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stringlengths 207
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united states women 's national water polo team
|
https://en.wikipedia.org/wiki/United_States_women%27s_national_water_polo_team
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16506555-1.html.csv
|
count
|
a total of two players on the united states women 's national water polo team play the gk position .
|
{'scope': 'all', 'criterion': 'equal', 'value': 'gk', 'result': '2', 'col': '2', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'pos', 'gk'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose pos record fuzzily matches to gk .', 'tostr': 'filter_eq { all_rows ; pos ; gk }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; pos ; gk } }', 'tointer': 'select the rows whose pos record fuzzily matches to gk . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; pos ; gk } } ; 2 } = true', 'tointer': 'select the rows whose pos record fuzzily matches to gk . the number of such rows is 2 .'}
|
eq { count { filter_eq { all_rows ; pos ; gk } } ; 2 } = true
|
select the rows whose pos record fuzzily matches to gk . 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, 'pos_5': 5, 'gk_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', 'pos_5': 'pos', 'gk_6': 'gk', '2_7': '2'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'pos_5': [0], 'gk_6': [0], '2_7': [2]}
|
['name', 'pos', 'height', 'weight', '2012 club']
|
[['elizabeth armstrong', 'gk', 'm', '-', 'great lakes wp club'], ['heather petri', 'd', 'm', '-', 'new york athletic club'], ['melissa seidemann', 'cb', 'm', '-', 'stanford university'], ['brenda villa', 'd', 'm', '-', 'orizzonte catania'], ['lauren wenger', 'd', 'm', '-', 'new york athletic club'], ['maggie steffens', 'cb', 'm', '-', 'diablo water polo'], ['courtney mathewson', 'd', 'm', '-', 'new york athletic club'], ['jessica steffens', 'cb', 'm', '-', 'new york athletic club'], ['elsie windes', 'cb', 'm', '-', 'tualatin hills wpc'], ['kelly rulon', 'd', 'm', '-', 'asd roma'], ['annika dries', 'cf', 'm', '-', 'stanford university'], ['kami craig', 'cf', 'm', '-', 'santa barbara wp foundation'], ['tumua anae', 'gk', 'm', '-', 'socal']]
|
bucknell bison men 's basketball
|
https://en.wikipedia.org/wiki/Bucknell_Bison_men%27s_basketball
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17016075-1.html.csv
|
comparative
|
the bisons lost to syracuse by a bigger margin than georgetown .
|
{'row_1': '2', 'row_2': '1', 'col': '5', 'col_other': '4', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}
|
{'func': 'greater', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', '2 syracuse'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opponent record fuzzily matches to 2 syracuse .', 'tostr': 'filter_eq { all_rows ; opponent ; 2 syracuse }'}, 'result / score'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; opponent ; 2 syracuse } ; result / score }', 'tointer': 'select the rows whose opponent record fuzzily matches to 2 syracuse . take the result / score record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', '1 georgetown'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose opponent record fuzzily matches to 1 georgetown .', 'tostr': 'filter_eq { all_rows ; opponent ; 1 georgetown }'}, 'result / score'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; opponent ; 1 georgetown } ; result / score }', 'tointer': 'select the rows whose opponent record fuzzily matches to 1 georgetown . take the result / score record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; opponent ; 2 syracuse } ; result / score } ; hop { filter_eq { all_rows ; opponent ; 1 georgetown } ; result / score } } = true', 'tointer': 'select the rows whose opponent record fuzzily matches to 2 syracuse . take the result / score record of this row . select the rows whose opponent record fuzzily matches to 1 georgetown . take the result / score record of this row . the first record is greater than the second record .'}
|
greater { hop { filter_eq { all_rows ; opponent ; 2 syracuse } ; result / score } ; hop { filter_eq { all_rows ; opponent ; 1 georgetown } ; result / score } } = true
|
select the rows whose opponent record fuzzily matches to 2 syracuse . take the result / score record of this row . select the rows whose opponent record fuzzily matches to 1 georgetown . take the result / 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, 'opponent_7': 7, '2 syracuse_8': 8, 'result / score_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'opponent_11': 11, '1 georgetown_12': 12, 'result / 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', 'opponent_7': 'opponent', '2 syracuse_8': '2 syracuse', 'result / score_9': 'result / score', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'opponent_11': 'opponent', '1 georgetown_12': '1 georgetown', 'result / score_13': 'result / score'}
|
{'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'opponent_7': [0], '2 syracuse_8': [0], 'result / score_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'opponent_11': [1], '1 georgetown_12': [1], 'result / score_13': [3]}
|
['year', 'seed', 'round', 'opponent', 'result / score']
|
[['1987', '16', 'first round', '1 georgetown', 'l 75 - 53'], ['1989', '15', 'first round', '2 syracuse', 'l 104 - 81'], ['2005', '14', 'first round second round', '3 kansas 6 wisconsin', 'w 64 - 63 l 71 - 62'], ['2006', '9', 'first round second round', '8 arkansas 1 memphis', 'w 59 - 55 l 72 - 56'], ['2011', '14', 'first round', '3 uconn', 'l 81 - 52'], ['2013', '11', 'first round', '6 butler', 'l 68 - 56']]
|
1970 boston patriots season
|
https://en.wikipedia.org/wiki/1970_Boston_Patriots_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10646877-1.html.csv
|
majority
|
the boston patriots lost most games in the month of november during the 1970 season .
|
{'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'fuzzily_match', 'value': 'l', 'subset': None}
|
{'func': 'most_str_eq', 'args': ['all_rows', 'result', 'l'], 'result': True, 'ind': 0, 'tointer': 'for the result records of all rows , most of them fuzzily match to l .', 'tostr': 'most_eq { all_rows ; result ; l } = true'}
|
most_eq { all_rows ; result ; l } = true
|
for the result records of all rows , most of them fuzzily match to l .
|
1
|
1
|
{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'result_3': 3, 'l_4': 4}
|
{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'result_3': 'result', 'l_4': 'l'}
|
{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'result_3': [0], 'l_4': [0]}
|
['week', 'date', 'opponent', 'result', 'attendance']
|
[['1', 'september 20 , 1970', 'miami dolphins', 'w 27 - 14', '32607'], ['2', 'september 27 , 1970', 'new york jets', 'l 31 - 21', '36040'], ['3', 'october 4 , 1970', 'baltimore colts', 'l 14 - 6', '38235'], ['4', 'october 11 , 1970', 'kansas city chiefs', 'l 23 - 10', '50698'], ['5', 'october 18 , 1970', 'new york giants', 'l 16 - 0', '39091'], ['6', 'october 25 , 1970', 'baltimore colts', 'l 27 - 3', '60240'], ['7', 'november 1 , 1970', 'buffalo bills', 'l 45 - 10', '31148'], ['8', 'november 8 , 1970', 'st louis cardinals', 'l 31 - 0', '46466'], ['9', 'november 15 , 1970', 'san diego chargers', 'l 16 - 14', '30597'], ['10', 'november 22 , 1970', 'new york jets', 'l 17 - 3', '61822'], ['11', 'november 29 , 1970', 'buffalo bills', 'w 14 - 10', '31427'], ['12', 'december 6 , 1970', 'miami dolphins', 'l 37 - 20', '51032'], ['13', 'december 13 , 1970', 'minnesota vikings', 'l 35 - 14', '37819'], ['14', 'december 20 , 1970', 'cincinnati bengals', 'l 45 - 7', '60157']]
|
principal officials accountability system
|
https://en.wikipedia.org/wiki/Principal_Officials_Accountability_System
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2263674-1.html.csv
|
count
|
four of the politicians in the principal officials accountability system were appointed at age 50 .
|
{'scope': 'all', 'criterion': 'equal', 'value': '50', 'result': '4', 'col': '3', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'age at appointment', '50'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose age at appointment record is equal to 50 .', 'tostr': 'filter_eq { all_rows ; age at appointment ; 50 }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; age at appointment ; 50 } }', 'tointer': 'select the rows whose age at appointment record is equal to 50 . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; age at appointment ; 50 } } ; 4 } = true', 'tointer': 'select the rows whose age at appointment record is equal to 50 . the number of such rows is 4 .'}
|
eq { count { filter_eq { all_rows ; age at appointment ; 50 } } ; 4 } = true
|
select the rows whose age at appointment record is equal to 50 . the number of such rows is 4 .
|
3
|
3
|
{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_eq_0': 0, 'all_rows_4': 4, 'age at appointment_5': 5, '50_6': 6, '4_7': 7}
|
{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'age at appointment_5': 'age at appointment', '50_6': '50', '4_7': '4'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'age at appointment_5': [0], '50_6': [0], '4_7': [2]}
|
['romanised name', 'chinese name', 'age at appointment', 'portfolio', 'prior occupation']
|
[['donald tsang yam - kuen', '曾蔭權', '58', 'chief secretary for administration ( cs )', 'chief secretary for administration ( cs )'], ['anthony leung kam - chung', '梁錦松', '50', 'financial secretary ( fs )', 'financial secretary ( fs )'], ['elsie leung oi - see', '梁愛詩', '63', 'secretary for justice ( sj )', 'secretary for justice ( sj )'], ['joseph wong wing - ping', '王永平', '54', 'secretary for civil service', 'secretary for civil service'], ['henry tang ying - yen', '唐英年', '50', 'secretary for commerce , industry and technology', 'chairman , federation of hong kong industries'], ['stephen ip shu - kwan', '葉澍堃', '50', 'secretary for economic development and labour', 'secretary for financial services'], ['frederick ma si - hang', '馬時亨', '50', 'secretary for financial services and the treasury', 'chief financial officer , pccw'], ['sarah liao sau - tung', '廖秀冬', '51', 'secretary for the environment , transport and works', 'md of greater china , ch2 m hill'], ['dr patrick ho chi - ping', '何志平', '52', 'secretary for home affairs', 'chairman , arts development council'], ['michael suen ming - yeung', '孫明揚', '58', 'secretary for housing , planning and lands', 'secretary for constitutional affairs'], ['arthur li kwok - cheung', '李國章', '57', 'secretary for education and manpower', 'vice - chancellor , chinese university'], ['yeoh eng - kiong', '楊永強', '56', 'secretary for health , welfare and food', 'secretary for health and welfare']]
|
paul mcnamee
|
https://en.wikipedia.org/wiki/Paul_McNamee
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1828666-1.html.csv
|
majority
|
the majority of matches were played on a clay surface .
|
{'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'clay', 'subset': None}
|
{'func': 'most_str_eq', 'args': ['all_rows', 'surface', 'clay'], 'result': True, 'ind': 0, 'tointer': 'for the surface records of all rows , most of them fuzzily match to clay .', 'tostr': 'most_eq { all_rows ; surface ; clay } = true'}
|
most_eq { all_rows ; surface ; clay } = true
|
for the surface records of all rows , most of them fuzzily match to clay .
|
1
|
1
|
{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'surface_3': 3, 'clay_4': 4}
|
{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'surface_3': 'surface', 'clay_4': 'clay'}
|
{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'surface_3': [0], 'clay_4': [0]}
|
['outcome', 'date', 'championship', 'surface', 'opponent in the final', 'score in the final']
|
[['winner', '1980', 'palm harbor , us', 'hard', 'stan smith', '6 - 4 , 6 - 3'], ['runner - up', '1980', 'palermo , italy', 'clay', 'guillermo vilas', '4 - 6 , 0 - 6 , 0 - 6'], ['winner', '1982', 'baltimore wct , us', 'carpet', 'guillermo vilas', '4 - 6 , 7 - 5 , 7 - 5 , 2 - 6 , 6 - 3'], ['runner - up', '1983', 'houston wct , us', 'clay', 'ivan lendl', '2 - 6 , 0 - 6 , 3 - 6'], ['runner - up', '1983', 'brisbane , australia', 'carpet', 'pat cash', '6 - 4 , 4 - 6 , 3 - 6'], ['runner - up', '1986', 'nice , france', 'clay', 'emilio sánchez', '1 - 6 , 3 - 6'], ['runner - up', '1986', 'st vincent , italy', 'clay', 'simone colombo', '6 - 2 , 3 - 6 , 6 - 7']]
|
tri - state collegiate hockey league
|
https://en.wikipedia.org/wiki/Tri-State_Collegiate_Hockey_League
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-16384648-2.html.csv
|
ordinal
|
bird arena has the second highest capacity of the home arenas among all institutions .
|
{'row': '5', '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', 'capacity', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; capacity ; 2 }'}, 'home arena'], 'result': 'bird arena', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; capacity ; 2 } ; home arena }'}, 'bird arena'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; capacity ; 2 } ; home arena } ; bird arena } = true', 'tointer': 'select the row whose capacity record of all rows is 2nd maximum . the home arena record of this row is bird arena .'}
|
eq { hop { nth_argmax { all_rows ; capacity ; 2 } ; home arena } ; bird arena } = true
|
select the row whose capacity record of all rows is 2nd maximum . the home arena record of this row is bird arena .
|
3
|
3
|
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'capacity_5': 5, '2_6': 6, 'home arena_7': 7, 'bird arena_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', 'capacity_5': 'capacity', '2_6': '2', 'home arena_7': 'home arena', 'bird arena_8': 'bird arena'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'capacity_5': [0], '2_6': [0], 'home arena_7': [1], 'bird arena_8': [2]}
|
['institution', 'location', 'team nickname', 'joined tschl', 'home arena', 'capacity', 'team website']
|
[['university of akron', 'akron , oh', 'zips', '2010', 'center ice sports complex', '900', 'zips hockey'], ['university of cincinnati', 'cincinnati , oh', 'bearcats', '2010', 'cincinnati gardens', '10208', 'cincinnati hockey'], ['university of dayton', 'dayton , oh', 'flyers', '2010', 'kettering rec center', '700', 'dayton hockey'], ['indiana university of pennsylvania', 'indiana , pa', 'crimson hawks', '2010', 's & t bank arena', '1000', 'iup hockey'], ['ohio university', 'athens , oh', 'bobcats', '2011', 'bird arena', '2000', 'ohio hockey'], ['university of toledo', 'toledo , oh', 'rockets', '2010', 'team toledo ice house', '1100', 'toledo hockey'], ['university of pittsburgh', 'pittsburgh , pa', 'panthers', '2010', 'bladerunners harmarville', '1200', 'pitt hockey'], ['west virginia university', 'morgantown , wv', 'mountaineers', '2010', 'morgantown municipal ice arena', '500', 'wvu hockey']]
|
1999 - 2000 chelsea f.c. season
|
https://en.wikipedia.org/wiki/1999%E2%80%932000_Chelsea_F.C._season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14768726-2.html.csv
|
unique
|
during the 1999-2000 chelsea f c season , the only time the opponent was leicester city was on january 30 , 2000 .
|
{'scope': 'all', 'row': '3', 'col': '3', 'col_other': '1', 'criterion': 'equal', 'value': 'leicester city', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'leicester city'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opponent record fuzzily matches to leicester city .', 'tostr': 'filter_eq { all_rows ; opponent ; leicester city }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; opponent ; leicester city } }', 'tointer': 'select the rows whose opponent record fuzzily matches to leicester city . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'leicester city'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opponent record fuzzily matches to leicester city .', 'tostr': 'filter_eq { all_rows ; opponent ; leicester city }'}, 'date'], 'result': '30 january 2000', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; opponent ; leicester city } ; date }'}, '30 january 2000'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; opponent ; leicester city } ; date } ; 30 january 2000 }', 'tointer': 'the date record of this unqiue row is 30 january 2000 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; opponent ; leicester city } } ; eq { hop { filter_eq { all_rows ; opponent ; leicester city } ; date } ; 30 january 2000 } } = true', 'tointer': 'select the rows whose opponent record fuzzily matches to leicester city . there is only one such row in the table . the date record of this unqiue row is 30 january 2000 .'}
|
and { only { filter_eq { all_rows ; opponent ; leicester city } } ; eq { hop { filter_eq { all_rows ; opponent ; leicester city } ; date } ; 30 january 2000 } } = true
|
select the rows whose opponent record fuzzily matches to leicester city . there is only one such row in the table . the date record of this unqiue row is 30 january 2000 .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'opponent_7': 7, 'leicester city_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'date_9': 9, '30 january 2000_10': 10}
|
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'opponent_7': 'opponent', 'leicester city_8': 'leicester city', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'date_9': 'date', '30 january 2000_10': '30 january 2000'}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'opponent_7': [0], 'leicester city_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'date_9': [2], '30 january 2000_10': [3]}
|
['date', 'round', 'opponent', 'venue', 'result', 'attendance', 'scorers']
|
[['11 december 1999', 'r3', 'hull city', 'a', '6 - 1', '10279', 'poyet ( 3 ) , sutton , di matteo , wise'], ['19 january 2000', 'r4', 'nottingham forest', 'h', '2 - 0', '30125', 'leboeuf , wise'], ['30 january 2000', 'r5', 'leicester city', 'h', '2 - 1', '30141', 'poyet , weah'], ['20 february 2000', 'qf', 'gillingham', 'h', '5 - 0', '34205', 'flo , terry , weah , zola ( pen ) , morris'], ['9 april 2000', 'sf', 'newcastle united', 'n', '2 - 1', '73876', 'poyet ( 2 )'], ['20 may 2000', 'f', 'aston villa', 'n', '1 - 0', '78217', 'di matteo']]
|
rei zulu
|
https://en.wikipedia.org/wiki/Rei_Zulu
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15573672-2.html.csv
|
aggregation
|
rei zulu 's matches lasted a total of 32 minutes and 26 seconds .
|
{'scope': 'all', 'col': '7', 'type': 'sum', 'result': '32:26', 'subset': None}
|
{'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'time'], 'result': '32:26', 'ind': 0, 'tostr': 'sum { all_rows ; time }'}, '32:26'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; time } ; 32:26 } = true', 'tointer': 'the sum of the time record of all rows is 32:26 .'}
|
round_eq { sum { all_rows ; time } ; 32:26 } = true
|
the sum of the time record of all rows is 32:26 .
|
2
|
2
|
{'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'time_4': 4, '32:26_5': 5}
|
{'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'time_4': 'time', '32:26_5': '32:26'}
|
{'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'time_4': [0], '32:26_5': [1]}
|
['res', 'record', 'opponent', 'method', 'event', 'round', 'time', 'location']
|
[['loss', '2 - 7', 'santos samurai', 'dq ( punches after the bell )', 'desafio de gigantes 10', '2', '5:00', 'macapá , brazil'], ['win', '2 - 6', 'wesslan evaristo de oliveira', 'submission ( punches )', 'zulu combat 1', '1', '0:28', 'são luís , maranhão , brazil'], ['loss', '1 - 6', 'enson inoue', 'tko ( elbows )', 'shooto : reconquista 2', '1', '0:45', 'tokyo , japan'], ['loss', '1 - 5', 'kunta kinte', 'submission ( fish hook )', 'bvf : circuito de lutas 7', '1', 'n / a', 'brazil'], ['loss', '1 - 4', 'ebenezer fontes braga', 'n / a', 'freestyle de belem 1', 'n / a', 'n / a', 'belém , brazil'], ['loss', '1 - 3', 'pedro otavio', 'disqualification', 'desafio - international vale tudo', '1', '11:54', 'brazil'], ['win', '1 - 2', 'sergio batarelli', 'submission ( guillotine choke )', 'jiu - jitsu vs martial arts', '1', '2:24', 'rio de janeiro , brazil'], ['loss', '0 - 2', 'rickson gracie', 'submission ( rear naked choke )', 'independent event', '1', 'n / a', 'rio de janeiro , brazil'], ['loss', '0 - 1', 'rickson gracie', 'submission ( rear naked choke )', 'independent event', '1', '11:55', 'brasília , brazil']]
|
list of magazines published by ascii media works
|
https://en.wikipedia.org/wiki/List_of_magazines_published_by_ASCII_Media_Works
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16704362-2.html.csv
|
comparative
|
dengeki game appli was published after the magazine entitled rekidama .
|
{'row_1': '6', 'row_2': '8', 'col': '5', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}
|
{'func': 'greater', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'title', 'dengeki game appli'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose title record fuzzily matches to dengeki game appli .', 'tostr': 'filter_eq { all_rows ; title ; dengeki game appli }'}, 'first published'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; title ; dengeki game appli } ; first published }', 'tointer': 'select the rows whose title record fuzzily matches to dengeki game appli . take the first published record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'title', 'rekidama'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose title record fuzzily matches to rekidama .', 'tostr': 'filter_eq { all_rows ; title ; rekidama }'}, 'first published'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; title ; rekidama } ; first published }', 'tointer': 'select the rows whose title record fuzzily matches to rekidama . take the first published record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; title ; dengeki game appli } ; first published } ; hop { filter_eq { all_rows ; title ; rekidama } ; first published } } = true', 'tointer': 'select the rows whose title record fuzzily matches to dengeki game appli . take the first published record of this row . select the rows whose title record fuzzily matches to rekidama . take the first published record of this row . the first record is greater than the second record .'}
|
greater { hop { filter_eq { all_rows ; title ; dengeki game appli } ; first published } ; hop { filter_eq { all_rows ; title ; rekidama } ; first published } } = true
|
select the rows whose title record fuzzily matches to dengeki game appli . take the first published record of this row . select the rows whose title record fuzzily matches to rekidama . take the first published 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, 'title_7': 7, 'dengeki game appli_8': 8, 'first published_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'title_11': 11, 'rekidama_12': 12, 'first published_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', 'title_7': 'title', 'dengeki game appli_8': 'dengeki game appli', 'first published_9': 'first published', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'title_11': 'title', 'rekidama_12': 'rekidama', 'first published_13': 'first published'}
|
{'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'title_7': [0], 'dengeki game appli_8': [0], 'first published_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'title_11': [1], 'rekidama_12': [1], 'first published_13': [3]}
|
['title', 'parent magazine', 'magazine type', 'frequency', 'first published']
|
[['character parfait comic & puzzle', 'character parfait', 'manga', 'bimonthly', 'april 27 , 2010'], ['character parfait puchi', 'character parfait', 'toy', 'quarterly', 'november 10 , 2011'], ["dengeki g 's comic", "dengeki g 's magazine", 'manga', 'monthly', 'october 15 , 2012'], ["dengeki g 's festival !", "dengeki g 's magazine", 'bishōjo game , eroge', 'variable', 'december 16 , 2004'], ["dengeki g 's festival ! comic", "dengeki g 's magazine", 'manga', 'bimonthly', 'november 26 , 2007'], ['dengeki game appli', 'mobile ascii', 'video game', 'bimonthly', 'december 14 , 2011'], ['dengeki moeoh', 'dengeki daioh', 'manga', 'bimonthly', 'march 26 , 2002'], ['rekidama', 'dengeki bunko magazine', 'history', 'bimonthly', 'december 6 , 2010'], ['viva tales of magazine', 'dengeki maoh', 'video game', 'monthly', 'february 25 , 2011']]
|
pol espargaró
|
https://en.wikipedia.org/wiki/Pol_Espargar%C3%B3
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16546257-1.html.csv
|
ordinal
|
2008 was the year that pol espargaró participated in the second lowest amount of races in his career .
|
{'row': '3', 'col': '2', 'order': '2', 'col_other': '1', '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', 'race', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; race ; 2 }'}, 'season'], 'result': '2008', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; race ; 2 } ; season }'}, '2008'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; race ; 2 } ; season } ; 2008 } = true', 'tointer': 'select the row whose race record of all rows is 2nd minimum . the season record of this row is 2008 .'}
|
eq { hop { nth_argmin { all_rows ; race ; 2 } ; season } ; 2008 } = true
|
select the row whose race record of all rows is 2nd minimum . the season record of this row is 2008 .
|
3
|
3
|
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'race_5': 5, '2_6': 6, 'season_7': 7, '2008_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', 'race_5': 'race', '2_6': '2', 'season_7': 'season', '2008_8': '2008'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'race_5': [0], '2_6': [0], 'season_7': [1], '2008_8': [2]}
|
['season', 'race', 'podium', 'pole', 'flap']
|
[['2006', '7', '0', '0', '0'], ['2007', '17', '1', '0', '0'], ['2008', '14', '3', '2', '1'], ['2009', '16', '5', '1', '1'], ['2010', '17', '12', '0', '3'], ['2011', '17', '2', '0', '1'], ['2012', '17', '11', '8', '5'], ['2013', '16', '10', '5', '4'], ['total', '121', '44', '16', '15']]
|
2009 - 10 atlanta hawks season
|
https://en.wikipedia.org/wiki/2009%E2%80%9310_Atlanta_Hawks_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-23248910-9.html.csv
|
majority
|
all games of the atlanta hawks ' in the 2009 - 10 season were scheduled for the month of march .
|
{'scope': 'all', 'col': '2', 'most_or_all': 'all', 'criterion': 'fuzzily_match', 'value': 'march', 'subset': None}
|
{'func': 'all_str_eq', 'args': ['all_rows', 'date', 'march'], 'result': True, 'ind': 0, 'tointer': 'for the date records of all rows , all of them fuzzily match to march .', 'tostr': 'all_eq { all_rows ; date ; march } = true'}
|
all_eq { all_rows ; date ; march } = true
|
for the date records of all rows , all of them fuzzily match to march .
|
1
|
1
|
{'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'date_3': 3, 'march_4': 4}
|
{'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'date_3': 'date', 'march_4': 'march'}
|
{'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'date_3': [0], 'march_4': [0]}
|
['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record']
|
[['60', 'march 1', 'bulls', 'w 116 - 92 ( ot )', 'j crawford ( 21 )', 'j smith ( 18 )', 'm williams ( 4 ) j smith ( 4 ) a horford ( 4 )', 'united center 19011', '38 - 21'], ['61', 'march 3', '76ers', 'w 112 - 93 ( ot )', 'm williams ( 21 )', 'm williams ( 8 ) a horford ( 8 ) j smith ( 8 )', 'j johnson ( 5 ) j smith ( 5 )', 'philips arena 15408', '39 - 21'], ['62', 'march 5', 'warriors', 'w 127 - 122 ( ot )', 'j smith ( 29 )', 'a horford ( 15 )', 'j johnson ( 8 )', 'philips arena 14066', '40 - 21'], ['63', 'march 6', 'heat', 'l 94 - 100 ( ot )', 'j crawford ( 24 )', 'a horford ( 9 )', 'j smith ( 5 )', 'american airlines arena 19600', '40 - 22'], ['64', 'march 8', 'knicks', 'l 98 - 99 ( ot )', 'j smith ( 25 )', 'a horford ( 12 )', 'j smith ( 6 )', 'madison square garden 19763', '40 - 23'], ['50', 'march 11', 'wizards', 'w 105 - 99 ( ot )', 'j crawford ( 29 )', 'j johnson ( 7 )', 'j johnson ( 5 ) j smith ( 5 ) m bibby ( 5 )', 'verizon center 13625', '41 - 23'], ['65', 'march 13', 'pistons', 'w 112 - 99 ( ot )', 'j crawford ( 29 )', 'j johnson ( 7 )', 'j johnson ( 5 ) j smith ( 5 ) m bibby ( 5 )', 'philips arena 18214', '42 - 23'], ['66', 'march 16', 'nets', 'w 108 - 84 ( ot )', 'j crawford ( 25 )', 'a horford ( 11 )', 'a horford ( 7 )', 'izod center 11128', '43 - 23'], ['67', 'march 17', 'raptors', 'l 105 - 106 ( ot )', 'j crawford ( 33 )', 'a horford ( 14 )', 'j smith ( 7 )', 'air canada centre 18441', '43 - 24'], ['68', 'march 19', 'bobcats', 'w 93 - 92 ( ot ) ot', 'j johnson ( 18 ) j smith ( 18 )', 'm williams ( 14 )', 'j smith ( 5 )', 'philips arena 17697', '44 - 24'], ['69', 'march 21', 'spurs', 'w 119 - 114 ( ot ) ot', 'm williams ( 26 )', 'a horford ( 18 )', 'j johnson ( 13 )', 'philips arena 18729', '45 - 24'], ['70', 'march 22', 'bucks', 'l 95 - 98 ( ot )', 'j johnson ( 27 )', 'a horford ( 12 )', 'a horford ( 4 )', 'bradley center 14186', '45 - 25'], ['71', 'march 24', 'magic', 'w 86 - 84 ( ot )', 'j johnson ( 17 )', 'a horford ( 11 )', 'j johnson ( 8 )', 'philips arena 16887', '46 - 25'], ['72', 'march 26', '76ers', 'l 98 - 105 ( ot )', 'j johnson ( 20 ) j smith ( 20 )', 'a horford ( 10 )', 'j johnson ( 6 )', 'wachovia center 13293', '46 - 26'], ['73', 'march 28', 'pacers', 'w 94 - 84 ( ot )', 'j smith ( 21 )', 'j smith ( 13 )', 'm bibby ( 8 )', 'philips arena 16646', '47 - 26']]
|
1935 vfl season
|
https://en.wikipedia.org/wiki/1935_VFL_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10790651-11.html.csv
|
unique
|
they only time a melbourne home team lost with a crowd more than 10000 was at mcg .
|
{'scope': 'subset', 'row': '1', 'col': '6', 'col_other': '5', 'criterion': 'greater_than', 'value': '10000', 'subset': {'col': '1', 'criterion': 'fuzzily_match', 'value': 'melbourne'}}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_greater', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'home team', 'melbourne'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; home team ; melbourne }', 'tointer': 'select the rows whose home team record fuzzily matches to melbourne .'}, 'crowd', '10000'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose home team record fuzzily matches to melbourne . among these rows , select the rows whose crowd record is greater than 10000 .', 'tostr': 'filter_greater { filter_eq { all_rows ; home team ; melbourne } ; crowd ; 10000 }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_greater { filter_eq { all_rows ; home team ; melbourne } ; crowd ; 10000 } }', 'tointer': 'select the rows whose home team record fuzzily matches to melbourne . among these rows , select the rows whose crowd record is greater than 10000 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_greater', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'home team', 'melbourne'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; home team ; melbourne }', 'tointer': 'select the rows whose home team record fuzzily matches to melbourne .'}, 'crowd', '10000'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose home team record fuzzily matches to melbourne . among these rows , select the rows whose crowd record is greater than 10000 .', 'tostr': 'filter_greater { filter_eq { all_rows ; home team ; melbourne } ; crowd ; 10000 }'}, 'venue'], 'result': 'mcg', 'ind': 3, 'tostr': 'hop { filter_greater { filter_eq { all_rows ; home team ; melbourne } ; crowd ; 10000 } ; venue }'}, 'mcg'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_greater { filter_eq { all_rows ; home team ; melbourne } ; crowd ; 10000 } ; venue } ; mcg }', 'tointer': 'the venue record of this unqiue row is mcg .'}], 'result': True, 'ind': 5, 'tostr': 'and { only { filter_greater { filter_eq { all_rows ; home team ; melbourne } ; crowd ; 10000 } } ; eq { hop { filter_greater { filter_eq { all_rows ; home team ; melbourne } ; crowd ; 10000 } ; venue } ; mcg } } = true', 'tointer': 'select the rows whose home team record fuzzily matches to melbourne . among these rows , select the rows whose crowd record is greater than 10000 . there is only one such row in the table . the venue record of this unqiue row is mcg .'}
|
and { only { filter_greater { filter_eq { all_rows ; home team ; melbourne } ; crowd ; 10000 } } ; eq { hop { filter_greater { filter_eq { all_rows ; home team ; melbourne } ; crowd ; 10000 } ; venue } ; mcg } } = true
|
select the rows whose home team record fuzzily matches to melbourne . among these rows , select the rows whose crowd record is greater than 10000 . there is only one such row in the table . the venue record of this unqiue row is mcg .
|
8
|
6
|
{'and_5': 5, 'result_6': 6, 'only_2': 2, 'filter_greater_1': 1, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'home team_8': 8, 'melbourne_9': 9, 'crowd_10': 10, '10000_11': 11, 'str_eq_4': 4, 'str_hop_3': 3, 'venue_12': 12, 'mcg_13': 13}
|
{'and_5': 'and', 'result_6': 'true', 'only_2': 'only', 'filter_greater_1': 'filter_greater', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'home team_8': 'home team', 'melbourne_9': 'melbourne', 'crowd_10': 'crowd', '10000_11': '10000', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'venue_12': 'venue', 'mcg_13': 'mcg'}
|
{'and_5': [6], 'result_6': [], 'only_2': [5], 'filter_greater_1': [2, 3], 'filter_str_eq_0': [1], 'all_rows_7': [0], 'home team_8': [0], 'melbourne_9': [0], 'crowd_10': [1], '10000_11': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'venue_12': [3], 'mcg_13': [4]}
|
['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date']
|
[['melbourne', '11.12 ( 78 )', 'south melbourne', '18.14 ( 122 )', 'mcg', '19086', '6 july 1935'], ['footscray', '15.13 ( 103 )', 'geelong', '16.7 ( 103 )', 'western oval', '11000', '6 july 1935'], ['collingwood', '17.20 ( 122 )', 'hawthorn', '12.3 ( 75 )', 'victoria park', '8000', '6 july 1935'], ['carlton', '19.21 ( 135 )', 'fitzroy', '8.6 ( 54 )', 'princes park', '24000', '6 july 1935'], ['st kilda', '10.14 ( 74 )', 'richmond', '7.8 ( 50 )', 'junction oval', '20000', '6 july 1935'], ['north melbourne', '10.13 ( 73 )', 'essendon', '10.14 ( 74 )', 'arden street oval', '8000', '6 july 1935']]
|
list of people in playboy 1980 - 89
|
https://en.wikipedia.org/wiki/List_of_people_in_Playboy_1980%E2%80%9389
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1566848-5.html.csv
|
unique
|
in the year 1984 , january was the only month playboy magazine did not have a model on their cover .
|
{'scope': 'all', 'row': '1', 'col': '2', 'col_other': '1', 'criterion': 'equal', 'value': 'no model pictured', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'cover model', 'no model pictured'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose cover model record fuzzily matches to no model pictured .', 'tostr': 'filter_eq { all_rows ; cover model ; no model pictured }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; cover model ; no model pictured } }', 'tointer': 'select the rows whose cover model record fuzzily matches to no model pictured . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'cover model', 'no model pictured'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose cover model record fuzzily matches to no model pictured .', 'tostr': 'filter_eq { all_rows ; cover model ; no model pictured }'}, 'date'], 'result': '1 - 84', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; cover model ; no model pictured } ; date }'}, '1 - 84'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; cover model ; no model pictured } ; date } ; 1 - 84 }', 'tointer': 'the date record of this unqiue row is 1 - 84 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; cover model ; no model pictured } } ; eq { hop { filter_eq { all_rows ; cover model ; no model pictured } ; date } ; 1 - 84 } } = true', 'tointer': 'select the rows whose cover model record fuzzily matches to no model pictured . there is only one such row in the table . the date record of this unqiue row is 1 - 84 .'}
|
and { only { filter_eq { all_rows ; cover model ; no model pictured } } ; eq { hop { filter_eq { all_rows ; cover model ; no model pictured } ; date } ; 1 - 84 } } = true
|
select the rows whose cover model record fuzzily matches to no model pictured . there is only one such row in the table . the date record of this unqiue row is 1 - 84 .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'cover model_7': 7, 'no model pictured_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'date_9': 9, '1 - 84_10': 10}
|
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'cover model_7': 'cover model', 'no model pictured_8': 'no model pictured', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'date_9': 'date', '1 - 84_10': '1 - 84'}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'cover model_7': [0], 'no model pictured_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'date_9': [2], '1 - 84_10': [3]}
|
['date', 'cover model', 'centerfold model', 'interview subject', 'pictorials']
|
[['1 - 84', 'no model pictured', 'penny baker', 'dan rather', 'mariel hemingway in star 80'], ['2 - 84', 'kimberly mcarthur', 'justine greiner', 'paul simon', 'carol wayne'], ['3 - 84', 'susie scott krabacher', 'dona speir', 'moses malone', 'big & beautiful , bridgette monet'], ['4 - 84', 'kathy shower', 'lesa ann pedriana', 'joan collins', 'sydney krueger'], ['5 - 84', 'rita jenrette , phillip anderson', 'patty duffek', 'calvin klein', 'rita jenrette , vicki lamotta , ola ray'], ['6 - 84', 'barbara edwards', 'tricia lange', 'jesse jackson', 'barbara edwards - pmoy'], ['7 - 84', 'bo derek', 'liz stewart', 'walid jumblat', 'bo derek'], ['8 - 84', 'terry moore', 'suzi schott', 'bobby knight', 'terry moore'], ['9 - 84', 'kimberly evenson', 'kimberly evenson', 'shirley maclaine', 'girls of the big ten , anne carlisle'], ['10 - 84', 'lesa pedriana', 'debi johnson', 'david letterman', 'babes of broadway , sonia braga'], ['11 - 84', 'christie brinkley', 'roberta vasquez', 'josé napoleón duarte', 'christie brinkley'], ['12 - 84', 'suzanne somers', 'karen velez', 'paul and linda mccartney', 'suzanne somers']]
|
1984 senior pga tour
|
https://en.wikipedia.org/wiki/1984_Senior_PGA_Tour
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11622840-4.html.csv
|
ordinal
|
arnold palmer had the third most earnings in the 1984 pga tour .
|
{'row': '3', 'col': '4', 'order': '3', 'col_other': '2', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}
|
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'earnings', '3'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; earnings ; 3 }'}, 'player'], 'result': 'arnold palmer', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; earnings ; 3 } ; player }'}, 'arnold palmer'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; earnings ; 3 } ; player } ; arnold palmer } = true', 'tointer': 'select the row whose earnings record of all rows is 3rd maximum . the player record of this row is arnold palmer .'}
|
eq { hop { nth_argmax { all_rows ; earnings ; 3 } ; player } ; arnold palmer } = true
|
select the row whose earnings record of all rows is 3rd maximum . the player record of this row is arnold palmer .
|
3
|
3
|
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'earnings_5': 5, '3_6': 6, 'player_7': 7, 'arnold palmer_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', 'earnings_5': 'earnings', '3_6': '3', 'player_7': 'player', 'arnold palmer_8': 'arnold palmer'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'earnings_5': [0], '3_6': [0], 'player_7': [1], 'arnold palmer_8': [2]}
|
['rank', 'player', 'country', 'earnings', 'wins']
|
[['1', 'don january', 'united states', '791990', '14'], ['2', 'miller barber', 'united states', '720134', '14'], ['3', 'arnold palmer', 'united states', '442974', '8'], ['4', 'billy casper', 'united states', '395386', '4'], ['5', 'gene littler', 'united states', '358770', '3']]
|
2005 - 06 toronto raptors season
|
https://en.wikipedia.org/wiki/2005%E2%80%9306_Toronto_Raptors_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15873014-7.html.csv
|
unique
|
in march of the 2005 - 06 toronto raptors season , there was only one game in which charlie villanueva had the most points .
|
{'scope': 'all', 'row': '14', 'col': '5', 'col_other': 'n/a', 'criterion': 'equal', 'value': 'charlie villanueva', 'subset': None}
|
{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'high points', 'charlie villanueva'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose high points record fuzzily matches to charlie villanueva .', 'tostr': 'filter_eq { all_rows ; high points ; charlie villanueva }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; high points ; charlie villanueva } } = true', 'tointer': 'select the rows whose high points record fuzzily matches to charlie villanueva . there is only one such row in the table .'}
|
only { filter_eq { all_rows ; high points ; charlie villanueva } } = true
|
select the rows whose high points record fuzzily matches to charlie villanueva . there is only one such row in the table .
|
2
|
2
|
{'only_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'high points_4': 4, 'charlie villanueva_5': 5}
|
{'only_1': 'only', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'high points_4': 'high points', 'charlie villanueva_5': 'charlie villanueva'}
|
{'only_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'high points_4': [0], 'charlie villanueva_5': [0]}
|
['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record']
|
[['57', 'march 1', 'atlanta', 'l 111 - 113 ( ot )', 'chris bosh ( 27 )', 'charlie villanueva ( 11 )', 'chris bosh ( 5 )', 'air canada centre 15137', '20 - 37'], ['58', 'march 4', 'new jersey', 'l 100 - 105 ( ot )', 'morris peterson ( 25 )', 'chris bosh , charlie villanueva ( 11 )', 'mike james ( 7 )', 'continental airlines arena 16215', '20 - 38'], ['59', 'march 5', 'boston', 'w 111 - 105 ( ot )', 'morris peterson ( 27 )', 'chris bosh ( 10 )', 'mike james ( 6 )', 'air canada centre 16623', '21 - 38'], ['60', 'march 7', 'cleveland', 'l 99 - 106 ( ot )', 'mike james ( 31 )', 'charlie villanueva ( 11 )', 'mike james ( 8 )', 'quicken loans arena 18077', '21 - 39'], ['61', 'march 8', 'cleveland', 'l 97 - 98 ( ot )', 'morris peterson ( 31 )', 'chris bosh ( 14 )', 'mike james ( 7 )', 'air canada centre 19800', '21 - 40'], ['62', 'march 10', 'denver', 'l 97 - 108 ( ot )', 'mike james ( 26 )', 'chris bosh ( 15 )', 'josé calderón ( 5 )', 'air canada centre 17806', '21 - 41'], ['63', 'march 12', 'indiana', 'w 93 - 89 ( ot )', 'morris peterson ( 25 )', 'chris bosh ( 8 )', 'mike james ( 4 )', 'air canada centre 17573', '22 - 41'], ['64', 'march 14', 'philadelphia', 'w 111 - 97 ( ot )', 'chris bosh ( 31 )', 'charlie villanueva ( 10 )', 'darrick martin ( 12 )', 'wachovia center 14917', '23 - 41'], ['65', 'march 15', 'detroit', 'l 98 - 105 ( ot )', 'mike james ( 24 )', 'chris bosh ( 11 )', 'mike james ( 11 )', 'air canada centre 19800', '23 - 42'], ['66', 'march 17', 'milwaukee', 'w 97 - 96 ( ot )', 'chris bosh ( 27 )', 'chris bosh ( 10 )', 'mike james ( 6 )', 'air canada centre 17273', '24 - 42'], ['67', 'march 21', 'new york', 'w 114 - 109 ( ot )', 'mike james ( 37 )', 'mike james , charlie villanueva ( 8 )', 'mike james ( 5 )', 'madison square garden 18131', '25 - 42'], ['68', 'march 22', 'boston', 'l 96 - 110 ( ot )', 'mike james ( 31 )', 'chris bosh ( 11 )', 'chris bosh ( 8 )', 'td banknorth garden 18624', '25 - 43'], ['69', 'march 24', 'minnesota', 'w 97 - 77 ( ot )', 'morris peterson ( 21 )', 'chris bosh ( 15 )', 'mike james ( 5 )', 'air canada centre 17493', '26 - 43'], ['70', 'march 26', 'milwaukee', 'l 116 - 125 ( ot )', 'charlie villanueva ( 48 )', 'charlie villanueva ( 9 )', 'mike james ( 10 )', 'bradley center 16317', '26 - 44'], ['71', 'march 29', 'miami', 'l 94 - 98 ( ot )', 'morris peterson ( 28 )', 'charlie villanueva ( 13 )', 'mike james ( 12 )', 'air canada centre 19973', '26 - 45'], ['72', 'march 31', 'phoenix', 'l 126 - 140 ( ot )', 'morris peterson ( 38 )', 'pape sow ( 15 )', 'mike james ( 10 )', 'air canada centre 19800', '26 - 46']]
|
jordan kerr
|
https://en.wikipedia.org/wiki/Jordan_Kerr
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15271798-2.html.csv
|
count
|
jordan kerr was runner-up in a total of six tennis doubles tournaments .
|
{'scope': 'all', 'criterion': 'equal', 'value': 'runner - up', 'result': '6', 'col': '1', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'outcome', 'runner - up'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose outcome record fuzzily matches to runner - up .', 'tostr': 'filter_eq { all_rows ; outcome ; runner - up }'}], 'result': '6', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; outcome ; runner - up } }', 'tointer': 'select the rows whose outcome record fuzzily matches to runner - up . the number of such rows is 6 .'}, '6'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; outcome ; runner - up } } ; 6 } = true', 'tointer': 'select the rows whose outcome record fuzzily matches to runner - up . the number of such rows is 6 .'}
|
eq { count { filter_eq { all_rows ; outcome ; runner - up } } ; 6 } = true
|
select the rows whose outcome record fuzzily matches to runner - up . the number of such rows is 6 .
|
3
|
3
|
{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'outcome_5': 5, 'runner - up_6': 6, '6_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', 'runner - up_6': 'runner - up', '6_7': '6'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'outcome_5': [0], 'runner - up_6': [0], '6_7': [2]}
|
['outcome', 'date', 'surface', 'partner', 'opponents in the final', 'score in the final']
|
[['winner', '2003', 'grass', 'david macpherson', 'julian knowle jürgen melzer', '7 - 6 ( 7 - 4 ) , 6 - 3'], ['winner', '2004', 'grass', 'jim thomas', 'grégory carraz nicolas mahut', '6 - 3 , 6 - 7 ( 5 - 7 ) , 6 - 3'], ['winner', '2004', 'hard', 'jim thomas', 'wayne black kevin ullyett', '6 - 7 ( 7 - 9 ) , 7 - 6 ( 7 - 3 ) , 6 - 3'], ['runner - up', '2005', 'hard', 'jim thomas', 'simon aspelin todd perry', '3 - 6 , 3 - 6'], ['winner', '2005', 'grass', 'jim thomas', 'graydon oliver travis parrott', '7 - 6 ( 7 - 5 ) , 7 - 6 ( 7 - 5 )'], ['winner', '2007', 'clay', 'david škoch', 'łukasz kubot oliver marach', '7 - 6 ( 7 - 4 ) , 1 - 6 ,'], ['winner', '2007', 'grass', 'jim thomas', 'nathan healey igor kunitsyn', '6 - 3 , 7 - 5'], ['winner', '2007', 'hard', 'robert lindstedt', 'frank dancevic stephen huss', '6 - 4 , 6 - 4'], ['winner', '2008', 'hard ( i )', 'paul hanley', 'christopher kas rogier wassen', '6 - 3 , 3 - 6 ,'], ['runner - up', '2009', 'clay', 'ashley fisher', 'jan hernych ivo minář', '4 - 6 , 4 - 6'], ['winner', '2009', 'grass', 'rajeev ram', 'michael kohlmann rogier wassen', '6 - 7 ( 6 - 8 ) , 7 - 6 ( 9 - 7 ) ,'], ['runner - up', '2009', 'hard', 'ashley fisher', 'ernests gulbis dmitry tursunov', '4 - 6 , 6 - 3 ,'], ['runner - up', '2009', 'hard', 'ross hutchins', 'julian knowle jürgen melzer', '2 - 6 , 7 - 5 ,'], ['runner - up', '2010', 'hard', 'ross hutchins', 'daniel nestor nenad zimonjić', '3 - 6 , 6 - 7 ( 5 - 7 )'], ['runner - up', '2010', 'hard ( i )', 'ross hutchins', 'john isner sam querrey', '4 - 6 , 4 - 6']]
|
memphis grizzlies all - time roster
|
https://en.wikipedia.org/wiki/Memphis_Grizzlies_all-time_roster
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-16494599-10.html.csv
|
ordinal
|
bobby jackson was the third player to be hired to play for the memphis grizzlies .
|
{'row': '1', 'col': '5', 'order': '3', 'col_other': '1', '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', 'years for grizzlies', '3'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; years for grizzlies ; 3 }'}, 'player'], 'result': 'bobby jackson', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; years for grizzlies ; 3 } ; player }'}, 'bobby jackson'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; years for grizzlies ; 3 } ; player } ; bobby jackson } = true', 'tointer': 'select the row whose years for grizzlies record of all rows is 3rd minimum . the player record of this row is bobby jackson .'}
|
eq { hop { nth_argmin { all_rows ; years for grizzlies ; 3 } ; player } ; bobby jackson } = true
|
select the row whose years for grizzlies record of all rows is 3rd minimum . the player record of this row is bobby jackson .
|
3
|
3
|
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'years for grizzlies_5': 5, '3_6': 6, 'player_7': 7, 'bobby jackson_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', 'years for grizzlies_5': 'years for grizzlies', '3_6': '3', 'player_7': 'player', 'bobby jackson_8': 'bobby jackson'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'years for grizzlies_5': [0], '3_6': [0], 'player_7': [1], 'bobby jackson_8': [2]}
|
['player', 'no', 'nationality', 'position', 'years for grizzlies', 'school / club team']
|
[['bobby jackson', '24', 'united states', 'guard', '2005 - 2006', 'minnesota'], ['casey jacobsen', '23', 'united states', 'guard - forward', '2007 - 2008', 'stanford'], ['alexander johnson', '32', 'united states', 'power forward', '2006 - 2007', 'florida state'], ['chris johnson', '4', 'united states', 'small forward', '2013', 'dayton'], ['bobby jones', '8', 'united states', 'forward', '2008', 'washington'], ['dahntay jones', '30', 'united states', 'guard - forward', '2003 - 2007', 'duke'], ['damon jones', '11', 'united states', 'shooting guard', '2000 - 2001', 'houston']]
|
list of american civil war generals
|
https://en.wikipedia.org/wiki/List_of_American_Civil_War_generals
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10648080-1.html.csv
|
comparative
|
john e. wool was appointed to his rank before david e. twiggs .
|
{'row_1': '7', 'row_2': '6', 'col': '4', 'col_other': '1', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}
|
{'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name', 'john e wool'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose name record fuzzily matches to john e wool .', 'tostr': 'filter_eq { all_rows ; name ; john e wool }'}, 'appointment date'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; name ; john e wool } ; appointment date }', 'tointer': 'select the rows whose name record fuzzily matches to john e wool . take the appointment date record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name', 'david e twiggs'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose name record fuzzily matches to david e twiggs .', 'tostr': 'filter_eq { all_rows ; name ; david e twiggs }'}, 'appointment date'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; name ; david e twiggs } ; appointment date }', 'tointer': 'select the rows whose name record fuzzily matches to david e twiggs . take the appointment date record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; name ; john e wool } ; appointment date } ; hop { filter_eq { all_rows ; name ; david e twiggs } ; appointment date } } = true', 'tointer': 'select the rows whose name record fuzzily matches to john e wool . take the appointment date record of this row . select the rows whose name record fuzzily matches to david e twiggs . take the appointment date record of this row . the first record is less than the second record .'}
|
less { hop { filter_eq { all_rows ; name ; john e wool } ; appointment date } ; hop { filter_eq { all_rows ; name ; david e twiggs } ; appointment date } } = true
|
select the rows whose name record fuzzily matches to john e wool . take the appointment date record of this row . select the rows whose name record fuzzily matches to david e twiggs . take the appointment 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, 'name_7': 7, 'john e wool_8': 8, 'appointment date_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'name_11': 11, 'david e twiggs_12': 12, 'appointment 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', 'name_7': 'name', 'john e wool_8': 'john e wool', 'appointment date_9': 'appointment date', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'name_11': 'name', 'david e twiggs_12': 'david e twiggs', 'appointment date_13': 'appointment date'}
|
{'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'name_7': [0], 'john e wool_8': [0], 'appointment date_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'name_11': [1], 'david e twiggs_12': [1], 'appointment date_13': [3]}
|
['name', 'date of birth', 'actual rank', 'appointment date', 'allegiance']
|
[['john garland', '1792', 'colonel 8th us infantry', 'may 7 , 1849', 'usa'], ['william s harney', 'august 27 , 1800', 'brigadier general', 'june 14 , 1858', 'usa'], ['albert s johnston', 'february 2 , 1803', 'colonel', 'may 1855', 'csa'], ['winfield scott', 'june 13 , 1786', 'major general', 'june 25 , 1841', 'usa'], ['edwin v sumner', 'january 30 , 1797', 'brigadier general', 'march 16 , 1861', 'usa'], ['david e twiggs', '1790', 'brigadier general', 'june 30 , 1846', 'csa'], ['john e wool', 'february 29 , 1784', 'brigadier general', 'june 25 , 1841', 'usa']]
|
2007 icc world twenty20 statistics
|
https://en.wikipedia.org/wiki/2007_ICC_World_Twenty20_statistics
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13219504-9.html.csv
|
count
|
johannesburg was the venue six times during the 2007 icc world twenty20 championship .
|
{'scope': 'all', 'criterion': 'equal', 'value': 'johannesburg', 'result': '6', 'col': '4', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'venue', 'johannesburg'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose venue record fuzzily matches to johannesburg .', 'tostr': 'filter_eq { all_rows ; venue ; johannesburg }'}], 'result': '6', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; venue ; johannesburg } }', 'tointer': 'select the rows whose venue record fuzzily matches to johannesburg . the number of such rows is 6 .'}, '6'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; venue ; johannesburg } } ; 6 } = true', 'tointer': 'select the rows whose venue record fuzzily matches to johannesburg . the number of such rows is 6 .'}
|
eq { count { filter_eq { all_rows ; venue ; johannesburg } } ; 6 } = true
|
select the rows whose venue record fuzzily matches to johannesburg . the number of such rows is 6 .
|
3
|
3
|
{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'venue_5': 5, 'johannesburg_6': 6, '6_7': 7}
|
{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'venue_5': 'venue', 'johannesburg_6': 'johannesburg', '6_7': '6'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'venue_5': [0], 'johannesburg_6': [0], '6_7': [2]}
|
['runs ( balls )', 'wicket', 'partnerships', 'venue', 'date']
|
[['145 ( 81 )', '1st', 'chris gayle / devon smith', 'johannesburg', '2007 - 09 - 11'], ['136 ( 88 )', '1st', 'gautam gambhir / virender sehwag', 'durban', '2007 - 09 - 19'], ['120 ( 57 )', '3rd', 'herschelle gibbs / justin kemp', 'johannesburg', '2007 - 09 - 11'], ['119 ( 75 )', '5th', 'shoaib malik / misbah - ul - haq', 'johannesburg', '2007 - 09 - 18'], ['109 ( 62 )', '3rd', 'aftab ahmed / mohammad ashraful', 'johannesburg', '2007 - 09 - 13'], ['104 ( 69 )', '1st', 'adam gilchrist / matthew hayden', 'cape town', '2007 - 09 - 16'], ['102 ( 62 )', '1st', 'adam gilchrist / matthew hayden', 'cape town', '2007 - 09 - 22'], ['101 ( 55 )', '4th', 'younis khan / shoaib malik', 'johannesburg', '2007 - 09 - 17'], ['100 ( 45 )', '4th', 'kevin pietersen / paul collingwood', 'cape town', '2007 - 09 - 13'], ['95 ( 79 )', '2nd', 'devon smith / shivnarine chanderpaul', 'johannesburg', '2007 - 09 - 13']]
|
tokyo indoor
|
https://en.wikipedia.org/wiki/Tokyo_Indoor
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17660329-1.html.csv
|
count
|
ivan lendl was the winner of a total of five tokyo indoor tennis tournaments .
|
{'scope': 'all', 'criterion': 'equal', 'value': 'ivan lendl', 'result': '5', 'col': '3', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'champions', 'ivan lendl'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose champions record fuzzily matches to ivan lendl .', 'tostr': 'filter_eq { all_rows ; champions ; ivan lendl }'}], 'result': '5', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; champions ; ivan lendl } }', 'tointer': 'select the rows whose champions record fuzzily matches to ivan lendl . the number of such rows is 5 .'}, '5'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; champions ; ivan lendl } } ; 5 } = true', 'tointer': 'select the rows whose champions record fuzzily matches to ivan lendl . the number of such rows is 5 .'}
|
eq { count { filter_eq { all_rows ; champions ; ivan lendl } } ; 5 } = true
|
select the rows whose champions record fuzzily matches to ivan lendl . the number of such rows is 5 .
|
3
|
3
|
{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'champions_5': 5, 'ivan lendl_6': 6, '5_7': 7}
|
{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'champions_5': 'champions', 'ivan lendl_6': 'ivan lendl', '5_7': '5'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'champions_5': [0], 'ivan lendl_6': [0], '5_7': [2]}
|
['year', 'name of tournament', 'champions', 'runners - up', 'score']
|
[['1978', 'seiko world super tennis', 'björn borg', 'brian teacher', '6 - 3 , 6 - 4'], ['1979', 'seiko world super tennis', 'björn borg', 'jimmy connors', '6 - 2 , 6 - 2'], ['1980', 'seiko world super tennis', 'jimmy connors', 'tom gullikson', '6 - 1 , 6 - 2'], ['1981', 'seiko world super tennis', 'vincent van patten', 'mark edmondson', '6 - 2 , 3 - 6 , 6 - 3'], ['1982', 'seiko world super tennis', 'john mcenroe', 'peter mcnamara', '7 - 6 , 7 - 5'], ['1983', 'seiko world super tennis', 'ivan lendl', 'scott davis', '3 - 6 , 6 - 3 , 6 - 4'], ['1984', 'seiko super tennis', 'jimmy connors', 'ivan lendl', '6 - 4 , 3 - 6 , 6 - 0'], ['1985', 'seiko super tennis', 'ivan lendl', 'mats wilander', '6 - 0 , 6 - 4'], ['1986', 'seiko super tennis', 'boris becker', 'stefan edberg', '7 - 6 , 6 - 1'], ['1987', 'seiko super tennis', 'stefan edberg', 'ivan lendl', '6 - 7 , 6 - 4 , 6 - 4'], ['1988', 'seiko super tennis', 'boris becker', 'john fitzgerald', '7 - 6 , 6 - 4'], ['1989', 'seiko super tennis', 'aaron krickstein', 'carl - uwe steeb', '6 - 2 , 6 - 2'], ['1990', 'seiko super tennis', 'ivan lendl', 'boris becker', '4 - 6 , 6 - 3 , 7 - 6'], ['1991', 'seiko super tennis', 'stefan edberg', 'derrick rostagno', '6 - 3 , 1 - 6 , 6 - 2'], ['1992', 'seiko super tennis', 'ivan lendl', 'henrik holm', '7 - 6 , 6 - 4'], ['1993', 'seiko super tennis', 'ivan lendl', 'todd martin', '6 - 4 , 6 - 4'], ['1994', 'seiko super tennis', 'goran ivanišević', 'michael chang', '6 - 4 , 6 - 4'], ['1995', 'seiko super tennis', 'michael chang', 'mark philippoussis', '6 - 3 , 6 - 4']]
|
q force
|
https://en.wikipedia.org/wiki/Q_Force
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12339816-1.html.csv
|
comparative
|
the serial number of surfer is higher than the serial number for shark .
|
{'row_1': '4', 'row_2': '1', 'col': '5', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}
|
{'func': 'greater', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'code name', 'surfer'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose code name record fuzzily matches to surfer .', 'tostr': 'filter_eq { all_rows ; code name ; surfer }'}, 'serial number'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; code name ; surfer } ; serial number }', 'tointer': 'select the rows whose code name record fuzzily matches to surfer . take the serial number record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'code name', 'shark'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose code name record fuzzily matches to shark .', 'tostr': 'filter_eq { all_rows ; code name ; shark }'}, 'serial number'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; code name ; shark } ; serial number }', 'tointer': 'select the rows whose code name record fuzzily matches to shark . take the serial number record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; code name ; surfer } ; serial number } ; hop { filter_eq { all_rows ; code name ; shark } ; serial number } } = true', 'tointer': 'select the rows whose code name record fuzzily matches to surfer . take the serial number record of this row . select the rows whose code name record fuzzily matches to shark . take the serial number record of this row . the first record is greater than the second record .'}
|
greater { hop { filter_eq { all_rows ; code name ; surfer } ; serial number } ; hop { filter_eq { all_rows ; code name ; shark } ; serial number } } = true
|
select the rows whose code name record fuzzily matches to surfer . take the serial number record of this row . select the rows whose code name record fuzzily matches to shark . take the serial number 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, 'code name_7': 7, 'surfer_8': 8, 'serial number_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'code name_11': 11, 'shark_12': 12, 'serial number_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', 'code name_7': 'code name', 'surfer_8': 'surfer', 'serial number_9': 'serial number', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'code name_11': 'code name', 'shark_12': 'shark', 'serial number_13': 'serial number'}
|
{'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'code name_7': [0], 'surfer_8': [0], 'serial number_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'code name_11': [1], 'shark_12': [1], 'serial number_13': [3]}
|
['code name', 'function ( figure )', 'real name', 'birthplace', 'serial number', 'primary military speciality', 'secondary military speciality', 'equipment']
|
[['shark', 'aqua trooper', 'jean - paul rives', 'toulouse', 'af 934038', 'torpedo technology', 'underwater demolition', 'breathing apparatus'], ['leviathan', 'deep sea defender', 'jamie hugh maclaren', 'glasgow', 'af 93403', 'naval battle tactics', 'gunnery', 'a red aerial and a red backpack'], ['phones', 'sonar officer', "patrick liam o'flaherty", 'dublin', 'af 934037', 'communications', 'survivor', 'radio pack and ak - 47'], ['surfer', 'sea skimmer pilot', 'hoxworth whipple', 'hawaii', 'af 934119', 'seaborne rescue', 'rocket assault', 'jet - ski ( sea skimmer ) armed with rockets'], ['dolphin', 'pilot of sealion', 'gareth morgan', 'cardiff', 'af 934332', 'underwater solo attack', 'deep sea exploration', 'pilot accompanying sealion vehicle']]
|
nasser al - attiyah
|
https://en.wikipedia.org/wiki/Nasser_Al-Attiyah
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12927587-5.html.csv
|
count
|
bmws were used 4 times during nasser al-attiyah , from 2204-2013 .
|
{'scope': 'all', 'criterion': 'equal', 'value': 'bmw', 'result': '4', 'col': '3', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'vehicle', 'bmw'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose vehicle record fuzzily matches to bmw .', 'tostr': 'filter_eq { all_rows ; vehicle ; bmw }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; vehicle ; bmw } }', 'tointer': 'select the rows whose vehicle record fuzzily matches to bmw . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; vehicle ; bmw } } ; 4 } = true', 'tointer': 'select the rows whose vehicle record fuzzily matches to bmw . the number of such rows is 4 .'}
|
eq { count { filter_eq { all_rows ; vehicle ; bmw } } ; 4 } = true
|
select the rows whose vehicle record fuzzily matches to bmw . 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, 'vehicle_5': 5, 'bmw_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', 'vehicle_5': 'vehicle', 'bmw_6': 'bmw', '4_7': '4'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'vehicle_5': [0], 'bmw_6': [0], '4_7': [2]}
|
['year', 'class', 'vehicle', 'position', 'stages won']
|
[['2004', 'car', 'mitsubishi', '10', '0'], ['2005', 'car', 'bmw', 'dnf', '0'], ['2006', 'car', 'bmw', 'dnf', '0'], ['2007', 'car', 'bmw', '6', '1'], ['2008', 'event cancelled - replaced by central europe rally', 'event cancelled - replaced by central europe rally', 'event cancelled - replaced by central europe rally', 'event cancelled - replaced by central europe rally'], ['2009', 'car', 'bmw', 'dsq', '2'], ['2010', 'car', 'volkswagen', '2', '4'], ['2011', 'car', 'volkswagen', '1', '4'], ['2012', 'car', 'hummer', 'dnf', '2'], ['2013', 'car', 'demon jefferies', 'dnf', '3']]
|
sophie ferguson
|
https://en.wikipedia.org/wiki/Sophie_Ferguson
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15179071-3.html.csv
|
unique
|
the tournament played in wuxi , china was the only one played in china .
|
{'scope': 'all', 'row': '1', 'col': '3', 'col_other': 'n/a', 'criterion': 'equal', 'value': 'china', 'subset': None}
|
{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'tournament', 'china'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose tournament record fuzzily matches to china .', 'tostr': 'filter_eq { all_rows ; tournament ; china }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; tournament ; china } } = true', 'tointer': 'select the rows whose tournament record fuzzily matches to china . there is only one such row in the table .'}
|
only { filter_eq { all_rows ; tournament ; china } } = true
|
select the rows whose tournament record fuzzily matches to china . there is only one such row in the table .
|
2
|
2
|
{'only_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'tournament_4': 4, 'china_5': 5}
|
{'only_1': 'only', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'tournament_4': 'tournament', 'china_5': 'china'}
|
{'only_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'tournament_4': [0], 'china_5': [0]}
|
['outcome', 'date', 'tournament', 'surface', 'partner', 'opponents in the final', 'score']
|
[['runner - up', '14 august 2005', 'wuxi , china', 'hard', 'casey dellacqua', 'mi - ra jeon wynne prakusya', '2 - 6 6 - 7 ( 6 )'], ['runner - up', '12 november 2006', 'mount gambier , australia', 'hard', 'daniella dominikovic', 'natalie grandin christina wheeler', '4 - 6 6 - 4 4 - 6'], ['runner - up', '20 april 2007', 'bari , italy', 'clay', 'katarina kachlikova', 'veronika kapshay mariya koryttseva', '5 - 7 2 - 6'], ['winner', '19 june 2007', 'noto , japan', 'carpet', 'anne yelsey', 'natsumi hamamura maria tanaka', '7 - 6 ( 8 ) 6 - 1'], ['runner - up', '16 november 2007', 'nuriootpa , australia', 'hard', 'trudi musgrave', 'natalie grandin robin stephenson', '4 - 6 5 - 7'], ['runner - up', '23 may 2007', 'mount gambier , australia', 'hard', 'trudi musgrave', 'antonia matic monica niculescu', '7 - 5 3 - 6'], ['runner - up', '16 may 2008', 'caserta , italy', 'clay', 'christina wheeler', 'xinyun han yi - fan xu', '6 - 4 4 - 6'], ['winner', '3 may 2009', 'gifu , japan', 'carpet', 'aiko nakamura', 'misaki doi kurumi nara', '6 - 2 6 - 1'], ['winner', '6 june 2009', 'brno , czech republic', 'clay', 'trudi musgrave', 'karin morgosova romana caroline tabak', '6 - 4 6 - 1'], ['runner - up', '5 march 2010', 'sydney , australia', 'hard', 'trudi musgrave', 'casey dellacqua jessica moore', 'w / o'], ['winner', '25 june 2010', 'rome , italy', 'clay', 'trudi musgrave', 'claudia giovine valentina sulpizio', '6 - 0 6 - 3'], ['winner', '09 - may - 2011', 'reggio emilia , italy', 'clay', 'sally peers', 'claudia giovine maria irigoyen', '6 - 4 6 - 1'], ['winner', '30 - may - 2011', 'rome - tiro a volo , italy', 'clay', 'sally peers', 'magda linette liana ungur', 'w / o']]
|
1991 buffalo bills season
|
https://en.wikipedia.org/wiki/1991_Buffalo_Bills_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15353123-1.html.csv
|
unique
|
in the 1991 buffalo bills season , the only player picked from clark university was millard hamilton .
|
{'scope': 'all', 'row': '5', 'col': '5', 'col_other': '3', 'criterion': 'equal', 'value': 'clark university', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'college', 'clark university'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose college record fuzzily matches to clark university .', 'tostr': 'filter_eq { all_rows ; college ; clark university }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; college ; clark university } }', 'tointer': 'select the rows whose college record fuzzily matches to clark university . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'college', 'clark university'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose college record fuzzily matches to clark university .', 'tostr': 'filter_eq { all_rows ; college ; clark university }'}, 'player'], 'result': 'millard hamilton', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; college ; clark university } ; player }'}, 'millard hamilton'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; college ; clark university } ; player } ; millard hamilton }', 'tointer': 'the player record of this unqiue row is millard hamilton .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; college ; clark university } } ; eq { hop { filter_eq { all_rows ; college ; clark university } ; player } ; millard hamilton } } = true', 'tointer': 'select the rows whose college record fuzzily matches to clark university . there is only one such row in the table . the player record of this unqiue row is millard hamilton .'}
|
and { only { filter_eq { all_rows ; college ; clark university } } ; eq { hop { filter_eq { all_rows ; college ; clark university } ; player } ; millard hamilton } } = true
|
select the rows whose college record fuzzily matches to clark university . there is only one such row in the table . the player record of this unqiue row is millard hamilton .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'college_7': 7, 'clark university_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'player_9': 9, 'millard hamilton_10': 10}
|
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'college_7': 'college', 'clark university_8': 'clark university', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'player_9': 'player', 'millard hamilton_10': 'millard hamilton'}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'college_7': [0], 'clark university_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'player_9': [2], 'millard hamilton_10': [3]}
|
['round', 'pick', 'player', 'position', 'college']
|
[['1', '26', 'henry jones', 'defensive back', 'illinois'], ['2', '54', 'phil hansen', 'defensive end', 'north dakota state'], ['3', '82', 'darryl wren', 'defensive back', 'pittsburg state'], ['4', '138', 'shawn wilbourn', 'defensive back', 'long beach state'], ['5', '166', 'millard hamilton', 'wide receiver', 'clark university'], ['6', '194', 'amir rasul', 'running back', 'florida a & m'], ['8', '222', 'brad lamb', 'wide receiver', 'anderson'], ['9', '249', 'mark maddox', 'linebacker', 'northern michigan'], ['10', '277', 'tony delorenzo', 'guard', 'new mexico state'], ['11', '305', 'dean kirkland', 'guard', 'washington'], ['12', '333', 'stephen clark', 'tight end', 'texas']]
|
list of sumo record holders
|
https://en.wikipedia.org/wiki/List_of_sumo_record_holders
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17634218-21.html.csv
|
ordinal
|
kyokunankai attended the second most tournaments out of sumo record holders .
|
{'row': '2', 'col': '2', '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', 'tournaments', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; tournaments ; 2 }'}, 'name'], 'result': 'kyokunankai', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; tournaments ; 2 } ; name }'}, 'kyokunankai'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; tournaments ; 2 } ; name } ; kyokunankai } = true', 'tointer': 'select the row whose tournaments record of all rows is 2nd maximum . the name record of this row is kyokunankai .'}
|
eq { hop { nth_argmax { all_rows ; tournaments ; 2 } ; name } ; kyokunankai } = true
|
select the row whose tournaments record of all rows is 2nd maximum . the name record of this row is kyokunankai .
|
3
|
3
|
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'tournaments_5': 5, '2_6': 6, 'name_7': 7, 'kyokunankai_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', 'tournaments_5': 'tournaments', '2_6': '2', 'name_7': 'name', 'kyokunankai_8': 'kyokunankai'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'tournaments_5': [0], '2_6': [0], 'name_7': [1], 'kyokunankai_8': [2]}
|
['name', 'tournaments', 'pro debut', 'top division debut', 'highest rank']
|
[['hoshiiwato', '115', 'may 1970', 'july 1989', 'maegashira 14'], ['kyokunankai', '105', 'march 1993', 'september 2010', 'maegashira 16'], ['yoshiazuma', '93', 'january 1996', 'september 2011', 'maegashira 12'], ['kotokasuga', '91', 'march 1993', 'may 2008', 'maegashira 7'], ['kototsubaki', '89', 'march 1976', 'january 1991', 'maegashira 3'], ['toyozakura', '88', 'march 1989', 'november 2003', 'maegashira 5'], ['takanomine', '87', 'september 1974', 'march 1989', 'maegashira 12'], ['kitazakura', '86', 'march 1987', 'july 2001', 'maegashira 9'], ['daimanazuru', '85', 'may 1992', 'july 2006', 'maegashira 16'], ['ånohana', '84', 'march 1974', 'march 1988', 'maegashira 13']]
|
1895 ahac season
|
https://en.wikipedia.org/wiki/1895_AHAC_Season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11756240-1.html.csv
|
unique
|
in the 1895 ahac season , the montreal victorias are the only team that won 6 games .
|
{'scope': 'all', 'row': '1', 'col': '3', 'col_other': '1', 'criterion': 'equal', 'value': '6', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'wins', '6'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose wins record is equal to 6 .', 'tostr': 'filter_eq { all_rows ; wins ; 6 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; wins ; 6 } }', 'tointer': 'select the rows whose wins record is equal to 6 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'wins', '6'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose wins record is equal to 6 .', 'tostr': 'filter_eq { all_rows ; wins ; 6 }'}, 'team'], 'result': 'montreal victorias', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; wins ; 6 } ; team }'}, 'montreal victorias'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; wins ; 6 } ; team } ; montreal victorias }', 'tointer': 'the team record of this unqiue row is montreal victorias .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; wins ; 6 } } ; eq { hop { filter_eq { all_rows ; wins ; 6 } ; team } ; montreal victorias } } = true', 'tointer': 'select the rows whose wins record is equal to 6 . there is only one such row in the table . the team record of this unqiue row is montreal victorias .'}
|
and { only { filter_eq { all_rows ; wins ; 6 } } ; eq { hop { filter_eq { all_rows ; wins ; 6 } ; team } ; montreal victorias } } = true
|
select the rows whose wins record is equal to 6 . there is only one such row in the table . the team record of this unqiue row is montreal victorias .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'wins_7': 7, '6_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'team_9': 9, 'montreal victorias_10': 10}
|
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'wins_7': 'wins', '6_8': '6', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'team_9': 'team', 'montreal victorias_10': 'montreal victorias'}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'wins_7': [0], '6_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'team_9': [2], 'montreal victorias_10': [3]}
|
['team', 'games played', 'wins', 'losses', 'ties', 'goals for', 'goals against']
|
[['montreal victorias', '8', '6', '2', '0', '35', '20'], ['montreal hockey club', '8', '4', '4', '0', '33', '22'], ['ottawa', '8', '4', '4', '0', '25', '24'], ['montreal crystals', '7', '3', '4', '0', '21', '39'], ['quebec', '7', '2', '5', '0', '18', '27']]
|
13th united states congress
|
https://en.wikipedia.org/wiki/13th_United_States_Congress
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-225096-4.html.csv
|
unique
|
in the 13th united states congress , the successor in kentucky 's 2nd district was the only one seated in the month of march .
|
{'scope': 'all', 'row': '8', 'col': '5', 'col_other': '1', 'criterion': 'fuzzily_match', 'value': 'march', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date successor seated', 'march'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose date successor seated record fuzzily matches to march .', 'tostr': 'filter_eq { all_rows ; date successor seated ; march }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; date successor seated ; march } }', 'tointer': 'select the rows whose date successor seated record fuzzily matches to march . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date successor seated', 'march'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose date successor seated record fuzzily matches to march .', 'tostr': 'filter_eq { all_rows ; date successor seated ; march }'}, 'district'], 'result': 'kentucky 2nd', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; date successor seated ; march } ; district }'}, 'kentucky 2nd'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; date successor seated ; march } ; district } ; kentucky 2nd }', 'tointer': 'the district record of this unqiue row is kentucky 2nd .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; date successor seated ; march } } ; eq { hop { filter_eq { all_rows ; date successor seated ; march } ; district } ; kentucky 2nd } } = true', 'tointer': 'select the rows whose date successor seated record fuzzily matches to march . there is only one such row in the table . the district record of this unqiue row is kentucky 2nd .'}
|
and { only { filter_eq { all_rows ; date successor seated ; march } } ; eq { hop { filter_eq { all_rows ; date successor seated ; march } ; district } ; kentucky 2nd } } = true
|
select the rows whose date successor seated record fuzzily matches to march . there is only one such row in the table . the district record of this unqiue row is kentucky 2nd .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'date successor seated_7': 7, 'march_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'district_9': 9, 'kentucky 2nd_10': 10}
|
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'date successor seated_7': 'date successor seated', 'march_8': 'march', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'district_9': 'district', 'kentucky 2nd_10': 'kentucky 2nd'}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'date successor seated_7': [0], 'march_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'district_9': [2], 'kentucky 2nd_10': [3]}
|
['district', 'vacator', 'reason for change', 'successor', 'date successor seated']
|
[['new york 15th', 'vacant', 'rep - elect william dowse died on february 18 , 1813', 'john m bowers ( f )', 'seated june 21 , 1813'], ['pennsylvania 5th', 'robert whitehill ( dr )', 'died april 8 , 1813', 'john rea ( dr )', 'seated may 28 , 1813'], ['new york 2nd', 'egbert benson ( f )', 'resigned august 2 , 1813', 'william irving ( dr )', 'seated january 22 , 1814'], ['pennsylvania 3rd', 'john gloninger ( f )', 'resigned august 2 , 1813', 'edward crouch ( dr )', 'seated december 6 , 1813'], ['pennsylvania 7th', 'john m hyneman ( dr )', 'resigned august 2 , 1813', 'daniel udree ( dr )', 'seated december 6 , 1813'], ['illinois territory at - large', 'shadrach bond', 'until august 2 , 1813', 'benjamin stephenson', 'seated november 14 , 1814'], ['tennessee 5th', 'felix grundy ( dr )', 'resigned sometime in 1814', 'newton cannon ( dr )', 'seated october 15 , 1814'], ['kentucky 2nd', 'henry clay ( dr )', 'resigned january 19 , 1814', 'joseph h hawkins ( dr )', 'seated march 29 , 1814'], ['virginia 11th', 'john dawson ( dr )', 'died march 31 , 1814', 'philip barbour ( dr )', 'seated september 19 , 1814'], ['massachusetts 4th', 'william m richardson ( dr )', 'resigned april 18 , 1814', 'samuel dana ( dr )', 'seated september 22 , 1814'], ['new jersey 3rd', 'jacob hufty ( f )', 'died may 20 , 1814', 'thomas bines ( dr )', 'seated november 2 , 1814'], ['ohio 6th', 'reasin beall ( dr )', 'resigned june 7 , 1814', 'david clendenin ( dr )', 'seated december 22 , 1814'], ['pennsylvania 3rd', 'james whitehill ( dr )', 'resigned september 1 , 1814', 'amos slaymaker ( f )', 'seated december 12 , 1814']]
|
1995 - 96 chicago bulls season
|
https://en.wikipedia.org/wiki/1995%E2%80%9396_Chicago_Bulls_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-13480122-5.html.csv
|
majority
|
all games of the 1995 - 96 chicago bulls ' season were scheduled for the month of january .
|
{'scope': 'all', 'col': '2', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'january', 'subset': None}
|
{'func': 'all_str_eq', 'args': ['all_rows', 'date', 'january'], 'result': True, 'ind': 0, 'tointer': 'for the date records of all rows , all of them fuzzily match to january .', 'tostr': 'all_eq { all_rows ; date ; january } = true'}
|
all_eq { all_rows ; date ; january } = true
|
for the date records of all rows , all of them fuzzily match to january .
|
1
|
1
|
{'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'date_3': 3, 'january_4': 4}
|
{'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'date_3': 'date', 'january_4': 'january'}
|
{'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'date_3': [0], 'january_4': [0]}
|
['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record']
|
[['29', 'january 3', 'houston', 'w 100 - 86', 'michael jordan ( 38 )', 'dennis rodman ( 15 )', 'scottie pippen ( 9 )', 'united center 23854', '26 - 3'], ['30', 'january 4', 'charlotte', 'w 117 - 93', 'michael jordan ( 27 )', 'dennis rodman ( 11 )', 'ron harper ( 7 )', 'charlotte coliseum 24042', '27 - 3'], ['31', 'january 6', 'milwaukee', 'w 113 - 84', 'michael jordan ( 32 )', 'dennis rodman ( 16 )', 'scottie pippen ( 6 )', 'united center 23801', '28 - 3'], ['32', 'january 10', 'seattle', 'w 113 - 87', 'michael jordan ( 35 )', 'michael jordan ( 14 )', 'michael jordan , luc longley , scottie pippen ( 5 )', 'united center 23877', '29 - 3'], ['33', 'january 13', 'philadelphia', 'w 120 - 93', 'michael jordan ( 48 )', 'dennis rodman ( 16 )', 'scottie pippen ( 10 )', 'the spectrum 18168', '30 - 3'], ['34', 'january 15', 'washington', 'w 116 - 109', 'michael jordan ( 46 )', 'dennis rodman ( 15 )', 'scottie pippen ( 6 )', 'usair arena 18756', '31 - 3'], ['35', 'january 16', 'philadelphia', 'w 116 - 104', 'michael jordan ( 32 )', 'dennis rodman ( 21 )', 'dennis rodman ( 10 )', 'united center 23587', '32 - 3'], ['36', 'january 18', 'toronto', 'w 92 - 89', 'michael jordan ( 38 )', 'dennis rodman ( 13 )', 'scottie pippen , dennis rodman ( 4 )', 'skydome 36118', '33 - 3'], ['37', 'january 21', 'detroit', 'w 111 - 96', 'michael jordan ( 36 )', 'dennis rodman ( 9 )', 'scottie pippen ( 6 )', 'the palace of auburn hills 21454', '34 - 3'], ['38', 'january 23', 'new york', 'w 99 - 79', 'michael jordan ( 33 )', 'dennis rodman ( 13 )', 'scottie pippen ( 6 )', 'madison square garden 19763', '35 - 3'], ['39', 'january 24', 'vancouver', 'w 104 - 84', 'scottie pippen ( 30 )', 'dennis rodman ( 16 )', 'ron harper ( 7 )', 'united center 23652', '36 - 3'], ['40', 'january 26', 'miami', 'w 102 - 80', 'michael jordan ( 25 )', 'dennis rodman ( 16 )', 'scottie pippen , dennis rodman ( 5 )', 'united center 23814', '37 - 3'], ['41', 'january 28', 'phoenix', 'w 93 - 82', 'michael jordan ( 31 )', 'dennis rodman ( 20 )', 'michael jordan ( 6 )', 'united center 23927', '38 - 3']]
|
2010 - 11 rugby - bundesliga
|
https://en.wikipedia.org/wiki/2010%E2%80%9311_Rugby-Bundesliga
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-30153446-1.html.csv
|
aggregation
|
on average , each club lost around 7 games in the 2010-2011 rugby league .
|
{'scope': 'all', 'col': '6', 'type': 'average', 'result': '7.125', 'subset': None}
|
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'lost'], 'result': '7.125', 'ind': 0, 'tostr': 'avg { all_rows ; lost }'}, '7.125'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; lost } ; 7.125 } = true', 'tointer': 'the average of the lost record of all rows is 7.125 .'}
|
round_eq { avg { all_rows ; lost } ; 7.125 } = true
|
the average of the lost record of all rows is 7.125 .
|
2
|
2
|
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'lost_4': 4, '7.125_5': 5}
|
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'lost_4': 'lost', '7.125_5': '7.125'}
|
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'lost_4': [0], '7.125_5': [1]}
|
['', 'club', 'played', 'won', 'drawn', 'lost', 'points for', 'points against', 'difference', 'bonus points', 'points']
|
[['1', 'heidelberger rk', '16', '15', '0', '1', '924', '120', '804', '15', '75'], ['2', 'sc 1880 frankfurt', '16', '14', '0', '2', '849', '237', '612', '12', '68'], ['3', 'tsv handschuhsheim', '16', '11', '0', '5', '468', '439', '29', '9', '53'], ['4', 'rg heidelberg', '16', '9', '0', '7', '512', '264', '248', '8', '44'], ['5', 'sc neuenheim', '16', '9', '0', '7', '380', '395', '- 15', '8', '44'], ['6', 'berliner rugby club', '16', '7', '0', '9', '281', '471', '- 190', '6', '34'], ['7', 'dsv 78 hannover', '16', '4', '0', '12', '265', '594', '- 329', '4', '20'], ['8', 'rk 03 berlin', '16', '2', '0', '14', '195', '688', '- 493', '2', '10']]
|
lonhro
|
https://en.wikipedia.org/wiki/Lonhro
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1360997-3.html.csv
|
majority
|
lonhro won most of the races they were entered in during the 2002/2003 season .
|
{'scope': 'all', 'col': '1', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'won', 'subset': None}
|
{'func': 'most_str_eq', 'args': ['all_rows', 'result', 'won'], 'result': True, 'ind': 0, 'tointer': 'for the result records of all rows , most of them fuzzily match to won .', 'tostr': 'most_eq { all_rows ; result ; won } = true'}
|
most_eq { all_rows ; result ; won } = true
|
for the result records of all rows , most of them fuzzily match to won .
|
1
|
1
|
{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'result_3': 3, 'won_4': 4}
|
{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'result_3': 'result', 'won_4': 'won'}
|
{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'result_3': [0], 'won_4': [0]}
|
['result', 'date', 'race', 'venue', 'group', 'distance', 'weight ( kg )', 'time', 'jockey', 'winner / 2nd']
|
[['won', '03 aug 2002', 'missile stakes', 'rosehill', 'g3', '1100 m', '57.5', '1:03.53', 'd beadman', '2nd - ancient song'], ['2nd', '24 aug 2002', 'warwick stakes', 'warwick farm', 'g2', '1400 m', '57.5', '1:21.85', 'd beadman', '1st - defier'], ['won', '07 sep 2002', 'chelmsford stakes', 'randwick', 'g2', '1600 m', '57.5', '1:36.30', 'd beadman', '2nd - platinum scissors'], ['4th', '29 sep 2002', 'george main stakes', 'randwick', 'g1', '1600 m', '57.5', '1:38.31', 'd beadman', '1st - defier'], ['won', '12 oct 2002', 'caulfield stakes', 'caulfield', 'g1', '2000 m', '57.5', '2:00.60', 'd beadman', '2nd - sunline'], ['6th', '26 oct 2002', 'cox plate', 'moonee valley', 'g1', '2040 m', '56.5', '2:06.27', 'd beadman', '1st - northerly'], ['won', '02 nov 2002', 'mackinnon stakes', 'flemington', 'g1', '2000 m', '57.5', '2:02.64', 'd beadman', '2nd - royal code'], ['won', '22 feb 2003', 'expressway stakes', 'randwick', 'g2', '1200 m', '57.5', '1:10.66', 'd beadman', '2nd - belle du jour'], ['won', '08 mar 2003', 'apollo stakes', 'randwick', 'g2', '1400 m', '58', '1:22.49', 'd beadman', '2nd - hoeburg'], ['won', '15 mar 2003', 'chipping norton stakes', 'warwick farm', 'g1', '1600 m', '58', '1:37.93', 'd beadman', '2nd - shogun lodge'], ['won', '05 apr 2003', 'george ryder stakes', 'rosehill', 'g1', '1500 m', '58', '1:30.71', 'd beadman', '2nd - dash for cash'], ['4th', '19 apr 2003', 'doncaster handicap', 'randwick', 'g1', '1600 m', '57.5', '1:36.85', 'd beadman', '1st - grand armee']]
|
peanut oil
|
https://en.wikipedia.org/wiki/Peanut_oil
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1195910-1.html.csv
|
majority
|
the majority of peanut oils have at least 20 g of monounsaturated fat .
|
{'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'greater_than_eq', 'value': '20 g', 'subset': None}
|
{'func': 'most_greater_eq', 'args': ['all_rows', 'monounsaturated fat', '20 g'], 'result': True, 'ind': 0, 'tointer': 'for the monounsaturated fat records of all rows , most of them are greater than or equal to 20 g .', 'tostr': 'most_greater_eq { all_rows ; monounsaturated fat ; 20 g } = true'}
|
most_greater_eq { all_rows ; monounsaturated fat ; 20 g } = true
|
for the monounsaturated fat records of all rows , most of them are greater than or equal to 20 g .
|
1
|
1
|
{'most_greater_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'monounsaturated fat_3': 3, '20 g_4': 4}
|
{'most_greater_eq_0': 'most_greater_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'monounsaturated fat_3': 'monounsaturated fat', '20 g_4': '20 g'}
|
{'most_greater_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'monounsaturated fat_3': [0], '20 g_4': [0]}
|
['total fat', 'saturated fat', 'monounsaturated fat', 'polyunsaturated fat', 'smoke point']
|
[['100 g', '11 g', '20 g ( 84 g in high oleic variety )', '69 g ( 4 g in high oleic variety )', 'degree'], ['100 g', '16 g', '23 g', '58 g', 'degree'], ['100 g', '7 g', '63 g', '28 g', 'degree'], ['100 g', '14 g', '73 g', '11 g', 'degree'], ['100 g', '15 g', '30 g', '55 g', 'degree'], ['100 g', '17 g', '46 g', '32 g', 'degree'], ['100 g', '25 g', '38 g', '37 g', 'degree'], ['71 g', '23 g ( 34 % )', '8 g ( 11 % )', '37 g ( 52 % )', 'degree'], ['100 g', '39 g', '45 g', '11 g', 'degree'], ['94 g', '52 g ( 55 % )', '32 g ( 34 % )', '3 g ( 3 % )', '200degree ( 400degree )'], ['81 g', '51 g ( 63 % )', '21 g ( 26 % )', '3 g ( 4 % )', 'degree']]
|
list of a league of their own episodes
|
https://en.wikipedia.org/wiki/List_of_A_League_of_Their_Own_episodes
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-29141354-7.html.csv
|
count
|
four of the episodes were first broadcast in september , 2013 .
|
{'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'september 2013', 'result': '4', 'col': '2', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'first broadcast', 'september 2013'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose first broadcast record fuzzily matches to september 2013 .', 'tostr': 'filter_eq { all_rows ; first broadcast ; september 2013 }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; first broadcast ; september 2013 } }', 'tointer': 'select the rows whose first broadcast record fuzzily matches to september 2013 . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; first broadcast ; september 2013 } } ; 4 } = true', 'tointer': 'select the rows whose first broadcast record fuzzily matches to september 2013 . the number of such rows is 4 .'}
|
eq { count { filter_eq { all_rows ; first broadcast ; september 2013 } } ; 4 } = true
|
select the rows whose first broadcast record fuzzily matches to september 2013 . 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, 'first broadcast_5': 5, 'september 2013_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', 'first broadcast_5': 'first broadcast', 'september 2013_6': 'september 2013', '4_7': '4'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'first broadcast_5': [0], 'september 2013_6': [0], '4_7': [2]}
|
['episode', 'first broadcast', 'andrew and jacks guest', 'jamies guests', 'scores']
|
[['07x01', '23 august 2013', 'amy williams', 'edgar davids , jimmy carr', '6 - 11'], ['07x02', '30 august 2013', 'sara cox', 'harry styles , louis tomlinson , niall horan', '13 - 14'], ['07x03', '6 september 2013', 'sarah storey', 'sam allardyce , david walliams', '9 - 7'], ['07x04', '13 september 2013', 'paula radcliffe', 'chris ashton , richard ayoade', '10 - 9'], ['07x05', '20 september 2013', 'gabby logan', 'joleon lescott , jon richardson', '9 - 11'], ['07x06', '27 september 2013', 'matt smith', 'jonathan ross , perri shakes - drayton', '13 - 8'], ['07x07', '4 october 2013', 'richard ayoade', 'nicola adams , david walliams', '6 - 11'], ['07x08', '11 october 2013', 'alan shearer', 'jason manford , frankie sandford', '13 - 12']]
|
tourism in costa rica
|
https://en.wikipedia.org/wiki/Tourism_in_Costa_Rica
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17781704-3.html.csv
|
unique
|
cuba is the only country with no 2011 tourist receipts and no revenue % of international goods .
|
{'scope': 'all', 'row': '7', 'col': '3', 'col_other': '1,6', 'criterion': 'equal', 'value': 'n/d', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'internl tourism receipts 2011 ( million usd )', 'n/d'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose internl tourism receipts 2011 ( million usd ) record fuzzily matches to n/d .', 'tostr': 'filter_eq { all_rows ; internl tourism receipts 2011 ( million usd ) ; n/d }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; internl tourism receipts 2011 ( million usd ) ; n/d } }', 'tointer': 'select the rows whose internl tourism receipts 2011 ( million usd ) record fuzzily matches to n/d . there is only one such row in the table .'}, {'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'internl tourism receipts 2011 ( million usd )', 'n/d'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose internl tourism receipts 2011 ( million usd ) record fuzzily matches to n/d .', 'tostr': 'filter_eq { all_rows ; internl tourism receipts 2011 ( million usd ) ; n/d }'}, 'selected caribbean and n latin america countries'], 'result': 'cuba', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; internl tourism receipts 2011 ( million usd ) ; n/d } ; selected caribbean and n latin america countries }'}, 'cuba'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; internl tourism receipts 2011 ( million usd ) ; n/d } ; selected caribbean and n latin america countries } ; cuba }', 'tointer': 'the selected caribbean and n latin america countries record of this unqiue row is cuba .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'internl tourism receipts 2011 ( million usd )', 'n/d'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose internl tourism receipts 2011 ( million usd ) record fuzzily matches to n/d .', 'tostr': 'filter_eq { all_rows ; internl tourism receipts 2011 ( million usd ) ; n/d }'}, 'revenues as % of exports goods and services 2011'], 'result': 'n / d', 'ind': 4, 'tostr': 'hop { filter_eq { all_rows ; internl tourism receipts 2011 ( million usd ) ; n/d } ; revenues as % of exports goods and services 2011 }'}, 'n / d'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; internl tourism receipts 2011 ( million usd ) ; n/d } ; revenues as % of exports goods and services 2011 } ; n / d }', 'tointer': 'the revenues as % of exports goods and services 2011 record of this unqiue row is n / d .'}], 'result': True, 'ind': 6, 'tostr': 'and { eq { hop { filter_eq { all_rows ; internl tourism receipts 2011 ( million usd ) ; n/d } ; selected caribbean and n latin america countries } ; cuba } ; eq { hop { filter_eq { all_rows ; internl tourism receipts 2011 ( million usd ) ; n/d } ; revenues as % of exports goods and services 2011 } ; n / d } }', 'tointer': 'the selected caribbean and n latin america countries record of this unqiue row is cuba . the revenues as % of exports goods and services 2011 record of this unqiue row is n / d .'}], 'result': True, 'ind': 7, 'tostr': 'and { only { filter_eq { all_rows ; internl tourism receipts 2011 ( million usd ) ; n/d } } ; and { eq { hop { filter_eq { all_rows ; internl tourism receipts 2011 ( million usd ) ; n/d } ; selected caribbean and n latin america countries } ; cuba } ; eq { hop { filter_eq { all_rows ; internl tourism receipts 2011 ( million usd ) ; n/d } ; revenues as % of exports goods and services 2011 } ; n / d } } } = true', 'tointer': 'select the rows whose internl tourism receipts 2011 ( million usd ) record fuzzily matches to n/d . there is only one such row in the table . the selected caribbean and n latin america countries record of this unqiue row is cuba . the revenues as % of exports goods and services 2011 record of this unqiue row is n / d .'}
|
and { only { filter_eq { all_rows ; internl tourism receipts 2011 ( million usd ) ; n/d } } ; and { eq { hop { filter_eq { all_rows ; internl tourism receipts 2011 ( million usd ) ; n/d } ; selected caribbean and n latin america countries } ; cuba } ; eq { hop { filter_eq { all_rows ; internl tourism receipts 2011 ( million usd ) ; n/d } ; revenues as % of exports goods and services 2011 } ; n / d } } } = true
|
select the rows whose internl tourism receipts 2011 ( million usd ) record fuzzily matches to n/d . there is only one such row in the table . the selected caribbean and n latin america countries record of this unqiue row is cuba . the revenues as % of exports goods and services 2011 record of this unqiue row is n / d .
|
10
|
8
|
{'and_7': 7, 'result_8': 8, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_9': 9, 'internl tourism receipts 2011 (million usd )_10': 10, 'n/d_11': 11, 'and_6': 6, 'str_eq_3': 3, 'str_hop_2': 2, 'selected caribbean and n latin america countries_12': 12, 'cuba_13': 13, 'str_eq_5': 5, 'str_hop_4': 4, 'revenues as % of exports goods and services 2011_14': 14, 'n / d_15': 15}
|
{'and_7': 'and', 'result_8': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_9': 'all_rows', 'internl tourism receipts 2011 (million usd )_10': 'internl tourism receipts 2011 ( million usd )', 'n/d_11': 'n/d', 'and_6': 'and', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'selected caribbean and n latin america countries_12': 'selected caribbean and n latin america countries', 'cuba_13': 'cuba', 'str_eq_5': 'str_eq', 'str_hop_4': 'str_hop', 'revenues as % of exports goods and services 2011_14': 'revenues as % of exports goods and services 2011', 'n / d_15': 'n / d'}
|
{'and_7': [8], 'result_8': [], 'only_1': [7], 'filter_str_eq_0': [1, 2, 4], 'all_rows_9': [0], 'internl tourism receipts 2011 (million usd )_10': [0], 'n/d_11': [0], 'and_6': [7], 'str_eq_3': [6], 'str_hop_2': [3], 'selected caribbean and n latin america countries_12': [2], 'cuba_13': [3], 'str_eq_5': [6], 'str_hop_4': [5], 'revenues as % of exports goods and services 2011_14': [4], 'n / d_15': [5]}
|
['selected caribbean and n latin america countries', 'internl tourist arrivals 2011 ( x1000 )', 'internl tourism receipts 2011 ( million usd )', 'receipts per arrival 2010 ( col 2 ) / ( col 1 ) ( usd )', 'receipts per capita 2005 usd', 'revenues as % of exports goods and services 2011']
|
[['bahamas ( 1 )', '1368', '2059', '1505', '6288', '74.6'], ['barbados', '568', '974', '1715', '2749', '58.5'], ['brazil', '5433', '6555', '1207', '18', '3.2'], ['chile', '3070', '1831', '596', '73', '5.3'], ['costa rica', '2196', '2156', '982', '343', '17.5'], ['colombia ( 1 )', '2385', '2083', '873', '25', '6.6'], ['cuba', '2688', 'n / d', 'n / d', '169', 'n / d'], ['dominican republic', '4306', '4353', '1011', '353', '36.2'], ['guatemala', '1225', '1350', '1102', '66', '16.0'], ['jamaica', '1952', '2012', '1031', '530', '49.2'], ['mexico', '23403', '11869', '507', '103', '5.7'], ['panama', '1473', '1926', '1308', '211', '10.6'], ['peru', '2598', '2360', '908', '41', '9.0']]
|
1985 world judo championships
|
https://en.wikipedia.org/wiki/1985_World_Judo_Championships
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15807751-2.html.csv
|
aggregation
|
on average , the countries in the 1985 world judo championship got 2.13 medals .
|
{'scope': 'all', 'col': '6', 'type': 'average', 'result': '2.13', 'subset': None}
|
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'total'], 'result': '2.13', 'ind': 0, 'tostr': 'avg { all_rows ; total }'}, '2.13'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; total } ; 2.13 } = true', 'tointer': 'the average of the total record of all rows is 2.13 .'}
|
round_eq { avg { all_rows ; total } ; 2.13 } = true
|
the average of the total record of all rows is 2.13 .
|
2
|
2
|
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'total_4': 4, '2.13_5': 5}
|
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'total_4': 'total', '2.13_5': '2.13'}
|
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'total_4': [0], '2.13_5': [1]}
|
['rank', 'nation', 'gold', 'silver', 'bronze', 'total']
|
[['1', 'japan', '4', '1', '1', '6'], ['2', 'south korea', '2', '2', '0', '4'], ['3', 'soviet union', '1', '0', '5', '6'], ['4', 'austria', '1', '0', '0', '1'], ['5', 'germany', '0', '1', '2', '3'], ['6', 'bulgaria', '0', '1', '1', '2'], ['7', 'egypt', '0', '1', '0', '1'], ['7', 'united states', '0', '1', '0', '1'], ['7', 'east germany', '0', '1', '0', '1'], ['10', 'great britain', '0', '0', '2', '2'], ['11', 'poland', '0', '0', '1', '1'], ['11', 'france', '0', '0', '1', '1'], ['11', 'belgium', '0', '0', '1', '1'], ['11', 'hungary', '0', '0', '1', '1'], ['11', 'netherlands', '0', '0', '1', '1']]
|
armageddon ( 2003 )
|
https://en.wikipedia.org/wiki/Armageddon_%282003%29
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18717672-3.html.csv
|
unique
|
the dudley boyz was the only tag team that was elminated by evolution in armageddon 2003 .
|
{'scope': 'all', 'row': '6', 'col': '4', 'col_other': '2', 'criterion': 'fuzzily_match', 'value': 'evolution', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'eliminated by', 'evolution'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose eliminated by record fuzzily matches to evolution .', 'tostr': 'filter_eq { all_rows ; eliminated by ; evolution }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; eliminated by ; evolution } }', 'tointer': 'select the rows whose eliminated by record fuzzily matches to evolution . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'eliminated by', 'evolution'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose eliminated by record fuzzily matches to evolution .', 'tostr': 'filter_eq { all_rows ; eliminated by ; evolution }'}, 'tag team'], 'result': 'dudley boyz', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; eliminated by ; evolution } ; tag team }'}, 'dudley boyz'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; eliminated by ; evolution } ; tag team } ; dudley boyz }', 'tointer': 'the tag team record of this unqiue row is dudley boyz .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; eliminated by ; evolution } } ; eq { hop { filter_eq { all_rows ; eliminated by ; evolution } ; tag team } ; dudley boyz } } = true', 'tointer': 'select the rows whose eliminated by record fuzzily matches to evolution . there is only one such row in the table . the tag team record of this unqiue row is dudley boyz .'}
|
and { only { filter_eq { all_rows ; eliminated by ; evolution } } ; eq { hop { filter_eq { all_rows ; eliminated by ; evolution } ; tag team } ; dudley boyz } } = true
|
select the rows whose eliminated by record fuzzily matches to evolution . there is only one such row in the table . the tag team record of this unqiue row is dudley boyz .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'eliminated by_7': 7, 'evolution_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'tag team_9': 9, 'dudley boyz_10': 10}
|
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'eliminated by_7': 'eliminated by', 'evolution_8': 'evolution', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'tag team_9': 'tag team', 'dudley boyz_10': 'dudley boyz'}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'eliminated by_7': [0], 'evolution_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'tag team_9': [2], 'dudley boyz_10': [3]}
|
['eliminated', 'tag team', 'entered', 'eliminated by', 'time']
|
[['1', 'la résistance ( robért conway and rené duprée )', '2', 'rosey and the hurricane', '03:16'], ['2', 'rosey and the hurricane', '1', 'mark jindrak and garrison cade', '03:34'], ['3', 'val venis and lance storm', '4', 'jindrak and cade', '07:17'], ['4', 'jindrak and cade', '3', 'the dudley boyz ( bubba ray and d - von )', '11:29'], ['5', 'test and scott steiner', '6', 'dudley boyz', '16:38'], ['6', 'dudley boyz', '5', 'evolution ( ric flair and batista )', '20:48'], ['n / a', 'evolution', '7', 'winners', 'winners']]
|
marine pharmacognosy
|
https://en.wikipedia.org/wiki/Marine_pharmacognosy
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12715053-1.html.csv
|
count
|
six of the marine sourced drugs are fda-approved clinical status .
|
{'scope': 'all', 'criterion': 'equal', 'value': 'fda - approved', 'result': '6', 'col': '1', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'clinical status', 'fda - approved'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose clinical status record fuzzily matches to fda - approved .', 'tostr': 'filter_eq { all_rows ; clinical status ; fda - approved }'}], 'result': '6', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; clinical status ; fda - approved } }', 'tointer': 'select the rows whose clinical status record fuzzily matches to fda - approved . the number of such rows is 6 .'}, '6'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; clinical status ; fda - approved } } ; 6 } = true', 'tointer': 'select the rows whose clinical status record fuzzily matches to fda - approved . the number of such rows is 6 .'}
|
eq { count { filter_eq { all_rows ; clinical status ; fda - approved } } ; 6 } = true
|
select the rows whose clinical status record fuzzily matches to fda - approved . the number of such rows is 6 .
|
3
|
3
|
{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'clinical status_5': 5, 'fda - approved_6': 6, '6_7': 7}
|
{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'clinical status_5': 'clinical status', 'fda - approved_6': 'fda - approved', '6_7': '6'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'clinical status_5': [0], 'fda - approved_6': [0], '6_7': [2]}
|
['clinical status', 'compound name', 'trademark', 'marine organism α', 'chemical class', 'molecular target', 'clinical trials β', 'disease area']
|
[['fda - approved', 'cytarabine ( ara - c )', 'cytosar - u ®', 'sponge', 'nucleoside', 'dna polymerase', '> 50 / 711', 'cancer'], ['fda - approved', 'vidarabine ( ara - a )', 'vira - a ®', 'sponge', 'nucleoside', 'viral dna polymerase', '0', 'antiviral'], ['fda - approved', 'ziconotide', 'prialt ®', 'cone snail', 'peptide', 'n - type ca 2 + channel', '2 / 5', 'analgesic'], ['fda - approved', 'eribulin mesylate ( e7389 )', 'halaven ®', 'sponge', 'macrolide', 's microtubule', '19 / 27', 'cancer'], ['fda - approved', 'omega - 3 - fatty acid ethyl esters', 'lovaza ®', 'fish', 'omega - 3 fatty acids', 'triglyceride - synthesizing enzymes', '45 / 94', 'hypertriglyceridemia'], ['fda - approved', 'trabectedin ( et - 743 ) eu approved only', 'yondelis ®', 'tunicate', 'alkaloid', 'minor groove of dna', '17 / 34', 'cancer'], ['phase iii', 'brentuximab vedotin ( sgn - 35 )', 'adcetris ®', 'mollusk', 'antibody - drug conjugate ( mm auristatin e )', 'cd30 and microtubules', '9 / 19', 'cancer'], ['phase iii', 'plitidepsin', 'aplidin ®', 'tunicate', 'depsipeptide', 'rac1 and jnk activation', '1 / 7', 'cancer'], ['phase ii', 'dmxba ( gts - 21 )', 'n / a', 'worm', 'alkaloid', 'alpha - 7 nicotinic acetylcholine receptor', '0 / 3', 'congnition , schizophrenia'], ['phase ii', 'plinabulin ( npi 2358 )', 'n / a', 'fungus', 'diketopiperazine', 'microtubules and jnk stress protein', '1 / 2', 'cancer'], ['phase ii', 'elisidepsin', 'irvalec ®', 'mollusk', 'depsipeptide', 'plasma membrane fluidity', '1 / 2', 'cancer'], ['phase ii', 'pm00104', 'zalypsis ®', 'nudibranch', 'alkaloid', 'dna - binding', '2 / 3', 'cancer'], ['phase ii', 'glembatumumab vedotin ( cdx - 011 )', 'n / a', 'mollusk', 'antibody drug conjugate ( mm auristatin e )', 'glycoprotein nmb and microtubules', '1 / 3', 'cancer'], ['phase i', 'marizomib ( salinosporamide a )', 'n / a', 'bacterium', 'beta - lactone - gamma lactam', '20s proteasome', '4 / 4', 'cancer'], ['phase i', 'pm01183', 'n / a', 'tunicate', 'alkaloid', 'minor groove of dna', 'n / a', 'cancer'], ['phase i', 'sgn - 75', 'n / a', 'mollusk', 'antibody drug conjugate ( mm auristatin f )', 'cd70 and microtubules', '2 / 2', 'cancer'], ['phase i', 'asg - 5 me', 'n / a', 'mollusk', 'antibody drug conjugate ( mm auristatin e )', 'asg - 5 and microtubules', '2 / 2', 'cancer'], ['phase i', 'hemiasterlin ( e7974 )', 'n / a', 'sponge', 'tripeptide', 'microtubules', '0 / 3', 'cancer'], ['phase i', 'bryostatin 1', 'n / a', 'bryozoa', 'polyketide', 'protein kinase c', '0 / 38', 'cancer , alzheimers'], ['phase i', 's pseudopterosin', 'n / a', 'soft coral', 'diterpene glycoside', 'eicosanoid metabolism', 'n / a', 'wound healing']]
|
1991 u.s. open ( golf )
|
https://en.wikipedia.org/wiki/1991_U.S._Open_%28golf%29
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17162268-2.html.csv
|
majority
|
in the 1991 u.s. open , most of the players are from the united states .
|
{'scope': 'all', 'col': '2', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'united states', 'subset': None}
|
{'func': 'most_str_eq', 'args': ['all_rows', 'country', 'united states'], 'result': True, 'ind': 0, 'tointer': 'for the country records of all rows , most of them fuzzily match to united states .', 'tostr': 'most_eq { all_rows ; country ; united states } = true'}
|
most_eq { all_rows ; country ; united states } = true
|
for the country records of all rows , most of them fuzzily match to united states .
|
1
|
1
|
{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'country_3': 3, 'united states_4': 4}
|
{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'country_3': 'country', 'united states_4': 'united states'}
|
{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'country_3': [0], 'united states_4': [0]}
|
['player', 'country', 'year ( s ) won', 'total', 'to par', 'finish']
|
[['scott simpson', 'united states', '1987', '282', '- 6', '2'], ['larry nelson', 'united states', '1983', '285', '- 3', 't3'], ['fuzzy zoeller', 'united states', '1984', '286', '- 2', '5'], ['raymond floyd', 'united states', '1986', '289', '+ 1', 't8'], ['hale irwin', 'united states', '1974 , 1979 , 1990', '290', '+ 2', 't11'], ['tom watson', 'united states', '1982', '291', '+ 3', 't16'], ['andy north', 'united states', '1978 , 1985', '295', '+ 7', 't37'], ['jack nicklaus', 'united states', '1962 , 1967 , 1972 , 1980', '297', '+ 9', 't46'], ['david graham', 'australia', '1981', '302', '+ 14', '60']]
|
juan garriga
|
https://en.wikipedia.org/wiki/Juan_Garriga
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14820149-3.html.csv
|
count
|
five of the events that juan garriga participated in were in the 250cc class .
|
{'scope': 'all', 'criterion': 'equal', 'value': '250cc', 'result': '5', 'col': '2', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'class', '250cc'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose class record fuzzily matches to 250cc .', 'tostr': 'filter_eq { all_rows ; class ; 250cc }'}], 'result': '5', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; class ; 250cc } }', 'tointer': 'select the rows whose class record fuzzily matches to 250cc . the number of such rows is 5 .'}, '5'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; class ; 250cc } } ; 5 } = true', 'tointer': 'select the rows whose class record fuzzily matches to 250cc . the number of such rows is 5 .'}
|
eq { count { filter_eq { all_rows ; class ; 250cc } } ; 5 } = true
|
select the rows whose class record fuzzily matches to 250cc . the number of such rows is 5 .
|
3
|
3
|
{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'class_5': 5, '250cc_6': 6, '5_7': 7}
|
{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'class_5': 'class', '250cc_6': '250cc', '5_7': '5'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'class_5': [0], '250cc_6': [0], '5_7': [2]}
|
['year', 'class', 'team', 'points', 'wins']
|
[['1984', '250cc', 'yamaha', '0', '0'], ['1985', '250cc', 'jj cobas', '8', '0'], ['1986', '500cc', 'cagiva', '4', '0'], ['1987', '250cc', 'ducados - yamaha', '46', '0'], ['1988', '250cc', 'ducados - yamaha', '221', '3'], ['1989', '250cc', 'ducados - yamaha', '98', '0'], ['1990', '500cc', 'ducados - yamaha', '121', '0'], ['1991', '500cc', 'ducados - yamaha', '121', '0'], ['1992', '500cc', 'ducados - yamaha', '61', '0'], ['1993', '500cc', 'cagiva', '7', '0']]
|
eurozone
|
https://en.wikipedia.org/wiki/Eurozone
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-184391-1.html.csv
|
majority
|
the population of most of the areas in the eurozone are greater than 511840 .
|
{'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '511840', 'subset': None}
|
{'func': 'most_greater', 'args': ['all_rows', 'population ( 2011 - 01 - 01 )', '511840'], 'result': True, 'ind': 0, 'tointer': 'for the population ( 2011 - 01 - 01 ) records of all rows , most of them are greater than 511840 .', 'tostr': 'most_greater { all_rows ; population ( 2011 - 01 - 01 ) ; 511840 } = true'}
|
most_greater { all_rows ; population ( 2011 - 01 - 01 ) ; 511840 } = true
|
for the population ( 2011 - 01 - 01 ) records of all rows , most of them are greater than 511840 .
|
1
|
1
|
{'most_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 'population (2011 - 01 - 01)_3': 3, '511840_4': 4}
|
{'most_greater_0': 'most_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 'population (2011 - 01 - 01)_3': 'population ( 2011 - 01 - 01 )', '511840_4': '511840'}
|
{'most_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 'population (2011 - 01 - 01)_3': [0], '511840_4': [0]}
|
['state', 'adopted', 'population ( 2011 - 01 - 01 )', 'nominal gdp world bank , 2009 ( million usd )', 'relative gdp of total ( nominal )', 'gdp per capita world bank , 2009 nominal ( usd )']
|
[['austria', '1999 - 01 - 01', '8404252', '384908', '3.09 %', '45799'], ['belgium', '1999 - 01 - 01', '10918405', '468522', '3.76 %', '42911'], ['cyprus ( incl uk military base )', '2008 - 01 - 01', '838896 14500', '24910', '0.20 %', '30966'], ['estonia', '2011 - 01 - 01', '1340194', '19120', '0.15 %', '14267'], ['finland', '1999 - 01 - 01', '5375276', '237512', '1.91 %', '44186'], ['france', '1999 - 01 - 01', '65075373', '2649390', '21.26 %', '40713'], ['germany', '1999 - 01 - 01', '81751602', '3330032', '26.73 %', '40734'], ['greece', '2001 - 01 - 01', '11325897', '329924', '2.65 %', '29130'], ['ireland', '1999 - 01 - 01', '4480858', '227193', '1.82 %', '50703'], ['italy', '1999 - 01 - 01', '60626442', '2112780', '16.96 %', '34849'], ['luxembourg', '1999 - 01 - 01', '511840', '52449', '0.42 %', '102471'], ['malta', '2008 - 01 - 01', '417617', '7449', '0.06 %', '17837'], ['netherlands', '1999 - 01 - 01', '16655799', '792128', '6.36 %', '47559'], ['portugal', '1999 - 01 - 01', '10636979', '227676', '1.83 %', '21404'], ['slovakia', '2009 - 01 - 01', '5435273', '87642', '0.70 %', '16125'], ['slovenia', '2007 - 01 - 01', '2050189', '48477', '0.39 %', '23645'], ['spain', '1999 - 01 - 01', '47190493', '1460250', '11.72 %', '30944'], ['eurozone', 'eurozone', '331963357', '12460362', '100 %', '37535']]
|
woden valley
|
https://en.wikipedia.org/wiki/Woden_Valley
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1174162-1.html.csv
|
majority
|
most of the places in woden valley had over 2000 people living in them .
|
{'scope': 'all', 'col': '2', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '2000', 'subset': None}
|
{'func': 'most_greater', 'args': ['all_rows', 'population ( in 2008 )', '2000'], 'result': True, 'ind': 0, 'tointer': 'for the population ( in 2008 ) records of all rows , most of them are greater than 2000 .', 'tostr': 'most_greater { all_rows ; population ( in 2008 ) ; 2000 } = true'}
|
most_greater { all_rows ; population ( in 2008 ) ; 2000 } = true
|
for the population ( in 2008 ) records of all rows , most of them are greater than 2000 .
|
1
|
1
|
{'most_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 'population (in 2008)_3': 3, '2000_4': 4}
|
{'most_greater_0': 'most_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 'population (in 2008)_3': 'population ( in 2008 )', '2000_4': '2000'}
|
{'most_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 'population (in 2008)_3': [0], '2000_4': [0]}
|
['suburb', 'population ( in 2008 )', 'median age ( in 2006 )', 'mean household size ( in 2006 )', 'area ( km square )', 'density ( / km square )', 'date first settled as a suburb', 'gazetted as a division name']
|
[['chifley', '2325', '36 years', '2.3 persons', '1.6', '1453', '1966', '12 may 1966'], ['curtin', '5133', '41 years', '2.5 persons', '4.8', '1069', '1962', '20 september 1962'], ['farrer', '3360', '41 years', '2.7 persons', '2.1', '1600', '1967', '12 may 1966'], ['garran', '3175', '39 years', '2.5 persons', '2.7', '1175', '1966', '12 may 1966'], ['hughes', '2898', '41 years', '2.5 persons', '1.8', '1610', '1963', '20 september 1962'], ['isaacs', '2424', '45 years', '2.6 persons', '3.1', '781', '1986', '12 may 1966'], ['lyons', '2444', '38 years', '2.1 persons', '2.3', '1062', '1965', '20 september 1962'], ['mawson', '2861', '40 years', '2.2 persons', '2.1', '1362', '1967', '12 may 1966'], ["o'malley", '684', '47 years', '3.1 persons', '2.6', '263', '1973', '12 may 1966'], ['pearce', '2509', '41 years', '2.3 persons', '1.7', '1475', '1967', '12 may 1966'], ['phillip', '1910', '32 years', '1.7 persons', '2.6', '734', '1966', '12 may 1966']]
|
100 metres
|
https://en.wikipedia.org/wiki/100_metres
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1231316-5.html.csv
|
aggregation
|
for 100 metre dash record holders , the average time of those from usa is 11.08 .
|
{'scope': 'subset', 'col': '2', 'type': 'average', 'result': '11.08', 'subset': {'col': '5', 'criterion': 'equal', 'value': 'united states'}}
|
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nation', 'united states'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; nation ; united states }', 'tointer': 'select the rows whose nation record fuzzily matches to united states .'}, 'fastest time ( s )'], 'result': '11.08', 'ind': 1, 'tostr': 'avg { filter_eq { all_rows ; nation ; united states } ; fastest time ( s ) }'}, '11.08'], 'result': True, 'ind': 2, 'tostr': 'round_eq { avg { filter_eq { all_rows ; nation ; united states } ; fastest time ( s ) } ; 11.08 } = true', 'tointer': 'select the rows whose nation record fuzzily matches to united states . the average of the fastest time ( s ) record of these rows is 11.08 .'}
|
round_eq { avg { filter_eq { all_rows ; nation ; united states } ; fastest time ( s ) } ; 11.08 } = true
|
select the rows whose nation record fuzzily matches to united states . the average of the fastest time ( s ) record of these rows is 11.08 .
|
3
|
3
|
{'eq_2': 2, 'result_3': 3, 'avg_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'nation_5': 5, 'united states_6': 6, 'fastest time (s)_7': 7, '11.08_8': 8}
|
{'eq_2': 'eq', 'result_3': 'true', 'avg_1': 'avg', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'nation_5': 'nation', 'united states_6': 'united states', 'fastest time (s)_7': 'fastest time ( s )', '11.08_8': '11.08'}
|
{'eq_2': [3], 'result_3': [], 'avg_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'nation_5': [0], 'united states_6': [0], 'fastest time (s)_7': [1], '11.08_8': [2]}
|
['rank', 'fastest time ( s )', 'wind ( m / s )', 'athlete', 'nation', 'date', 'location']
|
[['1', '10.88', '+ 2.0', 'marlies göhr', 'east germany', '1 july 1977', 'dresden'], ['2', '10.89', '+ 1.8', 'katrin krabbe', 'east germany', '20 july 1988', 'berlin'], ['3', '11.03', '+ 1.7', 'silke gladisch - möller', 'east germany', '8 june 1983', 'berlin'], ['3', '11.03', '+ 0.6', 'english gardner', 'united states', '14 may 2011', 'tucson'], ['5', '11.04', '+ 1.4', 'angela williams', 'united states', '5 june 1999', 'boise'], ['6', '11.07', '+ 0.7', 'bianca knight', 'united states', '27 june 2008', 'eugene'], ['7', '11.08', '+ 2.0', 'brenda morehead', 'united states', '21 june 1976', 'eugene'], ['8', '11.11', '+ 0.2', 'shakedia jones', 'united states', '2 may 1998', 'westwood'], ['8', '11.11', '+ 1.1', 'joan uduak ekah', 'nigeria', '2 july 1999', 'lausanne'], ['10', '11.12', '+ 2.0', 'veronica campbell - brown', 'jamaica', '18 october 2000', 'santiago'], ['10', '11.12', '+ 1.2', 'alexandria anderson', 'united states', '22 june 2006', 'indianapolis']]
|
swimming at the 2008 summer olympics - women 's 100 metre backstroke
|
https://en.wikipedia.org/wiki/Swimming_at_the_2008_Summer_Olympics_%E2%80%93_Women%27s_100_metre_backstroke
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18625437-4.html.csv
|
aggregation
|
the average time among swimmers in the women 's 100 metre backstroke was 1:00.26 .
|
{'scope': 'all', 'col': '5', 'type': 'average', 'result': '1:00.26', 'subset': None}
|
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'time'], 'result': '1:00.26', 'ind': 0, 'tostr': 'avg { all_rows ; time }'}, '1:00.26'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; time } ; 1:00.26 } = true', 'tointer': 'the average of the time record of all rows is 1:00.26 .'}
|
round_eq { avg { all_rows ; time } ; 1:00.26 } = true
|
the average of the time record of all rows is 1:00.26 .
|
2
|
2
|
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'time_4': 4, '1:00.26_5': 5}
|
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'time_4': 'time', '1:00.26_5': '1:00.26'}
|
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'time_4': [0], '1:00.26_5': [1]}
|
['rank', 'lane', 'name', 'nationality', 'time']
|
[['1', '5', 'natalie coughlin', 'united states', '59.43'], ['2', '4', 'reiko nakamura', 'japan', '59.64'], ['3', '3', 'gemma spofforth', 'great britain', '59.79'], ['4', '6', 'hanae ito', 'japan', '1:00.13'], ['5', '7', 'elizabeth simmonds', 'great britain', '1:00.39'], ['6', '2', 'julia wilkinson', 'canada', '1:00.60'], ['7', '1', 'sophie edington', 'australia', '1:01.05'], ['8', '8', 'kseniya moskvina', 'russia', '1:01.06']]
|
2013 games of the small states of europe
|
https://en.wikipedia.org/wiki/2013_Games_of_the_Small_States_of_Europe
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11729736-4.html.csv
|
majority
|
most of the nations in the 2013 games of the small states of europe were awarded more than 10 bronze medals .
|
{'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '10', 'subset': None}
|
{'func': 'most_greater', 'args': ['all_rows', 'bronze', '10'], 'result': True, 'ind': 0, 'tointer': 'for the bronze records of all rows , most of them are greater than 10 .', 'tostr': 'most_greater { all_rows ; bronze ; 10 } = true'}
|
most_greater { all_rows ; bronze ; 10 } = true
|
for the bronze records of all rows , most of them are greater than 10 .
|
1
|
1
|
{'most_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 'bronze_3': 3, '10_4': 4}
|
{'most_greater_0': 'most_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 'bronze_3': 'bronze', '10_4': '10'}
|
{'most_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 'bronze_3': [0], '10_4': [0]}
|
['nation', 'gold', 'silver', 'bronze', 'total']
|
[['luxembourg', '36', '39', '31', '106'], ['iceland', '28', '29', '30', '87'], ['cyprus', '28', '17', '24', '69'], ['liechtenstein', '11', '16', '8', '35'], ['montenegro', '9', '0', '2', '11'], ['monaco', '7', '8', '15', '30'], ['malta', '2', '11', '13', '26'], ['andorra', '2', '1', '3', '6'], ['san marino', '1', '4', '9', '14'], ['total', '124', '125', '135', '384']]
|
wru division one west
|
https://en.wikipedia.org/wiki/WRU_Division_One_West
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12792876-1.html.csv
|
majority
|
most of the clubs in the wru division one west division scored at least 400 points .
|
{'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '400', 'subset': None}
|
{'func': 'most_greater', 'args': ['all_rows', 'points for', '400'], 'result': True, 'ind': 0, 'tointer': 'for the points for records of all rows , most of them are greater than 400 .', 'tostr': 'most_greater { all_rows ; points for ; 400 } = true'}
|
most_greater { all_rows ; points for ; 400 } = true
|
for the points for records of all rows , most of them are greater than 400 .
|
1
|
1
|
{'most_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 'points for_3': 3, '400_4': 4}
|
{'most_greater_0': 'most_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 'points for_3': 'points for', '400_4': '400'}
|
{'most_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 'points for_3': [0], '400_4': [0]}
|
['club', 'played', 'drawn', 'lost', 'points for', 'points against', 'tries for', 'tries against', 'try bonus', 'losing bonus', 'points']
|
[['club', 'played', 'drawn', 'lost', 'points for', 'points against', 'tries for', 'tries against', 'try bonus', 'losing bonus', 'points'], ['corus ( port talbot ) rfc', '22', '1', '4', '598', '391', '73', '40', '9', '3', '82'], ['narberth rfc', '22', '0', '5', '623', '440', '81', '49', '10', '3', '81'], ['carmarthen athletic rfc', '22', '0', '8', '478', '359', '60', '36', '6', '5', '67'], ['llangennech rfc', '22', '0', '9', '455', '434', '51', '48', '4', '4', '60'], ['whitland rfc', '22', '1', '9', '387', '403', '36', '47', '1', '2', '53'], ['bridgend athletic rfc', '22', '1', '12', '461', '455', '52', '54', '7', '6', '51'], ['uwic rfc', '22', '1', '12', '442', '465', '52', '54', '6', '5', '49'], ['llanharan rfc', '22', '1', '12', '436', '447', '50', '54', '4', '7', '49'], ['tondu rfc', '22', '0', '13', '444', '460', '50', '47', '3', '8', '47'], ['waunarlwydd rfc', '22', '1', '14', '467', '572', '54', '71', '6', '8', '44'], ['bonymaen rfc', '22', '0', '14', '373', '472', '40', '54', '2', '6', '40'], ['ammanford rfc', '22', '2', '16', '349', '615', '35', '80', '4', '7', '31']]
|
central asian union
|
https://en.wikipedia.org/wiki/Central_Asian_Union
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-11780179-1.html.csv
|
ordinal
|
tajikistan has the second highest gdp among cau countries with populations below 10,000,000 .
|
{'scope': 'subset', 'row': '4', 'col': '4', 'order': '2', 'col_other': '1', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'subset': {'col': '2', 'criterion': 'less_than', 'value': '10,000,000'}}
|
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'population', '10,000,000'], 'result': None, 'ind': 0, 'tostr': 'filter_less { all_rows ; population ; 10,000,000 }', 'tointer': 'select the rows whose population record is less than 10,000,000 .'}, 'gdp ( nominal )', '2'], 'result': None, 'ind': 1, 'tostr': 'nth_argmax { filter_less { all_rows ; population ; 10,000,000 } ; gdp ( nominal ) ; 2 }'}, 'country'], 'result': 'tajikistan', 'ind': 2, 'tostr': 'hop { nth_argmax { filter_less { all_rows ; population ; 10,000,000 } ; gdp ( nominal ) ; 2 } ; country }'}, 'tajikistan'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { nth_argmax { filter_less { all_rows ; population ; 10,000,000 } ; gdp ( nominal ) ; 2 } ; country } ; tajikistan } = true', 'tointer': 'select the rows whose population record is less than 10,000,000 . select the row whose gdp ( nominal ) record of these rows is 2nd maximum . the country record of this row is tajikistan .'}
|
eq { hop { nth_argmax { filter_less { all_rows ; population ; 10,000,000 } ; gdp ( nominal ) ; 2 } ; country } ; tajikistan } = true
|
select the rows whose population record is less than 10,000,000 . select the row whose gdp ( nominal ) record of these rows is 2nd maximum . the country record of this row is tajikistan .
|
4
|
4
|
{'str_eq_3': 3, 'result_4': 4, 'str_hop_2': 2, 'nth_argmax_1': 1, 'filter_less_0': 0, 'all_rows_5': 5, 'population_6': 6, '10,000,000_7': 7, 'gdp (nominal)_8': 8, '2_9': 9, 'country_10': 10, 'tajikistan_11': 11}
|
{'str_eq_3': 'str_eq', 'result_4': 'true', 'str_hop_2': 'str_hop', 'nth_argmax_1': 'nth_argmax', 'filter_less_0': 'filter_less', 'all_rows_5': 'all_rows', 'population_6': 'population', '10,000,000_7': '10,000,000', 'gdp (nominal)_8': 'gdp ( nominal )', '2_9': '2', 'country_10': 'country', 'tajikistan_11': 'tajikistan'}
|
{'str_eq_3': [4], 'result_4': [], 'str_hop_2': [3], 'nth_argmax_1': [2], 'filter_less_0': [1], 'all_rows_5': [0], 'population_6': [0], '10,000,000_7': [0], 'gdp (nominal)_8': [1], '2_9': [1], 'country_10': [2], 'tajikistan_11': [3]}
|
['country', 'population', 'area ( km square )', 'gdp ( nominal )', 'gdp per capita ( nominal )']
|
[['kazakhstan', '16967000', '2724900', '196.4 billion', '11772'], ['kyrgyzstan', '5550239', '199900', '6.4 billion', '1152'], ['uzbekistan', '29559100', '447400', '52.0 billion', '1780'], ['tajikistan', '7616000', '143100', '7.2 billion', '903'], ['turkmenistan', '5125693', '488100', '29.9 billion', '5330']]
|
2007 - 08 serie d
|
https://en.wikipedia.org/wiki/2007%E2%80%9308_Serie_D
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12592501-25.html.csv
|
superlative
|
of the 2007-08 serie d play-off matches listed the tie between matera and quarto produced the highest number of goals .
|
{'scope': 'all', 'col_superlative': '2', 'row_superlative': '16', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1,3', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'agg'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; agg }'}, 'team 1'], 'result': 'matera ( h15 )', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; agg } ; team 1 }'}, 'matera ( h15 )'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; agg } ; team 1 } ; matera ( h15 ) }', 'tointer': 'select the row whose agg record of all rows is maximum . the team 1 record of this row is matera ( h15 ) .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'agg'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; agg }'}, 'team 2'], 'result': '( h14 ) quarto', 'ind': 3, 'tostr': 'hop { argmax { all_rows ; agg } ; team 2 }'}, '( h14 ) quarto'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { argmax { all_rows ; agg } ; team 2 } ; ( h14 ) quarto }', 'tointer': 'the team 2 record of this row is ( h14 ) quarto .'}], 'result': True, 'ind': 5, 'tostr': 'and { eq { hop { argmax { all_rows ; agg } ; team 1 } ; matera ( h15 ) } ; eq { hop { argmax { all_rows ; agg } ; team 2 } ; ( h14 ) quarto } } = true', 'tointer': 'select the row whose agg record of all rows is maximum . the team 1 record of this row is matera ( h15 ) . the team 2 record of this row is ( h14 ) quarto .'}
|
and { eq { hop { argmax { all_rows ; agg } ; team 1 } ; matera ( h15 ) } ; eq { hop { argmax { all_rows ; agg } ; team 2 } ; ( h14 ) quarto } } = true
|
select the row whose agg record of all rows is maximum . the team 1 record of this row is matera ( h15 ) . the team 2 record of this row is ( h14 ) quarto .
|
7
|
6
|
{'and_5': 5, 'result_6': 6, 'str_eq_2': 2, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_7': 7, 'agg_8': 8, 'team 1_9': 9, 'matera (h15)_10': 10, 'str_eq_4': 4, 'str_hop_3': 3, 'team 2_11': 11, '(h14) quarto_12': 12}
|
{'and_5': 'and', 'result_6': 'true', 'str_eq_2': 'str_eq', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_7': 'all_rows', 'agg_8': 'agg', 'team 1_9': 'team 1', 'matera (h15)_10': 'matera ( h15 )', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'team 2_11': 'team 2', '(h14) quarto_12': '( h14 ) quarto'}
|
{'and_5': [6], 'result_6': [], 'str_eq_2': [5], 'str_hop_1': [2], 'argmax_0': [1, 3], 'all_rows_7': [0], 'agg_8': [0], 'team 1_9': [1], 'matera (h15)_10': [2], 'str_eq_4': [5], 'str_hop_3': [4], 'team 2_11': [3], '(h14) quarto_12': [4]}
|
['team 1', 'agg', 'team 2', '1st leg', '2nd leg']
|
[['sanremese ( a16 )', '3 - 5', '( a13 ) casale', '1 - 3', '2 - 2'], ['imperia ( a15 )', '0 - 3', '( a14 ) novese', '0 - 3', 'n / a'], ['fanfulla ( b16 )', '6 - 1', '( b13 ) trento', '2 - 0', '4 - 1'], ['merate ( b15 )', '1 - 3', '( b14 ) ussestese', '0 - 2', '1 - 1'], ['montecchiom ( c16 )', '4 - 1', '( c13 ) sandonà', '1 - 1', '3 - 0'], ['virtusvecomp ( c15 )', '4 - 2', '( c14 ) este', '0 - 0', '4 - 2'], ['verucchio ( d16 )', '5 - 6', '( d13 ) castellana', '1 - 3', '4 - 3'], ['cagliese ( d15 )', '0 - 2', '( d14 ) russi', '0 - 1', '0 - 1'], ['torgiano ( e16 )', '2 - 2', '( e13 ) sansepolcro', '1 - 2', '1 - 0'], ['montevarchi ( e15 )', '3 - 2', '( e14 ) cecina', '2 - 1', '1 - 1'], ['tolentino ( f16 )', '2 - 2', '( f13 ) lucocanistro', '2 - 2', '0 - 0'], ['narnese ( f15 )', '1 - 2', '( f14 ) olympiaagnonese', '0 - 1', '1 - 1'], ['guidoniam ( g16 )', '1 - 3', '( g13 ) calangianus', '0 - 1', '1 - 2'], ['lupafrascati ( g15 )', '2 - 2', '( g14 ) morolo', '2 - 0', '0 - 2'], ['horatianavenosa ( h16 )', '2 - 3', '( h13 ) viribusunitis', '1 - 2', '1 - 1'], ['matera ( h15 )', '9 - 6', '( h14 ) quarto', '5 - 1', '4 - 5'], ['paternò ( i16 )', '1 - 1', "( i13 ) sant ' antonioabate", '1 - 0', '0 - 1'], ['castrovillari ( i15 )', '3 - 1', '( i14 ) caserta', '1 - 1', '2 - 0']]
|
dancing with the stars ( u.s. season 6 )
|
https://en.wikipedia.org/wiki/Dancing_with_the_Stars_%28U.S._season_6%29
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15116785-9.html.csv
|
aggregation
|
the average score on the 6th season of dancing with the stars was 22.8 .
|
{'scope': 'all', 'col': '2', 'type': 'average', 'result': '22.8', 'subset': None}
|
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'score'], 'result': '22.8', 'ind': 0, 'tostr': 'avg { all_rows ; score }'}, '22.8'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; score } ; 22.8 } = true', 'tointer': 'the average of the score record of all rows is 22.8 .'}
|
round_eq { avg { all_rows ; score } ; 22.8 } = true
|
the average of the score record of all rows is 22.8 .
|
2
|
2
|
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'score_4': 4, '22.8_5': 5}
|
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'score_4': 'score', '22.8_5': '22.8'}
|
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'score_4': [0], '22.8_5': [1]}
|
['couple', 'score', 'style', 'music', 'result']
|
[['marlee & fabian', '21 ( 7 , 7 , 7 )', 'jive', 'you may be right - billy joel', 'safe'], ['steve & anna', '21 ( 7 , 7 , 7 )', 'tango', "jalousie-alfred hause 's tango orchestra", 'eliminated'], ['cristián & cheryl', '25 ( 8 , 8 , 9 )', 'jive', "do n't stop me now - queen", 'safe'], ['mario & karina', '21 ( 7 , 6 , 8 )', 'tango', 'el tango de roxanne - moulin rouge ! soundtrack', 'last to be called safe'], ['shannon & derek', '24 ( 8 , 8 , 8 )', 'jive', 'goody two shoes - adam ant', 'safe'], ['adam & julianne', '21 ( 7 , 7 , 7 )', 'tango', "i ca n't tell a waltz from a tango - alma cogan", 'safe'], ['marissa & tony', '19 ( 6 , 7 , 6 )', 'jive', 'girlfriend - avril lavigne', 'safe'], ['priscilla & louis', '26 ( 8 , 9 , 9 )', 'tango', 'el choclo - lalo schifrin', 'safe'], ['jason & edyta', '23 ( 8 , 7 , 8 )', 'jive', 'i got a woman - ray charles', 'safe'], ['kristi & mark', '27 ( 9 , 9 , 9 )', 'tango', 'rio - duran duran', 'safe']]
|
list of olympic medalists in athletics ( men )
|
https://en.wikipedia.org/wiki/List_of_Olympic_medalists_in_athletics_%28men%29
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-22355-26.html.csv
|
aggregation
|
of the men 's olympic medalists in athletics , the average number of gold medals won is .82 .
|
{'scope': 'all', 'col': '5', 'type': 'average', 'result': '.82', 'subset': None}
|
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'gold'], 'result': '.82', 'ind': 0, 'tostr': 'avg { all_rows ; gold }'}, '.82'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; gold } ; .82 } = true', 'tointer': 'the average of the gold record of all rows is .82 .'}
|
round_eq { avg { all_rows ; gold } ; .82 } = true
|
the average of the gold record of all rows is .82 .
|
2
|
2
|
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'gold_4': 4, '.82_5': 5}
|
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'gold_4': 'gold', '.82_5': '.82'}
|
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'gold_4': [0], '.82_5': [1]}
|
['rank', 'athlete', 'nation', 'olympics', 'gold', 'silver', 'bronze', 'total ( min 2 medals )']
|
[['1', 'lee calhoun', 'united states ( usa )', '1952 - 1956', '2', '0', '0', '2'], ['1', 'roger kingdom', 'united states ( usa )', '1984 - 1988', '2', '0', '0', '2'], ['3', 'sydney atkinson', 'south africa ( rsa )', '1924 - 1928', '1', '1', '0', '2'], ['3', 'guy drut', 'france ( fra )', '1972 - 1976', '1', '1', '0', '2'], ['5', 'hayes jones', 'united states ( usa )', '1960 - 1964', '1', '0', '1', '2'], ['5', 'willie davenport', 'united states ( usa )', '1968 - 1976', '1', '0', '1', '2'], ['5', 'anier garcia', 'cuba ( cub )', '2000 - 2004', '1', '0', '1', '2'], ['8', 'jack davis', 'united states ( usa )', '1952 - 1956', '0', '2', '0', '2'], ['8', 'alejandro casanas', 'cuba ( cub )', '1976 - 1980', '0', '2', '0', '2'], ['8', 'terrence trammell', 'united states ( usa )', '2000 - 2004', '0', '2', '0', '2'], ['11', 'don finlay', 'great britain ( gbr )', '1932 - 1936', '0', '1', '1', '2']]
|
1964 vfl season
|
https://en.wikipedia.org/wiki/1964_VFL_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10784349-7.html.csv
|
majority
|
in the 1964 vfl season , all of the games took place on may 30 , 1964 .
|
{'scope': 'all', 'col': '7', 'most_or_all': 'all', 'criterion': 'equal', 'value': '30 may 1964', 'subset': None}
|
{'func': 'all_str_eq', 'args': ['all_rows', 'date', '30 may 1964'], 'result': True, 'ind': 0, 'tointer': 'for the date records of all rows , all of them fuzzily match to 30 may 1964 .', 'tostr': 'all_eq { all_rows ; date ; 30 may 1964 } = true'}
|
all_eq { all_rows ; date ; 30 may 1964 } = true
|
for the date records of all rows , all of them fuzzily match to 30 may 1964 .
|
1
|
1
|
{'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'date_3': 3, '30 may 1964_4': 4}
|
{'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'date_3': 'date', '30 may 1964_4': '30 may 1964'}
|
{'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'date_3': [0], '30 may 1964_4': [0]}
|
['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date']
|
[['hawthorn', '13.11 ( 89 )', 'richmond', '7.16 ( 58 )', 'glenferrie oval', '22000', '30 may 1964'], ['geelong', '11.23 ( 89 )', 'st kilda', '13.8 ( 86 )', 'kardinia park', '28000', '30 may 1964'], ['collingwood', '22.18 ( 150 )', 'north melbourne', '6.6 ( 42 )', 'victoria park', '34222', '30 may 1964'], ['carlton', '8.12 ( 60 )', 'fitzroy', '8.11 ( 59 )', 'princes park', '18945', '30 may 1964'], ['melbourne', '12.14 ( 86 )', 'footscray', '6.8 ( 44 )', 'mcg', '33129', '30 may 1964'], ['south melbourne', '11.18 ( 84 )', 'essendon', '14.12 ( 96 )', 'lake oval', '20470', '30 may 1964']]
|
swimming at the 2000 summer olympics - women 's 200 metre breaststroke
|
https://en.wikipedia.org/wiki/Swimming_at_the_2000_Summer_Olympics_%E2%80%93_Women%27s_200_metre_breaststroke
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12382876-4.html.csv
|
ordinal
|
in the women 's 200 metre breaststroke at the 2000 summer olympics , kristy kowalski came in first , with the fastest time of 2:25.46 .
|
{'row': '1', 'col': '5', 'order': '1', 'col_other': '1,3', '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', 'time', '1'], 'result': '2:25.46', 'ind': 0, 'tostr': 'nth_min { all_rows ; time ; 1 }', 'tointer': 'the 1st minimum time record of all rows is 2:25.46 .'}, '2:25.46'], 'result': True, 'ind': 1, 'tostr': 'eq { nth_min { all_rows ; time ; 1 } ; 2:25.46 }', 'tointer': 'the 1st minimum time record of all rows is 2:25.46 .'}, {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'time', '1'], 'result': None, 'ind': 2, 'tostr': 'nth_argmin { all_rows ; time ; 1 }'}, 'rank'], 'result': '1', 'ind': 3, 'tostr': 'hop { nth_argmin { all_rows ; time ; 1 } ; rank }'}, '1'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { nth_argmin { all_rows ; time ; 1 } ; rank } ; 1 }', 'tointer': 'the rank record of the row with 1st minimum time record is 1 .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'time', '1'], 'result': None, 'ind': 2, 'tostr': 'nth_argmin { all_rows ; time ; 1 }'}, 'name'], 'result': 'kristy kowal', 'ind': 5, 'tostr': 'hop { nth_argmin { all_rows ; time ; 1 } ; name }'}, 'kristy kowal'], 'result': True, 'ind': 6, 'tostr': 'eq { hop { nth_argmin { all_rows ; time ; 1 } ; name } ; kristy kowal }', 'tointer': 'the name record of the row with 1st minimum time record is kristy kowal .'}], 'result': True, 'ind': 7, 'tostr': 'and { eq { hop { nth_argmin { all_rows ; time ; 1 } ; rank } ; 1 } ; eq { hop { nth_argmin { all_rows ; time ; 1 } ; name } ; kristy kowal } }', 'tointer': 'the rank record of the row with 1st minimum time record is 1 . the name record of the row with 1st minimum time record is kristy kowal .'}], 'result': True, 'ind': 8, 'tostr': 'and { eq { nth_min { all_rows ; time ; 1 } ; 2:25.46 } ; and { eq { hop { nth_argmin { all_rows ; time ; 1 } ; rank } ; 1 } ; eq { hop { nth_argmin { all_rows ; time ; 1 } ; name } ; kristy kowal } } } = true', 'tointer': 'the 1st minimum time record of all rows is 2:25.46 . the rank record of the row with 1st minimum time record is 1 . the name record of the row with 1st minimum time record is kristy kowal .'}
|
and { eq { nth_min { all_rows ; time ; 1 } ; 2:25.46 } ; and { eq { hop { nth_argmin { all_rows ; time ; 1 } ; rank } ; 1 } ; eq { hop { nth_argmin { all_rows ; time ; 1 } ; name } ; kristy kowal } } } = true
|
the 1st minimum time record of all rows is 2:25.46 . the rank record of the row with 1st minimum time record is 1 . the name record of the row with 1st minimum time record is kristy kowal .
|
10
|
9
|
{'and_8': 8, 'result_9': 9, 'eq_1': 1, 'nth_min_0': 0, 'all_rows_10': 10, 'time_11': 11, '1_12': 12, '2:25.46_13': 13, 'and_7': 7, 'eq_4': 4, 'num_hop_3': 3, 'nth_argmin_2': 2, 'all_rows_14': 14, 'time_15': 15, '1_16': 16, 'rank_17': 17, '1_18': 18, 'str_eq_6': 6, 'str_hop_5': 5, 'name_19': 19, 'kristy kowal_20': 20}
|
{'and_8': 'and', 'result_9': 'true', 'eq_1': 'eq', 'nth_min_0': 'nth_min', 'all_rows_10': 'all_rows', 'time_11': 'time', '1_12': '1', '2:25.46_13': '2:25.46', 'and_7': 'and', 'eq_4': 'eq', 'num_hop_3': 'num_hop', 'nth_argmin_2': 'nth_argmin', 'all_rows_14': 'all_rows', 'time_15': 'time', '1_16': '1', 'rank_17': 'rank', '1_18': '1', 'str_eq_6': 'str_eq', 'str_hop_5': 'str_hop', 'name_19': 'name', 'kristy kowal_20': 'kristy kowal'}
|
{'and_8': [9], 'result_9': [], 'eq_1': [8], 'nth_min_0': [1], 'all_rows_10': [0], 'time_11': [0], '1_12': [0], '2:25.46_13': [1], 'and_7': [8], 'eq_4': [7], 'num_hop_3': [4], 'nth_argmin_2': [3, 5], 'all_rows_14': [2], 'time_15': [2], '1_16': [2], 'rank_17': [3], '1_18': [4], 'str_eq_6': [7], 'str_hop_5': [6], 'name_19': [5], 'kristy kowal_20': [6]}
|
['rank', 'lane', 'name', 'nationality', 'time']
|
[['1', '4', 'kristy kowal', 'united states', '2:25.46'], ['2', '6', 'sarah poewe', 'south africa', '2:25.54'], ['3', '7', 'luo xuejuan', 'china', '2:25.86'], ['4', '5', 'karine brãmond', 'france', '2:27.86'], ['5', '3', 'caroline hildreth', 'australia', '2:28.30'], ['6', '2', 'ku hyo - jin', 'south korea', '2:28.50'], ['7', '1', 'anne poleska', 'germany', '2:28.99'], ['8', '8', 'junko isoda', 'japan', '2:31.71']]
|
1985 open championship
|
https://en.wikipedia.org/wiki/1985_Open_Championship
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18153721-5.html.csv
|
count
|
in the 1985 open championship , there were three players from the country of australia .
|
{'scope': 'all', 'criterion': 'equal', 'value': 'australia', 'result': '3', 'col': '3', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'australia'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose country record fuzzily matches to australia .', 'tostr': 'filter_eq { all_rows ; country ; australia }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; country ; australia } }', 'tointer': 'select the rows whose country record fuzzily matches to australia . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; country ; australia } } ; 3 } = true', 'tointer': 'select the rows whose country record fuzzily matches to australia . the number of such rows is 3 .'}
|
eq { count { filter_eq { all_rows ; country ; australia } } ; 3 } = true
|
select the rows whose country record fuzzily matches to australia . 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, 'country_5': 5, 'australia_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', 'country_5': 'country', 'australia_6': 'australia', '3_7': '3'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'country_5': [0], 'australia_6': [0], '3_7': [2]}
|
['place', 'player', 'country', 'score', 'to par']
|
[['t1', 'david graham', 'australia', '68 + 71 = 139', '- 1'], ['t1', 'sandy lyle', 'scotland', '68 + 71 = 139', '- 1'], ['t3', 'tony johnstone', 'zimbabwe', '68 + 72 = 140', 'e'], ['t3', "christy o'connor jnr", 'ireland', '64 + 76 = 140', 'e'], ['t3', 'd a weibring', 'united states', '69 + 71 = 140', 'e'], ['t6', 'howard clark', 'england', '70 + 71 = 141', '+ 1'], ['t6', 'bernhard langer', 'west germany', '72 + 69 = 141', '+ 1'], ['t6', 'robert lee', 'england', '68 + 73 = 141', '+ 1'], ['t6', 'wayne riley', 'australia', '71 + 70 = 141', '+ 1'], ['t6', 'emilio rodríguez', 'spain', '71 + 70 = 141', '+ 1'], ['t6', 'peter senior', 'australia', '70 + 71 = 141', '+ 1'], ['t6', 'ian woosnam', 'wales', '70 + 71 = 141', '+ 1']]
|
chinese jia - a league
|
https://en.wikipedia.org/wiki/Chinese_Jia-A_League
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17632217-2.html.csv
|
comparative
|
there were more teams playing in the chinese jia - a league in the year of 2001 compared to the year of 1996 .
|
{'row_1': '8', 'row_2': '3', 'col': '6', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}
|
{'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'season', '2001'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose season record fuzzily matches to 2001 .', 'tostr': 'filter_eq { all_rows ; season ; 2001 }'}, 'number of clubs'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; season ; 2001 } ; number of clubs }', 'tointer': 'select the rows whose season record fuzzily matches to 2001 . take the number of clubs record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'season', '1996'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose season record fuzzily matches to 1996 .', 'tostr': 'filter_eq { all_rows ; season ; 1996 }'}, 'number of clubs'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; season ; 1996 } ; number of clubs }', 'tointer': 'select the rows whose season record fuzzily matches to 1996 . take the number of clubs record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; season ; 2001 } ; number of clubs } ; hop { filter_eq { all_rows ; season ; 1996 } ; number of clubs } } = true', 'tointer': 'select the rows whose season record fuzzily matches to 2001 . take the number of clubs record of this row . select the rows whose season record fuzzily matches to 1996 . take the number of clubs record of this row . the first record is greater than the second record .'}
|
greater { hop { filter_eq { all_rows ; season ; 2001 } ; number of clubs } ; hop { filter_eq { all_rows ; season ; 1996 } ; number of clubs } } = true
|
select the rows whose season record fuzzily matches to 2001 . take the number of clubs record of this row . select the rows whose season record fuzzily matches to 1996 . take the number of clubs 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, 'season_7': 7, '2001_8': 8, 'number of clubs_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'season_11': 11, '1996_12': 12, 'number of clubs_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', 'season_7': 'season', '2001_8': '2001', 'number of clubs_9': 'number of clubs', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'season_11': 'season', '1996_12': '1996', 'number of clubs_13': 'number of clubs'}
|
{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'season_7': [0], '2001_8': [0], 'number of clubs_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'season_11': [1], '1996_12': [1], 'number of clubs_13': [3]}
|
['season', 'winners', 'runners - up', 'third - place', 'fourth - placed', 'number of clubs']
|
[['1994', 'dalian wanda', 'guangzhou apollo', 'shanghai shenhua', 'liaoning yuandong', '12'], ['1995', 'shanghai shenhua', 'beijing guoan', 'dalian wanda', 'guangdong hongyuan', '12'], ['1996', 'dalian wanda', 'shanghai shenhua', 'august 1st', 'beijing guoan', '12'], ['1997', 'dalian wanda', 'shanghai shenhua', 'beijing guoan', 'yanbian aodong', '12'], ['1998', 'dalian wanda', 'shanghai shenhua', 'beijing guoan', 'guangzhou songri', '14'], ['1999', 'shandong luneng', 'liaoning fushun', 'sichuan quanxing', 'chongqing longxin', '14'], ['2000', 'dalian shide', 'shanghai shenhua', 'sichuan quanxing', 'chongqing longxin', '14'], ['2001', 'dalian shide', 'shanghai shenhua', 'liaoning fushun', 'sichuan quanxing', '14'], ['2002', 'dalian shide', "shenzhen ping ' an", 'beijing guoan', 'shandong luneng', '15'], ['2003', 'shanghai shenhua', 'shanghai international', 'dalian shide', 'shenzhen jianlibao', '15']]
|
al - wehdat sc
|
https://en.wikipedia.org/wiki/Al-Wehdat_SC
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2985714-2.html.csv
|
unique
|
the jordan premier league was the only competition in which al-wehdat had more than 20 wins .
|
{'scope': 'all', 'row': '1', 'col': '4', 'col_other': '2', 'criterion': 'greater_than', 'value': '20', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'al - wehdat wins', '20'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose al - wehdat wins record is greater than 20 .', 'tostr': 'filter_greater { all_rows ; al - wehdat wins ; 20 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_greater { all_rows ; al - wehdat wins ; 20 } }', 'tointer': 'select the rows whose al - wehdat wins record is greater than 20 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'al - wehdat wins', '20'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose al - wehdat wins record is greater than 20 .', 'tostr': 'filter_greater { all_rows ; al - wehdat wins ; 20 }'}, 'tournament'], 'result': 'jordan premier league', 'ind': 2, 'tostr': 'hop { filter_greater { all_rows ; al - wehdat wins ; 20 } ; tournament }'}, 'jordan premier league'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_greater { all_rows ; al - wehdat wins ; 20 } ; tournament } ; jordan premier league }', 'tointer': 'the tournament record of this unqiue row is jordan premier league .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_greater { all_rows ; al - wehdat wins ; 20 } } ; eq { hop { filter_greater { all_rows ; al - wehdat wins ; 20 } ; tournament } ; jordan premier league } } = true', 'tointer': 'select the rows whose al - wehdat wins record is greater than 20 . there is only one such row in the table . the tournament record of this unqiue row is jordan premier league .'}
|
and { only { filter_greater { all_rows ; al - wehdat wins ; 20 } } ; eq { hop { filter_greater { all_rows ; al - wehdat wins ; 20 } ; tournament } ; jordan premier league } } = true
|
select the rows whose al - wehdat wins record is greater than 20 . there is only one such row in the table . the tournament record of this unqiue row is jordan premier league .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_greater_0': 0, 'all_rows_6': 6, 'al - wehdat wins_7': 7, '20_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'tournament_9': 9, 'jordan premier league_10': 10}
|
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_greater_0': 'filter_greater', 'all_rows_6': 'all_rows', 'al - wehdat wins_7': 'al - wehdat wins', '20_8': '20', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'tournament_9': 'tournament', 'jordan premier league_10': 'jordan premier league'}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_greater_0': [1, 2], 'all_rows_6': [0], 'al - wehdat wins_7': [0], '20_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'tournament_9': [2], 'jordan premier league_10': [3]}
|
['', 'tournament', 'al - faisaly wins', 'al - wehdat wins', 'draws', 'total', 'al - faisaly goals', 'al - wehdat goals']
|
[['1', 'jordan premier league', '25', '26', '22', '73', '66', '69'], ['2', 'jordan fa cup', '6', '7', '5', '18', '23', '23'], ['3', 'jordan fa shield', '8', '5', '3', '16', '19', '14'], ['4', 'jordan super cup', '4', '5', '2', '11', '13', '13'], ['5', 'afc cup', '3', '0', '1', '4', '4', '2']]
|
united council of christian fraternities & sororities
|
https://en.wikipedia.org/wiki/United_Council_of_Christian_Fraternities_%26_Sororities
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-10054296-1.html.csv
|
count
|
2 of the classification of the members are fraternity and sorority .
|
{'scope': 'all', 'criterion': 'equal', 'value': 'fraternity & sorority', 'result': '2', 'col': '3', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'classification', 'fraternity & sorority'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose classification record fuzzily matches to fraternity & sorority .', 'tostr': 'filter_eq { all_rows ; classification ; fraternity & sorority }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; classification ; fraternity & sorority } }', 'tointer': 'select the rows whose classification record fuzzily matches to fraternity & sorority . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; classification ; fraternity & sorority } } ; 2 } = true', 'tointer': 'select the rows whose classification record fuzzily matches to fraternity & sorority . the number of such rows is 2 .'}
|
eq { count { filter_eq { all_rows ; classification ; fraternity & sorority } } ; 2 } = true
|
select the rows whose classification record fuzzily matches to fraternity & sorority . 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, 'classification_5': 5, 'fraternity & sorority_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', 'classification_5': 'classification', 'fraternity & sorority_6': 'fraternity & sorority', '2_7': '2'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'classification_5': [0], 'fraternity & sorority_6': [0], '2_7': [2]}
|
['member', 'headquarters', 'classification', 'chapters', 'founded', 'uccfs']
|
[['alpha nu omega', 'baltimore , maryland', 'fraternity & sorority', '26', '1988 at morgan state university', '2006'], ['men of god', 'san antonio , texas', 'fraternity', '5', '1999 at texas tech university', '2006'], ['delta psi epsilon', 'washington , dc', 'sorority', '12', '1999 in huntsville , alabama', '2006'], ['zeta phi zeta', 'chicago , illinois', 'fraternity & sorority', '7', '2001 at x - stream teens ministries', '2007'], ['gamma phi delta', 'austin , texas', 'fraternity', '16', '1988 at the university of texas at austin', '2011']]
|
2007 - 08 golden state warriors season
|
https://en.wikipedia.org/wiki/2007%E2%80%9308_Golden_State_Warriors_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11964379-7.html.csv
|
aggregation
|
for the 2007-08 golden state warriors season the total combined attendance was 197707 .
|
{'scope': 'all', 'col': '6', 'type': 'sum', 'result': '197707', 'subset': None}
|
{'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'attendance'], 'result': '197707', 'ind': 0, 'tostr': 'sum { all_rows ; attendance }'}, '197707'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; attendance } ; 197707 } = true', 'tointer': 'the sum of the attendance record of all rows is 197707 .'}
|
round_eq { sum { all_rows ; attendance } ; 197707 } = true
|
the sum of the attendance record of all rows is 197707 .
|
2
|
2
|
{'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'attendance_4': 4, '197707_5': 5}
|
{'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'attendance_4': 'attendance', '197707_5': '197707'}
|
{'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'attendance_4': [0], '197707_5': [1]}
|
['date', 'visitor', 'score', 'home', 'leading scorer', 'attendance', 'record']
|
[['2 / 1', 'charlotte bobcats', '127 - 94', 'golden state warriors', 'monta ellis', '20064', '29 - 19'], ['2 / 7', 'chicago bulls', '108 - 114', 'golden state warriors', 'monta ellis', '19596', '29 - 20'], ['2 / 9', 'sacramento kings', '105 - 102', 'golden state warriors', 'monta ellis', '20018', '30 - 20'], ['2 / 11', 'washington wizards', '120 - 117', 'golden state warriors', 'stephen jackson', '19043', '31 - 20'], ['2 / 13', 'phoenix suns', '120 - 118', 'golden state warriors', 'monta ellis', '19754', '32 - 20'], ['2 / 19', 'golden state warriors', '109 - 119', 'utah jazz', 'al harrington', '19911', '32 - 21'], ['2 / 20', 'boston celtics', '119 - 117', 'golden state warriors', 'baron davis', '20711', '33 - 21'], ['2 / 22', 'atlanta hawks', '110 - 117', 'golden state warriors', 'baron davis', '19596', '33 - 22'], ['2 / 26', 'seattle supersonics', '105 - 99', 'golden state warriors', 'monta ellis', '19412', '34 - 22'], ['2 / 29', 'philadelphia 76ers', '119 - 97', 'golden state warriors', 'mickaël piétrus', '19602', '35 - 22']]
|
2007 - 08 detroit pistons season
|
https://en.wikipedia.org/wiki/2007%E2%80%9308_Detroit_Pistons_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-11960944-11.html.csv
|
superlative
|
the td banknorth garden was the first location used by the detroit pistons in the 2007 - 08 season .
|
{'scope': 'all', 'col_superlative': '2', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '8', 'subset': None}
|
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'date'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; date }'}, 'location attendance'], 'result': 'td banknorth garden 18624', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; date } ; location attendance }'}, 'td banknorth garden 18624'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; date } ; location attendance } ; td banknorth garden 18624 } = true', 'tointer': 'select the row whose date record of all rows is minimum . the location attendance record of this row is td banknorth garden 18624 .'}
|
eq { hop { argmin { all_rows ; date } ; location attendance } ; td banknorth garden 18624 } = true
|
select the row whose date record of all rows is minimum . the location attendance record of this row is td banknorth garden 18624 .
|
3
|
3
|
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'date_5': 5, 'location attendance_6': 6, 'td banknorth garden 18624_7': 7}
|
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'date_5': 'date', 'location attendance_6': 'location attendance', 'td banknorth garden 18624_7': 'td banknorth garden 18624'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'date_5': [0], 'location attendance_6': [1], 'td banknorth garden 18624_7': [2]}
|
['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'series']
|
[['1', 'may 20', 'boston', 'l 88 - 79', 'prince ( 16 )', 'mcdyess ( 11 )', 'wallace ( 4 )', 'td banknorth garden 18624', '0 - 1'], ['2', 'may 22', 'boston', 'w 103 - 97', 'hamilton ( 25 )', 'wallace ( 10 )', 'billups ( 7 )', 'td banknorth garden 18624', '1 - 1'], ['3', 'may 24', 'boston', 'l 94 - 80', 'hamilton ( 26 )', 'mcdyess , wallace ( 8 )', 'billups , stuckey ( 4 )', 'the palace of auburn hills 22076', '1 - 2'], ['4', 'may 26', 'boston', 'w 94 - 75', 'mcdyess ( 21 )', 'mcdyess ( 17 )', 'hamilton ( 7 )', 'the palace of auburn hills 22076', '2 - 2'], ['5', 'may 28', 'boston', 'l 106 - 102', 'billups ( 26 )', 'billups , mcdyess ( 5 )', 'billups , hamilton ( 6 )', 'td banknorth garden 18624', '2 - 3']]
|
avc club volleyball championship
|
https://en.wikipedia.org/wiki/AVC_Club_Volleyball_Championship
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14841421-2.html.csv
|
comparative
|
at the avc club volleyball championship , japan won more bronze medals than indonesia .
|
{'row_1': '7', 'row_2': '9', 'col': '5', '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', 'nation', 'japan'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose nation record fuzzily matches to japan .', 'tostr': 'filter_eq { all_rows ; nation ; japan }'}, 'bronze'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; nation ; japan } ; bronze }', 'tointer': 'select the rows whose nation record fuzzily matches to japan . take the bronze record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nation', 'indonesia'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose nation record fuzzily matches to indonesia .', 'tostr': 'filter_eq { all_rows ; nation ; indonesia }'}, 'bronze'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; nation ; indonesia } ; bronze }', 'tointer': 'select the rows whose nation record fuzzily matches to indonesia . take the bronze record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; nation ; japan } ; bronze } ; hop { filter_eq { all_rows ; nation ; indonesia } ; bronze } } = true', 'tointer': 'select the rows whose nation record fuzzily matches to japan . take the bronze record of this row . select the rows whose nation record fuzzily matches to indonesia . take the bronze record of this row . the first record is greater than the second record .'}
|
greater { hop { filter_eq { all_rows ; nation ; japan } ; bronze } ; hop { filter_eq { all_rows ; nation ; indonesia } ; bronze } } = true
|
select the rows whose nation record fuzzily matches to japan . take the bronze record of this row . select the rows whose nation record fuzzily matches to indonesia . take the bronze 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, 'nation_7': 7, 'japan_8': 8, 'bronze_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'nation_11': 11, 'indonesia_12': 12, 'bronze_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', 'nation_7': 'nation', 'japan_8': 'japan', 'bronze_9': 'bronze', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'nation_11': 'nation', 'indonesia_12': 'indonesia', 'bronze_13': 'bronze'}
|
{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'nation_7': [0], 'japan_8': [0], 'bronze_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'nation_11': [1], 'indonesia_12': [1], 'bronze_13': [3]}
|
['rank', 'nation', 'gold', 'silver', 'bronze', 'total']
|
[['1', 'iran', '9', '4', '2', '15'], ['2', 'south korea', '2', '1', '0', '3'], ['3', 'kazakhstan', '1', '3', '2', '6'], ['4', 'qatar', '1', '2', '2', '5'], ['5', 'china', '1', '1', '4', '6'], ['6', 'saudi arabia', '0', '2', '0', '2'], ['7', 'japan', '0', '1', '2', '3'], ['8', 'chinese taipei', '0', '0', '1', '1'], ['8', 'indonesia', '0', '0', '1', '1'], ['total', 'total', '14', '14', '14', '42']]
|
list of azerbaijani submissions for the academy award for best foreign language film
|
https://en.wikipedia.org/wiki/List_of_Azerbaijani_submissions_for_the_Academy_Award_for_Best_Foreign_Language_Film
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17155980-1.html.csv
|
ordinal
|
shamil najafzadeh is the director of the 2nd earliest best foreign language film for the azerbaijani submission award .
|
{'row': '2', 'col': '1', 'order': '2', 'col_other': '5', '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 ( ceremony )', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; year ( ceremony ) ; 2 }'}, 'director'], 'result': 'shamil najafzadeh', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; year ( ceremony ) ; 2 } ; director }'}, 'shamil najafzadeh'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; year ( ceremony ) ; 2 } ; director } ; shamil najafzadeh } = true', 'tointer': 'select the row whose year ( ceremony ) record of all rows is 2nd minimum . the director record of this row is shamil najafzadeh .'}
|
eq { hop { nth_argmin { all_rows ; year ( ceremony ) ; 2 } ; director } ; shamil najafzadeh } = true
|
select the row whose year ( ceremony ) record of all rows is 2nd minimum . the director record of this row is shamil najafzadeh .
|
3
|
3
|
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'year (ceremony)_5': 5, '2_6': 6, 'director_7': 7, 'shamil najafzadeh_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 (ceremony)_5': 'year ( ceremony )', '2_6': '2', 'director_7': 'director', 'shamil najafzadeh_8': 'shamil najafzadeh'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'year (ceremony)_5': [0], '2_6': [0], 'director_7': [1], 'shamil najafzadeh_8': [2]}
|
['year ( ceremony )', 'film title used in nomination', 'original title', 'primary language ( s )', 'director', 'result']
|
[['2007 ( 80th )', 'caucasia', 'kavkaz ( кавказ )', 'russian', 'farid gumbatov', 'not nominated'], ['2008 ( 81st )', 'fortress', 'qala', 'azerbaijani', 'shamil najafzadeh', 'not nominated'], ['2010 ( 83rd )', 'the precinct', 'sahə', 'azerbaijani , russian', 'ilgar safat', 'not nominated'], ['2012 ( 85th )', 'buta', 'buta', 'azerbaijani', 'ilgar najaf', 'not nominated'], ['2013 ( 86th )', 'steppe man', 'çölçü', 'azerbaijani', 'shamil aliyev', 'tbd']]
|
1930 giro d'italia
|
https://en.wikipedia.org/wiki/1930_Giro_d%27Italia
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12606666-1.html.csv
|
majority
|
in the '30 giro d'italia , luigi marchisio achieved race leader status for most of the stages .
|
{'scope': 'all', 'col': '6', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'luigi marchisio ( ita )', 'subset': None}
|
{'func': 'most_str_eq', 'args': ['all_rows', 'race leader', 'luigi marchisio ( ita )'], 'result': True, 'ind': 0, 'tointer': 'for the race leader records of all rows , most of them fuzzily match to luigi marchisio ( ita ) .', 'tostr': 'most_eq { all_rows ; race leader ; luigi marchisio ( ita ) } = true'}
|
most_eq { all_rows ; race leader ; luigi marchisio ( ita ) } = true
|
for the race leader records of all rows , most of them fuzzily match to luigi marchisio ( ita ) .
|
1
|
1
|
{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'race leader_3': 3, 'luigi marchisio ( ita )_4': 4}
|
{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'race leader_3': 'race leader', 'luigi marchisio ( ita )_4': 'luigi marchisio ( ita )'}
|
{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'race leader_3': [0], 'luigi marchisio ( ita )_4': [0]}
|
['stage', 'date', 'course', 'distance', 'winner', 'race leader']
|
[['1', '17 may', 'messina to catania', '-', 'michele mara ( ita )', 'michele mara ( ita )'], ['2', '18 may', 'catania to palermo', '-', 'leonida frascarelli ( ita )', 'antonio negrini ( ita )'], ['3', '20 may', 'palermo to messina', '-', 'luigi marchisio ( ita )', 'luigi marchisio ( ita )'], ['4', '22 may', 'reggio calabria to catanzaro', '-', 'luigi marchisio ( ita )', 'luigi marchisio ( ita )'], ['5', '23 may', 'catanzaro to cosenza', '-', 'domenico piemontesi ( ita )', 'luigi marchisio ( ita )'], ['6', '25 may', 'cosenza to salerno', '-', 'allegro grandi ( ita )', 'luigi marchisio ( ita )'], ['7', '27 may', 'salerno to naples', '-', 'raffaele di paco ( ita )', 'luigi marchisio ( ita )'], ['8', '28 may', 'naples to rome', '-', 'learco guerra ( ita )', 'luigi marchisio ( ita )'], ['9', '30 may', 'rome to teramo', '-', 'michele mara ( ita )', 'luigi marchisio ( ita )'], ['10', '31 may', 'teramo to ancona', '-', 'michele mara ( ita )', 'luigi marchisio ( ita )'], ['11', '2 june', 'ancona to forlì', '-', 'learco guerra ( ita )', 'luigi marchisio ( ita )'], ['12', '3 june', 'forlì to rovigo', '-', 'michele mara ( ita )', 'luigi marchisio ( ita )'], ['13', '5 june', 'rovigo to asiago', '-', 'antonio pesenti ( ita )', 'luigi marchisio ( ita )'], ['14', '6 june', 'asiago to brescia', '-', 'leonida frascarelli ( ita )', 'luigi marchisio ( ita )'], ['15', '8 june', 'brescia to milan', '-', 'michele mara ( ita )', 'luigi marchisio ( ita )'], ['total', 'total', '-', 'km ( mi )', '-', 'km ( mi )']]
|
duffy waldorf
|
https://en.wikipedia.org/wiki/Duffy_Waldorf
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1781343-3.html.csv
|
unique
|
duffy waldorf only had one top 5 finish in tournaments played .
|
{'scope': 'all', 'row': '5', 'col': '3', 'col_other': 'n/a', 'criterion': 'equal', 'value': '1', 'subset': None}
|
{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'top - 5', '1'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose top - 5 record is equal to 1 .', 'tostr': 'filter_eq { all_rows ; top - 5 ; 1 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; top - 5 ; 1 } } = true', 'tointer': 'select the rows whose top - 5 record is equal to 1 . there is only one such row in the table .'}
|
only { filter_eq { all_rows ; top - 5 ; 1 } } = true
|
select the rows whose top - 5 record is equal to 1 . there is only one such row in the table .
|
2
|
2
|
{'only_1': 1, 'result_2': 2, 'filter_eq_0': 0, 'all_rows_3': 3, 'top - 5_4': 4, '1_5': 5}
|
{'only_1': 'only', 'result_2': 'true', 'filter_eq_0': 'filter_eq', 'all_rows_3': 'all_rows', 'top - 5_4': 'top - 5', '1_5': '1'}
|
{'only_1': [2], 'result_2': [], 'filter_eq_0': [1], 'all_rows_3': [0], 'top - 5_4': [0], '1_5': [0]}
|
['tournament', 'wins', 'top - 5', 'top - 10', 'top - 25', 'events', 'cuts made']
|
[['masters tournament', '0', '1', '1', '2', '6', '5'], ['us open', '0', '0', '1', '2', '13', '7'], ['the open championship', '0', '0', '0', '1', '8', '7'], ['pga championship', '0', '0', '1', '2', '12', '7'], ['totals', '0', '1', '3', '7', '39', '26']]
|
2007 - 08 guildford flames season
|
https://en.wikipedia.org/wiki/2007%E2%80%9308_Guildford_Flames_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15213262-9.html.csv
|
comparative
|
in the 2007-08 season the attendance at guildford flames ' home game against slough jets was greater than that of their home game against sheffield scimitars .
|
{'row_1': '11', 'row_2': '5', 'col': '5', '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', 'opponent', 'slough jets'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opponent record fuzzily matches to slough jets .', 'tostr': 'filter_eq { all_rows ; opponent ; slough jets }'}, 'attendance'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; opponent ; slough jets } ; attendance }', 'tointer': 'select the rows whose opponent record fuzzily matches to slough jets . take the attendance record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'sheffield scimitars'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose opponent record fuzzily matches to sheffield scimitars .', 'tostr': 'filter_eq { all_rows ; opponent ; sheffield scimitars }'}, 'attendance'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; opponent ; sheffield scimitars } ; attendance }', 'tointer': 'select the rows whose opponent record fuzzily matches to sheffield scimitars . take the attendance record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; opponent ; slough jets } ; attendance } ; hop { filter_eq { all_rows ; opponent ; sheffield scimitars } ; attendance } } = true', 'tointer': 'select the rows whose opponent record fuzzily matches to slough jets . take the attendance record of this row . select the rows whose opponent record fuzzily matches to sheffield scimitars . take the attendance record of this row . the first record is greater than the second record .'}
|
greater { hop { filter_eq { all_rows ; opponent ; slough jets } ; attendance } ; hop { filter_eq { all_rows ; opponent ; sheffield scimitars } ; attendance } } = true
|
select the rows whose opponent record fuzzily matches to slough jets . take the attendance record of this row . select the rows whose opponent record fuzzily matches to sheffield scimitars . 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, 'opponent_7': 7, 'slough jets_8': 8, 'attendance_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'opponent_11': 11, 'sheffield scimitars_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', 'opponent_7': 'opponent', 'slough jets_8': 'slough jets', 'attendance_9': 'attendance', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'opponent_11': 'opponent', 'sheffield scimitars_12': 'sheffield scimitars', 'attendance_13': 'attendance'}
|
{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'opponent_7': [0], 'slough jets_8': [0], 'attendance_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'opponent_11': [1], 'sheffield scimitars_12': [1], 'attendance_13': [3]}
|
['date', 'opponent', 'venue', 'result', 'attendance', 'competition']
|
[['1', 'peterborough phantoms', 'home', 'won 6 - 1', '1421', 'league'], ['2', 'peterborough phantoms', 'away', 'won 3 - 2 ( so )', '443', 'league'], ['8', 'swindon wildcats', 'away', 'won 3 - 2', '790', 'knockout cup'], ['9', 'chelmsford chieftains', 'away', 'won 5 - 3', '423', 'league'], ['15', 'sheffield scimitars', 'away', 'won 5 - 3', '351', 'league'], ['16', 'swindon wildcats', 'home', 'won 3 - 1', '1244', 'knockout cup'], ['22', 'romford raiders', 'home', 'won 5 - 2', '1551', 'knockout cup'], ['23', 'romford raiders', 'away', 'lost 2 - 6', 'unknown', 'league'], ['26', 'milton keynes lightning', 'home', 'won 4 - 3', '1228', 'premier cup'], ['29', 'chelmsford chieftains', 'home', 'won 5 - 0', '1117', 'league'], ['30', 'slough jets', 'home', 'won 4 - 3', '1921', 'league']]
|
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/2-15187735-13.html.csv
|
majority
|
all series ep of how it 's made episodes have a prefix of 13 .
|
{'scope': 'all', 'col': '1', 'most_or_all': 'all', 'criterion': 'fuzzily_match', 'value': '13 -', 'subset': None}
|
{'func': 'all_str_eq', 'args': ['all_rows', 'series ep', '13 -'], 'result': True, 'ind': 0, 'tointer': 'for the series ep records of all rows , all of them fuzzily match to 13 - .', 'tostr': 'all_eq { all_rows ; series ep ; 13 - } = true'}
|
all_eq { all_rows ; series ep ; 13 - } = true
|
for the series ep records of all rows , all of them fuzzily match to 13 - .
|
1
|
1
|
{'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'series ep_3': 3, '13 -_4': 4}
|
{'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'series ep_3': 'series ep', '13 -_4': '13 -'}
|
{'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'series ep_3': [0], '13 -_4': [0]}
|
['series ep', 'episode', 'segment a', 'segment b', 'segment c', 'segment d']
|
[['13 - 01', '157', 'hammers', 'swiss cheese', 'roller skates', 'coloured pencils'], ['13 - 02', '158', 'carbon fiber bicycles', 'blood products', 'forged chandeliers', 'ballpoint pens'], ['13 - 03', '159', 'swiss army knives', 'player piano rolls', 'oil tankers', 'racing wheels'], ['13 - 04', '160', 'bowling balls', 'barber poles', 'felt', 'radar guns'], ['13 - 05', '161', 'copper pipe fittings', 'cylinder music boxes', 'pepper mills', 'hot rod steering columns'], ['13 - 06', '162', 'gears', 'leather watchbands', 'vitrelle dishes', 'kitchen shears'], ['13 - 07', '163', 'pressure cookers', 'mechanical singing birds', 'oceanographic buoys', 'stainless - steel tank trailers'], ['13 - 08', '164', 'aluminium boats', 'alpine horns', 'es luxury watch ( part 1 )', 'es luxury watch ( part 2 )'], ['13 - 09', '165', 'all - terrain vehicles', 'alpine skis', 'laser cutters', 'marble sculptures'], ['13 - 10', '166', 'socket sets', 'leather shoes', 'aluminium water bottles', 'bike chains'], ['13 - 11', '167', 'carved wood sculptures', 'flatware', 'cow bells', 'fountain pens'], ['13 - 12', '168', 'olive oil', 'lift s truck', 'seamless rolled rings', 'ski boots'], ['13 - 13', '169', 'professional cookware', 'luxury inlaid boxes', 'high - efficiency water heaters', 'scooters']]
|
conference carolinas
|
https://en.wikipedia.org/wiki/Conference_Carolinas
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-11658094-1.html.csv
|
ordinal
|
the first college to be founded was erskine college in south carolina .
|
{'row': '4', 'col': '3', 'order': '1', 'col_other': '1,2', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'founded', '1'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; founded ; 1 }'}, 'institution'], 'result': 'erskine college', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; founded ; 1 } ; institution }'}, 'erskine college'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; founded ; 1 } ; institution } ; erskine college }', 'tointer': 'select the row whose founded record of all rows is 1st minimum . the institution record of this row is erskine college .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'founded', '1'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; founded ; 1 }'}, 'location'], 'result': 'due west , south carolina', 'ind': 3, 'tostr': 'hop { nth_argmin { all_rows ; founded ; 1 } ; location }'}, 'due west , south carolina'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { nth_argmin { all_rows ; founded ; 1 } ; location } ; due west , south carolina }', 'tointer': 'the location record of this row is due west , south carolina .'}], 'result': True, 'ind': 5, 'tostr': 'and { eq { hop { nth_argmin { all_rows ; founded ; 1 } ; institution } ; erskine college } ; eq { hop { nth_argmin { all_rows ; founded ; 1 } ; location } ; due west , south carolina } } = true', 'tointer': 'select the row whose founded record of all rows is 1st minimum . the institution record of this row is erskine college . the location record of this row is due west , south carolina .'}
|
and { eq { hop { nth_argmin { all_rows ; founded ; 1 } ; institution } ; erskine college } ; eq { hop { nth_argmin { all_rows ; founded ; 1 } ; location } ; due west , south carolina } } = true
|
select the row whose founded record of all rows is 1st minimum . the institution record of this row is erskine college . the location record of this row is due west , south carolina .
|
7
|
6
|
{'and_5': 5, 'result_6': 6, 'str_eq_2': 2, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_7': 7, 'founded_8': 8, '1_9': 9, 'institution_10': 10, 'erskine college_11': 11, 'str_eq_4': 4, 'str_hop_3': 3, 'location_12': 12, 'due west , south carolina_13': 13}
|
{'and_5': 'and', 'result_6': 'true', 'str_eq_2': 'str_eq', 'str_hop_1': 'str_hop', 'nth_argmin_0': 'nth_argmin', 'all_rows_7': 'all_rows', 'founded_8': 'founded', '1_9': '1', 'institution_10': 'institution', 'erskine college_11': 'erskine college', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'location_12': 'location', 'due west , south carolina_13': 'due west , south carolina'}
|
{'and_5': [6], 'result_6': [], 'str_eq_2': [5], 'str_hop_1': [2], 'nth_argmin_0': [1, 3], 'all_rows_7': [0], 'founded_8': [0], '1_9': [0], 'institution_10': [1], 'erskine college_11': [2], 'str_eq_4': [5], 'str_hop_3': [4], 'location_12': [3], 'due west , south carolina_13': [4]}
|
['institution', 'location', 'founded', 'type', 'enrollment', 'joined', 'nickname']
|
[['barton college', 'wilson , north carolina', '1902', 'private', '1200', '1930 1', 'bulldogs'], ['belmont abbey college', 'belmont , north carolina', '1876', 'private', '1320', '1989', 'crusaders'], ['converse college 2', 'spartanburg , south carolina', '1889', 'private', '750', '2008', 'valkyries'], ['erskine college', 'due west , south carolina', '1839', 'private', '920', '1995', 'flying fleet'], ['king university', 'bristol , tennessee', '1867', 'private', '1800', '2011', 'tornado'], ['leesmcrae college', 'banner elk , north carolina', '1899', 'private', '800', '1993', 'bobcats'], ['limestone college', 'gaffney , south carolina', '1845', 'private', '3300', '1998', 'saints'], ['mount olive college', 'mount olive , north carolina', '1951', 'private', '2500', '1988', 'trojans'], ['north greenville university', 'tigerville , south carolina', '1891', 'private', '2100', '2011', 'crusaders']]
|
1975 - 76 boston celtics season
|
https://en.wikipedia.org/wiki/1975%E2%80%9376_Boston_Celtics_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17342278-4.html.csv
|
count
|
there were 12 game dates in the 1975 - 76 boston celtics season .
|
{'scope': 'all', 'criterion': 'all', 'value': 'n/a', 'result': '12', 'col': '1', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_all', 'args': ['all_rows', 'game'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose game record is arbitrary .', 'tostr': 'filter_all { all_rows ; game }'}], 'result': '12', 'ind': 1, 'tostr': 'count { filter_all { all_rows ; game } }', 'tointer': 'select the rows whose game record is arbitrary . the number of such rows is 12 .'}, '12'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_all { all_rows ; game } } ; 12 } = true', 'tointer': 'select the rows whose game record is arbitrary . the number of such rows is 12 .'}
|
eq { count { filter_all { all_rows ; game } } ; 12 } = true
|
select the rows whose game record is arbitrary . the number of such rows is 12 .
|
3
|
3
|
{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_all_0': 0, 'all_rows_4': 4, 'game_5': 5, '12_6': 6}
|
{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_all_0': 'filter_all', 'all_rows_4': 'all_rows', 'game_5': 'game', '12_6': '12'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_all_0': [1], 'all_rows_4': [0], 'game_5': [0], '12_6': [2]}
|
['game', 'date', 'team', 'score', 'location attendance', 'record']
|
[['4', 'november 1', 'chicago', 'l 82 - 84', 'chicago stadium', '3 - 1'], ['5', 'november 5', 'buffalo', 'w 105 - 95', 'boston garden', '4 - 1'], ['6', 'november 7', 'milwaukee', 'l 101 - 104', 'mecca arena', '4 - 2'], ['7', 'november 8', 'detroit', 'w 118 - 104', 'cobo arena', '5 - 2'], ['8', 'november 11', 'atlanta', 'l 91 - 100', 'hartford civic center', '5 - 3'], ['9', 'november 13', 'washington', 'l 107 - 110', 'capital centre', '5 - 4'], ['10', 'november 14', 'philadelphia', 'l 109 - 119', 'boston garden', '5 - 5'], ['11', 'november 15', 'buffalo', 'w 112 - 110', 'buffalo memorial auditorium', '6 - 5'], ['12', 'november 21', 'new york', 'w 110 - 101', 'boston garden', '7 - 5'], ['13', 'november 23', 'cleveland', 'w 105 - 90', 'richfield coliseum', '8 - 5'], ['14', 'november 26', 'seattle', 'l 109 - 110', 'boston garden', '8 - 6'], ['15', 'november 28', 'atlanta', 'w 114 - 107', 'boston garden', '9 - 6']]
|
2007 calgary stampeders season
|
https://en.wikipedia.org/wiki/2007_Calgary_Stampeders_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12297537-1.html.csv
|
superlative
|
mike gyetvai was the highest picked player for the calgary stampeders in the 2007 draft .
|
{'scope': 'all', 'col_superlative': '2', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '3', 'subset': None}
|
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'pick'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; pick }'}, 'player'], 'result': 'mike gyetvai', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; pick } ; player }'}, 'mike gyetvai'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; pick } ; player } ; mike gyetvai } = true', 'tointer': 'select the row whose pick record of all rows is minimum . the player record of this row is mike gyetvai .'}
|
eq { hop { argmin { all_rows ; pick } ; player } ; mike gyetvai } = true
|
select the row whose pick record of all rows is minimum . the player record of this row is mike gyetvai .
|
3
|
3
|
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'pick_5': 5, 'player_6': 6, 'mike gyetvai_7': 7}
|
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'pick_5': 'pick', 'player_6': 'player', 'mike gyetvai_7': 'mike gyetvai'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'pick_5': [0], 'player_6': [1], 'mike gyetvai_7': [2]}
|
['round', 'pick', 'player', 'position', 'school / club team']
|
[['1', '3', 'mike gyetvai', 'ol', 'michigan state'], ['1', '5', 'justin phillips', 'lb', 'wilfrid laurier'], ['1', '6', 'jabari arthur', 'wr', 'akron'], ['2', '14', 'kevin challenger', 'wr', 'boston college'], ['3', '21', 'patrick macdonald', 'dl', 'alberta'], ['5', '35', 'henry bekkering', 'k', 'eastern washington'], ['5', '38', 'ian hazlett', 'lb', "queen 's"], ['6', '45', 'greg hetherington', 'sb', 'mcgill']]
|
2006 japanese television dramas
|
https://en.wikipedia.org/wiki/2006_Japanese_television_dramas
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18540022-3.html.csv
|
unique
|
the only 2006 japanese television drama with an average rating of 14.2 % is sapuri .
|
{'scope': 'all', 'row': '1', 'col': '5', 'col_other': '2', 'criterion': 'equal', 'value': '14.2 %', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'average ratings', '14.2 %'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose average ratings record fuzzily matches to 14.2 % .', 'tostr': 'filter_eq { all_rows ; average ratings ; 14.2 % }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; average ratings ; 14.2 % } }', 'tointer': 'select the rows whose average ratings record fuzzily matches to 14.2 % . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'average ratings', '14.2 %'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose average ratings record fuzzily matches to 14.2 % .', 'tostr': 'filter_eq { all_rows ; average ratings ; 14.2 % }'}, 'romaji title'], 'result': 'sapuri', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; average ratings ; 14.2 % } ; romaji title }'}, 'sapuri'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; average ratings ; 14.2 % } ; romaji title } ; sapuri }', 'tointer': 'the romaji title record of this unqiue row is sapuri .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; average ratings ; 14.2 % } } ; eq { hop { filter_eq { all_rows ; average ratings ; 14.2 % } ; romaji title } ; sapuri } } = true', 'tointer': 'select the rows whose average ratings record fuzzily matches to 14.2 % . there is only one such row in the table . the romaji title record of this unqiue row is sapuri .'}
|
and { only { filter_eq { all_rows ; average ratings ; 14.2 % } } ; eq { hop { filter_eq { all_rows ; average ratings ; 14.2 % } ; romaji title } ; sapuri } } = true
|
select the rows whose average ratings record fuzzily matches to 14.2 % . there is only one such row in the table . the romaji title record of this unqiue row is sapuri .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'average ratings_7': 7, '14.2%_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'romaji title_9': 9, 'sapuri_10': 10}
|
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'average ratings_7': 'average ratings', '14.2%_8': '14.2 %', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'romaji title_9': 'romaji title', 'sapuri_10': 'sapuri'}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'average ratings_7': [0], '14.2%_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'romaji title_9': [2], 'sapuri_10': [3]}
|
['japanese title', 'romaji title', 'tv station', 'episodes', 'average ratings']
|
[['サプリ', 'sapuri', 'fuji tv', '11', '14.2 %'], ['不信のとき ～ ウーマン ・ ウォーズ ～', 'fushin no toki ~ woman wars ~', 'fuji tv', '12', '12.9 %'], ['結婚できない男', 'kekkon dekinai otoko', 'fuji tv', '12', '17.1 %'], ['ダンドリ 。 ～ dance ☆ drill ～', 'dandori ~ dance ☆ drill ~', 'fuji tv', '11', '8.9 %'], ['誰よりもママを愛す', 'dare yorimo mama wo ai su', 'tbs', '11', '10.4 %'], ['花嫁は厄年ッ !', 'hanayome wa yakudoshi !', 'tbs', '12', '12.0 %'], ['タイヨウのうた', 'taiyou no uta', 'tbs', '10', '10.3 %'], ['レガッタ ～ 君といた永遠 ～', 'regatta ~ kimi to ita eien ~', 'tv - asahi', '9', '5.4 %'], ['下北サンデーズ', 'shimokita sundays', 'tv - asahi', '9', '7.3 %'], ['caとお呼びっ !', 'ca to oyobbi !', 'ntv', '11', '9.5 %'], ['マイ ☆ ボス マイ ☆ ヒーロー', 'my ☆ boss my ☆ hero', 'ntv', '10', '18.9 %']]
|
ed elisian
|
https://en.wikipedia.org/wiki/Ed_Elisian
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1252070-3.html.csv
|
majority
|
ed elisian drove all of his years with a offenhauser l4 type engine .
|
{'scope': 'all', 'col': '4', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'offenhauser l4', 'subset': None}
|
{'func': 'all_str_eq', 'args': ['all_rows', 'engine', 'offenhauser l4'], 'result': True, 'ind': 0, 'tointer': 'for the engine records of all rows , all of them fuzzily match to offenhauser l4 .', 'tostr': 'all_eq { all_rows ; engine ; offenhauser l4 } = true'}
|
all_eq { all_rows ; engine ; offenhauser l4 } = true
|
for the engine records of all rows , all of them fuzzily match to offenhauser l4 .
|
1
|
1
|
{'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'engine_3': 3, 'offenhauser l4_4': 4}
|
{'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'engine_3': 'engine', 'offenhauser l4_4': 'offenhauser l4'}
|
{'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'engine_3': [0], 'offenhauser l4_4': [0]}
|
['year', 'entrant', 'chassis', 'engine', 'points']
|
[['1954', 'ha chapman', 'stevens', 'offenhauser l4', '0'], ['1955', 'westwood gauge / wales', 'kurtis kraft 4000', 'offenhauser l4', '0'], ['1956', 'hoyt machine / fred sommer', 'kurtis kraft 500c', 'offenhauser l4', '0'], ['1957', 'mcnamara / kalamazoo sports', 'kurtis kraft 500d', 'offenhauser l4', '0'], ['1958', 'john zink', 'watson indy roadster', 'offenhauser l4', '0']]
|
khaled saad
|
https://en.wikipedia.org/wiki/Khaled_Saad
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14660578-1.html.csv
|
comparative
|
khaled saad scored three goals in competitive matches , but only two during friendly competition .
|
{'row_1': '2', 'row_2': '3', 'col': '5', 'col_other': '3', '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', 'score', '3 - 0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose score record fuzzily matches to 3 - 0 .', 'tostr': 'filter_eq { all_rows ; score ; 3 - 0 }'}, 'competition'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; score ; 3 - 0 } ; competition }', 'tointer': 'select the rows whose score record fuzzily matches to 3 - 0 . take the competition record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'score', '3 - 2'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose score record fuzzily matches to 3 - 2 .', 'tostr': 'filter_eq { all_rows ; score ; 3 - 2 }'}, 'competition'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; score ; 3 - 2 } ; competition }', 'tointer': 'select the rows whose score record fuzzily matches to 3 - 2 . take the competition record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_eq { all_rows ; score ; 3 - 0 } ; competition } ; hop { filter_eq { all_rows ; score ; 3 - 2 } ; competition } }', 'tointer': 'select the rows whose score record fuzzily matches to 3 - 0 . take the competition record of this row . select the rows whose score record fuzzily matches to 3 - 2 . take the competition 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', 'score', '3 - 0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose score record fuzzily matches to 3 - 0 .', 'tostr': 'filter_eq { all_rows ; score ; 3 - 0 }'}, 'competition'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; score ; 3 - 0 } ; competition }', 'tointer': 'select the rows whose score record fuzzily matches to 3 - 0 . take the competition record of this row .'}, 'friendly'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; score ; 3 - 0 } ; competition } ; friendly }', 'tointer': 'the competition record of the first row is friendly .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'score', '3 - 2'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose score record fuzzily matches to 3 - 2 .', 'tostr': 'filter_eq { all_rows ; score ; 3 - 2 }'}, 'competition'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; score ; 3 - 2 } ; competition }', 'tointer': 'select the rows whose score record fuzzily matches to 3 - 2 . take the competition record of this row .'}, 'friendly'], 'result': True, 'ind': 6, 'tostr': 'eq { hop { filter_eq { all_rows ; score ; 3 - 2 } ; competition } ; friendly }', 'tointer': 'the competition record of the second row is friendly .'}], 'result': True, 'ind': 7, 'tostr': 'and { eq { hop { filter_eq { all_rows ; score ; 3 - 0 } ; competition } ; friendly } ; eq { hop { filter_eq { all_rows ; score ; 3 - 2 } ; competition } ; friendly } }', 'tointer': 'the competition record of the first row is friendly . the competition record of the second row is friendly .'}], 'result': True, 'ind': 8, 'tostr': 'and { eq { hop { filter_eq { all_rows ; score ; 3 - 0 } ; competition } ; hop { filter_eq { all_rows ; score ; 3 - 2 } ; competition } } ; and { eq { hop { filter_eq { all_rows ; score ; 3 - 0 } ; competition } ; friendly } ; eq { hop { filter_eq { all_rows ; score ; 3 - 2 } ; competition } ; friendly } } } = true', 'tointer': 'select the rows whose score record fuzzily matches to 3 - 0 . take the competition record of this row . select the rows whose score record fuzzily matches to 3 - 2 . take the competition record of this row . the first record fuzzily matches to the second record . the competition record of the first row is friendly . the competition record of the second row is friendly .'}
|
and { eq { hop { filter_eq { all_rows ; score ; 3 - 0 } ; competition } ; hop { filter_eq { all_rows ; score ; 3 - 2 } ; competition } } ; and { eq { hop { filter_eq { all_rows ; score ; 3 - 0 } ; competition } ; friendly } ; eq { hop { filter_eq { all_rows ; score ; 3 - 2 } ; competition } ; friendly } } } = true
|
select the rows whose score record fuzzily matches to 3 - 0 . take the competition record of this row . select the rows whose score record fuzzily matches to 3 - 2 . take the competition record of this row . the first record fuzzily matches to the second record . the competition record of the first row is friendly . the competition record of the second row is friendly .
|
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, 'score_11': 11, '3 - 0_12': 12, 'competition_13': 13, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_14': 14, 'score_15': 15, '3 - 2_16': 16, 'competition_17': 17, 'and_7': 7, 'str_eq_5': 5, 'friendly_18': 18, 'str_eq_6': 6, 'friendly_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', 'score_11': 'score', '3 - 0_12': '3 - 0', 'competition_13': 'competition', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_14': 'all_rows', 'score_15': 'score', '3 - 2_16': '3 - 2', 'competition_17': 'competition', 'and_7': 'and', 'str_eq_5': 'str_eq', 'friendly_18': 'friendly', 'str_eq_6': 'str_eq', 'friendly_19': 'friendly'}
|
{'and_8': [9], 'result_9': [], 'str_eq_4': [8], 'str_hop_2': [4, 5], 'filter_str_eq_0': [2], 'all_rows_10': [0], 'score_11': [0], '3 - 0_12': [0], 'competition_13': [2], 'str_hop_3': [4, 6], 'filter_str_eq_1': [3], 'all_rows_14': [1], 'score_15': [1], '3 - 2_16': [1], 'competition_17': [3], 'and_7': [8], 'str_eq_5': [7], 'friendly_18': [5], 'str_eq_6': [7], 'friendly_19': [6]}
|
['date', 'venue', 'score', 'result', 'competition']
|
[['july 23 , 2004', 'jinan', '2 - 0', 'win', '2004 afc asian cup'], ['october 20 , 2004', 'tripoli', '3 - 0', 'win', 'friendly'], ['november 16 , 2005', 'tbilisi', '3 - 2', 'loss', 'friendly'], ['november 10 , 2006', 'lahore', '3 - 0', 'win', '2007 afc asian cup qualification'], ['june 20 , 2007', 'amman', '3 - 0', 'win', '2007 west asian football federation championship']]
|
olivier rochus
|
https://en.wikipedia.org/wiki/Olivier_Rochus
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1554464-7.html.csv
|
superlative
|
olivier rochus had the highest year-end ranking in the year 2005 .
|
{'scope': 'all', 'col_superlative': '7', 'row_superlative': '9', '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', '2005'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; 2005 }'}, 'tournament'], 'result': 'year end ranking', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; 2005 } ; tournament }'}, 'year end ranking'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; 2005 } ; tournament } ; year end ranking } = true', 'tointer': 'select the row whose 2005 record of all rows is maximum . the tournament record of this row is year end ranking .'}
|
eq { hop { argmax { all_rows ; 2005 } ; tournament } ; year end ranking } = true
|
select the row whose 2005 record of all rows is maximum . the tournament record of this row is year end ranking .
|
3
|
3
|
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, '2005_5': 5, 'tournament_6': 6, 'year end ranking_7': 7}
|
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', '2005_5': '2005', 'tournament_6': 'tournament', 'year end ranking_7': 'year end ranking'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], '2005_5': [0], 'tournament_6': [1], 'year end ranking_7': [2]}
|
['tournament', '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012']
|
[['grand slam tournaments', 'grand slam tournaments', 'grand slam tournaments', 'grand slam tournaments', 'grand slam tournaments', 'grand slam tournaments', 'grand slam tournaments', 'grand slam tournaments', 'grand slam tournaments', 'grand slam tournaments', 'grand slam tournaments', 'grand slam tournaments', 'grand slam tournaments', 'grand slam tournaments'], ['australian open', 'a', '1r', '1r', '2r', '1r', '4r', '2r', '2r', '1r', 'a', '1r', 'a', '2r'], ['french open', 'lq', '3r', '2r', '1r', '1r', '2r', '3r', '1r', '1r', 'q3', '2r', '1r', '1r'], ['wimbledon', '3r', '2r', '3r', '4r', '1r', '2r', '3r', '1r', '2r', 'q1', '1r', '2r', '1r'], ['us open', '1r', '1r', '1r', '1r', '4r', '3r', '3r', '1r', '1r', '2r', '1r', '1r', '1r'], ['win - loss', '2 - 2', '3 - 4', '3 - 4', '4 - 4', '3 - 4', '7 - 4', '7 - 4', '1 - 4', '1 - 4', '1 - 1', '1 - 4', '1 - 3', '1 - 4'], ['career statistics', 'career statistics', 'career statistics', 'career statistics', 'career statistics', 'career statistics', 'career statistics', 'career statistics', 'career statistics', 'career statistics', 'career statistics', 'career statistics', 'career statistics', 'career statistics'], ['titles - finals', '1 - 1', '0 - 0', '0 - 1', '0 - 1', '0 - 0', '0 - 1', '1 - 1', '0 - 1', '0 - 0', '0 - 1', '0 - 0', '0 - 1', '0 - 1'], ['year end ranking', '68', '114', '64', '48', '66', '27', '36', '48', '122', '57', '113', '67', '90']]
|
1998 - 99 fa cup
|
https://en.wikipedia.org/wiki/1998%E2%80%9399_FA_Cup
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15154539-6.html.csv
|
majority
|
in the 1998 - 99 fa cup , the majority of replays took place on 24 february 1999 .
|
{'scope': 'subset', 'col': '5', 'most_or_all': 'most', 'criterion': 'equal', 'value': '24 february 1999', 'subset': {'col': '1', 'criterion': 'equal', 'value': 'replay'}}
|
{'func': 'most_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'tie no', 'replay'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; tie no ; replay }', 'tointer': 'select the rows whose tie no record fuzzily matches to replay .'}, 'attendance', '24 february 1999'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose tie no record fuzzily matches to replay . for the attendance records of these rows , most of them fuzzily match to 24 february 1999 .', 'tostr': 'most_eq { filter_eq { all_rows ; tie no ; replay } ; attendance ; 24 february 1999 } = true'}
|
most_eq { filter_eq { all_rows ; tie no ; replay } ; attendance ; 24 february 1999 } = true
|
select the rows whose tie no record fuzzily matches to replay . for the attendance records of these rows , most of them fuzzily match to 24 february 1999 .
|
2
|
2
|
{'most_str_eq_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'tie no_4': 4, 'replay_5': 5, 'attendance_6': 6, '24 february 1999_7': 7}
|
{'most_str_eq_1': 'most_str_eq', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'tie no_4': 'tie no', 'replay_5': 'replay', 'attendance_6': 'attendance', '24 february 1999_7': '24 february 1999'}
|
{'most_str_eq_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'tie no_4': [0], 'replay_5': [0], 'attendance_6': [1], '24 february 1999_7': [1]}
|
['tie no', 'home team', 'score', 'away team', 'attendance']
|
[['1', 'sheffield wednesday', '0 - 1', 'chelsea', '13 february 1999'], ['2', 'everton', '2 - 1', 'coventry city', '13 february 1999'], ['3', 'newcastle united', '0 - 0', 'blackburn rovers', '14 february 1999'], ['replay', 'blackburn rovers', '0 - 1', 'newcastle united', '24 february 1999'], ['4', 'barnsley', '4 - 1', 'bristol rovers', '13 february 1999'], ['5', 'manchester united', '1 - 0', 'fulham', '14 february 1999'], ['6', 'huddersfield town', '2 - 2', 'derby county', '13 february 1999'], ['replay', 'derby county', '3 - 1', 'huddersfield town', '24 february 1999'], ['7', 'arsenal', '2 - 1', 'sheffield united', '13 february 1999'], ['replay', 'arsenal', '2 - 1', 'sheffield united', '23 february 1999'], ['8', 'leeds united', '1 - 1', 'tottenham hotspur', '13 february 1999'], ['replay', 'tottenham hotspur', '2 - 0', 'leeds united', '24 february 1999']]
|
2008 - 09 san antonio spurs season
|
https://en.wikipedia.org/wiki/2008%E2%80%9309_San_Antonio_Spurs_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17288845-9.html.csv
|
majority
|
during this period of the 2008-09 san antonio spurs spurs season , tony parker led the san antonio spurs in points in the majority of the games .
|
{'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'tony parker', 'subset': None}
|
{'func': 'most_str_eq', 'args': ['all_rows', 'high points', 'tony parker'], 'result': True, 'ind': 0, 'tointer': 'for the high points records of all rows , most of them fuzzily match to tony parker .', 'tostr': 'most_eq { all_rows ; high points ; tony parker } = true'}
|
most_eq { all_rows ; high points ; tony parker } = true
|
for the high points records of all rows , most of them fuzzily match to tony parker .
|
1
|
1
|
{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'high points_3': 3, 'tony parker_4': 4}
|
{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'high points_3': 'high points', 'tony parker_4': 'tony parker'}
|
{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'high points_3': [0], 'tony parker_4': [0]}
|
['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record']
|
[['58', 'march 1', 'portland', 'l 84 - 102 ( ot )', 'tony parker ( 15 )', 'fabricio oberto ( 6 )', 'george hill , tony parker ( 4 )', 'rose garden 20627', '39 - 19'], ['59', 'march 2', 'la clippers', 'w 106 - 78 ( ot )', 'tony parker ( 26 )', 'tim duncan ( 12 )', 'tony parker ( 10 )', 'staples center 17649', '40 - 19'], ['60', 'march 4', 'dallas', 'l 102 - 107 ( ot )', 'tony parker ( 37 )', 'tim duncan ( 12 )', 'tim duncan ( 5 )', 'american airlines center 20316', '40 - 20'], ['61', 'march 6', 'washington', 'w 100 - 78 ( ot )', 'tony parker ( 19 )', 'kurt thomas ( 7 )', 'tony parker ( 7 )', 'at & t center 18440', '41 - 20'], ['62', 'march 8', 'phoenix', 'w 103 - 98 ( ot )', 'tony parker ( 30 )', 'tim duncan ( 15 )', 'tony parker ( 9 )', 'at & t center 18797', '42 - 20'], ['63', 'march 10', 'charlotte', 'w 100 - 86 ( ot )', 'roger mason , tony parker ( 21 )', 'tim duncan ( 11 )', 'tony parker ( 7 )', 'at & t center 18254', '43 - 20'], ['64', 'march 12', 'la lakers', 'l 95 - 102 ( ot )', 'michael finley , tony parker ( 25 )', 'tim duncan ( 11 )', 'tony parker ( 9 )', 'at & t center 18797', '43 - 21'], ['65', 'march 14', 'houston', 'w 88 - 85 ( ot )', 'tony parker ( 28 )', 'tim duncan ( 12 )', 'tony parker ( 8 )', 'toyota center 18300', '44 - 21'], ['66', 'march 16', 'oklahoma city', 'l 76 - 78 ( ot )', 'tony parker ( 28 )', 'tim duncan ( 12 )', 'tony parker ( 7 )', 'ford center 19136', '44 - 22'], ['67', 'march 17', 'minnesota', 'w 93 - 86 ( ot )', 'tony parker ( 24 )', 'kurt thomas ( 10 )', 'tony parker , kurt thomas ( 6 )', 'at & t center 18797', '45 - 22'], ['68', 'march 20', 'boston', 'l 77 - 80 ( ot )', 'tony parker ( 25 )', 'tim duncan ( 9 )', 'tony parker ( 8 )', 'at & t center 18797', '45 - 23'], ['69', 'march 22', 'houston', 'l 85 - 87 ( ot )', 'tim duncan ( 23 )', 'kurt thomas ( 9 )', 'tony parker ( 12 )', 'at & t center 18797', '45 - 24'], ['70', 'march 24', 'golden state', 'w 107 - 106 ( ot )', 'tony parker ( 30 )', 'tim duncan ( 10 )', 'tony parker ( 10 )', 'at & t center 18797', '46 - 24'], ['71', 'march 25', 'atlanta', 'w 102 - 92 ( ot )', 'tony parker ( 42 )', 'kurt thomas ( 8 )', 'tony parker ( 10 )', 'philips arena 18529', '47 - 24'], ['72', 'march 27', 'la clippers', 'w 111 - 98 ( ot )', 'tony parker ( 18 )', 'roger mason ( 8 )', 'manu ginóbili ( 7 )', 'at & t center 18797', '48 - 24'], ['73', 'march 29', 'new orleans', 'l 86 - 90 ( ot )', 'tony parker ( 20 )', 'tim duncan ( 15 )', 'tony parker ( 7 )', 'new orleans arena 18204', '48 - 25'], ['74', 'march 31', 'oklahoma city', 'l 95 - 96 ( ot )', 'tim duncan ( 21 )', 'tim duncan ( 12 )', 'tim duncan , michael finley , tony parker ( 4 )', 'at & t center 18797', '48 - 26']]
|
2008 - 09 washington wizards season
|
https://en.wikipedia.org/wiki/2008%E2%80%9309_Washington_Wizards_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17311812-7.html.csv
|
superlative
|
the game played at the rose garden venue drew the highest crowd attendance in the 2008 - 09 washington wizards season .
|
{'scope': 'all', 'col_superlative': '7', 'row_superlative': '13', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '6', 'subset': None}
|
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'location attendance'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; location attendance }'}, 'high assists'], 'result': 'mike james ( 7 )', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; location attendance } ; high assists }'}, 'mike james ( 7 )'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; location attendance } ; high assists } ; mike james ( 7 ) } = true', 'tointer': 'select the row whose location attendance record of all rows is maximum . the high assists record of this row is mike james ( 7 ) .'}
|
eq { hop { argmax { all_rows ; location attendance } ; high assists } ; mike james ( 7 ) } = true
|
select the row whose location attendance record of all rows is maximum . the high assists record of this row is mike james ( 7 ) .
|
3
|
3
|
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'location attendance_5': 5, 'high assists_6': 6, 'mike james (7)_7': 7}
|
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'location attendance_5': 'location attendance', 'high assists_6': 'high assists', 'mike james (7)_7': 'mike james ( 7 )'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'location attendance_5': [0], 'high assists_6': [1], 'mike james (7)_7': [2]}
|
['game', 'date', 'team', 'score', 'high rebounds', 'high assists', 'location attendance', 'record']
|
[['31', 'january 2', 'boston', 'l 83 - 108 ( ot )', 'antawn jamison ( 9 )', 'caron butler ( 5 )', 'td banknorth garden 18624', '6 - 25'], ['32', 'january 4', 'cleveland', 'w 80 - 77 ( ot )', 'antawn jamison ( 13 )', 'andray blatche ( 4 )', 'verizon center 20173', '7 - 25'], ['33', 'january 6', 'orlando', 'l 80 - 89 ( ot )', 'antawn jamison ( 9 )', 'caron butler , mike james ( 5 )', 'amway arena 16011', '7 - 26'], ['34', 'january 7', 'toronto', 'l 93 - 99 ( ot )', 'antawn jamison ( 7 )', 'caron butler , javaris crittenton ( 6 )', 'verizon center 13864', '7 - 27'], ['35', 'january 9', 'chicago', 'l 86 - 98 ( ot )', 'antawn jamison ( 11 )', 'caron butler ( 6 )', 'united center 20125', '7 - 28'], ['36', 'january 10', 'charlotte', 'l 89 - 92 ( ot )', 'andray blatche ( 10 )', 'andray blatche ( 4 )', 'verizon center 20173', '7 - 29'], ['37', 'january 12', 'milwaukee', 'l 91 - 97 ( ot )', 'dominic mcguire ( 10 )', 'dominic mcguire ( 5 )', 'verizon center 13510', '7 - 30'], ['38', 'january 14', 'new york', 'l 122 - 128 ( ot )', 'antawn jamison ( 7 )', 'mike james ( 5 )', 'madison square garden 18020', '7 - 31'], ['39', 'january 16', 'new york', 'w 96 - 89 ( ot )', 'andray blatche ( 11 )', 'caron butler ( 7 )', 'verizon center 17526', '8 - 31'], ['40', 'january 19', 'golden state', 'l 98 - 119 ( ot )', 'dominic mcguire ( 11 )', 'dominic mcguire ( 6 )', 'oracle arena 19244', '8 - 32'], ['41', 'january 21', 'sacramento', 'w 110 - 107 ( ot )', 'dominic mcguire ( 12 )', 'caron butler ( 5 )', 'arco arena 10821', '9 - 32'], ['42', 'january 22', 'la lakers', 'l 97 - 117 ( ot )', 'javale mcgee ( 9 )', 'caron butler , mike james ( 6 )', 'staples center 18997', '9 - 33'], ['43', 'january 24', 'portland', 'l 87 - 100 ( ot )', 'caron butler ( 10 )', 'mike james ( 7 )', 'rose garden 20566', '9 - 34'], ['44', 'january 26', 'phoenix', 'l 87 - 103 ( ot )', 'antawn jamison ( 13 )', 'dominic mcguire ( 7 )', 'verizon center 17344', '9 - 35'], ['45', 'january 28', 'miami', 'l 71 - 93 ( ot )', 'antawn jamison ( 12 )', 'caron butler ( 6 )', 'american airlines arena 16424', '9 - 36'], ['46', 'january 30', 'philadelphia', 'l 94 - 104 ( ot )', 'antawn jamison ( 15 )', 'javaris crittenton ( 7 )', 'wachovia center 15528', '9 - 37'], ['47', 'january 31', 'la clippers', 'w 106 - 94 ( ot )', 'caron butler ( 13 )', 'caron butler ( 7 )', 'verizon center 18227', '10 - 37']]
|
list of best - selling music artists
|
https://en.wikipedia.org/wiki/List_of_best-selling_music_artists
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1291598-1.html.csv
|
count
|
four artists-elton john , pink floyd , led zepplin and the beatles-were the only artists to originate from the united kingdom on the list of best-selling music artists .
|
{'scope': 'all', 'criterion': 'equal', 'value': 'united kingdom', 'result': '4', 'col': '2', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country of origin', 'united kingdom'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose country of origin record fuzzily matches to united kingdom .', 'tostr': 'filter_eq { all_rows ; country of origin ; united kingdom }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; country of origin ; united kingdom } }', 'tointer': 'select the rows whose country of origin record fuzzily matches to united kingdom . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; country of origin ; united kingdom } } ; 4 } = true', 'tointer': 'select the rows whose country of origin record fuzzily matches to united kingdom . the number of such rows is 4 .'}
|
eq { count { filter_eq { all_rows ; country of origin ; united kingdom } } ; 4 } = true
|
select the rows whose country of origin record fuzzily matches to united kingdom . 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, 'country of origin_5': 5, 'united kingdom_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', 'country of origin_5': 'country of origin', 'united kingdom_6': 'united kingdom', '4_7': '4'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'country of origin_5': [0], 'united kingdom_6': [0], '4_7': [2]}
|
['artist', 'country of origin', 'period active', 'release - year of first charted record', 'genre', 'claimed sales']
|
[['the beatles', 'united kingdom', '1960 - 1970', '1962', 'rock / pop', '600 million'], ['elvis presley', 'united states', '1954 - 1977', '1954', 'rock and roll / pop / country', '600 million 500 million'], ['michael jackson', 'united states', '1964 - 2009', '1971', 'pop / rock / dance / r & b', '400 million 350 million 300 million'], ['madonna', 'united states', '1979 - present', '1982', 'pop / rock / dance', '300 million 275 million'], ['elton john', 'united kingdom', '1964 - present', '1969', 'pop / rock', '300 million 250 million'], ['led zeppelin', 'united kingdom', '1968 - 1980', '1969', 'hard rock / heavy metal', '300 million 200 million'], ['pink floyd', 'united kingdom', '1965 - 1996', '1967', 'progressive rock', '250 million 200 million']]
|
anna iljuštšenko
|
https://en.wikipedia.org/wiki/Anna_Ilju%C5%A1t%C5%A1enko
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18755785-1.html.csv
|
aggregation
|
between 2004 and 2013 , high jumper anna iljuštšenko averaged a jump measurement of 1.86 m.
|
{'scope': 'all', 'col': '5', 'type': 'average', 'result': '1.86', 'subset': None}
|
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'notes'], 'result': '1.86', 'ind': 0, 'tostr': 'avg { all_rows ; notes }'}, '1.86'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; notes } ; 1.86 } = true', 'tointer': 'the average of the notes record of all rows is 1.86 .'}
|
round_eq { avg { all_rows ; notes } ; 1.86 } = true
|
the average of the notes record of all rows is 1.86 .
|
2
|
2
|
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'notes_4': 4, '1.86_5': 5}
|
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'notes_4': 'notes', '1.86_5': '1.86'}
|
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'notes_4': [0], '1.86_5': [1]}
|
['year', 'competition', 'venue', 'position', 'notes']
|
[['2004', 'world junior championships', 'grosseto , italy', '15th', '1.75 m'], ['2005', 'european u23 championships', 'erfurt , germany', '11th', '1.70 m'], ['2006', 'european championships', 'gothenburg , sweden', '20th ( q )', '1.87 m'], ['2007', 'european u23 championships', 'debrecen , hungary', '13th ( q )', '1.81 m'], ['2007', 'universiade', 'bangkok , thailand', '9th', '1.80 m'], ['2008', 'olympics games', 'beijing , china', '21st ( q )', '1.89 m'], ['2009', 'european indoor championships', 'torino , italy', '10th ( q )', '1.85 m'], ['2009', 'universiade', 'belgrade , serbia', '5th', '1.88 m'], ['2009', 'world championships', 'berlin , germany', '17th ( q )', '1.89 m'], ['2010', 'world indoor championships', 'doha , qatar', '10th ( q )', '1.89 m'], ['2010', 'european championships', 'barcelona , spain', '11th', '1.85 m'], ['2011', 'european indoor championships', 'paris , france', '13th ( q )', '1.89 m'], ['2011', 'universiade', 'shenzhen , china', '3rd', '1.94 m'], ['2011', 'world championships', 'daegu , south korea', '12th', '1.89 m'], ['2012', 'world indoor championships', 'istanbul , turkey', '12th ( q )', '1.88 m'], ['2012', 'european championships', 'helsinki , finland', '13th ( q )', '1.87 m'], ['2012', 'olympic games', 'london , uk', '15th ( q )', '1.90 m'], ['2013', 'european indoor championships', 'gothenburg , sweden', '4th', '1.92 m'], ['2013', 'universiade', 'kazan , russia', '3rd', '1.94 m'], ['2013', 'world championships', 'moscow , russia', '16th ( q )', '1.88 m']]
|
lisa bonder
|
https://en.wikipedia.org/wiki/Lisa_Bonder
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15057113-3.html.csv
|
majority
|
lisa bonder won the majority of the tournaments .
|
{'scope': 'all', 'col': '1', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'winner', 'subset': None}
|
{'func': 'most_str_eq', 'args': ['all_rows', 'outcome', 'winner'], 'result': True, 'ind': 0, 'tointer': 'for the outcome records of all rows , most of them fuzzily match to winner .', 'tostr': 'most_eq { all_rows ; outcome ; winner } = true'}
|
most_eq { all_rows ; outcome ; winner } = true
|
for the outcome records of all rows , most of them fuzzily match to winner .
|
1
|
1
|
{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'outcome_3': 3, 'winner_4': 4}
|
{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'outcome_3': 'outcome', 'winner_4': 'winner'}
|
{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'outcome_3': [0], 'winner_4': [0]}
|
['outcome', 'date', 'tournament', 'surface', 'opponent', 'score']
|
[['winner', 'july 11 , 1982', 'hamburg', 'clay', 'renáta tomanová', '6 - 3 , 6 - 2'], ['winner', 'october 18 , 1982', 'tokyo', 'hard', 'shelley solomon', '2 - 6 , 6 - 0 , 6 - 3'], ['winner', 'september 18 , 1983', 'tokyo', 'carpet ( i )', 'andrea jaeger', '6 - 2 , 5 - 7 , 6 - 1'], ['winner', 'october 16 , 1983', 'tokyo', 'hard', 'laura arraya', '6 - 1 , 6 - 3'], ['runner - up', 'august 11 , 1984', 'indianapolis', 'clay', 'manuela maleeva', '6 - 4 , 6 - 3']]
|
1955 vfl season
|
https://en.wikipedia.org/wiki/1955_VFL_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10773753-1.html.csv
|
ordinal
|
the second highest number of people attended the 1955 vfl game in which richmond participated in .
|
{'row': '6', 'col': '6', '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', 'crowd', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; crowd ; 2 }'}, 'home team'], 'result': 'richmond', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; crowd ; 2 } ; home team }'}, 'richmond'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; crowd ; 2 } ; home team } ; richmond } = true', 'tointer': 'select the row whose crowd record of all rows is 2nd maximum . the home team record of this row is richmond .'}
|
eq { hop { nth_argmax { all_rows ; crowd ; 2 } ; home team } ; richmond } = true
|
select the row whose crowd record of all rows is 2nd maximum . the home team record of this row is richmond .
|
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, 'home team_7': 7, 'richmond_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', 'home team_7': 'home team', 'richmond_8': 'richmond'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'crowd_5': [0], '2_6': [0], 'home team_7': [1], 'richmond_8': [2]}
|
['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date']
|
[['geelong', '15.14 ( 104 )', 'south melbourne', '9.12 ( 66 )', 'kardinia park', '20976', '16 april 1955'], ['fitzroy', '13.15 ( 93 )', 'hawthorn', '7.16 ( 58 )', 'brunswick street oval', '16000', '16 april 1955'], ['collingwood', '6.12 ( 48 )', 'footscray', '15.14 ( 104 )', 'victoria park', '33398', '16 april 1955'], ['carlton', '19.20 ( 134 )', 'north melbourne', '10.5 ( 65 )', 'princes park', '25041', '16 april 1955'], ['st kilda', '8.11 ( 59 )', 'melbourne', '17.16 ( 118 )', 'junction oval', '20000', '16 april 1955'], ['richmond', '9.23 ( 77 )', 'essendon', '13.16 ( 94 )', 'punt road oval', '30000', '16 april 1955']]
|
86th united states congress
|
https://en.wikipedia.org/wiki/86th_United_States_Congress
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2159571-1.html.csv
|
count
|
3 of the changes occurred due to death .
|
{'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'died', 'result': '3', 'col': '3', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'reason for change', 'died'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose reason for change record fuzzily matches to died .', 'tostr': 'filter_eq { all_rows ; reason for change ; died }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; reason for change ; died } }', 'tointer': 'select the rows whose reason for change record fuzzily matches to died . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; reason for change ; died } } ; 3 } = true', 'tointer': 'select the rows whose reason for change record fuzzily matches to died . the number of such rows is 3 .'}
|
eq { count { filter_eq { all_rows ; reason for change ; died } } ; 3 } = true
|
select the rows whose reason for change record fuzzily matches to died . 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, 'reason for change_5': 5, 'died_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', 'reason for change_5': 'reason for change', 'died_6': 'died', '3_7': '3'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'reason for change_5': [0], 'died_6': [0], '3_7': [2]}
|
['state ( class )', 'vacator', 'reason for change', 'successor', 'date of successors formal installation']
|
[['hawaii ( 1 )', 'new seats', 'hawaii achieved statehood august 21 , 1959', 'hiram fong ( r )', 'august 21 , 1959'], ['hawaii ( 3 )', 'new seats', 'hawaii achieved statehood august 21 , 1959', 'oren e long ( d )', 'august 21 , 1959'], ['north dakota ( 1 )', 'william langer ( r )', 'died november 8 , 1959', 'clarence n brunsdale ( r )', 'november 19 , 1959'], ['oregon ( 2 )', 'richard l neuberger ( d )', 'died march 9 , 1960', 'hall s lusk ( d )', 'march 16 , 1960'], ['north dakota ( 1 )', 'clarence n brunsdale ( r )', 'successor elected august 7 , 1960', 'quentin n burdick ( d )', 'august 8 , 1960'], ['missouri ( 3 )', 'thomas c hennings , jr ( d )', 'died september 13 , 1960', 'edward v long ( d )', 'september 23 , 1960']]
|
1955 vfl season
|
https://en.wikipedia.org/wiki/1955_VFL_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10773753-5.html.csv
|
aggregation
|
the average attendance in the 1955 vfl season was around 21000-22000 fans per game .
|
{'scope': 'all', 'col': '6', 'type': 'average', 'result': '21000-22000', 'subset': None}
|
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'crowd'], 'result': '21000-22000', 'ind': 0, 'tostr': 'avg { all_rows ; crowd }'}, '21000-22000'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; crowd } ; 21000-22000 } = true', 'tointer': 'the average of the crowd record of all rows is 21000-22000 .'}
|
round_eq { avg { all_rows ; crowd } ; 21000-22000 } = true
|
the average of the crowd record of all rows is 21000-22000 .
|
2
|
2
|
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'crowd_4': 4, '21000-22000_5': 5}
|
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'crowd_4': 'crowd', '21000-22000_5': '21000-22000'}
|
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'crowd_4': [0], '21000-22000_5': [1]}
|
['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date']
|
[['hawthorn', '14.7 ( 91 )', 'north melbourne', '13.15 ( 93 )', 'glenferrie oval', '15000', '14 may 1955'], ['essendon', '8.11 ( 59 )', 'melbourne', '10.13 ( 73 )', 'windy hill', '25299', '14 may 1955'], ['carlton', '12.17 ( 89 )', 'collingwood', '17.12 ( 114 )', 'princes park', '37065', '14 may 1955'], ['south melbourne', '25.16 ( 166 )', 'st kilda', '4.8 ( 32 )', 'lake oval', '15000', '14 may 1955'], ['geelong', '12.12 ( 84 )', 'footscray', '10.12 ( 72 )', 'kardinia park', '28288', '14 may 1955'], ['richmond', '11.11 ( 77 )', 'fitzroy', '15.9 ( 99 )', 'punt road oval', '15000', '14 may 1955']]
|
polona hercog
|
https://en.wikipedia.org/wiki/Polona_Hercog
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17717526-9.html.csv
|
count
|
polona hercog partnered with stephanie vogt for two tournaments .
|
{'scope': 'all', 'criterion': 'equal', 'value': 'stephanie vogt', 'result': '2', 'col': '4', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'partner', 'stephanie vogt'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose partner record fuzzily matches to stephanie vogt .', 'tostr': 'filter_eq { all_rows ; partner ; stephanie vogt }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; partner ; stephanie vogt } }', 'tointer': 'select the rows whose partner record fuzzily matches to stephanie vogt . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; partner ; stephanie vogt } } ; 2 } = true', 'tointer': 'select the rows whose partner record fuzzily matches to stephanie vogt . the number of such rows is 2 .'}
|
eq { count { filter_eq { all_rows ; partner ; stephanie vogt } } ; 2 } = true
|
select the rows whose partner record fuzzily matches to stephanie vogt . 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, 'partner_5': 5, 'stephanie vogt_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', 'partner_5': 'partner', 'stephanie vogt_6': 'stephanie vogt', '2_7': '2'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'partner_5': [0], 'stephanie vogt_6': [0], '2_7': [2]}
|
['date', 'tournament', 'surface', 'partner', 'opponents', 'score']
|
[['15 january 2007', 'algiers 2 , algeria', 'clay', 'rushmi chakravarthi', 'barbora matusova anna savitskaya', '6 - 2 , 6 - 0'], ['11 february 2008', 'mallorca 2 , spain', 'clay', 'stephanie vogt', 'leticia costas - moreira maite gabarrus alonso', '7 - 6 ( 7 - 2 ) , 6 - 3'], ['28 april 2008', 'makarska , croatia', 'clay', 'stephanie vogt', 'tadeja majerić maša zec peškirič', '7 - 5 , 6 - 2'], ['8 september 2008', 'sarajevo 2 , bosnia - herzegovina', 'clay', 'alberta brianti', 'çağla büyükakçay julia glushko', '6 - 4 , 7 - 5'], ['8 february 2010', 'cali , colombia', 'clay', 'edina gallovits', 'estrella cabeza candella laura pous tió', '3 - 6 , 6 - 3 ,']]
|
wong chin hung
|
https://en.wikipedia.org/wiki/Wong_Chin_Hung
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13035867-2.html.csv
|
aggregation
|
the average score that wong chin hung had was .23 .
|
{'scope': 'all', 'col': '4', 'type': 'average', 'result': '.23', 'subset': None}
|
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'scored'], 'result': '.23', 'ind': 0, 'tostr': 'avg { all_rows ; scored }'}, '.23'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; scored } ; .23 } = true', 'tointer': 'the average of the scored record of all rows is .23 .'}
|
round_eq { avg { all_rows ; scored } ; .23 } = true
|
the average of the scored record of all rows is .23 .
|
2
|
2
|
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'scored_4': 4, '.23_5': 5}
|
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'scored_4': 'scored', '.23_5': '.23'}
|
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'scored_4': [0], '.23_5': [1]}
|
['date', 'venue', 'result', 'scored', 'competition']
|
[['19 november 2008', 'macau ust stadium , macau', '9 - 1', '0', 'friendly'], ['23 august 2009', 'world games stadium , kaohsiung , taiwan', '4 - 0', '0', '2010 eaff championship semi - finals'], ['27 august 2009', 'world games stadium , kaohsiung , taiwan', '12 - 0', '1', '2010 eaff championship semi - finals'], ['18 november 2009', 'hong kong stadium , hong kong', '0 - 4', '0', '2011 afc asian cup qualification'], ['11 february 2010', 'olympic stadium , tokyo , japan', '0 - 3', '0', '2010 east asian football championship'], ['14 february 2010', 'olympic stadium , tokyo , japan', '0 - 2', '0', '2010 east asian football championship'], ['3 march 2010', 'hong kong stadium , hong kong', '0 - 0', '0', '2011 afc asian cup qualification'], ['9 february 2011', 'shah alam stadium , kuala lumpur', '0 - 2', '0', 'friendly'], ['3 june 2011', 'siu sai wan sports ground , hong kong', '1 - 1', '0', 'friendly'], ['28 july 2011', 'siu sai wan sports ground , hong kong', '0 - 5', '0', '2014 fifa world cup qualification'], ['30 september 2011', 'kaohsiung national stadium , kaohsiung , taiwan', '3 - 3', '0', '2011 long teng cup'], ['2 october 2011', 'kaohsiung national stadium , kaohsiung , taiwan', '5 - 1', '2', '2011 long teng cup'], ['4 october 2011', 'kaohsiung national stadium , kaohsiung , taiwan', '6 - 0', '0', '2011 long teng cup']]
|
list of vancouver canucks draft picks
|
https://en.wikipedia.org/wiki/List_of_Vancouver_Canucks_draft_picks
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11636955-37.html.csv
|
ordinal
|
in rounds 4 through 7 , matt butcher was the 2nd person picked for the vancouver canucks .
|
{'scope': 'subset', 'row': '4', 'col': '2', 'order': '2', 'col_other': '3', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'subset': {'col': '1', 'criterion': 'greater_than_eq', 'value': '4'}}
|
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': [{'func': 'filter_greater_eq', 'args': ['all_rows', 'rd', '4'], 'result': None, 'ind': 0, 'tostr': 'filter_greater_eq { all_rows ; rd ; 4 }', 'tointer': 'select the rows whose rd record is greater than or equal to 4 .'}, 'pick', '2'], 'result': None, 'ind': 1, 'tostr': 'nth_argmin { filter_greater_eq { all_rows ; rd ; 4 } ; pick ; 2 }'}, 'player'], 'result': 'matt butcher', 'ind': 2, 'tostr': 'hop { nth_argmin { filter_greater_eq { all_rows ; rd ; 4 } ; pick ; 2 } ; player }'}, 'matt butcher'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { nth_argmin { filter_greater_eq { all_rows ; rd ; 4 } ; pick ; 2 } ; player } ; matt butcher } = true', 'tointer': 'select the rows whose rd record is greater than or equal to 4 . select the row whose pick record of these rows is 2nd minimum . the player record of this row is matt butcher .'}
|
eq { hop { nth_argmin { filter_greater_eq { all_rows ; rd ; 4 } ; pick ; 2 } ; player } ; matt butcher } = true
|
select the rows whose rd record is greater than or equal to 4 . select the row whose pick record of these rows is 2nd minimum . the player record of this row is matt butcher .
|
4
|
4
|
{'str_eq_3': 3, 'result_4': 4, 'str_hop_2': 2, 'nth_argmin_1': 1, 'filter_greater_eq_0': 0, 'all_rows_5': 5, 'rd_6': 6, '4_7': 7, 'pick_8': 8, '2_9': 9, 'player_10': 10, 'matt butcher_11': 11}
|
{'str_eq_3': 'str_eq', 'result_4': 'true', 'str_hop_2': 'str_hop', 'nth_argmin_1': 'nth_argmin', 'filter_greater_eq_0': 'filter_greater_eq', 'all_rows_5': 'all_rows', 'rd_6': 'rd', '4_7': '4', 'pick_8': 'pick', '2_9': '2', 'player_10': 'player', 'matt butcher_11': 'matt butcher'}
|
{'str_eq_3': [4], 'result_4': [], 'str_hop_2': [3], 'nth_argmin_1': [2], 'filter_greater_eq_0': [1], 'all_rows_5': [0], 'rd_6': [0], '4_7': [0], 'pick_8': [1], '2_9': [1], 'player_10': [2], 'matt butcher_11': [3]}
|
['rd', 'pick', 'player', 'team ( league )', 'reg gp', 'pl gp']
|
[['1', '10', 'luc bourdon', "val - d'or foreurs ( qmjhl )", '36', '0'], ['2', '51', 'mason raymond', 'camrose kodiaks ( ajhl )', '374', '55'], ['4', '114', 'alexandre vincent', 'chicoutimi saguenéens ( qmjhl )', '0', '0'], ['5', '138', 'matt butcher', 'chilliwack chiefs ( bchl )', '0', '0'], ['6', '185', 'kris fredheim', 'notre dame hounds ( sjhl )', '0', '0'], ['7', '205', 'mario bliznak', 'hc dukla trenčín ( slovak )', '6', '0']]
|
locomotives of the london and north eastern railway
|
https://en.wikipedia.org/wiki/Locomotives_of_the_London_and_North_Eastern_Railway
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1169568-2.html.csv
|
comparative
|
there were more 6ai locomotives of the london and north eastern railway built than 6d class locomotives .
|
{'row_1': '3', 'row_2': '4', 'col': '3', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}
|
{'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'class', '6ai'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose class record fuzzily matches to 6ai .', 'tostr': 'filter_eq { all_rows ; class ; 6ai }'}, 'quantity'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; class ; 6ai } ; quantity }', 'tointer': 'select the rows whose class record fuzzily matches to 6ai . take the quantity record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'class', '6d'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose class record fuzzily matches to 6d .', 'tostr': 'filter_eq { all_rows ; class ; 6d }'}, 'quantity'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; class ; 6d } ; quantity }', 'tointer': 'select the rows whose class record fuzzily matches to 6d . take the quantity record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; class ; 6ai } ; quantity } ; hop { filter_eq { all_rows ; class ; 6d } ; quantity } } = true', 'tointer': 'select the rows whose class record fuzzily matches to 6ai . take the quantity record of this row . select the rows whose class record fuzzily matches to 6d . take the quantity record of this row . the first record is greater than the second record .'}
|
greater { hop { filter_eq { all_rows ; class ; 6ai } ; quantity } ; hop { filter_eq { all_rows ; class ; 6d } ; quantity } } = true
|
select the rows whose class record fuzzily matches to 6ai . take the quantity record of this row . select the rows whose class record fuzzily matches to 6d . take the quantity 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, 'class_7': 7, '6ai_8': 8, 'quantity_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'class_11': 11, '6d_12': 12, 'quantity_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', 'class_7': 'class', '6ai_8': '6ai', 'quantity_9': 'quantity', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'class_11': 'class', '6d_12': '6d', 'quantity_13': 'quantity'}
|
{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'class_7': [0], '6ai_8': [0], 'quantity_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'class_11': [1], '6d_12': [1], 'quantity_13': [3]}
|
['class', 'type', 'quantity', 'date', 'lner class']
|
[['2', '4 - 4 - 0', '25', '1887 - 1892', 'd7'], ['3', '2 - 4 - 2t', '39', '1889 - 1892', 'f1'], ['6ai', '0 - 6 - 0', '12', '1888', 'j8'], ['6d', '2 - 4 - 0', '3', '1887', 'e2'], ['6db', '4 - 4 - 0', '3', '1888', 'd8'], ['9', '0 - 6 - 0', '6', '1888 - 89', 'j13'], ['9a', '0 - 6 - 2t', '55', '1889 - 92', 'n4'], ['9b & 9e', '0 - 6 - 0', '31', '1891 - 95', 'j9'], ['9c & 9f', '0 - 6 - 2t', '129', '1891 - 1901', 'n5'], ['9d , 9h & 9 m', '0 - 6 - 0', '124', '1892 - 1902', 'j10']]
|
50 metre running target mixed
|
https://en.wikipedia.org/wiki/50_metre_running_target_mixed
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18938213-3.html.csv
|
count
|
among the countries that won 0 gold medals at the 50 metre running target mixed at world championships , 2 of them won only 1 medal in total each .
|
{'scope': 'subset', 'criterion': 'equal', 'value': '1', 'result': '2', 'col': '6', 'subset': {'col': '3', 'criterion': 'equal', 'value': '0'}}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'gold', '0'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; gold ; 0 }', 'tointer': 'select the rows whose gold record is equal to 0 .'}, 'total', '1'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose gold record is equal to 0 . among these rows , select the rows whose total record is equal to 1 .', 'tostr': 'filter_eq { filter_eq { all_rows ; gold ; 0 } ; total ; 1 }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_eq { filter_eq { all_rows ; gold ; 0 } ; total ; 1 } }', 'tointer': 'select the rows whose gold record is equal to 0 . among these rows , select the rows whose total record is equal to 1 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_eq { all_rows ; gold ; 0 } ; total ; 1 } } ; 2 } = true', 'tointer': 'select the rows whose gold record is equal to 0 . among these rows , select the rows whose total record is equal to 1 . the number of such rows is 2 .'}
|
eq { count { filter_eq { filter_eq { all_rows ; gold ; 0 } ; total ; 1 } } ; 2 } = true
|
select the rows whose gold record is equal to 0 . among these rows , select the rows whose total record is equal to 1 . the number of such rows is 2 .
|
4
|
4
|
{'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_eq_1': 1, 'filter_eq_0': 0, 'all_rows_5': 5, 'gold_6': 6, '0_7': 7, 'total_8': 8, '1_9': 9, '2_10': 10}
|
{'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_eq_1': 'filter_eq', 'filter_eq_0': 'filter_eq', 'all_rows_5': 'all_rows', 'gold_6': 'gold', '0_7': '0', 'total_8': 'total', '1_9': '1', '2_10': '2'}
|
{'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_eq_1': [2], 'filter_eq_0': [1], 'all_rows_5': [0], 'gold_6': [0], '0_7': [0], 'total_8': [1], '1_9': [1], '2_10': [3]}
|
['rank', 'nation', 'gold', 'silver', 'bronze', 'total']
|
[['1', 'ussr', '13', '10', '2', '25'], ['2', 'czech republic', '4', '0', '3', '7'], ['3', 'russia', '3', '3', '1', '7'], ['4', 'hungary', '2', '4', '4', '10'], ['5', 'sweden', '2', '2', '5', '9'], ['6', 'china', '2', '2', '4', '8'], ['7', 'italy', '2', '0', '1', '3'], ['8', 'poland', '1', '1', '2', '4'], ['9', 'ukraine', '1', '1', '1', '3'], ['10', 'finland', '0', '3', '2', '5'], ['11', 'slovakia', '0', '2', '1', '3'], ['12', 'west germany', '0', '2', '0', '2'], ['13', 'united states', '0', '0', '2', '2'], ['14', 'colombia', '0', '0', '1', '1'], ['14', 'east germany', '0', '0', '1', '1'], ['total', 'total', '30', '30', '30', '90']]
|
1928 vfl season
|
https://en.wikipedia.org/wiki/1928_VFL_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10766119-3.html.csv
|
unique
|
in the 1928 vfl season , when the away team is from somewhere in melbourne , the only time the venue was windy hill was when the home team was essendon .
|
{'scope': 'subset', 'row': '2', 'col': '5', 'col_other': '1', 'criterion': 'equal', 'value': 'windy hill', 'subset': {'col': '3', 'criterion': 'fuzzily_match', 'value': 'melbourne'}}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'away team', 'melbourne'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; away team ; melbourne }', 'tointer': 'select the rows whose away team record fuzzily matches to melbourne .'}, 'venue', 'windy hill'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose away team record fuzzily matches to melbourne . among these rows , select the rows whose venue record fuzzily matches to windy hill .', 'tostr': 'filter_eq { filter_eq { all_rows ; away team ; melbourne } ; venue ; windy hill }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_eq { filter_eq { all_rows ; away team ; melbourne } ; venue ; windy hill } }', 'tointer': 'select the rows whose away team record fuzzily matches to melbourne . among these rows , select the rows whose venue record fuzzily matches to windy hill . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'away team', 'melbourne'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; away team ; melbourne }', 'tointer': 'select the rows whose away team record fuzzily matches to melbourne .'}, 'venue', 'windy hill'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose away team record fuzzily matches to melbourne . among these rows , select the rows whose venue record fuzzily matches to windy hill .', 'tostr': 'filter_eq { filter_eq { all_rows ; away team ; melbourne } ; venue ; windy hill }'}, 'home team'], 'result': 'essendon', 'ind': 3, 'tostr': 'hop { filter_eq { filter_eq { all_rows ; away team ; melbourne } ; venue ; windy hill } ; home team }'}, 'essendon'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_eq { filter_eq { all_rows ; away team ; melbourne } ; venue ; windy hill } ; home team } ; essendon }', 'tointer': 'the home team record of this unqiue row is essendon .'}], 'result': True, 'ind': 5, 'tostr': 'and { only { filter_eq { filter_eq { all_rows ; away team ; melbourne } ; venue ; windy hill } } ; eq { hop { filter_eq { filter_eq { all_rows ; away team ; melbourne } ; venue ; windy hill } ; home team } ; essendon } } = true', 'tointer': 'select the rows whose away team record fuzzily matches to melbourne . among these rows , select the rows whose venue record fuzzily matches to windy hill . there is only one such row in the table . the home team record of this unqiue row is essendon .'}
|
and { only { filter_eq { filter_eq { all_rows ; away team ; melbourne } ; venue ; windy hill } } ; eq { hop { filter_eq { filter_eq { all_rows ; away team ; melbourne } ; venue ; windy hill } ; home team } ; essendon } } = true
|
select the rows whose away team record fuzzily matches to melbourne . among these rows , select the rows whose venue record fuzzily matches to windy hill . there is only one such row in the table . the home team record of this unqiue row is essendon .
|
8
|
6
|
{'and_5': 5, 'result_6': 6, 'only_2': 2, 'filter_str_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'away team_8': 8, 'melbourne_9': 9, 'venue_10': 10, 'windy hill_11': 11, 'str_eq_4': 4, 'str_hop_3': 3, 'home team_12': 12, 'essendon_13': 13}
|
{'and_5': 'and', 'result_6': 'true', 'only_2': 'only', 'filter_str_eq_1': 'filter_str_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'away team_8': 'away team', 'melbourne_9': 'melbourne', 'venue_10': 'venue', 'windy hill_11': 'windy hill', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'home team_12': 'home team', 'essendon_13': 'essendon'}
|
{'and_5': [6], 'result_6': [], 'only_2': [5], 'filter_str_eq_1': [2, 3], 'filter_str_eq_0': [1], 'all_rows_7': [0], 'away team_8': [0], 'melbourne_9': [0], 'venue_10': [1], 'windy hill_11': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'home team_12': [3], 'essendon_13': [4]}
|
['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date']
|
[['fitzroy', '12.12 ( 84 )', 'melbourne', '17.16 ( 118 )', 'brunswick street oval', '17000', '5 may 1928'], ['essendon', '12.13 ( 85 )', 'south melbourne', '5.11 ( 41 )', 'windy hill', '22000', '5 may 1928'], ['st kilda', '11.11 ( 77 )', 'north melbourne', '10.15 ( 75 )', 'junction oval', '12000', '5 may 1928'], ['geelong', '10.17 ( 77 )', 'footscray', '12.9 ( 81 )', 'corio oval', '12500', '5 may 1928'], ['richmond', '5.14 ( 44 )', 'collingwood', '5.12 ( 42 )', 'punt road oval', '36000', '5 may 1928'], ['hawthorn', '7.17 ( 59 )', 'carlton', '14.9 ( 93 )', 'glenferrie oval', '14000', '5 may 1928']]
|
1989 masters tournament
|
https://en.wikipedia.org/wiki/1989_Masters_Tournament
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16514242-1.html.csv
|
majority
|
most of the players at the 1989 masters tournament represented the united states .
|
{'scope': 'all', 'col': '2', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'united states', 'subset': None}
|
{'func': 'most_str_eq', 'args': ['all_rows', 'country', 'united states'], 'result': True, 'ind': 0, 'tointer': 'for the country records of all rows , most of them fuzzily match to united states .', 'tostr': 'most_eq { all_rows ; country ; united states } = true'}
|
most_eq { all_rows ; country ; united states } = true
|
for the country records of all rows , most of them fuzzily match to united states .
|
1
|
1
|
{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'country_3': 3, 'united states_4': 4}
|
{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'country_3': 'country', 'united states_4': 'united states'}
|
{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'country_3': [0], 'united states_4': [0]}
|
['player', 'country', 'year ( s ) won', 'total', 'to par', 'finish']
|
[['ben crenshaw', 'united states', '1984', '284', '- 4', 't3'], ['seve ballesteros', 'spain', '1980 , 1983', '285', '- 3', 't5'], ['tom watson', 'united states', '1977 , 1981', '290', '+ 2', 't14'], ['jack nicklaus', 'united states', '1963 , 1965 , 1966 , 1984 , 1975 , 1986', '291', '+ 3', '18'], ['bernhard langer', 'west germany', '1985', '293', '+ 5', 't26'], ['larry mize', 'united states', '1987', '293', '+ 5', 't26'], ['fuzzy zoeller', 'united states', '1979', '293', '+ 5', 't26'], ['tommy aaron', 'united states', '1973', '298', '+ 10', 't38'], ['charles coody', 'united states', '1971', '298', '+ 10', 't38'], ['raymond floyd', 'united states', '1976', '298', '+ 10', 't38'], ['george archer', 'united states', '1969', '298', '+ 12', 't43']]
|
hampden football netball league
|
https://en.wikipedia.org/wiki/Hampden_Football_Netball_League
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18628904-27.html.csv
|
comparative
|
terang had 501 more wins than terang mortlake had .
|
{'row_1': '10', 'row_2': '11', 'col': '3', 'col_other': '1', 'relation': 'diff', 'record_mentioned': 'no', 'diff_result': {'diff_value': '501', 'bigger': 'row1'}}
|
{'func': 'eq', 'args': [{'func': 'diff', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'club', 'terang'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose club record fuzzily matches to terang .', 'tostr': 'filter_eq { all_rows ; club ; terang }'}, 'wins'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; club ; terang } ; wins }', 'tointer': 'select the rows whose club record fuzzily matches to terang . take the wins record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'club', 'terang mortlake'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose club record fuzzily matches to terang mortlake .', 'tostr': 'filter_eq { all_rows ; club ; terang mortlake }'}, 'wins'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; club ; terang mortlake } ; wins }', 'tointer': 'select the rows whose club record fuzzily matches to terang mortlake . take the wins record of this row .'}], 'result': '501', 'ind': 4, 'tostr': 'diff { hop { filter_eq { all_rows ; club ; terang } ; wins } ; hop { filter_eq { all_rows ; club ; terang mortlake } ; wins } }'}, '501'], 'result': True, 'ind': 5, 'tostr': 'eq { diff { hop { filter_eq { all_rows ; club ; terang } ; wins } ; hop { filter_eq { all_rows ; club ; terang mortlake } ; wins } } ; 501 } = true', 'tointer': 'select the rows whose club record fuzzily matches to terang . take the wins record of this row . select the rows whose club record fuzzily matches to terang mortlake . take the wins record of this row . the first record is 501 larger than the second record .'}
|
eq { diff { hop { filter_eq { all_rows ; club ; terang } ; wins } ; hop { filter_eq { all_rows ; club ; terang mortlake } ; wins } } ; 501 } = true
|
select the rows whose club record fuzzily matches to terang . take the wins record of this row . select the rows whose club record fuzzily matches to terang mortlake . take the wins record of this row . the first record is 501 larger than the second record .
|
6
|
6
|
{'eq_5': 5, 'result_6': 6, 'diff_4': 4, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'club_8': 8, 'terang_9': 9, 'wins_10': 10, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_11': 11, 'club_12': 12, 'terang mortlake_13': 13, 'wins_14': 14, '501_15': 15}
|
{'eq_5': 'eq', 'result_6': 'true', 'diff_4': 'diff', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'club_8': 'club', 'terang_9': 'terang', 'wins_10': 'wins', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_11': 'all_rows', 'club_12': 'club', 'terang mortlake_13': 'terang mortlake', 'wins_14': 'wins', '501_15': '501'}
|
{'eq_5': [6], 'result_6': [], 'diff_4': [5], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_7': [0], 'club_8': [0], 'terang_9': [0], 'wins_10': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_11': [1], 'club_12': [1], 'terang mortlake_13': [1], 'wins_14': [3], '501_15': [5]}
|
['club', 'active', 'wins', 'losses', 'draws', 'percentage wins', 'flags']
|
[['camperdown', '1930 - 2011', '723', '665', '15', '51.53 %', '6'], ['cobden', '1930 - 2011', '640', '733', '17', '46.04 %', '6'], ['colac', '1949 - 2000', '597', '373', '10', '60.92 %', '10'], ['coragulac', '1961 - 1979', '118', '225', '2', '33.91 %', '0'], ['koroit', '1961 - 2011', '431', '528', '8', '44.57 %', '5'], ['mortlake', '1930 - 1998', '473', '633', '18', '42.08 %', '3'], ['north warrnambool', '1997 - 2011', '52', '213', '3', '19.40 %', '0'], ['port fairy', '1949 - 2011', '410', '738', '2', '35.65 %', '1'], ['south warrnambool', '1933 - 2011', '745', '611', '17', '54.26 %', '11'], ['terang', '1930 - 2001', '642', '580', '10', '52.11 %', '8'], ['terang mortlake', '2002 - 2011', '141', '61', '1', '69.46 %', '3'], ['warrnambool', '1933 - 2011', '895', '490', '19', '63.75 %', '23'], ['western lions', '1999 - 2000', '2', '17', '0', '10.5 %', '0']]
|
2010 veikkausliiga
|
https://en.wikipedia.org/wiki/2010_Veikkausliiga
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-25129482-1.html.csv
|
unique
|
ratina stadion is the stadium is the only one to hold more than 16000 people .
|
{'scope': 'all', 'row': '12', 'col': '4', 'col_other': '3', 'criterion': 'greater_than', 'value': '16000', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'capacity', '16000'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose capacity record is greater than 16000 .', 'tostr': 'filter_greater { all_rows ; capacity ; 16000 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_greater { all_rows ; capacity ; 16000 } }', 'tointer': 'select the rows whose capacity record is greater than 16000 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'capacity', '16000'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose capacity record is greater than 16000 .', 'tostr': 'filter_greater { all_rows ; capacity ; 16000 }'}, 'stadium'], 'result': 'ratina stadion', 'ind': 2, 'tostr': 'hop { filter_greater { all_rows ; capacity ; 16000 } ; stadium }'}, 'ratina stadion'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_greater { all_rows ; capacity ; 16000 } ; stadium } ; ratina stadion }', 'tointer': 'the stadium record of this unqiue row is ratina stadion .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_greater { all_rows ; capacity ; 16000 } } ; eq { hop { filter_greater { all_rows ; capacity ; 16000 } ; stadium } ; ratina stadion } } = true', 'tointer': 'select the rows whose capacity record is greater than 16000 . there is only one such row in the table . the stadium record of this unqiue row is ratina stadion .'}
|
and { only { filter_greater { all_rows ; capacity ; 16000 } } ; eq { hop { filter_greater { all_rows ; capacity ; 16000 } ; stadium } ; ratina stadion } } = true
|
select the rows whose capacity record is greater than 16000 . there is only one such row in the table . the stadium record of this unqiue row is ratina stadion .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_greater_0': 0, 'all_rows_6': 6, 'capacity_7': 7, '16000_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'stadium_9': 9, 'ratina stadion_10': 10}
|
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_greater_0': 'filter_greater', 'all_rows_6': 'all_rows', 'capacity_7': 'capacity', '16000_8': '16000', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'stadium_9': 'stadium', 'ratina stadion_10': 'ratina stadion'}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_greater_0': [1, 2], 'all_rows_6': [0], 'capacity_7': [0], '16000_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'stadium_9': [2], 'ratina stadion_10': [3]}
|
['club', 'location', 'stadium', 'capacity', 'manager', 'kitmaker']
|
[['ac oulu', 'oulu', 'castrén', '4000', 'juha malinen', 'umbro'], ['fc honka', 'espoo', 'tapiolan urheilupuisto', '6000', 'mika lehkosuo', 'kappa'], ['fc inter', 'turku', 'veritas stadion', '9372', 'job dragtsma', 'nike'], ['fc lahti', 'lahti', 'lahden stadion', '15000', 'ilkka mäkelä', 'umbro'], ['ff jaro', 'jakobstad', 'jakobstads centralplan', '5000', 'alexei eremenko sr', 'errea'], ['haka', 'valkeakoski', 'tehtaan kenttä', '3516', 'sami ristilä', 'umbro'], ['hjk', 'helsinki', 'sonera stadium', '10770', 'antti muurinen', 'adidas'], ['ifk mariehamn', 'mariehamn', 'wiklöf holding arena', '4000', 'pekka lyyski', 'puma'], ['jjk', 'jyväskylä', 'harjun stadion', '3000', 'kari martonen', 'legea'], ['kups', 'kuopio', 'kuopion keskuskenttä', '5000', 'esa pekonen', 'puma'], ['mypa', 'anjalankoski', 'saviniemi', '4167', 'janne lindberg', 'puma'], ['tampere united', 'tampere', 'ratina stadion', '17000', 'ari hjelm', 'puma'], ['tps', 'turku', 'veritas stadion', '9372', 'marko rajamäki', 'puma']]
|
sterling marlin
|
https://en.wikipedia.org/wiki/Sterling_Marlin
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1708014-2.html.csv
|
ordinal
|
the 2nd highest number of starts that sterling martin had was in 1994 .
|
{'row': '7', 'col': '2', 'order': '2', 'col_other': '1', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'starts', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; starts ; 2 }'}, 'year'], 'result': '1994', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; starts ; 2 } ; year }'}, '1994'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; starts ; 2 } ; year } ; 1994 } = true', 'tointer': 'select the row whose starts record of all rows is 2nd maximum . the year record of this row is 1994 .'}
|
eq { hop { nth_argmax { all_rows ; starts ; 2 } ; year } ; 1994 } = true
|
select the row whose starts record of all rows is 2nd maximum . the year record of this row is 1994 .
|
3
|
3
|
{'eq_2': 2, 'result_3': 3, 'num_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'starts_5': 5, '2_6': 6, 'year_7': 7, '1994_8': 8}
|
{'eq_2': 'eq', 'result_3': 'true', 'num_hop_1': 'num_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'starts_5': 'starts', '2_6': '2', 'year_7': 'year', '1994_8': '1994'}
|
{'eq_2': [3], 'result_3': [], 'num_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'starts_5': [0], '2_6': [0], 'year_7': [1], '1994_8': [2]}
|
['year', 'starts', 'wins', 'top 5', 'top 10', 'poles', 'avg start', 'avg finish', 'winnings', 'position', 'team ( s )']
|
[['1986', '1', '0', '0', '0', '0', '29.0', '29.0', '830', '133rd', '69 hagan racing'], ['1988', '4', '0', '0', '0', '0', '19.2', '17.2', '6406', '46th', '44 hagan racing'], ['1989', '2', '0', '0', '0', '0', '17.5', '32.0', '12475', '77th', '48 hagan racing'], ['1990', '5', '1', '2', '2', '0', '16.8', '14.6', '81690', '48th', '48 fred turner racing'], ['1992', '2', '0', '1', '1', '0', '15.0', '21.5', '13169', '73rd', '10 fred turner racing'], ['1993', '8', '0', '1', '2', '0', '28.1', '18.8', '36493', '41st', '48 fred turner racing'], ['1994', '9', '0', '1', '3', '0', '21.9', '25.0', '49680', '44th', '4 fred turner racing'], ['1995', '1', '0', '0', '0', '0', '7.0', '36.0', '2085', '106th', '22 fred turner racing'], ['1996', '2', '0', '1', '1', '1', '8.5', '12.5', '31285', '60th', '22 fred turner racing 92 martin racing'], ['1997', '3', '0', '0', '0', '0', '27.0', '22.7', '17020', '69th', '92 martin racing 4 phoenix racing'], ['1998', '5', '0', '0', '2', '0', '25.0', '22.0', '35649', '58th', '1 sterling marlin racing'], ['1999', '7', '0', '1', '3', '0', '9.4', '18.7', '67565', '54th', '42 joe gibbs racing 14 sterling marlin racing'], ['2000', '4', '1', '2', '3', '0', '15.0', '14.0', '56575', '62nd', '82 / 01 team sabco'], ['2004', '2', '0', '0', '0', '0', '28.5', '29.0', '36458', '102nd', '1 phoenix racing'], ['2005', '19', '0', '3', '5', '0', '23.6', '20.5', '408295', '29th', '40 / 12 fitzbradshaw racing'], ['2007', '2', '0', '0', '0', '0', '13.5', '20.5', '39605', '106th', '1 phoenix racing'], ['2008', '1', '0', '0', '0', '0', '20.0', '22.0', '25284', '118th', '1 phoenix racing']]
|
united states house of representatives elections , 1950
|
https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1950
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1342198-18.html.csv
|
superlative
|
the representative from the 1950 louisiana house of representatives elected the earliest was overton brooks .
|
{'scope': 'all', 'col_superlative': '4', 'row_superlative': '4', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '3', 'subset': None}
|
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'first elected'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; first elected }'}, 'party'], 'result': 'democratic', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; first elected } ; party }'}, 'democratic'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; first elected } ; party } ; democratic } = true', 'tointer': 'select the row whose first elected record of all rows is minimum . the party record of this row is democratic .'}
|
eq { hop { argmin { all_rows ; first elected } ; party } ; democratic } = true
|
select the row whose first elected record of all rows is minimum . the party record of this row is democratic .
|
3
|
3
|
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'first elected_5': 5, 'party_6': 6, 'democratic_7': 7}
|
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'first elected_5': 'first elected', 'party_6': 'party', 'democratic_7': 'democratic'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'first elected_5': [0], 'party_6': [1], 'democratic_7': [2]}
|
['district', 'incumbent', 'party', 'first elected', 'result', 'candidates']
|
[['louisiana 1', 'f edward hebert', 'democratic', '1940', 're - elected', 'f edward hebert ( d ) unopposed'], ['louisiana 2', 'hale boggs', 'democratic', '1946', 're - elected', 'hale boggs ( d ) unopposed'], ['louisiana 3', 'edwin e willis', 'democratic', '1948', 're - elected', 'edwin e willis ( d ) unopposed'], ['louisiana 4', 'overton brooks', 'democratic', '1936', 're - elected', 'overton brooks ( d ) unopposed'], ['louisiana 5', 'otto passman', 'democratic', '1946', 're - elected', 'otto passman ( d ) unopposed'], ['louisiana 6', 'james h morrison', 'democratic', '1942', 're - elected', 'james h morrison ( d ) unopposed'], ['louisiana 7', 'henry d larcade , jr', 'democratic', '1942', 're - elected', 'henry d larcade , jr ( d ) unopposed']]
|
union of the centre ( 2008 )
|
https://en.wikipedia.org/wiki/Union_of_the_Centre_%282008%29
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-16070554-1.html.csv
|
aggregation
|
during the 1995 regional for the union of the centre , there was a total of 63.6 points .
|
{'scope': 'all', 'col': '3', 'type': 'sum', 'result': '63.6', 'subset': None}
|
{'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', '1995 regional'], 'result': '63.6', 'ind': 0, 'tostr': 'sum { all_rows ; 1995 regional }'}, '63.6'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; 1995 regional } ; 63.6 } = true', 'tointer': 'the sum of the 1995 regional record of all rows is 63.6 .'}
|
round_eq { sum { all_rows ; 1995 regional } ; 63.6 } = true
|
the sum of the 1995 regional record of all rows is 63.6 .
|
2
|
2
|
{'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, '1995 regional_4': 4, '63.6_5': 5}
|
{'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', '1995 regional_4': '1995 regional', '63.6_5': '63.6'}
|
{'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], '1995 regional_4': [0], '63.6_5': [1]}
|
['', '1994 general', '1995 regional', '1996 general', '1999 european', '2000 regional', '2001 general', '2004 european', '2005 regional', '2006 general', '2008 general', '2009 european', '2010 regional', '2013 general']
|
[['piedmont', 'with fi', '3.0', '4.4', '3.3', '4.5', '3.5', '5.0', '4.6', '6.2', '5.2', '6.1', '3.9', '1.2'], ['lombardy', 'with fi', '2.2', '4.6', '3.5', '4.1', '3.4', '3.6', '3.8', '5.9', '4.3', '5.0', '3.8', '1.1'], ['veneto', 'with fi', '3.6', '5.4', '5.4', '6.8', '5.0', '5.0', '6.4', '7.8', '5.6', '6.4', '4.9', '1.7'], ['emilia - romagna', 'with fi', '4.8', '4.8', '2.7', '3.7', '3.4', '2.8', '3.9', '5.8', '4.3', '4.7', '3.8', '1.1'], ['tuscany', 'with fi', '2.5', '4.8', '3.2', '4.2', '3.3', '3.3', '3.7', '5.9', '4.2', '4.6', '4.8', '1.1'], ['lazio', 'with fi', '4.2', '4.7', '4.8', '6.7', '4.8', '7.1', '7.8', '6.9', '4.8', '5.5', '6.1', '1.5'], ['campania', 'with fi', '9.7', '8.0', '6.8', '8.5', '7.5', '7.0', '6.7', '6.8', '6.5', '8.7', '9.4', '3.6'], ['apulia', 'with fi', '5.6', '7.6', '6.0', '6.2', '6.8', '8.1', '7.8', '7.8', '7.9', '9.1', '6.5', '2.0'], ['calabria', 'with fi', '9.0', '9.0', '9.4', '13.3', '9.5', '9.6', '10.4', '7.7', '8.2', '9.3', '9.4', '4.1'], ['sicily', 'with fi', '19.0 ( 1996 )', '8.1', '7.9', '24.3 ( 2001 )', '14.4', '14.0', '18.7 ( 2006 )', '10.0', '9.4', '11.9', '12.5 ( 2008 )', '2.8']]
|
fred funk
|
https://en.wikipedia.org/wiki/Fred_Funk
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1646050-1.html.csv
|
comparative
|
fred funk had a higher margin of victory at the shell houston open than the players championship .
|
{'row_1': '1', 'row_2': '7', 'col': '4', 'col_other': '2', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}
|
{'func': 'greater', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'tournament', 'shell houston open'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose tournament record fuzzily matches to shell houston open .', 'tostr': 'filter_eq { all_rows ; tournament ; shell houston open }'}, 'margin of victory'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; tournament ; shell houston open } ; margin of victory }', 'tointer': 'select the rows whose tournament record fuzzily matches to shell houston open . take the margin of victory record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'tournament', 'the players championship'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose tournament record fuzzily matches to the players championship .', 'tostr': 'filter_eq { all_rows ; tournament ; the players championship }'}, 'margin of victory'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; tournament ; the players championship } ; margin of victory }', 'tointer': 'select the rows whose tournament record fuzzily matches to the players championship . take the margin of victory record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; tournament ; shell houston open } ; margin of victory } ; hop { filter_eq { all_rows ; tournament ; the players championship } ; margin of victory } } = true', 'tointer': 'select the rows whose tournament record fuzzily matches to shell houston open . take the margin of victory record of this row . select the rows whose tournament record fuzzily matches to the players championship . take the margin of victory record of this row . the first record is greater than the second record .'}
|
greater { hop { filter_eq { all_rows ; tournament ; shell houston open } ; margin of victory } ; hop { filter_eq { all_rows ; tournament ; the players championship } ; margin of victory } } = true
|
select the rows whose tournament record fuzzily matches to shell houston open . take the margin of victory record of this row . select the rows whose tournament record fuzzily matches to the players championship . take the margin of victory 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, 'tournament_7': 7, 'shell houston open_8': 8, 'margin of victory_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'tournament_11': 11, 'the players championship_12': 12, 'margin of victory_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', 'tournament_7': 'tournament', 'shell houston open_8': 'shell houston open', 'margin of victory_9': 'margin of victory', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'tournament_11': 'tournament', 'the players championship_12': 'the players championship', 'margin of victory_13': 'margin of victory'}
|
{'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'tournament_7': [0], 'shell houston open_8': [0], 'margin of victory_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'tournament_11': [1], 'the players championship_12': [1], 'margin of victory_13': [3]}
|
['date', 'tournament', 'winning score', 'margin of victory', 'runner ( s ) - up']
|
[['may 1 , 1992', 'shell houston open', '- 16 ( 68 + 72 + 62 + 70 = 272 )', '2 strokes', 'kirk triplett'], ['jul 30 , 1995', 'ideon classic at pleasant valley', '- 20 ( 66 + 63 + 66 + 73 = 268 )', '1 stroke', 'jim mcgovern'], ['oct 6 , 1995', 'buick challenge', '- 16 ( 69 + 67 + 69 + 67 = 272 )', '1 stroke', 'john morse , loren roberts'], ['sep 21 , 1996', 'bc open 1', '- 19 ( 68 + 66 + 63 = 197 )', 'playoff', 'pete jordan'], ['jul 19 , 1998', 'deposit guaranty golf classic', '- 18 ( 69 + 64 + 69 + 68 = 270 )', '2 strokes', 'paul goydos , franklin langham , tim loustalot'], ['oct 3 , 2004', 'southern farm bureau classic', '- 22 ( 69 + 67 + 64 + 66 = 266 )', '1 stroke', 'ryan palmer'], ['mar 27 , 2005', 'the players championship', '- 9 ( 65 + 72 + 71 + 71 = 279 )', '1 stroke', 'luke donald , tom lehman , scott verplank'], ['feb 25 , 2007', 'mayakoba golf classic at riviera maya - cancun', '- 14 ( 62 + 69 + 64 + 71 = 266 )', 'playoff', 'josé cóceres']]
|
list of tallest buildings in mobile
|
https://en.wikipedia.org/wiki/List_of_tallest_buildings_in_Mobile
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17961233-1.html.csv
|
count
|
a total of 15 buildings have been listed as the tallest buildings in mobile .
|
{'scope': 'all', 'criterion': 'all', 'value': 'n/a', 'result': '15', 'col': '1', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_all', 'args': ['all_rows', 'rank'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose rank record is arbitrary .', 'tostr': 'filter_all { all_rows ; rank }'}], 'result': '15', 'ind': 1, 'tostr': 'count { filter_all { all_rows ; rank } }', 'tointer': 'select the rows whose rank record is arbitrary . the number of such rows is 15 .'}, '15'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_all { all_rows ; rank } } ; 15 } = true', 'tointer': 'select the rows whose rank record is arbitrary . the number of such rows is 15 .'}
|
eq { count { filter_all { all_rows ; rank } } ; 15 } = true
|
select the rows whose rank record is arbitrary . the number of such rows is 15 .
|
3
|
3
|
{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_all_0': 0, 'all_rows_4': 4, 'rank_5': 5, '15_6': 6}
|
{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_all_0': 'filter_all', 'all_rows_4': 'all_rows', 'rank_5': 'rank', '15_6': '15'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_all_0': [1], 'all_rows_4': [0], 'rank_5': [0], '15_6': [2]}
|
['rank', 'name', 'height ft ( m )', 'floors', 'year']
|
[['01.0 1', 'rsa battle house tower', '745 ( 227 )', '35', '2007'], ['02.0 2', 'rsabanktrust building', '424 ( 129 )', '34', '1965'], ['03.0 3', 'renaissance riverview plaza hotel', '374 ( 114 )', '28', '1983'], ['04.0 4 =', 'mobile government plaza', '325 ( 99 )', '12', '1994'], ['05.0 4 =', 'mobile marriott', '325 ( 99 )', '20', '1979'], ['06.0 6', 'regions bank building', '236 ( 72 )', '18', '1929'], ['07.0 7', 'wachovia building', '230 ( 70 )', '16', '1947'], ['08.0 8', 'lafayette plaza hotel', '180 ( 55 )', '17', '1975'], ['09.0 9', 'providence hospital', '170 ( 52 )', '11', '1987'], ['10.0 10', 'commerce building', '160 ( 49 )', '12', '1958'], ['11.0 11', 'radisson admiral semmes hotel', '136 ( 42 )', '12', '1940'], ['12.0 12', 'van antwerp building', '120 ( 37 )', '11', '1907'], ['13.0 13', 'battle house hotel', '119 ( 36 )', '7', '1908'], ['14.0 14', 'royal st francis building', '115 ( 35 )', '7', '1908'], ['15.0 15', 'cathedral basilica of the immaculate conception', '102 ( 31 )', '2', '1850']]
|
1907 michigan wolverines football team
|
https://en.wikipedia.org/wiki/1907_Michigan_Wolverines_football_team
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-25724294-2.html.csv
|
superlative
|
on the 1907 michigan wolverines football team , paul magoffin had the most points .
|
{'scope': 'all', 'col_superlative': '5', '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', 'points'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; points }'}, 'player'], 'result': 'paul magoffin', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; points } ; player }'}, 'paul magoffin'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; points } ; player } ; paul magoffin } = true', 'tointer': 'select the row whose points record of all rows is maximum . the player record of this row is paul magoffin .'}
|
eq { hop { argmax { all_rows ; points } ; player } ; paul magoffin } = true
|
select the row whose points record of all rows is maximum . the player record of this row is paul magoffin .
|
3
|
3
|
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'points_5': 5, 'player_6': 6, 'paul magoffin_7': 7}
|
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'points_5': 'points', 'player_6': 'player', 'paul magoffin_7': 'paul magoffin'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'points_5': [0], 'player_6': [1], 'paul magoffin_7': [2]}
|
['player', 'touchdowns', 'extra points', 'field goals', 'points']
|
[['paul magoffin', '7', '0', '0', '35'], ['walter rheinschild', '5', '0', '0', '25'], ['octy graham', '0', '7', '4', '24'], ['jack loell', '3', '0', '0', '15'], ['prentiss douglass', '1', '0', '0', '5'], ['dave allerdice', '0', '3', '0', '3'], ['harry s hammond', '0', '1', '0', '1']]
|
list of the green green grass episodes
|
https://en.wikipedia.org/wiki/List_of_The_Green_Green_Grass_episodes
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-17641206-2.html.csv
|
majority
|
all the episodes of the green green grass were written by john sullivan .
|
{'scope': 'all', 'col': '4', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'john sullivan', 'subset': None}
|
{'func': 'all_str_eq', 'args': ['all_rows', 'written by', 'john sullivan'], 'result': True, 'ind': 0, 'tointer': 'for the written by records of all rows , all of them fuzzily match to john sullivan .', 'tostr': 'all_eq { all_rows ; written by ; john sullivan } = true'}
|
all_eq { all_rows ; written by ; john sullivan } = true
|
for the written by records of all rows , all of them fuzzily match to john sullivan .
|
1
|
1
|
{'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'written by_3': 3, 'john sullivan_4': 4}
|
{'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'written by_3': 'written by', 'john sullivan_4': 'john sullivan'}
|
{'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'written by_3': [0], 'john sullivan_4': [0]}
|
['episode', 'title', 'directed by', 'written by', 'original airdate', 'duration', 'viewership']
|
[['1', 'keep on running', 'tony dow', 'john sullivan', '9 september 2005', '30 minutes', '8.88 million'], ['2', 'a rocky start', 'tony dow', 'john sullivan', '16 september 2005', '30 minutes', '6.34 million'], ['3', 'the country wife', 'tony dow', 'john sullivan', '23 september 2005', '30 minutes', '5.86 million'], ['4', 'hay fever', 'tony dow', 'john sullivan', '30 september 2005', '30 minutes', '6.33 million'], ['5', 'pillow talk', 'tony dow', 'john sullivan', '7 october 2005', '30 minutes', '6.63 million']]
|
1961 ohio state buckeyes football team
|
https://en.wikipedia.org/wiki/1961_Ohio_State_Buckeyes_football_team
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17814506-2.html.csv
|
count
|
in the 1961 ohio state buckeyes football team season , among the end players , 2 of them were drafter from nfl .
|
{'scope': 'subset', 'criterion': 'equal', 'value': 'nfl', 'result': '2', 'col': '2', 'subset': {'col': '5', 'criterion': 'equal', 'value': 'end'}}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'position', 'end'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; position ; end }', 'tointer': 'select the rows whose position record fuzzily matches to end .'}, 'draft', 'nfl'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose position record fuzzily matches to end . among these rows , select the rows whose draft record fuzzily matches to nfl .', 'tostr': 'filter_eq { filter_eq { all_rows ; position ; end } ; draft ; nfl }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_eq { filter_eq { all_rows ; position ; end } ; draft ; nfl } }', 'tointer': 'select the rows whose position record fuzzily matches to end . among these rows , select the rows whose draft record fuzzily matches to nfl . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_eq { all_rows ; position ; end } ; draft ; nfl } } ; 2 } = true', 'tointer': 'select the rows whose position record fuzzily matches to end . among these rows , select the rows whose draft record fuzzily matches to nfl . the number of such rows is 2 .'}
|
eq { count { filter_eq { filter_eq { all_rows ; position ; end } ; draft ; nfl } } ; 2 } = true
|
select the rows whose position record fuzzily matches to end . among these rows , select the rows whose draft record fuzzily matches to nfl . 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, 'position_6': 6, 'end_7': 7, 'draft_8': 8, 'nfl_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', 'position_6': 'position', 'end_7': 'end', 'draft_8': 'draft', 'nfl_9': 'nfl', '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], 'position_6': [0], 'end_7': [0], 'draft_8': [1], 'nfl_9': [1], '2_10': [3]}
|
['player', 'draft', 'round', 'pick', 'position', 'nfl club']
|
[['bob ferguson', 'nfl', '1', '5', 'fullback', 'pittsburgh steelers'], ['bob ferguson', 'afl', '1', '8', 'fullback', 'san diego chargers'], ['chuck bryant', 'nfl', '3', '34', 'end', 'st louis cardinals'], ['chuck bryant', 'afl', '13', '104', 'end', 'san diego chargers'], ['sam tidmore', 'nfl', '6', '81', 'linebacker', 'cleveland browns'], ['sam tidmore', 'afl', '20', '156', 'linebacker', 'buffalo bills'], ['john havlicek', 'nfl', '7', '95', 'end', 'cleveland browns'], ['jack roberts', 'nfl', '20', '273', 'tackle', 'chicago bears'], ['mike ingram', 'afl', '31', '246', 'guard', 'boston patriots']]
|
1983 nhl entry draft
|
https://en.wikipedia.org/wiki/1983_NHL_Entry_Draft
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2679061-9.html.csv
|
count
|
six players from the ohl were selected in picks 163 to 182 of the 1983 nhl draft .
|
{'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'ohl', 'result': '6', 'col': '6', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'college / junior / club team', 'ohl'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose college / junior / club team record fuzzily matches to ohl .', 'tostr': 'filter_eq { all_rows ; college / junior / club team ; ohl }'}], 'result': '6', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; college / junior / club team ; ohl } }', 'tointer': 'select the rows whose college / junior / club team record fuzzily matches to ohl . the number of such rows is 6 .'}, '6'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; college / junior / club team ; ohl } } ; 6 } = true', 'tointer': 'select the rows whose college / junior / club team record fuzzily matches to ohl . the number of such rows is 6 .'}
|
eq { count { filter_eq { all_rows ; college / junior / club team ; ohl } } ; 6 } = true
|
select the rows whose college / junior / club team record fuzzily matches to ohl . the number of such rows is 6 .
|
3
|
3
|
{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'college / junior / club team_5': 5, 'ohl_6': 6, '6_7': 7}
|
{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'college / junior / club team_5': 'college / junior / club team', 'ohl_6': 'ohl', '6_7': '6'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'college / junior / club team_5': [0], 'ohl_6': [0], '6_7': [2]}
|
['pick', 'player', 'position', 'nationality', 'nhl team', 'college / junior / club team']
|
[['163', 'marty ketola', 'right wing', 'united states', 'pittsburgh penguins', 'cloquet high school ( ushs - mn )'], ['164', 'bill fordy', 'left wing', 'canada', 'hartford whalers', 'guelph platers ( ohl )'], ['165', 'jay octeau', 'defence', 'united states', 'new jersey devils', 'mount st charles academy ( ushs - ri )'], ['166', 'dave sikorski', 'defence', 'united states', 'detroit red wings', 'cornwall royals ( ohl )'], ['167', 'bruce fishback', 'centre', 'united states', 'los angeles kings', 'white bear lake high school ( ushs - mn )'], ['168', 'cliff abrecht', 'defence', 'canada', 'toronto maple leafs', 'princeton university ( ecac )'], ['169', 'todd flichel', 'defence', 'canada', 'winnipeg jets', 'gloucester rangers ( cojhl )'], ['170', 'allan measures', 'defence', 'canada', 'vancouver canucks', 'calgary wranglers ( whl )'], ['171', 'rob kivell', 'defence', 'canada', 'calgary flames', 'victoria cougars ( whl )'], ['172', 'wayne groulx', 'centre', 'canada', 'quebec nordiques', 'sault ste marie greyhounds ( ohl )'], ['173', 'paul jerrard', 'right wing', 'canada', 'new york rangers', 'notre dame hounds ( sjhl )'], ['174', 'tim hoover', 'defence', 'canada', 'buffalo sabres', 'sault ste marie greyhounds ( ohl )'], ['175', 'dave cowan', 'left wing', 'united states', 'washington capitals', 'washburn high school ( ushs - mn )'], ['176', 'paul pulis', 'right wing', 'united states', 'minnesota north stars', 'hibbing high school ( ushs - mn )'], ['177', 'kevin vescio', 'defence', 'canada', 'new york islanders', 'north bay centennials ( ohl )'], ['178', 'grant mckay', 'defence', 'canada', 'montreal canadiens', 'university of calgary ( ciau )'], ['179', 'brian noonan', 'centre', 'united states', 'chicago black hawks', 'archbishop williams high school ( ushs - ma )'], ['180', 'dave roach', 'goaltender', 'canada', 'edmonton oilers', 'new westminster royals ( bcjhl )'], ['181', 'robbie nichols', 'left wing', 'canada', 'philadelphia flyers', 'kitchener rangers ( ohl )'], ['182', 'harri laurila', 'defence', 'finland', 'boston bruins', 'lahti ( finland )']]
|
1955 vfl season
|
https://en.wikipedia.org/wiki/1955_VFL_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10773753-1.html.csv
|
comparative
|
there were more people watching the richmond game than the geelong game .
|
{'row_1': '6', 'row_2': '1', 'col': '6', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}
|
{'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'home team', 'richmond'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose home team record fuzzily matches to richmond .', 'tostr': 'filter_eq { all_rows ; home team ; richmond }'}, 'crowd'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; home team ; richmond } ; crowd }', 'tointer': 'select the rows whose home team record fuzzily matches to richmond . take the crowd record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'home team', 'geelong'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose home team record fuzzily matches to geelong .', 'tostr': 'filter_eq { all_rows ; home team ; geelong }'}, 'crowd'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; home team ; geelong } ; crowd }', 'tointer': 'select the rows whose home team record fuzzily matches to geelong . take the crowd record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; home team ; richmond } ; crowd } ; hop { filter_eq { all_rows ; home team ; geelong } ; crowd } } = true', 'tointer': 'select the rows whose home team record fuzzily matches to richmond . take the crowd record of this row . select the rows whose home team record fuzzily matches to geelong . take the crowd record of this row . the first record is greater than the second record .'}
|
greater { hop { filter_eq { all_rows ; home team ; richmond } ; crowd } ; hop { filter_eq { all_rows ; home team ; geelong } ; crowd } } = true
|
select the rows whose home team record fuzzily matches to richmond . take the crowd record of this row . select the rows whose home team record fuzzily matches to geelong . take the crowd 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, 'home team_7': 7, 'richmond_8': 8, 'crowd_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'home team_11': 11, 'geelong_12': 12, 'crowd_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', 'home team_7': 'home team', 'richmond_8': 'richmond', 'crowd_9': 'crowd', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'home team_11': 'home team', 'geelong_12': 'geelong', 'crowd_13': 'crowd'}
|
{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'home team_7': [0], 'richmond_8': [0], 'crowd_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'home team_11': [1], 'geelong_12': [1], 'crowd_13': [3]}
|
['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date']
|
[['geelong', '15.14 ( 104 )', 'south melbourne', '9.12 ( 66 )', 'kardinia park', '20976', '16 april 1955'], ['fitzroy', '13.15 ( 93 )', 'hawthorn', '7.16 ( 58 )', 'brunswick street oval', '16000', '16 april 1955'], ['collingwood', '6.12 ( 48 )', 'footscray', '15.14 ( 104 )', 'victoria park', '33398', '16 april 1955'], ['carlton', '19.20 ( 134 )', 'north melbourne', '10.5 ( 65 )', 'princes park', '25041', '16 april 1955'], ['st kilda', '8.11 ( 59 )', 'melbourne', '17.16 ( 118 )', 'junction oval', '20000', '16 april 1955'], ['richmond', '9.23 ( 77 )', 'essendon', '13.16 ( 94 )', 'punt road oval', '30000', '16 april 1955']]
|
2006 u.s. open ( golf )
|
https://en.wikipedia.org/wiki/2006_U.S._Open_%28golf%29
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12523044-4.html.csv
|
unique
|
vijay singh was the only player from fiji in the 2006 u.s. open .
|
{'scope': 'all', 'row': '12', 'col': '3', 'col_other': '2', 'criterion': 'equal', 'value': 'fiji', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'fiji'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose country record fuzzily matches to fiji .', 'tostr': 'filter_eq { all_rows ; country ; fiji }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; country ; fiji } }', 'tointer': 'select the rows whose country record fuzzily matches to fiji . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'fiji'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose country record fuzzily matches to fiji .', 'tostr': 'filter_eq { all_rows ; country ; fiji }'}, 'player'], 'result': 'vijay singh', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; country ; fiji } ; player }'}, 'vijay singh'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; country ; fiji } ; player } ; vijay singh }', 'tointer': 'the player record of this unqiue row is vijay singh .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; country ; fiji } } ; eq { hop { filter_eq { all_rows ; country ; fiji } ; player } ; vijay singh } } = true', 'tointer': 'select the rows whose country record fuzzily matches to fiji . there is only one such row in the table . the player record of this unqiue row is vijay singh .'}
|
and { only { filter_eq { all_rows ; country ; fiji } } ; eq { hop { filter_eq { all_rows ; country ; fiji } ; player } ; vijay singh } } = true
|
select the rows whose country record fuzzily matches to fiji . there is only one such row in the table . the player record of this unqiue row is vijay singh .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'country_7': 7, 'fiji_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'player_9': 9, 'vijay singh_10': 10}
|
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'country_7': 'country', 'fiji_8': 'fiji', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'player_9': 'player', 'vijay singh_10': 'vijay singh'}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'country_7': [0], 'fiji_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'player_9': [2], 'vijay singh_10': [3]}
|
['place', 'player', 'country', 'score', 'to par']
|
[['1', 'colin montgomerie', 'scotland', '69', '- 1'], ['t2', 'jim furyk', 'united states', '70', 'e'], ['t2', 'david howell', 'england', '70', 'e'], ['t2', 'miguel ángel jiménez', 'spain', '70', 'e'], ['t2', 'phil mickelson', 'united states', '70', 'e'], ['t2', 'steve stricker', 'united states', '70', 'e'], ['t7', 'john cook', 'united states', '71', '+ 1'], ['t7', 'kenneth ferrie', 'england', '71', '+ 1'], ['t7', 'fred funk', 'united states', '71', '+ 1'], ['t7', 'graeme mcdowell', 'northern ireland', '71', '+ 1'], ['t7', 'geoff ogilvy', 'australia', '71', '+ 1'], ['t7', 'vijay singh', 'fiji', '71', '+ 1'], ['t7', 'mike weir', 'canada', '71', '+ 1']]
|
usa today all - usa high school football team
|
https://en.wikipedia.org/wiki/USA_Today_All-USA_high_school_football_team
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-11677691-3.html.csv
|
unique
|
nick o'leary was the only player on the usa today all - usa high school football team that went to florida state college .
|
{'scope': 'all', 'row': '4', 'col': '5', 'col_other': '1', 'criterion': 'equal', 'value': 'florida state', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'college', 'florida state'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose college record fuzzily matches to florida state .', 'tostr': 'filter_eq { all_rows ; college ; florida state }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; college ; florida state } }', 'tointer': 'select the rows whose college record fuzzily matches to florida state . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'college', 'florida state'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose college record fuzzily matches to florida state .', 'tostr': 'filter_eq { all_rows ; college ; florida state }'}, 'player'], 'result': "nick o'leary", 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; college ; florida state } ; player }'}, "nick o'leary"], 'result': True, 'ind': 3, 'tostr': "eq { hop { filter_eq { all_rows ; college ; florida state } ; player } ; nick o'leary }", 'tointer': "the player record of this unqiue row is nick o'leary ."}], 'result': True, 'ind': 4, 'tostr': "and { only { filter_eq { all_rows ; college ; florida state } } ; eq { hop { filter_eq { all_rows ; college ; florida state } ; player } ; nick o'leary } } = true", 'tointer': "select the rows whose college record fuzzily matches to florida state . there is only one such row in the table . the player record of this unqiue row is nick o'leary ."}
|
and { only { filter_eq { all_rows ; college ; florida state } } ; eq { hop { filter_eq { all_rows ; college ; florida state } ; player } ; nick o'leary } } = true
|
select the rows whose college record fuzzily matches to florida state . there is only one such row in the table . the player record of this unqiue row is nick o'leary .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'college_7': 7, 'Florida State_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'player_9': 9, "nick o'leary_10": 10}
|
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'college_7': 'college', 'Florida State_8': 'florida state', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'player_9': 'player', "nick o'leary_10": "nick o'leary"}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'college_7': [0], 'Florida State_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'player_9': [2], "nick o'leary_10": [3]}
|
['player', 'position', 'school', 'hometown', 'college']
|
[['cody kessler', 'quarterback', 'centennial high school', 'bakersfield , california', 'southern california'], ['mike bellamy', 'running back', 'charlotte high school', 'punta gorda , florida', 'clemson'], ['aaron green', 'running back', 'madison high school', 'san antonio , texas', 'nebraska'], ["nick o'leary", 'tight end', 'dwyer high school', 'west palm beach , florida', 'florida state'], ['trey metoyer', 'wide receiver', 'whitehouse high school', 'whitehouse , texas', 'oklahoma'], ['charone peake', 'wide receiver', 'dorman high school', 'roebuck , south carolina', 'clemson'], ['matt freeman', 'offensive line', 'cooper high school', 'abilene , texas', 'texas state'], ['ryne reeves', 'offensive line', 'crete high school', 'crete , nebraska', 'nebraska'], ['kiaro holts', 'offensive line', 'warren central high school', 'indianapolis , indiana', 'north carolina'], ['brey cook', 'offensive line', 'har - ber high school', 'springdale , arkansas', 'arkansas'], ['michael bennett', 'offensive line', 'centerville high school', 'centerville , ohio', 'ohio state']]
|
2009 atp world tour masters 1000
|
https://en.wikipedia.org/wiki/2009_ATP_World_Tour_Masters_1000
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17057363-1.html.csv
|
unique
|
at the 2009 atp world tour masters 1000 , when the court surface is hard , the only tournament in shanghai is the shanghai masters .
|
{'scope': 'subset', 'row': '8', 'col': '3', 'col_other': '1', 'criterion': 'equal', 'value': 'shanghai', 'subset': {'col': '6', 'criterion': 'fuzzily_match', 'value': 'hard'}}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'court surface', 'hard'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; court surface ; hard }', 'tointer': 'select the rows whose court surface record fuzzily matches to hard .'}, 'location', 'shanghai'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose court surface record fuzzily matches to hard . among these rows , select the rows whose location record fuzzily matches to shanghai .', 'tostr': 'filter_eq { filter_eq { all_rows ; court surface ; hard } ; location ; shanghai }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_eq { filter_eq { all_rows ; court surface ; hard } ; location ; shanghai } }', 'tointer': 'select the rows whose court surface record fuzzily matches to hard . among these rows , select the rows whose location record fuzzily matches to shanghai . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'court surface', 'hard'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; court surface ; hard }', 'tointer': 'select the rows whose court surface record fuzzily matches to hard .'}, 'location', 'shanghai'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose court surface record fuzzily matches to hard . among these rows , select the rows whose location record fuzzily matches to shanghai .', 'tostr': 'filter_eq { filter_eq { all_rows ; court surface ; hard } ; location ; shanghai }'}, 'tournament'], 'result': 'shanghai masters', 'ind': 3, 'tostr': 'hop { filter_eq { filter_eq { all_rows ; court surface ; hard } ; location ; shanghai } ; tournament }'}, 'shanghai masters'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_eq { filter_eq { all_rows ; court surface ; hard } ; location ; shanghai } ; tournament } ; shanghai masters }', 'tointer': 'the tournament record of this unqiue row is shanghai masters .'}], 'result': True, 'ind': 5, 'tostr': 'and { only { filter_eq { filter_eq { all_rows ; court surface ; hard } ; location ; shanghai } } ; eq { hop { filter_eq { filter_eq { all_rows ; court surface ; hard } ; location ; shanghai } ; tournament } ; shanghai masters } } = true', 'tointer': 'select the rows whose court surface record fuzzily matches to hard . among these rows , select the rows whose location record fuzzily matches to shanghai . there is only one such row in the table . the tournament record of this unqiue row is shanghai masters .'}
|
and { only { filter_eq { filter_eq { all_rows ; court surface ; hard } ; location ; shanghai } } ; eq { hop { filter_eq { filter_eq { all_rows ; court surface ; hard } ; location ; shanghai } ; tournament } ; shanghai masters } } = true
|
select the rows whose court surface record fuzzily matches to hard . among these rows , select the rows whose location record fuzzily matches to shanghai . there is only one such row in the table . the tournament record of this unqiue row is shanghai masters .
|
8
|
6
|
{'and_5': 5, 'result_6': 6, 'only_2': 2, 'filter_str_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'court surface_8': 8, 'hard_9': 9, 'location_10': 10, 'shanghai_11': 11, 'str_eq_4': 4, 'str_hop_3': 3, 'tournament_12': 12, 'shanghai masters_13': 13}
|
{'and_5': 'and', 'result_6': 'true', 'only_2': 'only', 'filter_str_eq_1': 'filter_str_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'court surface_8': 'court surface', 'hard_9': 'hard', 'location_10': 'location', 'shanghai_11': 'shanghai', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'tournament_12': 'tournament', 'shanghai masters_13': 'shanghai masters'}
|
{'and_5': [6], 'result_6': [], 'only_2': [5], 'filter_str_eq_1': [2, 3], 'filter_str_eq_0': [1], 'all_rows_7': [0], 'court surface_8': [0], 'hard_9': [0], 'location_10': [1], 'shanghai_11': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'tournament_12': [3], 'shanghai masters_13': [4]}
|
['tournament', 'country', 'location', 'current venue', 'began', 'court surface']
|
[['indian wells masters', 'united states', 'indian wells', 'indian wells tennis garden', '1987', 'hard'], ['miami masters', 'united states', 'miami', 'tennis center at crandon park', '1987', 'hard'], ['monte carlo masters', 'monaco', 'roquebrune - cap - martin , france', 'monte carlo country club', '1897', 'clay'], ['rome masters', 'italy', 'rome', 'foro italico', '1930', 'clay'], ['madrid masters', 'spain', 'madrid', 'park manzanares', '2002', 'clay'], ['canada masters', 'canada', 'montreal / toronto', 'stade uniprix / rexall centre', '1881', 'hard'], ['cincinnati masters', 'united states', 'mason , ohio', 'lindner family tennis center', '1899', 'hard'], ['shanghai masters', 'china', 'shanghai', 'qi zhong stadium', '2009', 'hard'], ['paris masters', 'france', 'paris', 'palais omnisports de paris - bercy', '1968', 'hard ( i )']]
|
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