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
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1948 vfl season | https://en.wikipedia.org/wiki/1948_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10809529-12.html.csv | ordinal | victoria park venue recorded the 2nd highest crowd participation during the 1948 vfl season . | {'row': '3', 'col': '6', 'order': '2', 'col_other': '5', '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', 'crowd', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; crowd ; 2 }'}, 'venue'], 'result': 'victoria park', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; crowd ; 2 } ; venue }'}, 'victoria park'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; crowd ; 2 } ; venue } ; victoria park } = true', 'tointer': 'select the row whose crowd record of all rows is 2nd maximum . the venue record of this row is victoria park .'} | eq { hop { nth_argmax { all_rows ; crowd ; 2 } ; venue } ; victoria park } = true | select the row whose crowd record of all rows is 2nd maximum . the venue record of this row is victoria park . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'crowd_5': 5, '2_6': 6, 'venue_7': 7, 'victoria park_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', 'crowd_5': 'crowd', '2_6': '2', 'venue_7': 'venue', 'victoria park_8': 'victoria park'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'crowd_5': [0], '2_6': [0], 'venue_7': [1], 'victoria park_8': [2]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['richmond', '21.18 ( 144 )', 'south melbourne', '9.13 ( 67 )', 'punt road oval', '21000', '10 july 1948'], ['essendon', '13.13 ( 91 )', 'melbourne', '9.11 ( 65 )', 'windy hill', '17000', '10 july 1948'], ['collingwood', '19.13 ( 127 )', 'north melbourne', '10.11 ( 71 )', 'victoria park', '19500', '10 july 1948'], ['carlton', '17.10 ( 112 )', 'hawthorn', '10.9 ( 69 )', 'princes park', '9000', '10 july 1948'], ['st kilda', '7.11 ( 53 )', 'footscray', '13.13 ( 91 )', 'junction oval', '7000', '10 july 1948'], ['geelong', '13.9 ( 87 )', 'fitzroy', '9.17 ( 71 )', 'kardinia park', '18500', '10 july 1948']] |
2007 - 08 commonwealth bank series statistics | https://en.wikipedia.org/wiki/2007%E2%80%9308_Commonwealth_Bank_Series_statistics | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15700367-5.html.csv | aggregation | the fourteen players that competed in the 2007-2008 commonwealth bank series scored a combined total of 1,097 runs . | {'scope': 'all', 'col': '3', 'type': 'sum', 'result': '1,097', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'runs scored'], 'result': '1,097', 'ind': 0, 'tostr': 'sum { all_rows ; runs scored }'}, '1,097'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; runs scored } ; 1,097 } = true', 'tointer': 'the sum of the runs scored record of all rows is 1,097 .'} | round_eq { sum { all_rows ; runs scored } ; 1,097 } = true | the sum of the runs scored record of all rows is 1,097 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'runs scored_4': 4, '1,097_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'runs scored_4': 'runs scored', '1,097_5': '1,097'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'runs scored_4': [0], '1,097_5': [1]} | ['name', 'innings', 'runs scored', 'balls faced', 'average', 'sr'] | [['upul tharanga', '1', '10', '18', '10.00', '55.56'], ['sanath jayasuriya', '7', '103', '116', '14.71', '88.79'], ['kumar sangakkara ( wk )', '7', '326', '448', '46.57', '72.77'], ['mahela jayawardene ( c )', '7', '214', '297', '35.67', '72.05'], ['chamara silva', '6', '80', '171', '16.00', '46.78'], ['tillakaratne dilshan', '7', '172', '182', '34.40', '94.51'], ['chamara kapugedera', '5', '86', '176', '17.20', '48.86'], ['chaminda vaas', '4', '19', '39', '6.33', '48.72'], ['lasith malinga', '4', '19', '65', '4.75', '29.23'], ['muttiah muralitharan', '4', '25', '43', '8.33', '58.14'], ['ishara amerasinghe', '3', '5', '7', '5.00', '71.43'], ['farveez maharoof', '2', '10', '20', '10.00', '50.00'], ['nuwan kulasekara', '2', '14', '29', '7.00', '48.28'], ['dilruwan perera', '3', '14', '36', '4.67', '38.89']] |
fundraising for the 2008 united states presidential election | https://en.wikipedia.org/wiki/Fundraising_for_the_2008_United_States_presidential_election | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12030247-2.html.csv | majority | most of the candidates of the 2008 united states presidential election received loans . | {'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '0', 'subset': None} | {'func': 'most_greater', 'args': ['all_rows', 'loans received', '0'], 'result': True, 'ind': 0, 'tointer': 'for the loans received records of all rows , most of them are greater than 0 .', 'tostr': 'most_greater { all_rows ; loans received ; 0 } = true'} | most_greater { all_rows ; loans received ; 0 } = true | for the loans received records of all rows , most of them are greater than 0 . | 1 | 1 | {'most_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 'loans received_3': 3, '0_4': 4} | {'most_greater_0': 'most_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 'loans received_3': 'loans received', '0_4': '0'} | {'most_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 'loans received_3': [0], '0_4': [0]} | ['candidate', 'contributions', 'loans received', 'all receipts', 'operating expenditures', 'all disbursements'] | [['hillary clinton', '107056586', '0', '118301659', '77804197', '106000000'], ['barack obama', '102092819', '0', '103802537', '84497445', '85176289'], ['john edwards', '34986088', '8974714', '44259386', '33513005', '36468929'], ['bill richardson', '22421742', '1000000', '23671031', '21401414', '21857565'], ['chris dodd', '10414392', '1302811', '16547015', '14040555', '14057455'], ['joe biden', '8245241', '1132114', '11405771', '9518537', '9538687'], ['dennis kucinich', '3869613', '0', '3870840', '3638219', '3641234'], ['combined total', '289086481', '12409639', '321858239', '244413372', '251093944']] |
1987 pittsburgh gladiators season | https://en.wikipedia.org/wiki/1987_Pittsburgh_Gladiators_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11938731-7.html.csv | aggregation | the players on the pittsburgh gladiators recorded a total of 153 solo tackles during the 1987 season . | {'scope': 'all', 'col': '3', 'type': 'sum', 'result': '153', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'solo'], 'result': '153', 'ind': 0, 'tostr': 'sum { all_rows ; solo }'}, '153'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; solo } ; 153 } = true', 'tointer': 'the sum of the solo record of all rows is 153 .'} | round_eq { sum { all_rows ; solo } ; 153 } = true | the sum of the solo record of all rows is 153 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'solo_4': 4, '153_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'solo_4': 'solo', '153_5': '153'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'solo_4': [0], '153_5': [1]} | ['player', 'tackles', 'solo', 'assisted', 'sack', 'yards', "td 's"] | [['joel gueli', '31', '29', '4', '3', '31', '1'], ['craig walls', '19', '15', '8', '13', '0', '0'], ['russell hairston', '17.5', '16', '0', '0', '50', '1'], ['creig federico', '17', '12', '10', '3', '0', '0'], ['scott dmitrenko', '15', '13', '4', '3', '0', '0'], ['mike stoops', '14.5', '11', '7', '0', '0', '0'], ['john mcclennon', '12.5', '9', '7', '0', '5', '0'], ['ricky mitchell', '11', '10', '2', '2', '0', '0'], ['jim rafferty', '10.5', '8', '2', '0', '4', '0'], ['thomas weaver', '9', '7', '4', '3', '2', '0'], ['earnest adams', '8', '6', '4', '5', '0', '0'], ['mike powell', '6', '5', '2', '0', '0', '0'], ['greg best', '6', '6', '0', '0', '0', '0'], ['willis yates', '5', '4', '2', '6', '0', '0'], ['lee larsen', '2.5', '2', '1', '0', '0', '0']] |
1965 vfl season | https://en.wikipedia.org/wiki/1965_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10788451-14.html.csv | comparative | the south melbourne club scored more points than the north melbourne club . | {'row_1': '3', 'row_2': '1', 'col': '4', 'col_other': '3', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'away team', 'south melbourne'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose away team record fuzzily matches to south melbourne .', 'tostr': 'filter_eq { all_rows ; away team ; south melbourne }'}, 'away team score'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; away team ; south melbourne } ; away team score }', 'tointer': 'select the rows whose away team record fuzzily matches to south melbourne . take the away team score record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'away team', 'north melbourne'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose away team record fuzzily matches to north melbourne .', 'tostr': 'filter_eq { all_rows ; away team ; north melbourne }'}, 'away team score'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; away team ; north melbourne } ; away team score }', 'tointer': 'select the rows whose away team record fuzzily matches to north melbourne . take the away team score record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; away team ; south melbourne } ; away team score } ; hop { filter_eq { all_rows ; away team ; north melbourne } ; away team score } } = true', 'tointer': 'select the rows whose away team record fuzzily matches to south melbourne . take the away team score record of this row . select the rows whose away team record fuzzily matches to north melbourne . take the away team score record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; away team ; south melbourne } ; away team score } ; hop { filter_eq { all_rows ; away team ; north melbourne } ; away team score } } = true | select the rows whose away team record fuzzily matches to south melbourne . take the away team score record of this row . select the rows whose away team record fuzzily matches to north melbourne . take the away team score 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, 'away team_7': 7, 'south melbourne_8': 8, 'away team score_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'away team_11': 11, 'north melbourne_12': 12, 'away team score_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', 'away team_7': 'away team', 'south melbourne_8': 'south melbourne', 'away team score_9': 'away team score', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'away team_11': 'away team', 'north melbourne_12': 'north melbourne', 'away team score_13': 'away team score'} | {'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'away team_7': [0], 'south melbourne_8': [0], 'away team score_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'away team_11': [1], 'north melbourne_12': [1], 'away team score_13': [3]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['geelong', '10.17 ( 77 )', 'north melbourne', '5.4 ( 34 )', 'kardinia park', '19658', '31 july 1965'], ['essendon', '13.18 ( 96 )', 'footscray', '6.11 ( 47 )', 'windy hill', '16800', '31 july 1965'], ['carlton', '9.19 ( 73 )', 'south melbourne', '13.12 ( 90 )', 'princes park', '20744', '31 july 1965'], ['st kilda', '14.12 ( 96 )', 'richmond', '11.17 ( 83 )', 'moorabbin oval', '34076', '31 july 1965'], ['melbourne', '12.11 ( 83 )', 'fitzroy', '11.15 ( 81 )', 'mcg', '30381', '31 july 1965'], ['hawthorn', '8.12 ( 60 )', 'collingwood', '12.22 ( 94 )', 'glenferrie oval', '18500', '31 july 1965']] |
list of earthquakes in iran | https://en.wikipedia.org/wiki/List_of_earthquakes_in_Iran | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10677198-2.html.csv | count | there are 2 recorded earthquakes that occurred in iran for the year 1997 . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': '1997', 'result': '2', 'col': '1', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', '1997'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose date record fuzzily matches to 1997 .', 'tostr': 'filter_eq { all_rows ; date ; 1997 }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; date ; 1997 } }', 'tointer': 'select the rows whose date record fuzzily matches to 1997 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; date ; 1997 } } ; 2 } = true', 'tointer': 'select the rows whose date record fuzzily matches to 1997 . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; date ; 1997 } } ; 2 } = true | select the rows whose date record fuzzily matches to 1997 . 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, 'date_5': 5, '1997_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', 'date_5': 'date', '1997_6': '1997', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'date_5': [0], '1997_6': [0], '2_7': [2]} | ['date', 'epicenter', 'magnitude', 'fatalities', 'name'] | [['march 14 , 1998', 'golbaf', '6.6', '5', '1998 golbaf earthquake'], ['may 10 , 1997', 'birjand - qaen', '7.3', '1567', '1997 qayen earthquake'], ['february 28 , 1997', 'ardabil', '6.0', '1100', '1997 ardabil earthquake'], ['june 20 , 1990', 'manjil ( - rudbar )', '7.4', 'least 40000', '1990 manjil - rudbar earthquake'], ['july 28 , 1981', 'southern iran', '7.3', '1500', 'july 1981 southern iran earthquake'], ['june 11 , 1981', 'southern iran', '6.9', '3000', 'june 1981 southern iran earthquake'], ['september 16 , 1978', 'tabas', '7.8', '15000', '1978 tabas earthquake'], ['april 10 , 1972', 'qir', '7.1', '5054', '1972 southern iran earthquake'], ['august 31 , 1968', 'dasht - e - bayaz - ferdows', '7.3', 'least 7000', '1968 dasht - e bayaz and ferdows earthquake'], ['february 10 , 1965', 'bostanabad - e bala', '5.1', '20', '1965 bostanabad - e bala earthquake'], ['september 1 , 1962', "bou'in - zahra", '7.1', '12225', "1962 bou'in - zahra earthquake"], ['december 13 , 1957', 'sahneh', '7.1', '1130', '1957 sahneh earthquake'], ['july 2 , 1957', 'm훮zandar훮n', '7.1', '1200', '1957 m훮zandar훮n earthquake'], ['february 12 , 1953', 'torud', '6.5', '970', '1953 torud earthquake'], ['august 5 , 1947', 'pasni', '7.3', '500', '1947 pasni earthquake'], ['may 6 , 1930', 'salmas', '7.2', '2 , 500', '1930 salmas earthquake'], ['may 1 , 1929', 'koppeh dagh', '7.4', '3800', '1929 koppeh dagh earthquake'], ['may 25 , 1923', 'torbat - e heydariyeh', '5.7', '2200', '1923 torbat - e heydariyeh earthquake'], ['january 23 , 1909', 'silakhor of borujerd', '7.3', '6000', '1909 borujerd earthquake']] |
atlanta falcons draft history | https://en.wikipedia.org/wiki/Atlanta_Falcons_draft_history | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15198842-45.html.csv | count | three of these players were the 19th pick for their team . | {'scope': 'all', 'criterion': 'equal', 'value': '19', 'result': '3', 'col': '2', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'pick', '19'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose pick record is equal to 19 .', 'tostr': 'filter_eq { all_rows ; pick ; 19 }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; pick ; 19 } }', 'tointer': 'select the rows whose pick record is equal to 19 . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; pick ; 19 } } ; 3 } = true', 'tointer': 'select the rows whose pick record is equal to 19 . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; pick ; 19 } } ; 3 } = true | select the rows whose pick record is equal to 19 . the number of such rows is 3 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_eq_0': 0, 'all_rows_4': 4, 'pick_5': 5, '19_6': 6, '3_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'pick_5': 'pick', '19_6': '19', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'pick_5': [0], '19_6': [0], '3_7': [2]} | ['round', 'pick', 'overall', 'name', 'position', 'college'] | [['1', '19', '19', 'sean weatherspoon', 'linebacker', 'missouri'], ['3', '19', '83', 'corey peters', 'defensive tackle', 'kentucky'], ['3', '34', '98', 'mike johnson', 'guard', 'alabama'], ['4', '19', '117', 'joe hawley', 'guard', 'unlv'], ['5', '4', '135', 'dominique franks', 'cornerback', 'oklahoma'], ['5', '34', '165', 'kerry meier', 'wide receiver', 'kansas'], ['6', '2', '171', 'shann schillinger', 'safety', 'montana']] |
european poker tour | https://en.wikipedia.org/wiki/European_Poker_Tour | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1296513-5.html.csv | ordinal | the 2008 european poker championships was the second earliest event . | {'row': '2', 'col': '1', 'order': '2', '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', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; date ; 2 }'}, 'event'], 'result': '2008 european poker championships', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; date ; 2 } ; event }'}, '2008 european poker championships'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; date ; 2 } ; event } ; 2008 european poker championships } = true', 'tointer': 'select the row whose date record of all rows is 2nd minimum . the event record of this row is 2008 european poker championships .'} | eq { hop { nth_argmin { all_rows ; date ; 2 } ; event } ; 2008 european poker championships } = true | select the row whose date record of all rows is 2nd minimum . the event record of this row is 2008 european poker championships . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'date_5': 5, '2_6': 6, 'event_7': 7, '2008 european poker championships_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', '2_6': '2', 'event_7': 'event', '2008 european poker championships_8': '2008 european poker championships'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'date_5': [0], '2_6': [0], 'event_7': [1], '2008 european poker championships_8': [2]} | ['date', 'city', 'event', 'winner', 'prize'] | [['10 - 14 september 2008', 'barcelona', 'ept barcelona open', 'sebastian ruthenberg', '1361000'], ['1 - 5 october 2008', 'london', '2008 european poker championships', 'michael martin', '1000000'], ['5 - 6 october 2008', 'london', 'ept london 1 million showdown', 'jason mercier', '516000'], ['28 oct - 1 nov 2008', 'budapest', 'ept hungarian open', 'will fry', '595839'], ['15 - 19 november 2008', 'warsaw', 'ept polish open', 'joão barbosa', 'zł1358420'], ['9 - 13 december 2008', 'prague', 'ept prague', 'salvatore bonavena', '774000'], ['5 - 10 january 2009', 'paradise island', 'ept pokerstars caribbean adventure', 'poorya nazari', '3000000'], ['20 - 24 january 2009', 'deauville', 'ept deauville', 'moritz kranich', '851400'], ['17 - 21 february 2009', 'copenhagen', 'ept scandinavian open', 'jens kyllönen', 'kr6542208'], ['10 - 14 march 2009', 'dortmund', 'ept german open', 'sandra naujoks', '917000'], ['18 - 23 april 2009', 'sanremo', 'ept sanremo', 'constant rijkenberg', '1508000'], ['28 apr - 3 may 2009', 'monte carlo', 'european poker tour grand final', 'pieter de korver', '2300000']] |
1979 - 80 philadelphia flyers season | https://en.wikipedia.org/wiki/1979%E2%80%9380_Philadelphia_Flyers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14208862-4.html.csv | unique | in the 1979 - 80 philadelphia flyers season , the only game with attendance under 10,000 was on december 26th . | {'scope': 'all', 'row': '12', 'col': '6', 'col_other': '1', 'criterion': 'less_than', 'value': '10000', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'attendance', '10000'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose attendance record is less than 10000 .', 'tostr': 'filter_less { all_rows ; attendance ; 10000 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_less { all_rows ; attendance ; 10000 } }', 'tointer': 'select the rows whose attendance record is less than 10000 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'attendance', '10000'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose attendance record is less than 10000 .', 'tostr': 'filter_less { all_rows ; attendance ; 10000 }'}, 'date'], 'result': 'december 26', 'ind': 2, 'tostr': 'hop { filter_less { all_rows ; attendance ; 10000 } ; date }'}, 'december 26'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_less { all_rows ; attendance ; 10000 } ; date } ; december 26 }', 'tointer': 'the date record of this unqiue row is december 26 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_less { all_rows ; attendance ; 10000 } } ; eq { hop { filter_less { all_rows ; attendance ; 10000 } ; date } ; december 26 } } = true', 'tointer': 'select the rows whose attendance record is less than 10000 . there is only one such row in the table . the date record of this unqiue row is december 26 .'} | and { only { filter_less { all_rows ; attendance ; 10000 } } ; eq { hop { filter_less { all_rows ; attendance ; 10000 } ; date } ; december 26 } } = true | select the rows whose attendance record is less than 10000 . there is only one such row in the table . the date record of this unqiue row is december 26 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_less_0': 0, 'all_rows_6': 6, 'attendance_7': 7, '10000_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'date_9': 9, 'december 26_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_less_0': 'filter_less', 'all_rows_6': 'all_rows', 'attendance_7': 'attendance', '10000_8': '10000', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'date_9': 'date', 'december 26_10': 'december 26'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_less_0': [1, 2], 'all_rows_6': [0], 'attendance_7': [0], '10000_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'date_9': [2], 'december 26_10': [3]} | ['date', 'visitor', 'score', 'home', 'decision', 'attendance', 'record'] | [['december 1', 'philadelphia', '4 - 4', 'toronto', 'myre', '16485', '17 - 1 - 4'], ['december 2', 'detroit', '4 - 4', 'philadelphia', 'peeters', '17077', '17 - 1 - 5'], ['december 4', 'boston', '2 - 2', 'philadelphia', 'myre', '17077', '17 - 1 - 6'], ['december 6', 'los angeles', '4 - 9', 'philadelphia', 'peeters', '17077', '18 - 1 - 6'], ['december 9', 'chicago', '4 - 4', 'philadelphia', 'myre', '17077', '18 - 1 - 7'], ['december 13', 'quebec', '4 - 6', 'philadelphia', 'peeters', '17077', '19 - 1 - 7'], ['december 15', 'buffalo', '2 - 3', 'philadelphia', 'peeters', '17077', '20 - 1 - 7'], ['december 16', 'philadelphia', '1 - 1', 'ny rangers', 'myre', '17404', '20 - 1 - 8'], ['december 20', 'pittsburgh', '1 - 1', 'philadelphia', 'peeters', '17077', '20 - 1 - 9'], ['december 22', 'philadelphia', '5 - 2', 'boston', 'myre', '14673', '21 - 1 - 9'], ['december 23', 'hartford', '2 - 4', 'philadelphia', 'peeters', '17077', '22 - 1 - 9'], ['december 26', 'philadelphia', '4 - 4', 'hartford', 'myre', '7627', '22 - 1 - 10'], ['december 28', 'philadelphia', '5 - 3', 'winnipeg', 'peeters', '16038', '23 - 1 - 10'], ['december 29', 'philadelphia', '3 - 2', 'colorado', 'myre', '16452', '24 - 1 - 10']] |
miami valley conference | https://en.wikipedia.org/wiki/Miami_Valley_Conference | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-13456202-1.html.csv | count | two of the schools in the miami valley conference have a private christian affiliation . | {'scope': 'all', 'criterion': 'equal', 'value': 'private christian', 'result': '2', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'affiliation', 'private christian'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose affiliation record fuzzily matches to private christian .', 'tostr': 'filter_eq { all_rows ; affiliation ; private christian }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; affiliation ; private christian } }', 'tointer': 'select the rows whose affiliation record fuzzily matches to private christian . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; affiliation ; private christian } } ; 2 } = true', 'tointer': 'select the rows whose affiliation record fuzzily matches to private christian . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; affiliation ; private christian } } ; 2 } = true | select the rows whose affiliation record fuzzily matches to private christian . 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, 'affiliation_5': 5, 'private christian_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', 'affiliation_5': 'affiliation', 'private christian_6': 'private christian', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'affiliation_5': [0], 'private christian_6': [0], '2_7': [2]} | ['school', 'location', 'founded', 'affiliation', 'mascot', 'division'] | [['cincinnati country day school', 'cincinnati , ohio', '1926', 'private', 'indians', 'gray'], ['cincinnati christian schools', 'fairfield , ohio', '1989', 'private christian', 'cougars', 'gray'], ['cincinnati hills christian academy', 'cincinnati , ohio', '1989', 'private christian', 'eagles', 'scarlet'], ['lockland high school', 'cincinnati , ohio', '1851', 'public / open enrollment', 'panthers', 'scarlet'], ['clark montessori high school', 'cincinnati , ohio', '1994', 'public', 'cougars', 'gray'], ['north college hill high school', 'cincinnati , ohio', '1901', 'public', 'trojans', 'scarlet'], ['new miami high school', 'new miami , ohio', '1972', 'public / open enrollment', 'vikings', 'gray'], ['seven hills school', 'cincinnati , ohio', '1906', 'private', 'stingers', 'scarlet'], ['st bernard - elmwood place high school', 'cincinnati , ohio', '1900', 'public / open enrollment', 'titans', 'gray']] |
list of formula one driver records | https://en.wikipedia.org/wiki/List_of_Formula_One_driver_records | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13599687-60.html.csv | aggregation | the drivers combined earned a total of 1,357 points . | {'scope': 'all', 'col': '2', 'type': 'sum', 'result': '1,357', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'points'], 'result': '1,357', 'ind': 0, 'tostr': 'sum { all_rows ; points }'}, '1,357'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; points } ; 1,357 } = true', 'tointer': 'the sum of the points record of all rows is 1,357 .'} | round_eq { sum { all_rows ; points } ; 1,357 } = true | the sum of the points record of all rows is 1,357 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'points_4': 4, '1,357_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'points_4': 'points', '1,357_5': '1,357'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'points_4': [0], '1,357_5': [1]} | ['driver', 'points', 'season', 'races', 'percentage of possible points'] | [['michael schumacher', '148', '2004', '18', '82.22 %'], ['michael schumacher', '144', '2002', '17', '84.71 %'], ['fernando alonso', '134', '2006', '18', '74.44 %'], ['fernando alonso', '133', '2005', '19', '70.00 %'], ['michael schumacher', '123', '2001', '17', '72.36 %'], ['michael schumacher', '121', '2006', '18', '67.22 %'], ['rubens barrichello', '114', '2004', '18', '63.33 %'], ['kimi räikkönen', '112', '2005', '19', '58.95 %'], ['kimi räikkönen', '110', '2007', '17', '64.71 %'], ['lewis hamilton', '109', '2007', '17', '64.12 %'], ['fernando alonso', '109', '2007', '17', '64.12 %']] |
list of state leaders in 860s bc | https://en.wikipedia.org/wiki/List_of_state_leaders_in_860s_BC | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17337639-10.html.csv | unique | the state of qi is the only one belonging to the royal house of jiang . | {'scope': 'all', 'row': '6', 'col': '5', 'col_other': '1', 'criterion': 'equal', 'value': 'jiang', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'royal house', 'jiang'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose royal house record fuzzily matches to jiang .', 'tostr': 'filter_eq { all_rows ; royal house ; jiang }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; royal house ; jiang } }', 'tointer': 'select the rows whose royal house record fuzzily matches to jiang . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'royal house', 'jiang'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose royal house record fuzzily matches to jiang .', 'tostr': 'filter_eq { all_rows ; royal house ; jiang }'}, 'state'], 'result': 'qi', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; royal house ; jiang } ; state }'}, 'qi'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; royal house ; jiang } ; state } ; qi }', 'tointer': 'the state record of this unqiue row is qi .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; royal house ; jiang } } ; eq { hop { filter_eq { all_rows ; royal house ; jiang } ; state } ; qi } } = true', 'tointer': 'select the rows whose royal house record fuzzily matches to jiang . there is only one such row in the table . the state record of this unqiue row is qi .'} | and { only { filter_eq { all_rows ; royal house ; jiang } } ; eq { hop { filter_eq { all_rows ; royal house ; jiang } ; state } ; qi } } = true | select the rows whose royal house record fuzzily matches to jiang . there is only one such row in the table . the state record of this unqiue row is qi . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'royal house_7': 7, 'jiang_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'state_9': 9, 'qi_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'royal house_7': 'royal house', 'jiang_8': 'jiang', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'state_9': 'state', 'qi_10': 'qi'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'royal house_7': [0], 'jiang_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'state_9': [2], 'qi_10': [3]} | ['state', 'type', 'name', 'title', 'royal house'] | [['cai', 'sovereign', 'li', 'marquis', 'ji'], ['cai', 'sovereign', 'wu', 'marquis', 'ji'], ['cao', 'sovereign', 'xiao', 'count', '-'], ['cao', 'sovereign', 'yi', 'count', '-'], ['lu', 'sovereign', 'xian', 'duke', 'ji'], ['qi', 'sovereign', 'ai', 'duke', 'jiang'], ['qin', 'sovereign', 'feizi', 'ruler', 'ying'], ['wey', 'sovereign', 'qing', 'marquis', '-'], ['yan', 'sovereign', 'hui', 'marquis', '-']] |
1953 u.s. open ( golf ) | https://en.wikipedia.org/wiki/1953_U.S._Open_%28golf%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17290169-1.html.csv | aggregation | the players have a score aggregation of 71 points in 1953 u.s golf opens . | {'scope': 'all', 'col': '4', 'type': 'average', 'result': '71', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'score'], 'result': '71', 'ind': 0, 'tostr': 'avg { all_rows ; score }'}, '71'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; score } ; 71 } = true', 'tointer': 'the average of the score record of all rows is 71 .'} | round_eq { avg { all_rows ; score } ; 71 } = true | the average of the score record of all rows is 71 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'score_4': 4, '71_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'score_4': 'score', '71_5': '71'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'score_4': [0], '71_5': [1]} | ['place', 'player', 'country', 'score', 'to par'] | [['1', 'ben hogan', 'united states', '67', '- 5'], ['t2', 'walter burkemo', 'united states', '70', '- 2'], ['t2', 'george fazio', 'united states', '70', '- 2'], ['t2', 'frank souchak ( a )', 'united states', '70', '- 2'], ['t5', 'jimmy demaret', 'united states', '71', '- 1'], ['t5', 'bill ogden', 'united states', '71', '- 1'], ['t7', 'lou barbaro', 'united states', '72', 'e'], ['t7', 'jerry barber', 'united states', '72', 'e'], ['t7', 'jay hebert', 'united states', '72', 'e'], ['t7', 'sam snead', 'united states', '72', 'e']] |
2008 - 09 fa cup | https://en.wikipedia.org/wiki/2008%E2%80%9309_FA_Cup | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-17814838-1.html.csv | aggregation | the prize money of the september rounds of the 2008-09 fa cup was 7500 . | {'scope': 'subset', 'col': '6', 'type': 'sum', 'result': '7500', 'subset': {'col': '2', 'criterion': 'fuzzily_match', 'value': 'september'}} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'main date', 'september'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; main date ; september }', 'tointer': 'select the rows whose main date record fuzzily matches to september .'}, 'prize money'], 'result': '7500', 'ind': 1, 'tostr': 'sum { filter_eq { all_rows ; main date ; september } ; prize money }'}, '7500'], 'result': True, 'ind': 2, 'tostr': 'round_eq { sum { filter_eq { all_rows ; main date ; september } ; prize money } ; 7500 } = true', 'tointer': 'select the rows whose main date record fuzzily matches to september . the sum of the prize money record of these rows is 7500 .'} | round_eq { sum { filter_eq { all_rows ; main date ; september } ; prize money } ; 7500 } = true | select the rows whose main date record fuzzily matches to september . the sum of the prize money record of these rows is 7500 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'sum_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'main date_5': 5, 'september_6': 6, 'prize money_7': 7, '7500_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'sum_1': 'sum', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'main date_5': 'main date', 'september_6': 'september', 'prize money_7': 'prize money', '7500_8': '7500'} | {'eq_2': [3], 'result_3': [], 'sum_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'main date_5': [0], 'september_6': [0], 'prize money_7': [1], '7500_8': [2]} | ['round', 'main date', 'number of fixtures', 'clubs', 'new entries this round', 'prize money', 'player of the round'] | [['extra preliminary round', '16 august 2008', '203', '761 → 558', '406 : 356th - 761st', '750', 'n / a'], ['preliminary round', '30 august 2008', '166', '558 → 392', '129 : 227th - 355th', '1500', 'n / a'], ['first round qualifying', '13 september 2008', '116', '392 → 276', '66 : 161st - 226th', '3000', 'derren ibrahim ( dartford )'], ['second round qualifying', '27 september 2008', '80', '276 → 196', '44 : 117th - 160th', '4500', 'dean lodge ( kingstonian )'], ['third round qualifying', '11 october 2008', '40', '196 → 156', 'none', '7500', 'craig davis ( afc totton )'], ['fourth round qualifying', '25 october 2008', '32', '156 → 124', '24 : 93rd - 116th', '12500', 'sam hatton ( afc wimbledon )'], ['first round proper', '8 november 2008', '40', '124 → 84', '48 : 45th - 92nd', '20000', 'jon adams ( afc telford united )'], ['second round proper', '29 november 2008', '20', '84 → 64', 'none', '30000', 'lindon meikle ( eastwood town )'], ['third round proper', '3 january 2009', '32', '64 → 32', '44 : 1st - 44th', '75000', 'nathan tyson ( nottingham forest )'], ['fourth round proper', '24 january 2009', '16', '32 → 16', 'none', '100000', 'scott parker ( west ham united )'], ['fifth round proper', '14 february 2009', '8', '16 → 8', 'none', '200000', 'mikel arteta ( everton )'], ['sixth round proper', '7 march 2009', '4', '8 → 4', 'none', '400000', 'robin van persie ( arsenal )'], ['semi - finals', '18 april 2009 19 april 2009', '2', '4 → 2', 'none', 'winners : 1000000 losers : 500000', 'phil jagielka ( everton )']] |
pete sampras career statistics | https://en.wikipedia.org/wiki/Pete_Sampras_career_statistics | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-22834834-2.html.csv | count | pete samparas was winner four out of five times from 1991-1997 . | {'scope': 'all', 'criterion': 'equal', 'value': 'winner', 'result': '4', 'col': '1', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'outcome', 'winner'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose outcome record fuzzily matches to winner .', 'tostr': 'filter_eq { all_rows ; outcome ; winner }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; outcome ; winner } }', 'tointer': 'select the rows whose outcome record fuzzily matches to winner . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; outcome ; winner } } ; 4 } = true', 'tointer': 'select the rows whose outcome record fuzzily matches to winner . the number of such rows is 4 .'} | eq { count { filter_eq { all_rows ; outcome ; winner } } ; 4 } = true | select the rows whose outcome record fuzzily matches to winner . 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, 'outcome_5': 5, 'winner_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', 'outcome_5': 'outcome', 'winner_6': 'winner', '4_7': '4'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'outcome_5': [0], 'winner_6': [0], '4_7': [2]} | ['outcome', 'year', 'championship', 'surface', 'opponent in the final', 'score in the final'] | [['winner', '1991', 'frankfurt', 'carpet ( i )', 'jim courier', '3 - 6 , 7 - 6 ( 7 - 5 ) , 6 - 3 , 6 - 4'], ['runner - up', '1993', 'frankfurt', 'carpet ( i )', 'michael stich', '6 - 7 ( 3 - 7 ) , 6 - 2 , 6 - 7 ( 7 - 9 ) , 2 - 6'], ['winner', '1994', 'frankfurt', 'carpet ( i )', 'boris becker', '4 - 6 , 6 - 3 , 7 - 5 , 6 - 4'], ['winner', '1996', 'hannover', 'carpet ( i )', 'boris becker', '3 - 6 , 7 - 6 ( 7 - 5 ) , 7 - 6 ( 7 - 4 ) , 6 - 7 ( 11 - 13 ) , 6 - 4'], ['winner', '1997', 'hannover', 'hard ( i )', 'yevgeny kafelnikov', '6 - 3 , 6 - 2 , 6 - 2']] |
the sunday night project | https://en.wikipedia.org/wiki/The_Sunday_Night_Project | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1590967-3.html.csv | superlative | in the sunday night project , the episode with the most recent air date was the episode where cheryl cole , kimberley walsh and sarah harding hosted . | {'scope': 'all', 'col_superlative': '2', 'row_superlative': '11', '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', 'air date'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; air date }'}, 'guest host'], 'result': 'cheryl cole , kimberley walsh and sarah harding', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; air date } ; guest host }'}, 'cheryl cole , kimberley walsh and sarah harding'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; air date } ; guest host } ; cheryl cole , kimberley walsh and sarah harding } = true', 'tointer': 'select the row whose air date record of all rows is maximum . the guest host record of this row is cheryl cole , kimberley walsh and sarah harding .'} | eq { hop { argmax { all_rows ; air date } ; guest host } ; cheryl cole , kimberley walsh and sarah harding } = true | select the row whose air date record of all rows is maximum . the guest host record of this row is cheryl cole , kimberley walsh and sarah harding . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'air date_5': 5, 'guest host_6': 6, 'cheryl cole , kimberley walsh and sarah harding_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'air date_5': 'air date', 'guest host_6': 'guest host', 'cheryl cole , kimberley walsh and sarah harding_7': 'cheryl cole , kimberley walsh and sarah harding'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'air date_5': [0], 'guest host_6': [1], 'cheryl cole , kimberley walsh and sarah harding_7': [2]} | ['episode number', 'air date', 'guest host', 'musical guest ( song performed )', 'who knows the most about the guest host panelists'] | [['1', '16 june 2006', 'jerry springer', 'orson ( bright idea )', 'zãe lucker and sam brodie'], ['2', '23 june 2006', 'patsy kensit', 'placebo ( infra - red )', 'jeremy edwards and grace adams - short'], ['3', '30 june 2006', 'rob lowe', 'the zutons ( valerie )', 'jennifer ellison and kirsty gallacher'], ['4', '7 july 2006', 'mischa barton', 'dirty pretty things ( deadwood )', 'camille coduri and harry judd'], ['5', '14 july 2006', 'ian wright', 'feeder ( just a day )', 'sally lindsay and lea walker'], ['6', '21 july 2006', 'jade goody', 'razorlight ( in the morning )', 'dominic wood and nikki grahame'], ['7', '28 july 2006', 'justin hawkins', 'kasabian ( empire )', 'holly willoughby and jayne kitt'], ['8', '4 august 2006', 'rupert everett', 'primal scream ( dolls ( sweet rock and roll ) )', 'jennie mcalpine and sarah beeny'], ['9', '11 august 2006', 'carol vorderman', 'the automatic ( recover )', 'gary lucy and susie verrico'], ['10', '18 august 2006', 'ross kemp', 'the feeling ( never be lonely )', 'matt willis and chantelle houghton'], ['11', '25 august 2006', 'cheryl cole , kimberley walsh and sarah harding', 'the fratellis ( chelsea dagger )', 'aisleyne horgan - wallace and glyn wise']] |
united states house of representatives elections , 1964 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1964 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1341865-37.html.csv | count | in the united states house of representatives election in 1964 , for those that were re-elected , two of the incumbents were first elected in 1960 . | {'scope': 'subset', 'criterion': 'equal', 'value': '1960', 'result': '2', 'col': '4', 'subset': {'col': '5', 'criterion': 'equal', 'value': 're - elected'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 're - elected'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; result ; re - elected }', 'tointer': 'select the rows whose result record fuzzily matches to re - elected .'}, 'first elected', '1960'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose result record fuzzily matches to re - elected . among these rows , select the rows whose first elected record is equal to 1960 .', 'tostr': 'filter_eq { filter_eq { all_rows ; result ; re - elected } ; first elected ; 1960 }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_eq { filter_eq { all_rows ; result ; re - elected } ; first elected ; 1960 } }', 'tointer': 'select the rows whose result record fuzzily matches to re - elected . among these rows , select the rows whose first elected record is equal to 1960 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_eq { all_rows ; result ; re - elected } ; first elected ; 1960 } } ; 2 } = true', 'tointer': 'select the rows whose result record fuzzily matches to re - elected . among these rows , select the rows whose first elected record is equal to 1960 . the number of such rows is 2 .'} | eq { count { filter_eq { filter_eq { all_rows ; result ; re - elected } ; first elected ; 1960 } } ; 2 } = true | select the rows whose result record fuzzily matches to re - elected . among these rows , select the rows whose first elected record is equal to 1960 . 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, 'result_6': 6, 're - elected_7': 7, 'first elected_8': 8, '1960_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', 'result_6': 'result', 're - elected_7': 're - elected', 'first elected_8': 'first elected', '1960_9': '1960', '2_10': '2'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_eq_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'result_6': [0], 're - elected_7': [0], 'first elected_8': [1], '1960_9': [1], '2_10': [3]} | ['district', 'incumbent', 'party', 'first elected', 'result', 'candidates'] | [['ohio 1', 'carl w rich', 'republican', '1962', 'lost re - election democratic gain', 'john j gilligan ( d ) 51.9 % carl w rich ( r ) 48.1 %'], ['ohio 2', 'donald d clancy', 'republican', '1960', 're - elected', 'donald d clancy ( r ) 60.5 % h a sand ( d ) 39.5 %'], ['ohio 3', 'paul f schenck', 'republican', '1951', 'lost re - election democratic gain', 'rodney m love ( d ) 52.0 % paul f schenck ( r ) 48.0 %'], ['ohio 5', 'del latta', 'republican', '1958', 're - elected', 'del latta ( r ) 65.9 % milford landis ( d ) 34.1 %'], ['ohio 6', 'bill harsha', 'republican', '1960', 're - elected', 'bill harsha ( r ) 60.1 % frank e smith ( d ) 39.9 %'], ['ohio 16', 'frank t bow', 'republican', '1950', 're - elected', 'frank t bow ( r ) 52.2 % robert d freeman ( d ) 47.8 %'], ['ohio 19', 'michael j kirwan', 'democratic', '1936', 're - elected', 'michael j kirwan ( d ) 76.3 % albert james ( r ) 23.7 %']] |
united states house of representatives elections , 1942 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1942 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1342256-5.html.csv | majority | all of the arkansas incumbents in the 1942 united states house of representatives elections were with the democratic party . | {'scope': 'all', 'col': '3', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'democratic', 'subset': None} | {'func': 'all_str_eq', 'args': ['all_rows', 'party', 'democratic'], 'result': True, 'ind': 0, 'tointer': 'for the party records of all rows , all of them fuzzily match to democratic .', 'tostr': 'all_eq { all_rows ; party ; democratic } = true'} | all_eq { all_rows ; party ; democratic } = true | for the party records of all rows , all of them fuzzily match to democratic . | 1 | 1 | {'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'party_3': 3, 'democratic_4': 4} | {'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'party_3': 'party', 'democratic_4': 'democratic'} | {'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'party_3': [0], 'democratic_4': [0]} | ['district', 'incumbent', 'party', 'first elected', 'result', 'candidates'] | [['arkansas 1', 'ezekiel c gathings', 'democratic', '1938', 're - elected', 'ezekiel c gathings ( d ) unopposed'], ['arkansas 2', 'wilbur mills', 'democratic', '1938', 're - elected', 'wilbur mills ( d ) unopposed'], ['arkansas 3', 'clyde t ellis', 'democratic', '1938', 'retired to run for u s senate democratic hold', 'j william fulbright ( d ) unopposed'], ['arkansas 4', 'william fadjo cravens', 'democratic', '1939', 're - elected', 'william fadjo cravens ( d ) unopposed'], ['arkansas 5', 'david d terry', 'democratic', '1933', 'retired to run for u s senate democratic hold', 'brooks hays ( d ) unopposed'], ['arkansas 6', 'william f norrell', 'democratic', '1938', 're - elected', 'william f norrell ( d ) unopposed']] |
naia independent football schools | https://en.wikipedia.org/wiki/NAIA_independent_football_schools | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15617076-1.html.csv | unique | haskell indian nations university is the only tribal institution among the naia independent football schools . | {'scope': 'all', 'row': '4', 'col': '4', 'col_other': '4', 'criterion': 'equal', 'value': 'tribal', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'type', 'tribal'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose type record fuzzily matches to tribal .', 'tostr': 'filter_eq { all_rows ; type ; tribal }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; type ; tribal } }', 'tointer': 'select the rows whose type record fuzzily matches to tribal . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'type', 'tribal'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose type record fuzzily matches to tribal .', 'tostr': 'filter_eq { all_rows ; type ; tribal }'}, 'type'], 'result': 'tribal', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; type ; tribal } ; type }'}, 'tribal'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; type ; tribal } ; type } ; tribal }', 'tointer': 'the type record of this unqiue row is tribal .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; type ; tribal } } ; eq { hop { filter_eq { all_rows ; type ; tribal } ; type } ; tribal } } = true', 'tointer': 'select the rows whose type record fuzzily matches to tribal . there is only one such row in the table . the type record of this unqiue row is tribal .'} | and { only { filter_eq { all_rows ; type ; tribal } } ; eq { hop { filter_eq { all_rows ; type ; tribal } ; type } ; tribal } } = true | select the rows whose type record fuzzily matches to tribal . there is only one such row in the table . the type record of this unqiue row is tribal . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'type_7': 7, 'tribal_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'type_9': 9, 'tribal_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'type_7': 'type', 'tribal_8': 'tribal', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'type_9': 'type', 'tribal_10': 'tribal'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'type_7': [0], 'tribal_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'type_9': [2], 'tribal_10': [3]} | ['institution', 'location', 'founded', 'type', 'enrollment', 'team', 'primary conference'] | [['ave maria university', 'ave maria , florida', '1998', 'private', '1200', 'gyrenes', 'the sun'], ['dakota state university', 'madison , south dakota', '1881', 'public', '3102', 'trojans', 'none'], ['edward waters college', 'jacksonville , florida', '1866', 'private', '800', 'tigers', 'gulf coast ( gcac )'], ['haskell indian nations university', 'lawrence , kansas', '1884', 'tribal', '1000', 'fighting indians', 'mcac'], ['jamestown college', 'jamestown , north dakota', '1883', 'private', '967', 'jimmies', 'none'], ['lindenwood universitybelleville', 'belleville , illinois', '2003', 'private', '2600', 'lynx', 'none'], ['mayville state university', 'mayville , north dakota', '1889', 'public', '825', 'comets', 'none'], ['menlo college', 'atherton , california', '1927', 'private', '650', 'oaks', 'calpac'], ['point university', 'west point , georgia', '1937', 'private', '1035', 'skyhawks', 'aac'], ['valley city state university', 'valley city , north dakota', '1890', 'public', '1340', 'vikings', 'none'], ['webber international university', 'babson park , florida', '1927', 'private', '616', 'warriors', 'the sun']] |
ai sugiyama | https://en.wikipedia.org/wiki/Ai_Sugiyama | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1514559-1.html.csv | aggregation | ai sugiyama 's average score in the finals is six points . | {'scope': 'all', 'col': '7', 'type': 'average', 'result': '6', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'score in the final'], 'result': '6', 'ind': 0, 'tostr': 'avg { all_rows ; score in the final }'}, '6'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; score in the final } ; 6 } = true', 'tointer': 'the average of the score in the final record of all rows is 6 .'} | round_eq { avg { all_rows ; score in the final } ; 6 } = true | the average of the score in the final record of all rows is 6 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'score in the final_4': 4, '6_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'score in the final_4': 'score in the final', '6_5': '6'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'score in the final_4': [0], '6_5': [1]} | ['outcome', 'year', 'championship', 'surface', 'partner', 'opponents in the final', 'score in the final'] | [['runner - up', '2000', 'wimbledon', 'grass', 'julie halard', 'serena williams venus williams', '6 - 3 , 6 - 2'], ['winner', '2000', 'us open', 'hard', 'julie halard', 'cara black elena likhovtseva', '6 - 0 , 1 - 6 , 6 - 1'], ['runner - up', '2001', 'wimbledon ( 2 )', 'grass', 'kim clijsters', 'lisa raymond rennae stubbs', '6 - 4 , 6 - 3'], ['winner', '2003', 'french open', 'clay', 'kim clijsters', 'virginia ruano pascual paola suárez', '6 - 7 , 6 - 2 , 9 - 7'], ['winner', '2003', 'wimbledon', 'grass', 'kim clijsters', 'virginia ruano pascual paola suárez', '6 - 4 6 - 4'], ['runner - up', '2004', 'wimbledon ( 3 )', 'grass', 'liezel huber', 'cara black rennae stubbs', '6 - 3 , 7 - 6'], ['runner - up', '2006', 'french open', 'clay', 'daniela hantuchová', 'lisa raymond samantha stosur', '6 - 3 , 6 - 2'], ['runner - up', '2007', 'french open ( 2 )', 'clay', 'katarina srebotnik', 'alicia molik mara santangelo', '7 - 6 , 6 - 4'], ['runner - up', '2007', 'wimbledon ( 4 )', 'grass', 'katarina srebotnik', 'cara black liezel huber', '3 - 6 , 6 - 3 , 6 - 2']] |
hubert hahne | https://en.wikipedia.org/wiki/Hubert_Hahne | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1233847-1.html.csv | unique | 1966 is the only year that hubert hahne drove for the tyrrell racing organisation . | {'scope': 'all', 'row': '1', 'col': '2', 'col_other': '1', 'criterion': 'equal', 'value': 'tyrrell racing organisation', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'entrant', 'tyrrell racing organisation'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose entrant record fuzzily matches to tyrrell racing organisation .', 'tostr': 'filter_eq { all_rows ; entrant ; tyrrell racing organisation }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; entrant ; tyrrell racing organisation } }', 'tointer': 'select the rows whose entrant record fuzzily matches to tyrrell racing organisation . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'entrant', 'tyrrell racing organisation'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose entrant record fuzzily matches to tyrrell racing organisation .', 'tostr': 'filter_eq { all_rows ; entrant ; tyrrell racing organisation }'}, 'year'], 'result': '1966', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; entrant ; tyrrell racing organisation } ; year }'}, '1966'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; entrant ; tyrrell racing organisation } ; year } ; 1966 }', 'tointer': 'the year record of this unqiue row is 1966 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; entrant ; tyrrell racing organisation } } ; eq { hop { filter_eq { all_rows ; entrant ; tyrrell racing organisation } ; year } ; 1966 } } = true', 'tointer': 'select the rows whose entrant record fuzzily matches to tyrrell racing organisation . there is only one such row in the table . the year record of this unqiue row is 1966 .'} | and { only { filter_eq { all_rows ; entrant ; tyrrell racing organisation } } ; eq { hop { filter_eq { all_rows ; entrant ; tyrrell racing organisation } ; year } ; 1966 } } = true | select the rows whose entrant record fuzzily matches to tyrrell racing organisation . there is only one such row in the table . the year record of this unqiue row is 1966 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'entrant_7': 7, 'tyrrell racing organisation_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'year_9': 9, '1966_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'entrant_7': 'entrant', 'tyrrell racing organisation_8': 'tyrrell racing organisation', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'year_9': 'year', '1966_10': '1966'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'entrant_7': [0], 'tyrrell racing organisation_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'year_9': [2], '1966_10': [3]} | ['year', 'entrant', 'chassis', 'engine', 'points'] | [['1966', 'tyrrell racing organisation', 'matra ms5 ( f2 )', 'brm straight - 4', '0'], ['1967', 'bayerische motoren werke', 'lola t100', 'bmw straight - 4', '0'], ['1968', 'bayerische motoren werke', 'lola t100', 'bmw straight - 4', '0'], ['1969', 'bayerische motoren werke', 'bmw t269 ( f2 )', 'bmw straight - 4', '0'], ['1970', 'hubert hahne', 'march 701', 'cosworth v8', '0']] |
1988 - 89 argentine primera división | https://en.wikipedia.org/wiki/1988%E2%80%9389_Argentine_Primera_Divisi%C3%B3n | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17968265-1.html.csv | count | two of the teams finished with 130 points . | {'scope': 'all', 'criterion': 'equal', 'value': '130', 'result': '2', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'points', '130'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose points record is equal to 130 .', 'tostr': 'filter_eq { all_rows ; points ; 130 }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; points ; 130 } }', 'tointer': 'select the rows whose points record is equal to 130 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; points ; 130 } } ; 2 } = true', 'tointer': 'select the rows whose points record is equal to 130 . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; points ; 130 } } ; 2 } = true | select the rows whose points record is equal to 130 . the number of such rows is 2 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_eq_0': 0, 'all_rows_4': 4, 'points_5': 5, '130_6': 6, '2_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'points_5': 'points', '130_6': '130', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'points_5': [0], '130_6': [0], '2_7': [2]} | ['team', 'average', 'points', 'played', '1986 - 87', '1987 - 88', '1988 - 89'] | [['independiente', '1.219', '139', '114', '47', '37', '55'], ["newell 's old boys", '1.193', '136', '114', '48', '55', '33'], ['san lorenzo', '1.184', '135', '114', '44', '49', '42'], ['racing club', '1.158', '132', '114', '44', '48', '40'], ['boca juniors', '1.140', '130', '114', '46', '35', '49'], ['river plate', '1.140', '130', '114', '39', '46', '45'], ['rosario central', '1.079', '123', '114', '49', '40', '34'], ['deportivo español', '1.070', '122', '114', '36', '40', '46'], ['gimnasia de la plata', '1.018', '116', '114', '37', '43', '36'], ['vélez sársfield', '1.009', '115', '114', '41', '41', '33'], ['estudiantes de la plata', '0.974', '111', '114', '37', '32', '42'], ['argentinos juniors', '0.965', '110', '114', '28', '40', '42'], ['talleres de córdoba', '0.956', '109', '114', '38', '27', '44'], ['ferro carril oeste', '0.939', '107', '114', '44', '33', '30'], ['deportivo mandiyú', '0.868', '33', '38', 'n / a', 'n / a', '33'], ['platense', '0.860', '98', '114', '27', '38', '33'], ['instituto de córdoba', '0.851', '97', '114', '41', '33', '23'], ['racing de córdoba', '0.851', '97', '114', '33', '31', '33'], ['san martín de tucumán', '0.842', '32', '38', 'n / a', 'n / a', '32'], ['deportivo armenio', '0.776', '59', '76', 'n / a', '34', '25']] |
united states house of representatives elections , 1954 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1954 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1342013-10.html.csv | superlative | georgia district 1 had the highest number of competing candidates in the united states house of representatives elections of 1954 . | {'scope': 'all', 'col_superlative': '6', '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', 'candidates'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; candidates }'}, 'district'], 'result': 'georgia 1', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; candidates } ; district }'}, 'georgia 1'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; candidates } ; district } ; georgia 1 } = true', 'tointer': 'select the row whose candidates record of all rows is maximum . the district record of this row is georgia 1 .'} | eq { hop { argmax { all_rows ; candidates } ; district } ; georgia 1 } = true | select the row whose candidates record of all rows is maximum . the district record of this row is georgia 1 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'candidates_5': 5, 'district_6': 6, 'georgia 1_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'candidates_5': 'candidates', 'district_6': 'district', 'georgia 1_7': 'georgia 1'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'candidates_5': [0], 'district_6': [1], 'georgia 1_7': [2]} | ['district', 'incumbent', 'party', 'first elected', 'result', 'candidates'] | [['georgia 1', 'prince hulon preston , jr', 'democratic', '1946', 're - elected', 'prince hulon preston , jr ( d ) 83.7 % others 16.3 %'], ['georgia 2', 'j l pilcher', 'democratic', '1953', 're - elected', 'j l pilcher ( d ) unopposed'], ['georgia 3', 'tic forrester', 'democratic', '1950', 're - elected', 'tic forrester ( d ) unopposed'], ['georgia 4', 'albert sidney camp', 'democratic', '1939', 'died in office democratic hold', 'john james flynt , jr ( d ) unopposed'], ['georgia 6', 'carl vinson', 'democratic', '1914', 're - elected', 'carl vinson ( d ) unopposed'], ['georgia 7', 'henderson lovelace lanham', 'democratic', '1946', 're - elected', 'henderson lovelace lanham ( d ) unopposed'], ['georgia 8', 'william m wheeler', 'democratic', '1946', 'lost renomination democratic hold', 'iris faircloth blitch ( d ) unopposed'], ['georgia 9', 'phillip m landrum', 'democratic', '1952', 're - elected', 'phillip m landrum ( d ) unopposed']] |
tiffany joh | https://en.wikipedia.org/wiki/Tiffany_Joh | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15870501-2.html.csv | ordinal | from 2007 - 2012 , tiffany joh 's third lowest scoring average was in 2011 . | {'row': '4', 'col': '7', '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', 'scoring average', '3'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; scoring average ; 3 }'}, 'year'], 'result': '2011', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; scoring average ; 3 } ; year }'}, '2011'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; scoring average ; 3 } ; year } ; 2011 } = true', 'tointer': 'select the row whose scoring average record of all rows is 3rd minimum . the year record of this row is 2011 .'} | eq { hop { nth_argmin { all_rows ; scoring average ; 3 } ; year } ; 2011 } = true | select the row whose scoring average record of all rows is 3rd minimum . the year record of this row is 2011 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'num_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'scoring average_5': 5, '3_6': 6, 'year_7': 7, '2011_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'num_hop_1': 'num_hop', 'nth_argmin_0': 'nth_argmin', 'all_rows_4': 'all_rows', 'scoring average_5': 'scoring average', '3_6': '3', 'year_7': 'year', '2011_8': '2011'} | {'eq_2': [3], 'result_3': [], 'num_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'scoring average_5': [0], '3_6': [0], 'year_7': [1], '2011_8': [2]} | ['year', 'tournaments played', 'cuts made', 'wins', 'best finish', 'earnings', 'scoring average'] | [['2007', '1', '1', '0', 't22', 'n / a', '71.66'], ['2009', '1', '1', '0', 't21', 'n / a', '72.50'], ['2010', '2', '0', '0', 'mc', '0', '79.00'], ['2011', '14', '12', '0', '2', '237365', '72.75'], ['2012', '20', '10', '0', 't33', '48695', '74.09']] |
list of european cup and uefa champions league winning managers | https://en.wikipedia.org/wiki/List_of_European_Cup_and_UEFA_Champions_League_winning_managers | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15205941-2.html.csv | majority | the majority of european cup and uefa champions league winning managers have 0 runner-up positions . | {'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'equal', 'value': '0', 'subset': None} | {'func': 'most_eq', 'args': ['all_rows', 'runner - up', '0'], 'result': True, 'ind': 0, 'tointer': 'for the runner - up records of all rows , most of them are equal to 0 .', 'tostr': 'most_eq { all_rows ; runner - up ; 0 } = true'} | most_eq { all_rows ; runner - up ; 0 } = true | for the runner - up records of all rows , most of them are equal to 0 . | 1 | 1 | {'most_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'runner - up_3': 3, '0_4': 4} | {'most_eq_0': 'most_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'runner - up_3': 'runner - up', '0_4': '0'} | {'most_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'runner - up_3': [0], '0_4': [0]} | ['rank', 'manager', 'runner - up', 'years won', 'clubs won'] | [['1', 'bob paisley', '0', '1977 , 1978 , 1981', 'liverpool'], ['2', 'alex ferguson', '2', '1999 , 2008', 'manchester united'], ['2', 'miguel muñoz', '2', '1960 , 1966', 'real madrid'], ['4', 'jupp heynckes', '1', '1998 , 2013', 'real madrid , bayern munich'], ['4', 'carlo ancelotti', '1', '2003 , 2007', 'milan'], ['4', 'ottmar hitzfeld', '1', '1997 , 2001', 'borussia dortmund , bayern munich'], ['4', 'ernst happel', '1', '1970 , 1983', 'feyenoord , hamburg'], ['4', 'helenio herrera', '1', '1964 , 1965', 'internazionale'], ['9', 'josep guardiola', '0', '2009 , 2011', 'barcelona'], ['9', 'josé mourinho', '0', '2004 , 2010', 'porto , internazionale'], ['9', 'vicente del bosque', '0', '2000 , 2002', 'real madrid'], ['9', 'arrigo sacchi', '0', '1989 , 1990', 'milan'], ['9', 'brian clough', '0', '1979 , 1980', 'nottingham forest'], ['9', 'dettmar cramer', '0', '1975 , 1976', 'bayern munich'], ['9', 'ștefan kovács', '0', '1972 , 1973', 'ajax'], ['9', 'nereo rocco', '0', '1963 , 1969', 'milan'], ['9', 'béla guttmann', '0', '1961 , 1962', 'benfica'], ['9', 'luis carniglia', '0', '1958 , 1959', 'real madrid'], ['9', 'josé villalonga', '0', '1956 , 1957', 'real madrid']] |
nick park | https://en.wikipedia.org/wiki/Nick_Park | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-149052-1.html.csv | comparative | chicken run was released after creature comforts was released . | {'row_1': '5', 'row_2': '1', 'col': '1', 'col_other': '2', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'title', 'chicken run'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose title record fuzzily matches to chicken run .', 'tostr': 'filter_eq { all_rows ; title ; chicken run }'}, 'year'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; title ; chicken run } ; year }', 'tointer': 'select the rows whose title record fuzzily matches to chicken run . take the year record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'title', 'creature comforts'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose title record fuzzily matches to creature comforts .', 'tostr': 'filter_eq { all_rows ; title ; creature comforts }'}, 'year'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; title ; creature comforts } ; year }', 'tointer': 'select the rows whose title record fuzzily matches to creature comforts . take the year record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; title ; chicken run } ; year } ; hop { filter_eq { all_rows ; title ; creature comforts } ; year } } = true', 'tointer': 'select the rows whose title record fuzzily matches to chicken run . take the year record of this row . select the rows whose title record fuzzily matches to creature comforts . take the year record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; title ; chicken run } ; year } ; hop { filter_eq { all_rows ; title ; creature comforts } ; year } } = true | select the rows whose title record fuzzily matches to chicken run . take the year record of this row . select the rows whose title record fuzzily matches to creature comforts . take the year 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, 'title_7': 7, 'chicken run_8': 8, 'year_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'title_11': 11, 'creature comforts_12': 12, 'year_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', 'title_7': 'title', 'chicken run_8': 'chicken run', 'year_9': 'year', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'title_11': 'title', 'creature comforts_12': 'creature comforts', 'year_13': 'year'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'title_7': [0], 'chicken run_8': [0], 'year_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'title_11': [1], 'creature comforts_12': [1], 'year_13': [3]} | ['year', 'title', 'director', 'writer', 'notes'] | [['1989', 'creature comforts', 'yes', 'yes', 'short film'], ['1989', 'wallace & gromit : a grand day out', 'yes', 'yes', 'short film'], ['1993', 'wallace & gromit : the wrong trousers', 'yes', 'yes', 'short film'], ['1995', 'wallace & gromit : a close shave', 'yes', 'yes', 'short film'], ['2000', 'chicken run', 'yes', 'yes', 'co - directed with peter lord'], ['2005', 'wallace & gromit : the curse of the were - rabbit', 'yes', 'yes', 'co - directed with steve box'], ['2008', 'wallace & gromit : a matter of loaf and death', 'yes', 'yes', 'short film']] |
1981 denver broncos season | https://en.wikipedia.org/wiki/1981_Denver_Broncos_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-17972136-1.html.csv | comparative | game attendance was higher on september 6 than on november 22 . | {'row_1': '1', 'row_2': '12', 'col': '7', 'col_other': '2', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'september 6'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose date record fuzzily matches to september 6 .', 'tostr': 'filter_eq { all_rows ; date ; september 6 }'}, 'attendance'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; date ; september 6 } ; attendance }', 'tointer': 'select the rows whose date record fuzzily matches to september 6 . take the attendance record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'november 22'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to november 22 .', 'tostr': 'filter_eq { all_rows ; date ; november 22 }'}, 'attendance'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; date ; november 22 } ; attendance }', 'tointer': 'select the rows whose date record fuzzily matches to november 22 . take the attendance record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; date ; september 6 } ; attendance } ; hop { filter_eq { all_rows ; date ; november 22 } ; attendance } } = true', 'tointer': 'select the rows whose date record fuzzily matches to september 6 . take the attendance record of this row . select the rows whose date record fuzzily matches to november 22 . take the attendance record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; date ; september 6 } ; attendance } ; hop { filter_eq { all_rows ; date ; november 22 } ; attendance } } = true | select the rows whose date record fuzzily matches to september 6 . take the attendance record of this row . select the rows whose date record fuzzily matches to november 22 . take the attendance 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, 'date_7': 7, 'september 6_8': 8, 'attendance_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'date_11': 11, 'november 22_12': 12, 'attendance_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', 'date_7': 'date', 'september 6_8': 'september 6', 'attendance_9': 'attendance', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'date_11': 'date', 'november 22_12': 'november 22', 'attendance_13': 'attendance'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'date_7': [0], 'september 6_8': [0], 'attendance_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'date_11': [1], 'november 22_12': [1], 'attendance_13': [3]} | ['week', 'date', 'opponent', 'result', 'game site', 'record', 'attendance'] | [['1', 'september 6', 'oakland raiders', 'w 9 - 7', 'mile high stadium', '1 - 0', '74796'], ['2', 'september 13', 'seattle seahawks', 'l 10 - 13', 'kingdome', '1 - 1', '58513'], ['3', 'september 20', 'baltimore colts', 'w 28 - 10', 'mile high stadium', '2 - 1', '74804'], ['4', 'september 27', 'san diego chargers', 'w 42 - 24', 'mile high stadium', '3 - 1', '74822'], ['5', 'october 4', 'oakland raiders', 'w 17 - 0', 'oakland - alameda county coliseum', '4 - 1', '51035'], ['6', 'october 11', 'detroit lions', 'w 27 - 21', 'mile high stadium', '5 - 1', '74816'], ['7', 'october 18', 'kansas city chiefs', 'l 14 - 28', 'arrowhead stadium', '5 - 2', '74672'], ['8', 'october 25', 'buffalo bills', 'l 7 - 9', 'rich stadium', '5 - 3', '77757'], ['9', 'november 2', 'minnesota vikings', 'w 19 - 17', 'mile high stadium', '6 - 3', '74834'], ['10', 'november 8', 'cleveland browns', 'w 23 - 20 ( ot )', 'mile high stadium', '7 - 3', '74859'], ['11', 'november 15', 'tampa bay buccaneers', 'w 24 - 7', 'tampa stadium', '8 - 3', '64518'], ['12', 'november 22', 'cincinnati bengals', 'l 21 - 38', 'riverfront stadium', '8 - 4', '57207'], ['13', 'november 29', 'san diego chargers', 'l 17 - 34', 'jack murphy stadium', '8 - 5', '51533'], ['14', 'december 6', 'kansas city chiefs', 'w 16 - 13', 'mile high stadium', '9 - 5', '74744'], ['15', 'december 13', 'seattle seahawks', 'w 23 - 13', 'mile high stadium', '10 - 5', '74527']] |
1975 england rugby union tour of australia | https://en.wikipedia.org/wiki/1975_England_rugby_union_tour_of_Australia | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17004899-1.html.csv | aggregation | the opposing teams scored a total of 119 against england in the 1975 england rugby union tour of australia . | {'scope': 'all', 'col': '2', 'type': 'sum', 'result': '119', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'against'], 'result': '119', 'ind': 0, 'tostr': 'sum { all_rows ; against }'}, '119'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; against } ; 119 } = true', 'tointer': 'the sum of the against record of all rows is 119 .'} | round_eq { sum { all_rows ; against } ; 119 } = true | the sum of the against record of all rows is 119 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'against_4': 4, '119_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'against_4': 'against', '119_5': '119'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'against_4': [0], '119_5': [1]} | ['opposing team', 'against', 'date', 'venue', 'status'] | [['western australia', '12', '10 / 05 / 1975', 'perry lakes stadium , perth', 'tour match'], ['sydney', '14', '13 / 05 / 1975', 'sydney cricket ground , sydney', 'tour match'], ['new south wales', '24', '17 / 05 / 1975', 'sydney sports ground , sydney', 'tour match'], ['new south wales country xv', '14', '20 / 05 / 1975', 'goulburn', 'tour match'], ['australia', '16', '24 / 05 / 1975', 'sydney cricket ground , sydney', 'first test'], ['queensland', '3', '27 / 05 / 1975', 'ballymore , brisbane', 'tour match'], ['australia', '30', '31 / 05 / 1975', 'ballymore , brisbane', 'second test'], ['queensland country', '6', '03 / 06 / 1975', 'townsville sports reserve , townsville', 'tour match']] |
paul caligiuri | https://en.wikipedia.org/wiki/Paul_Caligiuri | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1036039-1.html.csv | unique | of all of paul caligiuri 's competitions , the only one in italy was on june 10 , 1990 . | {'scope': 'all', 'row': '4', 'col': '2', 'col_other': '1', 'criterion': 'equal', 'value': 'italy', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'venue', 'italy'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose venue record fuzzily matches to italy .', 'tostr': 'filter_eq { all_rows ; venue ; italy }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; venue ; italy } }', 'tointer': 'select the rows whose venue record fuzzily matches to italy . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'venue', 'italy'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose venue record fuzzily matches to italy .', 'tostr': 'filter_eq { all_rows ; venue ; italy }'}, 'date'], 'result': 'june 10 , 1990', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; venue ; italy } ; date }'}, 'june 10 , 1990'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; venue ; italy } ; date } ; june 10 , 1990 }', 'tointer': 'the date record of this unqiue row is june 10 , 1990 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; venue ; italy } } ; eq { hop { filter_eq { all_rows ; venue ; italy } ; date } ; june 10 , 1990 } } = true', 'tointer': 'select the rows whose venue record fuzzily matches to italy . there is only one such row in the table . the date record of this unqiue row is june 10 , 1990 .'} | and { only { filter_eq { all_rows ; venue ; italy } } ; eq { hop { filter_eq { all_rows ; venue ; italy } ; date } ; june 10 , 1990 } } = true | select the rows whose venue record fuzzily matches to italy . there is only one such row in the table . the date record of this unqiue row is june 10 , 1990 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'venue_7': 7, 'italy_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'date_9': 9, 'june 10 , 1990_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'venue_7': 'venue', 'italy_8': 'italy', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'date_9': 'date', 'june 10 , 1990_10': 'june 10 , 1990'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'venue_7': [0], 'italy_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'date_9': [2], 'june 10 , 1990_10': [3]} | ['date', 'venue', 'score', 'result', 'competition'] | [['may 19 , 1985', 'torrance , california', '1 - 0', '1 - 0', '1986 world cup qualifying'], ['november 19 , 1989', 'port of spain , trinidad and tobago', '1 - 0', '1 - 0', '1990 world cup qualifying'], ['march 10 , 1990', 'tampa , florida', '1 - 0', '2 - 1', 'friendly'], ['june 10 , 1990', 'florence , italy', '1 - 3', '1 - 5', '1990 world cup'], ['may 28 , 1995', 'tampa , florida', '1 - 1', '1 - 2', 'friendly']] |
2011 the dominion tankard | https://en.wikipedia.org/wiki/2011_The_Dominion_Tankard | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-29565601-2.html.csv | unique | in the dominion tankard in 2011 , the only one with over 15 stolen ends was chris gardner . | {'scope': 'all', 'row': '6', 'col': '9', 'col_other': '1', 'criterion': 'greater_than', 'value': '15', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'stolen ends', '15'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose stolen ends record is greater than 15 .', 'tostr': 'filter_greater { all_rows ; stolen ends ; 15 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_greater { all_rows ; stolen ends ; 15 } }', 'tointer': 'select the rows whose stolen ends record is greater than 15 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'stolen ends', '15'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose stolen ends record is greater than 15 .', 'tostr': 'filter_greater { all_rows ; stolen ends ; 15 }'}, 'skip ( club )'], 'result': 'chris gardner ( renfrew )', 'ind': 2, 'tostr': 'hop { filter_greater { all_rows ; stolen ends ; 15 } ; skip ( club ) }'}, 'chris gardner ( renfrew )'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_greater { all_rows ; stolen ends ; 15 } ; skip ( club ) } ; chris gardner ( renfrew ) }', 'tointer': 'the skip ( club ) record of this unqiue row is chris gardner ( renfrew ) .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_greater { all_rows ; stolen ends ; 15 } } ; eq { hop { filter_greater { all_rows ; stolen ends ; 15 } ; skip ( club ) } ; chris gardner ( renfrew ) } } = true', 'tointer': 'select the rows whose stolen ends record is greater than 15 . there is only one such row in the table . the skip ( club ) record of this unqiue row is chris gardner ( renfrew ) .'} | and { only { filter_greater { all_rows ; stolen ends ; 15 } } ; eq { hop { filter_greater { all_rows ; stolen ends ; 15 } ; skip ( club ) } ; chris gardner ( renfrew ) } } = true | select the rows whose stolen ends record is greater than 15 . there is only one such row in the table . the skip ( club ) record of this unqiue row is chris gardner ( renfrew ) . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_greater_0': 0, 'all_rows_6': 6, 'stolen ends_7': 7, '15_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'skip (club)_9': 9, 'chris gardner (renfrew)_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_greater_0': 'filter_greater', 'all_rows_6': 'all_rows', 'stolen ends_7': 'stolen ends', '15_8': '15', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'skip (club)_9': 'skip ( club )', 'chris gardner (renfrew)_10': 'chris gardner ( renfrew )'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_greater_0': [1, 2], 'all_rows_6': [0], 'stolen ends_7': [0], '15_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'skip (club)_9': [2], 'chris gardner (renfrew)_10': [3]} | ['skip ( club )', 'w', 'l', 'pf', 'pa', 'ends won', 'ends lost', 'blank ends', 'stolen ends'] | [['peter corner ( brampton )', '8', '2', '69', '54', '41', '36', '8', '11'], ['glenn howard ( coldwater )', '8', '2', '79', '35', '40', '22', '8', '11'], ['greg balsdon ( loonie )', '7', '3', '80', '57', '46', '37', '5', '12'], ['john epping ( donalda )', '7', '3', '76', '64', '43', '41', '5', '10'], ['mark bice ( sarnia )', '6', '4', '70', '76', '45', '44', '8', '7'], ['chris gardner ( renfrew )', '5', '5', '73', '72', '47', '41', '7', '16'], ['dale matchett ( bradford )', '4', '6', '57', '75', '35', '42', '7', '7'], ['mark kean ( annandale )', '3', '7', '53', '67', '43', '35', '12', '8'], ['howard rajala ( rideau )', '3', '7', '67', '71', '43', '48', '5', '9'], ['nick rizzo ( brantford )', '3', '7', '56', '74', '35', '42', '4', '5']] |
wqln - fm | https://en.wikipedia.org/wiki/WQLN-FM | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14163566-1.html.csv | comparative | w207af and w211ae both share the same fcc info , fcc . | {'row_1': '1', 'row_2': '2', 'col': '5', 'col_other': '1', 'relation': 'equal', 'record_mentioned': 'yes', 'diff_result': None} | {'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'call sign', 'w207af'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose call sign record fuzzily matches to w207af .', 'tostr': 'filter_eq { all_rows ; call sign ; w207af }'}, 'fcc info'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; call sign ; w207af } ; fcc info }', 'tointer': 'select the rows whose call sign record fuzzily matches to w207af . take the fcc info record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'call sign', 'w211ae'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose call sign record fuzzily matches to w211ae .', 'tostr': 'filter_eq { all_rows ; call sign ; w211ae }'}, 'fcc info'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; call sign ; w211ae } ; fcc info }', 'tointer': 'select the rows whose call sign record fuzzily matches to w211ae . take the fcc info record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_eq { all_rows ; call sign ; w207af } ; fcc info } ; hop { filter_eq { all_rows ; call sign ; w211ae } ; fcc info } }', 'tointer': 'select the rows whose call sign record fuzzily matches to w207af . take the fcc info record of this row . select the rows whose call sign record fuzzily matches to w211ae . take the fcc info record of this row . the first record fuzzily matches to the second record .'}, {'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'call sign', 'w207af'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose call sign record fuzzily matches to w207af .', 'tostr': 'filter_eq { all_rows ; call sign ; w207af }'}, 'fcc info'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; call sign ; w207af } ; fcc info }', 'tointer': 'select the rows whose call sign record fuzzily matches to w207af . take the fcc info record of this row .'}, 'fcc'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; call sign ; w207af } ; fcc info } ; fcc }', 'tointer': 'the fcc info record of the first row is fcc .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'call sign', 'w211ae'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose call sign record fuzzily matches to w211ae .', 'tostr': 'filter_eq { all_rows ; call sign ; w211ae }'}, 'fcc info'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; call sign ; w211ae } ; fcc info }', 'tointer': 'select the rows whose call sign record fuzzily matches to w211ae . take the fcc info record of this row .'}, 'fcc'], 'result': True, 'ind': 6, 'tostr': 'eq { hop { filter_eq { all_rows ; call sign ; w211ae } ; fcc info } ; fcc }', 'tointer': 'the fcc info record of the second row is fcc .'}], 'result': True, 'ind': 7, 'tostr': 'and { eq { hop { filter_eq { all_rows ; call sign ; w207af } ; fcc info } ; fcc } ; eq { hop { filter_eq { all_rows ; call sign ; w211ae } ; fcc info } ; fcc } }', 'tointer': 'the fcc info record of the first row is fcc . the fcc info record of the second row is fcc .'}], 'result': True, 'ind': 8, 'tostr': 'and { eq { hop { filter_eq { all_rows ; call sign ; w207af } ; fcc info } ; hop { filter_eq { all_rows ; call sign ; w211ae } ; fcc info } } ; and { eq { hop { filter_eq { all_rows ; call sign ; w207af } ; fcc info } ; fcc } ; eq { hop { filter_eq { all_rows ; call sign ; w211ae } ; fcc info } ; fcc } } } = true', 'tointer': 'select the rows whose call sign record fuzzily matches to w207af . take the fcc info record of this row . select the rows whose call sign record fuzzily matches to w211ae . take the fcc info record of this row . the first record fuzzily matches to the second record . the fcc info record of the first row is fcc . the fcc info record of the second row is fcc .'} | and { eq { hop { filter_eq { all_rows ; call sign ; w207af } ; fcc info } ; hop { filter_eq { all_rows ; call sign ; w211ae } ; fcc info } } ; and { eq { hop { filter_eq { all_rows ; call sign ; w207af } ; fcc info } ; fcc } ; eq { hop { filter_eq { all_rows ; call sign ; w211ae } ; fcc info } ; fcc } } } = true | select the rows whose call sign record fuzzily matches to w207af . take the fcc info record of this row . select the rows whose call sign record fuzzily matches to w211ae . take the fcc info record of this row . the first record fuzzily matches to the second record . the fcc info record of the first row is fcc . the fcc info record of the second row is fcc . | 13 | 9 | {'and_8': 8, 'result_9': 9, 'str_eq_4': 4, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_10': 10, 'call sign_11': 11, 'w207af_12': 12, 'fcc info_13': 13, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_14': 14, 'call sign_15': 15, 'w211ae_16': 16, 'fcc info_17': 17, 'and_7': 7, 'str_eq_5': 5, 'fcc_18': 18, 'str_eq_6': 6, 'fcc_19': 19} | {'and_8': 'and', 'result_9': 'true', 'str_eq_4': 'str_eq', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_10': 'all_rows', 'call sign_11': 'call sign', 'w207af_12': 'w207af', 'fcc info_13': 'fcc info', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_14': 'all_rows', 'call sign_15': 'call sign', 'w211ae_16': 'w211ae', 'fcc info_17': 'fcc info', 'and_7': 'and', 'str_eq_5': 'str_eq', 'fcc_18': 'fcc', 'str_eq_6': 'str_eq', 'fcc_19': 'fcc'} | {'and_8': [9], 'result_9': [], 'str_eq_4': [8], 'str_hop_2': [4, 5], 'filter_str_eq_0': [2], 'all_rows_10': [0], 'call sign_11': [0], 'w207af_12': [0], 'fcc info_13': [2], 'str_hop_3': [4, 6], 'filter_str_eq_1': [3], 'all_rows_14': [1], 'call sign_15': [1], 'w211ae_16': [1], 'fcc info_17': [3], 'and_7': [8], 'str_eq_5': [7], 'fcc_18': [5], 'str_eq_6': [7], 'fcc_19': [6]} | ['call sign', 'frequency mhz', 'city of license', 'erp w', 'fcc info'] | [['w207af', '89.3 fm', 'meadville , pa', '4', 'fcc'], ['w211ae', '90.1 fm', 'mayville , ny', '3', 'fcc'], ['w218ap', '91.5 fm', 'titusville , pa', '13', 'fcc'], ['w220ba', '91.9 fm', 'oil city , pa', '10', 'fcc'], ['w255ae', '98.9 fm', 'warren , pa', '50', 'fcc']] |
henri leconte | https://en.wikipedia.org/wiki/Henri_Leconte | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1171445-6.html.csv | unique | the 1990 london / queen 's club , england tournament was the only one played against jeremy bates and kevin curren . | {'scope': 'all', 'row': '15', 'col': '6', 'col_other': '2,3', 'criterion': 'equal', 'value': 'jeremy bates kevin curren', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponents in the final', 'jeremy bates kevin curren'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opponents in the final record fuzzily matches to jeremy bates kevin curren .', 'tostr': 'filter_eq { all_rows ; opponents in the final ; jeremy bates kevin curren }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; opponents in the final ; jeremy bates kevin curren } }', 'tointer': 'select the rows whose opponents in the final record fuzzily matches to jeremy bates kevin curren . there is only one such row in the table .'}, {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponents in the final', 'jeremy bates kevin curren'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opponents in the final record fuzzily matches to jeremy bates kevin curren .', 'tostr': 'filter_eq { all_rows ; opponents in the final ; jeremy bates kevin curren }'}, 'date'], 'result': '1990', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; opponents in the final ; jeremy bates kevin curren } ; date }'}, '1990'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; opponents in the final ; jeremy bates kevin curren } ; date } ; 1990 }', 'tointer': 'the date record of this unqiue row is 1990 .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponents in the final', 'jeremy bates kevin curren'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opponents in the final record fuzzily matches to jeremy bates kevin curren .', 'tostr': 'filter_eq { all_rows ; opponents in the final ; jeremy bates kevin curren }'}, 'tournament'], 'result': "london / queen 's club , england", 'ind': 4, 'tostr': 'hop { filter_eq { all_rows ; opponents in the final ; jeremy bates kevin curren } ; tournament }'}, "london / queen 's club , england"], 'result': True, 'ind': 5, 'tostr': "eq { hop { filter_eq { all_rows ; opponents in the final ; jeremy bates kevin curren } ; tournament } ; london / queen 's club , england }", 'tointer': "the tournament record of this unqiue row is london / queen 's club , england ."}], 'result': True, 'ind': 6, 'tostr': "and { eq { hop { filter_eq { all_rows ; opponents in the final ; jeremy bates kevin curren } ; date } ; 1990 } ; eq { hop { filter_eq { all_rows ; opponents in the final ; jeremy bates kevin curren } ; tournament } ; london / queen 's club , england } }", 'tointer': "the date record of this unqiue row is 1990 . the tournament record of this unqiue row is london / queen 's club , england ."}], 'result': True, 'ind': 7, 'tostr': "and { only { filter_eq { all_rows ; opponents in the final ; jeremy bates kevin curren } } ; and { eq { hop { filter_eq { all_rows ; opponents in the final ; jeremy bates kevin curren } ; date } ; 1990 } ; eq { hop { filter_eq { all_rows ; opponents in the final ; jeremy bates kevin curren } ; tournament } ; london / queen 's club , england } } } = true", 'tointer': "select the rows whose opponents in the final record fuzzily matches to jeremy bates kevin curren . there is only one such row in the table . the date record of this unqiue row is 1990 . the tournament record of this unqiue row is london / queen 's club , england ."} | and { only { filter_eq { all_rows ; opponents in the final ; jeremy bates kevin curren } } ; and { eq { hop { filter_eq { all_rows ; opponents in the final ; jeremy bates kevin curren } ; date } ; 1990 } ; eq { hop { filter_eq { all_rows ; opponents in the final ; jeremy bates kevin curren } ; tournament } ; london / queen 's club , england } } } = true | select the rows whose opponents in the final record fuzzily matches to jeremy bates kevin curren . there is only one such row in the table . the date record of this unqiue row is 1990 . the tournament record of this unqiue row is london / queen 's club , england . | 10 | 8 | {'and_7': 7, 'result_8': 8, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_9': 9, 'opponents in the final_10': 10, 'jeremy bates kevin curren_11': 11, 'and_6': 6, 'eq_3': 3, 'num_hop_2': 2, 'date_12': 12, '1990_13': 13, 'str_eq_5': 5, 'str_hop_4': 4, 'tournament_14': 14, "london / queen 's club , england_15": 15} | {'and_7': 'and', 'result_8': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_9': 'all_rows', 'opponents in the final_10': 'opponents in the final', 'jeremy bates kevin curren_11': 'jeremy bates kevin curren', 'and_6': 'and', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'date_12': 'date', '1990_13': '1990', 'str_eq_5': 'str_eq', 'str_hop_4': 'str_hop', 'tournament_14': 'tournament', "london / queen 's club , england_15": "london / queen 's club , england"} | {'and_7': [8], 'result_8': [], 'only_1': [7], 'filter_str_eq_0': [1, 2, 4], 'all_rows_9': [0], 'opponents in the final_10': [0], 'jeremy bates kevin curren_11': [0], 'and_6': [7], 'eq_3': [6], 'num_hop_2': [3], 'date_12': [2], '1990_13': [3], 'str_eq_5': [6], 'str_hop_4': [5], 'tournament_14': [4], "london / queen 's club , england_15": [5]} | ['outcome', 'date', 'tournament', 'surface', 'partner', 'opponents in the final', 'score in the final'] | [['winner', '1981', 'bologna , italy', 'carpet', 'sammy giammalva jr', 'tomáš šmíd balázs taróczy', '7 - 6 , 6 - 4'], ['winner', '1982', 'nice , france', 'clay', 'yannick noah', 'paul mcnamee balázs taróczy', '5 - 7 , 6 - 4 , 6 - 3'], ['runner - up', '1982', 'bournemouth , england', 'clay', 'ilie năstase', 'paul mcnamee buster mottram', '6 - 3 , 6 - 7 , 3 - 6'], ['winner', '1982', 'basel , switzerland', 'hard ( i )', 'yannick noah', 'fritz buehning pavel složil', '6 - 2 , 6 - 2'], ['winner', '1982', 'vienna , austria', 'carpet', 'pavel složil', 'mark dickson terry moor', '6 - 1 , 7 - 6'], ['runner - up', '1983', 'monte carlo , monaco', 'clay', 'yannick noah', 'heinz günthardt balázs taróczy', '2 - 6 , 4 - 6'], ['winner', '1983', 'aix - en - provence , france', 'clay', 'gilles moretton', 'ivan camus sergio casal', '2 - 6 , 6 - 1 , 6 - 2'], ['runner - up', '1984', 'philadelphia , us', 'carpet', 'yannick noah', 'peter fleming john mcenroe', '2 - 6 , 3 - 6'], ['winner', '1984', 'french open , paris', 'clay', 'yannick noah', 'pavel složil tomáš šmíd', '6 - 4 , 2 - 6 , 3 - 6 , 6 - 3 , 6 - 2'], ['winner', '1984', 'kitzbühel , austria', 'clay', 'pascal portes', 'colin dowdeswell wojtek fibak', '2 - 6 , 7 - 6 , 7 - 6'], ['winner', '1984', 'stockholm , sweden', 'hard ( i )', 'tomáš šmíd', 'vijay amritraj ilie năstase', '3 - 6 , 7 - 6 , 6 - 4'], ['runner - up', '1985', 'us open , new york', 'hard', 'yannick noah', 'ken flach robert seguso', '7 - 6 , 6 - 7 , 6 - 7 , 0 - 6'], ['winner', '1988', 'nice , france', 'clay', 'guy forget', 'heinz günthardt diego nargiso', '4 - 6 , 6 - 3 , 6 - 4'], ['runner - up', '1988', 'monte carlo , monaco', 'clay', 'ivan lendl', 'sergio casal emilio sánchez', '0 - 6 , 3 - 6'], ['runner - up', '1990', "london / queen 's club , england", 'grass', 'ivan lendl', 'jeremy bates kevin curren', '2 - 6 , 6 - 7'], ['runner - up', '1991', 'indian wells , us', 'hard', 'guy forget', 'jim courier javier sánchez', '6 - 7 , 6 - 3 , 3 - 6'], ['runner - up', '1992', 'toulouse , france', 'hard ( i )', 'guy forget', 'brad pearce byron talbot', '1 - 6 , 6 - 3 , 3 - 6'], ['winner', '1993', 'indian wells , us', 'hard', 'guy forget', 'luke jensen scott melville', '6 - 4 , 7 - 5'], ['runner - up', '1994', 'halle , germany', 'grass', 'gary muller', 'olivier delaître guy forget', '4 - 6 , 7 - 6 , 4 - 6']] |
2010 - 11 san antonio spurs season | https://en.wikipedia.org/wiki/2010%E2%80%9311_San_Antonio_Spurs_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27715173-12.html.csv | aggregation | in the 2010-11 san antonio spurs season , for games where tim duncan had the high rebounds , his average number of rebounds was 11.5 . | {'scope': 'subset', 'col': '6', 'type': 'average', 'result': '11.5', 'subset': {'col': '6', 'criterion': 'equal', 'value': 'tim duncan'}} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'high rebounds', 'tim duncan'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; high rebounds ; tim duncan }', 'tointer': 'select the rows whose high rebounds record fuzzily matches to tim duncan .'}, 'high rebounds'], 'result': '11.5', 'ind': 1, 'tostr': 'avg { filter_eq { all_rows ; high rebounds ; tim duncan } ; high rebounds }'}, '11.5'], 'result': True, 'ind': 2, 'tostr': 'round_eq { avg { filter_eq { all_rows ; high rebounds ; tim duncan } ; high rebounds } ; 11.5 } = true', 'tointer': 'select the rows whose high rebounds record fuzzily matches to tim duncan . the average of the high rebounds record of these rows is 11.5 .'} | round_eq { avg { filter_eq { all_rows ; high rebounds ; tim duncan } ; high rebounds } ; 11.5 } = true | select the rows whose high rebounds record fuzzily matches to tim duncan . the average of the high rebounds record of these rows is 11.5 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'avg_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'high rebounds_5': 5, 'tim duncan_6': 6, 'high rebounds_7': 7, '11.5_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'avg_1': 'avg', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'high rebounds_5': 'high rebounds', 'tim duncan_6': 'tim duncan', 'high rebounds_7': 'high rebounds', '11.5_8': '11.5'} | {'eq_2': [3], 'result_3': [], 'avg_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'high rebounds_5': [0], 'tim duncan_6': [0], 'high rebounds_7': [1], '11.5_8': [2]} | ['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'series'] | [['1', 'april 17', 'memphis', 'l 98 - 101 ( ot )', 'tony parker ( 20 )', 'tim duncan ( 13 )', 'tony parker ( 5 )', 'at & t center 18581', '0 - 1'], ['2', 'april 20', 'memphis', 'w 93 - 87 ( ot )', 'manu ginóbili ( 17 )', 'tim duncan ( 10 )', 'tony parker ( 7 )', 'at & t center 18760', '1 - 1'], ['3', 'april 23', 'memphis', 'l 88 - 91 ( ot )', 'manu ginóbili ( 23 )', 'tim duncan ( 11 )', 'tim duncan ( 6 )', 'fedexforum 18119', '1 - 2'], ['4', 'april 25', 'memphis', 'l 86 - 104 ( ot )', 'tony parker ( 23 )', 'tiago splitter ( 9 )', 'manu ginóbili ( 4 )', 'fedexforum 18119', '1 - 3'], ['5', 'april 27', 'memphis', 'w 110 - 103 ( ot )', 'manu ginóbili ( 33 )', 'tim duncan ( 12 )', 'tony parker ( 9 )', 'at & t center 18581', '2 - 3']] |
list of supernanny episodes | https://en.wikipedia.org/wiki/List_of_Supernanny_episodes | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-19897294-10.html.csv | ordinal | the orm family was the 3rd family to be featured . | {'row': '3', 'col': '2', 'order': '3', 'col_other': '3', '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', 'no in season', '3'], 'result': '3', 'ind': 0, 'tostr': 'nth_min { all_rows ; no in season ; 3 }', 'tointer': 'the 3rd minimum no in season record of all rows is 3 .'}, '3'], 'result': True, 'ind': 1, 'tostr': 'eq { nth_min { all_rows ; no in season ; 3 } ; 3 }', 'tointer': 'the 3rd minimum no in season record of all rows is 3 .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'no in season', '3'], 'result': None, 'ind': 2, 'tostr': 'nth_argmin { all_rows ; no in season ; 3 }'}, 'family / families'], 'result': 'the orm family', 'ind': 3, 'tostr': 'hop { nth_argmin { all_rows ; no in season ; 3 } ; family / families }'}, 'the orm family'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { nth_argmin { all_rows ; no in season ; 3 } ; family / families } ; the orm family }', 'tointer': 'the family / families record of the row with 3rd minimum no in season record is the orm family .'}], 'result': True, 'ind': 5, 'tostr': 'and { eq { nth_min { all_rows ; no in season ; 3 } ; 3 } ; eq { hop { nth_argmin { all_rows ; no in season ; 3 } ; family / families } ; the orm family } } = true', 'tointer': 'the 3rd minimum no in season record of all rows is 3 . the family / families record of the row with 3rd minimum no in season record is the orm family .'} | and { eq { nth_min { all_rows ; no in season ; 3 } ; 3 } ; eq { hop { nth_argmin { all_rows ; no in season ; 3 } ; family / families } ; the orm family } } = true | the 3rd minimum no in season record of all rows is 3 . the family / families record of the row with 3rd minimum no in season record is the orm family . | 6 | 6 | {'and_5': 5, 'result_6': 6, 'eq_1': 1, 'nth_min_0': 0, 'all_rows_7': 7, 'no in season_8': 8, '3_9': 9, '3_10': 10, 'str_eq_4': 4, 'str_hop_3': 3, 'nth_argmin_2': 2, 'all_rows_11': 11, 'no in season_12': 12, '3_13': 13, 'family / families_14': 14, 'the orm family_15': 15} | {'and_5': 'and', 'result_6': 'true', 'eq_1': 'eq', 'nth_min_0': 'nth_min', 'all_rows_7': 'all_rows', 'no in season_8': 'no in season', '3_9': '3', '3_10': '3', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'nth_argmin_2': 'nth_argmin', 'all_rows_11': 'all_rows', 'no in season_12': 'no in season', '3_13': '3', 'family / families_14': 'family / families', 'the orm family_15': 'the orm family'} | {'and_5': [6], 'result_6': [], 'eq_1': [5], 'nth_min_0': [1], 'all_rows_7': [0], 'no in season_8': [0], '3_9': [0], '3_10': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'nth_argmin_2': [3], 'all_rows_11': [2], 'no in season_12': [2], '3_13': [2], 'family / families_14': [3], 'the orm family_15': [4]} | ['no in series', 'no in season', 'family / families', 'location ( s )', 'original air date'] | [['us1', '1', 'the jeans family', 'denver , co', '1 january 2005'], ['us2', '2', 'the bullard family', 'aurora , co', '24 january 2005'], ['us3', '3', 'the orm family', 'santa clarita , ca', '31 january 2005'], ['us4', '4', 'the wischmeyer family', 'colorado', '7 february 2005'], ['us5', '5', 'the weston family', 'florida', '14 february 2005'], ['us6', '6', 'the bailey family', 'n / a', '21 february 2005'], ['us7', '7', 'the gorbea family', 'california', '28 february 2005'], ['us8', '8', 'the ririe family', 'thousand oaks , ca', '21 march 2005'], ['us9', '9', 'the burnett family', 'n / a', '18 april 2005']] |
list of how it 's made episodes | https://en.wikipedia.org/wiki/List_of_How_It%27s_Made_episodes | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-15187735-12.html.csv | comparative | of the how it 's made episodes , the episode where segment a was metal detectors was one episode before the episode where segment a was riding mowers . | {'row_1': '8', 'row_2': '9', 'col': '2', 'col_other': '4', 'relation': 'diff', 'record_mentioned': 'yes', 'diff_result': {'diff_value': '1', 'bigger': 'row2'}} | {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'diff', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'segment a', 'metal detectors'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose segment a record fuzzily matches to metal detectors .', 'tostr': 'filter_eq { all_rows ; segment a ; metal detectors }'}, 'episode'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; segment a ; metal detectors } ; episode }', 'tointer': 'select the rows whose segment a record fuzzily matches to metal detectors . take the episode record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'segment a', 'riding mowers'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose segment a record fuzzily matches to riding mowers .', 'tostr': 'filter_eq { all_rows ; segment a ; riding mowers }'}, 'episode'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; segment a ; riding mowers } ; episode }', 'tointer': 'select the rows whose segment a record fuzzily matches to riding mowers . take the episode record of this row .'}], 'result': '-1', 'ind': 4, 'tostr': 'diff { hop { filter_eq { all_rows ; segment a ; metal detectors } ; episode } ; hop { filter_eq { all_rows ; segment a ; riding mowers } ; episode } }'}, '-1'], 'result': True, 'ind': 5, 'tostr': 'eq { diff { hop { filter_eq { all_rows ; segment a ; metal detectors } ; episode } ; hop { filter_eq { all_rows ; segment a ; riding mowers } ; episode } } ; -1 }', 'tointer': 'select the rows whose segment a record fuzzily matches to metal detectors . take the episode record of this row . select the rows whose segment a record fuzzily matches to riding mowers . take the episode record of this row . the second record is 1 larger than the first record .'}, {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'segment a', 'metal detectors'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose segment a record fuzzily matches to metal detectors .', 'tostr': 'filter_eq { all_rows ; segment a ; metal detectors }'}, 'episode'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; segment a ; metal detectors } ; episode }', 'tointer': 'select the rows whose segment a record fuzzily matches to metal detectors . take the episode record of this row .'}, '151'], 'result': True, 'ind': 6, 'tostr': 'eq { hop { filter_eq { all_rows ; segment a ; metal detectors } ; episode } ; 151 }', 'tointer': 'the episode record of the first row is 151 .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'segment a', 'riding mowers'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose segment a record fuzzily matches to riding mowers .', 'tostr': 'filter_eq { all_rows ; segment a ; riding mowers }'}, 'episode'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; segment a ; riding mowers } ; episode }', 'tointer': 'select the rows whose segment a record fuzzily matches to riding mowers . take the episode record of this row .'}, '152'], 'result': True, 'ind': 7, 'tostr': 'eq { hop { filter_eq { all_rows ; segment a ; riding mowers } ; episode } ; 152 }', 'tointer': 'the episode record of the second row is 152 .'}], 'result': True, 'ind': 8, 'tostr': 'and { eq { hop { filter_eq { all_rows ; segment a ; metal detectors } ; episode } ; 151 } ; eq { hop { filter_eq { all_rows ; segment a ; riding mowers } ; episode } ; 152 } }', 'tointer': 'the episode record of the first row is 151 . the episode record of the second row is 152 .'}], 'result': True, 'ind': 9, 'tostr': 'and { eq { diff { hop { filter_eq { all_rows ; segment a ; metal detectors } ; episode } ; hop { filter_eq { all_rows ; segment a ; riding mowers } ; episode } } ; -1 } ; and { eq { hop { filter_eq { all_rows ; segment a ; metal detectors } ; episode } ; 151 } ; eq { hop { filter_eq { all_rows ; segment a ; riding mowers } ; episode } ; 152 } } } = true', 'tointer': 'select the rows whose segment a record fuzzily matches to metal detectors . take the episode record of this row . select the rows whose segment a record fuzzily matches to riding mowers . take the episode record of this row . the second record is 1 larger than the first record . the episode record of the first row is 151 . the episode record of the second row is 152 .'} | and { eq { diff { hop { filter_eq { all_rows ; segment a ; metal detectors } ; episode } ; hop { filter_eq { all_rows ; segment a ; riding mowers } ; episode } } ; -1 } ; and { eq { hop { filter_eq { all_rows ; segment a ; metal detectors } ; episode } ; 151 } ; eq { hop { filter_eq { all_rows ; segment a ; riding mowers } ; episode } ; 152 } } } = true | select the rows whose segment a record fuzzily matches to metal detectors . take the episode record of this row . select the rows whose segment a record fuzzily matches to riding mowers . take the episode record of this row . the second record is 1 larger than the first record . the episode record of the first row is 151 . the episode record of the second row is 152 . | 14 | 10 | {'and_9': 9, 'result_10': 10, 'eq_5': 5, 'diff_4': 4, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_11': 11, 'segment a_12': 12, 'metal detectors_13': 13, 'episode_14': 14, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_15': 15, 'segment a_16': 16, 'riding mowers_17': 17, 'episode_18': 18, '-1_19': 19, 'and_8': 8, 'eq_6': 6, '151_20': 20, 'eq_7': 7, '152_21': 21} | {'and_9': 'and', 'result_10': 'true', 'eq_5': 'eq', 'diff_4': 'diff', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_11': 'all_rows', 'segment a_12': 'segment a', 'metal detectors_13': 'metal detectors', 'episode_14': 'episode', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_15': 'all_rows', 'segment a_16': 'segment a', 'riding mowers_17': 'riding mowers', 'episode_18': 'episode', '-1_19': '-1', 'and_8': 'and', 'eq_6': 'eq', '151_20': '151', 'eq_7': 'eq', '152_21': '152'} | {'and_9': [10], 'result_10': [], 'eq_5': [9], 'diff_4': [5], 'num_hop_2': [4, 6], 'filter_str_eq_0': [2], 'all_rows_11': [0], 'segment a_12': [0], 'metal detectors_13': [0], 'episode_14': [2], 'num_hop_3': [4, 7], 'filter_str_eq_1': [3], 'all_rows_15': [1], 'segment a_16': [1], 'riding mowers_17': [1], 'episode_18': [3], '-1_19': [5], 'and_8': [9], 'eq_6': [8], '151_20': [6], 'eq_7': [8], '152_21': [7]} | ['series ep', 'episode', 'netflix', 'segment a', 'segment b', 'segment c', 'segment d'] | [['12 - 01', '144', 's06e14', 'pneumatic impact wrenches', 'cultured marble sinks', 'plantain chips', 'nascar stock cars'], ['12 - 02', '145', 's06e15', 'jaws of life', 'artificial christmas trees', 'soda crackers', 'ratchets'], ['12 - 03', '146', 's06e16', 's thermometer', 'produce scales', 'aircraft painting', 'luxury s chocolate'], ['12 - 04', '147', 's06e17', 'carburetors', 'air conditioners', 'sugar ( part 1 )', 'sugar ( part 2 )'], ['12 - 05', '148', 's06e18', 'combination wrenches', 'deli meats', 'golf cars', 'airships'], ['12 - 06', '149', 's06e19', 'carbon fibre car parts', 'hand dryers', 'recycled polyester yarn', 'fleece'], ['12 - 07', '150', 's06e20', 'police badges', 'muffins', 'car washes', 'pressure gauges'], ['12 - 08', '151', 's06e21', 'metal detectors', 'rum', 'tiffany reproductions', 'aircraft engines'], ['12 - 09', '152', 's06e22', 'riding mowers', 'popcorn', 'adjustable beds', 'cultured diamonds'], ['12 - 10', '153', 's06e23', 'airstream trailers', 'horseradish', 'industrial steam s boiler', 'deodorant'], ['12 - 11', '154', 's06e24', 's screwdriver', 'compact track loaders', 'physician scales', 'carbon fibre bats'], ['12 - 12', '155', 's06e25', 's escalator', 'kevlar s canoe', 'goat cheese', 'disc music boxes']] |
fringe ( season 1 ) | https://en.wikipedia.org/wiki/Fringe_%28season_1%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-24648983-1.html.csv | majority | the majority of the episodes aired in 2008 . | {'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'fuzzily_match', 'value': '2008', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'original air date', '2008'], 'result': True, 'ind': 0, 'tointer': 'for the original air date records of all rows , most of them fuzzily match to 2008 .', 'tostr': 'most_eq { all_rows ; original air date ; 2008 } = true'} | most_eq { all_rows ; original air date ; 2008 } = true | for the original air date records of all rows , most of them fuzzily match to 2008 . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'original air date_3': 3, '2008_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'original air date_3': 'original air date', '2008_4': '2008'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'original air date_3': [0], '2008_4': [0]} | ['-', 'title', 'directed by', 'written by', 'original air date', 'production code', 'us viewers ( million )'] | [['1', 'pilot', 'alex graves', 'j j abrams & alex kurtzman & roberto orci', 'september 9 , 2008', '276038', '9.13'], ['3', 'the ghost network', 'frederick e o toye', 'david h goodman & j r orci', 'september 23 , 2008', '3t7652', '9.42'], ['4', 'the arrival', 'paul edwards', 'j j abrams & jeff pinkner', 'september 30 , 2008', '3t7653', '9.91'], ['5', 'power hungry', 'christopher misiano', 'jason cahill & julia cho', 'october 14 , 2008', '3t7654', '9.16'], ['6', 'the cure', 'bill eagles', 'felicia d henderson & brad caleb kane', 'october 21 , 2008', '3t7655', '8.91'], ['7', 'in which we meet mr jones', 'brad anderson', 'j j abrams & jeff pinkner', 'november 11 , 2008', '3t7656', '8.61'], ['8', 'the equation', 'gwyneth horder - payton', 'j r orci & david h goodman', 'november 18 , 2008', '3t7657', '9.18'], ['9', 'the dreamscape', 'frederick e o toye', 'zack whedon & julia cho', 'november 25 , 2008', '3t7658', '7.70'], ['10', 'safe', 'michael zinberg', 'david h goodman & jason cahill', 'december 2 , 2008', '3t7659', '8.54'], ['12', 'the no - brainer', 'john polson', 'david h goodman & brad caleb kane', 'january 27 , 2009', '3t7661', '11.62'], ['13', 'the transformation', 'brad anderson', 'zack whedon & j r orci', 'february 3 , 2009', '3t7662', '12.78'], ['15', 'inner child', 'frederick e o toye', 'brad caleb kane & julia cho', 'april 7 , 2009', '3t7664', '9.88'], ['16', 'unleashed', 'brad anderson', 'zack whedon & j r orci', 'april 14 , 2009', '3t7665', '10.15'], ['17', 'bad dreams', 'akiva goldsman', 'akiva goldsman', 'april 21 , 2009', '3t7666', '9.89']] |
2008 - 09 detroit red wings season | https://en.wikipedia.org/wiki/2008%E2%80%9309_Detroit_Red_Wings_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-17371135-30.html.csv | comparative | red wings player had a higher number of pick points over julian cayer during the 2008-2009 season . | {'row_1': '4', 'row_2': '3', 'col': '2', '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', 'player', 'julien cayer'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose player record fuzzily matches to julien cayer .', 'tostr': 'filter_eq { all_rows ; player ; julien cayer }'}, 'overall pick'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; player ; julien cayer } ; overall pick }', 'tointer': 'select the rows whose player record fuzzily matches to julien cayer . take the overall pick record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'gustav nyquist'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose player record fuzzily matches to gustav nyquist .', 'tostr': 'filter_eq { all_rows ; player ; gustav nyquist }'}, 'overall pick'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; player ; gustav nyquist } ; overall pick }', 'tointer': 'select the rows whose player record fuzzily matches to gustav nyquist . take the overall pick record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; player ; julien cayer } ; overall pick } ; hop { filter_eq { all_rows ; player ; gustav nyquist } ; overall pick } } = true', 'tointer': 'select the rows whose player record fuzzily matches to julien cayer . take the overall pick record of this row . select the rows whose player record fuzzily matches to gustav nyquist . take the overall pick record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; player ; julien cayer } ; overall pick } ; hop { filter_eq { all_rows ; player ; gustav nyquist } ; overall pick } } = true | select the rows whose player record fuzzily matches to julien cayer . take the overall pick record of this row . select the rows whose player record fuzzily matches to gustav nyquist . take the overall pick 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, 'player_7': 7, 'julien cayer_8': 8, 'overall pick_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'player_11': 11, 'gustav nyquist_12': 12, 'overall pick_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', 'player_7': 'player', 'julien cayer_8': 'julien cayer', 'overall pick_9': 'overall pick', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'player_11': 'player', 'gustav nyquist_12': 'gustav nyquist', 'overall pick_13': 'overall pick'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'player_7': [0], 'julien cayer_8': [0], 'overall pick_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'player_11': [1], 'gustav nyquist_12': [1], 'overall pick_13': [3]} | ['round', 'overall pick', 'player', 'position', 'nationality', 'college / junior / club team ( league )'] | [['1', '30', 'thomas mccollum', 'goaltender', 'united states', 'guelph storm ( ohl )'], ['3', '91', 'max nicastro', 'defenseman', 'united states', 'chicago steel ( ushl )'], ['4', '121', 'gustav nyquist', 'center', 'sweden', 'malmã redhawks ( sweden jr )'], ['5', '151', 'julien cayer', 'center', 'canada', 'northwood school ( hs - new york )'], ['6', '181', 'stephen johnston', 'left wing', 'canada', 'belleville bulls ( ohl )']] |
made ( tv series ) | https://en.wikipedia.org/wiki/Made_%28TV_series%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2140071-13.html.csv | unique | chris is made into a celebrity assistant was the only episode where bj coleman was the coach . | {'scope': 'all', 'row': '9', 'col': '6', 'col_other': '3', 'criterion': 'equal', 'value': 'bj coleman', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'coach', 'bj coleman'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose coach record fuzzily matches to bj coleman .', 'tostr': 'filter_eq { all_rows ; coach ; bj coleman }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; coach ; bj coleman } }', 'tointer': 'select the rows whose coach record fuzzily matches to bj coleman . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'coach', 'bj coleman'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose coach record fuzzily matches to bj coleman .', 'tostr': 'filter_eq { all_rows ; coach ; bj coleman }'}, 'episode summary'], 'result': 'chris is made into a celebrity assistant', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; coach ; bj coleman } ; episode summary }'}, 'chris is made into a celebrity assistant'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; coach ; bj coleman } ; episode summary } ; chris is made into a celebrity assistant }', 'tointer': 'the episode summary record of this unqiue row is chris is made into a celebrity assistant .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; coach ; bj coleman } } ; eq { hop { filter_eq { all_rows ; coach ; bj coleman } ; episode summary } ; chris is made into a celebrity assistant } } = true', 'tointer': 'select the rows whose coach record fuzzily matches to bj coleman . there is only one such row in the table . the episode summary record of this unqiue row is chris is made into a celebrity assistant .'} | and { only { filter_eq { all_rows ; coach ; bj coleman } } ; eq { hop { filter_eq { all_rows ; coach ; bj coleman } ; episode summary } ; chris is made into a celebrity assistant } } = true | select the rows whose coach record fuzzily matches to bj coleman . there is only one such row in the table . the episode summary record of this unqiue row is chris is made into a celebrity assistant . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'coach_7': 7, 'bj coleman_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'episode summary_9': 9, 'chris is made into a celebrity assistant_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'coach_7': 'coach', 'bj coleman_8': 'bj coleman', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'episode summary_9': 'episode summary', 'chris is made into a celebrity assistant_10': 'chris is made into a celebrity assistant'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'coach_7': [0], 'bj coleman_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'episode summary_9': [2], 'chris is made into a celebrity assistant_10': [3]} | ['season', 'episode', 'episode summary', 'premier date', 'external link', 'coach'] | [['13', '1', 'cara is made into an actress', 'june 18 , 2012', 'full episode', 'nikki deloach'], ['13', '2', 'felicia is made into a rapper', 'june 19 , 2012', 'full episode', 'killer mike | - |'], ['13', '3', 'rachel is made into a makeup mogul', 'june 20 , 2012', 'full episode', 'nikki robinson'], ['13', '4', 'megan is made into a model', 'june 22 , 2012', 'full episode', 'whitney thompson'], ['13', '5', 'rita is made into an actress', 'june 25 , 2012', 'full episode', 'amanda seales'], ['13', '6', 'aly is made into a roller derby girl', 'june 25 , 2012', 'full episode', 'tracy disco akers'], ['13', '7', 'dana is made into having her own show', 'june 26 , 2012', 'full episode', 'lashan browning'], ['13', '8', 'amber is made into a recording artist', 'june 29 , 2012', 'full episode', 'abesi manyando'], ['13', '9', 'chris is made into a celebrity assistant', 'october 9 , 2012', 'full episode', 'bj coleman'], ['13', '10', 'shane is made into a tough mudder', 'october 10 , 2012', 'full episode', 'chris'], ['13', '11', 'shambre is made into a gogo dancer', 'october 11 , 2012', 'full episode', 'katie kansas'], ['13', '12', 'richard is made into a drag queen', 'october 12 , 2012', 'full episode', 'manila luzon'], ['13', '13', 'ashley is made into a circus performer', 'october 15 , 2012', 'full episode', 'rebecca star'], ['13', '14', 'emilly and jeanette are made into actresses', 'october 17 , 2012', 'full episode', 'brian patacca'], ['13', '15', 'maeve is made into a pageant queen', 'november 3 , 2012', 'full episode', 'gina cerilli , elena laquatra']] |
automobiles gonfaronnaises sportives | https://en.wikipedia.org/wiki/Automobiles_Gonfaronnaises_Sportives | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1226665-1.html.csv | ordinal | ags jh22 was the second earliest chasis to be introduced in service among automobiles gonfaronnaises sportives . | {'row': '2', 'col': '1', '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', 'year', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; year ; 2 }'}, 'chassis'], 'result': 'ags jh22', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; year ; 2 } ; chassis }'}, 'ags jh22'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; year ; 2 } ; chassis } ; ags jh22 } = true', 'tointer': 'select the row whose year record of all rows is 2nd minimum . the chassis record of this row is ags jh22 .'} | eq { hop { nth_argmin { all_rows ; year ; 2 } ; chassis } ; ags jh22 } = true | select the row whose year record of all rows is 2nd minimum . the chassis record of this row is ags jh22 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'year_5': 5, '2_6': 6, 'chassis_7': 7, 'ags jh22_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', '2_6': '2', 'chassis_7': 'chassis', 'ags jh22_8': 'ags jh22'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'year_5': [0], '2_6': [0], 'chassis_7': [1], 'ags jh22_8': [2]} | ['year', 'chassis', 'engine', 'tyres', 'points'] | [['1986', 'ags jh21c', 'motori moderni 615 - 90 v6 ( t / c )', 'p', '0'], ['1987', 'ags jh22', 'ford dfz v8', 'g', '1'], ['1988', 'ags jh23', 'ford dfz v8', 'g', '0'], ['1989', 'ags jh23b ags jh24', 'ford dfr v8', 'g', '1'], ['1990', 'ags jh24 ags jh25', 'ford dfr v8', 'g', '0'], ['1991', 'ags jh25b ags jh27', 'ford dfr v8', 'g', '0']] |
united states house of representatives elections , 1834 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1834 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2668199-2.html.csv | majority | most of the incumbents in the 1834 house of representatives elections were re - elected . | {'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'equal', 'value': 're - elected', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'result', 're - elected'], 'result': True, 'ind': 0, 'tointer': 'for the result records of all rows , most of them fuzzily match to re - elected .', 'tostr': 'most_eq { all_rows ; result ; re - elected } = true'} | most_eq { all_rows ; result ; re - elected } = true | for the result records of all rows , most of them fuzzily match to re - elected . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'result_3': 3, 're - elected_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'result_3': 'result', 're - elected_4': 're - elected'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'result_3': [0], 're - elected_4': [0]} | ['district', 'incumbent', 'party', 'first elected', 'result', 'candidates'] | [['pennsylvania 1', 'joel b sutherland', 'jacksonian', '1826', 're - elected', 'joel b sutherland ( j ) 61.7 % james gowen 38.3 %'], ['pennsylvania 5', 'joel k mann', 'jacksonian', '1830', 'retired jacksonian hold', 'jacob fry , jr ( j ) 55.3 % james royer 44.7 %'], ['pennsylvania 6', 'robert ramsey', 'jacksonian', '1832', 'retired anti - jacksonian gain', 'mathias morris ( aj ) 52.4 % henry chapman ( j ) 47.6 %'], ['pennsylvania 10', 'william clark', 'anti - masonic', '1832', 're - elected', 'william clark ( am ) 54.0 % john c bucher ( j ) 46.0 %'], ['pennsylvania 12', 'george chambers', 'anti - masonic', '1832', 're - elected', 'george chambers ( am ) 59.8 % ludwig heck ( j ) 40.2 %'], ['pennsylvania 13', 'jesse miller', 'jacksonian', '1832', 're - elected', 'jesse miller ( j ) 51.4 % thomas whiteside ( am ) 48.6 %'], ['pennsylvania 17', 'john laporte', 'jacksonian', '1832', 're - elected', 'john laporte ( j ) 56.8 % horrace williston 43.2 %'], ['pennsylvania 18', 'george burd', 'anti - jacksonian', '1830', 'retired jacksonian gain', 'job mann ( j ) 54.6 % charles ogle ( am ) 45.4 %'], ['pennsylvania 22', 'harmar denny', 'anti - masonic', '1829 ( special )', 're - elected', 'harmar denny ( am ) 53.5 % john m snowden ( j ) 46.5 %'], ['pennsylvania 24', 'john banks', 'anti - masonic', '1830', 're - elected', 'john banks ( am ) 52.2 % samuel power ( j ) 47.8 %']] |
1994 foster 's cup | https://en.wikipedia.org/wiki/1994_Foster%27s_Cup | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-16387953-1.html.csv | aggregation | the average attendance in the first round of the 1994 foster 's cup was 18,978 . | {'scope': 'all', 'col': '6', 'type': 'average', 'result': '18978', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'crowd'], 'result': '18978', 'ind': 0, 'tostr': 'avg { all_rows ; crowd }'}, '18978'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; crowd } ; 18978 } = true', 'tointer': 'the average of the crowd record of all rows is 18978 .'} | round_eq { avg { all_rows ; crowd } ; 18978 } = true | the average of the crowd record of all rows is 18978 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'crowd_4': 4, '18978_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'crowd_4': 'crowd', '18978_5': '18978'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'crowd_4': [0], '18978_5': [1]} | ['home team', 'home team score', 'away team', 'away team score', 'ground', 'crowd', 'date', 'time'] | [['collingwood', '13.14 ( 92 )', 'north melbourne', '13.13 ( 91 )', 'waverley park', '25708', 'saturday , 19 february 1994', '8:00 pm'], ['st kilda', '14.12 ( 96 )', 'richmond', '17.14 ( 116 )', 'waverley park', '18662', 'monday , 21 february 1994', '8:00 pm'], ['adelaide', '16.17 ( 113 )', 'west coast', '14.10 ( 94 )', 'football park', '28776', 'wednesday 23 february 1994', '8:00 pm'], ['fitzroy', '12.13 ( 85 )', 'geelong', '10.11 ( 71 )', 'waverley park', '9080', 'wednesday 23 february 1994', '8:00 pm'], ['sydney', '18.11 ( 119 )', 'footscray', '16.10 ( 106 )', 'robertson oval , wagga wagga', '5525', 'saturday , 26 february 1994', '2:00 pm'], ['carlton', '11.18 ( 84 )', 'hawthorn', '14.15 ( 99 )', 'waverley park', '26117', 'saturday , 26 february 1994', '8:00 pm']] |
2009 belmont stakes | https://en.wikipedia.org/wiki/2009_Belmont_Stakes | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-22517564-3.html.csv | superlative | mine that bird had the best opening odds of all the other horses in the 2009 belmont stakes horse race . | {'scope': 'all', 'col_superlative': '5', 'row_superlative': '7', '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', 'opening odds'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; opening odds }'}, 'horse name'], 'result': 'mine that bird', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; opening odds } ; horse name }'}, 'mine that bird'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; opening odds } ; horse name } ; mine that bird } = true', 'tointer': 'select the row whose opening odds record of all rows is minimum . the horse name record of this row is mine that bird .'} | eq { hop { argmin { all_rows ; opening odds } ; horse name } ; mine that bird } = true | select the row whose opening odds record of all rows is minimum . the horse name record of this row is mine that bird . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'opening odds_5': 5, 'horse name_6': 6, 'mine that bird_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'opening odds_5': 'opening odds', 'horse name_6': 'horse name', 'mine that bird_7': 'mine that bird'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'opening odds_5': [0], 'horse name_6': [1], 'mine that bird_7': [2]} | ['post', 'horse name', 'trainer', 'jockey', 'opening odds', 'starting odds', 'finishing pos'] | [['1', 'chocolate candy', 'jerry hollendorfer', 'garrett gomez', '10 - 1', '9.50', '9'], ['2', 'dunkirk', 'todd pletcher', 'john velazquez', '4 - 1', '4.60', '2'], ['3', 'mr hot stuff', 'eoin harty', 'edgar prado', '15 - 1', '22.60', '8'], ['4', 'summer bird', 'tim ice', 'kent desormeaux', '12 - 1', '11.90', '1'], ['5', 'luv gov', 'd wayne lukas', 'miguel mena', '20 - 1', '22.40', '5'], ['6', 'charitable man', 'kiaran mclaughlin', 'alan garcia', '3 - 1', '4.60', '4'], ['7', 'mine that bird', 'bennie l woolley , jr', 'calvin borel', '2 - 1', '1.25', '3'], ['8', 'flying private', 'd wayne lukas', 'julien leparoux', '12 - 1', '17.30', '6'], ['9', "miner 's escape", 'nick zito', 'joze lezcano', '15 - 1', '22.00', '10']] |
portuguese legislative election , 1991 | https://en.wikipedia.org/wiki/Portuguese_legislative_election%2C_1991 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1886589-1.html.csv | majority | the majority of years had a total lead of at least 10 % . | {'scope': 'all', 'col': '7', 'most_or_all': 'most', 'criterion': 'greater_than_eq', 'value': '10', 'subset': None} | {'func': 'most_greater_eq', 'args': ['all_rows', 'lead', '10'], 'result': True, 'ind': 0, 'tointer': 'for the lead records of all rows , most of them are greater than or equal to 10 .', 'tostr': 'most_greater_eq { all_rows ; lead ; 10 } = true'} | most_greater_eq { all_rows ; lead ; 10 } = true | for the lead records of all rows , most of them are greater than or equal to 10 . | 1 | 1 | {'most_greater_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'lead_3': 3, '10_4': 4} | {'most_greater_eq_0': 'most_greater_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'lead_3': 'lead', '10_4': '10'} | {'most_greater_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'lead_3': [0], '10_4': [0]} | ['date released', 'polling institute', 'social democratic', 'socialist', 'green - communist', 'democratic and social centre', 'lead'] | [['october 6 , 1991', 'election results', '50.6 % 135 seats', '29.1 % 72 seats', '8.8 % 17 seats', '4.4 % 5 seats', '21.5 %'], ['october 6 , 1991', 'exit poll - rtp1 universidade católica', '48.0 % - 51.9 %', '28.5 % - 31.5 %', '7.5 % - 10.0 %', '4.5 % - 5.5 %', '19.5 % - 20.4 %'], ['october 6 , 1991', 'exit poll - tsf / expresso euroexpansão', '45.8 % - 50.2 %', '29.8 % - 33.9 %', '6.8 % - 9.1 %', '3.7 % - 5.5 %', '16.0 % - 16.3 %'], ['october 6 , 1991', 'exit poll - antena1 euroteste', '47.0 % - 50.0 %', '31.0 % - 34.0 %', '7.5 % - 10.0 %', '4.0 % - 5.0 %', '16.0 %'], ['september 28 , 1991', 'euroteste', '47.3 %', '35.5 %', '8.5 %', '4.1 %', '11.8 %'], ['september 28 , 1991', 'euroteste', '46.0 %', '37.0 %', '9.7 %', '3.9 %', '9.0 %'], ['september 28 , 1991', 'euroexpansão', '44.0 %', '33.0 %', '9.0 %', '6.0 %', '11.0 %'], ['september 27 , 1991', 'marktest', '43.1 %', '32.8 %', '7.7 %', '4.6 %', '10.3 %'], ['september 27 , 1991', 'pluriteste', '41.2 %', '34.7 %', '8.4 %', '8.1 %', '6.5 %'], ['september 20 , 1991', 'euroteste', '45.6 %', '35.5 %', '10.0 %', '4.4 %', '10.1 %'], ['september 20 , 1991', 'marktest', '41.9 %', '31.9 %', '7.3 %', '4.4 %', '10.0 %'], ['september 16 , 1991', 'pluriteste', '39.2 %', '26.6 %', '6.2 %', '6.0 %', '12.6 %'], ['september 16 , 1991', 'euroteste', '45.1 %', '34.5 %', '10.2 %', '5.2 %', '10.6 %'], ['september 14 , 1991', 'norma', '45.0 %', '37.5 %', '11.2 %', '3.5 %', '7.5 %'], ['august 28 , 1991', 'euroexpansão / marktest', '35.3 %', '36.8 %', '8.7 %', '4.9 %', '1.5 %'], ['august 4 , 1991', 'euroteste / jn', '47.5 %', '37.8 %', '12.3 %', '8.2 %', '7.7 %'], ['july 19 , 1987', '1987 election', '50.2 % 148 seats', '22.2 % 60 seats', '12.1 % 31 seats', '4.4 % 4 seats', '28.0 %']] |
2010 - 11 oklahoma city thunder season | https://en.wikipedia.org/wiki/2010%E2%80%9311_Oklahoma_City_Thunder_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27712702-11.html.csv | ordinal | in the 2010 - 11 oklahoma city thunder season , the second highest attendance was on march 13th . | {'row': '7', 'col': '8', 'order': '2', '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', 'location attendance', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; location attendance ; 2 }'}, 'date'], 'result': 'march 13', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; location attendance ; 2 } ; date }'}, 'march 13'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; location attendance ; 2 } ; date } ; march 13 } = true', 'tointer': 'select the row whose location attendance record of all rows is 2nd maximum . the date record of this row is march 13 .'} | eq { hop { nth_argmax { all_rows ; location attendance ; 2 } ; date } ; march 13 } = true | select the row whose location attendance record of all rows is 2nd maximum . the date record of this row is march 13 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'location attendance_5': 5, '2_6': 6, 'date_7': 7, 'march 13_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', 'location attendance_5': 'location attendance', '2_6': '2', 'date_7': 'date', 'march 13_8': 'march 13'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'location attendance_5': [0], '2_6': [0], 'date_7': [1], 'march 13_8': [2]} | ['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record'] | [['59', 'march 2', 'indiana', 'w 113 - 89 ( ot )', 'kevin durant , russell westbrook ( 21 )', 'serge ibaka ( 12 )', 'russell westbrook ( 9 )', 'oklahoma city arena 18203', '37 - 22'], ['60', 'march 4', 'atlanta', 'w 111 - 104 ( ot )', 'kevin durant ( 29 )', 'kevin durant ( 8 )', 'russell westbrook ( 9 )', 'philips arena 17916', '38 - 22'], ['61', 'march 6', 'phoenix', 'w 122 - 118 ( ot )', 'russell westbrook ( 32 )', 'nick collison , thabo sefolosha ( 9 )', 'russell westbrook ( 11 )', 'oklahoma city arena 18203', '39 - 22'], ['62', 'march 7', 'memphis', 'l 101 - 107 ( ot )', 'russell westbrook ( 27 )', 'kevin durant , james harden , serge ibaka ( 6 )', 'russell westbrook ( 7 )', 'fedexforum 13903', '39 - 23'], ['63', 'march 9', 'philadelphia', 'w 110 - 105 ( ot )', 'kevin durant ( 34 )', 'kevin durant ( 16 )', 'russell westbrook ( 12 )', 'wells fargo center 19283', '40 - 23'], ['64', 'march 11', 'detroit', 'w 104 - 94 ( ot )', 'kevin durant ( 24 )', 'kevin durant ( 9 )', 'russell westbrook ( 11 )', 'oklahoma city arena 18203', '41 - 23'], ['65', 'march 13', 'cleveland', 'w 95 - 75 ( ot )', 'russell westbrook ( 20 )', 'serge ibaka ( 14 )', 'eric maynor ( 8 )', 'quicken loans arena 19811', '42 - 23'], ['66', 'march 14', 'washington', 'w 116 - 89 ( ot )', 'kevin durant ( 32 )', 'kendrick perkins ( 9 )', 'russell westbrook ( 12 )', 'verizon center 17921', '43 - 23'], ['67', 'march 16', 'miami', 'w 96 - 85 ( ot )', 'kevin durant ( 29 )', 'serge ibaka ( 12 )', 'kevin durant ( 6 )', 'american airlines arena 20083', '44 - 23'], ['68', 'march 18', 'charlotte', 'w 99 - 82 ( ot )', 'kevin durant ( 25 )', 'serge ibaka ( 13 )', 'russell westbrook ( 7 )', 'oklahoma city arena 18203', '45 - 23'], ['70', 'march 23', 'utah', 'w 106 - 94 ( ot )', 'russell westbrook ( 31 )', 'serge ibaka ( 13 )', 'russell westbrook ( 5 )', 'oklahoma city arena 18203', '46 - 24'], ['71', 'march 25', 'minnesota', 'w 111 - 103 ( ot )', 'kevin durant ( 23 )', 'serge ibaka ( 10 )', 'russell westbrook ( 8 )', 'oklahoma city arena 18203', '47 - 24'], ['72', 'march 27', 'portland', 'w 99 - 90 ( ot )', 'russell westbrook ( 28 )', 'kendrick perkins ( 10 )', 'russell westbrook ( 7 )', 'oklahoma city arena 18203', '48 - 24'], ['73', 'march 29', 'golden state', 'w 115 - 114 ( ot )', 'kevin durant ( 39 )', 'kendrick perkins ( 13 )', 'russell westbrook ( 9 )', 'oklahoma city arena 18203', '49 - 24']] |
politics of veneto | https://en.wikipedia.org/wiki/Politics_of_Veneto | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10747104-1.html.csv | aggregation | the average number of inhabitants among italian provinces voting for the liga veneta party is 708,899 . | {'scope': 'subset', 'col': '2', 'type': 'average', 'result': '708899', 'subset': {'col': '4', 'criterion': 'equal', 'value': 'liga veneta'}} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'party', 'liga veneta'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; party ; liga veneta }', 'tointer': 'select the rows whose party record fuzzily matches to liga veneta .'}, 'inhabitants'], 'result': '708899', 'ind': 1, 'tostr': 'avg { filter_eq { all_rows ; party ; liga veneta } ; inhabitants }'}, '708899'], 'result': True, 'ind': 2, 'tostr': 'round_eq { avg { filter_eq { all_rows ; party ; liga veneta } ; inhabitants } ; 708899 } = true', 'tointer': 'select the rows whose party record fuzzily matches to liga veneta . the average of the inhabitants record of these rows is 708899 .'} | round_eq { avg { filter_eq { all_rows ; party ; liga veneta } ; inhabitants } ; 708899 } = true | select the rows whose party record fuzzily matches to liga veneta . the average of the inhabitants record of these rows is 708899 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'avg_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'party_5': 5, 'liga veneta_6': 6, 'inhabitants_7': 7, '708899_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', 'liga veneta_6': 'liga veneta', 'inhabitants_7': 'inhabitants', '708899_8': '708899'} | {'eq_2': [3], 'result_3': [], 'avg_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'party_5': [0], 'liga veneta_6': [0], 'inhabitants_7': [1], '708899_8': [2]} | ['province', 'inhabitants', 'president', 'party', 'election'] | [['padua', '934216', 'barbara degani', 'the people of freedom', '2009'], ['verona', '920158', 'giovanni miozzi', 'the people of freedom', '2009'], ['treviso', '888249', 'leonardo muraro', 'liga veneta', '2011'], ['vicenza', '870740', 'attilio schneck', 'liga veneta', '2007'], ['venice', '863133', 'francesca zaccariotto', 'liga veneta', '2009'], ['rovigo', '247884', 'tiziana virgili', 'democratic party', '2009'], ['belluno', '213474', 'gianpaolo bottacin', 'liga veneta', '2009']] |
chennai super kings | https://en.wikipedia.org/wiki/Chennai_Super_Kings | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-15829930-5.html.csv | unique | for the chennai super kings , when they were the runners-up , the only time they had 11 losses was in 2012 . | {'scope': 'subset', 'row': '5', 'col': '4', 'col_other': '1,9', 'criterion': 'equal', 'value': '11', 'subset': {'col': '9', 'criterion': 'equal', 'value': 'runners - up'}} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'summary', 'runners - up'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; summary ; runners - up }', 'tointer': 'select the rows whose summary record fuzzily matches to runners - up .'}, 'losses', '11'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose summary record fuzzily matches to runners - up . among these rows , select the rows whose losses record is equal to 11 .', 'tostr': 'filter_eq { filter_eq { all_rows ; summary ; runners - up } ; losses ; 11 }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_eq { filter_eq { all_rows ; summary ; runners - up } ; losses ; 11 } }', 'tointer': 'select the rows whose summary record fuzzily matches to runners - up . among these rows , select the rows whose losses record is equal to 11 . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'summary', 'runners - up'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; summary ; runners - up }', 'tointer': 'select the rows whose summary record fuzzily matches to runners - up .'}, 'losses', '11'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose summary record fuzzily matches to runners - up . among these rows , select the rows whose losses record is equal to 11 .', 'tostr': 'filter_eq { filter_eq { all_rows ; summary ; runners - up } ; losses ; 11 }'}, 'year'], 'result': '2012', 'ind': 3, 'tostr': 'hop { filter_eq { filter_eq { all_rows ; summary ; runners - up } ; losses ; 11 } ; year }'}, '2012'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_eq { filter_eq { all_rows ; summary ; runners - up } ; losses ; 11 } ; year } ; 2012 }', 'tointer': 'the year record of this unqiue row is 2012 .'}], 'result': True, 'ind': 5, 'tostr': 'and { only { filter_eq { filter_eq { all_rows ; summary ; runners - up } ; losses ; 11 } } ; eq { hop { filter_eq { filter_eq { all_rows ; summary ; runners - up } ; losses ; 11 } ; year } ; 2012 } } = true', 'tointer': 'select the rows whose summary record fuzzily matches to runners - up . among these rows , select the rows whose losses record is equal to 11 . there is only one such row in the table . the year record of this unqiue row is 2012 .'} | and { only { filter_eq { filter_eq { all_rows ; summary ; runners - up } ; losses ; 11 } } ; eq { hop { filter_eq { filter_eq { all_rows ; summary ; runners - up } ; losses ; 11 } ; year } ; 2012 } } = true | select the rows whose summary record fuzzily matches to runners - up . among these rows , select the rows whose losses record is equal to 11 . there is only one such row in the table . the year record of this unqiue row is 2012 . | 8 | 6 | {'and_5': 5, 'result_6': 6, 'only_2': 2, 'filter_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'summary_8': 8, 'runners - up_9': 9, 'losses_10': 10, '11_11': 11, 'eq_4': 4, 'num_hop_3': 3, 'year_12': 12, '2012_13': 13} | {'and_5': 'and', 'result_6': 'true', 'only_2': 'only', 'filter_eq_1': 'filter_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'summary_8': 'summary', 'runners - up_9': 'runners - up', 'losses_10': 'losses', '11_11': '11', 'eq_4': 'eq', 'num_hop_3': 'num_hop', 'year_12': 'year', '2012_13': '2012'} | {'and_5': [6], 'result_6': [], 'only_2': [5], 'filter_eq_1': [2, 3], 'filter_str_eq_0': [1], 'all_rows_7': [0], 'summary_8': [0], 'runners - up_9': [0], 'losses_10': [1], '11_11': [1], 'eq_4': [5], 'num_hop_3': [4], 'year_12': [3], '2012_13': [4]} | ['year', 'matches', 'wins', 'losses', 'no result', 'tied', 'success rate', 'position', 'summary'] | [['2008', '16', '9', '7', '0', '0', '56.25 %', '2nd', 'runners - up'], ['2009', '15', '8', '6', '1', '0', '53.33 %', '4th', 'semi - finalists'], ['2010', '16', '9', '7', '0', '0', '56.25 %', '1st', 'champions'], ['2011', '16', '11', '5', '0', '0', '68.75 %', '1st', 'champions'], ['2012', '19', '19', '11', '8', '0', '52.63 %', '2nd', 'runners - up'], ['2013', '18', '12', '6', '0', '0', '66.67 %', '2nd', 'runners - up']] |
1998 major league baseball draft | https://en.wikipedia.org/wiki/1998_Major_League_Baseball_Draft | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18468611-2.html.csv | comparative | jeff urban was selected later in the draft than chris george was . | {'row_1': '11', 'row_2': '1', 'col': '1', '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', 'player', 'jeff urban'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose player record fuzzily matches to jeff urban .', 'tostr': 'filter_eq { all_rows ; player ; jeff urban }'}, 'pick'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; player ; jeff urban } ; pick }', 'tointer': 'select the rows whose player record fuzzily matches to jeff urban . take the pick record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'chris george'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose player record fuzzily matches to chris george .', 'tostr': 'filter_eq { all_rows ; player ; chris george }'}, 'pick'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; player ; chris george } ; pick }', 'tointer': 'select the rows whose player record fuzzily matches to chris george . take the pick record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; player ; jeff urban } ; pick } ; hop { filter_eq { all_rows ; player ; chris george } ; pick } }', 'tointer': 'select the rows whose player record fuzzily matches to jeff urban . take the pick record of this row . select the rows whose player record fuzzily matches to chris george . take the pick 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', 'player', 'jeff urban'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose player record fuzzily matches to jeff urban .', 'tostr': 'filter_eq { all_rows ; player ; jeff urban }'}, 'pick'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; player ; jeff urban } ; pick }', 'tointer': 'select the rows whose player record fuzzily matches to jeff urban . take the pick record of this row .'}, '41'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; player ; jeff urban } ; pick } ; 41 }', 'tointer': 'the pick record of the first row is 41 .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'chris george'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose player record fuzzily matches to chris george .', 'tostr': 'filter_eq { all_rows ; player ; chris george }'}, 'pick'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; player ; chris george } ; pick }', 'tointer': 'select the rows whose player record fuzzily matches to chris george . take the pick record of this row .'}, '31'], 'result': True, 'ind': 6, 'tostr': 'eq { hop { filter_eq { all_rows ; player ; chris george } ; pick } ; 31 }', 'tointer': 'the pick record of the second row is 31 .'}], 'result': True, 'ind': 7, 'tostr': 'and { eq { hop { filter_eq { all_rows ; player ; jeff urban } ; pick } ; 41 } ; eq { hop { filter_eq { all_rows ; player ; chris george } ; pick } ; 31 } }', 'tointer': 'the pick record of the first row is 41 . the pick record of the second row is 31 .'}], 'result': True, 'ind': 8, 'tostr': 'and { greater { hop { filter_eq { all_rows ; player ; jeff urban } ; pick } ; hop { filter_eq { all_rows ; player ; chris george } ; pick } } ; and { eq { hop { filter_eq { all_rows ; player ; jeff urban } ; pick } ; 41 } ; eq { hop { filter_eq { all_rows ; player ; chris george } ; pick } ; 31 } } } = true', 'tointer': 'select the rows whose player record fuzzily matches to jeff urban . take the pick record of this row . select the rows whose player record fuzzily matches to chris george . take the pick record of this row . the first record is greater than the second record . the pick record of the first row is 41 . the pick record of the second row is 31 .'} | and { greater { hop { filter_eq { all_rows ; player ; jeff urban } ; pick } ; hop { filter_eq { all_rows ; player ; chris george } ; pick } } ; and { eq { hop { filter_eq { all_rows ; player ; jeff urban } ; pick } ; 41 } ; eq { hop { filter_eq { all_rows ; player ; chris george } ; pick } ; 31 } } } = true | select the rows whose player record fuzzily matches to jeff urban . take the pick record of this row . select the rows whose player record fuzzily matches to chris george . take the pick record of this row . the first record is greater than the second record . the pick record of the first row is 41 . the pick record of the second row is 31 . | 13 | 9 | {'and_8': 8, 'result_9': 9, 'greater_4': 4, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_10': 10, 'player_11': 11, 'jeff urban_12': 12, 'pick_13': 13, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_14': 14, 'player_15': 15, 'chris george_16': 16, 'pick_17': 17, 'and_7': 7, 'eq_5': 5, '41_18': 18, 'eq_6': 6, '31_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', 'player_11': 'player', 'jeff urban_12': 'jeff urban', 'pick_13': 'pick', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_14': 'all_rows', 'player_15': 'player', 'chris george_16': 'chris george', 'pick_17': 'pick', 'and_7': 'and', 'eq_5': 'eq', '41_18': '41', 'eq_6': 'eq', '31_19': '31'} | {'and_8': [9], 'result_9': [], 'greater_4': [8], 'num_hop_2': [4, 5], 'filter_str_eq_0': [2], 'all_rows_10': [0], 'player_11': [0], 'jeff urban_12': [0], 'pick_13': [2], 'num_hop_3': [4, 6], 'filter_str_eq_1': [3], 'all_rows_14': [1], 'player_15': [1], 'chris george_16': [1], 'pick_17': [3], 'and_7': [8], 'eq_5': [7], '41_18': [5], 'eq_6': [7], '31_19': [6]} | ['pick', 'player', 'team', 'position', 'school'] | [['31', 'chris george', 'kansas city royals', 'p', 'klein hs ( klein , tx )'], ['32', 'ben diggins', 'st louis cardinals', 'p', 'bradshaw mountain hs ( prescott valley , az )'], ['33', 'brad wilkerson', 'montreal expos', 'of', 'university of florida'], ['34', 'nate cornejo', 'detroit tigers', 'p', 'wellington hs ( wellington , ks )'], ['35', 'aaron rowand', 'chicago white sox', 'of', 'cal state fullerton university'], ['36', 'raphael freeman', 'colorado rockies', 'of', 'dallas christian school ( mesquite , tx )'], ['37', 'mike nannini', 'houston astros', 'p', 'green valley hs ( henderson , nv )'], ['38', 'chris jones', 'san francisco giants', 'p', 'south mecklenburg hs ( charlotte , nc )'], ['39', 'mamon tucker', 'baltimore orioles', 'of', 'stephen f austin hs ( austin , tx )'], ['40', 'jeff winchester', 'colorado rockies', 'c', 'archbishop rummel hs ( metairie , la )'], ['41', 'jeff urban', 'san francisco giants', 'p', 'ball state university'], ['42', 'eric valent', 'philadelphia phillies', 'of', 'ucla'], ['43', 'mark prior', 'new york yankees', 'p', 'university hs ( san diego , ca )']] |
dustley mulder | https://en.wikipedia.org/wiki/Dustley_Mulder | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11415108-1.html.csv | majority | dustley mulder played the majority of seasons with the club rkc waalwijk . | {'scope': 'all', 'col': '2', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'rkc waalwijk', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'club', 'rkc waalwijk'], 'result': True, 'ind': 0, 'tointer': 'for the club records of all rows , most of them fuzzily match to rkc waalwijk .', 'tostr': 'most_eq { all_rows ; club ; rkc waalwijk } = true'} | most_eq { all_rows ; club ; rkc waalwijk } = true | for the club records of all rows , most of them fuzzily match to rkc waalwijk . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'club_3': 3, 'rkc waalwijk_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'club_3': 'club', 'rkc waalwijk_4': 'rkc waalwijk'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'club_3': [0], 'rkc waalwijk_4': [0]} | ['season', 'club', 'apps', 'goals', 'division'] | [['2004 / 05', 'excelsior', '22', '3', '2'], ['2005 / 06', 'excelsior', '24', '1', '2'], ['2005 / 06', 'rkc waalwijk', '11', '0', '1'], ['2006 / 07', 'rkc waalwijk', '25', '1', '1'], ['2007 / 08', 'rkc waalwijk', '37', '1', '2'], ['2008 / 09', 'rkc waalwijk', '37', '1', '2'], ['2009 / 10', 'rkc waalwijk', '32', '1', '1']] |
2007 - 08 isthmian league | https://en.wikipedia.org/wiki/2007%E2%80%9308_Isthmian_League | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17494040-9.html.csv | aggregation | for the 2007-08 isthmian league the total combined attendance was 558 . | {'scope': 'all', 'col': '5', 'type': 'sum', 'result': '558', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'attendance'], 'result': '558', 'ind': 0, 'tostr': 'sum { all_rows ; attendance }'}, '558'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; attendance } ; 558 } = true', 'tointer': 'the sum of the attendance record of all rows is 558 .'} | round_eq { sum { all_rows ; attendance } ; 558 } = true | the sum of the attendance record of all rows is 558 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'attendance_4': 4, '558_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'attendance_4': 'attendance', '558_5': '558'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'attendance_4': [0], '558_5': [1]} | ['tie no', 'home team', 'score', 'away team', 'attendance'] | [['59', 'afc sudbury', '1 - 0', 'edgware town', '176'], ['60', 'carshalton athletic', '1 - 1', 'walton casuals', '95'], ['walton casuals advance 5 - 4 on penalties', 'walton casuals advance 5 - 4 on penalties', 'walton casuals advance 5 - 4 on penalties', 'walton casuals advance 5 - 4 on penalties', 'walton casuals advance 5 - 4 on penalties'], ['61', 'ramsgate', '1 - 1', 'tooting & mitcham united', '182'], ['ramsgate advance 4 - 3 on penalties', 'ramsgate advance 4 - 3 on penalties', 'ramsgate advance 4 - 3 on penalties', 'ramsgate advance 4 - 3 on penalties', 'ramsgate advance 4 - 3 on penalties'], ['62', 'wealdstone', '0 - 1', 'heybridge swifts', '105']] |
bluebird k7 | https://en.wikipedia.org/wiki/Bluebird_K7 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17829496-1.html.csv | majority | donald campbell was the pilot for all of the bluebird k7 speed record attempts . | {'scope': 'all', 'col': '3', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'donald campbell', 'subset': None} | {'func': 'all_str_eq', 'args': ['all_rows', 'pilot', 'donald campbell'], 'result': True, 'ind': 0, 'tointer': 'for the pilot records of all rows , all of them fuzzily match to donald campbell .', 'tostr': 'all_eq { all_rows ; pilot ; donald campbell } = true'} | all_eq { all_rows ; pilot ; donald campbell } = true | for the pilot records of all rows , all of them fuzzily match to donald campbell . | 1 | 1 | {'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'pilot_3': 3, 'donald campbell_4': 4} | {'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'pilot_3': 'pilot', 'donald campbell_4': 'donald campbell'} | {'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'pilot_3': [0], 'donald campbell_4': [0]} | ['speed', 'craft', 'pilot', 'location', 'date'] | [['-', 'bluebird k7', 'donald campbell', 'ullswater', '23 july 1955'], ['-', 'bluebird k7', 'donald campbell', 'lake mead', '16 november 1955'], ['-', 'bluebird k7', 'donald campbell', 'coniston water', '19 september 1956'], ['-', 'bluebird k7', 'donald campbell', 'coniston water', '7 november 1957'], ['-', 'bluebird k7', 'donald campbell', 'coniston water', '10 november 1958'], ['-', 'bluebird k7', 'donald campbell', 'coniston water', '14 may 1959'], ['-', 'bluebird k7', 'donald campbell', 'lake dumbleyung', '31 december 1964']] |
list of schools in the wellington region | https://en.wikipedia.org/wiki/List_of_schools_in_the_Wellington_Region | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12214488-8.html.csv | superlative | of the schools in the featherston area , st. teresa 's school has the highest roll . | {'scope': 'subset', 'col_superlative': '7', 'row_superlative': '8', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1,4', 'subset': {'col': '4', 'criterion': 'equal', 'value': 'featherston'}} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'area', 'featherston'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; area ; featherston }', 'tointer': 'select the rows whose area record fuzzily matches to featherston .'}, 'roll'], 'result': None, 'ind': 1, 'tostr': 'argmax { filter_eq { all_rows ; area ; featherston } ; roll }'}, 'name'], 'result': "st teresa 's school", 'ind': 2, 'tostr': 'hop { argmax { filter_eq { all_rows ; area ; featherston } ; roll } ; name }'}, "st teresa 's school"], 'result': True, 'ind': 3, 'tostr': "eq { hop { argmax { filter_eq { all_rows ; area ; featherston } ; roll } ; name } ; st teresa 's school } = true", 'tointer': "select the rows whose area record fuzzily matches to featherston . select the row whose roll record of these rows is maximum . the name record of this row is st teresa 's school ."} | eq { hop { argmax { filter_eq { all_rows ; area ; featherston } ; roll } ; name } ; st teresa 's school } = true | select the rows whose area record fuzzily matches to featherston . select the row whose roll record of these rows is maximum . the name record of this row is st teresa 's school . | 4 | 4 | {'str_eq_3': 3, 'result_4': 4, 'str_hop_2': 2, 'argmax_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'area_6': 6, 'featherston_7': 7, 'roll_8': 8, 'name_9': 9, "st teresa 's school_10": 10} | {'str_eq_3': 'str_eq', 'result_4': 'true', 'str_hop_2': 'str_hop', 'argmax_1': 'argmax', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'area_6': 'area', 'featherston_7': 'featherston', 'roll_8': 'roll', 'name_9': 'name', "st teresa 's school_10": "st teresa 's school"} | {'str_eq_3': [4], 'result_4': [], 'str_hop_2': [3], 'argmax_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'area_6': [0], 'featherston_7': [0], 'roll_8': [1], 'name_9': [2], "st teresa 's school_10": [3]} | ['name', 'years', 'gender', 'area', 'authority', 'decile', 'roll'] | [['featherston school', '1 - 8', 'coed', 'featherston', 'state', '3', '64'], ['greytown school', '1 - 8', 'coed', 'greytown', 'state', '6', '348'], ['kahutara school', '1 - 8', 'coed', 'kahutara', 'state', '7', '100'], ['kuranui college', '9 - 13', 'coed', 'greytown', 'state', '5', '488'], ['martinborough school', '1 - 8', 'coed', 'martinborough', 'state', '7', '251'], ['pirinoa school', '1 - 8', 'coed', 'pirinoa', 'state', '6', '26'], ['south featherston school', '1 - 8', 'coed', 'featherston', 'state', '5', '65'], ["st teresa 's school", '1 - 8', 'coed', 'featherston', 'integrated', '7', '111'], ['tuturumuri school', '1 - 8', 'coed', 'martinborough', 'state', '7', '17']] |
list of csi : ny characters | https://en.wikipedia.org/wiki/List_of_CSI%3A_NY_characters | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11240028-1.html.csv | count | there are 8 characters of csi : ny who have their last appearance on the episode today is life . | {'scope': 'all', 'criterion': 'equal', 'value': 'today is life', 'result': '8', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'last appearance', 'today is life'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose last appearance record fuzzily matches to today is life .', 'tostr': 'filter_eq { all_rows ; last appearance ; today is life }'}], 'result': '8', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; last appearance ; today is life } }', 'tointer': 'select the rows whose last appearance record fuzzily matches to today is life . the number of such rows is 8 .'}, '8'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; last appearance ; today is life } } ; 8 } = true', 'tointer': 'select the rows whose last appearance record fuzzily matches to today is life . the number of such rows is 8 .'} | eq { count { filter_eq { all_rows ; last appearance ; today is life } } ; 8 } = true | select the rows whose last appearance record fuzzily matches to today is life . the number of such rows is 8 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'last appearance_5': 5, 'today is life_6': 6, '8_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'last appearance_5': 'last appearance', 'today is life_6': 'today is life', '8_7': '8'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'last appearance_5': [0], 'today is life_6': [0], '8_7': [2]} | ['character', 'portrayed by', 'first appearance', 'last appearance', 'duration', 'episodes'] | [['mac taylor csi detective', 'gary sinise', 'blink 1 , 2 , 3', 'today is life', '1.01 - 9.17', '197'], ['jo danville csi detective', 'sela ward', 'the 34th floor', 'today is life', '7.01 - 9.17', '57'], ['danny messer csi detective', 'carmine giovinazzo', 'blink 1', 'today is life', '1.01 - 9.17', '197'], ['lindsay monroe messer csi detective', 'anna belknap', 'zoo york', 'today is life', '2.03 - 9.17', '172 4'], ['dr sid hammerback chief medical examiner', 'robert joy', 'dancing with the fishes', 'today is life', '2.05 - 9.17', '168 4'], ['adam ross lab technician', 'a j buckley', 'bad beat', 'today is life', '2.08 - 9.17', '141 4'], ['dr sheldon hawkes csi', 'hill harper', 'blink 1', 'today is life', '1.01 - 9.17', '197'], ['don flack homicide detective', 'eddie cahill', 'blink', 'today is life', '1.01 - 9.17', '197'], ['aiden burn csi detective', 'vanessa ferlito', 'blink 1', 'heroes', '1.01 - 2.02 , 2.23', '26'], ['stella bonasera csi detective', 'melina kanakaredes', 'blink 1', 'vacation getaway', '1.01 - 6.22', '140']] |
steve vigneault | https://en.wikipedia.org/wiki/Steve_Vigneault | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17442303-2.html.csv | superlative | the shortest fight that steve vigneault had was against jeff davis . | {'scope': 'all', 'col_superlative': '7', 'row_superlative': '15', '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', 'time'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; time }'}, 'opponent'], 'result': 'jeff davis', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; time } ; opponent }'}, 'jeff davis'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; time } ; opponent } ; jeff davis } = true', 'tointer': 'select the row whose time record of all rows is minimum . the opponent record of this row is jeff davis .'} | eq { hop { argmin { all_rows ; time } ; opponent } ; jeff davis } = true | select the row whose time record of all rows is minimum . the opponent record of this row is jeff davis . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'time_5': 5, 'opponent_6': 6, 'jeff davis_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'time_5': 'time', 'opponent_6': 'opponent', 'jeff davis_7': 'jeff davis'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'time_5': [0], 'opponent_6': [1], 'jeff davis_7': [2]} | ['res', 'record', 'opponent', 'method', 'event', 'round', 'time', 'location'] | [['win', '12 - 6', 'dan chambers', 'tko ( punches )', 'ringside mma - rivalry', '1', '4:03', 'quebec , canada'], ['loss', '11 - 6', 'mike swick', 'submission ( guillotine choke )', 'ufc 58', '1', '2:09', 'las vegas , nevada , united states'], ['win', '11 - 5', 'jason st louis', 'tko', 'tko 22 - lionheart', '2', '0:30', 'quebec , canada'], ['loss', '10 - 5', 'chris fontaine', 'tko ( knee )', 'tko 15 - unstoppable', '1', '1:14', 'quebec , canada'], ['loss', '10 - 4', 'patrick côté', 'ko', 'tko 14 - road warriors', '1', '1:08', 'quebec , canada'], ['win', '10 - 3', 'sean pierson', 'decision ( unanimous )', 'tko 13 - ultimate rush', '3', '5:00', 'montreal , quebec , canada'], ['win', '9 - 3', 'jermaine andre', 'decision ( unanimous )', 'ucc 12 - adrenaline', '3', '5:00', 'quebec , canada'], ['win', '8 - 3', 'jeromie sills', 'ko', 'ucc 10 - battle for the belts 2002', '2', '2:19', 'quebec , canada'], ['win', '7 - 3', 'jp cantin', 'submission ( punches )', 'ucc 8 - fast and furious', '1', '1:53', 'quebec , canada'], ['win', '6 - 3', 'mike kitchen', 'tko ( corner stoppage )', 'ucc 6 - redemption', '2', '4:38', 'montreal , quebec , canada'], ['win', '5 - 3', 'jason st louis', 'tko ( corner stoppage )', 'ucc 4 - return of the super strikers', '1', '10:00', 'quebec , canada'], ['win', '4 - 3', 'shawn peters', 'submission ( triangle choke )', 'ucc 3 - battle for the belts', '1', '0:32', 'quebec , canada'], ['loss', '3 - 3', 'david loiseau', 'tko ( corner stoppage )', 'ucc 2 - the moment of truth', '1', '10:00', 'montreal , quebec , canada'], ['win', '3 - 2', 'shawn tompkins', 'tko ( punches )', 'ucc 2 - the moment of truth', '1', '2:43', 'montreal , quebec , canada'], ['win', '2 - 2', 'jeff davis', 'tko ( punches )', 'ucc 1 - the new beginning', '1', '0:15', 'montreal , quebec , canada'], ['loss', '1 - 2', 'sean pierson', 'submission ( strikes )', 'ifc - battleground 2000', '1', '1:17', 'kahnawake , quebec , canada'], ['loss', '1 - 1', 'sean pierson', 'tko', 'ifc - montreal cage combat', '1', '1:07', 'quebec , canada'], ['win', '1 - 0', 'chris ellerton', 'tko', 'ifc - montreal cage combat', '1', '5:34', 'quebec , canada']] |
2004 arizona cardinals season | https://en.wikipedia.org/wiki/2004_Arizona_Cardinals_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18722259-2.html.csv | ordinal | during the 2004 season , the arizona cardinals game with the second lowest attendance was played in january 2005 . | {'row': '16', 'col': '5', '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', 'attendance', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; attendance ; 2 }'}, 'date'], 'result': 'january 2 , 2005', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; attendance ; 2 } ; date }'}, 'january 2 , 2005'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; attendance ; 2 } ; date } ; january 2 , 2005 } = true', 'tointer': 'select the row whose attendance record of all rows is 2nd minimum . the date record of this row is january 2 , 2005 .'} | eq { hop { nth_argmin { all_rows ; attendance ; 2 } ; date } ; january 2 , 2005 } = true | select the row whose attendance record of all rows is 2nd minimum . the date record of this row is january 2 , 2005 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'attendance_5': 5, '2_6': 6, 'date_7': 7, 'january 2 , 2005_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', 'attendance_5': 'attendance', '2_6': '2', 'date_7': 'date', 'january 2 , 2005_8': 'january 2 , 2005'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'attendance_5': [0], '2_6': [0], 'date_7': [1], 'january 2 , 2005_8': [2]} | ['week', 'date', 'opponent', 'result', 'attendance'] | [['1', 'september 12 , 2004', 'st louis rams', 'l 17 - 10', '65538'], ['2', 'september 19 , 2004', 'new england patriots', 'l 23 - 12', '51557'], ['3', 'september 26 , 2004', 'atlanta falcons', 'l 6 - 3', '70534'], ['4', 'october 3 , 2004', 'new orleans saints', 'w 34 - 10', '28109'], ['5', 'october 10 , 2004', 'san francisco 49ers', 'l 31 - 28 ot', '62836'], ['7', 'october 24 , 2004', 'seattle seahawks', 'w 25 - 17', '35695'], ['8', 'october 31 , 2004', 'buffalo bills', 'l 38 - 14', '65887'], ['9', 'november 7 , 2004', 'miami dolphins', 'w 24 - 23', '72612'], ['10', 'november 14 , 2004', 'new york giants', 'w 17 - 14', '42297'], ['11', 'november 21 , 2004', 'carolina panthers', 'l 35 - 10', '72796'], ['12', 'november 28 , 2004', 'new york jets', 'l 13 - 3', '35820'], ['13', 'december 5 , 2004', 'detroit lions', 'l 26 - 12', '62262'], ['14', 'december 12 , 2004', 'san francisco 49ers', 'l 31 - 28 ot', '35069'], ['15', 'december 19 , 2004', 'st louis rams', 'w 31 - 7', '40070'], ['16', 'december 26 , 2004', 'seattle seahawks', 'l 24 - 21', '65825'], ['17', 'january 2 , 2005', 'tampa bay buccaneers', 'w 12 - 7', '31650']] |
southern athletic conference of indiana | https://en.wikipedia.org/wiki/Southern_Athletic_Conference_of_Indiana | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18956862-1.html.csv | majority | the majority of the teams joined the southern athletic conference of indiana before 1978 . | {'scope': 'all', 'col': '7', 'most_or_all': 'most', 'criterion': 'less_than', 'value': '1978', 'subset': None} | {'func': 'most_less', 'args': ['all_rows', 'year joined', '1978'], 'result': True, 'ind': 0, 'tointer': 'for the year joined records of all rows , most of them are less than 1978 .', 'tostr': 'most_less { all_rows ; year joined ; 1978 } = true'} | most_less { all_rows ; year joined ; 1978 } = true | for the year joined records of all rows , most of them are less than 1978 . | 1 | 1 | {'most_less_0': 0, 'result_1': 1, 'all_rows_2': 2, 'year joined_3': 3, '1978_4': 4} | {'most_less_0': 'most_less', 'result_1': 'true', 'all_rows_2': 'all_rows', 'year joined_3': 'year joined', '1978_4': '1978'} | {'most_less_0': [1], 'result_1': [], 'all_rows_2': [0], 'year joined_3': [0], '1978_4': [0]} | ['school', 'location', 'mascot', 'county', 'enrollment', 'ihsaa class', 'year joined', 'previous conference'] | [['borden', 'borden', 'braves', '228', 'a', '10 clark', '1974', 'lost river'], ['crothersville', 'crothersville', 'tigers', '180', 'a', '36 jackson', '1974', 'mid - hoosier'], ['henryville', 'henryville', 'hornets', '347', 'aa', '10 clark', '1977', 'lost river'], ['lanesville', 'lanesville', 'eagles', '237', 'a', '31 harrison', '1979', 'blue river'], ['new washington', 'new washington', 'mustangs', '273', 'a', '10 clark', '1974', 'dixie - monon'], ['south central ( elizabeth )', 'elizabeth', 'rebels', '277', 'a', '31 harrison', '1979', 'blue river']] |
comparison of brainwave entrainment software | https://en.wikipedia.org/wiki/Comparison_of_brainwave_entrainment_software | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-15038373-1.html.csv | count | only three of the brainwave entrainment software programs are able to be used on the linux operating system . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'linux', 'result': '3', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'operating systems', 'linux'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose operating systems record fuzzily matches to linux .', 'tostr': 'filter_eq { all_rows ; operating systems ; linux }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; operating systems ; linux } }', 'tointer': 'select the rows whose operating systems record fuzzily matches to linux . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; operating systems ; linux } } ; 3 } = true', 'tointer': 'select the rows whose operating systems record fuzzily matches to linux . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; operating systems ; linux } } ; 3 } = true | select the rows whose operating systems record fuzzily matches to linux . 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, 'operating systems_5': 5, 'linux_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', 'operating systems_5': 'operating systems', 'linux_6': 'linux', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'operating systems_5': [0], 'linux_6': [0], '3_7': [2]} | ['software', 'version', 'operating systems', 'developer', 'license'] | [['beeone smod / hms', '3.1', 'windows', 'hemi - synths explorers', 'proprietary'], ['brainwave generator', '3.1', 'windows', 'noromaa solutions oy', 'proprietary'], ['gnaural', '1.0.20100707', 'freebsd , linux , mac os x , windows', 'gnaural', 'gpl'], ['brainigniter player', '6.0', 'windows', 'volition', 'proprietary'], ['neuro - programmer 3', '3.0.9.0', 'windows', 'transparent corp', 'proprietary'], ['mind workstation', '1.2.2.0', 'windows', 'transparent corp', 'proprietary'], ['sbagen', '1.4.4', 'dos , freebsd , linux , mac os x , windows , wince', 'uazu', 'gpl'], ['brainwave studio', '1.5', 'mac os x , ios', 'rcs software', 'proprietary'], ['discord', '3.2.1', 'linux', 'stan lysiak', 'gpl']] |
1903 in paleontology | https://en.wikipedia.org/wiki/1903_in_paleontology | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15689683-1.html.csv | comparative | in 1903 paleontology , a telmatosaurus was recorded in romania , whereas a brachiosaurus was recorded in colorado . | {'row_1': '5', 'row_2': '1', 'col': '6', 'col_other': '1', 'relation': 'not_equal', 'record_mentioned': 'yes', 'diff_result': None} | {'func': 'and', 'args': [{'func': 'not_str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name', 'telmatosaurus'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose name record fuzzily matches to telmatosaurus .', 'tostr': 'filter_eq { all_rows ; name ; telmatosaurus }'}, 'location'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; name ; telmatosaurus } ; location }', 'tointer': 'select the rows whose name record fuzzily matches to telmatosaurus . take the location record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name', 'brachiosaurus'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose name record fuzzily matches to brachiosaurus .', 'tostr': 'filter_eq { all_rows ; name ; brachiosaurus }'}, 'location'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; name ; brachiosaurus } ; location }', 'tointer': 'select the rows whose name record fuzzily matches to brachiosaurus . take the location record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'not_eq { hop { filter_eq { all_rows ; name ; telmatosaurus } ; location } ; hop { filter_eq { all_rows ; name ; brachiosaurus } ; location } }', 'tointer': 'select the rows whose name record fuzzily matches to telmatosaurus . take the location record of this row . select the rows whose name record fuzzily matches to brachiosaurus . take the location record of this row . the first record does not match to the second record .'}, {'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name', 'telmatosaurus'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose name record fuzzily matches to telmatosaurus .', 'tostr': 'filter_eq { all_rows ; name ; telmatosaurus }'}, 'location'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; name ; telmatosaurus } ; location }', 'tointer': 'select the rows whose name record fuzzily matches to telmatosaurus . take the location record of this row .'}, 'romania'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; name ; telmatosaurus } ; location } ; romania }', 'tointer': 'the location record of the first row is romania .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name', 'brachiosaurus'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose name record fuzzily matches to brachiosaurus .', 'tostr': 'filter_eq { all_rows ; name ; brachiosaurus }'}, 'location'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; name ; brachiosaurus } ; location }', 'tointer': 'select the rows whose name record fuzzily matches to brachiosaurus . take the location record of this row .'}, 'usa'], 'result': True, 'ind': 6, 'tostr': 'eq { hop { filter_eq { all_rows ; name ; brachiosaurus } ; location } ; usa }', 'tointer': 'the location record of the second row is usa .'}], 'result': True, 'ind': 7, 'tostr': 'and { eq { hop { filter_eq { all_rows ; name ; telmatosaurus } ; location } ; romania } ; eq { hop { filter_eq { all_rows ; name ; brachiosaurus } ; location } ; usa } }', 'tointer': 'the location record of the first row is romania . the location record of the second row is usa .'}], 'result': True, 'ind': 8, 'tostr': 'and { not_eq { hop { filter_eq { all_rows ; name ; telmatosaurus } ; location } ; hop { filter_eq { all_rows ; name ; brachiosaurus } ; location } } ; and { eq { hop { filter_eq { all_rows ; name ; telmatosaurus } ; location } ; romania } ; eq { hop { filter_eq { all_rows ; name ; brachiosaurus } ; location } ; usa } } } = true', 'tointer': 'select the rows whose name record fuzzily matches to telmatosaurus . take the location record of this row . select the rows whose name record fuzzily matches to brachiosaurus . take the location record of this row . the first record does not match to the second record . the location record of the first row is romania . the location record of the second row is usa .'} | and { not_eq { hop { filter_eq { all_rows ; name ; telmatosaurus } ; location } ; hop { filter_eq { all_rows ; name ; brachiosaurus } ; location } } ; and { eq { hop { filter_eq { all_rows ; name ; telmatosaurus } ; location } ; romania } ; eq { hop { filter_eq { all_rows ; name ; brachiosaurus } ; location } ; usa } } } = true | select the rows whose name record fuzzily matches to telmatosaurus . take the location record of this row . select the rows whose name record fuzzily matches to brachiosaurus . take the location record of this row . the first record does not match to the second record . the location record of the first row is romania . the location record of the second row is usa . | 13 | 9 | {'and_8': 8, 'result_9': 9, 'not_str_eq_4': 4, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_10': 10, 'name_11': 11, 'telmatosaurus_12': 12, 'location_13': 13, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_14': 14, 'name_15': 15, 'brachiosaurus_16': 16, 'location_17': 17, 'and_7': 7, 'str_eq_5': 5, 'romania_18': 18, 'str_eq_6': 6, 'usa_19': 19} | {'and_8': 'and', 'result_9': 'true', 'not_str_eq_4': 'not_str_eq', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_10': 'all_rows', 'name_11': 'name', 'telmatosaurus_12': 'telmatosaurus', 'location_13': 'location', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_14': 'all_rows', 'name_15': 'name', 'brachiosaurus_16': 'brachiosaurus', 'location_17': 'location', 'and_7': 'and', 'str_eq_5': 'str_eq', 'romania_18': 'romania', 'str_eq_6': 'str_eq', 'usa_19': 'usa'} | {'and_8': [9], 'result_9': [], 'not_str_eq_4': [8], 'str_hop_2': [4, 5], 'filter_str_eq_0': [2], 'all_rows_10': [0], 'name_11': [0], 'telmatosaurus_12': [0], 'location_13': [2], 'str_hop_3': [4, 6], 'filter_str_eq_1': [3], 'all_rows_14': [1], 'name_15': [1], 'brachiosaurus_16': [1], 'location_17': [3], 'and_7': [8], 'str_eq_5': [7], 'romania_18': [5], 'str_eq_6': [7], 'usa_19': [6]} | ['name', 'novelty', 'status', 'authors', 'unit', 'location'] | [['brachiosaurus', 'gen et sp', 'valid', 'riggs', 'morrison formation , colorado', 'usa'], ['haplocanthosaurus', 'gen et sp', 'valid , nomen conservandum', 'hatcher', 'morrison formation , colorado', 'usa'], ['haplocanthus', 'gen et sp', 'nomen oblitum', 'hatcher', 'morrison formation , colorado', 'usa'], ['ornitholestes', 'gen et sp', 'valid', 'osborn', 'morrison formation , wyoming', 'usa'], ['telmatosaurus', 'gen', 'valid', 'nopcsa', 'sãnpetru formation , transylvania', 'romania']] |
1956 cleveland browns season | https://en.wikipedia.org/wiki/1956_Cleveland_Browns_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10651573-1.html.csv | superlative | the game against the college all-stars at chicago drew the highest attendance in the 1956 cleveland browns season . | {'scope': 'all', 'col_superlative': '5', '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', 'attendance'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; attendance }'}, 'opponent'], 'result': 'college all - stars at chicago', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; attendance } ; opponent }'}, 'college all - stars at chicago'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; attendance } ; opponent } ; college all - stars at chicago } = true', 'tointer': 'select the row whose attendance record of all rows is maximum . the opponent record of this row is college all - stars at chicago .'} | eq { hop { argmax { all_rows ; attendance } ; opponent } ; college all - stars at chicago } = true | select the row whose attendance record of all rows is maximum . the opponent record of this row is college all - stars at chicago . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'attendance_5': 5, 'opponent_6': 6, 'college all - stars at chicago_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'attendance_5': 'attendance', 'opponent_6': 'opponent', 'college all - stars at chicago_7': 'college all - stars at chicago'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'attendance_5': [0], 'opponent_6': [1], 'college all - stars at chicago_7': [2]} | ['week', 'date', 'opponent', 'result', 'attendance'] | [['1', 'august 10 , 1956', 'college all - stars at chicago', 'w 26 - 0', '75000'], ['2', 'august 19 , 1956', 'san francisco 49ers', 'l 28 - 17', '38741'], ['3', 'august 24 , 1956', 'los angeles rams', 'l 17 - 6', '40175'], ['4', 'september 1 , 1956', 'green bay packers', 'l 21 - 20', '15456'], ['5', 'september 7 , 1956', 'detroit lions', 'l 17 - 0', '48105'], ['6', 'september 15 , 1956', 'detroit lions at akron', 'l 31 - 14', '28201'], ['7', 'september 21 , 1956', 'chicago bears', 'w 24 - 14', '56543']] |
1953 u.s. open ( golf ) | https://en.wikipedia.org/wiki/1953_U.S._Open_%28golf%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17290169-1.html.csv | superlative | ben hogan is the player with the lowest scores in 1953 u.s golf opens . | {'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': 'ben hogan', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; score } ; player }'}, 'ben hogan'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; score } ; player } ; ben hogan } = true', 'tointer': 'select the row whose score record of all rows is minimum . the player record of this row is ben hogan .'} | eq { hop { argmin { all_rows ; score } ; player } ; ben hogan } = true | select the row whose score record of all rows is minimum . the player record of this row is ben hogan . | 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, 'ben hogan_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', 'ben hogan_7': 'ben hogan'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'score_5': [0], 'player_6': [1], 'ben hogan_7': [2]} | ['place', 'player', 'country', 'score', 'to par'] | [['1', 'ben hogan', 'united states', '67', '- 5'], ['t2', 'walter burkemo', 'united states', '70', '- 2'], ['t2', 'george fazio', 'united states', '70', '- 2'], ['t2', 'frank souchak ( a )', 'united states', '70', '- 2'], ['t5', 'jimmy demaret', 'united states', '71', '- 1'], ['t5', 'bill ogden', 'united states', '71', '- 1'], ['t7', 'lou barbaro', 'united states', '72', 'e'], ['t7', 'jerry barber', 'united states', '72', 'e'], ['t7', 'jay hebert', 'united states', '72', 'e'], ['t7', 'sam snead', 'united states', '72', 'e']] |
bedford blues | https://en.wikipedia.org/wiki/Bedford_Blues | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1620305-1.html.csv | ordinal | zoo sport ltd is the most recent supplier for rugby union bedford blues . | {'row': '5', 'col': '1', 'order': '1', 'col_other': '1', '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', 'year', '1'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; year ; 1 }'}, 'year'], 'result': '2011 - 2014', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; year ; 1 } ; year }'}, '2011 - 2014'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; year ; 1 } ; year } ; 2011 - 2014 } = true', 'tointer': 'select the row whose year record of all rows is 1st maximum . the year record of this row is 2011 - 2014 .'} | eq { hop { nth_argmax { all_rows ; year ; 1 } ; year } ; 2011 - 2014 } = true | select the row whose year record of all rows is 1st maximum . the year record of this row is 2011 - 2014 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'year_5': 5, '1_6': 6, 'year_7': 7, '2011 - 2014_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', 'year_5': 'year', '1_6': '1', 'year_7': 'year', '2011 - 2014_8': '2011 - 2014'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'year_5': [0], '1_6': [0], 'year_7': [1], '2011 - 2014_8': [2]} | ['year', 'supplier', 'chest', 'sleeves', 'back'] | [['unknown', 'gilbert', 'unknown', 'unknown', 'unknown'], ['2006 - 2008', 'kooga', 'autoglass', 'wells bombardier', 'lifesure'], ['2008 - 2010', 'kooga', 'autoglass', 'wells bombardier', 'lifesure'], ['2010 - 2011', 'kooga', 'autoglass', 'wells bombardier', 'lifesure'], ['2011 - 2014', 'zoo sport ltd', 'autoglass', 'wells bombardier', 'lifesure']] |
list of one - day cricket records for new zealand | https://en.wikipedia.org/wiki/List_of_one-day_cricket_records_for_New_Zealand | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-13322378-10.html.csv | unique | only chris pringle had less than 3000 runs in the list of one - day cricket records for new zealand . | {'scope': 'all', 'row': '3', 'col': '3', 'col_other': '1', 'criterion': 'less_than', 'value': '3000', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'runs', '3000'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose runs record is less than 3000 .', 'tostr': 'filter_less { all_rows ; runs ; 3000 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_less { all_rows ; runs ; 3000 } }', 'tointer': 'select the rows whose runs record is less than 3000 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'runs', '3000'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose runs record is less than 3000 .', 'tostr': 'filter_less { all_rows ; runs ; 3000 }'}, ''], 'result': 'chris pringle', 'ind': 2, 'tostr': 'hop { filter_less { all_rows ; runs ; 3000 } ; }'}, 'chris pringle'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_less { all_rows ; runs ; 3000 } ; } ; chris pringle }', 'tointer': 'the record of this unqiue row is chris pringle .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_less { all_rows ; runs ; 3000 } } ; eq { hop { filter_less { all_rows ; runs ; 3000 } ; } ; chris pringle } } = true', 'tointer': 'select the rows whose runs record is less than 3000 . there is only one such row in the table . the record of this unqiue row is chris pringle .'} | and { only { filter_less { all_rows ; runs ; 3000 } } ; eq { hop { filter_less { all_rows ; runs ; 3000 } ; } ; chris pringle } } = true | select the rows whose runs record is less than 3000 . there is only one such row in the table . the record of this unqiue row is chris pringle . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_less_0': 0, 'all_rows_6': 6, 'runs_7': 7, '3000_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, '_9': 9, 'chris pringle_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_less_0': 'filter_less', 'all_rows_6': 'all_rows', 'runs_7': 'runs', '3000_8': '3000', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', '_9': '', 'chris pringle_10': 'chris pringle'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_less_0': [1, 2], 'all_rows_6': [0], 'runs_7': [0], '3000_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], '_9': [2], 'chris pringle_10': [3]} | ['', 'matches', 'runs', 'wickets', 'average', 'economy rate', 'best bowling', '4wi', '5wi'] | [['shane bond', '82', '3070', '147', '20.88', '4.28', '6 / 19', '7', '4'], ['richard hadlee', '115', '3407', '158', '21.56', '4.20', '5 / 25', '1', '5'], ['chris pringle', '64', '2459', '103', '23.87', '4.45', '5 / 45', '2', '1'], ['ewen chatfield', '114', '3618', '140', '25.84', '3.57', '5 / 34', '3', '1'], ['kyle mills', '129', '4998', '192', '26.03', '4.73', '5 / 25', '7', '1']] |
felice herrig | https://en.wikipedia.org/wiki/Felice_Herrig | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16182887-2.html.csv | unique | the event unconquered 1 : november reign was the only event with a time of 2:03 . | {'scope': 'all', 'row': '11', 'col': '7', 'col_other': '5', 'criterion': 'equal', 'value': '2:03', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'time', '2:03'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose time record fuzzily matches to 2:03 .', 'tostr': 'filter_eq { all_rows ; time ; 2:03 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; time ; 2:03 } }', 'tointer': 'select the rows whose time record fuzzily matches to 2:03 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'time', '2:03'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose time record fuzzily matches to 2:03 .', 'tostr': 'filter_eq { all_rows ; time ; 2:03 }'}, 'event'], 'result': 'unconquered 1 : november reign', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; time ; 2:03 } ; event }'}, 'unconquered 1 : november reign'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; time ; 2:03 } ; event } ; unconquered 1 : november reign }', 'tointer': 'the event record of this unqiue row is unconquered 1 : november reign .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; time ; 2:03 } } ; eq { hop { filter_eq { all_rows ; time ; 2:03 } ; event } ; unconquered 1 : november reign } } = true', 'tointer': 'select the rows whose time record fuzzily matches to 2:03 . there is only one such row in the table . the event record of this unqiue row is unconquered 1 : november reign .'} | and { only { filter_eq { all_rows ; time ; 2:03 } } ; eq { hop { filter_eq { all_rows ; time ; 2:03 } ; event } ; unconquered 1 : november reign } } = true | select the rows whose time record fuzzily matches to 2:03 . there is only one such row in the table . the event record of this unqiue row is unconquered 1 : november reign . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'time_7': 7, '2:03_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'event_9': 9, 'unconquered 1: november reign_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'time_7': 'time', '2:03_8': '2:03', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'event_9': 'event', 'unconquered 1: november reign_10': 'unconquered 1 : november reign'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'time_7': [0], '2:03_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'event_9': [2], 'unconquered 1: november reign_10': [3]} | ['res', 'record', 'opponent', 'method', 'event', 'round', 'time', 'location'] | [['win', '9 - 4', 'heather clark', 'decision ( split )', 'bellator 94', '3', '5:00', 'tampa , florida , united states'], ['win', '8 - 4', 'patricia vidonic', 'decision ( unanimous )', 'bellator 84', '3', '5:00', 'hammond , indiana , united states'], ['win', '7 - 4', 'simona soukupova', 'decision ( unanimous )', 'xfc 19 : charlotte showdown', '3', '5:00', 'charlotte , north carolina , united states'], ['win', '6 - 4', 'patricia vidonic', 'decision ( unanimous )', 'xfc 17 : apocalypse', '3', '5:00', 'jackson , tennessee , united states'], ['loss', '5 - 4', 'carla esparza', 'decision ( unanimous )', 'xfc 15 : tribute', '3', '5:00', 'tampa , florida , united states'], ['win', '5 - 3', 'nicdali rivera - calanoc', 'decision ( unanimous )', 'xtreme fighting organization 39', '3', '5:00', 'hoffman estates , illinois , united states'], ['win', '4 - 3', 'andrea miller', 'tko ( punches )', 'chicago cagefighting championship 3', '1', '3:30', 'villa park , illinois , united states'], ['loss', '3 - 3', 'barb honchak', 'decision ( unanimous )', 'hoosier fight club 6 : new years nemesis', '3', '5:00', 'valparaiso , indiana , united states'], ['win', '3 - 2', 'amanda lavoy', 'submission ( armbar )', 'xtreme fighting organization 37', '1', '3:35', 'chicago , illinois , united states'], ['win', '2 - 2', 'jessica rakoczy', 'decision ( split )', 'bellator 14', '3', '5:00', 'chicago , illinois , united states'], ['win', '1 - 2', 'michele gutierrez', 'submission ( armbar )', 'unconquered 1 : november reign', '2', '2:03', 'coral gables , florida , united states'], ['loss', '0 - 2', 'valerie coolbaugh', 'decision ( split )', 'xtreme fighting organization 29', '3', '5:00', 'lakemoor , illinois , united states'], ['loss', '0 - 1', 'iman achhal', 'decision ( split )', 'uwc : man o war', '3', '5:00', 'fairfax , virginia , united states']] |
anastasija sevastova | https://en.wikipedia.org/wiki/Anastasija_Sevastova | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16484261-3.html.csv | superlative | anastasija sevastova 's first match in 2006 took place in germany . | {'scope': 'subset', 'col_superlative': '1', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '4', 'subset': {'col': '1', 'criterion': 'fuzzily_match', 'value': '2006'}} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', '2006'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; date ; 2006 }', 'tointer': 'select the rows whose date record fuzzily matches to 2006 .'}, 'date'], 'result': None, 'ind': 1, 'tostr': 'argmin { filter_eq { all_rows ; date ; 2006 } ; date }'}, 'opponent'], 'result': 'josipa bek', 'ind': 2, 'tostr': 'hop { argmin { filter_eq { all_rows ; date ; 2006 } ; date } ; opponent }'}, 'josipa bek'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { argmin { filter_eq { all_rows ; date ; 2006 } ; date } ; opponent } ; josipa bek } = true', 'tointer': 'select the rows whose date record fuzzily matches to 2006 . select the row whose date record of these rows is minimum . the opponent record of this row is josipa bek .'} | eq { hop { argmin { filter_eq { all_rows ; date ; 2006 } ; date } ; opponent } ; josipa bek } = true | select the rows whose date record fuzzily matches to 2006 . select the row whose date record of these rows is minimum . the opponent record of this row is josipa bek . | 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, 'date_6': 6, '2006_7': 7, 'date_8': 8, 'opponent_9': 9, 'josipa bek_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', 'date_6': 'date', '2006_7': '2006', 'date_8': 'date', 'opponent_9': 'opponent', 'josipa bek_10': 'josipa bek'} | {'str_eq_3': [4], 'result_4': [], 'str_hop_2': [3], 'argmin_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'date_6': [0], '2006_7': [0], 'date_8': [1], 'opponent_9': [2], 'josipa bek_10': [3]} | ['date', 'tournament', 'surface', 'opponent', 'score'] | [['august 6 , 2006', 'bad saulgau , germany', 'clay', 'josipa bek', '6 - 1 , 6 - 0'], ['august 20 , 2006', 'bratislava , slovakia', 'clay', 'klaudia malenovska', '4 - 6 , 6 - 0 , 6 - 3'], ['march 22 , 2008', 'noida , india', 'hard', 'sunitha rao', '6 - 2 , 6 - 1'], ['june 1 , 2008', 'galatina , italy', 'clay', 'estrella cabeza candela', '6 - 4 , 6 - 4'], ['july 27 , 2008', 'les contamines , france', 'hard', 'agustina lepore', '6 - 4 , 3 - 6 , 6 - 3'], ['march 29 , 2009', 'la palma , spain', 'hard', 'kristína kučová', '4 - 6 , 6 - 1 , 6 - 1'], ['may 3 , 2009', 'johannesburg , south africa', 'hard', 'eva hrdinová', '6 - 2 , 6 - 2']] |
list of preakness stakes broadcasters | https://en.wikipedia.org/wiki/List_of_Preakness_Stakes_broadcasters | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-22654139-2.html.csv | majority | bob costas and tom hammond were the s hosts for all of the preaknass stakes broadcasts . | {'scope': 'all', 'col': '4', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'bob costas and tom hammond', 'subset': None} | {'func': 'all_str_eq', 'args': ['all_rows', 's host', 'bob costas and tom hammond'], 'result': True, 'ind': 0, 'tointer': 'for the s host records of all rows , all of them fuzzily match to bob costas and tom hammond .', 'tostr': 'all_eq { all_rows ; s host ; bob costas and tom hammond } = true'} | all_eq { all_rows ; s host ; bob costas and tom hammond } = true | for the s host records of all rows , all of them fuzzily match to bob costas and tom hammond . | 1 | 1 | {'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 's host_3': 3, 'bob costas and tom hammond_4': 4} | {'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 's host_3': 's host', 'bob costas and tom hammond_4': 'bob costas and tom hammond'} | {'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 's host_3': [0], 'bob costas and tom hammond_4': [0]} | ['year', 'network', 'race caller', 's host', 's analyst', 'reporters', 'trophy presentation'] | [['2009', 'nbc', 'tom durkin', 'bob costas and tom hammond', 'gary l stevens , bob neumeier and mike battaglia', 'kenny rice and donna barton brothers', 'bob costas and mike battaglia'], ['2008', 'nbc', 'tom durkin', 'bob costas and tom hammond', 'gary l stevens , bob neumeier and mike battaglia', 'kenny rice and donna barton brothers', 'bob costas and mike battaglia'], ['2007', 'nbc', 'tom durkin', 'bob costas and tom hammond', 'gary l stevens , bob neumeier and mike battaglia', 'kenny rice and donna barton brothers', 'bob costas and mike battaglia'], ['2006', 'nbc', 'tom durkin', 'bob costas and tom hammond', 'gary l stevens , bob neumeier and mike battaglia', 'kenny rice and donna barton brothers', 'bob costas and mike battaglia'], ['2005', 'nbc', 'tom durkin', 'bob costas and tom hammond', 'charlsie cantey , bob neumeier and mike battaglia', 'kenny rice and donna barton brothers', 'bob costas and mike battaglia'], ['2004', 'nbc', 'tom durkin', 'bob costas and tom hammond', 'charlsie cantey , bob neumeier and mike battaglia', 'kenny rice and donna barton brothers', 'bob costas and mike battaglia'], ['2003', 'nbc', 'tom durkin', 'bob costas and tom hammond', 'charlsie cantey , bob neumeier and mike battaglia', 'kenny rice and donna barton brothers', 'bob costas and mike battaglia'], ['2002', 'nbc', 'tom durkin', 'bob costas and tom hammond', 'charlsie cantey , bob neumeier and mike battaglia', 'kenny rice and donna barton brothers', 'bob costas and mike battaglia'], ['2001', 'nbc', 'tom durkin', 'bob costas and tom hammond', 'charlsie cantey , bob neumeier and mike battaglia', 'kenny rice and donna barton brothers', 'bob costas and mike battaglia']] |
2005 - 06 ottawa senators season | https://en.wikipedia.org/wiki/2005%E2%80%9306_Ottawa_Senators_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11622632-3.html.csv | count | hasek was the decision 4 times when the senators had ottawa as home games in the 2005-6 season . | {'scope': 'subset', 'criterion': 'equal', 'value': 'hasek', 'result': '4', 'col': '5', 'subset': {'col': '4', 'criterion': 'equal', 'value': 'ottawa'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'home', 'ottawa'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; home ; ottawa }', 'tointer': 'select the rows whose home record fuzzily matches to ottawa .'}, 'decision', 'hasek'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose home record fuzzily matches to ottawa . among these rows , select the rows whose decision record fuzzily matches to hasek .', 'tostr': 'filter_eq { filter_eq { all_rows ; home ; ottawa } ; decision ; hasek }'}], 'result': '4', 'ind': 2, 'tostr': 'count { filter_eq { filter_eq { all_rows ; home ; ottawa } ; decision ; hasek } }', 'tointer': 'select the rows whose home record fuzzily matches to ottawa . among these rows , select the rows whose decision record fuzzily matches to hasek . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_eq { all_rows ; home ; ottawa } ; decision ; hasek } } ; 4 } = true', 'tointer': 'select the rows whose home record fuzzily matches to ottawa . among these rows , select the rows whose decision record fuzzily matches to hasek . the number of such rows is 4 .'} | eq { count { filter_eq { filter_eq { all_rows ; home ; ottawa } ; decision ; hasek } } ; 4 } = true | select the rows whose home record fuzzily matches to ottawa . among these rows , select the rows whose decision record fuzzily matches to hasek . the number of such rows is 4 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_str_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'home_6': 6, 'ottawa_7': 7, 'decision_8': 8, 'hasek_9': 9, '4_10': 10} | {'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_str_eq_1': 'filter_str_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'home_6': 'home', 'ottawa_7': 'ottawa', 'decision_8': 'decision', 'hasek_9': 'hasek', '4_10': '4'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_str_eq_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'home_6': [0], 'ottawa_7': [0], 'decision_8': [1], 'hasek_9': [1], '4_10': [3]} | ['date', 'visitor', 'score', 'home', 'decision', 'attendance', 'record'] | [['october 5', 'ottawa', '3 - 2', 'toronto maple leafs', 'hasek', '19452', '1 - 0 - 0'], ['october 8', 'buffalo sabres', '0 - 5', 'ottawa', 'hasek', '19661', '2 - 0 - 0'], ['october 10', 'toronto maple leafs', '5 - 6', 'ottawa', 'hasek', '18680', '3 - 0 - 0'], ['october 11', 'ottawa', '4 - 2', 'montreal canadiens', 'emery', '21273', '4 - 0 - 0'], ['october 15', 'boston bruins', '1 - 5', 'ottawa', 'hasek', '19379', '5 - 0 - 0'], ['october 21', 'ottawa', '4 - 1', 'tampa bay lightning', 'hasek', '20494', '6 - 0 - 0'], ['october 24', 'ottawa', '2 - 3', 'carolina hurricanes', 'hasek', '12116', '6 - 1 - 0'], ['october 27', 'montreal canadiens', '3 - 4', 'ottawa', 'emery', '18840', '7 - 1 - 0'], ['october 29', 'ottawa', '8 - 0', 'toronto maple leafs', 'hasek', '19480', '8 - 1 - 0'], ['october 30', 'philadelphia flyers', '5 - 3', 'ottawa', 'hasek', '19335', '8 - 2 - 0']] |
naval campaign of the war of the pacific | https://en.wikipedia.org/wiki/Naval_Campaign_of_the_War_of_the_Pacific | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-23614702-1.html.csv | unique | the warship manco capac was the only ship that was equipped with 10 inch armour . | {'scope': 'all', 'row': '5', 'col': '5', 'col_other': '1', 'criterion': 'equal', 'value': '10', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'armour ( inch )', '10'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose armour ( inch ) record is equal to 10 .', 'tostr': 'filter_eq { all_rows ; armour ( inch ) ; 10 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; armour ( inch ) ; 10 } }', 'tointer': 'select the rows whose armour ( inch ) record is equal to 10 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'armour ( inch )', '10'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose armour ( inch ) record is equal to 10 .', 'tostr': 'filter_eq { all_rows ; armour ( inch ) ; 10 }'}, 'warship'], 'result': 'manco cápac', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; armour ( inch ) ; 10 } ; warship }'}, 'manco cápac'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; armour ( inch ) ; 10 } ; warship } ; manco cápac }', 'tointer': 'the warship record of this unqiue row is manco cápac .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; armour ( inch ) ; 10 } } ; eq { hop { filter_eq { all_rows ; armour ( inch ) ; 10 } ; warship } ; manco cápac } } = true', 'tointer': 'select the rows whose armour ( inch ) record is equal to 10 . there is only one such row in the table . the warship record of this unqiue row is manco cápac .'} | and { only { filter_eq { all_rows ; armour ( inch ) ; 10 } } ; eq { hop { filter_eq { all_rows ; armour ( inch ) ; 10 } ; warship } ; manco cápac } } = true | select the rows whose armour ( inch ) record is equal to 10 . there is only one such row in the table . the warship record of this unqiue row is manco cápac . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'armour (inch)_7': 7, '10_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'warship_9': 9, 'manco cápac_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'armour (inch)_7': 'armour ( inch )', '10_8': '10', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'warship_9': 'warship', 'manco cápac_10': 'manco cápac'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'armour (inch)_7': [0], '10_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'warship_9': [2], 'manco cápac_10': [3]} | ['warship', 'tons ( lton )', 'horse - power', 'speed ( knots )', 'armour ( inch )', 'main artillery', 'built year'] | [['cochrane', '3560', '2000', '9 - 12 , 8', 'up to 9', '6x9 inch', '1874'], ['blanco encalada', '3560', '3000', '9 - 12 , 8', 'up to 9', '6x9 inch', '1874'], ['huascar', '1130', '1200', '10 - 11', '4 ½', '2x300 - pounders', '1865'], ['independencia', '2004', '1500', '12 - 13', '4 ½', '2x150 - pounders', '1865'], ['manco cápac', '1034', '320', '6', '10', '2x500 - pounders', '1864']] |
howard county delegation | https://en.wikipedia.org/wiki/Howard_County_Delegation | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14009909-1.html.csv | count | two of the elected delegates for howard represent baltimore county . | {'scope': 'all', 'criterion': 'equal', 'value': 'baltimore county , howard', 'result': '2', 'col': '2', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'counties represented', 'baltimore county , howard'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose counties represented record fuzzily matches to baltimore county , howard .', 'tostr': 'filter_eq { all_rows ; counties represented ; baltimore county , howard }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; counties represented ; baltimore county , howard } }', 'tointer': 'select the rows whose counties represented record fuzzily matches to baltimore county , howard . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; counties represented ; baltimore county , howard } } ; 2 } = true', 'tointer': 'select the rows whose counties represented record fuzzily matches to baltimore county , howard . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; counties represented ; baltimore county , howard } } ; 2 } = true | select the rows whose counties represented record fuzzily matches to baltimore county , howard . 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, 'counties represented_5': 5, 'baltimore county, howard_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', 'counties represented_5': 'counties represented', 'baltimore county, howard_6': 'baltimore county , howard', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'counties represented_5': [0], 'baltimore county, howard_6': [0], '2_7': [2]} | ['district', 'counties represented', 'delegate', 'party', 'first elected', 'committee'] | [['09.1 9a', 'howard', 'bates , gail h gail h bates', 'republican', '2002', 'appropriations'], ['09.1 9a', 'howard', 'miller , warren e warren e miller', 'republican', '2003', 'economic matters'], ['12.1 12a', 'baltimore county , howard', 'deboy , steven j sr steven j deboy , sr', 'democratic', '2002', 'appropriations'], ['12.1 12a', 'baltimore county , howard', 'malone , james e jr james e malone , jr', 'democratic', '1994', 'environmental matters ( vice - chair )'], ['12.2 12b', 'howard', 'bobo , elizabeth elizabeth bobo', 'democratic', '1994', 'environmental matters'], ['13', 'howard', 'pendergrass , shane e shane pendergrass', 'democratic', '1994', 'health and government operations'], ['13', 'howard', 'guzzone , guy guy guzzone', 'democratic', '2006', 'appropriations'], ['13', 'howard', 'turner , frank s frank s turner', 'democratic', '1994', 'ways and means']] |
list of are you afraid of the dark ? episodes | https://en.wikipedia.org/wiki/List_of_Are_You_Afraid_of_the_Dark%3F_episodes | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-10470082-6.html.csv | count | there were four episodes in the 5th season of " are you afraid of the dark " where there was no villain . | {'scope': 'all', 'criterion': 'equal', 'value': 'none', 'result': '4', 'col': '8', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'villains', 'none'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose villains record fuzzily matches to none .', 'tostr': 'filter_eq { all_rows ; villains ; none }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; villains ; none } }', 'tointer': 'select the rows whose villains record fuzzily matches to none . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; villains ; none } } ; 4 } = true', 'tointer': 'select the rows whose villains record fuzzily matches to none . the number of such rows is 4 .'} | eq { count { filter_eq { all_rows ; villains ; none } } ; 4 } = true | select the rows whose villains record fuzzily matches to none . 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, 'villains_5': 5, 'none_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', 'villains_5': 'villains', 'none_6': 'none', '4_7': '4'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'villains_5': [0], 'none_6': [0], '4_7': [2]} | ['no', '-', 'title', 'director', 'writer', 'us air date', 'storyteller', 'villains'] | [['53', '1', "the tale of the dead man 's float", 'd j machale', 'will dixon', 'october 7 , 1995', 'stig', 'the pool zombie'], ['54', '2', 'the tale of the jagged sign', 'will dixon', 'susan kim', 'october 14 , 1995', 'kiki', 'none'], ['55', '3', 'the tale of station 109.1', 'ron oliver', 'scott peters', 'november 4 , 1995', 'stig', 'none'], ['56', '4', 'the tale of the mystical mirror', 'craig pryce', 'david wiechorek', 'november 11 , 1995', 'betty ann', 'ms valenti'], ['57', '5', 'the tale of the chameleons', 'iain patterson', 'mark d perry', 'november 18 , 1995', 'betty ann', 'the chameleon'], ['58', '6', "the tale of prisoner 's past", 'ron oliver', 'alan kingsberg', 'december 2 , 1995', 'tucker', 'none'], ['59', '7', 'the tale of c7', 'david winning', 'david preston', 'december 9 , 1995', 'sam', 'none'], ['60', '8', 'the tale of the manaha', 'will dixon', 'gerald wexler', 'december 30 , 1995', 'tucker', 'the shaman'], ['61', '9', 'the tale of the unexpected visitor', 'jacques laberge', 'alan kingsberg', 'january 13 , 1996', 'kiki', 'the alien kid and its mother'], ['62', '10', 'the tale of the vacant lot', 'lorette leblanc', 'gerald wexler', 'january 20 , 1996', 'kiki', 'marie'], ['63', '11', 'the tale of a door unlocked', 'ron oliver', 'scott peters', 'january 27 , 1996', 'gary', 'the toy door'], ['64', '12', 'the tale of the night shift', 'd j machale', 'chloe brown', 'february 3 , 1996', 'sam', 'the walking dead and the vampire']] |
rizal | https://en.wikipedia.org/wiki/Rizal | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-232458-1.html.csv | count | a total of two cities in rizal have a total number of eleven barangays . | {'scope': 'all', 'criterion': 'equal', 'value': '11', 'result': '2', 'col': '2', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'no of barangays', '11'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose no of barangays record is equal to 11 .', 'tostr': 'filter_eq { all_rows ; no of barangays ; 11 }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; no of barangays ; 11 } }', 'tointer': 'select the rows whose no of barangays record is equal to 11 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; no of barangays ; 11 } } ; 2 } = true', 'tointer': 'select the rows whose no of barangays record is equal to 11 . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; no of barangays ; 11 } } ; 2 } = true | select the rows whose no of barangays record is equal to 11 . the number of such rows is 2 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_eq_0': 0, 'all_rows_4': 4, 'no of barangays_5': 5, '11_6': 6, '2_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'no of barangays_5': 'no of barangays', '11_6': '11', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'no of barangays_5': [0], '11_6': [0], '2_7': [2]} | ['city / municipality', 'no of barangays', 'area ( km square )', 'population ( 2010 census )', 'pop density ( per km square )'] | [['angono', '10', '26.22', '102407', '3905.68'], ['antipolo', '16', '306.10', '677741', '2214.12'], ['baras', '10', '84.93', '32609', '383.95'], ['binangonan', '40', '66.34', '249872', '3766.54'], ['cainta', '7', '42.99', '311845', '7253.90'], ['cardona', '18', '28.56', '47414', '1660.15'], ['jalajala', '11', '44.12', '30074', '681.64'], ['morong', '8', '37.58', '52194', '1388.88'], ['pililla', '9', '69.95', '59527', '850.99'], ['rodriguez', '11', '312.70', '280904', '898.32'], ['san mateo', '15', '55.09', '205255', '3725.81'], ['tanay', '19', '200.00', '98879', '494.3'], ['taytay', '5', '38.80', '288956', '7447.32']] |
southeast asian games | https://en.wikipedia.org/wiki/Southeast_Asian_Games | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1575383-9.html.csv | majority | in the southeast asian games , for the countries that won over 200 gold medals , all of them won over 1000 total medals . | {'scope': 'subset', 'col': '5', 'most_or_all': 'all', 'criterion': 'greater_than', 'value': '1000', 'subset': {'col': '2', 'criterion': 'greater_than', 'value': '200'}} | {'func': 'all_greater', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'gold', '200'], 'result': None, 'ind': 0, 'tostr': 'filter_greater { all_rows ; gold ; 200 }', 'tointer': 'select the rows whose gold record is greater than 200 .'}, 'total', '1000'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose gold record is greater than 200 . for the total records of these rows , all of them are greater than 1000 .', 'tostr': 'all_greater { filter_greater { all_rows ; gold ; 200 } ; total ; 1000 } = true'} | all_greater { filter_greater { all_rows ; gold ; 200 } ; total ; 1000 } = true | select the rows whose gold record is greater than 200 . for the total records of these rows , all of them are greater than 1000 . | 2 | 2 | {'all_greater_1': 1, 'result_2': 2, 'filter_greater_0': 0, 'all_rows_3': 3, 'gold_4': 4, '200_5': 5, 'total_6': 6, '1000_7': 7} | {'all_greater_1': 'all_greater', 'result_2': 'true', 'filter_greater_0': 'filter_greater', 'all_rows_3': 'all_rows', 'gold_4': 'gold', '200_5': '200', 'total_6': 'total', '1000_7': '1000'} | {'all_greater_1': [2], 'result_2': [], 'filter_greater_0': [1], 'all_rows_3': [0], 'gold_4': [0], '200_5': [0], 'total_6': [1], '1000_7': [1]} | ['country', 'gold', 'silver', 'bronze', 'total'] | [['indonesia', '1602', '1413', '1395', '4410'], ['thailand', '1513', '1318', '1315', '4146'], ['philippines', '836', '971', '1191', '2998'], ['malaysia', '805', '772', '1067', '2644'], ['vietnam', '586', '540', '618', '1744'], ['singapore', '508', '559', '841', '1906'], ['myanmar', '249', '410', '579', '1238'], ['laos', '53', '60', '170', '283'], ['cambodia', '11', '26', '104', '151'], ['brunei', '10', '38', '120', '168'], ['timor - leste', '1', '1', '12', '14']] |
persons unknown ( tv series ) | https://en.wikipedia.org/wiki/Persons_Unknown_%28TV_series%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-25084227-1.html.csv | count | according to the list of episodes of persons unknown ( tv series ) , two of the episodes directed by jonathan frakes were written by linda mcgibney . | {'scope': 'subset', 'criterion': 'equal', 'value': 'linda mcgibney', 'result': '2', 'col': '4', 'subset': {'col': '3', 'criterion': 'equal', 'value': 'jonathan frakes'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'directed by', 'jonathan frakes'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; directed by ; jonathan frakes }', 'tointer': 'select the rows whose directed by record fuzzily matches to jonathan frakes .'}, 'written by', 'linda mcgibney'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose directed by record fuzzily matches to jonathan frakes . among these rows , select the rows whose written by record fuzzily matches to linda mcgibney .', 'tostr': 'filter_eq { filter_eq { all_rows ; directed by ; jonathan frakes } ; written by ; linda mcgibney }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_eq { filter_eq { all_rows ; directed by ; jonathan frakes } ; written by ; linda mcgibney } }', 'tointer': 'select the rows whose directed by record fuzzily matches to jonathan frakes . among these rows , select the rows whose written by record fuzzily matches to linda mcgibney . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_eq { all_rows ; directed by ; jonathan frakes } ; written by ; linda mcgibney } } ; 2 } = true', 'tointer': 'select the rows whose directed by record fuzzily matches to jonathan frakes . among these rows , select the rows whose written by record fuzzily matches to linda mcgibney . the number of such rows is 2 .'} | eq { count { filter_eq { filter_eq { all_rows ; directed by ; jonathan frakes } ; written by ; linda mcgibney } } ; 2 } = true | select the rows whose directed by record fuzzily matches to jonathan frakes . among these rows , select the rows whose written by record fuzzily matches to linda mcgibney . the number of such rows is 2 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_str_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'directed by_6': 6, 'jonathan frakes_7': 7, 'written by_8': 8, 'linda mcgibney_9': 9, '2_10': 10} | {'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_str_eq_1': 'filter_str_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'directed by_6': 'directed by', 'jonathan frakes_7': 'jonathan frakes', 'written by_8': 'written by', 'linda mcgibney_9': 'linda mcgibney', '2_10': '2'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_str_eq_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'directed by_6': [0], 'jonathan frakes_7': [0], 'written by_8': [1], 'linda mcgibney_9': [1], '2_10': [3]} | ['no', 'title', 'directed by', 'written by', 'original air date', 'production code', 'us viewers ( in millions )'] | [['1', 'pilot', 'michael rymer', 'christopher mcquarrie', 'june 7 , 2010', '4002 - 08 - 101', '4.29'], ['2', 'the edge', 'bill eagles', 'remi aubuchon', 'june 14 , 2010', '4002 - 08 - 102', '3.45'], ['3', 'the way through', 'bill eagles', 'sandy isaac', 'june 21 , 2010', '4002 - 08 - 103', '3.43'], ['4', 'exit one', 'leon ichaso', 'michael r perry', 'june 28 , 2010', '4002 - 08 - 104', '2.90'], ['5', 'incoming', 'jonathan frakes', 'linda mcgibney', 'july 5 , 2010', '4002 - 08 - 105', '2.96'], ['6', 'the truth', 'steve shill', 'sandy isaac', 'july 17 , 2010', '4002 - 08 - 106', '1.69'], ['7', 'smoke and steel', 'rod hardy', 'michael r perry', 'july 24 , 2010', '4002 - 08 - 107', '2.09'], ['8', 'saved', 'bill eagles', 'linda mcgibney', 'july 31 , 2010', '4002 - 08 - 108', '1.60'], ['9', 'static', 'michael offer', 'henry robles', 'august 7 , 2010', '4002 - 08 - 109', '1.26'], ['10', 'identity', 'jonathan frakes', 'sandy isaac', 'august 21 , 2010', '4002 - 08 - 110', '1.30'], ['11', 'seven sacrifices', 'tim matheson', 'michael r perry', 'august 21 , 2010 ( nbccom )', '4002 - 08 - 111', 'n / a'], ['12', 'and then there was one', 'jonathan frakes', 'linda mcgibney', 'august 28 , 2010', '4002 - 08 - 112', '2.97']] |
hunt - class mine countermeasures vessel | https://en.wikipedia.org/wiki/Hunt-class_mine_countermeasures_vessel | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1162013-1.html.csv | ordinal | ledbury is the second oldest commissioned hunt - class mine countermeasures vessel . | {'row': '2', 'col': '4', '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', 'commissioned', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; commissioned ; 2 }'}, 'name'], 'result': 'ledbury', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; commissioned ; 2 } ; name }'}, 'ledbury'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; commissioned ; 2 } ; name } ; ledbury } = true', 'tointer': 'select the row whose commissioned record of all rows is 2nd minimum . the name record of this row is ledbury .'} | eq { hop { nth_argmin { all_rows ; commissioned ; 2 } ; name } ; ledbury } = true | select the row whose commissioned record of all rows is 2nd minimum . the name record of this row is ledbury . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'commissioned_5': 5, '2_6': 6, 'name_7': 7, 'ledbury_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', 'commissioned_5': 'commissioned', '2_6': '2', 'name_7': 'name', 'ledbury_8': 'ledbury'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'commissioned_5': [0], '2_6': [0], 'name_7': [1], 'ledbury_8': [2]} | ['navy', 'name', 'pennant', 'commissioned', 'home port'] | [['royal navy', 'brecon', 'm29', '1980', 'hms raleigh'], ['royal navy', 'ledbury', 'm30', '1981', 'portsmouth'], ['royal navy', 'cattistock', 'm31', '1982', 'portsmouth'], ['royal navy', 'cottesmore', 'm32', '1983', 'portsmouth'], ['royal navy', 'brocklesby', 'm33', '1982', 'portsmouth'], ['royal navy', 'middleton', 'm34', '1984', 'portsmouth'], ['royal navy', 'dulverton', 'm35', '1983', 'portsmouth'], ['royal navy', 'bicester', 'm36', '1988', 'portsmouth'], ['royal navy', 'chiddingfold', 'm37', '1984', 'portsmouth'], ['royal navy', 'atherstone', 'm38', '1986', 'portsmouth'], ['royal navy', 'hurworth', 'm39', '1985', 'portsmouth'], ['royal navy', 'berkeley', 'm40', '1986', 'portsmouth'], ['royal navy', 'quorn', 'm41', '1989', 'portsmouth'], ['hellenic navy', 'europa', 'm62', '2001', 'salamis'], ['hellenic navy', 'kallisto', 'm63', '2000', 'salamis'], ['lithuanian naval force', 'skalvis', 'm53', '2011', 'klaipėda'], ['lithuanian naval force', 'kuršis', 'm51', '2011', 'klaipėda']] |
selima sfar | https://en.wikipedia.org/wiki/Selima_Sfar | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13604859-2.html.csv | ordinal | selima sfar won her second final in the tournament in moulins . | {'row': '2', 'col': '1', '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', 'date', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; date ; 2 }'}, 'tournament'], 'result': 'moulins', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; date ; 2 } ; tournament }'}, 'moulins'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; date ; 2 } ; tournament } ; moulins } = true', 'tointer': 'select the row whose date record of all rows is 2nd minimum . the tournament record of this row is moulins .'} | eq { hop { nth_argmin { all_rows ; date ; 2 } ; tournament } ; moulins } = true | select the row whose date record of all rows is 2nd minimum . the tournament record of this row is moulins . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'date_5': 5, '2_6': 6, 'tournament_7': 7, 'moulins_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', '2_6': '2', 'tournament_7': 'tournament', 'moulins_8': 'moulins'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'date_5': [0], '2_6': [0], 'tournament_7': [1], 'moulins_8': [2]} | ['date', 'tournament', 'surface', 'opponent in the final', 'score'] | [['august 14 , 1994', 'carthage', 'clay', 'anne - gaëlle sidot', '5 - 7 6 - 3 6 - 4'], ['march 26 , 1995', 'moulins', 'hard indoors', 'linda sentis', '3 - 6 6 - 3 6 - 2'], ['november 26 , 1995', 'le havre', 'clay indoors', 'émilie loit', '0 - 6 6 - 3 6 - 4'], ['february 4 , 1996', 'dinan', 'clay indoors', 'virginie massart', '6 - 4 7 - 6'], ['august 11 , 1996', 'carthage', 'clay', 'marielle bruens', '7 - 5 6 - 4'], ['december 14 , 1997', 'ismailia', 'clay', 'tzipora obziler', '5 - 7 7 - 5 6 - 4'], ['april 30 , 2000', 'bournemouth', 'clay', 'dragana zarić', '7 - 5 6 - 2'], ['september 22 , 2002', 'glasgow', 'hard indoors', 'anne keothavong', '7 - 6 2 - 6 7 - 6'], ['november 3 , 2002', 'nottingham', 'hard indoors', 'lilia osterloh', '6 - 2 6 - 2'], ['may 14 , 2006', 'jounieh', 'clay', 'anastasiya yakimova', '6 - 4 7 - 5'], ['may 13 , 2007', 'jounieh', 'clay', 'mariya koryttseva', '6 - 2 4 - 6 7 - 6']] |
list of football clubs in hong kong | https://en.wikipedia.org/wiki/List_of_football_clubs_in_Hong_Kong | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18408905-13.html.csv | majority | most of the football clubs in hong kong are in the first division . | {'scope': 'all', 'col': '2', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'first division', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'league / division', 'first division'], 'result': True, 'ind': 0, 'tointer': 'for the league / division records of all rows , most of them fuzzily match to first division .', 'tostr': 'most_eq { all_rows ; league / division ; first division } = true'} | most_eq { all_rows ; league / division ; first division } = true | for the league / division records of all rows , most of them fuzzily match to first division . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'league / division_3': 3, 'first division_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'league / division_3': 'league / division', 'first division_4': 'first division'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'league / division_3': [0], 'first division_4': [0]} | ['club', 'league / division', 'home ground', 'location', 'position in 2012 - 13'] | [['sai kung', 'fourth division', 'n / a', 'n / a', '7th , fourth division'], ['sai kung friends', 'fourth division', 'n / a', 'n / a', '5th , fourth division'], ['sham shui po', 'third division', 'n / a', 'n / a', '11th , second division ( relegated )'], ['shatin', 'second division', 'ma on shan recreation ground', 'ma on shan , new territories', '7th , second division'], ['solon', 'fourth division', 'n / a', 'n / a', '9th , fourth division'], ['south china', 'first division', 'hong kong stadium', 'so kon po , hong kong island', '1st , first division'], ['southern', 'first division', 'aberdeen sports ground', 'aberdeen , hong kong island', '4th , first division'], ["st joseph 's", 'fourth division', 'n / a', 'n / a', '6th , fourth division'], ['sunray cave jc sun hei', 'first division', 'tsing yi sports ground', 'tsing yi , new territories', '7th , first division'], ['sun pegasus', 'first division', 'mong kok stadium', 'mong kok , kowloon', '5th , first division'], ['sun source', 'third division', 'n / a', 'n / a', '4th , fourth division ( promoted )']] |
1982 atlanta falcons season | https://en.wikipedia.org/wiki/1982_Atlanta_Falcons_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-16710829-2.html.csv | comparative | the atlanta falcons had a game against the los angeles raiders earlier than the st louis cardinals in the 1982 season . | {'row_1': '2', 'row_2': '4', 'col': '2', 'col_other': '3', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'los angeles raiders'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opponent record fuzzily matches to los angeles raiders .', 'tostr': 'filter_eq { all_rows ; opponent ; los angeles raiders }'}, 'date'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; opponent ; los angeles raiders } ; date }', 'tointer': 'select the rows whose opponent record fuzzily matches to los angeles raiders . take the date record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'st louis cardinals'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose opponent record fuzzily matches to st louis cardinals .', 'tostr': 'filter_eq { all_rows ; opponent ; st louis cardinals }'}, 'date'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; opponent ; st louis cardinals } ; date }', 'tointer': 'select the rows whose opponent record fuzzily matches to st louis cardinals . take the date record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; opponent ; los angeles raiders } ; date } ; hop { filter_eq { all_rows ; opponent ; st louis cardinals } ; date } } = true', 'tointer': 'select the rows whose opponent record fuzzily matches to los angeles raiders . take the date record of this row . select the rows whose opponent record fuzzily matches to st louis cardinals . take the date record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; opponent ; los angeles raiders } ; date } ; hop { filter_eq { all_rows ; opponent ; st louis cardinals } ; date } } = true | select the rows whose opponent record fuzzily matches to los angeles raiders . take the date record of this row . select the rows whose opponent record fuzzily matches to st louis cardinals . 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, 'opponent_7': 7, 'los angeles raiders_8': 8, 'date_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'opponent_11': 11, 'st louis cardinals_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', 'opponent_7': 'opponent', 'los angeles raiders_8': 'los angeles raiders', 'date_9': 'date', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'opponent_11': 'opponent', 'st louis cardinals_12': 'st louis cardinals', 'date_13': 'date'} | {'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'opponent_7': [0], 'los angeles raiders_8': [0], 'date_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'opponent_11': [1], 'st louis cardinals_12': [1], 'date_13': [3]} | ['game', 'date', 'opponent', 'result', 'falcons points', 'opponents', 'record', 'attendance'] | [['1', 'sept 12', 'new york giants', 'win', '16', '14', '1 - 0', '74286'], ['2', 'sept 19', 'los angeles raiders', 'loss', '14', '38', '1 - 1', '54774'], ['3', 'nov 21', 'los angeles rams', 'win', '34', '17', '2 - 1', '39686'], ['4', 'nov 28', 'st louis cardinals', 'loss', '20', '23', '2 - 2', '33411'], ['5', 'dec 5', 'denver broncos', 'win', '34', '27', '3 - 2', '73984'], ['6', 'dec 12', 'new orleans saints', 'win', '35', '0', '4 - 2', '39535'], ['7', 'dec 19', 'san francisco 49ers', 'win', '17', '7', '5 - 2', '53234'], ['8', 'dec 26', 'green bay packers', 'loss', '7', '38', '5 - 3', '50245'], ['9', 'jan 2', 'new orleans saints', 'loss', '6', '35', '5 - 4', '47336']] |
list of vehicle speed records | https://en.wikipedia.org/wiki/List_of_vehicle_speed_records | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16343705-3.html.csv | unique | the schempp-hirth nimbus-4dm was the only vehicle that was manned by two people when achieving its speed record . | {'scope': 'all', 'row': '6', 'col': '5', 'col_other': '4', 'criterion': 'equal', 'value': 'and', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'pilot', 'and'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose pilot record fuzzily matches to and .', 'tostr': 'filter_eq { all_rows ; pilot ; and }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; pilot ; and } }', 'tointer': 'select the rows whose pilot record fuzzily matches to and . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'pilot', 'and'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose pilot record fuzzily matches to and .', 'tostr': 'filter_eq { all_rows ; pilot ; and }'}, 'vehicle'], 'result': 'schempp - hirth nimbus - 4dm', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; pilot ; and } ; vehicle }'}, 'schempp - hirth nimbus - 4dm'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; pilot ; and } ; vehicle } ; schempp - hirth nimbus - 4dm }', 'tointer': 'the vehicle record of this unqiue row is schempp - hirth nimbus - 4dm .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; pilot ; and } } ; eq { hop { filter_eq { all_rows ; pilot ; and } ; vehicle } ; schempp - hirth nimbus - 4dm } } = true', 'tointer': 'select the rows whose pilot record fuzzily matches to and . there is only one such row in the table . the vehicle record of this unqiue row is schempp - hirth nimbus - 4dm .'} | and { only { filter_eq { all_rows ; pilot ; and } } ; eq { hop { filter_eq { all_rows ; pilot ; and } ; vehicle } ; schempp - hirth nimbus - 4dm } } = true | select the rows whose pilot record fuzzily matches to and . there is only one such row in the table . the vehicle record of this unqiue row is schempp - hirth nimbus - 4dm . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'pilot_7': 7, 'and_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'vehicle_9': 9, 'schempp - hirth nimbus - 4dm_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'pilot_7': 'pilot', 'and_8': 'and', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'vehicle_9': 'vehicle', 'schempp - hirth nimbus - 4dm_10': 'schempp - hirth nimbus - 4dm'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'pilot_7': [0], 'and_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'vehicle_9': [2], 'schempp - hirth nimbus - 4dm_10': [3]} | ['category', 'speed ( km / h )', 'speed ( mph )', 'vehicle', 'pilot', 'date'] | [['rocket - powered aircraft', '7258', '4510', 'north american x - 15', 'william j knight', '3 oct 1967'], ['manned air - breathing craft', '3530', '2194', 'lockheed sr - 71 blackbird', 'eldon w joersz', '28 jul 1976'], ['propeller - driven aircraft', '870', '541', 'tupolev tu - 114', 'ivan soukhomline', '00 jan 1960'], ['piston - engined propeller - driven aircraft', '850.1', '528.33', 'grumman f8f bearcat rare bear ( n777l )', 'lyle shelton', '21 aug 1989'], ['helicopter', '401.0', '249.1', 'westland lynx 800 g - lynx', 'john egginton', '11 aug 1986'], ['glider ( sailplane )', '306.8', '190.6', 'schempp - hirth nimbus - 4dm', 'klaus ohlmann and matias garcia mazzaro', '22 dec 2006'], ['human - powered aircraft', '32', '19.8', 'mit monarch b', 'frank scarabino', '1 may 1984']] |
1930 vfl season | https://en.wikipedia.org/wiki/1930_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10767641-16.html.csv | ordinal | lake oval venue recorded the highest crowd participation during the 1930 vfl season . | {'row': '5', 'col': '6', 'order': '1', 'col_other': '5', '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', 'crowd', '1'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; crowd ; 1 }'}, 'venue'], 'result': 'lake oval', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; crowd ; 1 } ; venue }'}, 'lake oval'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; venue } ; lake oval } = true', 'tointer': 'select the row whose crowd record of all rows is 1st maximum . the venue record of this row is lake oval .'} | eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; venue } ; lake oval } = true | select the row whose crowd record of all rows is 1st maximum . the venue record of this row is lake oval . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'crowd_5': 5, '1_6': 6, 'venue_7': 7, 'lake oval_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', 'crowd_5': 'crowd', '1_6': '1', 'venue_7': 'venue', 'lake oval_8': 'lake oval'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'crowd_5': [0], '1_6': [0], 'venue_7': [1], 'lake oval_8': [2]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['essendon', '13.11 ( 89 )', 'melbourne', '12.7 ( 79 )', 'windy hill', '15000', '30 august 1930'], ['collingwood', '16.20 ( 116 )', 'footscray', '10.17 ( 77 )', 'victoria park', '10000', '30 august 1930'], ['carlton', '16.12 ( 108 )', 'st kilda', '15.7 ( 97 )', 'princes park', '20000', '30 august 1930'], ['richmond', '20.15 ( 135 )', 'north melbourne', '5.10 ( 40 )', 'punt road oval', '7000', '30 august 1930'], ['south melbourne', '12.16 ( 88 )', 'geelong', '15.14 ( 104 )', 'lake oval', '28000', '30 august 1930'], ['hawthorn', '3.16 ( 34 )', 'fitzroy', '15.14 ( 104 )', 'glenferrie oval', '7000', '30 august 1930']] |
1984 atlanta falcons season | https://en.wikipedia.org/wiki/1984_Atlanta_Falcons_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16710742-1.html.csv | aggregation | the average pick number for the 1984 atlanta falcons team was 154 . | {'scope': 'all', 'col': '2', 'type': 'average', 'result': '154', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'pick'], 'result': '154', 'ind': 0, 'tostr': 'avg { all_rows ; pick }'}, '154'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; pick } ; 154 } = true', 'tointer': 'the average of the pick record of all rows is 154 .'} | round_eq { avg { all_rows ; pick } ; 154 } = true | the average of the pick record of all rows is 154 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'pick_4': 4, '154_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'pick_4': 'pick', '154_5': '154'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'pick_4': [0], '154_5': [1]} | ['round', 'pick', 'player', 'position', 'school'] | [['1', '9', 'rick bryan', 'defensive end', 'oklahoma'], ['2', '32', 'scott case', 'defensive back', 'oklahoma'], ['2', '36', 'thomas benson', 'linebacker', 'oklahoma'], ['3', '63', 'rod mcswain', 'defensive back', 'clemson'], ['4', '94', 'rydell malancon', 'linebacker', 'louisiana state'], ['5', '132', 'cliff benson', 'tight end', 'purdue'], ['6', '148', 'ben bennett', 'quarterback', 'duke'], ['6', '163', 'dan ralph', 'defensive tackle', 'oregon'], ['7', '175', 'kirk dodge', 'linebacker', 'university of nevada - las vegas'], ['8', '206', 'jeff jackson', 'linebacker', 'auburn'], ['9', '233', 'glen howe', 'tackle', 'southern mississippi'], ['10', '260', 'derrick franklin', 'defensive back', 'fresno state'], ['11', '287', 'tommy norman', 'wide receiver', 'jackson state'], ['12', '318', 'don holmes', 'wide receiver', 'mesa']] |
kei nishikori | https://en.wikipedia.org/wiki/Kei_Nishikori | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12299543-2.html.csv | majority | of the finals that kei nishikori participated in , most of them were on a hard surface . | {'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'hard', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'surface', 'hard'], 'result': True, 'ind': 0, 'tointer': 'for the surface records of all rows , most of them fuzzily match to hard .', 'tostr': 'most_eq { all_rows ; surface ; hard } = true'} | most_eq { all_rows ; surface ; hard } = true | for the surface records of all rows , most of them fuzzily match to hard . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'surface_3': 3, 'hard_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'surface_3': 'surface', 'hard_4': 'hard'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'surface_3': [0], 'hard_4': [0]} | ['outcome', 'date', 'surface', 'opponent in the final', 'score in the final'] | [['winner', '11 february 2008', 'hard', 'james blake', '3 - 6 , 6 - 1 , 6 - 4'], ['runner - up', '10 april 2011', 'clay', 'ryan sweeting', '4 - 6 , 6 - 7 ( 3 - 7 )'], ['runner - up', '6 november 2011', 'hard ( i )', 'roger federer', '1 - 6 , 3 - 6'], ['winner', '7 october 2012', 'hard', 'milos raonic', '7 - 6 ( 7 - 5 ) , 3 - 6 , 6 - 0'], ['winner', '24 february 2013', 'hard ( i )', 'feliciano lópez', '6 - 2 , 6 - 3']] |
2005 - 06 columbus blue jackets season | https://en.wikipedia.org/wiki/2005%E2%80%9306_Columbus_Blue_Jackets_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13242342-5.html.csv | aggregation | the average attendance at columbus blue jackets games was 16013 . | {'scope': 'all', 'col': '6', 'type': 'average', 'result': '16013', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'attendance'], 'result': '16013', 'ind': 0, 'tostr': 'avg { all_rows ; attendance }'}, '16013'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; attendance } ; 16013 } = true', 'tointer': 'the average of the attendance record of all rows is 16013 .'} | round_eq { avg { all_rows ; attendance } ; 16013 } = true | the average of the attendance record of all rows is 16013 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'attendance_4': 4, '16013_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'attendance_4': 'attendance', '16013_5': '16013'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'attendance_4': [0], '16013_5': [1]} | ['date', 'visitor', 'score', 'home', 'decision', 'attendance', 'record'] | [['december 1', 'columbus', '1 - 4', 'st louis', 'leclaire', '12307', '7 - 19 - 0'], ['december 8', 'ny islanders', '3 - 4', 'columbus', 'leclaire', '15728', '8 - 19 - 0'], ['december 9', 'columbus', '2 - 5', 'atlanta', 'leclaire', '14260', '8 - 20 - 0'], ['december 11', 'new jersey', '2 - 3', 'columbus', 'denis', '17157', '9 - 20 - 0'], ['december 13', 'philadelphia', '3 - 1', 'columbus', 'denis', '16263', '9 - 21 - 0'], ['december 15', 'columbus', '1 - 2', 'carolina', 'leclaire', '11069', '9 - 22 - 0'], ['december 17', 'columbus', '3 - 7', 'nashville', 'denis', '16020', '9 - 23 - 0'], ['december 20', 'columbus', '3 - 4', 'detroit', 'leclaire', '20066', '9 - 23 - 1'], ['december 21', 'dallas', '5 - 3', 'columbus', 'leclaire', '16859', '9 - 24 - 1'], ['december 23', 'nashville', '5 - 4', 'columbus', 'denis', '16330', '9 - 25 - 1'], ['december 26', 'chicago', '3 - 4', 'columbus', 'denis', '17441', '10 - 25 - 1'], ['december 28', 'anaheim', '0 - 1', 'columbus', 'denis', '17387', '11 - 25 - 1'], ['december 30', 'columbus', '3 - 2', 'chicago', 'denis', '13229', '12 - 25 - 1'], ['december 31', 'columbus', '2 - 5', 'detroit', 'denis', '20066', '12 - 26 - 1']] |
south africa | https://en.wikipedia.org/wiki/South_Africa | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17416221-1.html.csv | ordinal | the province that has the second highest population in south africa is kwazulu-natal . | {'row': '4', 'col': '5', 'order': '2', 'col_other': '1', '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', 'population ( 2013 )', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; population ( 2013 ) ; 2 }'}, 'province'], 'result': 'kwazulu - natal', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; population ( 2013 ) ; 2 } ; province }'}, 'kwazulu - natal'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; population ( 2013 ) ; 2 } ; province } ; kwazulu - natal } = true', 'tointer': 'select the row whose population ( 2013 ) record of all rows is 2nd maximum . the province record of this row is kwazulu - natal .'} | eq { hop { nth_argmax { all_rows ; population ( 2013 ) ; 2 } ; province } ; kwazulu - natal } = true | select the row whose population ( 2013 ) record of all rows is 2nd maximum . the province record of this row is kwazulu - natal . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'population (2013)_5': 5, '2_6': 6, 'province_7': 7, 'kwazulu - natal_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', 'population (2013)_5': 'population ( 2013 )', '2_6': '2', 'province_7': 'province', 'kwazulu - natal_8': 'kwazulu - natal'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'population (2013)_5': [0], '2_6': [0], 'province_7': [1], 'kwazulu - natal_8': [2]} | ['province', 'provincial capital', 'largest city', 'area ( km 2 )', 'population ( 2013 )'] | [['eastern cape', 'bhisho', 'port elizabeth', '168966', '6620100'], ['free state', 'bloemfontein', 'bloemfontein', '129825', '2753200'], ['gauteng', 'johannesburg', 'johannesburg', '18178', '12728400'], ['kwazulu - natal', 'pietermaritzburg', 'durban', '94361', '10456900'], ['limpopo', 'polokwane', 'polokwane', '125754', '5518000'], ['mpumalanga', 'nelspruit', 'nelspruit', '76495', '4128000'], ['north west', 'mahikeng', 'rustenburg', '104882', '3597600'], ['northern cape', 'kimberley', 'kimberley', '372889', '1162900'], ['western cape', 'cape town', 'cape town', '129462', '6016900']] |
stefano modena | https://en.wikipedia.org/wiki/Stefano_Modena | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1226330-2.html.csv | unique | 1991 in the braun tyrell honda is the only event where stafano modena managed 10 points . | {'scope': 'all', 'row': '6', 'col': '5', 'col_other': '2', 'criterion': 'equal', 'value': '10', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'points', '10'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose points record is equal to 10 .', 'tostr': 'filter_eq { all_rows ; points ; 10 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; points ; 10 } }', 'tointer': 'select the rows whose points record is equal to 10 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'points', '10'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose points record is equal to 10 .', 'tostr': 'filter_eq { all_rows ; points ; 10 }'}, 'entrant'], 'result': 'braun tyrrell honda', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; points ; 10 } ; entrant }'}, 'braun tyrrell honda'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; points ; 10 } ; entrant } ; braun tyrrell honda }', 'tointer': 'the entrant record of this unqiue row is braun tyrrell honda .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; points ; 10 } } ; eq { hop { filter_eq { all_rows ; points ; 10 } ; entrant } ; braun tyrrell honda } } = true', 'tointer': 'select the rows whose points record is equal to 10 . there is only one such row in the table . the entrant record of this unqiue row is braun tyrrell honda .'} | and { only { filter_eq { all_rows ; points ; 10 } } ; eq { hop { filter_eq { all_rows ; points ; 10 } ; entrant } ; braun tyrrell honda } } = true | select the rows whose points record is equal to 10 . there is only one such row in the table . the entrant record of this unqiue row is braun tyrrell honda . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'points_7': 7, '10_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'entrant_9': 9, 'braun tyrrell honda_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'points_7': 'points', '10_8': '10', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'entrant_9': 'entrant', 'braun tyrrell honda_10': 'braun tyrrell honda'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'points_7': [0], '10_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'entrant_9': [2], 'braun tyrrell honda_10': [3]} | ['year', 'entrant', 'chassis', 'engine', 'points'] | [['1987', 'motor racing developments', 'brabham bt56', 'bmw str - 4', '0'], ['1988', 'eurobrun racing', 'eurobrun er188', 'cosworth v8', '0'], ['1989', 'motor racing developments', 'brabham bt58', 'judd v8', '4'], ['1990', 'motor racing developments', 'brabham bt58', 'judd v8', '2'], ['1990', 'motor racing developments', 'brabham bt59', 'judd v8', '2'], ['1991', 'braun tyrrell honda', 'tyrrell 020', 'honda v10', '10'], ['1992', 'sasol jordan yamaha', 'jordan 192', 'yamaha v12', '1']] |
2011 - 12 la liga | https://en.wikipedia.org/wiki/2011%E2%80%9312_La_Liga | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-29398373-2.html.csv | unique | caja granada only sponsored the granada football club in the 2011-12 season of la liga . | {'scope': 'all', 'row': '7', 'col': '6', 'col_other': '1', 'criterion': 'equal', 'value': 'caja granada', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'shirt sponsor', 'caja granada'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose shirt sponsor record fuzzily matches to caja granada .', 'tostr': 'filter_eq { all_rows ; shirt sponsor ; caja granada }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; shirt sponsor ; caja granada } }', 'tointer': 'select the rows whose shirt sponsor record fuzzily matches to caja granada . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'shirt sponsor', 'caja granada'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose shirt sponsor record fuzzily matches to caja granada .', 'tostr': 'filter_eq { all_rows ; shirt sponsor ; caja granada }'}, 'team'], 'result': 'granada', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; shirt sponsor ; caja granada } ; team }'}, 'granada'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; shirt sponsor ; caja granada } ; team } ; granada }', 'tointer': 'the team record of this unqiue row is granada .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; shirt sponsor ; caja granada } } ; eq { hop { filter_eq { all_rows ; shirt sponsor ; caja granada } ; team } ; granada } } = true', 'tointer': 'select the rows whose shirt sponsor record fuzzily matches to caja granada . there is only one such row in the table . the team record of this unqiue row is granada .'} | and { only { filter_eq { all_rows ; shirt sponsor ; caja granada } } ; eq { hop { filter_eq { all_rows ; shirt sponsor ; caja granada } ; team } ; granada } } = true | select the rows whose shirt sponsor record fuzzily matches to caja granada . there is only one such row in the table . the team record of this unqiue row is granada . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'shirt sponsor_7': 7, 'caja granada_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'team_9': 9, 'granada_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'shirt sponsor_7': 'shirt sponsor', 'caja granada_8': 'caja granada', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'team_9': 'team', 'granada_10': 'granada'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'shirt sponsor_7': [0], 'caja granada_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'team_9': [2], 'granada_10': [3]} | ['team', 'chairman', 'head coach', 'captain', 'kitmaker', 'shirt sponsor'] | [['athletic bilbao', 'josu urrutia', 'marcelo bielsa', 'carlos gurpegui', 'umbro', 'petronor'], ['atlético madrid', 'enrique cerezo', 'diego simeone', 'antonio lópez', 'nike', 'rixos hotels , huawei , and kyocera'], ['barcelona', 'sandro rosell', 'josep guardiola', 'carles puyol', 'nike', 'qatar foundation and unicef'], ['betis', 'miguel guillén', 'pepe mel', 'iriney', 'rbb', 'cirsa'], ['espanyol', 'ramon condal', 'mauricio pochettino', 'cristian álvarez', 'li ning', 'cancún'], ['getafe', 'ángel torres', 'luis garcía plaza', 'javier casquero', 'joma', 'burger king and confremar'], ['granada', 'quique pina', 'abel resino', 'manuel lucena', 'legea', 'caja granada'], ['levante', 'quico catalán', 'juan ignacio martínez', 'sergio ballesteros', 'luanvi', 'comunitat valenciana'], ['málaga', 'sheikh abdullah al thani', 'manuel pellegrini', 'jesús gámez', 'nike', 'unesco'], ['mallorca', 'jaume cladera', 'joaquín caparrós', 'nunes', 'macron', 'bet - at - homecom'], ['osasuna', 'patxi izco', 'josé luis mendilibar', 'francisco puñal', 'astore', 'can'], ['racing santander', 'francisco pernía', 'álvaro cervera', 'pedro munitis', 'slam', 'palacios'], ['rayo vallecano', 'raúl martín', 'josé ramón sandoval', 'míchel', 'erreà', 'los vengadores'], ['real madrid', 'florentino pérez', 'josé mourinho', 'iker casillas', 'adidas', 'bwin'], ['real sociedad', 'jokin aperribay', 'philippe montanier', 'mikel aranburu', 'nike', 'gipuzkoa euskararekin bat'], ['sevilla', 'josé maría del nido', 'míchel gonzález', 'andrés palop', 'li ning', 'interwetten'], ['sporting de gijón', 'manuel vega - arango', 'javier clemente', 'david barral', 'kappa', 'gijón / asturias'], ['valencia', 'manuel llorente', 'unai emery', 'david albelda', 'joma', 'jinko solar , herbalife and msc cruceros'], ['villarreal', 'fernando roig', 'miguel ángel lotina', 'marcos senna', 'xtep', 'comunitat valenciana']] |
senate of canada | https://en.wikipedia.org/wiki/Senate_of_Canada | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-123498-4.html.csv | aggregation | the average number of members in senate of canada from 1867 to 1999 was 88 . | {'scope': 'all', 'col': '2', 'type': 'average', 'result': '88', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'normal total'], 'result': '88', 'ind': 0, 'tostr': 'avg { all_rows ; normal total }'}, '88'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; normal total } ; 88 } = true', 'tointer': 'the average of the normal total record of all rows is 88 .'} | round_eq { avg { all_rows ; normal total } ; 88 } = true | the average of the normal total record of all rows is 88 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'normal total_4': 4, '88_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'normal total_4': 'normal total', '88_5': '88'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'normal total_4': [0], '88_5': [1]} | ['date enacted', 'normal total', 'ยง26 total', 'ont', 'que', 'ns', 'nb'] | [['july 1 , 1867', '72', '78', '24', '24', '12', '12'], ['july 15 , 1870', '74', '80', '24', '24', '12', '12'], ['july 20 , 1871', '77', '83', '24', '24', '12', '12'], ['july 1 , 1873', '77', '83', '24', '24', '10', '10'], ['september 1 , 1905', '85', '91', '24', '24', '10', '10'], ['may 19 , 1915', '96', '104', '24', '24', '10', '10'], ['march 31 , 1949', '102', '110', '24', '24', '10', '10'], ['june 19 , 1975', '104', '112', '24', '24', '10', '10'], ['april 1 , 1999', '105', '113', '24', '24', '10', '10'], ['date', 'normal total', 'ยง26 total', 'ont', 'que', 'ns', 'nb']] |
henlopen conference | https://en.wikipedia.org/wiki/Henlopen_Conference | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13054553-17.html.csv | aggregation | looking at their overall records , the teams of the henlopen conference averaged just over five wins each . | {'scope': 'all', 'col': '4', 'type': 'average', 'result': '5', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'overall record'], 'result': '5', 'ind': 0, 'tostr': 'avg { all_rows ; overall record }'}, '5'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; overall record } ; 5 } = true', 'tointer': 'the average of the overall record record of all rows is 5 .'} | round_eq { avg { all_rows ; overall record } ; 5 } = true | the average of the overall record record of all rows is 5 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'overall record_4': 4, '5_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'overall record_4': 'overall record', '5_5': '5'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'overall record_4': [0], '5_5': [1]} | ['school', 'team', 'division record', 'overall record', 'season outcome'] | [['sussex central', 'golden knights', '6 - 0', '7 - 4', 'loss in first round of div i playoffs'], ['dover', 'senators', '5 - 1', '8 - 4', 'loss in semi - finals of div i playoffs'], ['cape henlopen', 'vikings', '4 - 2', '8 - 2', 'failed to make playoffs'], ['caesar rodney', 'riders', '3 - 3', '3 - 7', 'failed to make playoffs'], ['smyrna', 'eagles', '2 - 4', '5 - 5', 'failed to make playoffs'], ['sussex tech', 'ravens', '1 - 5', '4 - 6', 'failed to make playoffs'], ['milford', 'buccaneers', '0 - 6', '1 - 9', 'failed to make playoffs']] |
york county , new brunswick | https://en.wikipedia.org/wiki/York_County%2C_New_Brunswick | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-176533-2.html.csv | ordinal | in york county , new brunswick , douglas has the highest area km 2 among those with population more than 4000 . | {'scope': 'subset', 'row': '2', 'col': '3', 'order': '1', 'col_other': '1', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'subset': {'col': '4', 'criterion': 'greater_than', 'value': '4000'}} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'population', '4000'], 'result': None, 'ind': 0, 'tostr': 'filter_greater { all_rows ; population ; 4000 }', 'tointer': 'select the rows whose population record is greater than 4000 .'}, 'area km 2', '1'], 'result': None, 'ind': 1, 'tostr': 'nth_argmax { filter_greater { all_rows ; population ; 4000 } ; area km 2 ; 1 }'}, 'official name'], 'result': 'douglas', 'ind': 2, 'tostr': 'hop { nth_argmax { filter_greater { all_rows ; population ; 4000 } ; area km 2 ; 1 } ; official name }'}, 'douglas'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { nth_argmax { filter_greater { all_rows ; population ; 4000 } ; area km 2 ; 1 } ; official name } ; douglas } = true', 'tointer': 'select the rows whose population record is greater than 4000 . select the row whose area km 2 record of these rows is 1st maximum . the official name record of this row is douglas .'} | eq { hop { nth_argmax { filter_greater { all_rows ; population ; 4000 } ; area km 2 ; 1 } ; official name } ; douglas } = true | select the rows whose population record is greater than 4000 . select the row whose area km 2 record of these rows is 1st maximum . the official name record of this row is douglas . | 4 | 4 | {'str_eq_3': 3, 'result_4': 4, 'str_hop_2': 2, 'nth_argmax_1': 1, 'filter_greater_0': 0, 'all_rows_5': 5, 'population_6': 6, '4000_7': 7, 'area km 2_8': 8, '1_9': 9, 'official name_10': 10, 'douglas_11': 11} | {'str_eq_3': 'str_eq', 'result_4': 'true', 'str_hop_2': 'str_hop', 'nth_argmax_1': 'nth_argmax', 'filter_greater_0': 'filter_greater', 'all_rows_5': 'all_rows', 'population_6': 'population', '4000_7': '4000', 'area km 2_8': 'area km 2', '1_9': '1', 'official name_10': 'official name', 'douglas_11': 'douglas'} | {'str_eq_3': [4], 'result_4': [], 'str_hop_2': [3], 'nth_argmax_1': [2], 'filter_greater_0': [1], 'all_rows_5': [0], 'population_6': [0], '4000_7': [0], 'area km 2_8': [1], '1_9': [1], 'official name_10': [2], 'douglas_11': [3]} | ['official name', 'status', 'area km 2', 'population', 'census ranking'] | [['kingsclear', 'parish', '303.26', '6689', '545 of 5008'], ['douglas', 'parish', '1446.50', '5774', '609 of 5008'], ['saint marys', 'parish', '753.06', '4224', '767 of 5008'], ['bright', 'parish', '404.00', '3159', '958 of 5008'], ['new maryland', 'parish', '375.40', '2348', '1193 of 5008'], ['manners sutton', 'parish', '525.47', '1863', '1394 of 5008'], ['stanley', 'parish', '2040.21', '1817', '1408 of 5008'], ['southampton', 'parish', '450.05', '1601', '1535 of 5008'], ['queensbury', 'parish', '301.22', '1215', '1811 of 5008'], ['prince william', 'parish', '287.92', '879', '2234 of 5008'], ['canterbury', 'parish', '557.22', '555', '2875 of 5008'], ['dumfries', 'parish', '305.23', '369', '3418 of 5008'], ['north lake', 'parish', '440.72', '300', '3678 of 5008'], ['mcadam', 'parish', '537.62', '80', '4512 of 5008']] |
rovers cup | https://en.wikipedia.org/wiki/Rovers_Cup | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14645146-1.html.csv | majority | most of the teams with at least 5 wins in the rover cup were last runners-up in 1987 or later . | {'scope': 'subset', 'col': '5', 'most_or_all': 'most', 'criterion': 'greater_than_eq', 'value': '1987', 'subset': {'col': '2', 'criterion': 'greater_than_eq', 'value': '5'}} | {'func': 'most_greater_eq', 'args': [{'func': 'filter_greater_eq', 'args': ['all_rows', 'wins', '5'], 'result': None, 'ind': 0, 'tostr': 'filter_greater_eq { all_rows ; wins ; 5 }', 'tointer': 'select the rows whose wins record is greater than or equal to 5 .'}, 'last runners - up', '1987'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose wins record is greater than or equal to 5 . for the last runners - up records of these rows , most of them are greater than or equal to 1987 .', 'tostr': 'most_greater_eq { filter_greater_eq { all_rows ; wins ; 5 } ; last runners - up ; 1987 } = true'} | most_greater_eq { filter_greater_eq { all_rows ; wins ; 5 } ; last runners - up ; 1987 } = true | select the rows whose wins record is greater than or equal to 5 . for the last runners - up records of these rows , most of them are greater than or equal to 1987 . | 2 | 2 | {'most_greater_eq_1': 1, 'result_2': 2, 'filter_greater_eq_0': 0, 'all_rows_3': 3, 'wins_4': 4, '5_5': 5, 'last runners - up_6': 6, '1987_7': 7} | {'most_greater_eq_1': 'most_greater_eq', 'result_2': 'true', 'filter_greater_eq_0': 'filter_greater_eq', 'all_rows_3': 'all_rows', 'wins_4': 'wins', '5_5': '5', 'last runners - up_6': 'last runners - up', '1987_7': '1987'} | {'most_greater_eq_1': [2], 'result_2': [], 'filter_greater_eq_0': [1], 'all_rows_3': [0], 'wins_4': [0], '5_5': [0], 'last runners - up_6': [1], '1987_7': [1]} | ['club', 'wins', 'last win', 'runners - up', 'last runners - up'] | [['mohun bagan ac', '14', '2000 - 01', '10', '1987'], ['east bengal club', '10', '1994', '4', '1988'], ['hyderabad police', '9', '1963', '1', '1943'], ['mohammedan sporting club', '6', '1987', '9', '1991'], ['dempo sc', '4', '1986', '1', '1989'], ['bangalore muslims', '3', '1948', '2', '1953'], ['salgaocar sc', '3', '1999', '1', '1985']] |
english cricket team in australia in 1911 - 12 | https://en.wikipedia.org/wiki/English_cricket_team_in_Australia_in_1911%E2%80%9312 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17470911-1.html.csv | aggregation | 456 is the sum of the results for the english cricket team in australia in 1911 - 12 . | {'scope': 'all', 'col': '5', 'type': 'sum', 'result': '456', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'result'], 'result': '456', 'ind': 0, 'tostr': 'sum { all_rows ; result }'}, '456'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; result } ; 456 } = true', 'tointer': 'the sum of the result record of all rows is 456 .'} | round_eq { sum { all_rows ; result } ; 456 } = true | the sum of the result record of all rows is 456 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'result_4': 4, '456_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'result_4': 'result', '456_5': '456'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'result_4': [0], '456_5': [1]} | ['date', 'home captain', 'away captain', 'venue', 'result'] | [['15 , 16 , 18 , 19 , 20 , 21 dec 1911', 'clem hill', 'johnny douglas', 'sydney cricket ground', 'aus by 146 runs'], ['30 dec , 1 , 2 , 3 jan 1911 / 2', 'clem hill', 'johnny douglas', 'melbourne cricket ground', 'eng by 8 wkts'], ['12 , 13 , 15 , 16 , 17 jan 1912', 'clem hill', 'johnny douglas', 'adelaide oval', 'eng by 7 wkts'], ['9 , 10 , 12 , 13 feb 1912', 'clem hill', 'johnny douglas', 'melbourne cricket ground', 'eng by inns & 225 runs'], ['23 , 24 , 26 , 27 , 28 , 29 feb , 1 mar 1912', 'clem hill', 'johnny douglas', 'sydney cricket ground', 'eng by 70 runs']] |
asian youth volleyball championship | https://en.wikipedia.org/wiki/Asian_Youth_Volleyball_Championship | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16744545-5.html.csv | ordinal | the rank 2 country in the asian youth volleyball championship received the second most bronze medals . | {'row': '2', 'col': '4', 'order': '2', 'col_other': '1', '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', 'bronze', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; bronze ; 2 }'}, 'rank'], 'result': '2', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; bronze ; 2 } ; rank }'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; bronze ; 2 } ; rank } ; 2 } = true', 'tointer': 'select the row whose bronze record of all rows is 2nd maximum . the rank record of this row is 2 .'} | eq { hop { nth_argmax { all_rows ; bronze ; 2 } ; rank } ; 2 } = true | select the row whose bronze record of all rows is 2nd maximum . the rank record of this row is 2 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'bronze_5': 5, '2_6': 6, 'rank_7': 7, '2_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', 'bronze_5': 'bronze', '2_6': '2', 'rank_7': 'rank', '2_8': '2'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'bronze_5': [0], '2_6': [0], 'rank_7': [1], '2_8': [2]} | ['rank', 'gold', 'silver', 'bronze', 'total'] | [['1', '5', '2', '0', '7'], ['2', '4', '3', '2', '9'], ['3', '0', '3', '1', '4'], ['4', '0', '1', '0', '1'], ['5', '0', '0', '3', '3'], ['total', '9', '9', '9', '27']] |
2005 world women 's curling championship | https://en.wikipedia.org/wiki/2005_World_Women%27s_Curling_Championship | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1554808-2.html.csv | majority | in the 2005 world women 's curling championship the majority of teams had a shot percentage over 65 % . | {'scope': 'all', 'col': '7', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '65 %', 'subset': None} | {'func': 'most_greater', 'args': ['all_rows', 'shot pct', '65 %'], 'result': True, 'ind': 0, 'tointer': 'for the shot pct records of all rows , most of them are greater than 65 % .', 'tostr': 'most_greater { all_rows ; shot pct ; 65 % } = true'} | most_greater { all_rows ; shot pct ; 65 % } = true | for the shot pct records of all rows , most of them are greater than 65 % . | 1 | 1 | {'most_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 'shot pct_3': 3, '65%_4': 4} | {'most_greater_0': 'most_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 'shot pct_3': 'shot pct', '65%_4': '65 %'} | {'most_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 'shot pct_3': [0], '65%_4': [0]} | ['locale', 'skip', 'ends won', 'ends lost', 'blank ends', 'stolen ends', 'shot pct'] | [['sweden', 'anette norberg', '56', '39', '11', '25', '75 %'], ['united states', 'cassandra johnson', '53', '38', '13', '22', '76 %'], ['canada', 'jennifer jones', '48', '45', '3', '21', '68 %'], ['norway', 'dordi nordby', '46', '40', '11', '19', '72 %'], ['russia', 'olga jarkova', '47', '45', '17', '10', '70 %'], ['scotland', 'kelly wood', '53', '40', '6', '23', '69 %'], ['china', 'wang bingyu', '42', '45', '14', '13', '68 %'], ['switzerland', 'mirjam ott', '45', '48', '11', '13', '72 %'], ['japan', 'ayumi onodera', '41', '48', '8', '12', '66 %'], ['denmark', 'madeleine dupont', '36', '50', '10', '12', '63 %'], ['italy', 'diana gaspari', '39', '46', '7', '12', '65 %'], ['finland', 'kirsi nykã ¤ nen', '32', '55', '3', '9', '58 %']] |
2009 world championships in athletics - men 's 1500 metres | https://en.wikipedia.org/wiki/2009_World_Championships_in_Athletics_%E2%80%93_Men%27s_1500_metres | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-23987362-2.html.csv | unique | the world record is the only record to be set in germany . | {'scope': 'all', 'row': '2', 'col': '4', 'col_other': '1', 'criterion': 'fuzzily_match', 'value': 'germany', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'rome , italy', 'germany'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose rome , italy record fuzzily matches to germany .', 'tostr': 'filter_eq { all_rows ; rome , italy ; germany }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; rome , italy ; germany } }', 'tointer': 'select the rows whose rome , italy record fuzzily matches to 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', 'rome , italy', 'germany'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose rome , italy record fuzzily matches to germany .', 'tostr': 'filter_eq { all_rows ; rome , italy ; germany }'}, 'world record'], 'result': 'world leading', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; rome , italy ; germany } ; world record }'}, 'world leading'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; rome , italy ; germany } ; world record } ; world leading }', 'tointer': 'the world record record of this unqiue row is world leading .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; rome , italy ; germany } } ; eq { hop { filter_eq { all_rows ; rome , italy ; germany } ; world record } ; world leading } } = true', 'tointer': 'select the rows whose rome , italy record fuzzily matches to germany . there is only one such row in the table . the world record record of this unqiue row is world leading .'} | and { only { filter_eq { all_rows ; rome , italy ; germany } } ; eq { hop { filter_eq { all_rows ; rome , italy ; germany } ; world record } ; world leading } } = true | select the rows whose rome , italy record fuzzily matches to germany . there is only one such row in the table . the world record record of this unqiue row is world leading . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'rome , italy_7': 7, 'germany_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'world record_9': 9, 'world leading_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'rome , italy_7': 'rome , italy', 'germany_8': 'germany', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'world record_9': 'world record', 'world leading_10': 'world leading'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'rome , italy_7': [0], 'germany_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'world record_9': [2], 'world leading_10': [3]} | ['world record', 'hicham el guerrouj ( mar )', '3:26.00', 'rome , italy', '14 july 1998'] | [['championship record', 'hicham el guerrouj ( mar )', '3:27.65', 'seville , spain', '14 august 1999'], ['world leading', 'augustine choge ( ken )', '3:29.47', 'berlin , germany', '14 june 2009'], ['african record', 'hicham el guerrouj ( mar )', '3:26.00', 'rome , italy', '14 july 1998'], ['asian record', 'rashid ramzi ( bhr )', '3:29.14', 'rome , italy', '14 july 2006'], ['north american record', 'bernard lagat ( usa )', '3:29.30', 'rieti , italy', '28 august 2005'], ['south american record', 'hudson de souza ( bra )', '3:33.25', 'rieti , italy', '28 august 2005'], ['european record', 'fermín cacho ( esp )', '3:28.95', 'zürich , switzerland', '13 august 1997']] |
marinne giraud | https://en.wikipedia.org/wiki/Marinne_Giraud | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15508602-2.html.csv | majority | the majority of marinne giraud 's tennis tournaments were on a hard surface . | {'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'hard', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'surface', 'hard'], 'result': True, 'ind': 0, 'tointer': 'for the surface records of all rows , most of them fuzzily match to hard .', 'tostr': 'most_eq { all_rows ; surface ; hard } = true'} | most_eq { all_rows ; surface ; hard } = true | for the surface records of all rows , most of them fuzzily match to hard . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'surface_3': 3, 'hard_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'surface_3': 'surface', 'hard_4': 'hard'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'surface_3': [0], 'hard_4': [0]} | ['outcome', 'date', 'tournament', 'surface', 'opponent in the final', 'score'] | [['winner', '24 - oct - 2005', 'pretoria', 'hard', 'alicia pillay', '6 - 4 6 - 2'], ['runner - up', '09 - oct - 2006', 'braga', 'carpet', 'eloisa compostizo de andres', '4 - 6 , 7 - 5 , 3 - 6'], ['winner', '14 april 2007', 'dubai', 'hard', 'çağla büyükakçay', '6 - 2 6 - 2'], ['winner', '14 - may - 2007', 'trivandrum', 'clay', 'agnes szatmari', '7 - 5 6 - 3'], ['winner', '20 - may - 2007', 'mumbai', 'hard', 'rushmi chakravarthi', '7 - 6 ( 7 ) 6 - 2']] |
lorenzo bandini | https://en.wikipedia.org/wiki/Lorenzo_Bandini | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1226484-1.html.csv | unique | lorenzo bandini used a cooper t53 chassis only in the 1961 season . | {'scope': 'all', 'row': '1', 'col': '3', 'col_other': '1', 'criterion': 'equal', 'value': 'cooper t53', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'chassis', 'cooper t53'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose chassis record fuzzily matches to cooper t53 .', 'tostr': 'filter_eq { all_rows ; chassis ; cooper t53 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; chassis ; cooper t53 } }', 'tointer': 'select the rows whose chassis record fuzzily matches to cooper t53 . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'chassis', 'cooper t53'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose chassis record fuzzily matches to cooper t53 .', 'tostr': 'filter_eq { all_rows ; chassis ; cooper t53 }'}, 'year'], 'result': '1961', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; chassis ; cooper t53 } ; year }'}, '1961'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; chassis ; cooper t53 } ; year } ; 1961 }', 'tointer': 'the year record of this unqiue row is 1961 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; chassis ; cooper t53 } } ; eq { hop { filter_eq { all_rows ; chassis ; cooper t53 } ; year } ; 1961 } } = true', 'tointer': 'select the rows whose chassis record fuzzily matches to cooper t53 . there is only one such row in the table . the year record of this unqiue row is 1961 .'} | and { only { filter_eq { all_rows ; chassis ; cooper t53 } } ; eq { hop { filter_eq { all_rows ; chassis ; cooper t53 } ; year } ; 1961 } } = true | select the rows whose chassis record fuzzily matches to cooper t53 . there is only one such row in the table . the year record of this unqiue row is 1961 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'chassis_7': 7, 'cooper t53_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'year_9': 9, '1961_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'chassis_7': 'chassis', 'cooper t53_8': 'cooper t53', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'year_9': 'year', '1961_10': '1961'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'chassis_7': [0], 'cooper t53_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'year_9': [2], '1961_10': [3]} | ['year', 'entrant', 'chassis', 'engine', 'points'] | [['1961', 'scuderia centro sud', 'cooper t53', 'maserati straight - 4', '0'], ['1962', 'scuderia ferrari', 'ferrari 156', 'ferrari v6', '4'], ['1963', 'scuderia centro sud', 'brm p57', 'brm v8', '6'], ['1963', 'scuderia ferrari', 'ferrari 156', 'ferrari v6', '6'], ['1964', 'scuderia ferrari', 'ferrari 156', 'ferrari v6', '23'], ['1964', 'scuderia ferrari', 'ferrari 158', 'ferrari v8', '23'], ['1964', 'north american racing team', 'ferrari 1512', 'ferrari flat - 12', '23'], ['1965', 'scuderia ferrari', 'ferrari 1512', 'ferrari flat - 12', '13'], ['1965', 'scuderia ferrari', 'ferrari 158', 'ferrari v8', '13'], ['1966', 'scuderia ferrari', 'ferrari 246', 'ferrari v6', '12'], ['1966', 'scuderia ferrari', 'ferrari 312 / 66', 'ferrari v12', '12'], ['1967', 'scuderia ferrari', 'ferrari 312 / 67', 'ferrari v12', '0']] |
1968 buffalo bills season | https://en.wikipedia.org/wiki/1968_Buffalo_Bills_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16982985-3.html.csv | aggregation | in the 1968 buffalo bills season , the average attendance for games in september was 36092.5 . | {'scope': 'all', 'col': '6', 'type': 'average', 'result': '36092.5', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'attendance'], 'result': '36092.5', 'ind': 0, 'tostr': 'avg { all_rows ; attendance }'}, '36092.5'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; attendance } ; 36092.5 } = true', 'tointer': 'the average of the attendance record of all rows is 36092.5 .'} | round_eq { avg { all_rows ; attendance } ; 36092.5 } = true | the average of the attendance record of all rows is 36092.5 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'attendance_4': 4, '36092.5_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'attendance_4': 'attendance', '36092.5_5': '36092.5'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'attendance_4': [0], '36092.5_5': [1]} | ['date', 'opponent', 'score', 'result', 'record', 'attendance'] | [['september 8', 'boston patriots', '16 - 7', 'loss', '0 - 1', '38865'], ['september 15', 'oakland raiders', '48 - 6', 'loss', '0 - 2', '43056'], ['september 22', 'cincinnati bengals', '34 - 23', 'loss', '0 - 3', '24405'], ['september 29', 'new york jets', '37 - 35', 'win', '1 - 3', '38044'], ['october 5', 'kansas city chiefs', '18 - 7', 'loss', '1 - 4', '40748'], ['october 12', 'miami dolphins', '14 - 14', 'tie', '1 - 4 - 1', '28559'], ['october 20', 'boston patriots', '23 - 6', 'loss', '1 - 5 - 1', '21082'], ['october 27', 'houston oilers', '30 - 7', 'loss', '1 - 6 - 1', '34339'], ['november 3', 'new york jets', '25 - 21', 'loss', '1 - 7 - 1', '61452'], ['november 10', 'miami dolphins', '21 - 17', 'loss', '1 - 8 - 1', '28751'], ['november 17', 'san diego chargers', '21 - 6', 'loss', '1 - 9 - 1', '27993'], ['november 24', 'denver broncos', '34 - 32', 'loss', '1 - 10 - 1', '35142'], ['november 28', 'oakland raiders', '13 - 10', 'loss', '1 - 11 - 1', '39883'], ['december 7', 'houston oilers', '35 - 6', 'loss', '1 - 12 - 1', '34110']] |
dorval | https://en.wikipedia.org/wiki/Dorval | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-189893-1.html.csv | ordinal | in 2011 , the third highest population group in dorval were those who spoke spanish as their mother tongue . | {'row': '5', 'col': '4', 'order': '3', 'col_other': '1', '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', 'population ( 2011 )', '3'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; population ( 2011 ) ; 3 }'}, 'mother tongue'], 'result': 'spanish', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; population ( 2011 ) ; 3 } ; mother tongue }'}, 'spanish'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; population ( 2011 ) ; 3 } ; mother tongue } ; spanish } = true', 'tointer': 'select the row whose population ( 2011 ) record of all rows is 3rd maximum . the mother tongue record of this row is spanish .'} | eq { hop { nth_argmax { all_rows ; population ( 2011 ) ; 3 } ; mother tongue } ; spanish } = true | select the row whose population ( 2011 ) record of all rows is 3rd maximum . the mother tongue record of this row is spanish . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'population (2011)_5': 5, '3_6': 6, 'mother tongue_7': 7, 'spanish_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', 'population (2011)_5': 'population ( 2011 )', '3_6': '3', 'mother tongue_7': 'mother tongue', 'spanish_8': 'spanish'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'population (2011)_5': [0], '3_6': [0], 'mother tongue_7': [1], 'spanish_8': [2]} | ['mother tongue', 'population ( 2006 )', 'percentage ( 2006 )', 'population ( 2011 )', 'percentage ( 2011 )'] | [['english', '8085', '45.22 %', '7615', '42.41 %'], ['french', '5400', '30.20 %', '5490', '30.57 %'], ['chinese languages', '650', '3.64 %', '470', '2.62 %'], ['italian', '590', '3.30 %', '510', '2.84 %'], ['spanish', '315', '1.76 %', '515', '2.87 %'], ['romanian', '300', '1.68 %', '235', '1.31 %'], ['arabic', '295', '1.65 %', '350', '1.95 %'], ['polish', '205', '1.15 %', '145', '0.81 %'], ['filipino', '170', '0.95 %', '200', '1.11 %'], ['english and french', '250', '1.40 %', '390', '2.17 %'], ['english and a non - official language', '120', '0.67 %', '190', '1.06 %'], ['french and a non - official language', '50', '0.28 %', '145', '0.81 %']] |
outcasts ( tv series ) | https://en.wikipedia.org/wiki/Outcasts_%28TV_series%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-29574579-1.html.csv | ordinal | episode 4 had the 5th highest amount of uk viewers of all outcasts episodes . | {'row': '4', 'col': '5', 'order': '5', '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', 'uk viewers ( million )', '5'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; uk viewers ( million ) ; 5 }'}, 'title'], 'result': 'episode 4', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; uk viewers ( million ) ; 5 } ; title }'}, 'episode 4'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; uk viewers ( million ) ; 5 } ; title } ; episode 4 } = true', 'tointer': 'select the row whose uk viewers ( million ) record of all rows is 5th maximum . the title record of this row is episode 4 .'} | eq { hop { nth_argmax { all_rows ; uk viewers ( million ) ; 5 } ; title } ; episode 4 } = true | select the row whose uk viewers ( million ) record of all rows is 5th maximum . the title record of this row is episode 4 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'uk viewers (million)_5': 5, '5_6': 6, 'title_7': 7, 'episode 4_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', 'uk viewers (million)_5': 'uk viewers ( million )', '5_6': '5', 'title_7': 'title', 'episode 4_8': 'episode 4'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'uk viewers (million)_5': [0], '5_6': [0], 'title_7': [1], 'episode 4_8': [2]} | ['episode', 'title', 'directed by', 'written by', 'uk viewers ( million )', 'share ( % )', 'original air date'] | [['1', 'episode 1', 'bharat nalluri', 'ben richards', '4.50', '17.9', '7 february 2011'], ['2', 'episode 2', 'bharat nalluri', 'ben richards', '3.30', '13.0', '8 february 2011'], ['3', 'episode 3', 'omar madha', 'ben richards and simon block', '2.95', '11.8', '14 february 2011'], ['4', 'episode 4', 'omar madha', 'jack lothian', '2.63', '10.05', '15 february 2011'], ['5', 'episode 5', 'andy goddard', 'ben richards and jimmy gardner', '2.70', '10.8', '21 february 2011'], ['6', 'episode 6', 'andy goddard', 'david farr', '1.52', '10.5', '27 february 2011'], ['7', 'episode 7', 'jamie payne', 'david farr', '1.33', '9.7', '6 march 2011']] |
list of countries with mcdonald 's restaurants | https://en.wikipedia.org/wiki/List_of_countries_with_McDonald%27s_restaurants | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1875327-2.html.csv | count | two of the mcdonald 's restaurants opened in the continent of asia . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'asia', 'result': '2', 'col': '1', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'continent', 'asia'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose continent record fuzzily matches to asia .', 'tostr': 'filter_eq { all_rows ; continent ; asia }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; continent ; asia } }', 'tointer': 'select the rows whose continent record fuzzily matches to asia . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; continent ; asia } } ; 2 } = true', 'tointer': 'select the rows whose continent record fuzzily matches to asia . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; continent ; asia } } ; 2 } = true | select the rows whose continent record fuzzily matches to asia . 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, 'continent_5': 5, 'asia_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', 'continent_5': 'continent', 'asia_6': 'asia', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'continent_5': [0], 'asia_6': [0], '2_7': [2]} | ['continent', 'location', 'city', 'date', 'year'] | [['north america', 'united states', 'san bernardino', 'may 15', '1940'], ['caribbean', 'puerto rico', 'san juan', 'november 10', '1967'], ['central america', 'costa rica', 'san josã', 'december 28', '1970'], ['oceania', 'australia', 'sydney', 'may 30', '1971'], ['asia 1', 'japan', 'tokyo', 'july 20', '1971'], ['europe', 'netherlands', 'zaandam', 'august 21', '1971'], ['south america', 'brazil', 'rio de janeiro', 'february 13', '1979'], ['asia 2', 'philippines', 'manila', 'september 27', '1981'], ['africa', 'morocco', 'casablanca', 'december 18', '1992'], ['middle east', 'israel', 'tel aviv', 'october 14', '1993']] |
jean - christophe boullion | https://en.wikipedia.org/wiki/Jean-Christophe_Boullion | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1235885-4.html.csv | majority | on the majority of occasions when jean - christophe boullion was driving for pescarolo sport the team completed more than 300 laps . | {'scope': 'subset', 'col': '5', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '300', 'subset': {'col': '2', 'criterion': 'equal', 'value': 'pescarolo sport'}} | {'func': 'most_greater', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'team', 'pescarolo sport'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; team ; pescarolo sport }', 'tointer': 'select the rows whose team record fuzzily matches to pescarolo sport .'}, 'laps', '300'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose team record fuzzily matches to pescarolo sport . for the laps records of these rows , most of them are greater than 300 .', 'tostr': 'most_greater { filter_eq { all_rows ; team ; pescarolo sport } ; laps ; 300 } = true'} | most_greater { filter_eq { all_rows ; team ; pescarolo sport } ; laps ; 300 } = true | select the rows whose team record fuzzily matches to pescarolo sport . for the laps records of these rows , most of them are greater than 300 . | 2 | 2 | {'most_greater_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'team_4': 4, 'pescarolo sport_5': 5, 'laps_6': 6, '300_7': 7} | {'most_greater_1': 'most_greater', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'team_4': 'team', 'pescarolo sport_5': 'pescarolo sport', 'laps_6': 'laps', '300_7': '300'} | {'most_greater_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'team_4': [0], 'pescarolo sport_5': [0], 'laps_6': [1], '300_7': [1]} | ['year', 'team', 'co - drivers', 'class', 'laps', 'pos', 'class pos'] | [['1994', 'michel hommell', 'alain cudini eric hélary', 'gt1', '230', 'dnf', 'dnf'], ['1997', 'dams', 'franck lagorce éric bernard', 'gt1', '149', 'dnf', 'dnf'], ['1998', 'jb racing', 'vincenzo sospiri jérôme policand', 'lmp1', '187', 'dnf', 'dnf'], ['2000', 'racing organisation course', 'jordi gené jérôme policand', 'lmp675', '72', 'dnf', 'dnf'], ['2001', 'pescarolo sport', 'sébastien bourdais laurent rédon', 'lmp900', '271', '13th', '4th'], ['2002', 'pescarolo sport', 'sébastien bourdais franck lagorce', 'lmp900', '343', '10th', '9th'], ['2003', 'pescarolo sport', 'stéphane sarrazin franck lagorce', 'lmp900', '356', '8th', '6th'], ['2005', 'pescarolo sport', 'emmanuel collard érik comas', 'lmp1', '368', '2nd', '2nd'], ['2007', 'pescarolo sport', 'emmanuel collard romain dumas', 'lmp1', '358', '3rd', '3rd'], ['2008', 'pescarolo sport', 'emmanuel collard romain dumas', 'lmp1', '238', 'dnf', 'dnf'], ['2009', 'pescarolo sport', 'simon pagenaud benoît tréluyer', 'lmp1', '210', 'dnf', 'dnf'], ['2010', 'rebellion racing', 'andrea belicchi guy smith', 'lmp1', '143', 'dnf', 'dnf'], ['2011', 'rebellion racing', 'andrea belicchi guy smith', 'lmp1', '190', 'dnf', 'dnf']] |
york county , new brunswick | https://en.wikipedia.org/wiki/York_County%2C_New_Brunswick | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-176533-2.html.csv | count | in york county , new brunswick , 4 of those with area km 2 more than 500 has population more than 1000 . | {'scope': 'subset', 'criterion': 'greater_than', 'value': '1000', 'result': '4', 'col': '4', 'subset': {'col': '3', 'criterion': 'greater_than', 'value': '500'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_greater', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'area km 2', '500'], 'result': None, 'ind': 0, 'tostr': 'filter_greater { all_rows ; area km 2 ; 500 }', 'tointer': 'select the rows whose area km 2 record is greater than 500 .'}, 'population', '1000'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose area km 2 record is greater than 500 . among these rows , select the rows whose population record is greater than 1000 .', 'tostr': 'filter_greater { filter_greater { all_rows ; area km 2 ; 500 } ; population ; 1000 }'}], 'result': '4', 'ind': 2, 'tostr': 'count { filter_greater { filter_greater { all_rows ; area km 2 ; 500 } ; population ; 1000 } }', 'tointer': 'select the rows whose area km 2 record is greater than 500 . among these rows , select the rows whose population record is greater than 1000 . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_greater { filter_greater { all_rows ; area km 2 ; 500 } ; population ; 1000 } } ; 4 } = true', 'tointer': 'select the rows whose area km 2 record is greater than 500 . among these rows , select the rows whose population record is greater than 1000 . the number of such rows is 4 .'} | eq { count { filter_greater { filter_greater { all_rows ; area km 2 ; 500 } ; population ; 1000 } } ; 4 } = true | select the rows whose area km 2 record is greater than 500 . among these rows , select the rows whose population record is greater than 1000 . the number of such rows is 4 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_greater_1': 1, 'filter_greater_0': 0, 'all_rows_5': 5, 'area km 2_6': 6, '500_7': 7, 'population_8': 8, '1000_9': 9, '4_10': 10} | {'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_greater_1': 'filter_greater', 'filter_greater_0': 'filter_greater', 'all_rows_5': 'all_rows', 'area km 2_6': 'area km 2', '500_7': '500', 'population_8': 'population', '1000_9': '1000', '4_10': '4'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_greater_1': [2], 'filter_greater_0': [1], 'all_rows_5': [0], 'area km 2_6': [0], '500_7': [0], 'population_8': [1], '1000_9': [1], '4_10': [3]} | ['official name', 'status', 'area km 2', 'population', 'census ranking'] | [['kingsclear', 'parish', '303.26', '6689', '545 of 5008'], ['douglas', 'parish', '1446.50', '5774', '609 of 5008'], ['saint marys', 'parish', '753.06', '4224', '767 of 5008'], ['bright', 'parish', '404.00', '3159', '958 of 5008'], ['new maryland', 'parish', '375.40', '2348', '1193 of 5008'], ['manners sutton', 'parish', '525.47', '1863', '1394 of 5008'], ['stanley', 'parish', '2040.21', '1817', '1408 of 5008'], ['southampton', 'parish', '450.05', '1601', '1535 of 5008'], ['queensbury', 'parish', '301.22', '1215', '1811 of 5008'], ['prince william', 'parish', '287.92', '879', '2234 of 5008'], ['canterbury', 'parish', '557.22', '555', '2875 of 5008'], ['dumfries', 'parish', '305.23', '369', '3418 of 5008'], ['north lake', 'parish', '440.72', '300', '3678 of 5008'], ['mcadam', 'parish', '537.62', '80', '4512 of 5008']] |
antonio ng | https://en.wikipedia.org/wiki/Antonio_Ng | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-14834801-1.html.csv | unique | 2005 was the only election year in which antonio ng received a hare quota score of over 10000 . | {'scope': 'all', 'row': '4', 'col': '3', 'col_other': '1', 'criterion': 'greater_than', 'value': '10000', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'hare quota', '10000'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose hare quota record is greater than 10000 .', 'tostr': 'filter_greater { all_rows ; hare quota ; 10000 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_greater { all_rows ; hare quota ; 10000 } }', 'tointer': 'select the rows whose hare quota record is greater than 10000 . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'hare quota', '10000'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose hare quota record is greater than 10000 .', 'tostr': 'filter_greater { all_rows ; hare quota ; 10000 }'}, 'year'], 'result': '2005', 'ind': 2, 'tostr': 'hop { filter_greater { all_rows ; hare quota ; 10000 } ; year }'}, '2005'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_greater { all_rows ; hare quota ; 10000 } ; year } ; 2005 }', 'tointer': 'the year record of this unqiue row is 2005 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_greater { all_rows ; hare quota ; 10000 } } ; eq { hop { filter_greater { all_rows ; hare quota ; 10000 } ; year } ; 2005 } } = true', 'tointer': 'select the rows whose hare quota record is greater than 10000 . there is only one such row in the table . the year record of this unqiue row is 2005 .'} | and { only { filter_greater { all_rows ; hare quota ; 10000 } } ; eq { hop { filter_greater { all_rows ; hare quota ; 10000 } ; year } ; 2005 } } = true | select the rows whose hare quota record is greater than 10000 . there is only one such row in the table . the year record of this unqiue row is 2005 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_greater_0': 0, 'all_rows_6': 6, 'hare quota_7': 7, '10000_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'year_9': 9, '2005_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_greater_0': 'filter_greater', 'all_rows_6': 'all_rows', 'hare quota_7': 'hare quota', '10000_8': '10000', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'year_9': 'year', '2005_10': '2005'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_greater_0': [1, 2], 'all_rows_6': [0], 'hare quota_7': [0], '10000_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'year_9': [2], '2005_10': [3]} | ['year', 'candidate', 'hare quota', 'mandate', 'list votes', 'list pct'] | [['1992', 'antónio ng ( anmd )', '3412', '№ 4', '3412', '12.39 %'], ['1996', 'antónio ng ( amdp )', '6331', '№ 6', '6331', '8.73 %'], ['2001', 'antónio ng ( amdp )', '8481', '№ 1', '16961', '20.95 %'], ['2005', 'antónio ng ( amdp )', '11745', '№ 1', '23489', '18.80 %'], ['2009', 'antónio ng ( apmd )', '8212', '№ 3', '16424', '11.58 %']] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.