmegen } ; traction type ; electric }'}, 'date ( from )'], 'result': '4 june 1911', 'ind': 3, 'tostr': 'hop { filter_eq { filter_eq { all_rows ; location ; nijmegen } ; traction type ; electric } ; date ( from ) }'}, '4 june 1911'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_eq { filter_eq { all_rows ; location ; nijmegen } ; traction type ; electric } ; date ( from ) } ; 4 june 1911 }', 'tointer': 'the date ( from ) record of this unqiue row is 4 june 1911 .'}], 'result': True, 'ind': 5, 'tostr': 'and { only { filter_eq { filter_eq { all_rows ; location ; nijmegen } ; traction type ; electric } } ; eq { hop { filter_eq { filter_eq { all_rows ; location ; nijmegen } ; traction type ; electric } ; date ( from ) } ; 4 june 1911 } } = true', 'tointer': 'select the rows whose location record fuzzily matches to nijmegen . among these rows , select the rows whose traction type record fuzzily matches to electric . there is only one such row in the table . the date ( from ) record of this unqiue row is 4 june 1911 .'} | and { only { filter_eq { filter_eq { all_rows ; location ; nijmegen } ; traction type ; electric } } ; eq { hop { filter_eq { filter_eq { all_rows ; location ; nijmegen } ; traction type ; electric } ; date ( from ) } ; 4 june 1911 } } = true | select the rows whose location record fuzzily matches to nijmegen . among these rows , select the rows whose traction type record fuzzily matches to electric . there is only one such row in the table . the date ( from ) record of this unqiue row is 4 june 1911 . | 8 | 6 | {'and_5': 5, 'result_6': 6, 'only_2': 2, 'filter_str_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'location_8': 8, 'nijmegen_9': 9, 'traction type_10': 10, 'electric_11': 11, 'str_eq_4': 4, 'str_hop_3': 3, 'date (from)_12': 12, '4 june 1911_13': 13} | {'and_5': 'and', 'result_6': 'true', 'only_2': 'only', 'filter_str_eq_1': 'filter_str_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'location_8': 'location', 'nijmegen_9': 'nijmegen', 'traction type_10': 'traction type', 'electric_11': 'electric', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'date (from)_12': 'date ( from )', '4 june 1911_13': '4 june 1911'} | {'and_5': [6], 'result_6': [], 'only_2': [5], 'filter_str_eq_1': [2, 3], 'filter_str_eq_0': [1], 'all_rows_7': [0], 'location_8': [0], 'nijmegen_9': [0], 'traction type_10': [1], 'electric_11': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'date (from)_12': [3], '4 june 1911_13': [4]} | ['name of system', 'location', 'traction type', 'date ( from )', 'date ( to )', 'notes'] | [['atm ( 1897 - 1917 ) gta ( 1919 - 1922 )', 'apeldoorn', 'horse', '12 august 1897', '11 november 1917', 'apeldoornsche tramweg - maatschappij'], ['atm ( 1897 - 1917 ) gta ( 1919 - 1922 )', 'apeldoorn', 'petrol ( gasoline )', '5 june 1919', '8 october 1922', 'gemeentetram apeldoorn'], ['atm ( 1880 - 1911 ) geta ( 1911 - 1944 )', 'arnhem', 'horse', '3 may 1880', '12 june 1912', 'arnhemsche tramweg - maatschappij'], ['hsm ( 1883 - 1910 ) gt ( 1915 - 1922 )', 'groenlo', 'steam', '29 may 1883', '31 december 1910', 'hollandshe ijzeren spoorweg - maatschappij'], ['hsm ( 1883 - 1910 ) gt ( 1915 - 1922 )', 'groenlo', 'petrol ( gasoline )', '6 august 1915', 'oct 1922', 'groenlosche tram'], ['hsm ( 1883 - 1910 ) gt ( 1915 - 1922 )', 'groenlo', 'horse', '1917', '1919', 'temporary use of horses because of lack of petrol'], ['ntm ( 1889 - 1912 ) m & w ( 1912 - 1921 ) gtn ( 1911 - 1955 )', 'nijmegen', 'horse', '1889', '1911', 'nijmeegsche tramweg - maatschappij'], ['ntm ( 1889 - 1912 ) m & w ( 1912 - 1921 ) gtn ( 1911 - 1955 )', 'nijmegen', 'steam', '30 june 1889', '31 december 1921', 'stoomtram maas en waal'], ['ntm ( 1889 - 1912 ) m & w ( 1912 - 1921 ) gtn ( 1911 - 1955 )', 'nijmegen', 'electric', '4 june 1911', '20 november 1955', 'gemeentetram nijmegen replaced by trolleybus'], ['gtz', 'zaltbommel', 'horse', '14 march 1910', '31 august 1923', 'gemeentetram zaltbommel']] |
bolt thrust | https://en.wikipedia.org/wiki/Bolt_thrust | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-26967904-2.html.csv | aggregation | all the chambering in the bolt thrust have an average p1 diameter of 13.425 mm . | {'scope': 'all', 'col': '2', 'type': 'average', 'result': '13.425', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'p1 diameter ( mm )'], 'result': '13.425', 'ind': 0, 'tostr': 'avg { all_rows ; p1 diameter ( mm ) }'}, '13.425'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; p1 diameter ( mm ) } ; 13.425 } = true', 'tointer': 'the average of the p1 diameter ( mm ) record of all rows is 13.425 .'} | round_eq { avg { all_rows ; p1 diameter ( mm ) } ; 13.425 } = true | the average of the p1 diameter ( mm ) record of all rows is 13.425 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'p1 diameter (mm)_4': 4, '13.425_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'p1 diameter (mm)_4': 'p1 diameter ( mm )', '13.425_5': '13.425'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'p1 diameter (mm)_4': [0], '13.425_5': [1]} | ['chambering', 'p1 diameter ( mm )', 'a external ( cm 2 )', 'p max ( bar )', 'f bolt ( kgf )', 'f bolt'] | [['5.45 x39 mm', '10.00', '0.7854', '3800', '2985', 'n ( lbf )'], ['.223 remington', '9.58', '0.7208', '4300', '3099', 'n ( lbf )'], ['7.62 x39 mm', '11.35', '1.0118', '3550', '3592', 'n ( lbf )'], ['.308 winchester', '11.96', '1.1234', '4150', '4662', 'n ( lbf )'], ['.300 winchester magnum', '13.03', '1.3335', '4300', '5734', 'n ( lbf )'], ['.300 wsm', '14.12', '1.5659', '4450', '6968', 'n ( lbf )'], ['.300 remington ultra magnum', '13.97', '1.5328', '4480', '6876', 'n ( lbf )'], ['.338 lapua magnum', '14.91', '1.7460', '4200', '7333', 'n ( lbf )'], ['.300 lapua magnum', '14.91', '1.7460', '4700', '8339', 'n ( lbf )'], ['.50 bmg', '20.42', '3.2749', '3700', '12117', 'n ( lbf )']] |
list of ngc objects ( 2001 - 3000 ) | https://en.wikipedia.org/wiki/List_of_NGC_objects_%282001%E2%80%933000%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11097664-6.html.csv | count | in the list of ngc objects ( 2001 - 3000 ) , 2 of the object type spiral galaxy has apparent magnitude of 13.0 . | {'scope': 'subset', 'criterion': 'equal', 'value': '13.0', 'result': '2', 'col': '6', 'subset': {'col': '2', 'criterion': 'equal', 'value': 'spiral galaxy'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'object type', 'spiral galaxy'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; object type ; spiral galaxy }', 'tointer': 'select the rows whose object type record fuzzily matches to spiral galaxy .'}, 'apparent magnitude', '13.0'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose object type record fuzzily matches to spiral galaxy . among these rows , select the rows whose apparent magnitude record is equal to 13.0 .', 'tostr': 'filter_eq { filter_eq { all_rows ; object type ; spiral galaxy } ; apparent magnitude ; 13.0 }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_eq { filter_eq { all_rows ; object type ; spiral galaxy } ; apparent magnitude ; 13.0 } }', 'tointer': 'select the rows whose object type record fuzzily matches to spiral galaxy . among these rows , select the rows whose apparent magnitude record is equal to 13.0 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_eq { all_rows ; object type ; spiral galaxy } ; apparent magnitude ; 13.0 } } ; 2 } = true', 'tointer': 'select the rows whose object type record fuzzily matches to spiral galaxy . among these rows , select the rows whose apparent magnitude record is equal to 13.0 . the number of such rows is 2 .'} | eq { count { filter_eq { filter_eq { all_rows ; object type ; spiral galaxy } ; apparent magnitude ; 13.0 } } ; 2 } = true | select the rows whose object type record fuzzily matches to spiral galaxy . among these rows , select the rows whose apparent magnitude record is equal to 13.0 . the number of such rows is 2 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'object type_6': 6, 'spiral galaxy_7': 7, 'apparent magnitude_8': 8, '13.0_9': 9, '2_10': 10} | {'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_eq_1': 'filter_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'object type_6': 'object type', 'spiral galaxy_7': 'spiral galaxy', 'apparent magnitude_8': 'apparent magnitude', '13.0_9': '13.0', '2_10': '2'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_eq_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'object type_6': [0], 'spiral galaxy_7': [0], 'apparent magnitude_8': [1], '13.0_9': [1], '2_10': [3]} | ['ngc number', 'object type', 'constellation', 'right ascension ( j2000 )', 'declination ( j2000 )', 'apparent magnitude'] | [['2516', 'open cluster', 'carina', '07h58 m', 'degree45 ′', '3.8'], ['2535', 'spiral galaxy', 'cancer', '08h11 m13 .6 s', 'degree12 ′ 24 ″', '13.0'], ['2536', 'spiral galaxy', 'cancer', '08h11 m16 .1 s', 'degree10 ′ 45 ″', '14.5'], ['2537', 'irregular galaxy', 'lynx', '08h13 m14 .6 s', 'degree59 ′ 30 ″', '11.7'], ['2541', 'spiral galaxy', 'lynx', '08h14 m40 .4 s', 'degree03 ′ 42 ″', '13.0'], ['2546', 'open cluster', 'puppis', '08h12 m', 'degree37 ′', '6.5'], ['2547', 'open cluster', 'vela', '08h10 m25 .7 s', 'degree10 ′ 03 ″', '4.8'], ['2548', 'open cluster', 'hydra', '08h14 m', 'degree45 ′', '6.1'], ['2549', 'lenticular galaxy', 'lynx', '08h18 m58 .4 s', 'degree48 ′ 10 ″', '12.1'], ['2550', 'spiral galaxy', 'camelopardalis', '08h24 m33 .9 s', 'degree00 ′ 43 ″', '13.1'], ['2551', 'spiral galaxy', 'camelopardalis', '08h24 m50 .5 s', 'degree24 ′ 44 ″', '12.7'], ['2552', 'irregular galaxy', 'lynx', '08h19 m19 .6 s', 'degree00 ′ 28 ″', '13.5']] |
señorita panamá 2001 | https://en.wikipedia.org/wiki/Se%C3%B1orita_Panam%C3%A1_2001 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-28062822-3.html.csv | aggregation | in the senorita panama 2001 , the average age of contestants with a hometown of panama city was 22.7 . | {'scope': 'subset', 'col': '3', 'type': 'average', 'result': '22.7', 'subset': {'col': '5', 'criterion': 'equal', 'value': 'panama city'}} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'hometown', 'panama city'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; hometown ; panama city }', 'tointer': 'select the rows whose hometown record fuzzily matches to panama city .'}, 'age'], 'result': '22.7', 'ind': 1, 'tostr': 'avg { filter_eq { all_rows ; hometown ; panama city } ; age }'}, '22.7'], 'result': True, 'ind': 2, 'tostr': 'round_eq { avg { filter_eq { all_rows ; hometown ; panama city } ; age } ; 22.7 } = true', 'tointer': 'select the rows whose hometown record fuzzily matches to panama city . the average of the age record of these rows is 22.7 .'} | round_eq { avg { filter_eq { all_rows ; hometown ; panama city } ; age } ; 22.7 } = true | select the rows whose hometown record fuzzily matches to panama city . the average of the age record of these rows is 22.7 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'avg_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'hometown_5': 5, 'panama city_6': 6, 'age_7': 7, '22.7_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'avg_1': 'avg', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'hometown_5': 'hometown', 'panama city_6': 'panama city', 'age_7': 'age', '22.7_8': '22.7'} | {'eq_2': [3], 'result_3': [], 'avg_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'hometown_5': [0], 'panama city_6': [0], 'age_7': [1], '22.7_8': [2]} | ['represent', 'contestant', 'age', 'height', 'hometown', 'sponsor'] | [['1', 'justine lissette pasek patiño', '23', '1.72 mts', 'panama city', 'yogurt vita slim'], ['2', 'karin natalie sempf kahn', '22', '1.75 mts', 'panama city', 'zero frizz'], ['3', 'bertha giovanna peric torres', '23', '1.78 mts', 'panama city', 'coca cola light'], ['4', 'lilibeth yanina camaño frías', '23', '1.76 mts', 'guararé', 'figali'], ['5', 'melissa del carmen bocharel', '25', '1.68 mts', 'panama city', 'saba'], ['6', 'jessica doralis segui barrios', '20', '1.70 mts', 'los santos', 'muebleria ancon'], ['7', 'katherine massiel quirós vásquez', '20', '1.71 mts', 'veraguas', 'jabon class'], ['8', 'lourdes cristina gonzález montenegro', '21', '1.73 mts', 'las tablas', "l'oreal"], ['9', 'beatriz del carmen nogueira domínguez', '18', '1.70 mts', 'coclé', 'mistolin'], ['10', 'carolina del carmen troncoso thayer', '19', '1.74 mts', 'panama city', 'skoda'], ['11', 'melina franco fonseca', '24', '1.75 mts', 'panama city', 'max factor']] |
croatian party of rights of bosnia and herzegovina | https://en.wikipedia.org/wiki/Croatian_Party_of_Rights_of_Bosnia_and_Herzegovina | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2002282-1.html.csv | count | the croatian party of rights of bosnia and herzegovina has conducted elections 6 times . | {'scope': 'all', 'criterion': 'all', 'value': 'n/a', 'result': '6', 'col': '1', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_all', 'args': ['all_rows', 'election'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose election record is arbitrary .', 'tostr': 'filter_all { all_rows ; election }'}], 'result': '6', 'ind': 1, 'tostr': 'count { filter_all { all_rows ; election } }', 'tointer': 'select the rows whose election record is arbitrary . the number of such rows is 6 .'}, '6'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_all { all_rows ; election } } ; 6 } = true', 'tointer': 'select the rows whose election record is arbitrary . the number of such rows is 6 .'} | eq { count { filter_all { all_rows ; election } } ; 6 } = true | select the rows whose election record is arbitrary . the number of such rows is 6 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_all_0': 0, 'all_rows_4': 4, 'election_5': 5, '6_6': 6} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_all_0': 'filter_all', 'all_rows_4': 'all_rows', 'election_5': 'election', '6_6': '6'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_all_0': [1], 'all_rows_4': [0], 'election_5': [0], '6_6': [2]} | ['election', 'in coalition with', 'votes won', 'percentage', 'seats won', 'change'] | [['november 1990', 'none', '0', '0', '0 / 42', '± 0'], ['september 1996', 'none', '14879', '6', '0 / 42', '± 0'], ['september 1998', 'none', '10305', '6', '0 / 42', '± 0'], ['november 2000', 'none', '1366', '7', '0 / 42', '± 0'], ['october 2002', 'none', '4401', '44', '0 / 42', '± 0'], ['october 2006', 'new croatian initiative', '19486', '23', '0 / 42', '± 0']] |
manhunt international | https://en.wikipedia.org/wiki/Manhunt_International | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2290097-4.html.csv | ordinal | australia was the country with the fourth most semifinalists in the manhunt international . | {'row': '6', 'col': '8', 'order': '4', 'col_other': '2', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'semifinalists', '4'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; semifinalists ; 4 }'}, 'country / territory'], 'result': 'australia', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; semifinalists ; 4 } ; country / territory }'}, 'australia'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; semifinalists ; 4 } ; country / territory } ; australia } = true', 'tointer': 'select the row whose semifinalists record of all rows is 4th maximum . the country / territory record of this row is australia .'} | eq { hop { nth_argmax { all_rows ; semifinalists ; 4 } ; country / territory } ; australia } = true | select the row whose semifinalists record of all rows is 4th maximum . the country / territory record of this row is australia . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'semifinalists_5': 5, '4_6': 6, 'country / territory_7': 7, 'australia_8': 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'semifinalists_5': 'semifinalists', '4_6': '4', 'country / territory_7': 'country / territory', 'australia_8': 'australia'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'semifinalists_5': [0], '4_6': [0], 'country / territory_7': [1], 'australia_8': [2]} | ['rank', 'country / territory', 'manhunt international', '1st runner - up', '2nd runner - up', '3rd runner - up', '4th runner - up', 'semifinalists', 'total'] | [['1', 'china', '2', '1', '1', '1', '0', '5', '10'], ['2', 'india', '1', '2', '0', '0', '3', '5', '11'], ['3', 'sweden', '1', '2', '0', '0', '0', '3', '6'], ['4', 'venezuela', '1', '1', '1', '1', '1', '6', '11'], ['5', 'turkey', '1', '1', '1', '1', '0', '3', '7'], ['6', 'australia', '1', '1', '0', '1', '0', '4', '7'], ['7', 'germany', '1', '1', '0', '0', '0', '1', '3'], ['8', 'usa', '1', '0', '3', '1', '0', '3', '8'], ['9', 'philippines', '1', '0', '1', '1', '0', '3', '6'], ['10', 'greece', '1', '0', '1', '0', '0', '3', '5'], ['11', 'south africa', '1', '0', '0', '0', '1', '3', '5'], ['12', 'slovakia', '1', '0', '0', '0', '1', '0', '2'], ['13', 'france', '1', '0', '0', '0', '0', '2', '3'], ['14', 'morocco', '1', '0', '0', '0', '0', '0', '1']] |
miss universe | https://en.wikipedia.org/wiki/Miss_Universe | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-150340-3.html.csv | ordinal | sweden is the country with the third most semifinalists in the miss universe competition . | {'row': '4', 'col': '8', 'order': '3', 'col_other': '2', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'semifinalists', '3'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; semifinalists ; 3 }'}, 'country'], 'result': 'sweden', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; semifinalists ; 3 } ; country }'}, 'sweden'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; semifinalists ; 3 } ; country } ; sweden } = true', 'tointer': 'select the row whose semifinalists record of all rows is 3rd maximum . the country record of this row is sweden .'} | eq { hop { nth_argmax { all_rows ; semifinalists ; 3 } ; country } ; sweden } = true | select the row whose semifinalists record of all rows is 3rd maximum . the country record of this row is sweden . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'semifinalists_5': 5, '3_6': 6, 'country_7': 7, 'sweden_8': 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'semifinalists_5': 'semifinalists', '3_6': '3', 'country_7': 'country', 'sweden_8': 'sweden'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'semifinalists_5': [0], '3_6': [0], 'country_7': [1], 'sweden_8': [2]} | ['rank', 'country', 'miss universe', '1st runner - up', '2nd runner - up', '3rd runner - up', '4th runner - up', 'semifinalists', 'total'] | [['1', 'usa', '8', '8', '6', '1', '5', '28', '56'], ['2', 'venezuela', '6', '6', '6', '4', '2', '14', '38'], ['3', 'puerto rico', '5', '1', '2', '1', '3', '7', '19'], ['4', 'sweden', '3', '1', '2', '3', '4', '16', '29'], ['5', 'brazil', '2', '5', '1', '2', '4', '17', '31'], ['6', 'finland', '2', '3', '5', '1', '1', '6', '18'], ['7', 'philippines', '2', '2', '0', '4', '2', '7', '17'], ['8', 'australia', '2', '1', '3', '2', '2', '6', '16'], ['9', 'japan', '2', '1', '1', '2', '3', '9', '18'], ['10', 'india', '2', '1', '1', '1', '1', '14', '20'], ['11', 'canada', '2', '1', '1', '1', '0', '11', '16'], ['12', 'mexico', '2', '0', '1', '2', '2', '10', '17'], ['13', 'trinidad & tobago', '2', '0', '1', '0', '1', '4', '8'], ['14', 'thailand', '2', '0', '1', '0', '0', '4', '7']] |
list of presidents of india by longevity | https://en.wikipedia.org/wiki/List_of_Presidents_of_India_by_longevity | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18596194-1.html.csv | ordinal | on january 26 , 1950 , rajendra prasad was the first inaugurated president of india . | {'row': '1', 'col': '3', 'order': '1', 'col_other': '1', 'max_or_min': 'min_to_max', 'value_mentioned': 'yes', 'scope': 'all', 'subset': None} | {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'nth_min', 'args': ['all_rows', 'date of inauguration', '1'], 'result': '26 january 1950', 'ind': 0, 'tostr': 'nth_min { all_rows ; date of inauguration ; 1 }', 'tointer': 'the 1st minimum date of inauguration record of all rows is 26 january 1950 .'}, '26 january 1950'], 'result': True, 'ind': 1, 'tostr': 'eq { nth_min { all_rows ; date of inauguration ; 1 } ; 26 january 1950 }', 'tointer': 'the 1st minimum date of inauguration record of all rows is 26 january 1950 .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'date of inauguration', '1'], 'result': None, 'ind': 2, 'tostr': 'nth_argmin { all_rows ; date of inauguration ; 1 }'}, 'president'], 'result': 'prasad , rajendra rajendra prasad', 'ind': 3, 'tostr': 'hop { nth_argmin { all_rows ; date of inauguration ; 1 } ; president }'}, 'prasad , rajendra rajendra prasad'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { nth_argmin { all_rows ; date of inauguration ; 1 } ; president } ; prasad , rajendra rajendra prasad }', 'tointer': 'the president record of the row with 1st minimum date of inauguration record is prasad , rajendra rajendra prasad .'}], 'result': True, 'ind': 5, 'tostr': 'and { eq { nth_min { all_rows ; date of inauguration ; 1 } ; 26 january 1950 } ; eq { hop { nth_argmin { all_rows ; date of inauguration ; 1 } ; president } ; prasad , rajendra rajendra prasad } } = true', 'tointer': 'the 1st minimum date of inauguration record of all rows is 26 january 1950 . the president record of the row with 1st minimum date of inauguration record is prasad , rajendra rajendra prasad .'} | and { eq { nth_min { all_rows ; date of inauguration ; 1 } ; 26 january 1950 } ; eq { hop { nth_argmin { all_rows ; date of inauguration ; 1 } ; president } ; prasad , rajendra rajendra prasad } } = true | the 1st minimum date of inauguration record of all rows is 26 january 1950 . the president record of the row with 1st minimum date of inauguration record is prasad , rajendra rajendra prasad . | 6 | 6 | {'and_5': 5, 'result_6': 6, 'eq_1': 1, 'nth_min_0': 0, 'all_rows_7': 7, 'date of inauguration_8': 8, '1_9': 9, '26 january 1950_10': 10, 'str_eq_4': 4, 'str_hop_3': 3, 'nth_argmin_2': 2, 'all_rows_11': 11, 'date of inauguration_12': 12, '1_13': 13, 'president_14': 14, 'prasad , rajendra rajendra prasad_15': 15} | {'and_5': 'and', 'result_6': 'true', 'eq_1': 'eq', 'nth_min_0': 'nth_min', 'all_rows_7': 'all_rows', 'date of inauguration_8': 'date of inauguration', '1_9': '1', '26 january 1950_10': '26 january 1950', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'nth_argmin_2': 'nth_argmin', 'all_rows_11': 'all_rows', 'date of inauguration_12': 'date of inauguration', '1_13': '1', 'president_14': 'president', 'prasad , rajendra rajendra prasad_15': 'prasad , rajendra rajendra prasad'} | {'and_5': [6], 'result_6': [], 'eq_1': [5], 'nth_min_0': [1], 'all_rows_7': [0], 'date of inauguration_8': [0], '1_9': [0], '26 january 1950_10': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'nth_argmin_2': [3], 'all_rows_11': [2], 'date of inauguration_12': [2], '1_13': [2], 'president_14': [3], 'prasad , rajendra rajendra prasad_15': [4]} | ['president', 'date of birth', 'date of inauguration', 'age at inauguration', 'end of term', 'length of retirement', 'date of death', 'lifespan'] | [['prasad , rajendra rajendra prasad', '1884 - 12 - 03 3 december 1884', '26 january 1950', '65 - 054 65years , 54days', '13 may 1962', '0291 days', '1963 - 02 - 28 28 february 1963', 'days ( 78years , 87days )'], ['radhakrishnan , sarvepalli sarvepalli radhakrishnan', '1888 - 09 - 05 5 september 1888', '13 may 1962', '73 - 250 73years , 250days', '13 may 1967', '2896 days', '1975 - 04 - 17 17 april 1975', 'days ( 86years , 224days )'], ['hussain , zakir zakir hussain', '1897 - 02 - 08 8 february 1897', '13 may 1967', '70 - 094 70years , 94days', '3 may 1969', '0000 n / a', '1969 - 05 - 03 3 may 1969', 'days ( 72years , 84days )'], ['giri , v v vv giri', '1894 - 08 - 10 10 august 1894', '24 august 1969', '75 - 014 75years , 14days', '24 august 1974', '2130 days', '1980 - 06 - 23 23 june 1980', 'days ( 85years , 318days )'], ['ahmed , fakhruddin fakhruddin ahmed', '1905 - 05 - 13 13 may 1905', '24 august 1974', '69 - 103 69years , 103days', '11 february 1977', '0000 n / a', '1977 - 02 - 11 11 february 1977', 'days ( 71years , 274days )'], ['reddy , neelam neelam reddy', '1913 - 05 - 19 19 may 1913', '25 july 1977', '64 - 067 64years , 67days', '25 july 1982', '5060 days', '1996 - 06 - 01 1 june 1996', 'days ( 83years , 13days )'], ['singh , zail zail singh', '1916 - 05 - 05 5 may 1916', '25 july 1982', '66 - 081 66years , 81days', '25 july 1987', '2710 days', '1994 - 12 - 25 25 december 1994', 'days ( 78years , 234days )'], ['venkataraman , ramaswamy ramaswamy venkataraman', '1910 - 12 - 04 4 december 1910', '25 july 1987', '76 - 233 76years , 233days', '25 july 1992', '6030 days', '2009 - 01 - 27 27 january 2009', 'days ( 98years , 54days )'], ['sharma , shankar shankar dayal sharma', '1918 - 08 - 19 19 august 1918', '25 july 1992', '73 - 341 73years , 341days', '25 july 1997', '0884 days', '1999 - 12 - 26 26 december 1999', 'days ( 81years , 129days )'], ['narayanan , k r kr narayanan', '1920 - 10 - 27 27 october 1920', '25 july 1997', '76 - 271 76years , 271days', '25 july 2002', '1203 days', '2005 - 11 - 09 9 november 2005', 'days ( 85years , 13days )'], ['kalam , a p j apjabdul kalam', '1931 - 10 - 15 15 october 1931', '25 july 2002', '70 - 283 70years , 283days', '25 july 2007', '0 , 2383 days', '2014 - 02 - 1', 'days ( 82years , 109days )'], ['patil , pratibha pratibha patil', '1934 - 12 - 19 19 december 1934', '25 july 2007', '72 - 218 72years , 218days', '25 july 2012', '0 , 556 days', '2014 - 02 - 1', 'days ( 79years , 44days )'], ['mukherjee , pranab pranab mukherjee', '1934 - 12 - 19 11 december 1935', '25 july 2012', '76years , 227days', 'incumbent', '0000 incumbent', '2014 - 02 - 1', 'days ( 78years , 52days )'], ['president', 'date of birth', 'date of inauguration', 'age at inauguration', 'end of term', 'length of retirement', 'date of death 25 - 7 - 2012', 'lifespan']] |
1925 vfl season | https://en.wikipedia.org/wiki/1925_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10746200-11.html.csv | aggregation | in the 1925 vfl season , for games where the home team 's score was less than 10 , the average crowd size was 16600 . | {'scope': 'subset', 'col': '6', 'type': 'average', 'result': '16600', 'subset': {'col': '2', 'criterion': 'less_than', 'value': '10'}} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'home team score', '10'], 'result': None, 'ind': 0, 'tostr': 'filter_less { all_rows ; home team score ; 10 }', 'tointer': 'select the rows whose home team score record is less than 10 .'}, 'crowd'], 'result': '16600', 'ind': 1, 'tostr': 'avg { filter_less { all_rows ; home team score ; 10 } ; crowd }'}, '16600'], 'result': True, 'ind': 2, 'tostr': 'round_eq { avg { filter_less { all_rows ; home team score ; 10 } ; crowd } ; 16600 } = true', 'tointer': 'select the rows whose home team score record is less than 10 . the average of the crowd record of these rows is 16600 .'} | round_eq { avg { filter_less { all_rows ; home team score ; 10 } ; crowd } ; 16600 } = true | select the rows whose home team score record is less than 10 . the average of the crowd record of these rows is 16600 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'avg_1': 1, 'filter_less_0': 0, 'all_rows_4': 4, 'home team score_5': 5, '10_6': 6, 'crowd_7': 7, '16600_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'avg_1': 'avg', 'filter_less_0': 'filter_less', 'all_rows_4': 'all_rows', 'home team score_5': 'home team score', '10_6': '10', 'crowd_7': 'crowd', '16600_8': '16600'} | {'eq_2': [3], 'result_3': [], 'avg_1': [2], 'filter_less_0': [1], 'all_rows_4': [0], 'home team score_5': [0], '10_6': [0], 'crowd_7': [1], '16600_8': [2]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['richmond', '8.14 ( 62 )', 'st kilda', '6.9 ( 45 )', 'punt road oval', '20000', '18 july 1925'], ['hawthorn', '6.10 ( 46 )', 'melbourne', '14.18 ( 102 )', 'glenferrie oval', '6000', '18 july 1925'], ['essendon', '9.11 ( 65 )', 'geelong', '11.10 ( 76 )', 'windy hill', '30000', '18 july 1925'], ['collingwood', '16.17 ( 113 )', 'north melbourne', '11.4 ( 70 )', 'victoria park', '9000', '18 july 1925'], ['carlton', '9.12 ( 66 )', 'footscray', '7.11 ( 53 )', 'princes park', '12000', '18 july 1925'], ['south melbourne', '6.11 ( 47 )', 'fitzroy', '12.12 ( 84 )', 'lake oval', '15000', '18 july 1925']] |
2011 newfoundland and labrador tankard | https://en.wikipedia.org/wiki/2011_Newfoundland_and_Labrador_Tankard | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-29565858-2.html.csv | unique | brad gushue was the only player who has zero losses at the 2011 newfoundland and labrador tankard . | {'scope': 'all', 'row': '1', 'col': '3', 'col_other': '1', 'criterion': 'equal', 'value': '0', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'l', '0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose l record is equal to 0 .', 'tostr': 'filter_eq { all_rows ; l ; 0 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; l ; 0 } }', 'tointer': 'select the rows whose l record is equal to 0 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'l', '0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose l record is equal to 0 .', 'tostr': 'filter_eq { all_rows ; l ; 0 }'}, 'skip ( club )'], 'result': 'brad gushue ( bally haly )', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; l ; 0 } ; skip ( club ) }'}, 'brad gushue ( bally haly )'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; l ; 0 } ; skip ( club ) } ; brad gushue ( bally haly ) }', 'tointer': 'the skip ( club ) record of this unqiue row is brad gushue ( bally haly ) .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; l ; 0 } } ; eq { hop { filter_eq { all_rows ; l ; 0 } ; skip ( club ) } ; brad gushue ( bally haly ) } } = true', 'tointer': 'select the rows whose l record is equal to 0 . there is only one such row in the table . the skip ( club ) record of this unqiue row is brad gushue ( bally haly ) .'} | and { only { filter_eq { all_rows ; l ; 0 } } ; eq { hop { filter_eq { all_rows ; l ; 0 } ; skip ( club ) } ; brad gushue ( bally haly ) } } = true | select the rows whose l record is equal to 0 . there is only one such row in the table . the skip ( club ) record of this unqiue row is brad gushue ( bally haly ) . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'l_7': 7, '0_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'skip (club)_9': 9, 'brad gushue (bally haly)_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'l_7': 'l', '0_8': '0', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'skip (club)_9': 'skip ( club )', 'brad gushue (bally haly)_10': 'brad gushue ( bally haly )'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'l_7': [0], '0_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'skip (club)_9': [2], 'brad gushue (bally haly)_10': [3]} | ['skip ( club )', 'w', 'l', 'pf', 'pa', 'ends won', 'ends lost', 'blank ends', 'stolen ends'] | [['brad gushue ( bally haly )', '5', '0', '35', '15', '22', '12', '9', '6'], ['alex smith ( re / max centre )', '3', '2', '39', '34', '21', '23', '3', '5'], ['andrew symonds ( re / max centre )', '3', '2', '35', '36', '20', '21', '6', '1'], ['keith ryan ( carroll )', '2', '3', '29', '32', '19', '23', '5', '5'], ['ken peddigrew ( re / max centre )', '1', '4', '24', '32', '19', '17', '7', '4']] |
breakaway ( kelly clarkson album ) | https://en.wikipedia.org/wiki/Breakaway_%28Kelly_Clarkson_album%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1254205-10.html.csv | count | kelly clarkson had 5 limited edition cd releases . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'limited edition', 'result': '5', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'format', 'limited edition'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose format record fuzzily matches to limited edition .', 'tostr': 'filter_eq { all_rows ; format ; limited edition }'}], 'result': '5', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; format ; limited edition } }', 'tointer': 'select the rows whose format record fuzzily matches to limited edition . the number of such rows is 5 .'}, '5'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; format ; limited edition } } ; 5 } = true', 'tointer': 'select the rows whose format record fuzzily matches to limited edition . the number of such rows is 5 .'} | eq { count { filter_eq { all_rows ; format ; limited edition } } ; 5 } = true | select the rows whose format record fuzzily matches to limited edition . the number of such rows is 5 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'format_5': 5, 'limited edition_6': 6, '5_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'format_5': 'format', 'limited edition_6': 'limited edition', '5_7': '5'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'format_5': [0], 'limited edition_6': [0], '5_7': [2]} | ['region', 'date', 'label', 'format', 'catalog'] | [['canada', 'november 30 , 2004', 'bertelsmann music group', 'standard cd release', '82876 - 64491 - 2'], ['united states', 'november 30 , 2004', 'rca records , 19 recordings , s records', 'standard cd release', '82876 - 64491 - 2'], ['australia', 'january 3 , 2005', 'bertelsmann music group', 'standard cd release', '82876 - 64491 - 2'], ['europe', 'january 8 , 2005', 'bertelsmann music group', 'standard cd release', '82876 - 70291 - 2'], ['japan', 'january 26 , 2005', 'bmg japan', 'limited edition cd release', 'bvcp - 24059'], ['united kingdom', 'july 18 , 2005', 'rca records', 'limited edition cd release', '82876 - 69026 - 2'], ['france', 'july 18 , 2005', 'jive records', 'limited edition cd release', '82876 - 69026 - 2'], ['china', 'september 30 , 2005', 'sony bmg music entertainment', 'limited edition cd release', '9787799420134'], ['united states', 'november 25 , 2005', 'rca records , 19 recordings , s records', 'special edition cd + dvd reissue', '82876 - 74553 - 2'], ['australia', 'november 29 , 2005', 'sony bmg music entertainment', 'special edition cd + dvd reissue', '82876 - 74553 - 2'], ['japan', 'december 21 , 2005', 'bmg japan', 'special edition cd + dvd reissue', 'bvcp - 28053'], ['france', 'march 2 , 2006', 'jive records', 'limited edition cd reissue', '82876 - 70291 - 2']] |
1937 vfl season | https://en.wikipedia.org/wiki/1937_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10806194-8.html.csv | unique | in the 1937 vfl season , the only game on 12 june 1937 that drew more than 20,000 people was between richmond and carlton . | {'scope': 'subset', 'row': '3', 'col': '6', 'col_other': '1,3', 'criterion': 'greater_than', 'value': '20000', 'subset': {'col': '7', 'criterion': 'equal', 'value': '12 june 1937'}} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_greater', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', '12 june 1937'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; date ; 12 june 1937 }', 'tointer': 'select the rows whose date record fuzzily matches to 12 june 1937 .'}, 'crowd', '20000'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to 12 june 1937 . among these rows , select the rows whose crowd record is greater than 20000 .', 'tostr': 'filter_greater { filter_eq { all_rows ; date ; 12 june 1937 } ; crowd ; 20000 }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_greater { filter_eq { all_rows ; date ; 12 june 1937 } ; crowd ; 20000 } }', 'tointer': 'select the rows whose date record fuzzily matches to 12 june 1937 . among these rows , select the rows whose crowd record is greater than 20000 . there is only one such row in the table .'}, {'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_greater', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', '12 june 1937'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; date ; 12 june 1937 }', 'tointer': 'select the rows whose date record fuzzily matches to 12 june 1937 .'}, 'crowd', '20000'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to 12 june 1937 . among these rows , select the rows whose crowd record is greater than 20000 .', 'tostr': 'filter_greater { filter_eq { all_rows ; date ; 12 june 1937 } ; crowd ; 20000 }'}, 'home team'], 'result': 'richmond', 'ind': 3, 'tostr': 'hop { filter_greater { filter_eq { all_rows ; date ; 12 june 1937 } ; crowd ; 20000 } ; home team }'}, 'richmond'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_greater { filter_eq { all_rows ; date ; 12 june 1937 } ; crowd ; 20000 } ; home team } ; richmond }', 'tointer': 'the home team record of this unqiue row is richmond .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_greater', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', '12 june 1937'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; date ; 12 june 1937 }', 'tointer': 'select the rows whose date record fuzzily matches to 12 june 1937 .'}, 'crowd', '20000'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to 12 june 1937 . among these rows , select the rows whose crowd record is greater than 20000 .', 'tostr': 'filter_greater { filter_eq { all_rows ; date ; 12 june 1937 } ; crowd ; 20000 }'}, 'away team'], 'result': 'carlton', 'ind': 5, 'tostr': 'hop { filter_greater { filter_eq { all_rows ; date ; 12 june 1937 } ; crowd ; 20000 } ; away team }'}, 'carlton'], 'result': True, 'ind': 6, 'tostr': 'eq { hop { filter_greater { filter_eq { all_rows ; date ; 12 june 1937 } ; crowd ; 20000 } ; away team } ; carlton }', 'tointer': 'the away team record of this unqiue row is carlton .'}], 'result': True, 'ind': 7, 'tostr': 'and { eq { hop { filter_greater { filter_eq { all_rows ; date ; 12 june 1937 } ; crowd ; 20000 } ; home team } ; richmond } ; eq { hop { filter_greater { filter_eq { all_rows ; date ; 12 june 1937 } ; crowd ; 20000 } ; away team } ; carlton } }', 'tointer': 'the home team record of this unqiue row is richmond . the away team record of this unqiue row is carlton .'}], 'result': True, 'ind': 8, 'tostr': 'and { only { filter_greater { filter_eq { all_rows ; date ; 12 june 1937 } ; crowd ; 20000 } } ; and { eq { hop { filter_greater { filter_eq { all_rows ; date ; 12 june 1937 } ; crowd ; 20000 } ; home team } ; richmond } ; eq { hop { filter_greater { filter_eq { all_rows ; date ; 12 june 1937 } ; crowd ; 20000 } ; away team } ; carlton } } } = true', 'tointer': 'select the rows whose date record fuzzily matches to 12 june 1937 . among these rows , select the rows whose crowd record is greater than 20000 . there is only one such row in the table . the home team record of this unqiue row is richmond . the away team record of this unqiue row is carlton .'} | and { only { filter_greater { filter_eq { all_rows ; date ; 12 june 1937 } ; crowd ; 20000 } } ; and { eq { hop { filter_greater { filter_eq { all_rows ; date ; 12 june 1937 } ; crowd ; 20000 } ; home team } ; richmond } ; eq { hop { filter_greater { filter_eq { all_rows ; date ; 12 june 1937 } ; crowd ; 20000 } ; away team } ; carlton } } } = true | select the rows whose date record fuzzily matches to 12 june 1937 . among these rows , select the rows whose crowd record is greater than 20000 . there is only one such row in the table . the home team record of this unqiue row is richmond . the away team record of this unqiue row is carlton . | 13 | 9 | {'and_8': 8, 'result_9': 9, 'only_2': 2, 'filter_greater_1': 1, 'filter_str_eq_0': 0, 'all_rows_10': 10, 'date_11': 11, '12 june 1937_12': 12, 'crowd_13': 13, '20000_14': 14, 'and_7': 7, 'str_eq_4': 4, 'str_hop_3': 3, 'home team_15': 15, 'richmond_16': 16, 'str_eq_6': 6, 'str_hop_5': 5, 'away team_17': 17, 'carlton_18': 18} | {'and_8': 'and', 'result_9': 'true', 'only_2': 'only', 'filter_greater_1': 'filter_greater', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_10': 'all_rows', 'date_11': 'date', '12 june 1937_12': '12 june 1937', 'crowd_13': 'crowd', '20000_14': '20000', 'and_7': 'and', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'home team_15': 'home team', 'richmond_16': 'richmond', 'str_eq_6': 'str_eq', 'str_hop_5': 'str_hop', 'away team_17': 'away team', 'carlton_18': 'carlton'} | {'and_8': [9], 'result_9': [], 'only_2': [8], 'filter_greater_1': [2, 3, 5], 'filter_str_eq_0': [1], 'all_rows_10': [0], 'date_11': [0], '12 june 1937_12': [0], 'crowd_13': [1], '20000_14': [1], 'and_7': [8], 'str_eq_4': [7], 'str_hop_3': [4], 'home team_15': [3], 'richmond_16': [4], 'str_eq_6': [7], 'str_hop_5': [6], 'away team_17': [5], 'carlton_18': [6]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['geelong', '16.13 ( 109 )', 'st kilda', '11.15 ( 81 )', 'corio oval', '12600', '12 june 1937'], ['essendon', '13.11 ( 89 )', 'collingwood', '19.14 ( 128 )', 'windy hill', '13000', '12 june 1937'], ['richmond', '14.24 ( 108 )', 'carlton', '13.19 ( 97 )', 'punt road oval', '27000', '12 june 1937'], ['hawthorn', '12.10 ( 82 )', 'melbourne', '15.15 ( 105 )', 'glenferrie oval', '18000', '14 june 1937'], ['fitzroy', '14.15 ( 99 )', 'footscray', '8.14 ( 62 )', 'brunswick street oval', '20000', '14 june 1937'], ['south melbourne', '16.18 ( 114 )', 'north melbourne', '10.10 ( 70 )', 'lake oval', '16000', '14 june 1937']] |
braathens | https://en.wikipedia.org/wiki/Braathens | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-150761-1.html.csv | superlative | the boeing had the greatest quantity that was manufactured for braathens . | {'scope': 'all', 'col_superlative': '3', 'row_superlative': '8', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'quantity'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; quantity }'}, 'manufacturer'], 'result': 'boeing', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; quantity } ; manufacturer }'}, 'boeing'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; quantity } ; manufacturer } ; boeing } = true', 'tointer': 'select the row whose quantity record of all rows is maximum . the manufacturer record of this row is boeing .'} | eq { hop { argmax { all_rows ; quantity } ; manufacturer } ; boeing } = true | select the row whose quantity record of all rows is maximum . the manufacturer record of this row is boeing . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'quantity_5': 5, 'manufacturer_6': 6, 'boeing_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'quantity_5': 'quantity', 'manufacturer_6': 'manufacturer', 'boeing_7': 'boeing'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'quantity_5': [0], 'manufacturer_6': [1], 'boeing_7': [2]} | ['manufacturer', 'model', 'quantity', 'introduced', 'retired'] | [['douglas', 'c - 54 skymaster', '6', '1947', '1966'], ['douglas', 'c - 47 dakota', '2', '1947', '1964'], ['de havilland', 'dh114 heron', '7', '1952', '1960'], ['fokker', 'f - 27 friendship', '8', '1958', '1977'], ['douglas', 'dc - 6a / c', '1', '1961', '1965'], ['douglas', 'dc - 6b', '7', '1962', '1973'], ['fokker', 'f - 28 fellowship', '6', '1969', '1986'], ['boeing', '737 - 200', '20', '1969', '1994'], ['boeing', '767 - 200', '2', '1984', '1986'], ['boeing', '737 - 400', '7', '1989', '2004'], ['boeing', '737 - 500', '17', '1990', '2004'], ['fokker', '100', '5', '1997', '1999'], ['boeing', '737 - 300', '1', '1997', '1999'], ['boeing', '737 - 700', '13', '1998', '2004'], ['british aerospace', '146200', '10', '1998', '2001']] |
ddr - liga | https://en.wikipedia.org/wiki/DDR-Liga | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16087792-5.html.csv | unique | tsg wismar has only been in staffel a once . | {'scope': 'all', 'row': '1', 'col': '2', 'col_other': 'n/a', 'criterion': 'equal', 'value': 'tsg wismar', 'subset': None} | {'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'staffel a', 'tsg wismar'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose staffel a record fuzzily matches to tsg wismar .', 'tostr': 'filter_eq { all_rows ; staffel a ; tsg wismar }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; staffel a ; tsg wismar } } = true', 'tointer': 'select the rows whose staffel a record fuzzily matches to tsg wismar . there is only one such row in the table .'} | only { filter_eq { all_rows ; staffel a ; tsg wismar } } = true | select the rows whose staffel a record fuzzily matches to tsg wismar . there is only one such row in the table . | 2 | 2 | {'only_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'staffel a_4': 4, 'tsg wismar_5': 5} | {'only_1': 'only', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'staffel a_4': 'staffel a', 'tsg wismar_5': 'tsg wismar'} | {'only_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'staffel a_4': [0], 'tsg wismar_5': [0]} | ['season', 'staffel a', 'staffel b', 'staffel c', 'staffel d', 'staffel e'] | [['1971 - 72', 'tsg wismar', 'dynamo berlin ii', 'chemie leipzig', 'motor werdau', 'rot - weiß erfurt'], ['1972 - 73', 'vorwärts stralsund', 'dynamo berlin ii', 'vorwärts leipzig', 'dynamo dresden ii', 'chemie zeitz'], ['1973 - 74', 'vorwärts stralsund', '1 . fc union berlin', 'hallescher fc chemie', 'chemie böhlen', 'wismut gera'], ['1974 - 75', 'dynamo schwerin', '1 . fc union berlin', 'chemie leipzig', 'energie cottbus', 'wismut gera'], ['1975 - 76', 'hansa rostock', '1 . fc union berlin', 'hallescher fc chemie ii', 'motor werdau', 'fc carl zeiss jena ii'], ['1976 - 77', 'vorwärts stralsund', 'stahl hennigsdorf', 'chemie leipzig', 'chemie böhlen', 'wismut gera'], ['1977 - 78', 'hansa rostock', 'vorwärts neubrandenburg', 'chemie leipzig', 'lok dresden', 'stahl riesa'], ['1978 - 79', 'tsg bau rostock', 'vorwärts frankfurt', 'chemie leipzig', 'energie cottbus', 'motor suhl'], ['1979 - 80', 'hansa rostock', 'dynamo fürstenwalde', 'chemie böhlen', 'energie cottbus', 'wismut gera'], ['1980 - 81', 'schiffahrt / hafen rostock', '1 . fc union berlin', 'chemie schkopau', 'energie cottbus', 'motor suhl'], ['1981 - 82', 'vorwärts stralsund', '1 . fc union berlin', 'chemie böhlen', 'stahl riesa', 'motor nordhausen'], ['1982 - 83', 'schiffahrt / hafen rostock', 'stahl brandenburg', 'chemie leipzig', 'stahl riesa', 'wismut gera'], ['1983 - 84', 'vorwärts neubrandenburg', 'stahl brandenburg', 'vorwärts dessau', 'sachsenring zwickau', 'motor suhl']] |
dragons ' den ( uk ) | https://en.wikipedia.org/wiki/Dragons%27_Den_%28UK%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12617978-7.html.csv | superlative | the earliest episode of dragons ' den to air was when james seddon was the entrepreneur . | {'scope': 'all', 'col_superlative': '2', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '3', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'first aired'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; first aired }'}, 'entrepreneur ( s )'], 'result': 'james seddon', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; first aired } ; entrepreneur ( s ) }'}, 'james seddon'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; first aired } ; entrepreneur ( s ) } ; james seddon } = true', 'tointer': 'select the row whose first aired record of all rows is minimum . the entrepreneur ( s ) record of this row is james seddon .'} | eq { hop { argmin { all_rows ; first aired } ; entrepreneur ( s ) } ; james seddon } = true | select the row whose first aired record of all rows is minimum . the entrepreneur ( s ) record of this row is james seddon . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'first aired_5': 5, 'entrepreneur (s)_6': 6, 'james seddon_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'first aired_5': 'first aired', 'entrepreneur (s)_6': 'entrepreneur ( s )', 'james seddon_7': 'james seddon'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'first aired_5': [0], 'entrepreneur (s)_6': [1], 'james seddon_7': [2]} | ['episode', 'first aired', 'entrepreneur ( s )', 'company or product name', 'money requested', 'investing dragon ( s )'] | [['episode 1', '3 august 2006', 'james seddon', 'eggxactly', '75000', 'richard farleigh & peter jones'], ['episode 2', '10 august 2006', 'gary taylor', 'alpine cleaning', '200000', 'deborah meaden & theo paphitis'], ['episode 3', '17 august 2006', 'matthew hazell', 'first light solutions', '100000', 'richard farleigh'], ['episode 4', '24 august 2006', 'ian chamings', 'mixalbum', '150000', 'deborah meaden & theo paphitis'], ['episode 5', '31 august 2006', 'richard lee & daren duraidi', 'dr cap', '150000', 'duncan bannatyne'], ['episode 6', '7 september 2006', 'stephen bellis', 'nuts poker league', '50000 ( but received 65000 )', 'theo paphitis & deborah meaden'], ['episode 7', '14 september 2006', 'peter sesay', 'autosafe', '100000', 'peter jones & duncan bannatyne'], ['episode 8', '21 september 2006', 'ian daintith & richard adams', 'coin metrics', '200000', 'deborah meaden & theo paphitis']] |
1997 u.s. open ( golf ) | https://en.wikipedia.org/wiki/1997_U.S._Open_%28golf%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17162179-6.html.csv | aggregation | at the 1997 u.s. open , the average number of strokes to par was .92 . | {'scope': 'all', 'col': '5', 'type': 'average', 'result': '0.92', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'to par'], 'result': '0.92', 'ind': 0, 'tostr': 'avg { all_rows ; to par }'}, '0.92'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; to par } ; 0.92 } = true', 'tointer': 'the average of the to par record of all rows is 0.92 .'} | round_eq { avg { all_rows ; to par } ; 0.92 } = true | the average of the to par record of all rows is 0.92 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'to par_4': 4, '0.92_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'to par_4': 'to par', '0.92_5': '0.92'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'to par_4': [0], '0.92_5': [1]} | ['place', 'player', 'country', 'score', 'to par', 'money'] | [['1', 'ernie els', 'south africa', '71 + 67 + 69 + 69 = 276', '- 4', '465000'], ['2', 'colin montgomerie', 'scotland', '65 + 76 + 67 + 69 = 277', '- 3', '275000'], ['3', 'tom lehman', 'united states', '67 + 70 + 68 + 73 = 278', '- 2', '172828'], ['4', 'jeff maggert', 'united states', '73 + 66 + 68 + 74 = 281', '+ 1', '120454'], ['t5', 'olin browne', 'united states', '71 + 71 + 69 + 71 = 282', '+ 2', '79875'], ['t5', 'jim furyk', 'united states', '74 + 68 + 69 + 71 = 282', '+ 2', '79875'], ['t5', 'jay haas', 'united states', '73 + 69 + 68 + 72 = 282', '+ 2', '79875'], ['t5', 'tommy tolles', 'united states', '74 + 67 + 69 + 72 = 282', '+ 2', '79875'], ['t5', 'bob tway', 'united states', '71 + 71 + 70 + 70 = 282', '+ 2', '79875'], ['t10', 'scott hoch', 'united states', '71 + 68 + 72 + 72 = 283', '+ 3', '56949'], ['t10', 'scott mccarron', 'united states', '73 + 71 + 69 + 70 = 283', '+ 3', '56949'], ['t10', 'david ogrin', 'united states', '70 + 69 + 71 + 73 = 283', '+ 3', '56949']] |
history of test cricket from 1901 to 1914 | https://en.wikipedia.org/wiki/History_of_Test_cricket_from_1901_to_1914 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1598207-2.html.csv | majority | the away captain 's position was always held by joe darling . | {'scope': 'all', 'col': '3', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'joe darling', 'subset': None} | {'func': 'all_str_eq', 'args': ['all_rows', 'away captain', 'joe darling'], 'result': True, 'ind': 0, 'tointer': 'for the away captain records of all rows , all of them fuzzily match to joe darling .', 'tostr': 'all_eq { all_rows ; away captain ; joe darling } = true'} | all_eq { all_rows ; away captain ; joe darling } = true | for the away captain records of all rows , all of them fuzzily match to joe darling . | 1 | 1 | {'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'away captain_3': 3, 'joe darling_4': 4} | {'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'away captain_3': 'away captain', 'joe darling_4': 'joe darling'} | {'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'away captain_3': [0], 'joe darling_4': [0]} | ['date', 'home captain', 'away captain', 'venue', 'result'] | [['29 , 3031 may 1902', 'archie maclaren', 'joe darling', 'edgbaston', 'draw'], ['12 , 13 , 14 jun 1902', 'archie maclaren', 'joe darling', "lord 's", 'draw'], ['3 , 4 , 5 jul 1902', 'archie maclaren', 'joe darling', 'bramall lane', 'aus by 143 runs'], ['24 , 25 , 26 jul 1902', 'archie maclaren', 'joe darling', 'old trafford', 'aus by 3 runs'], ['11 , 12 , 13 aug 1902', 'archie maclaren', 'joe darling', 'oval', 'eng by 1 wkt']] |
u.s. cities with teams from four major league sports | https://en.wikipedia.org/wiki/U.S._cities_with_teams_from_four_major_league_sports | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1205598-2.html.csv | comparative | new york had teams from four major league sports before chicago did . | {'row_1': '5', 'row_2': '2', 'col': '3', 'col_other': '1', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'metropolitan area', 'new york , new york'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose metropolitan area record fuzzily matches to new york , new york .', 'tostr': 'filter_eq { all_rows ; metropolitan area ; new york , new york }'}, 'since'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; metropolitan area ; new york , new york } ; since }', 'tointer': 'select the rows whose metropolitan area record fuzzily matches to new york , new york . take the since record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'metropolitan area', 'chicago , illinois'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose metropolitan area record fuzzily matches to chicago , illinois .', 'tostr': 'filter_eq { all_rows ; metropolitan area ; chicago , illinois }'}, 'since'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; metropolitan area ; chicago , illinois } ; since }', 'tointer': 'select the rows whose metropolitan area record fuzzily matches to chicago , illinois . take the since record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; metropolitan area ; new york , new york } ; since } ; hop { filter_eq { all_rows ; metropolitan area ; chicago , illinois } ; since } } = true', 'tointer': 'select the rows whose metropolitan area record fuzzily matches to new york , new york . take the since record of this row . select the rows whose metropolitan area record fuzzily matches to chicago , illinois . take the since record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; metropolitan area ; new york , new york } ; since } ; hop { filter_eq { all_rows ; metropolitan area ; chicago , illinois } ; since } } = true | select the rows whose metropolitan area record fuzzily matches to new york , new york . take the since record of this row . select the rows whose metropolitan area record fuzzily matches to chicago , illinois . take the since record of this row . the first record is less than the second record . | 5 | 5 | {'less_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'metropolitan area_7': 7, 'new york , new york_8': 8, 'since_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'metropolitan area_11': 11, 'chicago , illinois_12': 12, 'since_13': 13} | {'less_4': 'less', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'metropolitan area_7': 'metropolitan area', 'new york , new york_8': 'new york , new york', 'since_9': 'since', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'metropolitan area_11': 'metropolitan area', 'chicago , illinois_12': 'chicago , illinois', 'since_13': 'since'} | {'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'metropolitan area_7': [0], 'new york , new york_8': [0], 'since_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'metropolitan area_11': [1], 'chicago , illinois_12': [1], 'since_13': [3]} | ['metropolitan area', 'media market ranking', 'since', 'mlb team ( s )', 'nba team ( s )'] | [['boston , massachusetts', '7', '1996', 'red sox', 'celtics'], ['chicago , illinois', '3', '1998', 'cubs white sox', 'bulls'], ['dallasfort worth metroplex , texas', '5', '1996', 'rangers ( arlington , tx )', 'mavericks'], ['denver , colorado', '16', '1996', 'rockies', 'nuggets'], ['new york , new york', '1', '1996', 'mets yankees', 'knicks nets'], ['philadelphia , pennsylvania', '4', '2010', 'phillies', '76ers'], ['san francisco bay area , california', '6', '2008', 'giants ( san francisco , ca ) athletics ( oakland )', 'warriors ( oakland )'], ['washington , dc', '9', '2005', 'nationals', 'wizards']] |
david sigachev | https://en.wikipedia.org/wiki/David_Sigachev | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-25421463-1.html.csv | count | david sagachev finished with no points in 2 different series . | {'scope': 'all', 'criterion': 'equal', 'value': 'n / a', 'result': '2', 'col': '6', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'points', 'n / a'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose points record fuzzily matches to n / a .', 'tostr': 'filter_eq { all_rows ; points ; n / a }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; points ; n / a } }', 'tointer': 'select the rows whose points record fuzzily matches to n / a . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; points ; n / a } } ; 2 } = true', 'tointer': 'select the rows whose points record fuzzily matches to n / a . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; points ; n / a } } ; 2 } = true | select the rows whose points record fuzzily matches to n / a . the number of such rows is 2 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'points_5': 5, 'n / a_6': 6, '2_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'points_5': 'points', 'n / a_6': 'n / a', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'points_5': [0], 'n / a_6': [0], '2_7': [2]} | ['season', 'series', 'team name', 'races', 'wins', 'points', 'final placing'] | [['2007', 'formula renault 2.0 nec', 'sl formula racing', '16', '0', '67', '21st'], ['2007', 'eurocup formula renault 2.0', 'sl formula racing', '2', '0', 'n / a', 'nc'], ['2009', 'porsche carrera cup germany', 'tolimit seyffarth motorsport', '9', '0', '38', '13th'], ['2009', 'porsche supercup', 'tolimit seyffarth motorsport', '2', '0', 'n / a', 'nc'], ['2010', 'porsche carrera cup germany', 'seyffarth motorsport', '9', '0', '42', '14th']] |
united states house of representatives elections , 1922 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1922 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1342426-18.html.csv | comparative | of the incumbents in the 1922 for the united states house of representatives , james o'connor was first elected 10 years after henry garland dupre was first elected . | {'row_1': '1', 'row_2': '2', 'col': '4', 'col_other': '2', 'relation': 'diff', 'record_mentioned': 'no', 'diff_result': {'diff_value': '10', 'bigger': 'row1'}} | {'func': 'eq', 'args': [{'func': 'diff', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'incumbent', "james o'connor"], 'result': None, 'ind': 0, 'tointer': "select the rows whose incumbent record fuzzily matches to james o'connor .", 'tostr': "filter_eq { all_rows ; incumbent ; james o'connor }"}, 'first elected'], 'result': None, 'ind': 2, 'tostr': "hop { filter_eq { all_rows ; incumbent ; james o'connor } ; first elected }", 'tointer': "select the rows whose incumbent record fuzzily matches to james o'connor . take the first elected record of this row ."}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'incumbent', 'henry garland dupré'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose incumbent record fuzzily matches to henry garland dupré .', 'tostr': 'filter_eq { all_rows ; incumbent ; henry garland dupré }'}, 'first elected'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; incumbent ; henry garland dupré } ; first elected }', 'tointer': 'select the rows whose incumbent record fuzzily matches to henry garland dupré . take the first elected record of this row .'}], 'result': '10', 'ind': 4, 'tostr': "diff { hop { filter_eq { all_rows ; incumbent ; james o'connor } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; henry garland dupré } ; first elected } }"}, '10'], 'result': True, 'ind': 5, 'tostr': "eq { diff { hop { filter_eq { all_rows ; incumbent ; james o'connor } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; henry garland dupré } ; first elected } } ; 10 } = true", 'tointer': "select the rows whose incumbent record fuzzily matches to james o'connor . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to henry garland dupré . take the first elected record of this row . the first record is 10 larger than the second record ."} | eq { diff { hop { filter_eq { all_rows ; incumbent ; james o'connor } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; henry garland dupré } ; first elected } } ; 10 } = true | select the rows whose incumbent record fuzzily matches to james o'connor . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to henry garland dupré . take the first elected record of this row . the first record is 10 larger than the second record . | 6 | 6 | {'eq_5': 5, 'result_6': 6, 'diff_4': 4, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'incumbent_8': 8, "james o'connor_9": 9, 'first elected_10': 10, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_11': 11, 'incumbent_12': 12, 'henry garland dupré_13': 13, 'first elected_14': 14, '10_15': 15} | {'eq_5': 'eq', 'result_6': 'true', 'diff_4': 'diff', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'incumbent_8': 'incumbent', "james o'connor_9": "james o'connor", 'first elected_10': 'first elected', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_11': 'all_rows', 'incumbent_12': 'incumbent', 'henry garland dupré_13': 'henry garland dupré', 'first elected_14': 'first elected', '10_15': '10'} | {'eq_5': [6], 'result_6': [], 'diff_4': [5], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_7': [0], 'incumbent_8': [0], "james o'connor_9": [0], 'first elected_10': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_11': [1], 'incumbent_12': [1], 'henry garland dupré_13': [1], 'first elected_14': [3], '10_15': [5]} | ['district', 'incumbent', 'party', 'first elected', 'result', 'candidates'] | [['louisiana 1', "james o'connor", 'democratic', '1918', 're - elected', "james o'connor ( d ) unopposed"], ['louisiana 2', 'henry garland dupré', 'democratic', '1908', 're - elected', 'henry garland dupré ( d ) unopposed'], ['louisiana 3', 'whitmell p martin', 'democratic', '1914', 're - elected', 'whitmell p martin ( d ) unopposed'], ['louisiana 4', 'john n sandlin', 'democratic', '1920', 're - elected', 'john n sandlin ( d ) unopposed'], ['louisiana 5', 'riley joseph wilson', 'democratic', '1914', 're - elected', 'riley joseph wilson ( d ) unopposed'], ['louisiana 6', 'george k favrot', 'democratic', '1920', 're - elected', 'george k favrot ( d ) unopposed'], ['louisiana 7', 'ladislas lazaro', 'democratic', '1912', 're - elected', 'ladislas lazaro ( d ) unopposed']] |
lubbock , texas | https://en.wikipedia.org/wiki/Lubbock%2C_Texas | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-136320-2.html.csv | count | in lubbock texas , for buildings completed before 2000 , two of them had exactly 12 floors . | {'scope': 'subset', 'criterion': 'equal', 'value': '12', 'result': '2', 'col': '4', 'subset': {'col': '5', 'criterion': 'less_than', 'value': '2000'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'year completed', '2000'], 'result': None, 'ind': 0, 'tostr': 'filter_less { all_rows ; year completed ; 2000 }', 'tointer': 'select the rows whose year completed record is less than 2000 .'}, 'floors ( stories )', '12'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose year completed record is less than 2000 . among these rows , select the rows whose floors ( stories ) record is equal to 12 .', 'tostr': 'filter_eq { filter_less { all_rows ; year completed ; 2000 } ; floors ( stories ) ; 12 }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_eq { filter_less { all_rows ; year completed ; 2000 } ; floors ( stories ) ; 12 } }', 'tointer': 'select the rows whose year completed record is less than 2000 . among these rows , select the rows whose floors ( stories ) record is equal to 12 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_less { all_rows ; year completed ; 2000 } ; floors ( stories ) ; 12 } } ; 2 } = true', 'tointer': 'select the rows whose year completed record is less than 2000 . among these rows , select the rows whose floors ( stories ) record is equal to 12 . the number of such rows is 2 .'} | eq { count { filter_eq { filter_less { all_rows ; year completed ; 2000 } ; floors ( stories ) ; 12 } } ; 2 } = true | select the rows whose year completed record is less than 2000 . among these rows , select the rows whose floors ( stories ) record is equal to 12 . the number of such rows is 2 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_eq_1': 1, 'filter_less_0': 0, 'all_rows_5': 5, 'year completed_6': 6, '2000_7': 7, 'floors (stories)_8': 8, '12_9': 9, '2_10': 10} | {'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_eq_1': 'filter_eq', 'filter_less_0': 'filter_less', 'all_rows_5': 'all_rows', 'year completed_6': 'year completed', '2000_7': '2000', 'floors (stories)_8': 'floors ( stories )', '12_9': '12', '2_10': '2'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_eq_1': [2], 'filter_less_0': [1], 'all_rows_5': [0], 'year completed_6': [0], '2000_7': [0], 'floors (stories)_8': [1], '12_9': [1], '2_10': [3]} | ['rank', 'name', 'height ft ( m )', 'floors ( stories )', 'year completed'] | [['1', 'metro tower', '274 ( 84 )', '20', '1955'], ['2', 'wells fargo building', '209 ( 64 )', '15', '1968'], ['3', 'ttu media and communication', '208 ( 63 )', '12', '1969'], ['4', 'overton hotel', '165 ( 50 )', '15', '2009'], ['5', 'park tower', '150 ( 46 )', '15', '1968'], ['6', 'bank of america tower', '143 ( 44 )', '12', '1940'], ['7', 'victory tower', '96 ( 29 )', '8', '1999']] |
list of ottawa senators draft picks | https://en.wikipedia.org/wiki/List_of_Ottawa_Senators_draft_picks | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11803648-5.html.csv | count | a total of two of the players drafted by the ottawa senators have canadian nationality . | {'scope': 'all', 'criterion': 'equal', 'value': 'canada', 'result': '2', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nationality', 'canada'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose nationality record fuzzily matches to canada .', 'tostr': 'filter_eq { all_rows ; nationality ; canada }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; nationality ; canada } }', 'tointer': 'select the rows whose nationality record fuzzily matches to canada . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; nationality ; canada } } ; 2 } = true', 'tointer': 'select the rows whose nationality record fuzzily matches to canada . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; nationality ; canada } } ; 2 } = true | select the rows whose nationality record fuzzily matches to canada . the number of such rows is 2 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'nationality_5': 5, 'canada_6': 6, '2_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'nationality_5': 'nationality', 'canada_6': 'canada', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'nationality_5': [0], 'canada_6': [0], '2_7': [2]} | ['round', 'overall', 'player', 'nationality', 'club team'] | [['1', '1', 'chris phillips', 'canada', 'prince albert raiders ( whl )'], ['4', '81', 'antti - jussi niemi', 'finland', 'jokerit ( finland )'], ['6', '136', 'andreas dackell', 'sweden', 'brynäs if gävle ( sweden )'], ['7', '163', 'françois hardy', 'canada', "val - d'or foreurs ( qmjhl )"], ['8', '212', 'erich goldmann', 'germany', 'mannheim eagles ( germany )'], ['9', '216', 'ivan ciernik', 'slovakia', 'mhc plastika nitra ( slovakia )'], ['9', '239', 'sami salo', 'finland', 'tps ( finland )']] |
2008 in video gaming | https://en.wikipedia.org/wiki/2008_in_video_gaming | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10060114-4.html.csv | count | 5 video game titles were published with the wii platform in 2008 . | {'scope': 'all', 'criterion': 'equal', 'value': 'wii', 'result': '5', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'platform', 'wii'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose platform record fuzzily matches to wii .', 'tostr': 'filter_eq { all_rows ; platform ; wii }'}], 'result': '5', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; platform ; wii } }', 'tointer': 'select the rows whose platform record fuzzily matches to wii . the number of such rows is 5 .'}, '5'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; platform ; wii } } ; 5 } = true', 'tointer': 'select the rows whose platform record fuzzily matches to wii . the number of such rows is 5 .'} | eq { count { filter_eq { all_rows ; platform ; wii } } ; 5 } = true | select the rows whose platform record fuzzily matches to wii . the number of such rows is 5 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'platform_5': 5, 'wii_6': 6, '5_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'platform_5': 'platform', 'wii_6': 'wii', '5_7': '5'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'platform_5': [0], 'wii_6': [0], '5_7': [2]} | ['place', 'title', 'platform', 'publisher', 'units sold'] | [['1', 'monster hunter portable 2nd g', 'psp', 'capcom', '2452111'], ['2', 'pokémon platinum', 'nds', 'pokémon company', '2187337'], ['3', 'wii fit', 'wii', 'nintendo', '2149131'], ['4', 'mario kart wii', 'wii', 'nintendo', '2003315'], ['5', 'super smash bros brawl', 'wii', 'nintendo', '1747113'], ['6', 'rhythm heaven', 'nds', 'nintendo', '1350671'], ['7', 'dragon quest v : hand of the heavenly bride', 'nds', 'square enix', '1176082'], ['8', 'animal crossing : city folk', 'wii', 'nintendo', '895302'], ['9', 'kirby super star ultra', 'nds', 'nintendo', '855427'], ['10', 'wii sports', 'wii', 'nintendo', '841736']] |
rural community | https://en.wikipedia.org/wiki/Rural_community | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-26321719-1.html.csv | aggregation | the average population in 2011 for the rural communities in new brunswick is 2,262 . | {'scope': 'all', 'col': '2', 'type': 'average', 'result': '2262', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'population ( 2011 )'], 'result': '2262', 'ind': 0, 'tostr': 'avg { all_rows ; population ( 2011 ) }'}, '2262'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; population ( 2011 ) } ; 2262 } = true', 'tointer': 'the average of the population ( 2011 ) record of all rows is 2262 .'} | round_eq { avg { all_rows ; population ( 2011 ) } ; 2262 } = true | the average of the population ( 2011 ) record of all rows is 2262 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'population (2011)_4': 4, '2262_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'population (2011)_4': 'population ( 2011 )', '2262_5': '2262'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'population (2011)_4': [0], '2262_5': [1]} | ['name', 'population ( 2011 )', 'population ( 2006 )', 'change ( % )', 'area ( km square )', 'population density'] | [['beaubassin east', '6200', '6429', '- 3.6', '291.12', '21.3'], ['campobello island', '925', '1056', '- 12.4', '39.67', '23.3'], ['kedgwick', '993', '1146', '- 13.4', '4.28', '232.2'], ['saint - andré', '819', '868', '- 5.6', '8.12', '100.8'], ['upper miramichi', '2373', '2414', '- 1.7', '1835.01', '1.3']] |
united states house of representatives elections , 1962 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1962 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1341884-40.html.csv | aggregation | republicans in the united states house of representatives elections , 1962 averaged 50.12 % of the vote . | {'scope': 'subset', 'col': '6', 'type': 'average', 'result': '50.12', 'subset': {'col': '3', 'criterion': 'equal', 'value': 'republican'}} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'party', 'republican'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; party ; republican }', 'tointer': 'select the rows whose party record fuzzily matches to republican .'}, 'candidates'], 'result': '50.12', 'ind': 1, 'tostr': 'avg { filter_eq { all_rows ; party ; republican } ; candidates }'}, '50.12'], 'result': True, 'ind': 2, 'tostr': 'round_eq { avg { filter_eq { all_rows ; party ; republican } ; candidates } ; 50.12 } = true', 'tointer': 'select the rows whose party record fuzzily matches to republican . the average of the candidates record of these rows is 50.12 .'} | round_eq { avg { filter_eq { all_rows ; party ; republican } ; candidates } ; 50.12 } = true | select the rows whose party record fuzzily matches to republican . the average of the candidates record of these rows is 50.12 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'avg_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'party_5': 5, 'republican_6': 6, 'candidates_7': 7, '50.12_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'avg_1': 'avg', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'party_5': 'party', 'republican_6': 'republican', 'candidates_7': 'candidates', '50.12_8': '50.12'} | {'eq_2': [3], 'result_3': [], 'avg_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'party_5': [0], 'republican_6': [0], 'candidates_7': [1], '50.12_8': [2]} | ['district', 'incumbent', 'party', 'first elected', 'result', 'candidates'] | [['pennsylvania 3', 'james a byrne', 'democratic', '1952', 're - elected', 'james a byrne ( d ) 59.3 % joseph r burns ( r ) 40.7 %'], ['pennsylvania 4', 'herman toll redistricted from 6th', 'democratic', '1958', 're - elected', 'herman toll ( d ) 56.0 % frank j barbera ( r ) 44.0 %'], ['pennsylvania 16', 'john c kunkel', 'republican', '1961', 're - elected', 'john c kunkel ( r ) 66.7 % john a walter ( d ) 33.3 %'], ['pennsylvania 21', 'john h dent', 'democratic', '1958', 're - elected', 'john h dent ( d ) 59.6 % charles e scalf ( r ) 40.4 %'], ['pennsylvania 22', 'john p saylor', 'republican', '1949', 're - elected', 'john p saylor ( r ) 57.5 % donald j perry ( d ) 42.5 %'], ['pennsylvania 24', 'carroll d kearns', 'republican', '1946', 'lost renomination republican hold', 'james d weaver ( r ) 51.4 % peter j joyce ( d ) 48.6 %']] |
list of corporations by market capitalization | https://en.wikipedia.org/wiki/List_of_corporations_by_market_capitalization | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14094649-20.html.csv | superlative | the corporation that has the most value on the market is microsoft . | {'scope': 'all', 'col_superlative': '5', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '2', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'market value ( usd million )'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; market value ( usd million ) }'}, 'name'], 'result': 'microsoft', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; market value ( usd million ) } ; name }'}, 'microsoft'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; market value ( usd million ) } ; name } ; microsoft } = true', 'tointer': 'select the row whose market value ( usd million ) record of all rows is maximum . the name record of this row is microsoft .'} | eq { hop { argmax { all_rows ; market value ( usd million ) } ; name } ; microsoft } = true | select the row whose market value ( usd million ) record of all rows is maximum . the name record of this row is microsoft . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'market value ( usd million)_5': 5, 'name_6': 6, 'microsoft_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'market value ( usd million)_5': 'market value ( usd million )', 'name_6': 'name', 'microsoft_7': 'microsoft'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'market value ( usd million)_5': [0], 'name_6': [1], 'microsoft_7': [2]} | ['rank', 'name', 'headquarters', 'primary industry', 'market value ( usd million )'] | [['1', 'microsoft', 'united states', 'software industry', '586197'], ['2', 'general electric', 'united states', 'conglomerate', '474956'], ['3', 'ntt docomo', 'japan', 'telecommunications', '366204'], ['4', 'cisco systems', 'united states', 'networking hardware', '348965'], ['5', 'wal - mart', 'united states', 'retail', '286153'], ['6', 'intel corporation', 'united states', 'computer hardware', '277096'], ['7', 'nippon telegraph and telephone', 'japan', 'telecommunications', '274905'], ['8', 'exxon mobil', 'united states', 'oil and gas', '265894'], ['9', 'lucent technologies', 'united states', 'telecommunications', '237668'], ['10', 'deutsche telekom', 'germany', 'telecommunications', '209628']] |
list of sumo record holders | https://en.wikipedia.org/wiki/List_of_sumo_record_holders | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17634218-20.html.csv | comparative | of the sumo record holders , aran participated in one less tournament than itai . | {'row_1': '4', 'row_2': '5', 'col': '2', 'col_other': '1', 'relation': 'diff', 'record_mentioned': 'no', 'diff_result': {'diff_value': '1', 'bigger': 'row2'}} | {'func': 'eq', 'args': [{'func': 'diff', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name', 'aran'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose name record fuzzily matches to aran .', 'tostr': 'filter_eq { all_rows ; name ; aran }'}, 'tournaments'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; name ; aran } ; tournaments }', 'tointer': 'select the rows whose name record fuzzily matches to aran . take the tournaments record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name', 'itai'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose name record fuzzily matches to itai .', 'tostr': 'filter_eq { all_rows ; name ; itai }'}, 'tournaments'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; name ; itai } ; tournaments }', 'tointer': 'select the rows whose name record fuzzily matches to itai . take the tournaments record of this row .'}], 'result': '-1', 'ind': 4, 'tostr': 'diff { hop { filter_eq { all_rows ; name ; aran } ; tournaments } ; hop { filter_eq { all_rows ; name ; itai } ; tournaments } }'}, '-1'], 'result': True, 'ind': 5, 'tostr': 'eq { diff { hop { filter_eq { all_rows ; name ; aran } ; tournaments } ; hop { filter_eq { all_rows ; name ; itai } ; tournaments } } ; -1 } = true', 'tointer': 'select the rows whose name record fuzzily matches to aran . take the tournaments record of this row . select the rows whose name record fuzzily matches to itai . take the tournaments record of this row . the second record is 1 larger than the first record .'} | eq { diff { hop { filter_eq { all_rows ; name ; aran } ; tournaments } ; hop { filter_eq { all_rows ; name ; itai } ; tournaments } } ; -1 } = true | select the rows whose name record fuzzily matches to aran . take the tournaments record of this row . select the rows whose name record fuzzily matches to itai . take the tournaments record of this row . the second record is 1 larger than the first record . | 6 | 6 | {'eq_5': 5, 'result_6': 6, 'diff_4': 4, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'name_8': 8, 'aran_9': 9, 'tournaments_10': 10, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_11': 11, 'name_12': 12, 'itai_13': 13, 'tournaments_14': 14, '-1_15': 15} | {'eq_5': 'eq', 'result_6': 'true', 'diff_4': 'diff', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'name_8': 'name', 'aran_9': 'aran', 'tournaments_10': 'tournaments', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_11': 'all_rows', 'name_12': 'name', 'itai_13': 'itai', 'tournaments_14': 'tournaments', '-1_15': '-1'} | {'eq_5': [6], 'result_6': [], 'diff_4': [5], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_7': [0], 'name_8': [0], 'aran_9': [0], 'tournaments_10': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_11': [1], 'name_12': [1], 'itai_13': [1], 'tournaments_14': [3], '-1_15': [5]} | ['name', 'tournaments', 'pro debut', 'top division debut', 'highest rank'] | [['jōkōryū', '9', 'may 2011', 'november 2012', 'maegashira 7'], ['ōsunaarashi', '10', 'march 2012', 'november 2013', 'maegashira 15'], ['kotoōshū', '11', 'november 2002', 'september 2004', 'ōzeki'], ['aran', '11', 'january 2007', 'november 2008', 'sekiwake'], ['itai', '12', 'september 1978', 'september 1980', 'komusubi'], ['konishiki', '12', 'july 1982', 'july 1984', 'ōzeki'], ['tochiazuma ii', '12', 'november 1994', 'november 1996', 'ōzeki'], ['asashōryū', '12', 'january 1999', 'january 2001', 'yokozuna'], ['tokitenkū', '12', 'july 2002', 'july 2004', 'komusubi'], ['yoshikaze', '12', 'january 2004', 'january 2006', 'maegashira 1'], ['baruto', '12', 'may 2004', 'may 2006', 'ōzeki'], ['sakaizawa', '12', 'march 2006', 'march 2008', 'maegashira 15'], ['yamamotoyama', '12', 'january 2007', 'january 2009', 'maegashira 9']] |
abdelmalek cherrad | https://en.wikipedia.org/wiki/Abdelmalek_Cherrad | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1646643-1.html.csv | count | abdelmalek cherrad scored a total of three goals in the 2006 fifa world cup qualification . | {'scope': 'all', 'criterion': 'equal', 'value': '2006 fifa world cup qualification', 'result': '3', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'competition', '2006 fifa world cup qualification'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose competition record fuzzily matches to 2006 fifa world cup qualification .', 'tostr': 'filter_eq { all_rows ; competition ; 2006 fifa world cup qualification }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; competition ; 2006 fifa world cup qualification } }', 'tointer': 'select the rows whose competition record fuzzily matches to 2006 fifa world cup qualification . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; competition ; 2006 fifa world cup qualification } } ; 3 } = true', 'tointer': 'select the rows whose competition record fuzzily matches to 2006 fifa world cup qualification . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; competition ; 2006 fifa world cup qualification } } ; 3 } = true | select the rows whose competition record fuzzily matches to 2006 fifa world cup qualification . the number of such rows is 3 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'competition_5': 5, '2006 fifa world cup qualification_6': 6, '3_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'competition_5': 'competition', '2006 fifa world cup qualification_6': '2006 fifa world cup qualification', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'competition_5': [0], '2006 fifa world cup qualification_6': [0], '3_7': [2]} | ['date', 'venue', 'score', 'result', 'competition'] | [['24 april 2003', 'stade de la licorne , amiens , france', '3 - 0', '3 - 1', 'friendly match'], ['4 september 2003', 'stade paul audrain , dinard , france', '1 - 0', '1 - 0', 'friendly match'], ['14 november 2003', 'stade 5 juillet 1962 , algiers , algeria', '1 - 0', '6 - 0', '2006 fifa world cup qualification'], ['14 november 2003', 'stade 5 juillet 1962 , algiers , algeria', '2 - 0', '6 - 0', '2006 fifa world cup qualification'], ['8 february 2004', 'stade taïeb el mhiri , sfax , tunisia', '1 - 0', '1 - 3', '2004 african cup of nations'], ['30 may 2004', 'stade 19 mai 1956 , annaba , algeria', '1 - 1', '1 - 1', 'friendly match'], ['20 june 2004', 'national stadium , harare , zimbabwe', '1 - 0', '1 - 1', '2006 fifa world cup qualification']] |
1905 in brazilian football | https://en.wikipedia.org/wiki/1905_in_Brazilian_football | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15421748-1.html.csv | superlative | among the 1905 brazilian football teams , paulistano achieved the highest amount of points . | {'scope': 'all', 'col_superlative': '3', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '2', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'points'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; points }'}, 'team'], 'result': 'paulistano', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; points } ; team }'}, 'paulistano'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; points } ; team } ; paulistano } = true', 'tointer': 'select the row whose points record of all rows is maximum . the team record of this row is paulistano .'} | eq { hop { argmax { all_rows ; points } ; team } ; paulistano } = true | select the row whose points record of all rows is maximum . the team record of this row is paulistano . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'points_5': 5, 'team_6': 6, 'paulistano_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'points_5': 'points', 'team_6': 'team', 'paulistano_7': 'paulistano'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'points_5': [0], 'team_6': [1], 'paulistano_7': [2]} | ['position', 'team', 'points', 'played', 'drawn', 'lost', 'against', 'difference'] | [['1', 'paulistano', '18', '10', '2', '0', '3', '17'], ['2', 'germnia', '13', '10', '3', '2', '16', '14'], ['3', 'sc internacional de são paulo', '11', '10', '3', '3', '19', '- 4'], ['4', 'são paulo athletic', '8', '10', '0', '6', '26', '- 10'], ['5', 'mackenzie', '7', '10', '1', '6', '27', '0'], ['6', 'aa das palmeiras', '3', '10', '1', '8', '27', '- 17']] |
2009 nll season | https://en.wikipedia.org/wiki/2009_NLL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-14132239-3.html.csv | unique | only one game in the 2009 nll season was played in april . | {'scope': 'all', 'row': '14', 'col': '1', 'col_other': 'n/a', 'criterion': 'equal', 'value': 'april', 'subset': None} | {'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'month', 'april'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose month record fuzzily matches to april .', 'tostr': 'filter_eq { all_rows ; month ; april }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; month ; april } } = true', 'tointer': 'select the rows whose month record fuzzily matches to april . there is only one such row in the table .'} | only { filter_eq { all_rows ; month ; april } } = true | select the rows whose month record fuzzily matches to april . there is only one such row in the table . | 2 | 2 | {'only_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'month_4': 4, 'april_5': 5} | {'only_1': 'only', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'month_4': 'month', 'april_5': 'april'} | {'only_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'month_4': [0], 'april_5': [0]} | ['month', 'week', 'overall', 'offensive', 'defensive', 'transition', 'rookie'] | [['january', '1', 'blaine manning', 'casey powell', 'kevin croswell', 'scott stewart', 'andrew watt'], ['january', '2', 'gary gait', 'pat maddalena', 'ken montour', 'brodie merrill', 'sean thomson'], ['january', '3', 'mark steenhuis', 'mark steenhuis', 'ken montour', 'greg peyser', 'daryl veltman'], ['january', '4', 'dan teat', 'dan dawson', 'michael thompson', 'tyler codron', 'daryl veltman'], ['january', '5', 'matt disher', 'mike accursi', 'matt disher', 'curtis hodgson', 'matt danowski'], ['february', '6', 'gary bining', 'tracey kelusky', 'pat campbell', 'chris driscoll', 'gary bining'], ['february', '7', 'mark steenhuis', 'mark steenhuis', 'anthony cosmo', 'jason bloom', 'tyler crompton'], ['february', '8', 'dan dawson', 'mark steenhuis', 'jon harnett', 'bobby mcbride', 'rhys duch'], ['february', '9', 'shawn evans', 'shawn evans', 'matt disher', 'kyle ross', 'kevin buchanan'], ['march', '10', 'shawn evans', 'shawn evans', 'sandy chapman', 'pat mccready', 'kevin buchanan'], ['march', '11', 'bob watson', 'john tavares', 'ken montour', 'paul rabil', 'tyler crompton'], ['march', '12', 'athan iannucci', 'andy secore', 'matt vinc', 'brodie merrill', 'rhys duch'], ['march', '13', 'john tavares', 'colin doyle', 'tyler richards', 'brodie merrill', 'rhys duch'], ['april', '14', 'anthony cosmo', 'merrick thomson', 'matt disher', 'scott stewart', 'rhys duch']] |
1985 masters tournament | https://en.wikipedia.org/wiki/1985_Masters_Tournament | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16488699-1.html.csv | comparative | in the 1985 masters tournament , the money earned by craig stadler was 5863 more than fred couples . | {'row_1': '9', 'row_2': '10', 'col': '6', 'col_other': '2', 'relation': 'diff', 'record_mentioned': 'no', 'diff_result': {'diff_value': '5863', 'bigger': 'row1'}} | {'func': 'eq', 'args': [{'func': 'diff', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'craig stadler'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose player record fuzzily matches to craig stadler .', 'tostr': 'filter_eq { all_rows ; player ; craig stadler }'}, 'money'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; player ; craig stadler } ; money }', 'tointer': 'select the rows whose player record fuzzily matches to craig stadler . take the money record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'fred couples'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose player record fuzzily matches to fred couples .', 'tostr': 'filter_eq { all_rows ; player ; fred couples }'}, 'money'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; player ; fred couples } ; money }', 'tointer': 'select the rows whose player record fuzzily matches to fred couples . take the money record of this row .'}], 'result': '5863', 'ind': 4, 'tostr': 'diff { hop { filter_eq { all_rows ; player ; craig stadler } ; money } ; hop { filter_eq { all_rows ; player ; fred couples } ; money } }'}, '5863'], 'result': True, 'ind': 5, 'tostr': 'eq { diff { hop { filter_eq { all_rows ; player ; craig stadler } ; money } ; hop { filter_eq { all_rows ; player ; fred couples } ; money } } ; 5863 } = true', 'tointer': 'select the rows whose player record fuzzily matches to craig stadler . take the money record of this row . select the rows whose player record fuzzily matches to fred couples . take the money record of this row . the first record is 5863 larger than the second record .'} | eq { diff { hop { filter_eq { all_rows ; player ; craig stadler } ; money } ; hop { filter_eq { all_rows ; player ; fred couples } ; money } } ; 5863 } = true | select the rows whose player record fuzzily matches to craig stadler . take the money record of this row . select the rows whose player record fuzzily matches to fred couples . take the money record of this row . the first record is 5863 larger than the second record . | 6 | 6 | {'eq_5': 5, 'result_6': 6, 'diff_4': 4, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'player_8': 8, 'craig stadler_9': 9, 'money_10': 10, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_11': 11, 'player_12': 12, 'fred couples_13': 13, 'money_14': 14, '5863_15': 15} | {'eq_5': 'eq', 'result_6': 'true', 'diff_4': 'diff', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'player_8': 'player', 'craig stadler_9': 'craig stadler', 'money_10': 'money', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_11': 'all_rows', 'player_12': 'player', 'fred couples_13': 'fred couples', 'money_14': 'money', '5863_15': '5863'} | {'eq_5': [6], 'result_6': [], 'diff_4': [5], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_7': [0], 'player_8': [0], 'craig stadler_9': [0], 'money_10': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_11': [1], 'player_12': [1], 'fred couples_13': [1], 'money_14': [3], '5863_15': [5]} | ['place', 'player', 'country', 'score', 'to par', 'money'] | [['1', 'bernhard langer', 'west germany', '72 + 74 + 68 + 68 = 282', '- 6', '126000'], ['t2', 'seve ballesteros', 'spain', '72 + 71 + 71 + 70 = 284', '- 4', '52267'], ['t2', 'raymond floyd', 'united states', '70 + 73 + 69 + 72 = 284', '- 4', '52267'], ['t2', 'curtis strange', 'united states', '80 + 65 + 68 + 71 = 284', '- 4', '52267'], ['5', 'jay haas', 'united states', '73 + 73 + 72 + 67 = 285', '- 3', '28000'], ['t6', 'gary hallberg', 'united states', '68 + 73 + 75 + 70 = 286', '- 2', '22663'], ['t6', 'bruce lietzke', 'united states', '72 + 71 + 73 + 70 = 286', '- 2', '22663'], ['t6', 'jack nicklaus', 'united states', '71 + 74 + 72 + 69 = 286', '- 2', '22663'], ['t6', 'craig stadler', 'united states', '73 + 67 + 76 + 70 = 286', '- 2', '22663'], ['t10', 'fred couples', 'united states', '75 + 73 + 69 + 70 = 287', '- 1', '16800'], ['t10', 'david graham', 'australia', '74 + 71 + 71 + 71 = 287', '- 1', '16800'], ['t10', 'lee trevino', 'united states', '70 + 73 + 72 + 72 = 287', '- 1', '16800'], ['t10', 'tom watson', 'united states', '69 + 71 + 75 + 72 = 287', '- 1', '16800']] |
dancing with the stars ( u.s. season 3 ) | https://en.wikipedia.org/wiki/Dancing_with_the_Stars_%28U.S._season_3%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10535525-3.html.csv | count | there were seven perfect scores ( 30 ) in dancing with the stars , season 3 . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': '30', 'result': '7', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'best score', '30'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose best score record fuzzily matches to 30 .', 'tostr': 'filter_eq { all_rows ; best score ; 30 }'}], 'result': '7', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; best score ; 30 } }', 'tointer': 'select the rows whose best score record fuzzily matches to 30 . the number of such rows is 7 .'}, '7'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; best score ; 30 } } ; 7 } = true', 'tointer': 'select the rows whose best score record fuzzily matches to 30 . the number of such rows is 7 .'} | eq { count { filter_eq { all_rows ; best score ; 30 } } ; 7 } = true | select the rows whose best score record fuzzily matches to 30 . the number of such rows is 7 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'best score_5': 5, '30_6': 6, '7_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'best score_5': 'best score', '30_6': '30', '7_7': '7'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'best score_5': [0], '30_6': [0], '7_7': [2]} | ['dance', 'best dancer', 'best score', 'worst dancer', 'worst score'] | [['cha - cha - cha', 'emmitt smith', '30', 'tucker carlson', '12'], ['foxtrot', 'mario lopez joey lawrence', '29', 'sara evans', '15'], ['mambo', 'emmitt smith', '30', 'sara evans', '21'], ['quickstep', 'joey lawrence', '29', 'jerry springer', '19'], ['jive', 'monique coleman mario lopez', '27', 'joey lawrence willa ford', '22'], ['tango', 'mario lopez', '30', 'emmitt smith', '19'], ['waltz', 'emmitt smith', '29', 'jerry springer', '22'], ['rumba', 'joey lawrence', '30', 'joey lawrence', '24'], ['paso doble', 'mario lopez', '30', 'jerry springer', '18'], ['samba', 'emmitt smith', '30', 'monique coleman', '23'], ['freestyle', 'mario lopez', '30', 'emmitt smith', '29']] |
wisconsin badgers men 's basketball | https://en.wikipedia.org/wiki/Wisconsin_Badgers_men%27s_basketball | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10631744-2.html.csv | comparative | steve yoder coached more wisconsin badgers men 's basketball games than his predecessor , bill cofield . | {'row_1': '11', 'row_2': '10', 'col': '3', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'coach', 'steve yoder'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose coach record fuzzily matches to steve yoder .', 'tostr': 'filter_eq { all_rows ; coach ; steve yoder }'}, 'record'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; coach ; steve yoder } ; record }', 'tointer': 'select the rows whose coach record fuzzily matches to steve yoder . take the record record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'coach', 'bill cofield'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose coach record fuzzily matches to bill cofield .', 'tostr': 'filter_eq { all_rows ; coach ; bill cofield }'}, 'record'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; coach ; bill cofield } ; record }', 'tointer': 'select the rows whose coach record fuzzily matches to bill cofield . take the record record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; coach ; steve yoder } ; record } ; hop { filter_eq { all_rows ; coach ; bill cofield } ; record } } = true', 'tointer': 'select the rows whose coach record fuzzily matches to steve yoder . take the record record of this row . select the rows whose coach record fuzzily matches to bill cofield . take the record record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; coach ; steve yoder } ; record } ; hop { filter_eq { all_rows ; coach ; bill cofield } ; record } } = true | select the rows whose coach record fuzzily matches to steve yoder . take the record record of this row . select the rows whose coach record fuzzily matches to bill cofield . take the record record of this row . the first record is greater than the second record . | 5 | 5 | {'greater_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'coach_7': 7, 'steve yoder_8': 8, 'record_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'coach_11': 11, 'bill cofield_12': 12, 'record_13': 13} | {'greater_4': 'greater', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'coach_7': 'coach', 'steve yoder_8': 'steve yoder', 'record_9': 'record', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'coach_11': 'coach', 'bill cofield_12': 'bill cofield', 'record_13': 'record'} | {'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'coach_7': [0], 'steve yoder_8': [0], 'record_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'coach_11': [1], 'bill cofield_12': [1], 'record_13': [3]} | ['coach', 'years', 'record', 'conference record', 'overall win percentage'] | [['james c elsom', '1898 - 1904', '25 - 14', '-', '64 %'], ['emmett angell', '1904 - 1908', '43 - 15', '19 - 5', '74 %'], ['haskell noyes', '1908 - 1911', '26 - 15', '18 - 15', '63 %'], ['walter meanwell', '1911 - 1917', '92 - 9', '63 - 9', '91 %'], ['guy lowman', '1917 - 1920', '34 - 19', '19 - 17', '64 %'], ['walter meanwell', '1920 - 1934', '154 - 90', '95 - 71', '63 %'], ['bud foster', '1934 - 1959', '265 - 267', '143 - 182', '49 %'], ['john e erickson', '1959 - 1968', '100 - 114', '52 - 74', '46 %'], ['john powless', '1968 - 1976', '88 - 108', '42 - 78', '44 %'], ['bill cofield', '1976 - 1982', '63 - 101', '32 - 76', '38 %'], ['steve yoder', '1982 - 1992', '128 - 165', '50 - 130', '43 %'], ['stu jackson', '1992 - 1994', '32 - 25', '15 - 21', '56 %'], ['stan van gundy', '1994 - 1995', '13 - 14', '7 - 11', '48 %'], ['dick bennett', '1995 - 2000', '93 - 69', '39 - 45', '57 %'], ['brad soderberg', '2000 - 2001', '16 - 10', '9 - 7', '61 %'], ['bo ryan', '2001 - present', '291 - 113', '144 - 60', '72 %'], ['total', '1898 - 2013', '1463 - 1148', '747 - 801', '56 %']] |
olivier occéan | https://en.wikipedia.org/wiki/Olivier_Occ%C3%A9an | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1109407-1.html.csv | majority | the majority of olivier occéan 's goals were in the 2014 fifa world cup qualification . | {'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'equal', 'value': '2014 fifa world cup qualification', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'competition', '2014 fifa world cup qualification'], 'result': True, 'ind': 0, 'tointer': 'for the competition records of all rows , most of them fuzzily match to 2014 fifa world cup qualification .', 'tostr': 'most_eq { all_rows ; competition ; 2014 fifa world cup qualification } = true'} | most_eq { all_rows ; competition ; 2014 fifa world cup qualification } = true | for the competition records of all rows , most of them fuzzily match to 2014 fifa world cup qualification . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'competition_3': 3, '2014 fifa world cup qualification_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'competition_3': 'competition', '2014 fifa world cup qualification_4': '2014 fifa world cup qualification'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'competition_3': [0], '2014 fifa world cup qualification_4': [0]} | ['date', 'venue', 'score', 'result', 'competition'] | [['february 9 , 2005', 'windsor park , belfast , northern ireland', '1 - 0', '1 - 0', 'friendly'], ['august 22 , 2007', 'laugardalsvöllur , reykjavík , iceland', '1 - 1', '1 - 1', 'friendly'], ['october 7 , 2011', 'beausejour stadium , gros islet , saint lucia', '3 - 0', '7 - 0', '2014 fifa world cup qualification'], ['october 7 , 2011', 'beausejour stadium , gros islet , saint lucia', '5 - 0', '7 - 0', '2014 fifa world cup qualification'], ['november 15 , 2011', 'bmo field , toronto , canada', '1 - 0', '4 - 0', '2014 fifa world cup qualification'], ['june 8 , 2012', 'estadio pedro marrero , havana , cuba', '1 - 0', '1 - 0', '2014 fifa world cup qualification']] |
eurovision song contest 1962 | https://en.wikipedia.org/wiki/Eurovision_Song_Contest_1962 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-181142-1.html.csv | aggregation | a total number of 96 points were scored in the 1962 eurovision song contest . | {'scope': 'all', 'col': '6', 'type': 'sum', 'result': '96', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'points'], 'result': '96', 'ind': 0, 'tostr': 'sum { all_rows ; points }'}, '96'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; points } ; 96 } = true', 'tointer': 'the sum of the points record of all rows is 96 .'} | round_eq { sum { all_rows ; points } ; 96 } = true | the sum of the points record of all rows is 96 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'points_4': 4, '96_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'points_4': 'points', '96_5': '96'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'points_4': [0], '96_5': [1]} | ['draw', 'language', 'artist', 'english translation', 'place', 'points'] | [['01', 'finnish', 'marion rung', 'chirpy chirp', '7', '4'], ['02', 'french', 'fud leclerc', 'your name', '13', '0'], ['03', 'spanish', 'victor balaguer', 'call me', '13', '0'], ['04', 'german', 'eleonore schwarz', 'only in the vienna air', '13', '0'], ['05', 'danish', 'ellen winther', 'lullaby', '10', '2'], ['06', 'swedish', 'inger berggren', 'sun and spring', '7', '4'], ['07', 'german', 'conny froboess', 'two little italians', '6', '9'], ['08', 'dutch', 'de spelbrekers', '-', '13', '0'], ['09', 'french', 'isabelle aubret', 'a first love', '1', '26'], ['10', 'norwegian', 'inger jacobsen', 'come sun , come rain', '10', '2'], ['11', 'french', 'jean philippe', 'the return', '10', '2'], ['12', 'serbian', 'lola novaković', "do n't turn the lights on at twilight", '4', '10'], ['13', 'english', 'ronnie carroll', '-', '4', '10'], ['14', 'french', 'camillo felgen', 'little chap', '3', '11'], ['15', 'italian', 'claudio villa', 'goodbye , goodbye', '9', '3'], ['16', 'french', 'françois deguelt', 'say nothing', '2', '13']] |
1977 san francisco 49ers season | https://en.wikipedia.org/wiki/1977_San_Francisco_49ers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18673955-1.html.csv | ordinal | the san francisco 49ers ' game against new york giants recorded their highest attendance of the 1977 season . | {'row': '5', 'col': '5', 'order': '1', 'col_other': '3', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'attendance', '1'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; attendance ; 1 }'}, 'opponent'], 'result': 'new york giants', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; attendance ; 1 } ; opponent }'}, 'new york giants'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; attendance ; 1 } ; opponent } ; new york giants } = true', 'tointer': 'select the row whose attendance record of all rows is 1st maximum . the opponent record of this row is new york giants .'} | eq { hop { nth_argmax { all_rows ; attendance ; 1 } ; opponent } ; new york giants } = true | select the row whose attendance record of all rows is 1st maximum . the opponent record of this row is new york giants . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'attendance_5': 5, '1_6': 6, 'opponent_7': 7, 'new york giants_8': 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'attendance_5': 'attendance', '1_6': '1', 'opponent_7': 'opponent', 'new york giants_8': 'new york giants'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'attendance_5': [0], '1_6': [0], 'opponent_7': [1], 'new york giants_8': [2]} | ['week', 'date', 'opponent', 'result', 'attendance'] | [['1', 'september 19 , 1977', 'pittsburgh steelers', 'l 27 - 0', '48046'], ['2', 'september 25 , 1977', 'miami dolphins', 'l 19 - 15', '40503'], ['3', 'october 2 , 1977', 'los angeles rams', 'l 34 - 14', '55466'], ['4', 'october 9 , 1977', 'atlanta falcons', 'l 7 - 0', '38009'], ['5', 'october 16 , 1977', 'new york giants', 'l 20 - 17', '70366'], ['6', 'october 23 , 1977', 'detroit lions', 'w 28 - 7', '39392'], ['7', 'october 30 , 1977', 'tampa bay buccaneers', 'w 20 - 10', '34700'], ['8', 'november 6 , 1977', 'atlanta falcons', 'w 10 - 3', '46577'], ['9', 'november 13 , 1977', 'new orleans saints', 'w 10 - 7', '41564'], ['10', 'november 20 , 1977', 'los angeles rams', 'l 23 - 10', '56779'], ['11', 'november 27 , 1977', 'new orleans saints', 'w 20 - 17', '33702'], ['12', 'december 4 , 1977', 'minnesota vikings', 'l 28 - 27', '40745'], ['13', 'december 12 , 1977', 'dallas cowboys', 'l 42 - 35', '55851'], ['14', 'december 18 , 1977', 'green bay packers', 'l 16 - 14', '44902']] |
rowing at the 2008 summer olympics - women 's single sculls | https://en.wikipedia.org/wiki/Rowing_at_the_2008_Summer_Olympics_%E2%80%93_Women%27s_single_sculls | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18662695-8.html.csv | unique | inga dudchenko was the only participant from kazakhstan in the 2008 summer olympics - women 's single sculls . | {'scope': 'all', 'row': '5', 'col': '3', 'col_other': '2', 'criterion': 'equal', 'value': 'kazakhstan', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'kazakhstan'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose country record fuzzily matches to kazakhstan .', 'tostr': 'filter_eq { all_rows ; country ; kazakhstan }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; country ; kazakhstan } }', 'tointer': 'select the rows whose country record fuzzily matches to kazakhstan . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'kazakhstan'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose country record fuzzily matches to kazakhstan .', 'tostr': 'filter_eq { all_rows ; country ; kazakhstan }'}, 'athlete'], 'result': 'inga dudchenko', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; country ; kazakhstan } ; athlete }'}, 'inga dudchenko'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; country ; kazakhstan } ; athlete } ; inga dudchenko }', 'tointer': 'the athlete record of this unqiue row is inga dudchenko .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; country ; kazakhstan } } ; eq { hop { filter_eq { all_rows ; country ; kazakhstan } ; athlete } ; inga dudchenko } } = true', 'tointer': 'select the rows whose country record fuzzily matches to kazakhstan . there is only one such row in the table . the athlete record of this unqiue row is inga dudchenko .'} | and { only { filter_eq { all_rows ; country ; kazakhstan } } ; eq { hop { filter_eq { all_rows ; country ; kazakhstan } ; athlete } ; inga dudchenko } } = true | select the rows whose country record fuzzily matches to kazakhstan . there is only one such row in the table . the athlete record of this unqiue row is inga dudchenko . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'country_7': 7, 'kazakhstan_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'athlete_9': 9, 'inga dudchenko_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'country_7': 'country', 'kazakhstan_8': 'kazakhstan', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'athlete_9': 'athlete', 'inga dudchenko_10': 'inga dudchenko'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'country_7': [0], 'kazakhstan_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'athlete_9': [2], 'inga dudchenko_10': [3]} | ['rank', 'athlete', 'country', 'time', 'notes'] | [['1', 'michelle guerette', 'united states', '7:28.91', 'sa / b'], ['2', 'julia michalska', 'poland', '7:31.90', 'sa / b'], ['3', 'gabriella bascelli', 'italy', '7:36.68', 'sa / b'], ['4', 'nuria domã\xadnguez', 'spain', '7:49.60', 'sc / d'], ['5', 'inga dudchenko', 'kazakhstan', '8:15.88', 'sc / d'], ['6', 'elana hill', 'zimbabwe', '8:20.84', 'sc / d']] |
2003 cfl draft | https://en.wikipedia.org/wiki/2003_CFL_Draft | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-21321804-3.html.csv | unique | dounia whitehouse was the only player in the cb position selected in the first eight picks of round three of the 2003 cfl draft . | {'scope': 'all', 'row': '2', 'col': '4', 'col_other': '3', 'criterion': 'equal', 'value': 'cb', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'position', 'cb'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose position record fuzzily matches to cb .', 'tostr': 'filter_eq { all_rows ; position ; cb }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; position ; cb } }', 'tointer': 'select the rows whose position record fuzzily matches to cb . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'position', 'cb'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose position record fuzzily matches to cb .', 'tostr': 'filter_eq { all_rows ; position ; cb }'}, 'player'], 'result': 'dounia whitehouse', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; position ; cb } ; player }'}, 'dounia whitehouse'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; position ; cb } ; player } ; dounia whitehouse }', 'tointer': 'the player record of this unqiue row is dounia whitehouse .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; position ; cb } } ; eq { hop { filter_eq { all_rows ; position ; cb } ; player } ; dounia whitehouse } } = true', 'tointer': 'select the rows whose position record fuzzily matches to cb . there is only one such row in the table . the player record of this unqiue row is dounia whitehouse .'} | and { only { filter_eq { all_rows ; position ; cb } } ; eq { hop { filter_eq { all_rows ; position ; cb } ; player } ; dounia whitehouse } } = true | select the rows whose position record fuzzily matches to cb . there is only one such row in the table . the player record of this unqiue row is dounia whitehouse . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'position_7': 7, 'cb_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'player_9': 9, 'dounia whitehouse_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'position_7': 'position', 'cb_8': 'cb', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'player_9': 'player', 'dounia whitehouse_10': 'dounia whitehouse'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'position_7': [0], 'cb_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'player_9': [2], 'dounia whitehouse_10': [3]} | ['pick', 'cfl team', 'player', 'position', 'college'] | [['19', 'ottawa renegades', 'patrick kabongo', 'dt', 'nebraska'], ['20', 'edmonton eskimos', 'dounia whitehouse', 'cb', 'charleston southern'], ['21', 'hamilton tiger - cats', 'kevin scott', 'lb', 'california pa'], ['22', 'winnipeg blue bombers', 'todd krenbring', 'ol', 'regina'], ['23', 'saskatchewan roughriders', 'mike mccullough', 'lb', 'st francis xavier'], ['24', 'bc lions', 'carl gourgues', 'ol', 'laval'], ['25', 'calgary stampeders', 'mike labinjo', 'dl', 'michigan state'], ['26', 'edmonton eskimos', 'joseph bonaventura', 'lb', "saint mary 's"]] |
united states house of representatives elections , 2006 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_2006 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1805191-24.html.csv | comparative | of the incumbents in the 2006 election for the united states house of representatives , john kline was first elected 12 years after jim ramstad was first elected . | {'row_1': '2', 'row_2': '3', 'col': '4', 'col_other': '2', 'relation': 'greater', 'record_mentioned': 'yes', 'diff_result': None} | {'func': 'and', 'args': [{'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'incumbent', 'john kline'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose incumbent record fuzzily matches to john kline .', 'tostr': 'filter_eq { all_rows ; incumbent ; john kline }'}, 'first elected'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; incumbent ; john kline } ; first elected }', 'tointer': 'select the rows whose incumbent record fuzzily matches to john kline . take the first elected record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'incumbent', 'jim ramstad'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose incumbent record fuzzily matches to jim ramstad .', 'tostr': 'filter_eq { all_rows ; incumbent ; jim ramstad }'}, 'first elected'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; incumbent ; jim ramstad } ; first elected }', 'tointer': 'select the rows whose incumbent record fuzzily matches to jim ramstad . take the first elected record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; incumbent ; john kline } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; jim ramstad } ; first elected } }', 'tointer': 'select the rows whose incumbent record fuzzily matches to john kline . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to jim ramstad . take the first elected record of this row . the first record is greater than the second record .'}, {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'incumbent', 'john kline'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose incumbent record fuzzily matches to john kline .', 'tostr': 'filter_eq { all_rows ; incumbent ; john kline }'}, 'first elected'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; incumbent ; john kline } ; first elected }', 'tointer': 'select the rows whose incumbent record fuzzily matches to john kline . take the first elected record of this row .'}, '2002'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; incumbent ; john kline } ; first elected } ; 2002 }', 'tointer': 'the first elected record of the first row is 2002 .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'incumbent', 'jim ramstad'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose incumbent record fuzzily matches to jim ramstad .', 'tostr': 'filter_eq { all_rows ; incumbent ; jim ramstad }'}, 'first elected'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; incumbent ; jim ramstad } ; first elected }', 'tointer': 'select the rows whose incumbent record fuzzily matches to jim ramstad . take the first elected record of this row .'}, '1990'], 'result': True, 'ind': 6, 'tostr': 'eq { hop { filter_eq { all_rows ; incumbent ; jim ramstad } ; first elected } ; 1990 }', 'tointer': 'the first elected record of the second row is 1990 .'}], 'result': True, 'ind': 7, 'tostr': 'and { eq { hop { filter_eq { all_rows ; incumbent ; john kline } ; first elected } ; 2002 } ; eq { hop { filter_eq { all_rows ; incumbent ; jim ramstad } ; first elected } ; 1990 } }', 'tointer': 'the first elected record of the first row is 2002 . the first elected record of the second row is 1990 .'}], 'result': True, 'ind': 8, 'tostr': 'and { greater { hop { filter_eq { all_rows ; incumbent ; john kline } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; jim ramstad } ; first elected } } ; and { eq { hop { filter_eq { all_rows ; incumbent ; john kline } ; first elected } ; 2002 } ; eq { hop { filter_eq { all_rows ; incumbent ; jim ramstad } ; first elected } ; 1990 } } } = true', 'tointer': 'select the rows whose incumbent record fuzzily matches to john kline . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to jim ramstad . take the first elected record of this row . the first record is greater than the second record . the first elected record of the first row is 2002 . the first elected record of the second row is 1990 .'} | and { greater { hop { filter_eq { all_rows ; incumbent ; john kline } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; jim ramstad } ; first elected } } ; and { eq { hop { filter_eq { all_rows ; incumbent ; john kline } ; first elected } ; 2002 } ; eq { hop { filter_eq { all_rows ; incumbent ; jim ramstad } ; first elected } ; 1990 } } } = true | select the rows whose incumbent record fuzzily matches to john kline . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to jim ramstad . take the first elected record of this row . the first record is greater than the second record . the first elected record of the first row is 2002 . the first elected record of the second row is 1990 . | 13 | 9 | {'and_8': 8, 'result_9': 9, 'greater_4': 4, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_10': 10, 'incumbent_11': 11, 'john kline_12': 12, 'first elected_13': 13, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_14': 14, 'incumbent_15': 15, 'jim ramstad_16': 16, 'first elected_17': 17, 'and_7': 7, 'eq_5': 5, '2002_18': 18, 'eq_6': 6, '1990_19': 19} | {'and_8': 'and', 'result_9': 'true', 'greater_4': 'greater', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_10': 'all_rows', 'incumbent_11': 'incumbent', 'john kline_12': 'john kline', 'first elected_13': 'first elected', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_14': 'all_rows', 'incumbent_15': 'incumbent', 'jim ramstad_16': 'jim ramstad', 'first elected_17': 'first elected', 'and_7': 'and', 'eq_5': 'eq', '2002_18': '2002', 'eq_6': 'eq', '1990_19': '1990'} | {'and_8': [9], 'result_9': [], 'greater_4': [8], 'num_hop_2': [4, 5], 'filter_str_eq_0': [2], 'all_rows_10': [0], 'incumbent_11': [0], 'john kline_12': [0], 'first elected_13': [2], 'num_hop_3': [4, 6], 'filter_str_eq_1': [3], 'all_rows_14': [1], 'incumbent_15': [1], 'jim ramstad_16': [1], 'first elected_17': [3], 'and_7': [8], 'eq_5': [7], '2002_18': [5], 'eq_6': [7], '1990_19': [6]} | ['district', 'incumbent', 'party', 'first elected', 'results'] | [['minnesota 1', 'gil gutknecht', 'republican', '1994', 'lost re - election democratic gain'], ['minnesota 2', 'john kline', 'republican', '2002', 're - elected'], ['minnesota 3', 'jim ramstad', 'republican', '1990', 're - elected'], ['minnesota 4', 'betty mccollum', 'democratic', '2000', 're - elected'], ['minnesota 5', 'martin sabo', 'democratic', '1978', 'retired democratic hold'], ['minnesota 6', 'mark kennedy', 'republican', '2000', 'retired to run for us senate republican hold'], ['minnesota 7', 'collin peterson', 'democratic', '1990', 're - elected'], ['minnesota 8', 'jim oberstar', 'democratic', '1974', 're - elected']] |
rosi sexton | https://en.wikipedia.org/wiki/Rosi_Sexton | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18916132-2.html.csv | comparative | rosi sexton 's fight against debi purcell lasted more rounds than her fight against tonomi sunaba . | {'row_1': '8', 'row_2': '10', 'col': '6', 'col_other': '3', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'debi purcell'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opponent record fuzzily matches to debi purcell .', 'tostr': 'filter_eq { all_rows ; opponent ; debi purcell }'}, 'round'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; opponent ; debi purcell } ; round }', 'tointer': 'select the rows whose opponent record fuzzily matches to debi purcell . take the round record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'tomomi sunaba'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose opponent record fuzzily matches to tomomi sunaba .', 'tostr': 'filter_eq { all_rows ; opponent ; tomomi sunaba }'}, 'round'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; opponent ; tomomi sunaba } ; round }', 'tointer': 'select the rows whose opponent record fuzzily matches to tomomi sunaba . take the round record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; opponent ; debi purcell } ; round } ; hop { filter_eq { all_rows ; opponent ; tomomi sunaba } ; round } } = true', 'tointer': 'select the rows whose opponent record fuzzily matches to debi purcell . take the round record of this row . select the rows whose opponent record fuzzily matches to tomomi sunaba . take the round record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; opponent ; debi purcell } ; round } ; hop { filter_eq { all_rows ; opponent ; tomomi sunaba } ; round } } = true | select the rows whose opponent record fuzzily matches to debi purcell . take the round record of this row . select the rows whose opponent record fuzzily matches to tomomi sunaba . take the round record of this row . the first record is greater than the second record . | 5 | 5 | {'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'opponent_7': 7, 'debi purcell_8': 8, 'round_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'opponent_11': 11, 'tomomi sunaba_12': 12, 'round_13': 13} | {'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'opponent_7': 'opponent', 'debi purcell_8': 'debi purcell', 'round_9': 'round', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'opponent_11': 'opponent', 'tomomi sunaba_12': 'tomomi sunaba', 'round_13': 'round'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'opponent_7': [0], 'debi purcell_8': [0], 'round_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'opponent_11': [1], 'tomomi sunaba_12': [1], 'round_13': [3]} | ['res', 'record', 'opponent', 'method', 'event', 'round', 'time', 'location'] | [['loss', '13 - 4', 'jessica andrade', 'decision ( unanimous )', 'ufc fight night : machida vs munoz', '3', '5:00', 'manchester , england'], ['loss', '13 - 3', 'alexis davis', 'decision ( unanimous )', 'ufc 161', '3', '5:00', 'winnipeg , manitoba , canada'], ['win', '13 - 2', 'aisling daly', 'decision ( unanimous )', 'cage warriors fighting championship 47', '3', '5:00', 'dublin , ireland'], ['win', '12 - 2', 'roxanne modafferi', 'decision ( unanimous )', 'cage warriors fighting championship 40', '3', '5:00', 'north london , england'], ['win', '11 - 2', 'sally krumdiack', 'tko ( punches )', 'cage warriors fighting championship 39', '2', '4:07', 'cork , ireland'], ['loss', '10 - 2', 'zoila frausto gurgel', 'ko ( knee and punches )', 'bellator 23', '1', '2:00', 'louisville , kentucky , united states'], ['win', '10 - 1', 'valerie coolbaugh', 'submission ( armbar )', 'bellator 12', '1', '3:40', 'hollywood , florida , united states'], ['win', '9 - 1', 'debi purcell', 'decision ( split )', 'shoxc : suganuma vs hamman ii', '3', '3:00', 'friant , california , united states'], ['win', '8 - 1', 'julia berezikova', 'submission ( armbar )', 'bodogfight - vancouver', '2', '1:49', 'vancouver , british columbia , canada'], ['win', '7 - 1', 'tomomi sunaba', 'technical decision ( unanimous )', 'bodogfight - costa rica', '2', '1:05', 'san josã , costa rica'], ['win', '6 - 1', 'carina damm', 'submission ( armbar )', 'bodogfight - st petersburg', '1', '4:15', 'saint petersburg , russia'], ['loss', '5 - 1', 'gina carano', 'ko ( punch )', 'world pro fighting championships', '2', '4:55', 'las vegas , nevada , united states'], ['win', '5 - 0', 'dina van den hooven', 'tko ( corner stoppage )', 'cwfc - strike force 4', '3', '5:00', 'english midlands , england'], ['win', '4 - 0', 'kelli salone', 'submission ( armbar )', 'p & g 1 - pride and glory 1', '1', '3:45', 'cardiff , wales'], ['win', '3 - 0', "carla o ' sullivan", 'submission ( rear - naked choke )', 'cwfc 3 - cage warriors 3', '1', 'n / a', 'hampshire , england'], ['win', '2 - 0', 'serena saunders', 'submission ( armbar )', 'cwfc 1 - armageddon', '1', '0:40', 'london , england'], ['win', '1 - 0', 'angela boyce', 'submission ( armbar )', 'g & s 5 - grapple & strike 5', '3', 'n / a', 'worcester , england']] |
new york court of appeals | https://en.wikipedia.org/wiki/New_York_Court_of_Appeals | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1531632-1.html.csv | unique | judge robert s smith is the only judge appointed in the year 2004 . | {'scope': 'all', 'row': '4', 'col': '2', 'col_other': '1', 'criterion': 'equal', 'value': '2004', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'appointed', '2004'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose appointed record is equal to 2004 .', 'tostr': 'filter_eq { all_rows ; appointed ; 2004 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; appointed ; 2004 } }', 'tointer': 'select the rows whose appointed record is equal to 2004 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'appointed', '2004'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose appointed record is equal to 2004 .', 'tostr': 'filter_eq { all_rows ; appointed ; 2004 }'}, 'name'], 'result': 'judge robert s smith', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; appointed ; 2004 } ; name }'}, 'judge robert s smith'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; appointed ; 2004 } ; name } ; judge robert s smith }', 'tointer': 'the name record of this unqiue row is judge robert s smith .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; appointed ; 2004 } } ; eq { hop { filter_eq { all_rows ; appointed ; 2004 } ; name } ; judge robert s smith } } = true', 'tointer': 'select the rows whose appointed record is equal to 2004 . there is only one such row in the table . the name record of this unqiue row is judge robert s smith .'} | and { only { filter_eq { all_rows ; appointed ; 2004 } } ; eq { hop { filter_eq { all_rows ; appointed ; 2004 } ; name } ; judge robert s smith } } = true | select the rows whose appointed record is equal to 2004 . there is only one such row in the table . the name record of this unqiue row is judge robert s smith . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'appointed_7': 7, '2004_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'name_9': 9, 'judge robert s smith_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'appointed_7': 'appointed', '2004_8': '2004', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'name_9': 'name', 'judge robert s smith_10': 'judge robert s smith'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'appointed_7': [0], '2004_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'name_9': [2], 'judge robert s smith_10': [3]} | ['name', 'appointed', 'term expiration', 'appointing governor', 'law school attended'] | [['chief judge jonathan lippman', '2009', '2015', 'david paterson , democrat', 'new york university school of law'], ['judge victoria a graffeo', '2000', '2014', 'george pataki , republican', 'albany law school'], ['judge susan phillips read', '2003', '2017', 'george pataki , republican', 'university of chicago law school'], ['judge robert s smith', '2004', '2014', 'george pataki , republican', 'columbia law school'], ['judge eugene f pigott , jr', '2006', '2016', 'george pataki , republican', 'university at buffalo law school'], ['judge jenny rivera', '2013', '2027', 'andrew cuomo , democrat', 'new york university school of law'], ['judge sheila abdus - salaam', '2013', '2027', 'andrew cuomo , democrat', 'columbia law school']] |
tomas behrend | https://en.wikipedia.org/wiki/Tomas_Behrend | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15290654-2.html.csv | majority | all of tomas behrend 's tennis tournaments were played on the clay surface . | {'scope': 'all', 'col': '3', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'clay', 'subset': None} | {'func': 'all_str_eq', 'args': ['all_rows', 'surface', 'clay'], 'result': True, 'ind': 0, 'tointer': 'for the surface records of all rows , all of them fuzzily match to clay .', 'tostr': 'all_eq { all_rows ; surface ; clay } = true'} | all_eq { all_rows ; surface ; clay } = true | for the surface records of all rows , all of them fuzzily match to clay . | 1 | 1 | {'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'surface_3': 3, 'clay_4': 4} | {'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'surface_3': 'surface', 'clay_4': 'clay'} | {'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'surface_3': [0], 'clay_4': [0]} | ['date', 'tournament', 'surface', 'opponent in the final', 'score'] | [['1998', 'germany f13', 'clay', 'mehdi tahiri', '6 - 3 , 7 - 6'], ['1998', 'budva', 'clay', 'fredrik jonsson', '3 - 6 , 6 - 3 , 6 - 2'], ['2002', 'sofia', 'clay', 'werner eschauer', '6 - 0 , 6 - 3'], ['2003', 'san remo', 'clay', 'werner eschauer', '6 - 4 , 6 - 2'], ['2003', 'weiden', 'clay', 'björn phau', '2 - 6 , 6 - 4 , 6 - 1'], ['2004', 'kish island', 'clay', 'mathieu montcourt', '7 - 6 ( 3 ) , 6 - 1'], ['2005', 'santiago', 'clay', 'adrián garcía', '7 - 6 ( 3 ) , 4 - 6 , 6 - 2'], ['2005', 'olbia', 'clay', 'alessio di mauro', '6 - 4 , 7 - 6 ( 3 )']] |
serbia fed cup team | https://en.wikipedia.org/wiki/Serbia_Fed_Cup_team | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11233501-3.html.csv | unique | the only time that the team played in the world cup was when they were on the serbia team . | {'scope': 'all', 'row': '4', 'col': '2', 'col_other': '5', 'criterion': 'equal', 'value': 'serbia ( srb )', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name of the country', 'serbia ( srb )'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose name of the country record fuzzily matches to serbia ( srb ) .', 'tostr': 'filter_eq { all_rows ; name of the country ; serbia ( srb ) }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; name of the country ; serbia ( srb ) } }', 'tointer': 'select the rows whose name of the country record fuzzily matches to serbia ( srb ) . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name of the country', 'serbia ( srb )'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose name of the country record fuzzily matches to serbia ( srb ) .', 'tostr': 'filter_eq { all_rows ; name of the country ; serbia ( srb ) }'}, 'years in world group'], 'result': '3 ( 2 - 3 )', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; name of the country ; serbia ( srb ) } ; years in world group }'}, '3 ( 2 - 3 )'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; name of the country ; serbia ( srb ) } ; years in world group } ; 3 ( 2 - 3 ) }', 'tointer': 'the years in world group record of this unqiue row is 3 ( 2 - 3 ) .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; name of the country ; serbia ( srb ) } } ; eq { hop { filter_eq { all_rows ; name of the country ; serbia ( srb ) } ; years in world group } ; 3 ( 2 - 3 ) } } = true', 'tointer': 'select the rows whose name of the country record fuzzily matches to serbia ( srb ) . there is only one such row in the table . the years in world group record of this unqiue row is 3 ( 2 - 3 ) .'} | and { only { filter_eq { all_rows ; name of the country ; serbia ( srb ) } } ; eq { hop { filter_eq { all_rows ; name of the country ; serbia ( srb ) } ; years in world group } ; 3 ( 2 - 3 ) } } = true | select the rows whose name of the country record fuzzily matches to serbia ( srb ) . there is only one such row in the table . the years in world group record of this unqiue row is 3 ( 2 - 3 ) . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'name of the country_7': 7, 'serbia (srb)_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'years in world group_9': 9, '3 (2 - 3)_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'name of the country_7': 'name of the country', 'serbia (srb)_8': 'serbia ( srb )', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'years in world group_9': 'years in world group', '3 (2 - 3)_10': '3 ( 2 - 3 )'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'name of the country_7': [0], 'serbia (srb)_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'years in world group_9': [2], '3 (2 - 3)_10': [3]} | ['year', 'name of the country', 'years played', 'ties played', 'years in world group', 'best result'] | [['1969 - 1992', 'socialist federal republic of yugoslavia ( sfrj )', '19', '53 ( 24 - 29 )', '-', 'main draw semifinals 1984'], ['1995 - 2003', 'federal republic of yugoslavia ( srj )', '9', '34 ( 20 - 14 )', '0', 'europe / africa zone , group i play - offs 2002 , 2003'], ['2004 - 2006', 'serbia and montenegro ( scg )', '3', '11 ( 7 - 4 )', '0', 'europe / africa zone , group i play - offs 2004 , 2006'], ['2007 -', 'serbia ( srb )', '7', '20 ( 13 - 7 )', '3 ( 2 - 3 )', 'final 2012'], ['1969 -', 'overall', '38', '118 ( 64 - 54 )', '3 ( 2 - 3 )', 'final 2012']] |
1972 - 73 new york rangers season | https://en.wikipedia.org/wiki/1972%E2%80%9373_New_York_Rangers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17324893-6.html.csv | superlative | the new york rangers got the highest score against the boston bruins . | {'scope': 'all', 'col_superlative': '4', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '3', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'score'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; score }'}, 'opponent'], 'result': 'boston bruins', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; score } ; opponent }'}, 'boston bruins'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; score } ; opponent } ; boston bruins } = true', 'tointer': 'select the row whose score record of all rows is maximum . the opponent record of this row is boston bruins .'} | eq { hop { argmax { all_rows ; score } ; opponent } ; boston bruins } = true | select the row whose score record of all rows is maximum . the opponent record of this row is boston bruins . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'score_5': 5, 'opponent_6': 6, 'boston bruins_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'score_5': 'score', 'opponent_6': 'opponent', 'boston bruins_7': 'boston bruins'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'score_5': [0], 'opponent_6': [1], 'boston bruins_7': [2]} | ['game', 'february', 'opponent', 'score', 'record'] | [['52', '3', 'boston bruins', '7 - 3', '35 - 13 - 4'], ['53', '4', 'atlanta flames', '6 - 0', '36 - 13 - 4'], ['54', '7', 'new york islanders', '6 - 0', '37 - 13 - 4'], ['55', '10', 'new york islanders', '6 - 0', '38 - 13 - 4'], ['56', '11', 'montreal canadiens', '2 - 2', '38 - 13 - 5'], ['57', '14', 'montreal canadiens', '6 - 3', '38 - 14 - 5'], ['58', '15', 'buffalo sabres', '4 - 1', '38 - 15 - 5'], ['59', '18', 'new york islanders', '3 - 2', '39 - 15 - 5'], ['60', '21', 'los angeles kings', '4 - 3', '40 - 15 - 5'], ['61', '23', 'california golden seals', '5 - 3', '40 - 16 - 5'], ['62', '25', 'minnesota north stars', '6 - 5', '41 - 16 - 5'], ['63', '28', 'chicago black hawks', '3 - 3', '41 - 16 - 6']] |
2007 - 08 uefa cup | https://en.wikipedia.org/wiki/2007%E2%80%9308_UEFA_Cup | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10124937-14.html.csv | unique | the 1st leg match between bolton wanderers and sporting cp was the only match in the 2007 - 08 uefa cup to end in a 1-1 score . | {'scope': 'all', 'row': '3', 'col': '4', 'col_other': '1,3', 'criterion': 'equal', 'value': '1 - 1', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', '1st leg', '1 - 1'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose 1st leg record fuzzily matches to 1 - 1 .', 'tostr': 'filter_eq { all_rows ; 1st leg ; 1 - 1 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; 1st leg ; 1 - 1 } }', 'tointer': 'select the rows whose 1st leg record fuzzily matches to 1 - 1 . there is only one such row in the table .'}, {'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', '1st leg', '1 - 1'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose 1st leg record fuzzily matches to 1 - 1 .', 'tostr': 'filter_eq { all_rows ; 1st leg ; 1 - 1 }'}, 'team 1'], 'result': 'bolton wanderers', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; 1st leg ; 1 - 1 } ; team 1 }'}, 'bolton wanderers'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; 1st leg ; 1 - 1 } ; team 1 } ; bolton wanderers }', 'tointer': 'the team 1 record of this unqiue row is bolton wanderers .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', '1st leg', '1 - 1'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose 1st leg record fuzzily matches to 1 - 1 .', 'tostr': 'filter_eq { all_rows ; 1st leg ; 1 - 1 }'}, 'team 2'], 'result': 'sporting cp', 'ind': 4, 'tostr': 'hop { filter_eq { all_rows ; 1st leg ; 1 - 1 } ; team 2 }'}, 'sporting cp'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; 1st leg ; 1 - 1 } ; team 2 } ; sporting cp }', 'tointer': 'the team 2 record of this unqiue row is sporting cp .'}], 'result': True, 'ind': 6, 'tostr': 'and { eq { hop { filter_eq { all_rows ; 1st leg ; 1 - 1 } ; team 1 } ; bolton wanderers } ; eq { hop { filter_eq { all_rows ; 1st leg ; 1 - 1 } ; team 2 } ; sporting cp } }', 'tointer': 'the team 1 record of this unqiue row is bolton wanderers . the team 2 record of this unqiue row is sporting cp .'}], 'result': True, 'ind': 7, 'tostr': 'and { only { filter_eq { all_rows ; 1st leg ; 1 - 1 } } ; and { eq { hop { filter_eq { all_rows ; 1st leg ; 1 - 1 } ; team 1 } ; bolton wanderers } ; eq { hop { filter_eq { all_rows ; 1st leg ; 1 - 1 } ; team 2 } ; sporting cp } } } = true', 'tointer': 'select the rows whose 1st leg record fuzzily matches to 1 - 1 . there is only one such row in the table . the team 1 record of this unqiue row is bolton wanderers . the team 2 record of this unqiue row is sporting cp .'} | and { only { filter_eq { all_rows ; 1st leg ; 1 - 1 } } ; and { eq { hop { filter_eq { all_rows ; 1st leg ; 1 - 1 } ; team 1 } ; bolton wanderers } ; eq { hop { filter_eq { all_rows ; 1st leg ; 1 - 1 } ; team 2 } ; sporting cp } } } = true | select the rows whose 1st leg record fuzzily matches to 1 - 1 . there is only one such row in the table . the team 1 record of this unqiue row is bolton wanderers . the team 2 record of this unqiue row is sporting cp . | 10 | 8 | {'and_7': 7, 'result_8': 8, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_9': 9, '1st leg_10': 10, '1 - 1_11': 11, 'and_6': 6, 'str_eq_3': 3, 'str_hop_2': 2, 'team 1_12': 12, 'bolton wanderers_13': 13, 'str_eq_5': 5, 'str_hop_4': 4, 'team 2_14': 14, 'sporting cp_15': 15} | {'and_7': 'and', 'result_8': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_9': 'all_rows', '1st leg_10': '1st leg', '1 - 1_11': '1 - 1', 'and_6': 'and', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'team 1_12': 'team 1', 'bolton wanderers_13': 'bolton wanderers', 'str_eq_5': 'str_eq', 'str_hop_4': 'str_hop', 'team 2_14': 'team 2', 'sporting cp_15': 'sporting cp'} | {'and_7': [8], 'result_8': [], 'only_1': [7], 'filter_str_eq_0': [1, 2, 4], 'all_rows_9': [0], '1st leg_10': [0], '1 - 1_11': [0], 'and_6': [7], 'str_eq_3': [6], 'str_hop_2': [3], 'team 1_12': [2], 'bolton wanderers_13': [3], 'str_eq_5': [6], 'str_hop_4': [5], 'team 2_14': [4], 'sporting cp_15': [5]} | ['team 1', 'agg', 'team 2', '1st leg', '2nd leg'] | [['anderlecht', '2 - 6', 'bayern munich', '0 - 5', '2 - 1'], ['rangers', '2 - 1', 'werder bremen', '2 - 0', '0 - 1'], ['bolton wanderers', '1 - 2', 'sporting cp', '1 - 1', '0 - 1'], ['bayer leverkusen', '3 - 3 ( a )', 'hamburg', '1 - 0', '2 - 3'], ['benfica', '1 - 3', 'getafe', '1 - 2', '0 - 1'], ['fiorentina', '2 - 2 ( 4 - 2 p )', 'everton', '2 - 0', '0 - 2 ( aet )'], ['tottenham hotspur', '1 - 1 ( 5 - 6 p )', 'psv eindhoven', '0 - 1', '1 - 0 ( aet )'], ['marseille', '3 - 3 ( a )', 'zenit st petersburg', '3 - 1', '0 - 2']] |
gauliga | https://en.wikipedia.org/wiki/Gauliga | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17418169-1.html.csv | aggregation | there were 119000 people in total to spectate at the games in 1934 and 1935 . | {'scope': 'subset', 'col': '7', 'type': 'sum', 'result': '119000', 'subset': {'col': '1', 'criterion': 'less_than_eq', 'value': '1935'}} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': [{'func': 'filter_less_eq', 'args': ['all_rows', 'year', '1935'], 'result': None, 'ind': 0, 'tostr': 'filter_less_eq { all_rows ; year ; 1935 }', 'tointer': 'select the rows whose year record is less than or equal to 1935 .'}, 'attendance'], 'result': '119000', 'ind': 1, 'tostr': 'sum { filter_less_eq { all_rows ; year ; 1935 } ; attendance }'}, '119000'], 'result': True, 'ind': 2, 'tostr': 'round_eq { sum { filter_less_eq { all_rows ; year ; 1935 } ; attendance } ; 119000 } = true', 'tointer': 'select the rows whose year record is less than or equal to 1935 . the sum of the attendance record of these rows is 119000 .'} | round_eq { sum { filter_less_eq { all_rows ; year ; 1935 } ; attendance } ; 119000 } = true | select the rows whose year record is less than or equal to 1935 . the sum of the attendance record of these rows is 119000 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'sum_1': 1, 'filter_less_eq_0': 0, 'all_rows_4': 4, 'year_5': 5, '1935_6': 6, 'attendance_7': 7, '119000_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'sum_1': 'sum', 'filter_less_eq_0': 'filter_less_eq', 'all_rows_4': 'all_rows', 'year_5': 'year', '1935_6': '1935', 'attendance_7': 'attendance', '119000_8': '119000'} | {'eq_2': [3], 'result_3': [], 'sum_1': [2], 'filter_less_eq_0': [1], 'all_rows_4': [0], 'year_5': [0], '1935_6': [0], 'attendance_7': [1], '119000_8': [2]} | ['year', 'champion', 'runner - up', 'result', 'date', 'venue', 'attendance'] | [['1944', 'dresdner sc', 'luftwaffen - sv hamburg', '4 - 0', '18 june 1944', 'berlin', '70000'], ['1943', 'dresdner sc', 'fv saarbrã ¼ cken', '3 - 0', '27 june 1943', 'berlin', '80000'], ['1942', 'fc schalke 04', 'first vienna fc', '2 - 0', '5 july 1942', 'berlin', '90000'], ['1941', 'rapid wien', 'fc schalke 04', '4 - 3', '22 june 1941', 'berlin', '95000'], ['1940', 'fc schalke 04', 'dresdner sc', '1 - 0', '21 july 1940', 'berlin', '95000'], ['1939', 'fc schalke 04', 'admira wien', '9 - 0', '18 june 1939', 'berlin', '100000'], ['1938', 'hannover 96', 'fc schalke 04', '3 - 3 aet 4 - 3 aet', '26 june 1938 3 july 1938', 'berlin berlin', '100000 100000'], ['1937', 'fc schalke 04', '1 . fc nuremberg', '2 - 0', '20 june 1937', 'berlin', '100000'], ['1936', '1 . fc nuremberg', 'fortuna dã ¼ sseldorf', '2 - 1 aet', '21 june 1936', 'berlin', '45000'], ['1935', 'fc schalke 04', 'vfb stuttgart', '6 - 4', '23 june 1935', 'cologne', '74000'], ['1934', 'fc schalke 04', '1 . fc nuremberg', '2 - 1', '24 june 1934', 'berlin', '45000']] |
chinese jia - a league | https://en.wikipedia.org/wiki/Chinese_Jia-A_League | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17632217-2.html.csv | aggregation | the average number of teams playing in the chinese jia - a league between 1994 to 2003 was around 13 teams . | {'scope': 'all', 'col': '6', 'type': 'average', 'result': '13', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'number of clubs'], 'result': '13', 'ind': 0, 'tostr': 'avg { all_rows ; number of clubs }'}, '13'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; number of clubs } ; 13 } = true', 'tointer': 'the average of the number of clubs record of all rows is 13 .'} | round_eq { avg { all_rows ; number of clubs } ; 13 } = true | the average of the number of clubs record of all rows is 13 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'number of clubs_4': 4, '13_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'number of clubs_4': 'number of clubs', '13_5': '13'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'number of clubs_4': [0], '13_5': [1]} | ['season', 'winners', 'runners - up', 'third - place', 'fourth - placed', 'number of clubs'] | [['1994', 'dalian wanda', 'guangzhou apollo', 'shanghai shenhua', 'liaoning yuandong', '12'], ['1995', 'shanghai shenhua', 'beijing guoan', 'dalian wanda', 'guangdong hongyuan', '12'], ['1996', 'dalian wanda', 'shanghai shenhua', 'august 1st', 'beijing guoan', '12'], ['1997', 'dalian wanda', 'shanghai shenhua', 'beijing guoan', 'yanbian aodong', '12'], ['1998', 'dalian wanda', 'shanghai shenhua', 'beijing guoan', 'guangzhou songri', '14'], ['1999', 'shandong luneng', 'liaoning fushun', 'sichuan quanxing', 'chongqing longxin', '14'], ['2000', 'dalian shide', 'shanghai shenhua', 'sichuan quanxing', 'chongqing longxin', '14'], ['2001', 'dalian shide', 'shanghai shenhua', 'liaoning fushun', 'sichuan quanxing', '14'], ['2002', 'dalian shide', "shenzhen ping ' an", 'beijing guoan', 'shandong luneng', '15'], ['2003', 'shanghai shenhua', 'shanghai international', 'dalian shide', 'shenzhen jianlibao', '15']] |
2001 arizona cardinals season | https://en.wikipedia.org/wiki/2001_Arizona_Cardinals_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16434134-1.html.csv | comparative | bill gramatica was drafted by the arizona cardinals earlier than renaldo hill . | {'row_1': '5', 'row_2': '9', 'col': '1', 'col_other': '3', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'bill gramatica'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose player record fuzzily matches to bill gramatica .', 'tostr': 'filter_eq { all_rows ; player ; bill gramatica }'}, 'round'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; player ; bill gramatica } ; round }', 'tointer': 'select the rows whose player record fuzzily matches to bill gramatica . take the round record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'renaldo hill'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose player record fuzzily matches to renaldo hill .', 'tostr': 'filter_eq { all_rows ; player ; renaldo hill }'}, 'round'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; player ; renaldo hill } ; round }', 'tointer': 'select the rows whose player record fuzzily matches to renaldo hill . take the round record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; player ; bill gramatica } ; round } ; hop { filter_eq { all_rows ; player ; renaldo hill } ; round } } = true', 'tointer': 'select the rows whose player record fuzzily matches to bill gramatica . take the round record of this row . select the rows whose player record fuzzily matches to renaldo hill . take the round record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; player ; bill gramatica } ; round } ; hop { filter_eq { all_rows ; player ; renaldo hill } ; round } } = true | select the rows whose player record fuzzily matches to bill gramatica . take the round record of this row . select the rows whose player record fuzzily matches to renaldo hill . take the round record of this row . the first record is less than the second record . | 5 | 5 | {'less_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'player_7': 7, 'bill gramatica_8': 8, 'round_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'player_11': 11, 'renaldo hill_12': 12, 'round_13': 13} | {'less_4': 'less', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'player_7': 'player', 'bill gramatica_8': 'bill gramatica', 'round_9': 'round', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'player_11': 'player', 'renaldo hill_12': 'renaldo hill', 'round_13': 'round'} | {'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'player_7': [0], 'bill gramatica_8': [0], 'round_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'player_11': [1], 'renaldo hill_12': [1], 'round_13': [3]} | ['round', 'pick', 'player', 'position', 'school / club team'] | [['1', '2', 'leonard davis', 'tackle', 'texas'], ['2', '34', 'kyle vanden bosch', 'defensive end', 'nebraska'], ['2', '54', 'michael stone', 'defensive back', 'memphis'], ['3', '64', 'adrian wilson ( american football )', 'defensive back', 'north carolina state'], ['4', '98', 'bill gramatica', 'kicker', 'south florida'], ['4', '123', 'marcus bell', 'defensive tackle', 'memphis'], ['5', '133', 'mario fatafehi', 'defensive tackle', 'kansas state'], ['6', '166', 'bobby newcombe', 'wide receiver', 'nebraska'], ['7', '202', 'renaldo hill', 'defensive back', 'michigan state'], ['7', '246', 'tevita ofahengaue', 'tight end', 'brigham young university']] |
reasons to be pretty | https://en.wikipedia.org/wiki/Reasons_to_be_pretty | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18963715-1.html.csv | majority | the majority of the time reasons to be pretty was nominated but did n't win . | {'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'fuzzily_match', 'value': 'nominated', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'result', 'nominated'], 'result': True, 'ind': 0, 'tointer': 'for the result records of all rows , most of them fuzzily match to nominated .', 'tostr': 'most_eq { all_rows ; result ; nominated } = true'} | most_eq { all_rows ; result ; nominated } = true | for the result records of all rows , most of them fuzzily match to nominated . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'result_3': 3, 'nominated_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'result_3': 'result', 'nominated_4': 'nominated'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'result_3': [0], 'nominated_4': [0]} | ['year', 'award ceremony', 'category', 'nominee', 'result'] | [['2009', 'tony award', 'best play', 'neil labute', 'nominated'], ['2009', 'tony award', 'best performance by a leading actor in a play', 'thomas sadoski', 'nominated'], ['2009', 'tony award', 'best performance by a featured actress in a play', 'marin ireland', 'nominated'], ['2009', 'drama desk award', 'outstanding play', 'outstanding play', 'nominated'], ['2009', 'drama desk award', 'outstanding actor in a play', 'thomas sadoski', 'nominated'], ['2009', 'drama desk award', 'outstanding director of a play', 'terry kinney', 'nominated'], ['2009', 'theatre world award', 'theatre world award', 'marin ireland', 'won']] |
1972 isle of man tt | https://en.wikipedia.org/wiki/1972_Isle_of_Man_TT | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15753390-2.html.csv | majority | the majority of the riders in the 1972 isle of man tt were from united kingdom . | {'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'united kingdom', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'country', 'united kingdom'], 'result': True, 'ind': 0, 'tointer': 'for the country records of all rows , most of them fuzzily match to united kingdom .', 'tostr': 'most_eq { all_rows ; country ; united kingdom } = true'} | most_eq { all_rows ; country ; united kingdom } = true | for the country records of all rows , most of them fuzzily match to united kingdom . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'country_3': 3, 'united kingdom_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'country_3': 'country', 'united kingdom_4': 'united kingdom'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'country_3': [0], 'united kingdom_4': [0]} | ['place', 'rider', 'country', 'machine', 'speed', 'time', 'points'] | [['1', 'siegfried schauzu / wolfgang kalauch', 'west germany', 'bmw', '91.85 mph', '1:13.57.2', '15'], ['2', 'heinz luthringshauser / jcusnik', 'west germany', 'bmw', '91.70 mph', '1:14.04.6', '12'], ['3', 'gerry boret / nick boret', 'united kingdom', 'konig', '84.43 mph', '1:20.27.4', '10'], ['4', 'wklenk / nscheerer', 'west germany', 'bmw', '83.62 mph', '1:21.31.8', '8'], ['5', 'barry dungworth / rwturrington', 'united kingdom', 'bmw', '82.32 mph', '1:22.30.6', '6'], ['6', 'roy hanks / jpmann', 'united kingdom', 'bsa', '80.07 mph', '1:24.49.6', '5'], ['7', 'rwoodhouse / dwoodhouse', 'united kingdom', 'bsa', '79.83 mph', '1.25.05.40', '4'], ['8', 'roger dutton / tony wright', 'united kingdom', 'bmw', '79.63 mph', '1.25.18.0', '3'], ['9', "george o'dell / bill boldison", 'united kingdom', 'bsa', '79.60 mph', '1.25.20.2', '2'], ['10', 'jbarker / amacfadzean', 'united kingdom', 'bsa', '79.52 mph', '1.25.28.2', '1']] |
cricket in world war ii | https://en.wikipedia.org/wiki/Cricket_in_World_War_II | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10791018-1.html.csv | count | three of england 's cricket players during world war ii came from the sussex club . | {'scope': 'all', 'criterion': 'equal', 'value': 'sussex', 'result': '3', 'col': '2', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'club', 'sussex'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose club record fuzzily matches to sussex .', 'tostr': 'filter_eq { all_rows ; club ; sussex }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; club ; sussex } }', 'tointer': 'select the rows whose club record fuzzily matches to sussex . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; club ; sussex } } ; 3 } = true', 'tointer': 'select the rows whose club record fuzzily matches to sussex . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; club ; sussex } } ; 3 } = true | select the rows whose club record fuzzily matches to sussex . the number of such rows is 3 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'club_5': 5, 'sussex_6': 6, '3_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'club_5': 'club', 'sussex_6': 'sussex', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'club_5': [0], 'sussex_6': [0], '3_7': [2]} | ['name', 'club', 'birth date', 'batting style', 'bowling style'] | [['a j holmes', 'sussex', '30 june 1899 ( aged 40 )', 'right - handed', 'none'], ['h t bartlett', 'sussex', '07 october 1914 ( aged 24 )', 'left - handed', 'none'], ['h e dollery', 'warwickshire', '14 october 1914 ( aged 24 )', 'right - handed', 'none'], ['h gimblett', 'somerset', '19 october 1914 ( aged 24 )', 'right - handed', 'right arm medium pace'], ['r h c human', 'worcestershire', '11 may 1909 ( aged 30 )', 'right - handed', 'right arm medium pace'], ['j g langridge', 'sussex', '10 february 1910 ( aged 29 )', 'right - handed', 'right arm medium pace'], ['r e s wyatt', 'warwickshire', '02 may 1901 ( aged 38 )', 'right - handed', 'right arm medium pace']] |
united states house of representatives elections , 1928 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1928 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1342370-42.html.csv | unique | l burd was the only candidate who ran and managed to receive zero percent of the votes . | {'scope': 'all', 'row': '10', 'col': '6', 'col_other': 'n/a', 'criterion': 'fuzzily_match', 'value': 'l burd ( i ) 0.0 %', 'subset': None} | {'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'candidates', 'l burd ( i ) 0.0 %'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose candidates record fuzzily matches to l burd ( i ) 0.0 % .', 'tostr': 'filter_eq { all_rows ; candidates ; l burd ( i ) 0.0 % }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; candidates ; l burd ( i ) 0.0 % } } = true', 'tointer': 'select the rows whose candidates record fuzzily matches to l burd ( i ) 0.0 % . there is only one such row in the table .'} | only { filter_eq { all_rows ; candidates ; l burd ( i ) 0.0 % } } = true | select the rows whose candidates record fuzzily matches to l burd ( i ) 0.0 % . there is only one such row in the table . | 2 | 2 | {'only_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'candidates_4': 4, 'l burd (i) 0.0%_5': 5} | {'only_1': 'only', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'candidates_4': 'candidates', 'l burd (i) 0.0%_5': 'l burd ( i ) 0.0 %'} | {'only_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'candidates_4': [0], 'l burd (i) 0.0%_5': [0]} | ['district', 'incumbent', 'party', 'first elected', 'result', 'candidates'] | [['texas 2', 'john c box', 'democratic', '1918', 're - elected', 'john c box ( d ) unopposed'], ['texas 3', 'morgan g sanders', 'democratic', '1920', 're - elected', 'morgan g sanders ( d ) unopposed'], ['texas 4', 'sam rayburn', 'democratic', '1912', 're - elected', 'sam rayburn ( d ) 84.2 % floyd harry ( r ) 15.8 %'], ['texas 5', 'hatton w sumners', 'democratic', '1914', 're - elected', 'hatton w sumners ( d ) unopposed'], ['texas 6', 'luther a johnson', 'democratic', '1922', 're - elected', 'luther a johnson ( d ) 90.7 % h lee monroe ( r ) 9.3 %'], ['texas 7', 'clay stone briggs', 'democratic', '1918', 're - elected', 'clay stone briggs ( d ) 88.4 % a j long ( r ) 11.6 %'], ['texas 11', 'tom connally', 'democratic', '1916', 'retired to run for u s senate democratic hold', 'oliver h cross ( d ) 90.9 % r c bush ( r ) 9.1 %'], ['texas 12', 'fritz g lanham', 'democratic', '1919', 're - elected', 'fritz g lanham ( d ) 79.6 % david sutton ( r ) 20.4 %'], ['texas 13', 'guinn williams', 'democratic', '1922', 're - elected', 'guinn williams ( d ) 88.5 % p a welty ( r ) 11.5 %'], ['texas 15', 'john nance garner', 'democratic', '1902', 're - elected', 'john nance garner ( d ) 100.0 % j l burd ( i ) 0.0 %'], ['texas 16', 'claude benton hudspeth', 'democratic', '1918', 're - elected', 'claude benton hudspeth ( d ) unopposed'], ['texas 17', 'thomas l blanton', 'democratic', '1916', 'retired to run for u s senate democratic hold', 'robert quincy lee ( d ) unopposed']] |
2007 - 08 new orleans hornets season | https://en.wikipedia.org/wiki/2007%E2%80%9308_New_Orleans_Hornets_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11963536-8.html.csv | comparative | the match on 7 march 2008 had lower attendance than the match on 8 march 2008 . | {'row_1': '4', 'row_2': '5', 'col': '6', 'col_other': '1', 'relation': 'less', 'record_mentioned': 'yes', 'diff_result': None} | {'func': 'and', 'args': [{'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', '7 march 2008'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose date record fuzzily matches to 7 march 2008 .', 'tostr': 'filter_eq { all_rows ; date ; 7 march 2008 }'}, 'attendance'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; date ; 7 march 2008 } ; attendance }', 'tointer': 'select the rows whose date record fuzzily matches to 7 march 2008 . take the attendance record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', '8 march 2008'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to 8 march 2008 .', 'tostr': 'filter_eq { all_rows ; date ; 8 march 2008 }'}, 'attendance'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; date ; 8 march 2008 } ; attendance }', 'tointer': 'select the rows whose date record fuzzily matches to 8 march 2008 . take the attendance record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; date ; 7 march 2008 } ; attendance } ; hop { filter_eq { all_rows ; date ; 8 march 2008 } ; attendance } }', 'tointer': 'select the rows whose date record fuzzily matches to 7 march 2008 . take the attendance record of this row . select the rows whose date record fuzzily matches to 8 march 2008 . take the attendance record of this row . the first record is less than the second record .'}, {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', '7 march 2008'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose date record fuzzily matches to 7 march 2008 .', 'tostr': 'filter_eq { all_rows ; date ; 7 march 2008 }'}, 'attendance'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; date ; 7 march 2008 } ; attendance }', 'tointer': 'select the rows whose date record fuzzily matches to 7 march 2008 . take the attendance record of this row .'}, '17225'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; date ; 7 march 2008 } ; attendance } ; 17225 }', 'tointer': 'the attendance record of the first row is 17225 .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', '8 march 2008'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to 8 march 2008 .', 'tostr': 'filter_eq { all_rows ; date ; 8 march 2008 }'}, 'attendance'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; date ; 8 march 2008 } ; attendance }', 'tointer': 'select the rows whose date record fuzzily matches to 8 march 2008 . take the attendance record of this row .'}, '18279'], 'result': True, 'ind': 6, 'tostr': 'eq { hop { filter_eq { all_rows ; date ; 8 march 2008 } ; attendance } ; 18279 }', 'tointer': 'the attendance record of the second row is 18279 .'}], 'result': True, 'ind': 7, 'tostr': 'and { eq { hop { filter_eq { all_rows ; date ; 7 march 2008 } ; attendance } ; 17225 } ; eq { hop { filter_eq { all_rows ; date ; 8 march 2008 } ; attendance } ; 18279 } }', 'tointer': 'the attendance record of the first row is 17225 . the attendance record of the second row is 18279 .'}], 'result': True, 'ind': 8, 'tostr': 'and { less { hop { filter_eq { all_rows ; date ; 7 march 2008 } ; attendance } ; hop { filter_eq { all_rows ; date ; 8 march 2008 } ; attendance } } ; and { eq { hop { filter_eq { all_rows ; date ; 7 march 2008 } ; attendance } ; 17225 } ; eq { hop { filter_eq { all_rows ; date ; 8 march 2008 } ; attendance } ; 18279 } } } = true', 'tointer': 'select the rows whose date record fuzzily matches to 7 march 2008 . take the attendance record of this row . select the rows whose date record fuzzily matches to 8 march 2008 . take the attendance record of this row . the first record is less than the second record . the attendance record of the first row is 17225 . the attendance record of the second row is 18279 .'} | and { less { hop { filter_eq { all_rows ; date ; 7 march 2008 } ; attendance } ; hop { filter_eq { all_rows ; date ; 8 march 2008 } ; attendance } } ; and { eq { hop { filter_eq { all_rows ; date ; 7 march 2008 } ; attendance } ; 17225 } ; eq { hop { filter_eq { all_rows ; date ; 8 march 2008 } ; attendance } ; 18279 } } } = true | select the rows whose date record fuzzily matches to 7 march 2008 . take the attendance record of this row . select the rows whose date record fuzzily matches to 8 march 2008 . take the attendance record of this row . the first record is less than the second record . the attendance record of the first row is 17225 . the attendance record of the second row is 18279 . | 13 | 9 | {'and_8': 8, 'result_9': 9, 'less_4': 4, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_10': 10, 'date_11': 11, '7 march 2008_12': 12, 'attendance_13': 13, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_14': 14, 'date_15': 15, '8 march 2008_16': 16, 'attendance_17': 17, 'and_7': 7, 'eq_5': 5, '17225_18': 18, 'eq_6': 6, '18279_19': 19} | {'and_8': 'and', 'result_9': 'true', 'less_4': 'less', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_10': 'all_rows', 'date_11': 'date', '7 march 2008_12': '7 march 2008', 'attendance_13': 'attendance', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_14': 'all_rows', 'date_15': 'date', '8 march 2008_16': '8 march 2008', 'attendance_17': 'attendance', 'and_7': 'and', 'eq_5': 'eq', '17225_18': '17225', 'eq_6': 'eq', '18279_19': '18279'} | {'and_8': [9], 'result_9': [], 'less_4': [8], 'num_hop_2': [4, 5], 'filter_str_eq_0': [2], 'all_rows_10': [0], 'date_11': [0], '7 march 2008_12': [0], 'attendance_13': [2], 'num_hop_3': [4, 6], 'filter_str_eq_1': [3], 'all_rows_14': [1], 'date_15': [1], '8 march 2008_16': [1], 'attendance_17': [3], 'and_7': [8], 'eq_5': [7], '17225_18': [5], 'eq_6': [7], '18279_19': [6]} | ['date', 'visitor', 'score', 'home', 'leading scorer', 'attendance', 'record'] | [['2 march 2008', 'hornets', 'l 84 - 101 ( ot )', 'wizards', 'peja stojakovic ( 17 )', '20173', '39 - 19'], ['3 march 2008', 'hornets', 'w 100 - 88 ( ot )', 'knicks', 'chris paul ( 27 )', '18467', '40 - 19'], ['5 march 2008', 'hawks', 'w 116 - 101 ( ot )', 'hornets', 'peja stojakovic ( 29 )', '17430', '41 - 19'], ['7 march 2008', 'nets', 'w 107 - 96 ( ot )', 'hornets', 'chris paul ( 25 )', '17225', '42 - 19'], ['8 march 2008', 'hornets', 'l 96 - 106 ( ot )', 'rockets', 'chris paul ( 37 )', '18279', '42 - 20'], ['12 march 2008', 'spurs', 'w 100 - 75 ( ot )', 'hornets', 'david west ( 29 )', '17419', '43 - 20'], ['14 march 2008', 'lakers', 'w 108 - 98 ( ot )', 'hornets', 'chris paul ( 27 )', '18299', '44 - 20'], ['16 march 2008', 'hornets', 'l 84 - 105 ( ot )', 'pistons', 'peja stojakovic ( 21 )', '22076', '44 - 21'], ['17 march 2008', 'bulls', 'w 108 - 97 ( ot )', 'hornets', 'chris paul ( 37 )', '17337', '45 - 21'], ['19 march 2008', 'rockets', 'w 90 - 69 ( ot )', 'hornets', 'bonzi wells ( 25 )', '18056', '46 - 21'], ['22 march 2008', 'celtics', 'w 113 - 106 ( ot )', 'hornets', 'david west ( 37 )', '18380', '47 - 21'], ['25 march 2008', 'hornets', 'w 114 - 106 ( ot )', 'pacers', 'david west ( 35 )', '10829', '48 - 21'], ['26 march 2008', 'hornets', 'w 100 - 99 ( ot )', 'cavaliers', 'peja stojakovic ( 25 )', '20562', '49 - 21'], ['28 march 2008', 'hornets', 'l 92 - 112 ( ot )', 'celtics', 'chris paul ( 22 )', '18624', '49 - 22'], ['30 march 2008', 'hornets', 'w 118 - 111 ( ot )', 'raptors', 'david west ( 32 )', '19800', '50 - 22']] |
curlin | https://en.wikipedia.org/wiki/Curlin | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10731284-1.html.csv | ordinal | the maiden race recorded curlin 's fastest time in any race . | {'row': '16', 'col': '5', 'order': '1', 'col_other': '2', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'time', '1'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; time ; 1 }'}, 'race'], 'result': 'maiden', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; time ; 1 } ; race }'}, 'maiden'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; time ; 1 } ; race } ; maiden } = true', 'tointer': 'select the row whose time record of all rows is 1st minimum . the race record of this row is maiden .'} | eq { hop { nth_argmin { all_rows ; time ; 1 } ; race } ; maiden } = true | select the row whose time record of all rows is 1st minimum . the race record of this row is maiden . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'time_5': 5, '1_6': 6, 'race_7': 7, 'maiden_8': 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmin_0': 'nth_argmin', 'all_rows_4': 'all_rows', 'time_5': 'time', '1_6': '1', 'race_7': 'race', 'maiden_8': 'maiden'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'time_5': [0], '1_6': [0], 'race_7': [1], 'maiden_8': [2]} | ['finish', 'race', 'distance', 'jockey', 'time', 'grade', 'runner up / winner', 'track'] | [['4th', "breeders ' cup classic", '1\xa01⁄4 mi', 'robby albarado', '1:59.27', 'i', 'ravens pass', 'santa anita'], ['1st', 'jockey club gold cup', '1\xa01⁄4 mi', 'robby albarado', '2:01.93', 'i', "wanderin ' boy", 'belmont park'], ['1st', 'woodward stakes', '1\xa01⁄8 mi', 'robby albarado', '1:49.34', 'i', 'past the point', 'saratoga'], ['2nd', "man o ' war stakes", '1\xa03⁄8 mi', 'robby albarado', '2:13.89', 'i', 'red rocks', 'belmont park'], ['1st', 'stephen foster handicap', '1\xa01⁄8 mi', 'robby albarado', '1:49.68', 'i', 'einstein', 'churchill downs'], ['1st', 'dubai world cup', '1\xa01⁄4 mi', 'robby albarado', '2:00.15', 'i', 'asiatic boy', 'nad al sheba'], ['1st', 'jaguar trophy handicap', '1\xa01⁄4 mi', 'robby albarado', '2:00.60', 'ii', 'familiar territory', 'nad al sheba'], ['1st', "breeders ' cup classic", '1\xa01⁄4 mi', 'robby albarado', '2:00.59', 'i', 'hard spun', 'monmouth park'], ['1st', 'jockey club gold cup', '1\xa01⁄4 mi', 'robby albarado', '2:01.20', 'i', 'lawyer ron', 'belmont park'], ['3rd', 'haskell invitational', '1\xa01⁄8 mi', 'robby albarado', '1:48.35', 'i', 'any given saturday', 'monmouth park'], ['2nd', 'belmont stakes', '1\xa01⁄2 mi', 'robby albarado', '2:28.74', 'i', 'rags to riches', 'belmont park'], ['1st', 'preakness stakes', '1\xa03⁄16 mi', 'robby albarado', '1:53.46', 'i', 'street sense', 'pimlico race course'], ['3rd', 'kentucky derby', '1\xa01⁄4 mi', 'robby albarado', '2:02.17', 'i', 'street sense', 'churchill downs'], ['1st', 'arkansas derby', '1\xa01⁄8 mi', 'robby albarado', '1:50.09', 'ii', 'storm in may', 'oaklawn park'], ['1st', 'rebel stakes', '1\xa01⁄16 mi', 'robby albarado', '1:44.70', 'ii', 'officer rocket', 'oaklawn park'], ['1st', 'maiden', '7 fur', 'rafael bejarano', '1:20.22', 'none', 'winstrella', 'gulfstream park']] |
colonial turf cup | https://en.wikipedia.org/wiki/Colonial_Turf_Cup | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11237859-1.html.csv | ordinal | paddy o'prado has the second lowest time of all these horses . | {'row': '2', 'col': '7', 'order': '2', 'col_other': '2', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'time', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; time ; 2 }'}, 'winner'], 'result': "paddy o'prado", 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; time ; 2 } ; winner }'}, "paddy o'prado"], 'result': True, 'ind': 2, 'tostr': "eq { hop { nth_argmin { all_rows ; time ; 2 } ; winner } ; paddy o'prado } = true", 'tointer': "select the row whose time record of all rows is 2nd minimum . the winner record of this row is paddy o'prado ."} | eq { hop { nth_argmin { all_rows ; time ; 2 } ; winner } ; paddy o'prado } = true | select the row whose time record of all rows is 2nd minimum . the winner record of this row is paddy o'prado . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'time_5': 5, '2_6': 6, 'winner_7': 7, "paddy o'prado_8": 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmin_0': 'nth_argmin', 'all_rows_4': 'all_rows', 'time_5': 'time', '2_6': '2', 'winner_7': 'winner', "paddy o'prado_8": "paddy o'prado"} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'time_5': [0], '2_6': [0], 'winner_7': [1], "paddy o'prado_8": [2]} | ['year', 'winner', 'jockey', 'trainer', 'owner', 'distance ( miles )', 'time'] | [['2011', 'rahystrada', 'sheldon russell', 'byron hughes', 'robert courtney', '1 - 3 / 16', '1:54.68'], ['2010', "paddy o'prado", 'kent desormeaux', 'dale romans', 'donegal racing', '1 - 3 / 16', '1:54.20'], ['2009', 'battle of hastings', 'tyler baze', 'jeff mullins', 'michael house', '1 - 3 / 16', '1:57.79'], ['2008', "sailor 's cap", 'alan garcia', 'james j toner', 'team valor international', '1 - 3 / 16', '2:04.42'], ['2007', 'summer doldrums', 'jose lezcano', 'richard a violette , jr', 'klaravich stables', '1 - 3 / 16', '1:55.68'], ['2006', 'showing up', 'cornelio velã ¡ squez', 'barclay tagg', 'lael stables', '1 - 3 / 16', '1:52.98'], ['2005', 'english channel', 'john velazquez', 'todd pletcher', 'james t scatuorchio', '1 - 3 / 16', '1:56:37']] |
demographics of imperial japan | https://en.wikipedia.org/wiki/Demographics_of_Imperial_Japan | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1980653-5.html.csv | majority | the majority of cities were not included in the 1890 census of imperail japan . | {'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'fuzzily_match', 'value': 'na', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', '1890 census', 'na'], 'result': True, 'ind': 0, 'tointer': 'for the 1890 census records of all rows , most of them fuzzily match to na .', 'tostr': 'most_eq { all_rows ; 1890 census ; na } = true'} | most_eq { all_rows ; 1890 census ; na } = true | for the 1890 census records of all rows , most of them fuzzily match to na . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, '1890 census_3': 3, 'na_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', '1890 census_3': '1890 census', 'na_4': 'na'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], '1890 census_3': [0], 'na_4': [0]} | ['rank', 'city', '1890 census', '1910 census', '1920 census', '1930 census', '1940 census'] | [['1', 'keijō', '1165000', '230000', '247000', '350000', '1100000'], ['2', 'fuzan', 'na', '81000', '74000', '130000', '400000'], ['3', 'heijō', 'na', '40000', '60000', '137000', '286000'], ['4', 'jinsen', 'na', '30000', '40000', '54000', '171000'], ['5', 'taihoku', '78000', '95000', '164000', '249000', '326000'], ['6', 'tainan', 'na', '44000', '112000', '166000', '296000']] |
list of republic of doyle episodes | https://en.wikipedia.org/wiki/List_of_Republic_of_Doyle_episodes | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27547668-3.html.csv | comparative | episode 14 of republic of doyle had more viewers than episode 15 . | {'row_1': '2', 'row_2': '3', 'col': '6', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', '', '14'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose record fuzzily matches to 14 .', 'tostr': 'filter_eq { all_rows ; ; 14 }'}, 'viewers'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; ; 14 } ; viewers }', 'tointer': 'select the rows whose record fuzzily matches to 14 . take the viewers record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', '', '15'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose record fuzzily matches to 15 .', 'tostr': 'filter_eq { all_rows ; ; 15 }'}, 'viewers'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; ; 15 } ; viewers }', 'tointer': 'select the rows whose record fuzzily matches to 15 . take the viewers record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; ; 14 } ; viewers } ; hop { filter_eq { all_rows ; ; 15 } ; viewers } } = true', 'tointer': 'select the rows whose record fuzzily matches to 14 . take the viewers record of this row . select the rows whose record fuzzily matches to 15 . take the viewers record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; ; 14 } ; viewers } ; hop { filter_eq { all_rows ; ; 15 } ; viewers } } = true | select the rows whose record fuzzily matches to 14 . take the viewers record of this row . select the rows whose record fuzzily matches to 15 . take the viewers record of this row . the first record is greater than the second record . | 5 | 5 | {'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, '_7': 7, '14_8': 8, 'viewers_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, '_11': 11, '15_12': 12, 'viewers_13': 13} | {'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', '_7': '', '14_8': '14', 'viewers_9': 'viewers', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', '_11': '', '15_12': '15', 'viewers_13': 'viewers'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], '_7': [0], '14_8': [0], 'viewers_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], '_11': [1], '15_12': [1], 'viewers_13': [3]} | ['', 'no', 'title', 'directed by', 'written by', 'viewers', 'original airdate', 'prod code'] | [['13', '1', 'live and let doyle', 'james allodi', 'allan hawco', '1038000', 'january 12 , 2011', '201'], ['14', '2', 'popeye doyle', 'steve scaini', 'allan hawco', '944000', 'january 19 , 2011', '202'], ['15', '3', 'a stand up guy', 'steve scaini', 'perry chafe', '776000', 'january 26 , 2011', '203'], ['16', '4', 'the son also rises', 'steve dimarco', 'jesse mckeown', '899000', 'february 2 , 2011', '204'], ['17', '5', 'something old , someone blue', 'james allodi', 'adam higgs & jackie may', '854000', 'february 9 , 2011', '205'], ['18', '6', 'the ryans and the pittmans', 'steve dimarco', 'greg nelson', '843000', 'february 16 , 2011', '206'], ['19', '7', 'crashing on the couch', 'keith samples', 'jackie may', '760000', 'february 23 , 2011', '207'], ['20', '8', 'sympathy for the devil', 'stacey curtis', 'john callaghan', '834400', 'march 2 , 2011', '208'], ['21', '9', 'will the real des courtney please stand up', 'keith samples', 'greg nelson', '1026000', 'march 9 , 2011', '209'], ['22', '10', 'the special detective', 'steve scaini', 'adam higgs', '836000', 'march 16 , 2011', '210'], ['23', '11', "do n't gamble with city hall", 'john vatcher', 'jackie may', '1021000', 'march 23 , 2011', '211'], ['24', '12', "st john 's town", 'keith samples', 'perry chafe', '730000', 'march 30 , 2011', '212']] |
cycling at the 2008 summer olympics - men 's bmx | https://en.wikipedia.org/wiki/Cycling_at_the_2008_Summer_Olympics_%E2%80%93_Men%27s_BMX | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18603914-3.html.csv | ordinal | emilio falla ranked 6th in the men 's bmx at the 2008 summer olympics . | {'row': '6', 'col': '1', 'order': '6', 'col_other': '2', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'rank', '6'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; rank ; 6 }'}, 'name'], 'result': 'emilio falla ( ecu )', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; rank ; 6 } ; name }'}, 'emilio falla ( ecu )'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; rank ; 6 } ; name } ; emilio falla ( ecu ) } = true', 'tointer': 'select the row whose rank record of all rows is 6th minimum . the name record of this row is emilio falla ( ecu ) .'} | eq { hop { nth_argmin { all_rows ; rank ; 6 } ; name } ; emilio falla ( ecu ) } = true | select the row whose rank record of all rows is 6th minimum . the name record of this row is emilio falla ( ecu ) . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'rank_5': 5, '6_6': 6, 'name_7': 7, 'emilio falla ( ecu )_8': 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmin_0': 'nth_argmin', 'all_rows_4': 'all_rows', 'rank_5': 'rank', '6_6': '6', 'name_7': 'name', 'emilio falla ( ecu )_8': 'emilio falla ( ecu )'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'rank_5': [0], '6_6': [0], 'name_7': [1], 'emilio falla ( ecu )_8': [2]} | ['rank', 'name', '1st run', '2nd run', '3rd run', 'total'] | [['1', 'mike day ( usa )', '36.170 ( 1 )', '36.080 ( 1 )', '36.122 ( 1 )', '3'], ['2', 'marc willers ( nzl )', '47.614 ( 4 )', '36.253 ( 3 )', '36.278 ( 2 )', '9'], ['3', 'donny robinson ( usa )', '48.906 ( 6 )', '36.235 ( 2 )', '36.490 ( 3 )', '11'], ['4', 'andrés jiménez caicedo ( col )', '36.619 ( 2 )', '36.939 ( 5 )', '36.660 ( 4 )', '11'], ['5', 'jonathan suárez ( ven )', '53.614 ( 8 )', '36.481 ( 4 )', '36.789 ( 5 )', '17'], ['6', 'emilio falla ( ecu )', '37.080 ( 3 )', '37.381 ( 6 )', '1:02.877 ( 8 )', '17'], ['7', 'akifumi sakamoto ( jpn )', '48.487 ( 5 )', '42.614 ( 8 )', '40.046 ( 6 )', '19'], ['8', 'ivo lakučs ( lat )', '53.300 ( 7 )', '39.213 ( 7 )', '57.461 ( 7 )', '21']] |
2008 - 09 lega pro seconda divisione | https://en.wikipedia.org/wiki/2008%E2%80%9309_Lega_Pro_Seconda_Divisione | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17702363-2.html.csv | aggregation | the average 2008 - 09 lega pro seconda divisione stadium can hold around 7000-7500 people . | {'scope': 'all', 'col': '4', 'type': 'average', 'result': '7000-7500', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'capacity'], 'result': '7000-7500', 'ind': 0, 'tostr': 'avg { all_rows ; capacity }'}, '7000-7500'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; capacity } ; 7000-7500 } = true', 'tointer': 'the average of the capacity record of all rows is 7000-7500 .'} | round_eq { avg { all_rows ; capacity } ; 7000-7500 } = true | the average of the capacity record of all rows is 7000-7500 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'capacity_4': 4, '7000-7500_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'capacity_4': 'capacity', '7000-7500_5': '7000-7500'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'capacity_4': [0], '7000-7500_5': [1]} | ['club', 'city', 'stadium', 'capacity', '200708 season'] | [['bassano virtus 55 st', 'bassano del grappa', 'stadio rino mercante', '3900', '2nd in serie c2 / b'], ['ac bellaria igea marina', 'bellaria - igea marina', 'stadio enrico nanni', '2500', '7th in serie c2 / b'], ['carrarese calcio', 'carrara', 'stadio dei marmi', '15000', '13th in serie c2 / b'], ['celano fc olimpia', 'celano', 'stadio comunale', '3200', '5th in serie c2 / c'], ['as cisco calcio roma', 'rome', 'stadio flaminio', '25000', '9th in serie c2 / c'], ['vf colligiana', "colle di val d'elsa", 'stadio gino manni', '3000', '2nd in serie d / e'], ['cuoiopelli cappiano r', "santa croce sull ' arno", 'stadio libero masini', '3350', '11th in serie c2 / b'], ['as figline', 'figline valdarno', 'stadio goffredo del buffa', '1700', '1st serie d / e'], ['ac giacomense', 'masi torello ( playing in ferrara )', 'stadio paolo mazza', '19000', '1st serie d / d'], ['giulianova calcio', 'giulianova', 'stadio rubens fadini', '5625', '12th in serie c2 / b'], ['as gubbio 1910', 'gubbio', 'stadio polisportivo san biagio', '5000', '10th in serie c2 / b'], ['us poggibonsi', 'poggibonsi', 'stadio stefano lotti', '3621', '6th in serie c2 / b'], ['ac prato', 'prato', 'stadio lungobisenzio', '6800', '9th in serie c2 / b'], ['rovigo calcio', 'rovigo', 'stadio francesco gabrielli', '3200', '16th in serie c2 / b'], ['san marino calcio', 'serravalle , san marino', 'stadio olimpico', '7000', '5th in serie c2 / b'], ['ac sangiovannese 1927', 'san giovanni valdarno', 'stadio virgilio fedini', '3800', '17th in serie c1 / b'], ['ac sangiustese', 'monte san giusto', 'stadio villa san filippo', '1487', '1st serie d / f'], ['fc esperia viareggio', 'viareggio', 'stadio dei pini', '4700', '14th in serie c2 / b']] |
i 'm a celebrity ... get me out of here ! ( uk tv series ) | https://en.wikipedia.org/wiki/I%27m_a_Celebrity...Get_Me_Out_of_Here%21_%28UK_TV_series%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14345690-4.html.csv | majority | the majority of celebrities who exited i 'm a celebrity ... get me out of here ( uk tv series ) before day 16 finished above 9th place . | {'scope': 'subset', 'col': '5', 'most_or_all': 'most', 'criterion': 'less_than', 'value': '9th', 'subset': {'col': '4', 'criterion': 'less_than', 'value': 'day 16'}} | {'func': 'most_less', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'exited', 'day 16'], 'result': None, 'ind': 0, 'tostr': 'filter_less { all_rows ; exited ; day 16 }', 'tointer': 'select the rows whose exited record is less than day 16 .'}, 'finished', '9th'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose exited record is less than day 16 . for the finished records of these rows , most of them are less than 9th .', 'tostr': 'most_less { filter_less { all_rows ; exited ; day 16 } ; finished ; 9th } = true'} | most_less { filter_less { all_rows ; exited ; day 16 } ; finished ; 9th } = true | select the rows whose exited record is less than day 16 . for the finished records of these rows , most of them are less than 9th . | 2 | 2 | {'most_less_1': 1, 'result_2': 2, 'filter_less_0': 0, 'all_rows_3': 3, 'exited_4': 4, 'day 16_5': 5, 'finished_6': 6, '9th_7': 7} | {'most_less_1': 'most_less', 'result_2': 'true', 'filter_less_0': 'filter_less', 'all_rows_3': 'all_rows', 'exited_4': 'exited', 'day 16_5': 'day 16', 'finished_6': 'finished', '9th_7': '9th'} | {'most_less_1': [2], 'result_2': [], 'filter_less_0': [1], 'all_rows_3': [0], 'exited_4': [0], 'day 16_5': [0], 'finished_6': [1], '9th_7': [1]} | ['celebrity', 'famous for', 'entered', 'exited', 'finished'] | [['kerry katona', 'singer in atomic kitten', 'day 1', 'day 16', '1st'], ['jennie bond', 'former royal correspondent for the bbc', 'day 1', 'day 16', '2nd'], ['peter andre', 'pop singer', 'day 1', 'day 16', '3rd'], ['lord brocket', 'aristocrat', 'day 1', 'day 15', '4th'], ['katie price ( first appearance )', 'page 3 model', 'day 1', 'day 14', '5th'], ['alex best', 'second wife of footballer george best', 'day 1', 'day 13', '6th'], ['neil ruddock', 'ex - footballer', 'day 1', 'day 11', '7th'], ['john lydon', 'sex pistols & public image ltd frontman', 'day 1', 'day 11', '8th'], ['diane modahl', 'athlete', 'day 1', 'day 10', '9th'], ['mike read', 'radio dj', 'day 1', 'day 9', '10th']] |
chung kyung - ho | https://en.wikipedia.org/wiki/Chung_Kyung-Ho | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1385043-2.html.csv | superlative | of the games that chung kyung - ho played in 2003 , the one with the highest winning score was the september 29 game of the 2004 afc asian cup qualification . | {'scope': 'all', 'col_superlative': '4', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'result'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; result }'}, 'date'], 'result': 'september 29 , 2003', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; result } ; date }'}, 'september 29 , 2003'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; result } ; date } ; september 29 , 2003 } = true', 'tointer': 'select the row whose result record of all rows is maximum . the date record of this row is september 29 , 2003 .'} | eq { hop { argmax { all_rows ; result } ; date } ; september 29 , 2003 } = true | select the row whose result record of all rows is maximum . the date record of this row is september 29 , 2003 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'result_5': 5, 'date_6': 6, 'september 29 , 2003_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'result_5': 'result', 'date_6': 'date', 'september 29 , 2003_7': 'september 29 , 2003'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'result_5': [0], 'date_6': [1], 'september 29 , 2003_7': [2]} | ['date', 'venue', 'score', 'result', 'competition'] | [['september 29 , 2003', 'incheon', '1 goal', '16 - 0', '2004 afc asian cup qualification'], ['october 21 , 2003', 'muscat', '1 goal', '1 - 3', '2004 afc asian cup qualification'], ['october 24 , 2003', 'muscat', '1 goal', '7 - 0', '2004 afc asian cup qualification'], ['january 15 , 2005', 'los angeles', '1 goal', '1 - 2', 'friendly match'], ['january 22 , 2005', 'carson', '1 goal', '1 - 1', 'friendly match'], ['june 8 , 2005', 'kuwait city', '1 goal', '4 - 0', '2006 fifa world cup qualification']] |
mont ventoux | https://en.wikipedia.org/wiki/Mont_Ventoux | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-162439-2.html.csv | count | the mont ventoux race finished in avignon in three of the years that it took place . | {'scope': 'all', 'criterion': 'equal', 'value': 'avignon', 'result': '3', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'finish', 'avignon'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose finish record fuzzily matches to avignon .', 'tostr': 'filter_eq { all_rows ; finish ; avignon }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; finish ; avignon } }', 'tointer': 'select the rows whose finish record fuzzily matches to avignon . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; finish ; avignon } } ; 3 } = true', 'tointer': 'select the rows whose finish record fuzzily matches to avignon . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; finish ; avignon } } ; 3 } = true | select the rows whose finish record fuzzily matches to avignon . the number of such rows is 3 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'finish_5': 5, 'avignon_6': 6, '3_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'finish_5': 'finish', 'avignon_6': 'avignon', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'finish_5': [0], 'avignon_6': [0], '3_7': [2]} | ['year', 'stage', 'category', 'start', 'finish', 'leader at the summit'] | [['1994', '15', 'hc', 'montpellier', 'carpentras', 'eros poli ( ita )'], ['1974', '12', '1', 'savines - le - lac', 'orange', 'gonzalo aja ( esp )'], ['1967', '13', '1', 'marseille', 'carpentras', 'julio jimãnez ( esp )'], ['1955', '11', '1', 'marseille', 'avignon', 'louison bobet ( fra )'], ['1952', '14', '1', 'aix - en - provence', 'avignon', 'jean robic ( fra )'], ['1951', '18', '1', 'montpellier', 'avignon', 'lucien lazarides ( fra )']] |
list of people in playboy 2000 - 09 | https://en.wikipedia.org/wiki/List_of_people_in_Playboy_2000%E2%80%9309 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1566852-5.html.csv | ordinal | sandra hubby was the third person featured as a playboy centerfold model in april . | {'row': '3', 'col': '1', 'order': '3', 'col_other': '3', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'date', '3'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; date ; 3 }'}, 'centerfold model'], 'result': 'sandra hubby', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; date ; 3 } ; centerfold model }'}, 'sandra hubby'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; date ; 3 } ; centerfold model } ; sandra hubby } = true', 'tointer': 'select the row whose date record of all rows is 3rd minimum . the centerfold model record of this row is sandra hubby .'} | eq { hop { nth_argmin { all_rows ; date ; 3 } ; centerfold model } ; sandra hubby } = true | select the row whose date record of all rows is 3rd minimum . the centerfold model record of this row is sandra hubby . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'date_5': 5, '3_6': 6, 'centerfold model_7': 7, 'sandra hubby_8': 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmin_0': 'nth_argmin', 'all_rows_4': 'all_rows', 'date_5': 'date', '3_6': '3', 'centerfold model_7': 'centerfold model', 'sandra hubby_8': 'sandra hubby'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'date_5': [0], '3_6': [0], 'centerfold model_7': [1], 'sandra hubby_8': [2]} | ['date', 'cover model', 'centerfold model', 'interview subject', '20 questions'] | [['1 - 04', 'none', 'colleen shannon', 'jack nicholson', 'al franken'], ['2 - 04', 'jaime pressly', 'aliya wolf', 'kiefer sutherland', 'dave matthews'], ['3 - 04', 'rena mero , torrie wilson ( two alternative covers )', 'sandra hubby', 'jim carrey', 'william petersen'], ['4 - 04', 'rachel hunter', 'krista kelly', '50 cent', 'kevin smith'], ['5 - 04', 'pamela anderson', 'nicole whitehead', 'johnny depp', 'matthew perry'], ['6 - 04', 'charisma carpenter', 'hiromi oshima', 'derek jeter', 'jude law'], ['7 - 04', 'peta wilson', 'stephanie glasson', 'michael moore', 'christina applegate'], ['8 - 04', 'eva herzigova', 'pilar lastra', 'matt damon', 'spike lee'], ['9 - 04', 'amy acuff', 'scarlett keegan', 'sergey brin & larry page', 'terrel owens'], ['10 - 04', 'evelyn gery', 'kimberly holland', 'donald trump', 'jimmy fallon'], ['11 - 04', 'brooke burke', 'cara zavaleta', 'oliver stone', 'john carmack'], ['12 - 04', 'denise richards', 'tiffany fallon', 'bernie mac', 'dustin hoffman']] |
women 's british open | https://en.wikipedia.org/wiki/Women%27s_British_Open | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1520559-1.html.csv | comparative | the 2011 women 's british open had a higher purse than the 2005 women 's british open . | {'row_1': '3', 'row_2': '9', 'col': '10', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'year', '2011'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose year record fuzzily matches to 2011 .', 'tostr': 'filter_eq { all_rows ; year ; 2011 }'}, 'purse'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; year ; 2011 } ; purse }', 'tointer': 'select the rows whose year record fuzzily matches to 2011 . take the purse record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'year', '2005'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose year record fuzzily matches to 2005 .', 'tostr': 'filter_eq { all_rows ; year ; 2005 }'}, 'purse'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; year ; 2005 } ; purse }', 'tointer': 'select the rows whose year record fuzzily matches to 2005 . take the purse record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; year ; 2011 } ; purse } ; hop { filter_eq { all_rows ; year ; 2005 } ; purse } } = true', 'tointer': 'select the rows whose year record fuzzily matches to 2011 . take the purse record of this row . select the rows whose year record fuzzily matches to 2005 . take the purse record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; year ; 2011 } ; purse } ; hop { filter_eq { all_rows ; year ; 2005 } ; purse } } = true | select the rows whose year record fuzzily matches to 2011 . take the purse record of this row . select the rows whose year record fuzzily matches to 2005 . take the purse record of this row . the first record is greater than the second record . | 5 | 5 | {'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'year_7': 7, '2011_8': 8, 'purse_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'year_11': 11, '2005_12': 12, 'purse_13': 13} | {'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'year_7': 'year', '2011_8': '2011', 'purse_9': 'purse', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'year_11': 'year', '2005_12': '2005', 'purse_13': 'purse'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'year_7': [0], '2011_8': [0], 'purse_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'year_11': [1], '2005_12': [1], 'purse_13': [3]} | ['year', 'dates', 'venue', 'champion', 'country', 'score', 'to par', 'margin of victory', 'runner ( s ) - up', 'purse', "winner 's share"] | [['2013', 'aug 1 - 4', 'old course at st andrews', 'stacy lewis', 'united states', '280', '- 8', '2 strokes', 'na yeon choi hee young park', '2750000', '402583'], ['2012', 'sep 13 - 16', 'royal liverpool golf club', 'jiyai shin', 'south korea', '279', '- 9', '9 strokes', 'inbee park', '2750000', '428650'], ['2011', 'july 28 - 31', 'carnoustie golf links', 'yani tseng', 'taiwan', '272', '- 16', '4 strokes', 'brittany lang', '2500000', '392133'], ['2010', 'july 29 - aug 1', 'royal birkdale golf club', 'yani tseng', 'taiwan', '277', '- 11', '1 stroke', 'katherine hull', '2500000', '408714'], ['2009', 'july 30 - aug 2', 'royal lytham & st annes golf club', 'catriona matthew', 'scotland', '285', '- 3', '3 strokes', 'karrie webb', '2200000', '335000'], ['2008', 'july 31 - aug 3', 'sunningdale golf club', 'jiyai shin', 'south korea', '270', '- 18', '3 strokes', 'yani tseng', '2100000', '314464'], ['2007', 'aug 2 - 5', 'old course at st andrews', 'lorena ochoa', 'mexico', '287', '- 5', '4 strokes', 'maria hjorth jee young lee', '2000000', '320512'], ['2006', 'aug 3 - 6', 'royal lytham & st annes golf club', 'sherri steinhauer', 'united states', '281', '- 7', '3 strokes', 'sophie gustafson cristie kerr', '1800000', '305440'], ['2005', 'july 28 - 31', 'royal birkdale golf club', 'jeong jang', 'south korea', '272', '- 16', '4 strokes', 'sophie gustafson', '1800000', '280208'], ['2004', 'july 29 - aug 1', 'sunningdale golf club', 'karen stupples', 'england', '269', '- 19', '5 strokes', 'rachel hetherington', '1600000', '290880'], ['2003', 'july 31 - aug 3', 'royal lytham & st annes golf club', 'annika sörenstam', 'sweden', '278', '- 10', '1 stroke', 'se ri pak', '1600000', '254880'], ['2002', 'aug 8 - 11', 'turnberry - ailsa course', 'karrie webb', 'australia', '273', '- 15', '2 strokes', 'michelle ellis paula martí', '1500000', '236383'], ['2001', 'aug 2 - 5', 'sunningdale golf club', 'se ri pak', 'south korea', '277', '- 11', '2 strokes', 'mi hyun kim', '1500000', '221650']] |
1911 michigan wolverines football team | https://en.wikipedia.org/wiki/1911_Michigan_Wolverines_football_team | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-25730123-2.html.csv | aggregation | for the 1911 michigan wolverines the total number of touchdowns scored was 14 . | {'scope': 'all', 'col': '2', 'type': 'sum', 'result': '14', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'touchdowns'], 'result': '14', 'ind': 0, 'tostr': 'sum { all_rows ; touchdowns }'}, '14'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; touchdowns } ; 14 } = true', 'tointer': 'the sum of the touchdowns record of all rows is 14 .'} | round_eq { sum { all_rows ; touchdowns } ; 14 } = true | the sum of the touchdowns record of all rows is 14 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'touchdowns_4': 4, '14_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'touchdowns_4': 'touchdowns', '14_5': '14'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'touchdowns_4': [0], '14_5': [1]} | ['player', 'touchdowns', 'extra points', 'field goals', 'points'] | [['george c thomson', '7', '0', '0', '35'], ['frederick l conklin', '2', '10', '2', '26'], ['stanfield wells', '4', '0', '0', '20'], ['jimmy craig', '1', '0', '0', '5'], ['thomas a bogle , jr', '0', '1', '1', '4']] |
pablo andújar | https://en.wikipedia.org/wiki/Pablo_And%C3%BAjar | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16949333-3.html.csv | unique | the match july 17 , 2011 is the only one played in germany . | {'scope': 'all', 'row': '3', 'col': '3', 'col_other': '2', 'criterion': 'equal', 'value': 'mercedescup , stuttgart , germany', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'tournament', 'mercedescup , stuttgart , germany'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose tournament record fuzzily matches to mercedescup , stuttgart , germany .', 'tostr': 'filter_eq { all_rows ; tournament ; mercedescup , stuttgart , germany }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; tournament ; mercedescup , stuttgart , germany } }', 'tointer': 'select the rows whose tournament record fuzzily matches to mercedescup , stuttgart , germany . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'tournament', 'mercedescup , stuttgart , germany'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose tournament record fuzzily matches to mercedescup , stuttgart , germany .', 'tostr': 'filter_eq { all_rows ; tournament ; mercedescup , stuttgart , germany }'}, 'date'], 'result': 'july 17 , 2011', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; tournament ; mercedescup , stuttgart , germany } ; date }'}, 'july 17 , 2011'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; tournament ; mercedescup , stuttgart , germany } ; date } ; july 17 , 2011 }', 'tointer': 'the date record of this unqiue row is july 17 , 2011 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; tournament ; mercedescup , stuttgart , germany } } ; eq { hop { filter_eq { all_rows ; tournament ; mercedescup , stuttgart , germany } ; date } ; july 17 , 2011 } } = true', 'tointer': 'select the rows whose tournament record fuzzily matches to mercedescup , stuttgart , germany . there is only one such row in the table . the date record of this unqiue row is july 17 , 2011 .'} | and { only { filter_eq { all_rows ; tournament ; mercedescup , stuttgart , germany } } ; eq { hop { filter_eq { all_rows ; tournament ; mercedescup , stuttgart , germany } ; date } ; july 17 , 2011 } } = true | select the rows whose tournament record fuzzily matches to mercedescup , stuttgart , germany . there is only one such row in the table . the date record of this unqiue row is july 17 , 2011 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'tournament_7': 7, 'mercedescup , stuttgart , germany_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'date_9': 9, 'july 17 , 2011_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'tournament_7': 'tournament', 'mercedescup , stuttgart , germany_8': 'mercedescup , stuttgart , germany', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'date_9': 'date', 'july 17 , 2011_10': 'july 17 , 2011'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'tournament_7': [0], 'mercedescup , stuttgart , germany_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'date_9': [2], 'july 17 , 2011_10': [3]} | ['outcome', 'date', 'tournament', 'surface', 'opponent', 'score'] | [['runner - up', 'september 26 , 2010', 'brd năstase ţiriac trophy , bucharest , romania', 'clay', 'juan ignacio chela', '5 - 7 , 1 - 6'], ['winner', 'april 10 , 2011', 'grand prix hassan ii , casablanca , morocco ( 1 )', 'clay', 'potito starace', '6 - 1 , 6 - 2'], ['runner - up', 'july 17 , 2011', 'mercedescup , stuttgart , germany', 'clay', 'juan carlos ferrero', '4 - 6 , 0 - 6'], ['runner - up', 'september 25 , 2011', 'brd năstase ţiriac trophy , bucharest , romania', 'clay', 'florian mayer', '3 - 6 , 1 - 6'], ['winner', 'april 15 , 2012', 'grand prix hassan ii , casablanca , morocco ( 2 )', 'clay', 'albert ramos', '6 - 1 , 7 - 6 ( 7 - 5 )']] |
1990 u.s. open ( golf ) | https://en.wikipedia.org/wiki/1990_U.S._Open_%28golf%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17129548-4.html.csv | superlative | at the 1990 u.s. open , the player with the lowest score was tim simpson . | {'scope': 'all', 'col_superlative': '4', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '2', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'score'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; score }'}, 'player'], 'result': 'tim simpson', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; score } ; player }'}, 'tim simpson'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; score } ; player } ; tim simpson } = true', 'tointer': 'select the row whose score record of all rows is minimum . the player record of this row is tim simpson .'} | eq { hop { argmin { all_rows ; score } ; player } ; tim simpson } = true | select the row whose score record of all rows is minimum . the player record of this row is tim simpson . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'score_5': 5, 'player_6': 6, 'tim simpson_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'score_5': 'score', 'player_6': 'player', 'tim simpson_7': 'tim simpson'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'score_5': [0], 'player_6': [1], 'tim simpson_7': [2]} | ['place', 'player', 'country', 'score', 'to par'] | [['1', 'tim simpson', 'united states', '66 + 69 = 135', '- 9'], ['2', 'jeff sluman', 'united states', '66 + 70 = 136', '- 8'], ['3', 'mike donald', 'united states', '67 + 70 = 137', '- 7'], ['4', 'mark brooks', 'united states', '68 + 70 = 138', '- 6'], ['t5', 'hale irwin', 'united states', '69 + 70 = 139', '- 5'], ['t5', 'scott simpson', 'united states', '66 + 73 = 139', '- 5'], ['t7', 'billy ray brown', 'united states', '69 + 71 = 140', '- 4'], ['t7', 'jim gallagher , jr', 'united states', '71 + 69 = 140', '- 4'], ['t7', 'john huston', 'united states', '68 + 72 = 140', '- 4'], ['t7', 'ian woosnam', 'wales', '70 + 70 = 140', '- 4']] |
first championship | https://en.wikipedia.org/wiki/FIRST_Championship | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15584199-3.html.csv | unique | falcons japan was the only team from tokyo , japan in the first championship . | {'scope': 'all', 'row': '1', 'col': '5', 'col_other': '3', 'criterion': 'equal', 'value': 'tokyo , japan', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'city , state / country', 'tokyo , japan'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose city , state / country record fuzzily matches to tokyo , japan .', 'tostr': 'filter_eq { all_rows ; city , state / country ; tokyo , japan }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; city , state / country ; tokyo , japan } }', 'tointer': 'select the rows whose city , state / country record fuzzily matches to tokyo , japan . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'city , state / country', 'tokyo , japan'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose city , state / country record fuzzily matches to tokyo , japan .', 'tostr': 'filter_eq { all_rows ; city , state / country ; tokyo , japan }'}, 'team name'], 'result': 'falcons japan', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; city , state / country ; tokyo , japan } ; team name }'}, 'falcons japan'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; city , state / country ; tokyo , japan } ; team name } ; falcons japan }', 'tointer': 'the team name record of this unqiue row is falcons japan .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; city , state / country ; tokyo , japan } } ; eq { hop { filter_eq { all_rows ; city , state / country ; tokyo , japan } ; team name } ; falcons japan } } = true', 'tointer': 'select the rows whose city , state / country record fuzzily matches to tokyo , japan . there is only one such row in the table . the team name record of this unqiue row is falcons japan .'} | and { only { filter_eq { all_rows ; city , state / country ; tokyo , japan } } ; eq { hop { filter_eq { all_rows ; city , state / country ; tokyo , japan } ; team name } ; falcons japan } } = true | select the rows whose city , state / country record fuzzily matches to tokyo , japan . there is only one such row in the table . the team name record of this unqiue row is falcons japan . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'city , state / country_7': 7, 'tokyo , japan_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'team name_9': 9, 'falcons japan_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'city , state / country_7': 'city , state / country', 'tokyo , japan_8': 'tokyo , japan', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'team name_9': 'team name', 'falcons japan_10': 'falcons japan'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'city , state / country_7': [0], 'tokyo , japan_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'team name_9': [2], 'falcons japan_10': [3]} | ['year / theme', 'award name', 'team name', 'team number', 'city , state / country'] | [['2012 / food factor', 'championship winner - 1st place', 'falcons japan', '15650', 'tokyo , japan'], ['2012 / food factor', 'championship winner - 2nd place', 'blue gear ticks', '252', 'lincoln , ma , usa'], ['2012 / food factor', 'championship winner - 3rd place', 'nxtremers', '15200', 'bengaluru , india'], ['2011 / body forward', 'championship winner - 1st place', 'the sentinels', '3663', 'oakville , on , canada'], ['2011 / body forward', 'championship winner - 2nd place', 'sap g33k', '13300', 'mpumalanga , south africa'], ['2011 / body forward', 'championship winner - 3rd place', 'hammerheads', '4129', 'umatilla , fl , usa'], ['2011 / body forward', 'robot performance award', 'hammerheads', '4129', 'umatilla , fl , usa'], ['2010 / smart move', 'championship winner - 3rd place', 'cougar robotics team', '437', 'columbus , oh , usa'], ['2009 / climate connections', 'championship winner - 1st place', 'da peeps', '55', 'swartz creek , mi , usa'], ['2009 / climate connections', 'championship winner - 2nd place', 'steele', '1232', 'illinois , usa'], ['2009 / climate connections', 'championship winner - 3rd place', 'nxt generation', '9201', 'nordborg , denmark'], ['2009 / climate connections', 'robot performance award - 1st place', 'emerotecos', '8004', 'brazil'], ['2009 / climate connections', 'robot performance award - 2nd place', 'team singapore', '8254', 'singapore'], ['2009 / climate connections', 'robot performance award - 3rd place', 'giant panda', '8060', 'china'], ['2008 / power puzzle', 'championship winner - 1st place', 'external fusion', '8095', 'singapore'], ['2008 / power puzzle', 'championship winner - 2nd place', 'pixelation', '2560', 'north branch , mn , usa'], ['2008 / power puzzle', 'championship winner - 3rd place', 'power peeps', '334', 'swartz creek , mi , usa'], ['2008 / power puzzle', 'robot performance award - 1st place', 'black ocean current', '8110', 'kaohsiung , taiwan'], ['2008 / power puzzle', 'robot performance award - 1st place', 'green man group', '1', 'windham , nh , usa'], ['2008 / power puzzle', 'robot performance award - 3rd place', 'landroids', '2254', 'livingston , nj , usa']] |
composition of the human body | https://en.wikipedia.org/wiki/Composition_of_the_human_body | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13248239-2.html.csv | comparative | there is a higher percent of mass in protein than s lipid in a normal human body composition . | {'row_1': '5', 'row_2': '2', 'col': '2', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'molecule', 'protein'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose molecule record fuzzily matches to protein .', 'tostr': 'filter_eq { all_rows ; molecule ; protein }'}, 'percent of mass'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; molecule ; protein } ; percent of mass }', 'tointer': 'select the rows whose molecule record fuzzily matches to protein . take the percent of mass record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'molecule', 'other s inorganic'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose molecule record fuzzily matches to other s inorganic .', 'tostr': 'filter_eq { all_rows ; molecule ; other s inorganic }'}, 'percent of mass'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; molecule ; other s inorganic } ; percent of mass }', 'tointer': 'select the rows whose molecule record fuzzily matches to other s inorganic . take the percent of mass record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; molecule ; protein } ; percent of mass } ; hop { filter_eq { all_rows ; molecule ; other s inorganic } ; percent of mass } } = true', 'tointer': 'select the rows whose molecule record fuzzily matches to protein . take the percent of mass record of this row . select the rows whose molecule record fuzzily matches to other s inorganic . take the percent of mass record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; molecule ; protein } ; percent of mass } ; hop { filter_eq { all_rows ; molecule ; other s inorganic } ; percent of mass } } = true | select the rows whose molecule record fuzzily matches to protein . take the percent of mass record of this row . select the rows whose molecule record fuzzily matches to other s inorganic . take the percent of mass record of this row . the first record is greater than the second record . | 5 | 5 | {'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'molecule_7': 7, 'protein_8': 8, 'percent of mass_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'molecule_11': 11, 'other s inorganic_12': 12, 'percent of mass_13': 13} | {'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'molecule_7': 'molecule', 'protein_8': 'protein', 'percent of mass_9': 'percent of mass', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'molecule_11': 'molecule', 'other s inorganic_12': 'other s inorganic', 'percent of mass_13': 'percent of mass'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'molecule_7': [0], 'protein_8': [0], 'percent of mass_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'molecule_11': [1], 'other s inorganic_12': [1], 'percent of mass_13': [3]} | ['molecule', 'percent of mass', 'molweight ( daltons )', 'molecules', 'percent of molecules'] | [['water', '65', '18', '1.74 e14', '98.73'], ['other s inorganic', '1.5', 'n / a', '1.31 e12', '0.74'], ['s lipid', '12', 'n / a', '8.4 e11', '0.475'], ['other s organic', '0.4', 'n / a', '7.7 e10', '0.044'], ['protein', '20', 'n / a', '1.9 e10', '0.011'], ['rna', '1.0', 'n / a', '5e7', '3e - 5'], ['dna', '0.1', '1e11', '46', '3e - 11']] |
list of manly - warringah sea eagles honours | https://en.wikipedia.org/wiki/List_of_Manly-Warringah_Sea_Eagles_honours | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12573519-8.html.csv | ordinal | the manly - warringah sea eagles ' game against the south sydney rabbitohs is the earliest in their honours ' list . | {'row': '1', 'col': '1', 'order': '1', 'col_other': '2', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'year', '1'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; year ; 1 }'}, 'opponent'], 'result': 'south sydney rabbitohs', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; year ; 1 } ; opponent }'}, 'south sydney rabbitohs'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; year ; 1 } ; opponent } ; south sydney rabbitohs } = true', 'tointer': 'select the row whose year record of all rows is 1st minimum . the opponent record of this row is south sydney rabbitohs .'} | eq { hop { nth_argmin { all_rows ; year ; 1 } ; opponent } ; south sydney rabbitohs } = true | select the row whose year record of all rows is 1st minimum . the opponent record of this row is south sydney rabbitohs . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'year_5': 5, '1_6': 6, 'opponent_7': 7, 'south sydney rabbitohs_8': 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmin_0': 'nth_argmin', 'all_rows_4': 'all_rows', 'year_5': 'year', '1_6': '1', 'opponent_7': 'opponent', 'south sydney rabbitohs_8': 'south sydney rabbitohs'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'year_5': [0], '1_6': [0], 'opponent_7': [1], 'south sydney rabbitohs_8': [2]} | ['year', 'opponent', 'competition', 'score', 'venue', 'attendance'] | [['1951', 'south sydney rabbitohs', 'nswrfl', '14 - 42', 'sydney sports ground', '28505'], ['1957', 'st george dragons', 'nswrfl', '9 - 31', 'sydney cricket ground', '54399'], ['1959', 'st george dragons', 'nswrfl', '0 - 20', 'sydney cricket ground', '49457'], ['1968', 'south sydney rabbitohs', 'nswrfl', '9 - 13', 'sydney cricket ground', '54255'], ['1970', 'south sydney rabbitohs', 'nswrfl', '12 - 23', 'sydney cricket ground', '53241'], ['1982', 'parramatta eels', 'nswrfl', '8 - 21', 'sydney cricket ground', '52186'], ['1983', 'parramatta eels', 'nswrfl', '6 - 18', 'sydney cricket ground', '40285'], ['1995', 'sydney bulldogs', 'arl', '4 - 17', 'sydney football stadium', '41127'], ['1997', 'newcastle knights', 'arl', '16 - 22', 'sydney football stadium', '42482'], ['2007', 'melbourne storm', 'nrl', '8 - 34', 'anz stadium', '81392']] |
romania in the eurovision song contest 2008 | https://en.wikipedia.org/wiki/Romania_in_the_Eurovision_Song_Contest_2008 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15016411-1.html.csv | unique | nico & vlad mirita were the only ones that placed 1st . | {'scope': 'all', 'row': '1', 'col': '5', 'col_other': '1', 'criterion': 'equal', 'value': '1', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'place', '1'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose place record is equal to 1 .', 'tostr': 'filter_eq { all_rows ; place ; 1 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; place ; 1 } }', 'tointer': 'select the rows whose place record is equal to 1 . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'place', '1'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose place record is equal to 1 .', 'tostr': 'filter_eq { all_rows ; place ; 1 }'}, 'draw'], 'result': '1', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; place ; 1 } ; draw }'}, '1'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; place ; 1 } ; draw } ; 1 }', 'tointer': 'the draw record of this unqiue row is 1 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; place ; 1 } } ; eq { hop { filter_eq { all_rows ; place ; 1 } ; draw } ; 1 } } = true', 'tointer': 'select the rows whose place record is equal to 1 . there is only one such row in the table . the draw record of this unqiue row is 1 .'} | and { only { filter_eq { all_rows ; place ; 1 } } ; eq { hop { filter_eq { all_rows ; place ; 1 } ; draw } ; 1 } } = true | select the rows whose place record is equal to 1 . there is only one such row in the table . the draw record of this unqiue row is 1 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'place_7': 7, '1_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'draw_9': 9, '1_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'place_7': 'place', '1_8': '1', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'draw_9': 'draw', '1_10': '1'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'place_7': [0], '1_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'draw_9': [2], '1_10': [3]} | ['draw', 'artist', 'song', 'points', 'place'] | [['1', 'nico & vlad mirita', 'pe - o margine de lume', '284', '1'], ['2', 'inesa', 'la storia della pioggia', '89', '11'], ['3', 'adrian enache', 'te iubesc', '114', '8'], ['4', 'lagaylia frazier', 'dr frankenstein', '174', '5'], ['5', 'new effect feat gentiana', 'zamira', '119', '7'], ['6', 'tabasco', 'love is all i need', '170', '6'], ['7', 'cătălin josan', "when we 're together", '217', '3'], ['8', 'daniela nicol', 'why', '112', '10'], ['9', 'yana', "c'est la vie", '113', '9'], ['10', 'leo iorga & pacifica', 'prea mici sunt cuvintele', '206', '4'], ['11', 'ana mardare & irvin doomes', 'heaven', '87', '12'], ['12', 'simona nae', 'the key of life', '229', '2']] |
test matches ( 1991 - 2000 ) | https://en.wikipedia.org/wiki/Test_matches_%281991%E2%80%932000%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12410929-70.html.csv | aggregation | of the cricket test matches ( 1991 - 2000 ) won by the west indies ( wi ) , they were won with an average of 8 wickets . | {'scope': 'subset', 'col': '5', 'type': 'average', 'result': '8', 'subset': {'col': '5', 'criterion': 'fuzzily_match', 'value': 'wi'}} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 'wi'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; result ; wi }', 'tointer': 'select the rows whose result record fuzzily matches to wi .'}, 'result'], 'result': '8', 'ind': 1, 'tostr': 'avg { filter_eq { all_rows ; result ; wi } ; result }'}, '8'], 'result': True, 'ind': 2, 'tostr': 'round_eq { avg { filter_eq { all_rows ; result ; wi } ; result } ; 8 } = true', 'tointer': 'select the rows whose result record fuzzily matches to wi . the average of the result record of these rows is 8 .'} | round_eq { avg { filter_eq { all_rows ; result ; wi } ; result } ; 8 } = true | select the rows whose result record fuzzily matches to wi . the average of the result record of these rows is 8 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'avg_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'result_5': 5, 'wi_6': 6, 'result_7': 7, '8_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'avg_1': 'avg', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'result_5': 'result', 'wi_6': 'wi', 'result_7': 'result', '8_8': '8'} | {'eq_2': [3], 'result_3': [], 'avg_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'result_5': [0], 'wi_6': [0], 'result_7': [1], '8_8': [2]} | ['date', 'home captain', 'away captain', 'venue', 'result'] | [['22 , 23 , 24 , 25 , 26 november 1996', 'mark taylor', 'courtney walsh', 'brisbane cricket ground', 'aus by 123 runs'], ['29 , 30 november , 1 , 2 , 3 december 1996', 'mark taylor', 'courtney walsh', 'sydney cricket ground', 'aus by 124 runs'], ['26 , 27 , 28 december 1996', 'mark taylor', 'courtney walsh', 'melbourne cricket ground', 'wi by 6 wkts'], ['25 , 26 , 27 , 28 january 1997', 'mark taylor', 'courtney walsh', 'adelaide oval', 'aus by inns & 183 runs'], ['1 , 2 , 3 february 1997', 'mark taylor', 'courtney walsh', 'waca ground', 'wi by 10 wkts']] |
united states house of representatives elections , 2012 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_2012 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-25030512-36.html.csv | comparative | howard coble has a first elected year which is earlier than that of mike mcintyre . | {'row_1': '3', 'row_2': '4', 'col': '4', 'col_other': '2', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'incumbent', 'howard coble'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose incumbent record fuzzily matches to howard coble .', 'tostr': 'filter_eq { all_rows ; incumbent ; howard coble }'}, 'first elected'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; incumbent ; howard coble } ; first elected }', 'tointer': 'select the rows whose incumbent record fuzzily matches to howard coble . take the first elected record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'incumbent', 'mike mcintyre'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose incumbent record fuzzily matches to mike mcintyre .', 'tostr': 'filter_eq { all_rows ; incumbent ; mike mcintyre }'}, 'first elected'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; incumbent ; mike mcintyre } ; first elected }', 'tointer': 'select the rows whose incumbent record fuzzily matches to mike mcintyre . take the first elected record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; incumbent ; howard coble } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; mike mcintyre } ; first elected } } = true', 'tointer': 'select the rows whose incumbent record fuzzily matches to howard coble . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to mike mcintyre . take the first elected record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; incumbent ; howard coble } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; mike mcintyre } ; first elected } } = true | select the rows whose incumbent record fuzzily matches to howard coble . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to mike mcintyre . take the first elected record of this row . the first record is less than the second record . | 5 | 5 | {'less_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'incumbent_7': 7, 'howard coble_8': 8, 'first elected_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'incumbent_11': 11, 'mike mcintyre_12': 12, 'first elected_13': 13} | {'less_4': 'less', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'incumbent_7': 'incumbent', 'howard coble_8': 'howard coble', 'first elected_9': 'first elected', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'incumbent_11': 'incumbent', 'mike mcintyre_12': 'mike mcintyre', 'first elected_13': 'first elected'} | {'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'incumbent_7': [0], 'howard coble_8': [0], 'first elected_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'incumbent_11': [1], 'mike mcintyre_12': [1], 'first elected_13': [3]} | ['district', 'incumbent', 'party', 'first elected', 'result', 'candidates'] | [['north carolina 3', 'walter jones jr', 'republican', '1994', 're - elected', 'walter jones jr ( r ) 63.2 % erik anderson ( d ) 36.8 %'], ['north carolina 4', 'david price', 'democratic', '1996', 're - elected', "david price ( d ) 74.4 % tim d'annunzio ( r ) 25.6 %"], ['north carolina 6', 'howard coble', 'republican', '1984', 're - elected', 'howard coble ( r ) 60.9 % tony foriest ( d ) 39.1 %'], ['north carolina 7', 'mike mcintyre', 'democratic', '1996', 're - elected', 'mike mcintyre ( d ) 50.1 % david rouzer ( r ) 49.9 %'], ['north carolina 8', 'larry kissell', 'democratic', '2008', 'lost re - election republican gain', 'richard hudson ( r ) 54.1 % larry kissell ( d ) 45.9 %'], ['north carolina 10', 'patrick mchenry', 'republican', '2004', 're - elected', 'patrick mchenry ( r ) 57.0 % patsy keever ( d ) 43.0 %'], ['north carolina 11', 'heath shuler', 'democratic', '2006', 'retired republican gain', 'mark meadows ( r ) 57.4 % hayden rogers ( d ) 42.6 %'], ['north carolina 12', 'mel watt', 'democratic', '1992', 're - elected', 'mel watt ( d ) 79.7 % jack brosch ( r ) 20.3 %']] |
1996 u.s. open ( golf ) | https://en.wikipedia.org/wiki/1996_U.S._Open_%28golf%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17162199-6.html.csv | unique | vijay singh was the only player from fiji in the 1996 u.s. open . | {'scope': 'all', 'row': '9', 'col': '3', 'col_other': '2', 'criterion': 'equal', 'value': 'fiji', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'fiji'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose country record fuzzily matches to fiji .', 'tostr': 'filter_eq { all_rows ; country ; fiji }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; country ; fiji } }', 'tointer': 'select the rows whose country record fuzzily matches to fiji . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'fiji'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose country record fuzzily matches to fiji .', 'tostr': 'filter_eq { all_rows ; country ; fiji }'}, 'player'], 'result': 'vijay singh', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; country ; fiji } ; player }'}, 'vijay singh'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; country ; fiji } ; player } ; vijay singh }', 'tointer': 'the player record of this unqiue row is vijay singh .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; country ; fiji } } ; eq { hop { filter_eq { all_rows ; country ; fiji } ; player } ; vijay singh } } = true', 'tointer': 'select the rows whose country record fuzzily matches to fiji . there is only one such row in the table . the player record of this unqiue row is vijay singh .'} | and { only { filter_eq { all_rows ; country ; fiji } } ; eq { hop { filter_eq { all_rows ; country ; fiji } ; player } ; vijay singh } } = true | select the rows whose country record fuzzily matches to fiji . there is only one such row in the table . the player record of this unqiue row is vijay singh . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'country_7': 7, 'fiji_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'player_9': 9, 'vijay singh_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'country_7': 'country', 'fiji_8': 'fiji', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'player_9': 'player', 'vijay singh_10': 'vijay singh'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'country_7': [0], 'fiji_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'player_9': [2], 'vijay singh_10': [3]} | ['place', 'player', 'country', 'score', 'to par', 'money'] | [['1', 'steve jones', 'united states', '74 + 66 + 69 + 69 = 278', '- 2', '425000'], ['t2', 'tom lehman', 'united states', '71 + 72 + 65 + 71 = 279', '- 1', '204801'], ['t2', 'davis love iii', 'united states', '71 + 69 + 70 + 69 = 279', '- 1', '204801'], ['4', 'john morse', 'united states', '68 + 74 + 68 + 70 = 280', 'e', '111235'], ['t5', 'ernie els', 'south africa', '72 + 67 + 72 + 70 = 281', '+ 1', '84965'], ['t5', 'jim furyk', 'united states', '72 + 69 + 70 + 70 = 281', '+ 1', '84965'], ['t7', 'ken green', 'united states', '73 + 67 + 72 + 70 = 282', '+ 2', '66295'], ['t7', 'scott hoch', 'united states', '73 + 71 + 71 + 67 = 282', '+ 2', '66295'], ['t7', 'vijay singh', 'fiji', '71 + 72 + 70 + 69 = 282', '+ 2', '66295'], ['t10', 'lee janzen', 'united states', '68 + 75 + 71 + 69 = 283', '+ 3', '52591'], ['t10', 'colin montgomerie', 'scotland', '70 + 72 + 69 + 72 = 283', '+ 3', '52591'], ['t10', 'greg norman', 'australia', '73 + 66 + 74 + 70 = 283', '+ 3', '52591']] |
1953 argentine grand prix | https://en.wikipedia.org/wiki/1953_Argentine_Grand_Prix | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1122075-2.html.csv | comparative | in the 1953 argentine grand prix , luigi villoresi completed more laps than alan brown . | {'row_1': '2', 'row_2': '9', 'col': '3', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'driver', 'luigi villoresi'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose driver record fuzzily matches to luigi villoresi .', 'tostr': 'filter_eq { all_rows ; driver ; luigi villoresi }'}, 'laps'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; driver ; luigi villoresi } ; laps }', 'tointer': 'select the rows whose driver record fuzzily matches to luigi villoresi . take the laps record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'driver', 'alan brown'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose driver record fuzzily matches to alan brown .', 'tostr': 'filter_eq { all_rows ; driver ; alan brown }'}, 'laps'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; driver ; alan brown } ; laps }', 'tointer': 'select the rows whose driver record fuzzily matches to alan brown . take the laps record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; driver ; luigi villoresi } ; laps } ; hop { filter_eq { all_rows ; driver ; alan brown } ; laps } } = true', 'tointer': 'select the rows whose driver record fuzzily matches to luigi villoresi . take the laps record of this row . select the rows whose driver record fuzzily matches to alan brown . take the laps record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; driver ; luigi villoresi } ; laps } ; hop { filter_eq { all_rows ; driver ; alan brown } ; laps } } = true | select the rows whose driver record fuzzily matches to luigi villoresi . take the laps record of this row . select the rows whose driver record fuzzily matches to alan brown . take the laps record of this row . the first record is greater than the second record . | 5 | 5 | {'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'driver_7': 7, 'luigi villoresi_8': 8, 'laps_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'driver_11': 11, 'alan brown_12': 12, 'laps_13': 13} | {'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'driver_7': 'driver', 'luigi villoresi_8': 'luigi villoresi', 'laps_9': 'laps', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'driver_11': 'driver', 'alan brown_12': 'alan brown', 'laps_13': 'laps'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'driver_7': [0], 'luigi villoresi_8': [0], 'laps_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'driver_11': [1], 'alan brown_12': [1], 'laps_13': [3]} | ['driver', 'constructor', 'laps', 'time / retired', 'grid'] | [['alberto ascari', 'ferrari', '97', '3:01:04.6', '1'], ['luigi villoresi', 'ferrari', '96', '+ 1 lap', '3'], ['josé froilán gonzález', 'maserati', '96', '+ 1 lap', '5'], ['mike hawthorn', 'ferrari', '96', '+ 1 lap', '6'], ['oscar alfredo gálvez', 'maserati', '96', '+ 1 lap', '9'], ['jean behra', 'gordini', '94', '+ 3 laps', '11'], ['maurice trintignant harry schell', 'gordini', '91', '+ 6 laps', '7'], ['john barber', 'cooper - bristol', '90', '+ 7 laps', '16'], ['alan brown', 'cooper - bristol', '87', '+ 10 laps', '12'], ['robert manzon', 'gordini', '67', 'wheel', '8'], ['juan manuel fangio', 'maserati', '36', 'transmission', '2'], ['felice bonetto', 'maserati', '32', 'transmission', '15'], ['nino farina', 'ferrari', '31', 'accident', '4'], ['carlos menditeguy', 'gordini', '24', 'gearbox', '10'], ['pablo birger', 'simca - gordini - gordini', '21', 'differential', '14'], ['adolfo schwelm cruz', 'cooper - bristol', '20', 'wheel', '13']] |
1994 fei world equestrian games | https://en.wikipedia.org/wiki/1994_FEI_World_Equestrian_Games | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11871998-2.html.csv | count | 12 nations were represented in the fei world equestrian games of 1994 . | {'scope': 'all', 'criterion': 'all', 'value': 'n/a', 'result': '12', 'col': '1', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_all', 'args': ['all_rows', 'nation'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose nation record is arbitrary .', 'tostr': 'filter_all { all_rows ; nation }'}], 'result': '12', 'ind': 1, 'tostr': 'count { filter_all { all_rows ; nation } }', 'tointer': 'select the rows whose nation record is arbitrary . the number of such rows is 12 .'}, '12'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_all { all_rows ; nation } } ; 12 } = true', 'tointer': 'select the rows whose nation record is arbitrary . the number of such rows is 12 .'} | eq { count { filter_all { all_rows ; nation } } ; 12 } = true | select the rows whose nation record is arbitrary . the number of such rows is 12 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_all_0': 0, 'all_rows_4': 4, 'nation_5': 5, '12_6': 6} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_all_0': 'filter_all', 'all_rows_4': 'all_rows', 'nation_5': 'nation', '12_6': '12'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_all_0': [1], 'all_rows_4': [0], 'nation_5': [0], '12_6': [2]} | ['nation', 'gold', 'silver', 'bronze', 'total'] | [['germany', '7', '4', '5', '16'], ['france', '1', '4', '1', '6'], ['united states', '1', '2', '1', '4'], ['netherlands', '1', '1', '3', '5'], ['united kingdom', '1', '1', '1', '3'], ['switzerland', '1', '-', '1', '2'], ['denmark', '1', '-', '-', '1'], ['new zealand', '1', '-', '-', '1'], ['belgium', '-', '1', '-', '1'], ['spain', '-', '1', '-', '1'], ['australia', '-', '-', '1', '1'], ['sweden', '-', '-', '1', '1']] |
rowing at the 2008 summer olympics - women 's quadruple sculls | https://en.wikipedia.org/wiki/Rowing_at_the_2008_Summer_Olympics_%E2%80%93_Women%27s_quadruple_sculls | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18662713-5.html.csv | ordinal | in the 2008 summer olympics women 's quadruple sculls , the 3rd best time ( 6:41:39 ) was achieved by the austrian team . | {'scope': 'all', 'row': '3', 'col': '4', 'order': '3', 'col_other': '1,3', 'max_or_min': 'min_to_max', 'value_mentioned': 'yes', 'subset': None} | {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'nth_min', 'args': ['all_rows', 'time', '3'], 'result': '6:41.39', 'ind': 0, 'tostr': 'nth_min { all_rows ; time ; 3 }', 'tointer': 'the 3rd minimum time record of all rows is 6:41.39 .'}, '6:41.39'], 'result': True, 'ind': 1, 'tostr': 'eq { nth_min { all_rows ; time ; 3 } ; 6:41.39 }', 'tointer': 'the 3rd minimum time record of all rows is 6:41.39 .'}, {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'time', '3'], 'result': None, 'ind': 2, 'tostr': 'nth_argmin { all_rows ; time ; 3 }'}, 'rank'], 'result': '3', 'ind': 3, 'tostr': 'hop { nth_argmin { all_rows ; time ; 3 } ; rank }'}, '3'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { nth_argmin { all_rows ; time ; 3 } ; rank } ; 3 }', 'tointer': 'the rank record of the row with 3rd minimum time record is 3 .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'time', '3'], 'result': None, 'ind': 2, 'tostr': 'nth_argmin { all_rows ; time ; 3 }'}, 'country'], 'result': 'australia', 'ind': 5, 'tostr': 'hop { nth_argmin { all_rows ; time ; 3 } ; country }'}, 'australia'], 'result': True, 'ind': 6, 'tostr': 'eq { hop { nth_argmin { all_rows ; time ; 3 } ; country } ; australia }', 'tointer': 'the country record of the row with 3rd minimum time record is australia .'}], 'result': True, 'ind': 7, 'tostr': 'and { eq { hop { nth_argmin { all_rows ; time ; 3 } ; rank } ; 3 } ; eq { hop { nth_argmin { all_rows ; time ; 3 } ; country } ; australia } }', 'tointer': 'the rank record of the row with 3rd minimum time record is 3 . the country record of the row with 3rd minimum time record is australia .'}], 'result': True, 'ind': 8, 'tostr': 'and { eq { nth_min { all_rows ; time ; 3 } ; 6:41.39 } ; and { eq { hop { nth_argmin { all_rows ; time ; 3 } ; rank } ; 3 } ; eq { hop { nth_argmin { all_rows ; time ; 3 } ; country } ; australia } } } = true', 'tointer': 'the 3rd minimum time record of all rows is 6:41.39 . the rank record of the row with 3rd minimum time record is 3 . the country record of the row with 3rd minimum time record is australia .'} | and { eq { nth_min { all_rows ; time ; 3 } ; 6:41.39 } ; and { eq { hop { nth_argmin { all_rows ; time ; 3 } ; rank } ; 3 } ; eq { hop { nth_argmin { all_rows ; time ; 3 } ; country } ; australia } } } = true | the 3rd minimum time record of all rows is 6:41.39 . the rank record of the row with 3rd minimum time record is 3 . the country record of the row with 3rd minimum time record is australia . | 10 | 9 | {'and_8': 8, 'result_9': 9, 'eq_1': 1, 'nth_min_0': 0, 'all_rows_10': 10, 'time_11': 11, '3_12': 12, '6:41.39_13': 13, 'and_7': 7, 'eq_4': 4, 'num_hop_3': 3, 'nth_argmin_2': 2, 'all_rows_14': 14, 'time_15': 15, '3_16': 16, 'rank_17': 17, '3_18': 18, 'str_eq_6': 6, 'str_hop_5': 5, 'country_19': 19, 'australia_20': 20} | {'and_8': 'and', 'result_9': 'true', 'eq_1': 'eq', 'nth_min_0': 'nth_min', 'all_rows_10': 'all_rows', 'time_11': 'time', '3_12': '3', '6:41.39_13': '6:41.39', 'and_7': 'and', 'eq_4': 'eq', 'num_hop_3': 'num_hop', 'nth_argmin_2': 'nth_argmin', 'all_rows_14': 'all_rows', 'time_15': 'time', '3_16': '3', 'rank_17': 'rank', '3_18': '3', 'str_eq_6': 'str_eq', 'str_hop_5': 'str_hop', 'country_19': 'country', 'australia_20': 'australia'} | {'and_8': [9], 'result_9': [], 'eq_1': [8], 'nth_min_0': [1], 'all_rows_10': [0], 'time_11': [0], '3_12': [0], '6:41.39_13': [1], 'and_7': [8], 'eq_4': [7], 'num_hop_3': [4], 'nth_argmin_2': [3, 5], 'all_rows_14': [2], 'time_15': [2], '3_16': [2], 'rank_17': [3], '3_18': [4], 'str_eq_6': [7], 'str_hop_5': [6], 'country_19': [5], 'australia_20': [6]} | ['rank', 'rowers', 'country', 'time', 'notes'] | [['1', 'oppelt , lutze , boron , schiller', 'germany', '6:36.17', 'fa'], ['2', 'pernell , meyer , kaido , shumway', 'united states', '6:39.53', 'fa'], ['3', 'ives , hore , uphill , bradley', 'australia', '6:41.39', 'fa'], ['4', 'spiryukhova , olefirenko , lyal ™ chuk , kolesnikova', 'ukraine', '6:41.45', 'fa'], ['5', 'de jong , de zwager , hanson , guloien', 'canada', '6:46.60', 'fb'], ['6', 'kalinovskaya , dorodnova , merk , levina', 'russia', '6:51.14', 'fb']] |
1996 pga championship | https://en.wikipedia.org/wiki/1996_PGA_Championship | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18096431-5.html.csv | unique | in the 1996 pga championship , vijay singh was the only player from fiji to compete . | {'scope': 'all', 'row': '5', 'col': '3', 'col_other': '2', 'criterion': 'equal', 'value': 'fiji', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'fiji'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose country record fuzzily matches to fiji .', 'tostr': 'filter_eq { all_rows ; country ; fiji }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; country ; fiji } }', 'tointer': 'select the rows whose country record fuzzily matches to fiji . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'fiji'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose country record fuzzily matches to fiji .', 'tostr': 'filter_eq { all_rows ; country ; fiji }'}, 'player'], 'result': 'vijay singh', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; country ; fiji } ; player }'}, 'vijay singh'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; country ; fiji } ; player } ; vijay singh }', 'tointer': 'the player record of this unqiue row is vijay singh .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; country ; fiji } } ; eq { hop { filter_eq { all_rows ; country ; fiji } ; player } ; vijay singh } } = true', 'tointer': 'select the rows whose country record fuzzily matches to fiji . there is only one such row in the table . the player record of this unqiue row is vijay singh .'} | and { only { filter_eq { all_rows ; country ; fiji } } ; eq { hop { filter_eq { all_rows ; country ; fiji } ; player } ; vijay singh } } = true | select the rows whose country record fuzzily matches to fiji . there is only one such row in the table . the player record of this unqiue row is vijay singh . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'country_7': 7, 'fiji_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'player_9': 9, 'vijay singh_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'country_7': 'country', 'fiji_8': 'fiji', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'player_9': 'player', 'vijay singh_10': 'vijay singh'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'country_7': [0], 'fiji_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'player_9': [2], 'vijay singh_10': [3]} | ['place', 'player', 'country', 'score', 'to par'] | [['1', 'phil mickelson', 'united states', '67 + 67 = 134', '- 10'], ['2', 'justin leonard', 'united states', '71 + 66 = 137', '- 7'], ['t3', 'mark brooks', 'united states', '68 + 70 = 138', '- 6'], ['t3', 'kenny perry', 'united states', '66 + 72 = 138', '- 6'], ['t3', 'vijay singh', 'fiji', '69 + 69 = 138', '- 6'], ['t6', 'lee janzen', 'united states', '68 + 71 = 139', '- 5'], ['t6', 'nick price', 'zimbabwe', '68 + 72 = 139', '- 5'], ['t8', 'mike brisky', 'united states', '71 + 69 = 140', '- 4'], ['t8', 'russ cochran', 'united states', '68 + 72 = 140', '- 4'], ['t8', 'david edwards', 'united states', '69 + 71 = 140', '- 4'], ['t8', 'brad faxon', 'united states', '72 + 68 = 140', '- 4'], ['t8', 'jim furyk', 'united states', '70 + 70 = 140', '- 4'], ['t8', 'greg norman', 'australia', '68 + 72 = 140', '- 4'], ['t8', 'jesper parnevik', 'sweden', '73 + 67 = 140', '- 4'], ['t8', 'tommy tolles', 'united states', '69 + 71 = 140', '- 4'], ['t8', 'tom watson', 'united states', '69 + 71 = 140', '- 4'], ['t8', 'ian woosnam', 'wales', '68 + 72 = 140', '- 4']] |
chala kelele | https://en.wikipedia.org/wiki/Chala_Kelele | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12736407-1.html.csv | ordinal | chala kelele had a 3rd place result in the year 1996 . | {'row': '6', 'col': '4', 'order': '3', 'col_other': '1', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'result', '3'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; result ; 3 }'}, 'year'], 'result': '1996', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; result ; 3 } ; year }'}, '1996'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; result ; 3 } ; year } ; 1996 } = true', 'tointer': 'select the row whose result record of all rows is 3rd minimum . the year record of this row is 1996 .'} | eq { hop { nth_argmin { all_rows ; result ; 3 } ; year } ; 1996 } = true | select the row whose result record of all rows is 3rd minimum . the year record of this row is 1996 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'num_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'result_5': 5, '3_6': 6, 'year_7': 7, '1996_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'num_hop_1': 'num_hop', 'nth_argmin_0': 'nth_argmin', 'all_rows_4': 'all_rows', 'result_5': 'result', '3_6': '3', 'year_7': 'year', '1996_8': '1996'} | {'eq_2': [3], 'result_3': [], 'num_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'result_5': [0], '3_6': [0], 'year_7': [1], '1996_8': [2]} | ['year', 'tournament', 'venue', 'result', 'extra'] | [['1988', 'world cross country championships', 'auckland , new zealand', '2nd', 'team competition'], ['1991', 'world cross country championships', 'antwerp , belgium', '7th', 'long race'], ['1991', 'world cross country championships', 'antwerp , belgium', '2nd', 'team competition'], ['1995', 'world cross country championships', 'durham , england', '27th', 'long race'], ['1995', 'world cross country championships', 'durham , england', '5th', 'team competition'], ['1996', 'world cross country championships', 'stellenbosch , south africa', '3rd', 'team competition']] |
b " rowing at the 2008 summer olympics - men 's single sculls " | https://en.wikipedia.org/wiki/Rowing_at_the_2008_Summer_Olympics_%E2%80%93_Men%27s_single_sculls | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18662643-6.html.csv | count | 5 nations were represented in the 2008 olympics men 's single sculls rowing competition . | {'scope': 'all', 'criterion': 'all', 'value': 'n/a', 'result': '5', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_all', 'args': ['all_rows', 'country'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose country record is arbitrary .', 'tostr': 'filter_all { all_rows ; country }'}], 'result': '5', 'ind': 1, 'tostr': 'count { filter_all { all_rows ; country } }', 'tointer': 'select the rows whose country record is arbitrary . the number of such rows is 5 .'}, '5'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_all { all_rows ; country } } ; 5 } = true', 'tointer': 'select the rows whose country record is arbitrary . the number of such rows is 5 .'} | eq { count { filter_all { all_rows ; country } } ; 5 } = true | select the rows whose country record is arbitrary . the number of such rows is 5 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_all_0': 0, 'all_rows_4': 4, 'country_5': 5, '5_6': 6} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_all_0': 'filter_all', 'all_rows_4': 'all_rows', 'country_5': 'country', '5_6': '5'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_all_0': [1], 'all_rows_4': [0], 'country_5': [0], '5_6': [2]} | ['rank', 'athlete', 'country', 'time', 'notes'] | [['1', 'alan campbell', 'great britain', '7:14.98', 'q'], ['2', 'peter hardcastle', 'australia', '7:17.74', 'q'], ['3', 'patrick loliger', 'mexico', '7:22.55', 'q'], ['4', 'ken jurkowski', 'united states', '7:25.13', 'q'], ['5', 'ruslan naurzaliev', 'uzbekistan', '7:58.43', 'se / f']] |
türk telekom arena | https://en.wikipedia.org/wiki/T%C3%BCrk_Telekom_Arena | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12243387-1.html.csv | comparative | the final plan for the türk telekom arena called for more seats than the original plan . | {'row_1': '5', 'row_2': '1', 'col': '4', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'yes', 'diff_result': None} | {'func': 'and', 'args': [{'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'project', 'özhan canaydın project'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose project record fuzzily matches to özhan canaydın project .', 'tostr': 'filter_eq { all_rows ; project ; özhan canaydın project }'}, 'capacity'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; project ; özhan canaydın project } ; capacity }', 'tointer': 'select the rows whose project record fuzzily matches to özhan canaydın project . take the capacity record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'project', 'faruk süren project'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose project record fuzzily matches to faruk süren project .', 'tostr': 'filter_eq { all_rows ; project ; faruk süren project }'}, 'capacity'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; project ; faruk süren project } ; capacity }', 'tointer': 'select the rows whose project record fuzzily matches to faruk süren project . take the capacity record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; project ; özhan canaydın project } ; capacity } ; hop { filter_eq { all_rows ; project ; faruk süren project } ; capacity } }', 'tointer': 'select the rows whose project record fuzzily matches to özhan canaydın project . take the capacity record of this row . select the rows whose project record fuzzily matches to faruk süren project . take the capacity record of this row . the first record is greater than the second record .'}, {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'project', 'özhan canaydın project'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose project record fuzzily matches to özhan canaydın project .', 'tostr': 'filter_eq { all_rows ; project ; özhan canaydın project }'}, 'capacity'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; project ; özhan canaydın project } ; capacity }', 'tointer': 'select the rows whose project record fuzzily matches to özhan canaydın project . take the capacity record of this row .'}, '52652'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; project ; özhan canaydın project } ; capacity } ; 52652 }', 'tointer': 'the capacity record of the first row is 52652 .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'project', 'faruk süren project'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose project record fuzzily matches to faruk süren project .', 'tostr': 'filter_eq { all_rows ; project ; faruk süren project }'}, 'capacity'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; project ; faruk süren project } ; capacity }', 'tointer': 'select the rows whose project record fuzzily matches to faruk süren project . take the capacity record of this row .'}, '40482'], 'result': True, 'ind': 6, 'tostr': 'eq { hop { filter_eq { all_rows ; project ; faruk süren project } ; capacity } ; 40482 }', 'tointer': 'the capacity record of the second row is 40482 .'}], 'result': True, 'ind': 7, 'tostr': 'and { eq { hop { filter_eq { all_rows ; project ; özhan canaydın project } ; capacity } ; 52652 } ; eq { hop { filter_eq { all_rows ; project ; faruk süren project } ; capacity } ; 40482 } }', 'tointer': 'the capacity record of the first row is 52652 . the capacity record of the second row is 40482 .'}], 'result': True, 'ind': 8, 'tostr': 'and { greater { hop { filter_eq { all_rows ; project ; özhan canaydın project } ; capacity } ; hop { filter_eq { all_rows ; project ; faruk süren project } ; capacity } } ; and { eq { hop { filter_eq { all_rows ; project ; özhan canaydın project } ; capacity } ; 52652 } ; eq { hop { filter_eq { all_rows ; project ; faruk süren project } ; capacity } ; 40482 } } } = true', 'tointer': 'select the rows whose project record fuzzily matches to özhan canaydın project . take the capacity record of this row . select the rows whose project record fuzzily matches to faruk süren project . take the capacity record of this row . the first record is greater than the second record . the capacity record of the first row is 52652 . the capacity record of the second row is 40482 .'} | and { greater { hop { filter_eq { all_rows ; project ; özhan canaydın project } ; capacity } ; hop { filter_eq { all_rows ; project ; faruk süren project } ; capacity } } ; and { eq { hop { filter_eq { all_rows ; project ; özhan canaydın project } ; capacity } ; 52652 } ; eq { hop { filter_eq { all_rows ; project ; faruk süren project } ; capacity } ; 40482 } } } = true | select the rows whose project record fuzzily matches to özhan canaydın project . take the capacity record of this row . select the rows whose project record fuzzily matches to faruk süren project . take the capacity record of this row . the first record is greater than the second record . the capacity record of the first row is 52652 . the capacity record of the second row is 40482 . | 13 | 9 | {'and_8': 8, 'result_9': 9, 'greater_4': 4, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_10': 10, 'project_11': 11, 'özhan canaydın project_12': 12, 'capacity_13': 13, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_14': 14, 'project_15': 15, 'faruk süren project_16': 16, 'capacity_17': 17, 'and_7': 7, 'eq_5': 5, '52652_18': 18, 'eq_6': 6, '40482_19': 19} | {'and_8': 'and', 'result_9': 'true', 'greater_4': 'greater', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_10': 'all_rows', 'project_11': 'project', 'özhan canaydın project_12': 'özhan canaydın project', 'capacity_13': 'capacity', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_14': 'all_rows', 'project_15': 'project', 'faruk süren project_16': 'faruk süren project', 'capacity_17': 'capacity', 'and_7': 'and', 'eq_5': 'eq', '52652_18': '52652', 'eq_6': 'eq', '40482_19': '40482'} | {'and_8': [9], 'result_9': [], 'greater_4': [8], 'num_hop_2': [4, 5], 'filter_str_eq_0': [2], 'all_rows_10': [0], 'project_11': [0], 'özhan canaydın project_12': [0], 'capacity_13': [2], 'num_hop_3': [4, 6], 'filter_str_eq_1': [3], 'all_rows_14': [1], 'project_15': [1], 'faruk süren project_16': [1], 'capacity_17': [3], 'and_7': [8], 'eq_5': [7], '52652_18': [5], 'eq_6': [7], '40482_19': [6]} | ['project', 'year', 'location', 'capacity', 'suites', 'architect', 'cost'] | [['faruk süren project', '1997 - 2001', 'mecidiyeköy', '40482', '125 + 72 boxes without outside seating', 'bbb architects', '118.5 million ( in 2014 dollars )'], ['mehmet cansun project', '2001', 'mecidiyeköy', '35000', '132', 'gs member architecture group', '35 million ( in 2014 dollars )'], ["özhan canaydın : back to süren 's project", '2002 - 2005', 'aslantepe', '40482', '125', 'bbb architects', '90 million ( in 2014 dollars )'], ['eren talu bidding project', '2007', 'aslantepe', '52000', '150', 'populous', 'n / a'], ['özhan canaydın project', '2007', 'aslantepe', '52652', '157', 'asp stuttgart', '250 million ( in 2014 dollars )']] |
2009 deutsche tourenwagen masters season | https://en.wikipedia.org/wiki/2009_Deutsche_Tourenwagen_Masters_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-21321935-2.html.csv | comparative | mattias ekström started in pole position before oliver jarvis had the chance to . | {'row_1': '1', 'row_2': '4', 'col': '3', 'col_other': '4', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'pole position', 'mattias ekström'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose pole position record fuzzily matches to mattias ekström .', 'tostr': 'filter_eq { all_rows ; pole position ; mattias ekström }'}, 'date'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; pole position ; mattias ekström } ; date }', 'tointer': 'select the rows whose pole position record fuzzily matches to mattias ekström . take the date record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'pole position', 'oliver jarvis'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose pole position record fuzzily matches to oliver jarvis .', 'tostr': 'filter_eq { all_rows ; pole position ; oliver jarvis }'}, 'date'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; pole position ; oliver jarvis } ; date }', 'tointer': 'select the rows whose pole position record fuzzily matches to oliver jarvis . take the date record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; pole position ; mattias ekström } ; date } ; hop { filter_eq { all_rows ; pole position ; oliver jarvis } ; date } } = true', 'tointer': 'select the rows whose pole position record fuzzily matches to mattias ekström . take the date record of this row . select the rows whose pole position record fuzzily matches to oliver jarvis . take the date record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; pole position ; mattias ekström } ; date } ; hop { filter_eq { all_rows ; pole position ; oliver jarvis } ; date } } = true | select the rows whose pole position record fuzzily matches to mattias ekström . take the date record of this row . select the rows whose pole position record fuzzily matches to oliver jarvis . take the date record of this row . the first record is less than the second record . | 5 | 5 | {'less_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'pole position_7': 7, 'mattias ekström_8': 8, 'date_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'pole position_11': 11, 'oliver jarvis_12': 12, 'date_13': 13} | {'less_4': 'less', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'pole position_7': 'pole position', 'mattias ekström_8': 'mattias ekström', 'date_9': 'date', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'pole position_11': 'pole position', 'oliver jarvis_12': 'oliver jarvis', 'date_13': 'date'} | {'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'pole position_7': [0], 'mattias ekström_8': [0], 'date_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'pole position_11': [1], 'oliver jarvis_12': [1], 'date_13': [3]} | ['round', 'circuit', 'date', 'pole position', 'fastest lap', 'winning driver', 'winning team'] | [['1', 'hockenheimring', '17 may', 'mattias ekström', 'mattias ekström', 'tom kristensen', 'abt sportsline'], ['2', 'eurospeedway lausitz', '31 may', 'mattias ekström', 'jamie green', 'gary paffett', 'hwa team'], ['3', 'norisring , nuremberg', '28 june', 'timo scheider', 'katherine legge', 'jamie green', 'persson motorsport'], ['4', 'circuit park zandvoort', '19 july', 'oliver jarvis', 'mattias ekström', 'gary paffett', 'hwa team'], ['5', 'motorsport arena oschersleben', '2 august', 'tom kristensen', 'timo scheider', 'timo scheider', 'abt sportsline'], ['6', 'nürburgring', '16 august', 'martin tomczyk', 'mattias ekström', 'martin tomczyk', 'abt sportsline'], ['7', 'brands hatch , kent', '6 september', 'paul di resta', 'paul di resta', 'paul di resta', 'hwa team'], ['8', 'circuit de catalunya , barcelona', '20 september', 'tom kristensen', 'timo scheider', 'timo scheider', 'abt sportsline'], ['9', 'dijon - prenois', '11 october', 'bruno spengler', 'paul di resta', 'gary paffett', 'hwa team']] |
karen walker ( footballer ) | https://en.wikipedia.org/wiki/Karen_Walker_%28footballer%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17150259-1.html.csv | unique | only one of the matches was played in reykjavik . | {'scope': 'all', 'row': '19', 'col': '3', 'col_other': 'n/a', 'criterion': 'fuzzily_match', 'value': 'reykjavík', 'subset': None} | {'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'venue', 'reykjavík'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose venue record fuzzily matches to reykjavík .', 'tostr': 'filter_eq { all_rows ; venue ; reykjavík }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; venue ; reykjavík } } = true', 'tointer': 'select the rows whose venue record fuzzily matches to reykjavík . there is only one such row in the table .'} | only { filter_eq { all_rows ; venue ; reykjavík } } = true | select the rows whose venue record fuzzily matches to reykjavík . there is only one such row in the table . | 2 | 2 | {'only_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'venue_4': 4, 'reykjavík_5': 5} | {'only_1': 'only', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'venue_4': 'venue', 'reykjavík_5': 'reykjavík'} | {'only_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'venue_4': [0], 'reykjavík_5': [0]} | ['goal', 'date', 'venue', 'result', 'competition', 'scored'] | [['3', '25 september 1993', 'bežigrad stadium , ljubljana', '10 - 0', '1995 uefa championship qual', '3'], ['5', '6 november 1993', 'kvv coxyde , koksijde', '3 - 0', '1995 uefa championship qual', '2'], ['7', '13 march 1994', 'city ground , nottingham', '6 - 0', '1995 uefa championship qual', '2'], ['9', '17 april 1994', 'griffin park , brentford', '10 - 0', '1995 uefa championship qual', '2'], ['10', '8 june 1995', 'tingvalla ip , karlstad', '3 - 2', '1995 world cup', '1'], ['12', '19 november 1995', 'the valley , london', '5 - 0', '1997 uefa championship qual', '2'], ['13', '23 may 1998', 'sportpark olympia , waalwijk', '1 - 2', '1999 world cup qual', '1'], ['15', '13 september 1998', 'stadionul poiana , cmpina', '4 - 1', '1999 world cup qual', '2'], ['16', '11 october 1998', 'adams park , wycombe', '2 - 1', '1999 world cup qual', '1'], ['17', '26 may 1999', 'lugo , emilia - romagna', '1 - 4', 'friendly', '1'], ['18', '22 august 1999', 'odense stadion , odense', '1 - 0', 'friendly', '1'], ['19', '17 october 1999', 'sportanlagen trinermatten , zofingen', '3 - 0', '2001 uefa championship qual', '1'], ['20', '20 february 2000', 'oakwell , barnsley', '2 - 0', '2001 uefa championship qual', '1'], ['21', '30 october 2000', 'kolos stadium , boryspil', '2 - 1', '2001 uefa championship qual', '1'], ['22', '24 november 2001', 'complexo desportivo da gafanha , gafanha da nazaré', '1 - 1', '2003 world cup qual', '1'], ['24', '5 march 2002', 'estádio municipal , lagos', '3 - 6', 'algarve cup', '2'], ['25', '7 march 2002', 'estádio municipal , quarteira', '4 - 1', 'algarve cup', '1'], ['25', '23 march 2002', 'zuiderpark stadion , the hague', '4 - 0', '2003 world cup qual', '1'], ['27', '16 september 2002', 'laugardalsvöllur , reykjavík', '2 - 2', '2003 world cup qual', '2']] |
indiana high school athletics conferences : allen county - metropolitan | https://en.wikipedia.org/wiki/Indiana_High_School_Athletics_Conferences%3A_Allen_County_%E2%80%93_Metropolitan | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13986492-6.html.csv | count | among the schools in the allen county - metropolitan division ( indiana high school athletics conference ) , five have an enrollment over 2000 . | {'scope': 'all', 'criterion': 'greater_than', 'value': '2000', 'result': '5', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'enrollment', '2000'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose enrollment record is greater than 2000 .', 'tostr': 'filter_greater { all_rows ; enrollment ; 2000 }'}], 'result': '5', 'ind': 1, 'tostr': 'count { filter_greater { all_rows ; enrollment ; 2000 } }', 'tointer': 'select the rows whose enrollment record is greater than 2000 . the number of such rows is 5 .'}, '5'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_greater { all_rows ; enrollment ; 2000 } } ; 5 } = true', 'tointer': 'select the rows whose enrollment record is greater than 2000 . the number of such rows is 5 .'} | eq { count { filter_greater { all_rows ; enrollment ; 2000 } } ; 5 } = true | select the rows whose enrollment record is greater than 2000 . the number of such rows is 5 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_greater_0': 0, 'all_rows_4': 4, 'enrollment_5': 5, '2000_6': 6, '5_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_greater_0': 'filter_greater', 'all_rows_4': 'all_rows', 'enrollment_5': 'enrollment', '2000_6': '2000', '5_7': '5'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_greater_0': [1], 'all_rows_4': [0], 'enrollment_5': [0], '2000_6': [0], '5_7': [2]} | ['school', 'mascot', 'location', 'enrollment', 'ihsaa class', 'ihsaa football class', 'county'] | [['chesterton', 'trojans', 'chesterton', '1986', 'aaaa', 'aaaaa', '64 porter'], ['crown point', 'bulldogs', 'crown point', '2532', 'aaaa', 'aaaaa', '45 lake'], ['laporte', 'slicers', 'laporte', '1839', 'aaaa', 'aaaaa', '46 laporte'], ['lake central', 'indians', 'saint john', '3225', 'aaaa', 'aaaaa', '45 lake'], ['merrillville', 'pirates', 'merrillville', '2396', 'aaaa', 'aaaaa', '45 lake'], ['michigan city', 'wolves', 'michigan city', '1909', 'aaaa', 'aaaaa', '46 laporte'], ['portage', 'indians', 'portage', '2668', 'aaaa', 'aaaaa', '64 porter'], ['valparaiso', 'vikings', 'valparaiso', '2114', 'aaaa', 'aaaaa', '64 porter']] |
mark mccumber | https://en.wikipedia.org/wiki/Mark_McCumber | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1598242-1.html.csv | majority | most of the tournaments took place after the year 1980 . | {'scope': 'all', 'col': '1', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '1980', 'subset': None} | {'func': 'most_greater', 'args': ['all_rows', 'date', '1980'], 'result': True, 'ind': 0, 'tointer': 'for the date records of all rows , most of them are greater than 1980 .', 'tostr': 'most_greater { all_rows ; date ; 1980 } = true'} | most_greater { all_rows ; date ; 1980 } = true | for the date records of all rows , most of them are greater than 1980 . | 1 | 1 | {'most_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 'date_3': 3, '1980_4': 4} | {'most_greater_0': 'most_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 'date_3': 'date', '1980_4': '1980'} | {'most_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 'date_3': [0], '1980_4': [0]} | ['date', 'tournament', 'winning score', 'margin of victory', 'runner - up'] | [['mar 18 , 1979', 'doral - eastern open', '- 9 ( 67 + 71 + 69 + 72 = 279 )', '1 stroke', 'bill rogers'], ['jul 3 , 1983', 'western open', '- 4 ( 74 + 71 + 68 + 71 = 284 )', '1 stroke', 'tom watson'], ['oct 30 , 1983', 'pensacola open', '- 18 ( 68 + 68 + 65 + 65 = 266 )', '4 strokes', 'lon hinkle'], ['feb 24 , 1985', 'doral - eastern open', '- 4 ( 70 + 71 + 72 + 71 = 284 )', '1 stroke', 'tom kite'], ['jul 12 , 1987', 'anheuser - busch golf classic', '- 17 ( 65 + 69 + 67 + 66 = 267 )', '1 stroke', 'bobby clampett'], ['mar 27 , 1988', 'the players championship', '- 15 ( 65 + 72 + 67 + 69 = 273 )', '4 strokes', 'mike reid'], ['jul 3 , 1989', 'beatrice western open', '- 13 ( 68 + 67 + 71 + 69 = 275 )', 'playoff', 'peter jacobsen'], ['jul 10 , 1994', 'anheuser - busch golf classic', '- 17 ( 67 + 69 + 65 + 66 = 267 )', '3 strokes', 'glen day'], ['sep 25 , 1994', "hardee 's golf classic", '- 15 ( 66 + 67 + 65 + 67 = 265 )', '1 stroke', 'kenny perry'], ['oct 30 , 1994', 'the tour championship', '- 10 ( 66 + 71 + 69 + 68 = 274 )', 'playoff', 'fuzzy zoeller']] |
1970 - 71 cleveland cavaliers season | https://en.wikipedia.org/wiki/1970%E2%80%9371_Cleveland_Cavaliers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16275352-7.html.csv | superlative | in the 1970 - 71 season , the cleveland cavaliers ' highest point total was 118 . | {'scope': 'all', 'col_superlative': '4', 'row_superlative': '12', 'value_mentioned': 'yes', 'max_or_min': 'max', 'other_col': 'n/a', 'subset': None} | {'func': 'eq', 'args': [{'func': 'max', 'args': ['all_rows', 'score'], 'result': '118 - 104', 'ind': 0, 'tostr': 'max { all_rows ; score }', 'tointer': 'the maximum score record of all rows is 118 - 104 .'}, '118 - 104'], 'result': True, 'ind': 1, 'tostr': 'eq { max { all_rows ; score } ; 118 - 104 } = true', 'tointer': 'the maximum score record of all rows is 118 - 104 .'} | eq { max { all_rows ; score } ; 118 - 104 } = true | the maximum score record of all rows is 118 - 104 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'max_0': 0, 'all_rows_3': 3, 'score_4': 4, '118 - 104_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'max_0': 'max', 'all_rows_3': 'all_rows', 'score_4': 'score', '118 - 104_5': '118 - 104'} | {'eq_1': [2], 'result_2': [], 'max_0': [1], 'all_rows_3': [0], 'score_4': [0], '118 - 104_5': [1]} | ['date', 'h / a / n', 'opponent', 'score', 'record'] | [['january 1', 'h', 'baltimore bullets', '105 - 128', '5 - 40'], ['january 2', 'a', 'milwaukee bucks', '73 - 118', '5 - 41'], ['january 4', 'h', 'portland trail blazers', '106 - 119', '5 - 42'], ['january 6', 'h', 'new york knicks', '94 - 127', '5 - 43'], ['january 7', 'h', 'los angeles lakers', '105 - 110', '5 - 44'], ['january 9', 'h', 'buffalo braves', '111 - 89', '6 - 44'], ['january 14', 'a', 'detroit pistons', '106 - 108', '6 - 45'], ['january 16', 'a', 'philadelphia 76ers', '96 - 115', '6 - 46'], ['january 19', 'n', 'buffalo braves', '111 - 79', '7 - 46'], ['january 24', 'a', 'boston celtics', '110 - 121', '7 - 47'], ['january 25', 'h', 'boston celtics', '117 - 116', '8 - 47'], ['january 27', 'h', 'portland trail blazers', '118 - 104', '9 - 47'], ['january 29', 'a', 'atlanta hawks', '111 - 119', '9 - 48'], ['january 31', 'h', 'buffalo braves', '117 - 108', '10 - 48']] |
1984 - 85 philadelphia 76ers season | https://en.wikipedia.org/wiki/1984%E2%80%9385_Philadelphia_76ers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14828562-1.html.csv | unique | in the 1984-1985 season , charles barkley was the only player for the philadelphia 76ers who played for auburn . | {'scope': 'all', 'row': '1', 'col': '5', 'col_other': '3', 'criterion': 'equal', 'value': 'auburn', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'college', 'auburn'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose college record fuzzily matches to auburn .', 'tostr': 'filter_eq { all_rows ; college ; auburn }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; college ; auburn } }', 'tointer': 'select the rows whose college record fuzzily matches to auburn . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'college', 'auburn'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose college record fuzzily matches to auburn .', 'tostr': 'filter_eq { all_rows ; college ; auburn }'}, 'player'], 'result': 'charles barkley', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; college ; auburn } ; player }'}, 'charles barkley'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; college ; auburn } ; player } ; charles barkley }', 'tointer': 'the player record of this unqiue row is charles barkley .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; college ; auburn } } ; eq { hop { filter_eq { all_rows ; college ; auburn } ; player } ; charles barkley } } = true', 'tointer': 'select the rows whose college record fuzzily matches to auburn . there is only one such row in the table . the player record of this unqiue row is charles barkley .'} | and { only { filter_eq { all_rows ; college ; auburn } } ; eq { hop { filter_eq { all_rows ; college ; auburn } ; player } ; charles barkley } } = true | select the rows whose college record fuzzily matches to auburn . there is only one such row in the table . the player record of this unqiue row is charles barkley . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'college_7': 7, 'auburn_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'player_9': 9, 'charles barkley_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'college_7': 'college', 'auburn_8': 'auburn', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'player_9': 'player', 'charles barkley_10': 'charles barkley'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'college_7': [0], 'auburn_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'player_9': [2], 'charles barkley_10': [3]} | ['round', 'pick', 'player', 'nationality', 'college'] | [['1', '5', 'charles barkley', 'united states', 'auburn'], ['1', '10', 'leon wood', 'united states', 'california state - fullerton'], ['1', '22', 'tom sewell', 'united states', 'lamar'], ['3', '48', 'james banks', 'united states', 'georgia'], ['3', '68', 'butch graves', 'united states', 'yale'], ['4', '91', 'earl harrison', 'united states', 'morehead state'], ['5', '114', 'dan federman', 'united states', 'tennessee'], ['6', '137', 'gary springer', 'united states', 'iona'], ['7', '160', 'rich congo', 'united states', 'drexel'], ['8', '183', 'franks dobbs', 'united states', 'villanova'], ['9', '205', 'michael mitchell', 'united states', 'drexel'], ['10', '227', 'martin clark', 'united states', 'boston college']] |
2003 - 04 european challenge cup | https://en.wikipedia.org/wiki/2003%E2%80%9304_European_Challenge_Cup | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27987767-3.html.csv | superlative | montferrand has the lowest aggregate score of the european challenge cup . | {'scope': 'all', 'col_superlative': '3', 'row_superlative': '7', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '1', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'aggregate score'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; aggregate score }'}, 'proceed to quarter - final'], 'result': 'montferrand', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; aggregate score } ; proceed to quarter - final }'}, 'montferrand'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; aggregate score } ; proceed to quarter - final } ; montferrand } = true', 'tointer': 'select the row whose aggregate score record of all rows is minimum . the proceed to quarter - final record of this row is montferrand .'} | eq { hop { argmin { all_rows ; aggregate score } ; proceed to quarter - final } ; montferrand } = true | select the row whose aggregate score record of all rows is minimum . the proceed to quarter - final record of this row is montferrand . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'aggregate score_5': 5, 'proceed to quarter - final_6': 6, 'montferrand_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'aggregate score_5': 'aggregate score', 'proceed to quarter - final_6': 'proceed to quarter - final', 'montferrand_7': 'montferrand'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'aggregate score_5': [0], 'proceed to quarter - final_6': [1], 'montferrand_7': [2]} | ['proceed to quarter - final', 'match points', 'aggregate score', 'points margin', 'eliminated from competition'] | [['nec harlequins', '4 - 0', '89 - 25', '64', 'montauban'], ['béziers', '4 - 0', '43 - 23', '20', 'grenoble'], ['bath', '4 - 0', '58 - 42', '16', 'colomiers'], ['connacht', '2 - 2', '35 - 17', '18', 'pau'], ['narbonne', '2 - 2', '42 - 30', '12', 'london irish'], ['brive', '2 - 2', '58 - 48', '10', 'castres olympique'], ['montferrand', '2 - 2', '28 - 23', '5', 'newcastle falcons']] |
amor en custodia ( tv series ) | https://en.wikipedia.org/wiki/Amor_en_Custodia_%28TV_series%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-19938261-2.html.csv | unique | season 10 of amor en custodia was the only season in the monday - friday 10:00 pm timeslot . | {'scope': 'all', 'row': '9', 'col': '2', 'col_other': '1', 'criterion': 'equal', 'value': 'monday - friday 10:00 pm', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'timeslot ( edt )', 'monday - friday 10:00 pm'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose timeslot ( edt ) record fuzzily matches to monday - friday 10:00 pm .', 'tostr': 'filter_eq { all_rows ; timeslot ( edt ) ; monday - friday 10:00 pm }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; timeslot ( edt ) ; monday - friday 10:00 pm } }', 'tointer': 'select the rows whose timeslot ( edt ) record fuzzily matches to monday - friday 10:00 pm . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'timeslot ( edt )', 'monday - friday 10:00 pm'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose timeslot ( edt ) record fuzzily matches to monday - friday 10:00 pm .', 'tostr': 'filter_eq { all_rows ; timeslot ( edt ) ; monday - friday 10:00 pm }'}, 'season'], 'result': '10', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; timeslot ( edt ) ; monday - friday 10:00 pm } ; season }'}, '10'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; timeslot ( edt ) ; monday - friday 10:00 pm } ; season } ; 10 }', 'tointer': 'the season record of this unqiue row is 10 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; timeslot ( edt ) ; monday - friday 10:00 pm } } ; eq { hop { filter_eq { all_rows ; timeslot ( edt ) ; monday - friday 10:00 pm } ; season } ; 10 } } = true', 'tointer': 'select the rows whose timeslot ( edt ) record fuzzily matches to monday - friday 10:00 pm . there is only one such row in the table . the season record of this unqiue row is 10 .'} | and { only { filter_eq { all_rows ; timeslot ( edt ) ; monday - friday 10:00 pm } } ; eq { hop { filter_eq { all_rows ; timeslot ( edt ) ; monday - friday 10:00 pm } ; season } ; 10 } } = true | select the rows whose timeslot ( edt ) record fuzzily matches to monday - friday 10:00 pm . there is only one such row in the table . the season record of this unqiue row is 10 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'timeslot ( edt )_7': 7, 'monday - friday 10:00 pm_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'season_9': 9, '10_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'timeslot ( edt )_7': 'timeslot ( edt )', 'monday - friday 10:00 pm_8': 'monday - friday 10:00 pm', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'season_9': 'season', '10_10': '10'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'timeslot ( edt )_7': [0], 'monday - friday 10:00 pm_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'season_9': [2], '10_10': [3]} | ['season', 'timeslot ( edt )', 'season premiere', 'season finale', 'tv season', 'rank ( spanish language )', 'viewers ( in millions )'] | [['2', 'monday - friday 9:00 pm', 'january 16 , 2006', 'april 6 , 2006', '2006', '1', '9.4'], ['3', 'monday - friday 9:00 pm', 'april 10 , 2006', 'september 1 , 2006', '2006', '1', '9.9'], ['4', 'monday - friday 9:00 pm', 'september 4 , 2006', 'january 6 , 2007', '2006 - 2007', '2', '9.7'], ['5', 'monday - friday 9:00 pm', 'january 8 , 2007', 'march 28 , 2007', '2007', '1', '10.1'], ['6', 'monday - friday 9:00 pm', 'april 2 , 2007', 'august 9 , 2007', '2007', '3', '7.9'], ['7', 'monday - friday 9:00 pm', 'august 13 , 2007', 'december 10 , 2007', '2007', '2', '9.9'], ['8', 'monday - friday 9:00 pm', 'december 13 , 2007', 'february 15 , 2008', '2007 - 2008', '7', '5.1'], ['9', 'monday - friday 9:00 pm', 'february 18 , 2008', 'may 30 , 2008', '2008', '1', '10.4'], ['10', 'monday - friday 10:00 pm', 'june 2 , 2008', 'august 29 , 2008', '2008', '1', '9.4']] |
list of kraft nabisco championship champions | https://en.wikipedia.org/wiki/List_of_Kraft_Nabisco_Championship_champions | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27864661-6.html.csv | superlative | the united states has the highest number of wins among all the other nations on the list of kraft nabisco championship champions . | {'scope': 'all', 'col_superlative': '7', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '2', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'total wins'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; total wins }'}, 'nationality'], 'result': 'united states', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; total wins } ; nationality }'}, 'united states'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; total wins } ; nationality } ; united states } = true', 'tointer': 'select the row whose total wins record of all rows is maximum . the nationality record of this row is united states .'} | eq { hop { argmax { all_rows ; total wins } ; nationality } ; united states } = true | select the row whose total wins record of all rows is maximum . the nationality record of this row is united states . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'total wins_5': 5, 'nationality_6': 6, 'united states_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'total wins_5': 'total wins', 'nationality_6': 'nationality', 'united states_7': 'united states'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'total wins_5': [0], 'nationality_6': [1], 'united states_7': [2]} | ['rank', 'nationality', 'non - major wins', 'non - major winners', 'major wins', 'major winners', 'total wins', 'total winners', 'first title', 'last title'] | [['1', 'united states', '8', '8', '19', '13', '27', '21', '1972', '2011'], ['2', 'sweden', '0', '0', '4', '2', '4', '2', '1993', '2005'], ['3', 'south korea', '0', '0', '3', '3', '3', '3', '2004', '2013'], ['t4', 'australia', '0', '0', '2', '1', '2', '1', '2000', '2006'], ['t4', 'canada', '2', '1', '0', '0', '2', '1', '1978', '1979'], ['t6', 'france', '0', '0', '1', '1', '1', '1', '2003', '2003'], ['t6', 'mexico', '0', '0', '1', '1', '1', '1', '2008', '2008'], ['t6', 'south africa', '1', '1', '0', '0', '1', '1', '1982', '1982']] |
republican party presidential primaries , 2012 | https://en.wikipedia.org/wiki/Republican_Party_presidential_primaries%2C_2012 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-20246201-9.html.csv | superlative | in the republican party presidential primaries-2012 , buddy roemer was the candidate from the former governor office with the lowest popular vote . | {'scope': 'subset', 'col_superlative': '4', 'row_superlative': '4', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '1,2', 'subset': {'col': '2', 'criterion': 'equal', 'value': 'former governor'}} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'office', 'former governor'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; office ; former governor }', 'tointer': 'select the rows whose office record fuzzily matches to former governor .'}, 'popular vote'], 'result': None, 'ind': 1, 'tostr': 'argmin { filter_eq { all_rows ; office ; former governor } ; popular vote }'}, 'candidate'], 'result': 'buddy roemer', 'ind': 2, 'tostr': 'hop { argmin { filter_eq { all_rows ; office ; former governor } ; popular vote } ; candidate }'}, 'buddy roemer'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { argmin { filter_eq { all_rows ; office ; former governor } ; popular vote } ; candidate } ; buddy roemer } = true', 'tointer': 'select the rows whose office record fuzzily matches to former governor . select the row whose popular vote record of these rows is minimum . the candidate record of this row is buddy roemer .'} | eq { hop { argmin { filter_eq { all_rows ; office ; former governor } ; popular vote } ; candidate } ; buddy roemer } = true | select the rows whose office record fuzzily matches to former governor . select the row whose popular vote record of these rows is minimum . the candidate record of this row is buddy roemer . | 4 | 4 | {'str_eq_3': 3, 'result_4': 4, 'str_hop_2': 2, 'argmin_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'office_6': 6, 'former governor_7': 7, 'popular vote_8': 8, 'candidate_9': 9, 'buddy roemer_10': 10} | {'str_eq_3': 'str_eq', 'result_4': 'true', 'str_hop_2': 'str_hop', 'argmin_1': 'argmin', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'office_6': 'office', 'former governor_7': 'former governor', 'popular vote_8': 'popular vote', 'candidate_9': 'candidate', 'buddy roemer_10': 'buddy roemer'} | {'str_eq_3': [4], 'result_4': [], 'str_hop_2': [3], 'argmin_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'office_6': [0], 'former governor_7': [0], 'popular vote_8': [1], 'candidate_9': [2], 'buddy roemer_10': [3]} | ['candidate', 'office', 'home state', 'popular vote', 'states - first place', 'states - second place', 'states - third place'] | [['jon huntsman', 'former governor', 'utah', '83173', '0', '0', '1 new hampshire'], ['rick perry', 'governor', 'texas', '42251', '0', '0', '0'], ['michele bachmann', 'us representative', 'minnesota', '35089', '0', '0', '0'], ['buddy roemer', 'former governor', 'louisiana', '33212', '0', '0', '0'], ['herman cain', 'none', 'georgia', '13538', '0', '0', '0']] |
wpxn - tv | https://en.wikipedia.org/wiki/WPXN-TV | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-188003-1.html.csv | count | four different wxpn - tv channels have an aspect ratio of 4:3 . | {'scope': 'all', 'criterion': 'equal', 'value': '4:3', 'result': '4', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'aspect', '4:3'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose aspect record fuzzily matches to 4:3 .', 'tostr': 'filter_eq { all_rows ; aspect ; 4:3 }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; aspect ; 4:3 } }', 'tointer': 'select the rows whose aspect record fuzzily matches to 4:3 . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; aspect ; 4:3 } } ; 4 } = true', 'tointer': 'select the rows whose aspect record fuzzily matches to 4:3 . the number of such rows is 4 .'} | eq { count { filter_eq { all_rows ; aspect ; 4:3 } } ; 4 } = true | select the rows whose aspect record fuzzily matches to 4:3 . the number of such rows is 4 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'aspect_5': 5, '4:3_6': 6, '4_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'aspect_5': 'aspect', '4:3_6': '4:3', '4_7': '4'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'aspect_5': [0], '4:3_6': [0], '4_7': [2]} | ['channel', 'video', 'aspect', 'psip short name', 'network'] | [['31.1', '720p', '16:9', 'ion', 'ion television'], ['31.2', '480i', '4:3', 'qubo', 'qubo'], ['31.3', '480i', '4:3', 'ionlife', 'ion life'], ['31.4', '480i', '4:3', 'shop', 'ion shop'], ['31.5', '480i', '4:3', 'qvc', 'qvc']] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.