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fiji national rugby union team | https://en.wikipedia.org/wiki/Fiji_national_rugby_union_team | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1074616-5.html.csv | count | three players in the fiji national rugby union team had draws . | {'scope': 'all', 'criterion': 'greater_than', 'value': '0', 'result': '3', 'col': '9', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'draw', '0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose draw record is greater than 0 .', 'tostr': 'filter_greater { all_rows ; draw ; 0 }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_greater { all_rows ; draw ; 0 } }', 'tointer': 'select the rows whose draw record is greater than 0 . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_greater { all_rows ; draw ; 0 } } ; 3 } = true', 'tointer': 'select the rows whose draw record is greater than 0 . the number of such rows is 3 .'} | eq { count { filter_greater { all_rows ; draw ; 0 } } ; 3 } = true | select the rows whose draw record is greater than 0 . the number of such rows is 3 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_greater_0': 0, 'all_rows_4': 4, 'draw_5': 5, '0_6': 6, '3_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_greater_0': 'filter_greater', 'all_rows_4': 'all_rows', 'draw_5': 'draw', '0_6': '0', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_greater_0': [1], 'all_rows_4': [0], 'draw_5': [0], '0_6': [0], '3_7': [2]} | ['player', 'span', 'start', 'tries', 'conv', 'pens', 'drop', 'lost', 'draw'] | [['nicky little', '1996 - 2011', '60', '2', '117', '140', '2', '34', '0'], ['jacob rauluni', '1995 - 2006', '40', '6', '0', '0', '0', '23', '0'], ['joeli veitayaki', '1994 - 2003', '45', '3', '0', '0', '0', '23', '0'], ['emori katalau', '1995 - 2003', '39', '4', '0', '0', '0', '23', '0'], ['norman ligairi', '2000 - 2010', '39', '16', '0', '0', '0', '22', '0'], ['seremaia bai', '2000 -', '44', '4', '47', '51', '1', '22', '1'], ['sisa koyamaibole', '2001 - 2011', '35', '3', '0', '0', '0', '25', '1'], ['ifereimi tawake', '1986 - 1999', '38', '4', '0', '2', '0', '29', '1'], ['mosese rauluni', '1996 - 2009', '36', '4', '0', '0', '0', '21', '0'], ['greg smith', '1995 - 2003', '44', '1', '0', '0', '0', '20', '0']] |
wru division one east | https://en.wikipedia.org/wiki/WRU_Division_One_East | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12784856-3.html.csv | unique | of the teams in the wru division one east only bedlinog rfc has no try bp . | {'scope': 'all', 'row': '8', 'col': '5', 'col_other': '1', 'criterion': 'equal', 'value': '0', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'try bp', '0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose try bp record is equal to 0 .', 'tostr': 'filter_eq { all_rows ; try bp ; 0 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; try bp ; 0 } }', 'tointer': 'select the rows whose try bp record is equal to 0 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'try bp', '0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose try bp record is equal to 0 .', 'tostr': 'filter_eq { all_rows ; try bp ; 0 }'}, 'club'], 'result': 'bedlinog rfc', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; try bp ; 0 } ; club }'}, 'bedlinog rfc'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; try bp ; 0 } ; club } ; bedlinog rfc }', 'tointer': 'the club record of this unqiue row is bedlinog rfc .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; try bp ; 0 } } ; eq { hop { filter_eq { all_rows ; try bp ; 0 } ; club } ; bedlinog rfc } } = true', 'tointer': 'select the rows whose try bp record is equal to 0 . there is only one such row in the table . the club record of this unqiue row is bedlinog rfc .'} | and { only { filter_eq { all_rows ; try bp ; 0 } } ; eq { hop { filter_eq { all_rows ; try bp ; 0 } ; club } ; bedlinog rfc } } = true | select the rows whose try bp record is equal to 0 . there is only one such row in the table . the club record of this unqiue row is bedlinog rfc . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'try bp_7': 7, '0_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'club_9': 9, 'bedlinog rfc_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'try bp_7': 'try bp', '0_8': '0', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'club_9': 'club', 'bedlinog rfc_10': 'bedlinog rfc'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'try bp_7': [0], '0_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'club_9': [2], 'bedlinog rfc_10': [3]} | ['club', 'played', 'drawn', 'lost', 'try bp', 'losing bp'] | [['club', 'played', 'drawn', 'lost', 'try bp', 'losing bp'], ['uwic rfc', '22', '0', '3', '10', '2'], ['llanharan rfc', '22', '0', '5', '13', '3'], ['blackwood rfc', '22', '0', '6', '9', '4'], ['bargoed rfc', '22', '0', '6', '10', '2'], ['newbridge rfc', '22', '0', '9', '7', '2'], ['rumney rfc', '22', '0', '12', '5', '3'], ['bedlinog rfc', '22', '0', '13', '0', '5'], ['merthyr rfc', '22', '1', '14', '5', '5'], ['ystrad rhondda rfc', '22', '0', '15', '6', '3'], ['beddau rfc', '22', '1', '14', '3', '4'], ['tredegar rfc', '22', '0', '15', '4', '4'], ['caerphilly rfc', '22', '0', '19', '1', '5']] |
united states house of representatives elections , 1942 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1942 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1342256-6.html.csv | count | 6 incumbents were re - elected during the 1942 united states house of representatives elections . | {'scope': 'all', 'criterion': 'equal', 'value': 're - elected', 'result': '6', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 're - elected'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to re - elected .', 'tostr': 'filter_eq { all_rows ; result ; re - elected }'}], 'result': '6', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; result ; re - elected } }', 'tointer': 'select the rows whose result record fuzzily matches to re - elected . the number of such rows is 6 .'}, '6'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; result ; re - elected } } ; 6 } = true', 'tointer': 'select the rows whose result record fuzzily matches to re - elected . the number of such rows is 6 .'} | eq { count { filter_eq { all_rows ; result ; re - elected } } ; 6 } = true | select the rows whose result record fuzzily matches to re - elected . 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, 'result_5': 5, 're - elected_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', 'result_5': 'result', 're - elected_6': 're - elected', '6_7': '6'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'result_5': [0], 're - elected_6': [0], '6_7': [2]} | ['district', 'incumbent', 'party', 'first elected', 'result', 'candidates'] | [['california 2', 'harry lane englebright', 'republican', '1926', 're - elected', 'harry lane englebright ( r ) unopposed'], ['california 4', 'thomas rolph', 'republican', '1940', 're - elected', 'thomas rolph ( r ) 98.3 % archie brown ( w / i ) 1.7 %'], ['california 7', 'john h tolan', 'democratic', '1934', 're - elected', 'john h tolan ( d ) unopposed'], ['california 9', 'bertrand w gearhart', 'republican', '1934', 're - elected', 'bertrand w gearhart ( r ) unopposed'], ['california 10', 'alfred j elliott', 'democratic', '1937', 're - elected', 'alfred j elliott ( d ) unopposed'], ['california 17', 'cecil r king', 'democratic', 'august 25 , 1942', 're - elected', 'cecil r king ( d ) unopposed'], ['california 22', 'none ( district created )', 'none ( district created )', 'none ( district created )', 'new seat republican gain', 'john j phillips ( r ) 57.6 % n e west ( d ) 42.4 %']] |
wyfk | https://en.wikipedia.org/wiki/WYFK | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14369924-1.html.csv | superlative | the wyfk radio channel with the call sign w273ae broadcasts with the highest erp wattage . | {'scope': 'all', 'col_superlative': '4', 'row_superlative': '3', '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', 'erp w'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; erp w }'}, 'call sign'], 'result': 'w273ae', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; erp w } ; call sign }'}, 'w273ae'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; erp w } ; call sign } ; w273ae } = true', 'tointer': 'select the row whose erp w record of all rows is maximum . the call sign record of this row is w273ae .'} | eq { hop { argmax { all_rows ; erp w } ; call sign } ; w273ae } = true | select the row whose erp w record of all rows is maximum . the call sign record of this row is w273ae . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'erp w_5': 5, 'call sign_6': 6, 'w273ae_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'erp w_5': 'erp w', 'call sign_6': 'call sign', 'w273ae_7': 'w273ae'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'erp w_5': [0], 'call sign_6': [1], 'w273ae_7': [2]} | ['call sign', 'frequency mhz', 'city of license', 'erp w', 'class', 'fcc info'] | [['w230av', '93.9 fm', 'gadsden , alabama', '10', 'd', 'fcc'], ['w269ax', '101.7 fm', 'anniston , alabama', '10', 'd', 'fcc'], ['w273ae', '102.5 fm', 'albany , georgia', '55', 'd', 'fcc'], ['w282ae', '104.3 fm', 'macon , georgia', '27', 'd', 'fcc'], ['w290ag', '105.9 fm', 'griffin , georgia', '27', 'd', 'fcc']] |
2003 - 04 manchester united f.c. season | https://en.wikipedia.org/wiki/2003%E2%80%9304_Manchester_United_F.C._season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12950804-5.html.csv | aggregation | for the 2003-04 manchester united f.c. season , the average attendance was 48976.33 . | {'scope': 'all', 'col': '5', 'type': 'average', 'result': '48976.33', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'attendance'], 'result': '48976.33', 'ind': 0, 'tostr': 'avg { all_rows ; attendance }'}, '48976.33'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; attendance } ; 48976.33 } = true', 'tointer': 'the average of the attendance record of all rows is 48976.33 .'} | round_eq { avg { all_rows ; attendance } ; 48976.33 } = true | the average of the attendance record of all rows is 48976.33 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'attendance_4': 4, '48976.33_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'attendance_4': 'attendance', '48976.33_5': '48976.33'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'attendance_4': [0], '48976.33_5': [1]} | ['date', 'round', 'opponents', 'result f - a', 'attendance'] | [['4 january 2004', 'round 3', 'aston villa', '2 - 1', '40371'], ['25 january 2004', 'round 4', 'northampton town', '3 - 0', '7356'], ['14 february 2004', 'round 5', 'manchester city', '4 - 2', '67228'], ['6 march 2004', 'round 6', 'fulham', '2 - 1', '67614'], ['3 april 2004', 'semi - final', 'arsenal', '1 - 0', '39939'], ['22 may 2004', 'final', 'millwall', '3 - 0', '71350']] |
1980 san francisco 49ers season | https://en.wikipedia.org/wiki/1980_San_Francisco_49ers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18680817-1.html.csv | majority | the san francisco 49ers lost all games in the month of october during the 1980 season . | {'scope': 'subset', 'col': '4', 'most_or_all': 'all', 'criterion': 'fuzzily_match', 'value': 'l', 'subset': {'col': '2', 'criterion': 'fuzzily_match', 'value': 'october'}} | {'func': 'all_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'october'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; date ; october }', 'tointer': 'select the rows whose date record fuzzily matches to october .'}, 'result', 'l'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to october . for the result records of these rows , all of them fuzzily match to l .', 'tostr': 'all_eq { filter_eq { all_rows ; date ; october } ; result ; l } = true'} | all_eq { filter_eq { all_rows ; date ; october } ; result ; l } = true | select the rows whose date record fuzzily matches to october . for the result records of these rows , all of them fuzzily match to l . | 2 | 2 | {'all_str_eq_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'date_4': 4, 'october_5': 5, 'result_6': 6, 'l_7': 7} | {'all_str_eq_1': 'all_str_eq', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'date_4': 'date', 'october_5': 'october', 'result_6': 'result', 'l_7': 'l'} | {'all_str_eq_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'date_4': [0], 'october_5': [0], 'result_6': [1], 'l_7': [1]} | ['week', 'date', 'opponent', 'result', 'attendance'] | [['1', 'september 7 , 1980', 'new orleans saints', 'w 26 - 23', '58621'], ['2', 'september 14 , 1980', 'st louis cardinals', 'w 24 - 21', '49999'], ['3', 'september 21 , 1980', 'new york jets', 'w 37 - 27', '50608'], ['4', 'september 28 , 1980', 'atlanta falcons', 'l 20 - 17', '56518'], ['5', 'october 5 , 1980', 'los angeles rams', 'l 48 - 26', '62188'], ['6', 'october 12 , 1980', 'dallas cowboys', 'l 59 - 14', '63399'], ['7', 'october 19 , 1980', 'los angeles rams', 'l 31 - 17', '55360'], ['8', 'october 26 , 1980', 'tampa bay buccaneers', 'l 24 - 23', '51925'], ['9', 'november 2 , 1980', 'detroit lions', 'l 17 - 13', '78845'], ['10', 'november 9 , 1980', 'green bay packers', 'l 23 - 16', '54475'], ['11', 'november 16 , 1980', 'miami dolphins', 'l 17 - 13', '45135'], ['12', 'november 23 , 1980', 'new york giants', 'w 12 - 0', '38574'], ['13', 'november 30 , 1980', 'new england patriots', 'w 21 - 17', '45254'], ['14', 'december 7 , 1980', 'new orleans saints', 'w 38 - 35', '37949'], ['15', 'december 14 , 1980', 'atlanta falcons', 'l 35 - 10', '55767'], ['16', 'december 21 , 1980', 'buffalo bills', 'l 18 - 13', '37476']] |
1978 houston oilers season | https://en.wikipedia.org/wiki/1978_Houston_Oilers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15984957-2.html.csv | superlative | the houston oilers had the highest number of first downs during the 1978 season when playing against the new england patriots . | {'scope': 'all', 'col_superlative': '7', 'row_superlative': '11', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '3', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'oilers first downs'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; oilers first downs }'}, 'opponent'], 'result': 'new england patriots', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; oilers first downs } ; opponent }'}, 'new england patriots'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; oilers first downs } ; opponent } ; new england patriots } = true', 'tointer': 'select the row whose oilers first downs record of all rows is maximum . the opponent record of this row is new england patriots .'} | eq { hop { argmax { all_rows ; oilers first downs } ; opponent } ; new england patriots } = true | select the row whose oilers first downs record of all rows is maximum . the opponent record of this row is new england patriots . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'oilers first downs_5': 5, 'opponent_6': 6, 'new england patriots_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'oilers first downs_5': 'oilers first downs', 'opponent_6': 'opponent', 'new england patriots_7': 'new england patriots'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'oilers first downs_5': [0], 'opponent_6': [1], 'new england patriots_7': [2]} | ['game', 'date', 'opponent', 'result', 'oilers points', 'opponents', 'oilers first downs', 'record', 'attendance'] | [['1', 'sept 3', 'atlanta falcons', 'loss', '14', '20', '13', '0 - 1', '57328'], ['2', 'sept 10', 'kansas city chiefs', 'win', '20', '17', '15', '1 - 1', '40213'], ['3', 'sept 17', 'san francisco 49ers', 'win', '20', '19', '23', '2 - 1', '46161'], ['4', 'sept 24', 'los angeles rams', 'loss', '6', '10', '10', '2 - 2', '45749'], ['5', 'oct 1', 'cleveland browns', 'win', '16', '13', '20', '3 - 2', '72776'], ['6', 'oct 8', 'oakland raiders', 'loss', '17', '21', '20', '3 - 3', '52550'], ['7', 'oct 15', 'buffalo bills', 'win', '17', '10', '17', '4 - 3', '47727'], ['8', 'oct 23', 'pittsburgh steelers', 'win', '24', '17', '22', '5 - 3', '48021'], ['9', 'oct 29', 'cincinnati bengals', 'loss', '13', '28', '15', '5 - 4', '50532'], ['10', 'nov 5', 'cleveland browns', 'win', '14', '10', '18', '6 - 4', '45827'], ['11', 'nov 12', 'new england patriots', 'win', '26', '23', '24', '7 - 4', '60356'], ['12', 'nov 20', 'miami dolphins', 'win', '35', '30', '23', '8 - 4', '50290'], ['13', 'nov 26', 'cincinnati bengals', 'win', '17', '10', '17', '9 - 4', '43245'], ['14', 'dec 3', 'pittsburgh steelers', 'loss', '3', '13', '9', '9 - 5', '54261'], ['15', 'dec 10', 'new orleans saints', 'win', '17', '12', '16', '10 - 5', '63169'], ['16', 'dec 17', 'san diego chargers', 'loss', '24', '45', '14', '10 - 6', '49554']] |
volkswagen polo mk5 | https://en.wikipedia.org/wiki/Volkswagen_Polo_Mk5 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-21888587-1.html.csv | aggregation | the 1.2 tsi model of the volkswagen polo mk5 averaged 4750 power rpm . | {'scope': 'subset', 'col': '3', 'type': 'average', 'result': '4750', 'subset': {'col': '1', 'criterion': 'equal', 'value': '1.2 tsi'}} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'model', '1.2 tsi'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; model ; 1.2 tsi }', 'tointer': 'select the rows whose model record fuzzily matches to 1.2 tsi .'}, 'power rpm'], 'result': '4750', 'ind': 1, 'tostr': 'avg { filter_eq { all_rows ; model ; 1.2 tsi } ; power rpm }'}, '4750'], 'result': True, 'ind': 2, 'tostr': 'round_eq { avg { filter_eq { all_rows ; model ; 1.2 tsi } ; power rpm } ; 4750 } = true', 'tointer': 'select the rows whose model record fuzzily matches to 1.2 tsi . the average of the power rpm record of these rows is 4750 .'} | round_eq { avg { filter_eq { all_rows ; model ; 1.2 tsi } ; power rpm } ; 4750 } = true | select the rows whose model record fuzzily matches to 1.2 tsi . the average of the power rpm record of these rows is 4750 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'avg_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'model_5': 5, '1.2 tsi_6': 6, 'power rpm_7': 7, '4750_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'avg_1': 'avg', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'model_5': 'model', '1.2 tsi_6': '1.2 tsi', 'power rpm_7': 'power rpm', '4750_8': '4750'} | {'eq_2': [3], 'result_3': [], 'avg_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'model_5': [0], '1.2 tsi_6': [0], 'power rpm_7': [1], '4750_8': [2]} | ['model', 'engine', 'power rpm', 'torque rpm', '0 - 100 km / h acceleration', 'top speed', 'transmission', 'co 2 emissions'] | [['1.2', 'cc ( cuin ) i3', '5200', '3000', '16.1 s', 'n / a', '5 - spd manual', '128 g / km'], ['1.2', 'cc ( cuin ) i3', '5400', '3000', '14.1 s', 'n / a', '5 - spd manual', '128 g / km'], ['1.4', 'cc ( cuin ) i4', '5000', '3800', '11.9 s', 'n / a', '5 - spd manual , 7 - spd dsg ( optional )', '139 g / km 135 g / km'], ['1.2 tsi', 'cc ( cuin ) turbo i4', '5000', '1550 - 4100', '9.7 s', 'n / a', '6 - spd manual , 7 - spd dsg ( optional )', '124 g / km 124 g / km'], ['1.2 tsi', 'cc ( cuin ) turbo i4', '4500', '1500 - 3500', '10.9 s', 'n / a', '5 - spd manual , 7 - spd dsg ( optional )', '119 g / km 124 g / km'], ['bluegt', 'cc ( cuin ) turbo i4', '4500 - 6000', '1500 - 3500', '7.9 s', 'n / a', '6 - spd manual , 7 - spd dsg ( optional )', '107 g / km 105 g / km'], ['gti', 'cc ( cuin ) twincharger i4', '6200', '2000 - 4500', '6.9 s', 'n / a', '7 - spd dsg', '139 g / km']] |
list of amusement park rankings | https://en.wikipedia.org/wiki/List_of_amusement_park_rankings | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16578883-3.html.csv | aggregation | the top ten amusement parks drew an average of 8133819 in attendance for the year 2008 . | {'scope': 'all', 'col': '3', 'type': 'average', 'result': '8133819', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', '2008'], 'result': '8133819', 'ind': 0, 'tostr': 'avg { all_rows ; 2008 }'}, '8133819'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; 2008 } ; 8133819 } = true', 'tointer': 'the average of the 2008 record of all rows is 8133819 .'} | round_eq { avg { all_rows ; 2008 } ; 8133819 } = true | the average of the 2008 record of all rows is 8133819 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, '2008_4': 4, '8133819_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', '2008_4': '2008', '8133819_5': '8133819'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], '2008_4': [0], '8133819_5': [1]} | ['rank', 'location', '2008', '2009', '2010', '2011', '2012'] | [['1', 'lake buena vista , florida , usa', '17063000', '17233000', '16972000', '17142000', '17536000'], ['2', 'anaheim , california , usa', '14721000', '15900000', '15980000', '16140000', '15963000'], ['3', 'lake buena vista , florida , usa', '10935000', '10990000', '10825000', '10825000', '11063000'], ['4', 'lake buena vista , florida , usa', '9540000', '9590000', '9686000', '9783000', '9998000'], ['5', 'lake buena vista , florida , usa', '9608000', '9700000', '9603000', '9699000', '9912000'], ['6', 'orlando , florida , usa', '5297000', '4627000', '5949000', '7674000', '7981000'], ['7', 'anaheim , california , usa', '5566000', '6095000', '6278000', '6341000', '7775000'], ['8', 'orlando , florida , usa', '6231000', '5530000', '5925000', '6044000', '6195000'], ['9', 'universal city , california , usa', '4583000', '4308000', '5040000', '5141000', '5912000'], ['10', 'orlando , florida , usa', '5926000', '5800000', '5100000', '5202000', '5358000']] |
c - class destroyer ( 1943 ) | https://en.wikipedia.org/wiki/C-class_destroyer_%281943%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1206583-1.html.csv | superlative | cambrian ( ex - spitfire ) was the earliest c - class destroyer to be laid down . | {'scope': 'all', 'col_superlative': '4', 'row_superlative': '5', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '1', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'laid down'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; laid down }'}, 'name'], 'result': 'cambrian ( ex - spitfire )', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; laid down } ; name }'}, 'cambrian ( ex - spitfire )'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; laid down } ; name } ; cambrian ( ex - spitfire ) } = true', 'tointer': 'select the row whose laid down record of all rows is minimum . the name record of this row is cambrian ( ex - spitfire ) .'} | eq { hop { argmin { all_rows ; laid down } ; name } ; cambrian ( ex - spitfire ) } = true | select the row whose laid down record of all rows is minimum . the name record of this row is cambrian ( ex - spitfire ) . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'laid down_5': 5, 'name_6': 6, 'cambrian (ex - spitfire)_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'laid down_5': 'laid down', 'name_6': 'name', 'cambrian (ex - spitfire)_7': 'cambrian ( ex - spitfire )'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'laid down_5': [0], 'name_6': [1], 'cambrian (ex - spitfire)_7': [2]} | ['name', 'pennant', 'builder', 'laid down', 'launched', 'commissioned'] | [['caprice ( ex - swallow )', 'r01 later d01', 'yarrow , scotstoun', '24 september 1942', '16 september 1943', '5 april 1944'], ['cassandra ( ex - tourmaline )', 'r62 later d10', 'yarrow , scotstoun', '30 january 1943', '29 november 1943', '28 july 1944'], ['caesar ( ex - ranger )', 'r07 later d07', 'john brown , clydebank', '3 april 1943', '14 february 1944', '5 october 1944'], ['cavendish ( ex - sibyl )', 'r15 later d15', 'john brown , clydebank', '19 may 1943', '12 april 1944', '13 december 1944'], ['cambrian ( ex - spitfire )', 'r85 later d85', 'scotts , greenock', '14 august 1942', '10 december 1943', '17 july 1944 by john brown'], ['carron ( ex - strenuous )', 'r30 later d30', 'scotts , greenock', '26 november 1942', '28 march 1944', '6 november 1944'], ['cavalier ( ex - pellew )', 'r73 later d73', 'white , cowes', '28 february 1943', '7 april 1944', '22 november 1944'], ['carysfort ( ex - pique )', 'r25 later d25', 'white , cowes', '12 may 1943', '25 july 1944', '20 february 1945']] |
1994 - 95 philadelphia flyers season | https://en.wikipedia.org/wiki/1994%E2%80%9395_Philadelphia_Flyers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14022127-5.html.csv | count | philadelphia was the home team 8 times during the 1994 - 95 season . | {'scope': 'all', 'criterion': 'equal', 'value': 'philadelphia', 'result': '8', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'home', 'philadelphia'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose home record fuzzily matches to philadelphia .', 'tostr': 'filter_eq { all_rows ; home ; philadelphia }'}], 'result': '8', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; home ; philadelphia } }', 'tointer': 'select the rows whose home record fuzzily matches to philadelphia . the number of such rows is 8 .'}, '8'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; home ; philadelphia } } ; 8 } = true', 'tointer': 'select the rows whose home record fuzzily matches to philadelphia . the number of such rows is 8 .'} | eq { count { filter_eq { all_rows ; home ; philadelphia } } ; 8 } = true | select the rows whose home record fuzzily matches to philadelphia . the number of such rows is 8 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'home_5': 5, 'philadelphia_6': 6, '8_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'home_5': 'home', 'philadelphia_6': 'philadelphia', '8_7': '8'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'home_5': [0], 'philadelphia_6': [0], '8_7': [2]} | ['date', 'visitor', 'score', 'home', 'decision', 'attendance', 'record'] | [['april 1', 'philadelphia', '2 - 3', 'pittsburgh', 'hextall', '17181', '17 - 13 - 4'], ['april 2', 'ny rangers', '2 - 4', 'philadelphia', 'hextall', '17380', '18 - 13 - 4'], ['april 6', 'tampa bay', '4 - 5', 'philadelphia', 'hextall', '17245', '19 - 13 - 4'], ['april 8', 'philadelphia', '3 - 1', 'washington', 'hextall', '18130', '20 - 13 - 4'], ['april 12', 'montreal', '2 - 3', 'philadelphia', 'hextall', '17380', '21 - 13 - 4'], ['april 14', 'tampa bay', '2 - 3', 'philadelphia', 'roussel', '17380', '22 - 13 - 4'], ['april 16', 'pittsburgh', '3 - 4', 'philadelphia', 'hextall', '17380', '23 - 13 - 4'], ['april 18', 'philadelphia', '3 - 1', 'florida', 'hextall', '14703', '24 - 13 - 4'], ['april 20', 'ny islanders', '1 - 2', 'philadelphia', 'hextall', '17380', '25 - 13 - 4'], ['april 22', 'philadelphia', '4 - 3', 'new jersey', 'roussel', '19040', '26 - 13 - 4'], ['april 23', 'philadelphia', '2 - 4', 'buffalo', 'hextall', '16230', '26 - 14 - 4'], ['april 26', 'ottawa', '5 - 2', 'philadelphia', 'hextall', '17380', '26 - 15 - 4'], ['april 28', 'philadelphia', '4 - 3', 'hartford', 'hextall', '15550', '27 - 15 - 4'], ['april 30', 'ny rangers', '2 - 0', 'philadelphia', 'roussel', '17380', '27 - 16 - 4']] |
philippe streiff | https://en.wikipedia.org/wiki/Philippe_Streiff | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1226517-1.html.csv | aggregation | phillipe streiff scored a total of 8 points in the year 1985 . | {'scope': 'subset', 'col': '5', 'type': 'sum', 'result': '8', 'subset': {'col': '1', 'criterion': 'equal', 'value': '1985'}} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'year', '1985'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; year ; 1985 }', 'tointer': 'select the rows whose year record is equal to 1985 .'}, 'points'], 'result': '8', 'ind': 1, 'tostr': 'sum { filter_eq { all_rows ; year ; 1985 } ; points }'}, '8'], 'result': True, 'ind': 2, 'tostr': 'round_eq { sum { filter_eq { all_rows ; year ; 1985 } ; points } ; 8 } = true', 'tointer': 'select the rows whose year record is equal to 1985 . the sum of the points record of these rows is 8 .'} | round_eq { sum { filter_eq { all_rows ; year ; 1985 } ; points } ; 8 } = true | select the rows whose year record is equal to 1985 . the sum of the points record of these rows is 8 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'sum_1': 1, 'filter_eq_0': 0, 'all_rows_4': 4, 'year_5': 5, '1985_6': 6, 'points_7': 7, '8_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'sum_1': 'sum', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'year_5': 'year', '1985_6': '1985', 'points_7': 'points', '8_8': '8'} | {'eq_2': [3], 'result_3': [], 'sum_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'year_5': [0], '1985_6': [0], 'points_7': [1], '8_8': [2]} | ['year', 'entrant', 'chassis', 'engine', 'points'] | [['1984', 'equipe renault elf', 'renault re50', 'renault v6', '0'], ['1985', 'equipe ligier gitanes', 'ligier js25', 'renault v6', '4'], ['1985', 'tyrrell racing organisation', 'tyrrell 014', 'renault v6', '4'], ['1986', 'data general team tyrrell', 'tyrrell 014', 'renault v6', '3'], ['1986', 'data general team tyrrell', 'tyrrell 015', 'renault v6', '3'], ['1987', 'data general team tyrrell', 'tyrrell dg016', 'cosworth v8', '4'], ['1988', 'automobiles gonfaronnaises sportives', 'ags jh23', 'cosworth v8', '0'], ['1989', 'automobiles gonfaronnaises sportives', 'ags jh23b', 'cosworth v8', '0']] |
atlantic hurricane season | https://en.wikipedia.org/wiki/Atlantic_hurricane_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2930244-3.html.csv | unique | the 1864 atlantic hurricane season is the only one in which there were no deaths recorded . | {'scope': 'all', 'row': '5', 'col': '5', 'col_other': '1', 'criterion': 'equal', 'value': 'none', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'deaths', 'none'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose deaths record fuzzily matches to none .', 'tostr': 'filter_eq { all_rows ; deaths ; none }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; deaths ; none } }', 'tointer': 'select the rows whose deaths record fuzzily matches to none . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'deaths', 'none'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose deaths record fuzzily matches to none .', 'tostr': 'filter_eq { all_rows ; deaths ; none }'}, 'year'], 'result': '1864', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; deaths ; none } ; year }'}, '1864'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; deaths ; none } ; year } ; 1864 }', 'tointer': 'the year record of this unqiue row is 1864 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; deaths ; none } } ; eq { hop { filter_eq { all_rows ; deaths ; none } ; year } ; 1864 } } = true', 'tointer': 'select the rows whose deaths record fuzzily matches to none . there is only one such row in the table . the year record of this unqiue row is 1864 .'} | and { only { filter_eq { all_rows ; deaths ; none } } ; eq { hop { filter_eq { all_rows ; deaths ; none } ; year } ; 1864 } } = true | select the rows whose deaths record fuzzily matches to none . there is only one such row in the table . the year record of this unqiue row is 1864 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'deaths_7': 7, 'none_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'year_9': 9, '1864_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'deaths_7': 'deaths', 'none_8': 'none', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'year_9': 'year', '1864_10': '1864'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'deaths_7': [0], 'none_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'year_9': [2], '1864_10': [3]} | ['year', 'number of tropical storms', 'number of hurricanes', 'number of major hurricanes', 'deaths', 'strongest storm'] | [['1860', '1', '5', '1', '60 +', 'one'], ['1861', '2', '6', '0', '22 +', 'one and three'], ['1862', '3', '3', '0', '3', 'two and three'], ['1863', '4', '5', '0', '90', 'one , two , three & four'], ['1864', '2', '3', '0', 'none', 'one , three & five'], ['1865', '4', '3', '0', '326', 'four & seven'], ['1866', '1', '5', '1', '383', 'six'], ['1867', '2', '6', '0', '811', "' san narciso '"], ['1868', '1', '3', '0', '2', 'one , two & four']] |
watch ( tv channel ) | https://en.wikipedia.org/wiki/Watch_%28TV_channel%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18383702-1.html.csv | majority | on the watch channel most of the dynamo : magician impossible shows attracted more than 1000000 viewers . | {'scope': 'subset', 'col': '4', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '1000000', 'subset': {'col': '2', 'criterion': 'equal', 'value': 'dynamo : magician impossible'}} | {'func': 'most_greater', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'show', 'dynamo : magician impossible'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; show ; dynamo : magician impossible }', 'tointer': 'select the rows whose show record fuzzily matches to dynamo : magician impossible .'}, 'number of viewers', '1000000'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose show record fuzzily matches to dynamo : magician impossible . for the number of viewers records of these rows , most of them are greater than 1000000 .', 'tostr': 'most_greater { filter_eq { all_rows ; show ; dynamo : magician impossible } ; number of viewers ; 1000000 } = true'} | most_greater { filter_eq { all_rows ; show ; dynamo : magician impossible } ; number of viewers ; 1000000 } = true | select the rows whose show record fuzzily matches to dynamo : magician impossible . for the number of viewers records of these rows , most of them are greater than 1000000 . | 2 | 2 | {'most_greater_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'show_4': 4, 'dynamo : magician impossible_5': 5, 'number of viewers_6': 6, '1000000_7': 7} | {'most_greater_1': 'most_greater', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'show_4': 'show', 'dynamo : magician impossible_5': 'dynamo : magician impossible', 'number of viewers_6': 'number of viewers', '1000000_7': '1000000'} | {'most_greater_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'show_4': [0], 'dynamo : magician impossible_5': [0], 'number of viewers_6': [1], '1000000_7': [1]} | ['rank', 'show', 'episode', 'number of viewers', 'date'] | [['1', 'dynamo : magician impossible', '2.01', '1927000', '5 july 2012'], ['2', 'dynamo : magician impossible', '2.02', '1826000', '12 july 2012'], ['3', 'dynamo : magician impossible', '2.03', '1793000', '19 july 2012'], ['4', 'dynamo : magician impossible', '1.04', '1441000', '28 july 2011'], ['5', 'dynamo : magician impossible', '1.02', '1391000', '14 july 2011'], ['6', 'dynamo : magician impossible', '1.03', '1384000', '21 july 2011'], ['6', 'dynamo : magician impossible', '2.04', '1384000', '26 july 2012'], ['7', 'alcatraz', '1.01 - pilot', '1299000', '13 march 2012'], ['8', 'alcatraz', '1.06 - paxton petty', '1229000', '17 april 2012'], ['9', 'alcatraz', '1.08 - the ames brothers', '1193000', '1 may 2012'], ['10', 'dynamo : magician impossible', '3.01', '1193000', '11 july 2013']] |
portuguese legislative election , 1991 | https://en.wikipedia.org/wiki/Portuguese_legislative_election%2C_1991 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1886589-1.html.csv | aggregation | the social demographic of the portuguese legislative election in 1991 was an average of 45 % . | {'scope': 'all', 'col': '3', 'type': 'average', 'result': '45', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'social democratic'], 'result': '45', 'ind': 0, 'tostr': 'avg { all_rows ; social democratic }'}, '45'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; social democratic } ; 45 } = true', 'tointer': 'the average of the social democratic record of all rows is 45 .'} | round_eq { avg { all_rows ; social democratic } ; 45 } = true | the average of the social democratic record of all rows is 45 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'social democratic_4': 4, '45_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'social democratic_4': 'social democratic', '45_5': '45'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'social democratic_4': [0], '45_5': [1]} | ['date released', 'polling institute', 'social democratic', 'socialist', 'green - communist', 'democratic and social centre', 'lead'] | [['october 6 , 1991', 'election results', '50.6 % 135 seats', '29.1 % 72 seats', '8.8 % 17 seats', '4.4 % 5 seats', '21.5 %'], ['october 6 , 1991', 'exit poll - rtp1 universidade católica', '48.0 % - 51.9 %', '28.5 % - 31.5 %', '7.5 % - 10.0 %', '4.5 % - 5.5 %', '19.5 % - 20.4 %'], ['october 6 , 1991', 'exit poll - tsf / expresso euroexpansão', '45.8 % - 50.2 %', '29.8 % - 33.9 %', '6.8 % - 9.1 %', '3.7 % - 5.5 %', '16.0 % - 16.3 %'], ['october 6 , 1991', 'exit poll - antena1 euroteste', '47.0 % - 50.0 %', '31.0 % - 34.0 %', '7.5 % - 10.0 %', '4.0 % - 5.0 %', '16.0 %'], ['september 28 , 1991', 'euroteste', '47.3 %', '35.5 %', '8.5 %', '4.1 %', '11.8 %'], ['september 28 , 1991', 'euroteste', '46.0 %', '37.0 %', '9.7 %', '3.9 %', '9.0 %'], ['september 28 , 1991', 'euroexpansão', '44.0 %', '33.0 %', '9.0 %', '6.0 %', '11.0 %'], ['september 27 , 1991', 'marktest', '43.1 %', '32.8 %', '7.7 %', '4.6 %', '10.3 %'], ['september 27 , 1991', 'pluriteste', '41.2 %', '34.7 %', '8.4 %', '8.1 %', '6.5 %'], ['september 20 , 1991', 'euroteste', '45.6 %', '35.5 %', '10.0 %', '4.4 %', '10.1 %'], ['september 20 , 1991', 'marktest', '41.9 %', '31.9 %', '7.3 %', '4.4 %', '10.0 %'], ['september 16 , 1991', 'pluriteste', '39.2 %', '26.6 %', '6.2 %', '6.0 %', '12.6 %'], ['september 16 , 1991', 'euroteste', '45.1 %', '34.5 %', '10.2 %', '5.2 %', '10.6 %'], ['september 14 , 1991', 'norma', '45.0 %', '37.5 %', '11.2 %', '3.5 %', '7.5 %'], ['august 28 , 1991', 'euroexpansão / marktest', '35.3 %', '36.8 %', '8.7 %', '4.9 %', '1.5 %'], ['august 4 , 1991', 'euroteste / jn', '47.5 %', '37.8 %', '12.3 %', '8.2 %', '7.7 %']] |
rebecca soni | https://en.wikipedia.org/wiki/Rebecca_Soni | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18301533-3.html.csv | count | rebecca soni attained three of her world records in london , uk . | {'scope': 'all', 'criterion': 'equal', 'value': 'london , uk', 'result': '3', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'location', 'london , uk'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose location record fuzzily matches to london , uk .', 'tostr': 'filter_eq { all_rows ; location ; london , uk }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; location ; london , uk } }', 'tointer': 'select the rows whose location record fuzzily matches to london , uk . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; location ; london , uk } } ; 3 } = true', 'tointer': 'select the rows whose location record fuzzily matches to london , uk . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; location ; london , uk } } ; 3 } = true | select the rows whose location record fuzzily matches to london , uk . 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, 'location_5': 5, 'london , uk_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', 'location_5': 'location', 'london , uk_6': 'london , uk', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'location_5': [0], 'london , uk_6': [0], '3_7': [2]} | ['distance', 'event', 'time', 'meet', 'location'] | [['200 m', 'breaststroke', '2:20.22', '2008 summer olympics', 'beijing , chn'], ['100 m', 'breaststroke', '1:04.84', '2009 world aquatics championships', 'rome , ita'], ['200 m', 'breaststroke ( sc )', '2:14.57', '2009 duel in the pool', 'manchester , uk'], ['100 m', 'breaststroke ( sc )', '1:02.70', '2009 duel in the pool', 'manchester , uk'], ['4100 m', 'medley relay ( sc )', '3:45.56', '2011 duel in the pool', 'atlanta , georgia , us'], ['200 m', 'breaststroke', '2:20.00', '2012 summer olympics', 'london , uk'], ['200 m', 'breaststroke', '2:19.59', '2012 summer olympics', 'london , uk'], ['4100 m', 'medley relay', '3:52.05', '2012 summer olympics', 'london , uk']] |
ningde | https://en.wikipedia.org/wiki/Ningde | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2013618-1.html.csv | superlative | gutian county is the administrative region in ningde with the highest area . | {'scope': 'all', 'col_superlative': '6', 'row_superlative': '5', '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', 'area'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; area }'}, 'english name'], 'result': 'gutian county', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; area } ; english name }'}, 'gutian county'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; area } ; english name } ; gutian county } = true', 'tointer': 'select the row whose area record of all rows is maximum . the english name record of this row is gutian county .'} | eq { hop { argmax { all_rows ; area } ; english name } ; gutian county } = true | select the row whose area record of all rows is maximum . the english name record of this row is gutian county . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'area_5': 5, 'english name_6': 6, 'gutian county_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'area_5': 'area', 'english name_6': 'english name', 'gutian county_7': 'gutian county'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'area_5': [0], 'english name_6': [1], 'gutian county_7': [2]} | ['english name', 'simplified', 'traditional', 'pinyin', 'foochow', 'area', 'population', 'density'] | [['jiaocheng district', '蕉城区', '蕉城區', 'jiāochéng qū', 'ciĕu - siàng - kṳ̆', '1537', '429260', '279'], ["fu'an city", '福安市', '福安市', "fú ' ān shì", 'hók - ăng - chê', '1795', '563640', '314'], ['fuding city', '福鼎市', '福鼎市', 'fúdǐng shì', 'hók - tīng - chê', '1526', '529534', '347'], ['xiapu county', '霞浦县', '霞蒲縣', 'xiápǔ xiàn', 'hà - puō - ging', '1716', '461176', '269'], ['gutian county', '古田县', '古田縣', 'gǔtián xiàn', 'kŭ - chèng - ging', '2377', '323700', '136'], ['pingnan county', '屏南县', '屏南縣', 'píngnán xiàn', 'bìng - nàng - ging', '1485', '137724', '93'], ['shouning county', '寿宁县', '壽寧縣', 'shòuníng xiàn', 'sêu - nìng - ging', '1425', '175874', '123'], ['zherong county', '柘荣县', '柘榮縣', 'zhèróng xiàn', 'ciá - ìng - ging', '544', '88387', '162']] |
kyaz | https://en.wikipedia.org/wiki/KYAZ | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1755656-1.html.csv | aggregation | 100 % of kyaz aspect ratios were shown in 4:3 . | {'scope': 'all', 'col': '3', 'type': 'average', 'result': '4:3', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'aspect'], 'result': '4:3', 'ind': 0, 'tostr': 'avg { all_rows ; aspect }'}, '4:3'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; aspect } ; 4:3 } = true', 'tointer': 'the average of the aspect record of all rows is 4:3 .'} | round_eq { avg { all_rows ; aspect } ; 4:3 } = true | the average of the aspect record of all rows is 4:3 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'aspect_4': 4, '4:3_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'aspect_4': 'aspect', '4:3_5': '4:3'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'aspect_4': [0], '4:3_5': [1]} | ['channel', 'video', 'aspect', 'psip short name', 'programming'] | [['51.1', '480i', '4:3', 'kyaz - 1', 'azteca amãrica'], ['51.2', '480i', '4:3', 'kyaz - 2', 'vietface tv'], ['51.3', '480i', '4:3', 'kyaz - 3', 'saigon network television'], ['51.4', '480i', '4:3', 'kyaz - 4', 'new tang dynasty television'], ['51.5', '480i', '4:3', 'kyaz - 5', 'global tv'], ['51.6', '480i', '4:3', 'kyaz - 6', 'latv'], ['51.7', '480i', '4:3', 'kyaz - 7', 'vietmax']] |
utah jazz all - time roster | https://en.wikipedia.org/wiki/Utah_Jazz_all-time_roster | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-11545282-6.html.csv | ordinal | derrick favors is the player with the second lowest jersey number on the utah jazz all-time roster . | {'row': '2', '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', 'no', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; no ; 2 }'}, 'player'], 'result': 'derrick favors', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; no ; 2 } ; player }'}, 'derrick favors'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; no ; 2 } ; player } ; derrick favors } = true', 'tointer': 'select the row whose no record of all rows is 2nd minimum . the player record of this row is derrick favors .'} | eq { hop { nth_argmin { all_rows ; no ; 2 } ; player } ; derrick favors } = true | select the row whose no record of all rows is 2nd minimum . the player record of this row is derrick favors . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'no_5': 5, '2_6': 6, 'player_7': 7, 'derrick favors_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', 'no_5': 'no', '2_6': '2', 'player_7': 'player', 'derrick favors_8': 'derrick favors'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'no_5': [0], '2_6': [0], 'player_7': [1], 'derrick favors_8': [2]} | ['player', 'no', 'nationality', 'position', 'years for jazz', 'school / club team'] | [['jim farmer', '30', 'united states', 'guard', '1988 - 89', 'alabama'], ['derrick favors', '15', 'united states', 'forward', '2011 - present', 'georgia tech'], ['kyrylo fesenko', '44', 'ukraine', 'center', '2007 - 11', 'cherkasy monkeys ( ukraine )'], ['derek fisher', '2', 'united states', 'guard', '2006 - 2007', 'arkansas - little rock'], ['greg foster', '44', 'united states', 'center / forward', '1995 - 99', 'utep'], ['bernie fryer', '25', 'united states', 'guard', '1975 - 76', 'byu'], ['todd fuller', '52', 'united states', 'center', '1998 - 99', 'north carolina state']] |
1965 - 66 segunda división | https://en.wikipedia.org/wiki/1965%E2%80%9366_Segunda_Divisi%C3%B3n | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17832085-2.html.csv | count | two clubs in the 1965 - 66 segunda división had a goal difference of - 2 . | {'scope': 'all', 'criterion': 'equal', 'value': '-2', 'result': '2', 'col': '10', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'goal difference', '-2'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose goal difference record is equal to -2 .', 'tostr': 'filter_eq { all_rows ; goal difference ; -2 }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; goal difference ; -2 } }', 'tointer': 'select the rows whose goal difference record is equal to -2 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; goal difference ; -2 } } ; 2 } = true', 'tointer': 'select the rows whose goal difference record is equal to -2 . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; goal difference ; -2 } } ; 2 } = true | select the rows whose goal difference record is equal to -2 . the number of such rows is 2 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_eq_0': 0, 'all_rows_4': 4, 'goal difference_5': 5, '-2_6': 6, '2_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'goal difference_5': 'goal difference', '-2_6': '-2', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'goal difference_5': [0], '-2_6': [0], '2_7': [2]} | ['position', 'club', 'played', 'points', 'wins', 'draws', 'losses', 'goals for', 'goals against', 'goal difference'] | [['1', 'deportivo de la coruña', '30', '43', '18', '7', '5', '53', '19', '+ 34'], ['2', 'celta de vigo', '30', '39', '17', '5', '8', '54', '28', '+ 26'], ['3', 'real gijón', '30', '36', '15', '6', '9', '67', '50', '+ 17'], ['4', 'real oviedo', '30', '34', '13', '8', '9', '38', '22', '+ 16'], ['5', 'burgos cf', '30', '32', '13', '6', '11', '42', '41', '+ 1'], ['6', 'sd indauchu', '30', '32', '14', '4', '12', '47', '47', '0'], ['7', 'cd condal', '30', '32', '14', '4', '12', '55', '46', '+ 9'], ['8', 'real santander', '30', '31', '11', '9', '10', '38', '40', '- 2'], ['9', 'ca osasuna', '30', '31', '13', '5', '12', '39', '41', '- 2'], ['10', 'real sociedad', '30', '31', '13', '5', '12', '50', '48', '+ 2'], ['11', 'ud lérida', '30', '30', '10', '10', '10', '37', '31', '+ 6'], ['12', 'cf badalona', '30', '30', '13', '4', '13', '32', '41', '- 9'], ['13', 'up langreo', '30', '23', '10', '3', '17', '37', '58', '- 21'], ['14', 'cd europa', '30', '23', '9', '5', '16', '33', '49', '- 16'], ['15', 'cd hospitalet', '30', '19', '9', '1', '20', '37', '63', '- 26'], ['16', 'baracaldo ah', '30', '14', '5', '4', '21', '24', '59', '- 35']] |
list of tallest buildings in portland , oregon | https://en.wikipedia.org/wiki/List_of_tallest_buildings_in_Portland%2C_Oregon | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13949437-2.html.csv | superlative | wells fargo center has the highest number of floors among the other buildings at 41 floors . | {'scope': 'all', 'col_superlative': '5', 'row_superlative': '9', 'value_mentioned': 'yes', 'max_or_min': 'max', 'other_col': '1', 'subset': None} | {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'max', 'args': ['all_rows', 'floors'], 'result': '41', 'ind': 0, 'tostr': 'max { all_rows ; floors }', 'tointer': 'the maximum floors record of all rows is 41 .'}, '41'], 'result': True, 'ind': 1, 'tostr': 'eq { max { all_rows ; floors } ; 41 }', 'tointer': 'the maximum floors record of all rows is 41 .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'floors'], 'result': None, 'ind': 2, 'tostr': 'argmax { all_rows ; floors }'}, 'name'], 'result': 'wells fargo center', 'ind': 3, 'tostr': 'hop { argmax { all_rows ; floors } ; name }'}, 'wells fargo center'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { argmax { all_rows ; floors } ; name } ; wells fargo center }', 'tointer': 'the name record of the row with superlative floors record is wells fargo center .'}], 'result': True, 'ind': 5, 'tostr': 'and { eq { max { all_rows ; floors } ; 41 } ; eq { hop { argmax { all_rows ; floors } ; name } ; wells fargo center } } = true', 'tointer': 'the maximum floors record of all rows is 41 . the name record of the row with superlative floors record is wells fargo center .'} | and { eq { max { all_rows ; floors } ; 41 } ; eq { hop { argmax { all_rows ; floors } ; name } ; wells fargo center } } = true | the maximum floors record of all rows is 41 . the name record of the row with superlative floors record is wells fargo center . | 6 | 6 | {'and_5': 5, 'result_6': 6, 'eq_1': 1, 'max_0': 0, 'all_rows_7': 7, 'floors_8': 8, '41_9': 9, 'str_eq_4': 4, 'str_hop_3': 3, 'argmax_2': 2, 'all_rows_10': 10, 'floors_11': 11, 'name_12': 12, 'wells fargo center_13': 13} | {'and_5': 'and', 'result_6': 'true', 'eq_1': 'eq', 'max_0': 'max', 'all_rows_7': 'all_rows', 'floors_8': 'floors', '41_9': '41', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'argmax_2': 'argmax', 'all_rows_10': 'all_rows', 'floors_11': 'floors', 'name_12': 'name', 'wells fargo center_13': 'wells fargo center'} | {'and_5': [6], 'result_6': [], 'eq_1': [5], 'max_0': [1], 'all_rows_7': [0], 'floors_8': [0], '41_9': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'argmax_2': [3], 'all_rows_10': [2], 'floors_11': [2], 'name_12': [3], 'wells fargo center_13': [4]} | ['name', 'street address', 'years as tallest', 'height feet / m', 'floors'] | [['wells fargo building', '309 sw 6th avenue', '1907 - 1911', '185 / 56', '12'], ['yeon building', '522 sw 5th avenue', '1911 - 1913', '194 / 59', '15'], ['american bank building', '621 sw morrison street', '1913 - 1927', '207 / 63', '15'], ['public service building', '920 sw sixth avenue', '1927 - 1962', '220 / 67', '16'], ['hilton portland hotel', '921 sw sixth avenue', '1963 - 1965', '241 / 73', '22'], ['harrison west condominium tower', '200 sw harrison', '1965 - 1969', '256 / 78', '25'], ['union bank of california tower', '707 sw washington street', '1969 - 1970', '268 / 82', '15'], ['standard insurance center', '900 sw fifth avenue', '1970 - 1972', '367 / 112', '27'], ['wells fargo center', '1300 sw 5th avenue', '1972 - present', '546 / 166', '41']] |
list of castle episodes | https://en.wikipedia.org/wiki/List_of_Castle_episodes | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-23958944-2.html.csv | unique | the 6th episode of castle was the only one to have less than 8 million us viewers . | {'scope': 'all', 'row': '6', 'col': '7', 'col_other': '1', 'criterion': 'less_than', 'value': '8', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'us viewers ( in millions )', '8'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose us viewers ( in millions ) record is less than 8 .', 'tostr': 'filter_less { all_rows ; us viewers ( in millions ) ; 8 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_less { all_rows ; us viewers ( in millions ) ; 8 } }', 'tointer': 'select the rows whose us viewers ( in millions ) record is less than 8 . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'us viewers ( in millions )', '8'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose us viewers ( in millions ) record is less than 8 .', 'tostr': 'filter_less { all_rows ; us viewers ( in millions ) ; 8 }'}, 'no by series'], 'result': '6', 'ind': 2, 'tostr': 'hop { filter_less { all_rows ; us viewers ( in millions ) ; 8 } ; no by series }'}, '6'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_less { all_rows ; us viewers ( in millions ) ; 8 } ; no by series } ; 6 }', 'tointer': 'the no by series record of this unqiue row is 6 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_less { all_rows ; us viewers ( in millions ) ; 8 } } ; eq { hop { filter_less { all_rows ; us viewers ( in millions ) ; 8 } ; no by series } ; 6 } } = true', 'tointer': 'select the rows whose us viewers ( in millions ) record is less than 8 . there is only one such row in the table . the no by series record of this unqiue row is 6 .'} | and { only { filter_less { all_rows ; us viewers ( in millions ) ; 8 } } ; eq { hop { filter_less { all_rows ; us viewers ( in millions ) ; 8 } ; no by series } ; 6 } } = true | select the rows whose us viewers ( in millions ) record is less than 8 . there is only one such row in the table . the no by series record of this unqiue row is 6 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_less_0': 0, 'all_rows_6': 6, 'us viewers (in millions)_7': 7, '8_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'no by series_9': 9, '6_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_less_0': 'filter_less', 'all_rows_6': 'all_rows', 'us viewers (in millions)_7': 'us viewers ( in millions )', '8_8': '8', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'no by series_9': 'no by series', '6_10': '6'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_less_0': [1, 2], 'all_rows_6': [0], 'us viewers (in millions)_7': [0], '8_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'no by series_9': [2], '6_10': [3]} | ['no by series', 'title', 'directed by', 'written by', 'original air date', 'production number', 'us viewers ( in millions )'] | [['1', 'flowers for your grave', 'rob bowman', 'andrew w marlowe', 'march 9 , 2009', '101', '10.76'], ['2', 'nanny mcdead', 'john terlesky', 'barry schindel', 'march 16 , 2009', '103', '10.97'], ['3', 'hedge fund homeboys', 'rob bowman', 'david grae', 'march 23 , 2009', '104', '9.14'], ['4', 'hell hath no fury', 'rob bowman', 'andrew w marlowe', 'march 30 , 2009', '102', '9.09'], ['5', 'a chill goes through her veins', 'bryan spicer', 'charles murray', 'april 6 , 2009', '105', '9.03'], ['6', 'always buy retail', 'jamie babbit', 'gabrielle stanton & harry werksman', 'april 13 , 2009', '107', '7.73'], ['7', 'home is where the heart stops', 'dean white', 'will beall', 'april 20 , 2009', '106', '8.21'], ['8', 'ghosts', 'bryan spicer', 'moira kirland', 'april 27 , 2009', '108', '8.24']] |
eren derdiyok | https://en.wikipedia.org/wiki/Eren_Derdiyok | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12318000-2.html.csv | unique | eren derdiyok 's competition on february 6th , 2008 , was the only competition in london , england . | {'scope': 'all', 'row': '1', 'col': '2', 'col_other': '1', 'criterion': 'equal', 'value': 'london , england', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'venue', 'london , england'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose venue record fuzzily matches to london , england .', 'tostr': 'filter_eq { all_rows ; venue ; london , england }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; venue ; london , england } }', 'tointer': 'select the rows whose venue record fuzzily matches to london , england . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'venue', 'london , england'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose venue record fuzzily matches to london , england .', 'tostr': 'filter_eq { all_rows ; venue ; london , england }'}, 'date'], 'result': '6 february 2008', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; venue ; london , england } ; date }'}, '6 february 2008'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; venue ; london , england } ; date } ; 6 february 2008 }', 'tointer': 'the date record of this unqiue row is 6 february 2008 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; venue ; london , england } } ; eq { hop { filter_eq { all_rows ; venue ; london , england } ; date } ; 6 february 2008 } } = true', 'tointer': 'select the rows whose venue record fuzzily matches to london , england . there is only one such row in the table . the date record of this unqiue row is 6 february 2008 .'} | and { only { filter_eq { all_rows ; venue ; london , england } } ; eq { hop { filter_eq { all_rows ; venue ; london , england } ; date } ; 6 february 2008 } } = true | select the rows whose venue record fuzzily matches to london , england . there is only one such row in the table . the date record of this unqiue row is 6 february 2008 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'venue_7': 7, 'london , england_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'date_9': 9, '6 february 2008_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'venue_7': 'venue', 'london , england_8': 'london , england', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'date_9': 'date', '6 february 2008_10': '6 february 2008'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'venue_7': [0], 'london , england_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'date_9': [2], '6 february 2008_10': [3]} | ['date', 'venue', 'score', 'result', 'competition'] | [['6 february 2008', 'london , england', '1 - 1', '2 - 1', 'friendly'], ['9 september 2009', 'riga , latvia', '2 - 2', '2 - 2', '2010 fifa world cup qualification'], ['10 august 2011', 'vaduz , liechtenstein', '1 - 0', '1 - 2', 'friendly'], ['11 october 2011', 'basel , switzerland', '1 - 0', '2 - 0', 'uefa euro 2012 qualifying'], ['26 may 2012', 'basel , switzerland', '1 - 0', '5 - 3', 'friendly'], ['26 may 2012', 'basel , switzerland', '2 - 0', '5 - 3', 'friendly'], ['26 may 2012', 'basel , switzerland', '3 - 1', '5 - 3', 'friendly'], ['14 november 2012', 'sousse , tunisia', '1 - 0', '2 - 1', 'friendly']] |
1999 - 2000 philadelphia flyers season | https://en.wikipedia.org/wiki/1999%E2%80%932000_Philadelphia_Flyers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14173105-18.html.csv | comparative | in the 1999-2000 philadelphia flyers season , jeff feniak was selected two rounds before konstantin rudenko . | {'row_1': '2', 'row_2': '3', 'col': '1', 'col_other': '2', 'relation': 'diff', 'record_mentioned': 'no', 'diff_result': {'diff_value': '2', 'bigger': 'row2'}} | {'func': 'eq', 'args': [{'func': 'diff', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'jeff feniak'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose player record fuzzily matches to jeff feniak .', 'tostr': 'filter_eq { all_rows ; player ; jeff feniak }'}, 'round'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; player ; jeff feniak } ; round }', 'tointer': 'select the rows whose player record fuzzily matches to jeff feniak . take the round record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'konstantin rudenko'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose player record fuzzily matches to konstantin rudenko .', 'tostr': 'filter_eq { all_rows ; player ; konstantin rudenko }'}, 'round'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; player ; konstantin rudenko } ; round }', 'tointer': 'select the rows whose player record fuzzily matches to konstantin rudenko . take the round record of this row .'}], 'result': '-2', 'ind': 4, 'tostr': 'diff { hop { filter_eq { all_rows ; player ; jeff feniak } ; round } ; hop { filter_eq { all_rows ; player ; konstantin rudenko } ; round } }'}, '-2'], 'result': True, 'ind': 5, 'tostr': 'eq { diff { hop { filter_eq { all_rows ; player ; jeff feniak } ; round } ; hop { filter_eq { all_rows ; player ; konstantin rudenko } ; round } } ; -2 } = true', 'tointer': 'select the rows whose player record fuzzily matches to jeff feniak . take the round record of this row . select the rows whose player record fuzzily matches to konstantin rudenko . take the round record of this row . the second record is 2 larger than the first record .'} | eq { diff { hop { filter_eq { all_rows ; player ; jeff feniak } ; round } ; hop { filter_eq { all_rows ; player ; konstantin rudenko } ; round } } ; -2 } = true | select the rows whose player record fuzzily matches to jeff feniak . take the round record of this row . select the rows whose player record fuzzily matches to konstantin rudenko . take the round record of this row . the second record is 2 larger than the first record . | 6 | 6 | {'eq_5': 5, 'result_6': 6, 'diff_4': 4, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'player_8': 8, 'jeff feniak_9': 9, 'round_10': 10, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_11': 11, 'player_12': 12, 'konstantin rudenko_13': 13, 'round_14': 14, '-2_15': 15} | {'eq_5': 'eq', 'result_6': 'true', 'diff_4': 'diff', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'player_8': 'player', 'jeff feniak_9': 'jeff feniak', 'round_10': 'round', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_11': 'all_rows', 'player_12': 'player', 'konstantin rudenko_13': 'konstantin rudenko', 'round_14': 'round', '-2_15': '-2'} | {'eq_5': [6], 'result_6': [], 'diff_4': [5], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_7': [0], 'player_8': [0], 'jeff feniak_9': [0], 'round_10': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_11': [1], 'player_12': [1], 'konstantin rudenko_13': [1], 'round_14': [3], '-2_15': [5]} | ['round', 'player', 'position', 'nationality', 'college / junior / club team ( league )'] | [['1', 'maxime ouellet', 'goaltender', 'canada', 'quebec remparts ( qmjhl )'], ['4', 'jeff feniak', 'defense', 'canada', 'calgary hitmen ( whl )'], ['6', 'konstantin rudenko', 'forward', 'russia', 'severstal cherepovets ( rus )'], ['7', 'pavel kasparik', 'center', 'czech republic', 'ihc pisek ( cze )'], ['7', 'vaclav pletka', 'left wing', 'czech republic', 'hc oceláři třinec ( cze )'], ['8', 'david nystrom', 'right wing', 'sweden', 'frölunda hc ( elitserien )']] |
list of argumental episodes | https://en.wikipedia.org/wiki/List_of_Argumental_episodes | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-19930660-2.html.csv | majority | the blue team won the majority of episodes in argumental season 2 . | {'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'fuzzily_match', 'value': 'blue', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'winner', 'blue'], 'result': True, 'ind': 0, 'tointer': 'for the winner records of all rows , most of them fuzzily match to blue .', 'tostr': 'most_eq { all_rows ; winner ; blue } = true'} | most_eq { all_rows ; winner ; blue } = true | for the winner records of all rows , most of them fuzzily match to blue . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'winner_3': 3, 'blue_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'winner_3': 'winner', 'blue_4': 'blue'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'winner_3': [0], 'blue_4': [0]} | ['episode', 'first broadcast', 'rufus guest', 'marcus guest', 'winner'] | [['2x01', '23 march 2009', 'chris addison', 'dara ó briain', 'red ( 3 - 2 )'], ['2x02', '30 march 2009', 'mark watson', "ardal o'hanlon", 'red ( 2 - 2 )'], ['2x03', '6 april 2009', 'jo caulfield', 'katy brand', 'red ( 3 - 2 )'], ['2x05', '27 april 2009', 'reginald d hunter', 'sean hughes', 'blue ( 3 - 2 )'], ['2x06', '4 may 2009', 'frankie boyle', 'lucy porter', 'blue ( 3 - 2 )'], ['2x07', '13 october 2009', 'andrew maxwell', 'frankie boyle', 'blue ( 3 - 1 )'], ['2x08', '20 october 2009', 'sean lock', 'phill jupitus', 'blue ( 3 - 1 )'], ['2x09', '27 october 2009', 'dara ó briain', 'will smith', 'blue ( 4 - 1 )'], ['2x10', '3 november 2009', 'simon day', 'charlie higson', 'blue ( 5 - 0 )'], ['2x11', '10 november 2009', 'rory mcgrath', 'sean hughes', 'blue ( 3 - 1 )'], ['2x12', '17 november 2009', 'hugh dennis', 'mark watson', 'red ( 3 - 2 )'], ['2x13', '24 november 2009', 'clips show : episodes 1 - 6', 'clips show : episodes 1 - 6', 'n / a']] |
elena pampoulova | https://en.wikipedia.org/wiki/Elena_Pampoulova | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18330817-8.html.csv | count | of all of elena pampoulova 's tournaments , five were on a clay surface . | {'scope': 'all', 'criterion': 'equal', 'value': 'clay', 'result': '5', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'surface', 'clay'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose surface record fuzzily matches to clay .', 'tostr': 'filter_eq { all_rows ; surface ; clay }'}], 'result': '5', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; surface ; clay } }', 'tointer': 'select the rows whose surface record fuzzily matches to clay . the number of such rows is 5 .'}, '5'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; surface ; clay } } ; 5 } = true', 'tointer': 'select the rows whose surface record fuzzily matches to clay . the number of such rows is 5 .'} | eq { count { filter_eq { all_rows ; surface ; clay } } ; 5 } = true | select the rows whose surface record fuzzily matches to clay . 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, 'surface_5': 5, 'clay_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', 'surface_5': 'surface', 'clay_6': 'clay', '5_7': '5'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'surface_5': [0], 'clay_6': [0], '5_7': [2]} | ['outcome', 'date', 'tournament', 'surface', 'partner', 'opponents in the final', 'score in the final'] | [['runner - ups', 'november 4 , 1988', 'melbourne , australia itf 10000', 'hard', 'kristin godridge', 'natalia leipus bernadette randall - marshall', '4 - 6 , 7 - 6 ( 7 - 5 ) , 2 - 6'], ['winners', 'april 9 , 1989', 'bari , italy itf 10000', 'clay', 'marion maruska', 'andrea noszály eva maria schuerhoff', 'w / o'], ['winners', 'june 14 , 1992', 'modena , italy itf 25000', 'clay', 'ruxandra dragomir', 'alexandra fusai nathalie tschan', '6 - 3 , 7 - 6 ( 7 - 5 )'], ['runner - ups', 'august 4 , 1992', 'vaihingen , germany itf 25000', 'clay', 'joannette kruger', 'eva martincová pavlína rajzlová', '4 - 6 , 0 - 6'], ['runner - ups', 'november 15 , 1992', 'manchester , united kingdom itf 25000', 'carpet ( i )', 'nathalie tschan', 'elena likhovtseva elena makarova', '3 - 6 , 4 - 6'], ['winners', 'november 22 , 1992', 'nottingham , united kingdom itf 25000', 'carpet ( i )', 'els callens', 'ruxandra dragomir irina spîrlea', '7 - 6 ( 7 - 3 ) , 6 - 4'], ['winners', 'april 11 , 1993', 'limoges , france itf 25000', 'carpet ( i )', 'silvia farina elia', 'stephanie reece danielle scott', '6 - 2 , 6 - 7 ( 5 - 7 ) , 6 - 2'], ['winners', 'april 11 , 1993', 'poitiers , france itf 25000', 'hard', 'olga lugina', 'els callens nancy feber', '6 - 4 , 3 - 6 , 6 - 3'], ['winners', 'december 11 , 1994', 'cergy - pontoise , france itf 50000', 'hard ( i )', 'angelique olivier', 'kateřina šišková eva melicharová', '6 - 1 , 6 - 4'], ['winners', 'october 29 , 1995', 'lakeland , fl , usa itf 50000', 'hard', 'eva martincová', 'sandra cacic tracey rodgers', '1 - 6 , 6 - 2 , 6 - 1'], ['runner - ups', 'december 3 , 1995', 'limoges , france itf 50000', 'hard', 'eva martincová', 'eva melicharová helena vildová', '3 - 6 , 6 - 0 , 4 - 6'], ['winners', 'october 29 , 1997', 'makarska , croatia itf 75000', 'clay', 'olga lugina', 'maria goloviznina evgenia kulikovskaya', '5 - 7 , 7 - 5 , 7 - 5'], ['runner - ups', 'april 26 , 1998', 'prostějov , czech republic itf 75000', 'clay', 'olga lugina', 'lenka cenková kateřina šišková', '4 - 6 , 6 - 4 , 4 - 6']] |
2008 - 09 west ham united f.c. season | https://en.wikipedia.org/wiki/2008%E2%80%9309_West_Ham_United_F.C._season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18539546-24.html.csv | comparative | west ham united f.c. played against peterborough united before playig against villarreal . | {'row_1': '4', 'row_2': '7', 'col': '2', 'col_other': '5', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'peterborough united'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opponent record fuzzily matches to peterborough united .', 'tostr': 'filter_eq { all_rows ; opponent ; peterborough united }'}, 'date'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; opponent ; peterborough united } ; date }', 'tointer': 'select the rows whose opponent record fuzzily matches to peterborough united . take the date record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'villarreal'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose opponent record fuzzily matches to villarreal .', 'tostr': 'filter_eq { all_rows ; opponent ; villarreal }'}, 'date'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; opponent ; villarreal } ; date }', 'tointer': 'select the rows whose opponent record fuzzily matches to villarreal . take the date record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; opponent ; peterborough united } ; date } ; hop { filter_eq { all_rows ; opponent ; villarreal } ; date } } = true', 'tointer': 'select the rows whose opponent record fuzzily matches to peterborough united . take the date record of this row . select the rows whose opponent record fuzzily matches to villarreal . take the date record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; opponent ; peterborough united } ; date } ; hop { filter_eq { all_rows ; opponent ; villarreal } ; date } } = true | select the rows whose opponent record fuzzily matches to peterborough united . take the date record of this row . select the rows whose opponent record fuzzily matches to villarreal . take the date record of this row . the first record is less than the second record . | 5 | 5 | {'less_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'opponent_7': 7, 'peterborough united_8': 8, 'date_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'opponent_11': 11, 'villarreal_12': 12, 'date_13': 13} | {'less_4': 'less', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'opponent_7': 'opponent', 'peterborough united_8': 'peterborough united', 'date_9': 'date', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'opponent_11': 'opponent', 'villarreal_12': 'villarreal', 'date_13': 'date'} | {'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'opponent_7': [0], 'peterborough united_8': [0], 'date_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'opponent_11': [1], 'villarreal_12': [1], 'date_13': [3]} | ['match', 'date', 'competition or tour', 'ground', 'opponent', 'score1', 'scorers'] | [['1', '17 july 2008', 'friendly', 'a', 'hampton & richmond', '4 - 2', "bellamy 37 ' & 60 ' , noble 41 ' , hines 81 '"], ['2', '20 july 2008', 'friendly', 'a', 'columbus crew', '3 - 1', "ashton 6 ' , evans og 26 ' , reid 53 '"], ['3', '24 july 2008', 'major league soccer all - star game', 'a', 'major league all stars', '2 - 3', "ashton 26 ' & 67 '"], ['4', '29 july 2008', 'friendly', 'a', 'peterborough united', '2 - 0', "bellamy 3 ' & 42 '"], ['5', '1 august 2008', 'friendly', 'a', 'southampton', '2 - 2', "davenport 45 ' , hines 65 '"], ['6', '4 august 2008', 'friendly', 'a', 'ipswich town', '5 - 3', "ashton 4 ' , 57 ' & 74 ' , bellamy 10 ' , noble 79 '"], ['7', '9 august 2008', 'the bobby moore cup', 'h', 'villarreal', '1 - 1', "cole 1 '"]] |
handball at the asian games | https://en.wikipedia.org/wiki/Handball_at_the_Asian_Games | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14306965-3.html.csv | ordinal | playing handball at the asian games , japan ranked second in total medal count . | {'row': '4', 'col': '6', 'order': '2', 'col_other': '2', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'total', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; total ; 2 }'}, 'nation'], 'result': 'japan ( jpn )', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; total ; 2 } ; nation }'}, 'japan ( jpn )'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; total ; 2 } ; nation } ; japan ( jpn ) } = true', 'tointer': 'select the row whose total record of all rows is 2nd maximum . the nation record of this row is japan ( jpn ) .'} | eq { hop { nth_argmax { all_rows ; total ; 2 } ; nation } ; japan ( jpn ) } = true | select the row whose total record of all rows is 2nd maximum . the nation record of this row is japan ( jpn ) . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'total_5': 5, '2_6': 6, 'nation_7': 7, 'japan (jpn)_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', 'total_5': 'total', '2_6': '2', 'nation_7': 'nation', 'japan (jpn)_8': 'japan ( jpn )'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'total_5': [0], '2_6': [0], 'nation_7': [1], 'japan (jpn)_8': [2]} | ['rank', 'nation', 'gold', 'silver', 'bronze', 'total'] | [['1', 'south korea ( kor )', '11', '0', '2', '13'], ['2', 'china ( chn )', '2', '2', '3', '7'], ['3', 'kuwait ( kuw )', '1', '2', '0', '3'], ['4', 'japan ( jpn )', '0', '5', '5', '10'], ['5', 'kazakhstan ( kaz )', '0', '2', '0', '2'], ['6', 'iran ( iri )', '0', '1', '1', '2'], ['6', 'qatar ( qat )', '0', '1', '1', '2'], ['8', 'north korea ( prk )', '0', '1', '0', '1'], ['9', 'chinese taipei ( tpe )', '0', '0', '1', '1'], ['9', 'saudi arabia ( ksa )', '0', '0', '1', '1'], ['total', 'total', '14', '14', '14', '42']] |
2007 world championships in athletics - men 's 200 metres | https://en.wikipedia.org/wiki/2007_World_Championships_in_Athletics_%E2%80%93_Men%27s_200_metres | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18912995-9.html.csv | unique | of all of these athletes , only anastasios gousis is from greece . | {'scope': 'all', 'row': '8', 'col': '3', 'col_other': '2', 'criterion': 'equal', 'value': 'greece', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nationality', 'greece'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose nationality record fuzzily matches to greece .', 'tostr': 'filter_eq { all_rows ; nationality ; greece }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; nationality ; greece } }', 'tointer': 'select the rows whose nationality record fuzzily matches to greece . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nationality', 'greece'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose nationality record fuzzily matches to greece .', 'tostr': 'filter_eq { all_rows ; nationality ; greece }'}, 'name'], 'result': 'anastasios gousis', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; nationality ; greece } ; name }'}, 'anastasios gousis'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; nationality ; greece } ; name } ; anastasios gousis }', 'tointer': 'the name record of this unqiue row is anastasios gousis .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; nationality ; greece } } ; eq { hop { filter_eq { all_rows ; nationality ; greece } ; name } ; anastasios gousis } } = true', 'tointer': 'select the rows whose nationality record fuzzily matches to greece . there is only one such row in the table . the name record of this unqiue row is anastasios gousis .'} | and { only { filter_eq { all_rows ; nationality ; greece } } ; eq { hop { filter_eq { all_rows ; nationality ; greece } ; name } ; anastasios gousis } } = true | select the rows whose nationality record fuzzily matches to greece . there is only one such row in the table . the name record of this unqiue row is anastasios gousis . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'nationality_7': 7, 'greece_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'name_9': 9, 'anastasios gousis_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'nationality_7': 'nationality', 'greece_8': 'greece', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'name_9': 'name', 'anastasios gousis_10': 'anastasios gousis'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'nationality_7': [0], 'greece_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'name_9': [2], 'anastasios gousis_10': [3]} | ['lane', 'name', 'nationality', 'react', 'time'] | [['4', 'tyson gay', 'united states', '0.143', '19.76'], ['5', 'usain bolt', 'jamaica', '0.159', '19.91'], ['6', 'wallace spearmon', 'united states', '0.144', '20.05'], ['8', 'rodney martin', 'united states', '0.186', '20.06'], ['3', 'churandy martina', 'netherlands antilles', '0.144', '20.28'], ['7', 'marvin anderson', 'jamaica', '0.171', '20.28'], ['9', 'christopher williams', 'jamaica', '0.154', '20.57'], ['7', 'anastasios gousis', 'greece', '0.143', '20.75']] |
christian vietoris | https://en.wikipedia.org/wiki/Christian_Vietoris | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-10705060-1.html.csv | aggregation | christian vietoris had an average of almost 57 points in his career between the years 2005 and 2012 . | {'scope': 'all', 'col': '7', 'type': 'average', 'result': '57', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'points'], 'result': '57', 'ind': 0, 'tostr': 'avg { all_rows ; points }'}, '57'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; points } ; 57 } = true', 'tointer': 'the average of the points record of all rows is 57 .'} | round_eq { avg { all_rows ; points } ; 57 } = true | the average of the points record of all rows is 57 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'points_4': 4, '57_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'points_4': 'points', '57_5': '57'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'points_4': [0], '57_5': [1]} | ['season', 'series', 'team name', 'races', 'poles', 'wins', 'points', 'position'] | [['2005', 'formula bmw adac', 'eifelland racing', '19', '0', '0', '17', '16th'], ['2006', 'formula bmw adac', 'josef kaufmann racing', '18', '9', '9', '277', '1st'], ['2007', 'german formula three', 'josef kaufmann racing', '12', '2', '1', '62', '6th'], ['2008', 'formula 3 euro series', 'mücke motorsport', '20', '1', '1', '36', '6th'], ['2009', 'formula 3 euro series', 'mücke motorsport', '18', '0', '4', '75', '2nd'], ['2009 - 10', 'gp2 asia series', 'dams', '8', '0', '1', '9', '10th'], ['2010', 'gp2 series', 'racing engineering', '18', '0', '1', '29', '9th'], ['2011', 'gp2 series', 'racing engineering', '14', '1', '2', '35', '7th'], ['2011', 'deutsche tourenwagen masters', 'persson motorsport', '10', '0', '0', '4', '14th'], ['2012', 'deutsche tourenwagen masters', 'hwa team', '10', '0', '0', '25', '12th']] |
2002 honda indy 300 | https://en.wikipedia.org/wiki/2002_Honda_Indy_300 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15162303-2.html.csv | comparative | in the results for the 2002 honda indy 300 given , kenny bräck finished before jmmy vasser . | {'row_1': '4', 'row_2': '12', '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', 'driver', 'kenny bräck'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose driver record fuzzily matches to kenny bräck .', 'tostr': 'filter_eq { all_rows ; driver ; kenny bräck }'}, 'time / retired'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; driver ; kenny bräck } ; time / retired }', 'tointer': 'select the rows whose driver record fuzzily matches to kenny bräck . take the time / retired record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'driver', 'jimmy vasser'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose driver record fuzzily matches to jimmy vasser .', 'tostr': 'filter_eq { all_rows ; driver ; jimmy vasser }'}, 'time / retired'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; driver ; jimmy vasser } ; time / retired }', 'tointer': 'select the rows whose driver record fuzzily matches to jimmy vasser . take the time / retired record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; driver ; kenny bräck } ; time / retired } ; hop { filter_eq { all_rows ; driver ; jimmy vasser } ; time / retired } } = true', 'tointer': 'select the rows whose driver record fuzzily matches to kenny bräck . take the time / retired record of this row . select the rows whose driver record fuzzily matches to jimmy vasser . take the time / retired record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; driver ; kenny bräck } ; time / retired } ; hop { filter_eq { all_rows ; driver ; jimmy vasser } ; time / retired } } = true | select the rows whose driver record fuzzily matches to kenny bräck . take the time / retired record of this row . select the rows whose driver record fuzzily matches to jimmy vasser . take the time / retired 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, 'driver_7': 7, 'kenny bräck_8': 8, 'time / retired_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'driver_11': 11, 'jimmy vasser_12': 12, 'time / retired_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', 'driver_7': 'driver', 'kenny bräck_8': 'kenny bräck', 'time / retired_9': 'time / retired', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'driver_11': 'driver', 'jimmy vasser_12': 'jimmy vasser', 'time / retired_13': 'time / retired'} | {'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'driver_7': [0], 'kenny bräck_8': [0], 'time / retired_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'driver_11': [1], 'jimmy vasser_12': [1], 'time / retired_13': [3]} | ['driver', 'team', 'laps', 'time / retired', 'grid'] | [['mario domínguez', 'herdez competition', '40', '2:00:06.524', '18'], ['patrick carpentier', 'forsythe racing', '40', '+ 2.2 secs', '15'], ['paul tracy', 'team green', '40', '+ 2.5 secs', '5'], ['kenny bräck', 'chip ganassi racing', '40', '+ 2.7 secs', '4'], ['tony kanaan', 'mo nunn racing', '40', '+ 4.8 secs', '3'], ['alex tagliani', 'forsythe racing', '40', '+ 7.8 secs', '14'], ['dario franchitti', 'team green', '40', '+ 9.9 secs', '8'], ['cristiano da matta', 'newman - haas racing', '40', '+ 12.3 secs', '1'], ['michael andretti', 'team green', '40', '+ 13.5 secs', '16'], ['michel jourdain , jr', 'team rahal', '40', '+ 13.6 secs', '12'], ['christian fittipaldi', 'newman - haas racing', '40', '+ 14.8 secs', '13'], ['jimmy vasser', 'team rahal', '40', '+ 16.2 secs', '11'], ['shinji nakano', 'fernández racing', '40', '+ 17.6 secs', '7'], ['bruno junqueira', 'chip ganassi racing', '40', '+ 18.1 secs', '2'], ['scott dixon', 'chip ganassi racing', '24', 'mechanical', '6'], ['oriol servià', 'patrick racing', '11', 'mechanical', '9'], ['adrián fernández', 'fernández racing', '0', 'contact', '10'], ['tora takagi', 'walker racing', '0', 'contact', '17']] |
list of asian and pacific countries by gdp ( ppp ) | https://en.wikipedia.org/wiki/List_of_Asian_and_Pacific_countries_by_GDP_%28PPP%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2248784-4.html.csv | comparative | of the list of asian and pacific countries by gdp iran has a greater gdp than lebanon . | {'row_1': '1', 'row_2': '11', 'col': '5', 'col_other': '4', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'iran'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose country record fuzzily matches to iran .', 'tostr': 'filter_eq { all_rows ; country ; iran }'}, '2011 gdp ( ppp ) billions of usd'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; country ; iran } ; 2011 gdp ( ppp ) billions of usd }', 'tointer': 'select the rows whose country record fuzzily matches to iran . take the 2011 gdp ( ppp ) billions of usd record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'lebanon'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose country record fuzzily matches to lebanon .', 'tostr': 'filter_eq { all_rows ; country ; lebanon }'}, '2011 gdp ( ppp ) billions of usd'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; country ; lebanon } ; 2011 gdp ( ppp ) billions of usd }', 'tointer': 'select the rows whose country record fuzzily matches to lebanon . take the 2011 gdp ( ppp ) billions of usd record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; country ; iran } ; 2011 gdp ( ppp ) billions of usd } ; hop { filter_eq { all_rows ; country ; lebanon } ; 2011 gdp ( ppp ) billions of usd } } = true', 'tointer': 'select the rows whose country record fuzzily matches to iran . take the 2011 gdp ( ppp ) billions of usd record of this row . select the rows whose country record fuzzily matches to lebanon . take the 2011 gdp ( ppp ) billions of usd record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; country ; iran } ; 2011 gdp ( ppp ) billions of usd } ; hop { filter_eq { all_rows ; country ; lebanon } ; 2011 gdp ( ppp ) billions of usd } } = true | select the rows whose country record fuzzily matches to iran . take the 2011 gdp ( ppp ) billions of usd record of this row . select the rows whose country record fuzzily matches to lebanon . take the 2011 gdp ( ppp ) billions of usd 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, 'country_7': 7, 'iran_8': 8, '2011 gdp (ppp) billions of usd_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'country_11': 11, 'lebanon_12': 12, '2011 gdp (ppp) billions of usd_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', 'country_7': 'country', 'iran_8': 'iran', '2011 gdp (ppp) billions of usd_9': '2011 gdp ( ppp ) billions of usd', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'country_11': 'country', 'lebanon_12': 'lebanon', '2011 gdp (ppp) billions of usd_13': '2011 gdp ( ppp ) billions of usd'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'country_7': [0], 'iran_8': [0], '2011 gdp (ppp) billions of usd_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'country_11': [1], 'lebanon_12': [1], '2011 gdp (ppp) billions of usd_13': [3]} | ['rank mideast', 'rank asia', 'rank world', 'country', '2011 gdp ( ppp ) billions of usd'] | [['1', '6', '17', 'iran', '930.236'], ['2', '9', '23', 'saudi arabia', '677.663'], ['3', '18', '48', 'united arab emirates', '261.189'], ['4', '19', '50', 'israel', '235.446'], ['5', '21', '55', 'qatar', '181.912'], ['6', '22', '58', 'kuwait', '150.002'], ['7', '23', '60', 'iraq', '127.348'], ['8', '26', '66', 'syria', '107.803'], ['9', '29', '76', 'oman', '81.005'], ['10', '30', '83', 'yemen', '63.344'], ['11', '31', '84', 'lebanon', '61.738'], ['12', '35', '97', 'jordan', '36.897'], ['13', '37', '104', 'bahrain', '30.889']] |
list of csi : ny characters | https://en.wikipedia.org/wiki/List_of_CSI%3A_NY_characters | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11240028-5.html.csv | superlative | henry darius had the highest murder count out of csi : ny criminals from the show . | {'scope': 'subset', 'col_superlative': '3', 'row_superlative': '3', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': {'col': '3', 'criterion': 'fuzzily_match', 'value': 'murder'}} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'crime', 'murder'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; crime ; murder }', 'tointer': 'select the rows whose crime record fuzzily matches to murder .'}, 'crime'], 'result': None, 'ind': 1, 'tostr': 'argmax { filter_eq { all_rows ; crime ; murder } ; crime }'}, 'character'], 'result': 'henry darius', 'ind': 2, 'tostr': 'hop { argmax { filter_eq { all_rows ; crime ; murder } ; crime } ; character }'}, 'henry darius'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { argmax { filter_eq { all_rows ; crime ; murder } ; crime } ; character } ; henry darius } = true', 'tointer': 'select the rows whose crime record fuzzily matches to murder . select the row whose crime record of these rows is maximum . the character record of this row is henry darius .'} | eq { hop { argmax { filter_eq { all_rows ; crime ; murder } ; crime } ; character } ; henry darius } = true | select the rows whose crime record fuzzily matches to murder . select the row whose crime record of these rows is maximum . the character record of this row is henry darius . | 4 | 4 | {'str_eq_3': 3, 'result_4': 4, 'str_hop_2': 2, 'argmax_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'crime_6': 6, 'murder_7': 7, 'crime_8': 8, 'character_9': 9, 'henry darius_10': 10} | {'str_eq_3': 'str_eq', 'result_4': 'true', 'str_hop_2': 'str_hop', 'argmax_1': 'argmax', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'crime_6': 'crime', 'murder_7': 'murder', 'crime_8': 'crime', 'character_9': 'character', 'henry darius_10': 'henry darius'} | {'str_eq_3': [4], 'result_4': [], 'str_hop_2': [3], 'argmax_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'crime_6': [0], 'murder_7': [0], 'crime_8': [1], 'character_9': [2], 'henry darius_10': [3]} | ['character', 'portrayed by', 'crime', 'first appearance', 'last appearance'] | [['sonny sassone', 'michael deluise', 'murder ( 2 counts )', 'tanglewood', 'run silent , run deep'], ['frankie mala', 'ed quinn', 'attempted murder ( attacked stella )', 'grand murder at central station', 'all access'], ['henry darius', 'james badge dale', 'murder ( 15 counts )', 'felony flight ( csi : miami crossover )', 'manhattan manhunt'], ['dj pratt', 'chad williams', 'murder / rape ( 1 / 2 counts ) ( killed aiden )', 'summer in the city', 'heroes'], ['shane casey', 'edward furlong', 'murder ( 8 counts )', 'hung out to dry', 'the 34th floor'], ['clay dobson', 'joey lawrence', 'murder ( 3 counts )', 'past imperfect', 'comes around'], ['andrew drew bedford ( aka 333 stalker )', 'kerr smith', 'attempted murder ( 6 counts )', 'the deep', 'the thing about heroes'], ['suspect x', 'kam heskin', 'murder ( 6 counts )', 'down the rabbit hole', 'doa for a day'], ['cabbie killer', 'ryan locke', 'murder ( 6 counts )', 'like water for murder', 'taxi'], ['ethan scott ( aka joe )', 'elias koteas', 'murder ( 2 counts )', 'hostage', 'veritas'], ['sebastian diakos', 'adoni maropis', 'murder ( 2 counts )', 'the cost of living', 'point of no return'], ['george kolovos', 'paul papadakis', 'murder ( 1 count )', 'the cost of living', 'grounds for deception'], ['hollis eckhart ( aka the compass killer )', 'skeet ulrich', 'murder ( 3 counts )', "lat 40 degree 47 ' n / long 73 degree 58 ' w", 'manhattanhenge'], ['raymond harris', 'clifton collins , jr', 'murder ( 2 counts )', 'nothing for something', 'life sentence'], ['john curtis', 'jason wiles', 'rape ( 5 counts )', 'crushed', 'means to an end']] |
john karlen | https://en.wikipedia.org/wiki/John_Karlen | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1287443-1.html.csv | unique | the role of geoffrey fitton was the only time that john karlen performed at the royale theatre . | {'scope': 'all', 'row': '5', 'col': '5', 'col_other': '4', 'criterion': 'equal', 'value': 'royale theatre', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'theatre', 'royale theatre'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose theatre record fuzzily matches to royale theatre .', 'tostr': 'filter_eq { all_rows ; theatre ; royale theatre }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; theatre ; royale theatre } }', 'tointer': 'select the rows whose theatre record fuzzily matches to royale theatre . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'theatre', 'royale theatre'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose theatre record fuzzily matches to royale theatre .', 'tostr': 'filter_eq { all_rows ; theatre ; royale theatre }'}, 'role'], 'result': 'geoffrey fitton', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; theatre ; royale theatre } ; role }'}, 'geoffrey fitton'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; theatre ; royale theatre } ; role } ; geoffrey fitton }', 'tointer': 'the role record of this unqiue row is geoffrey fitton .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; theatre ; royale theatre } } ; eq { hop { filter_eq { all_rows ; theatre ; royale theatre } ; role } ; geoffrey fitton } } = true', 'tointer': 'select the rows whose theatre record fuzzily matches to royale theatre . there is only one such row in the table . the role record of this unqiue row is geoffrey fitton .'} | and { only { filter_eq { all_rows ; theatre ; royale theatre } } ; eq { hop { filter_eq { all_rows ; theatre ; royale theatre } ; role } ; geoffrey fitton } } = true | select the rows whose theatre record fuzzily matches to royale theatre . there is only one such row in the table . the role record of this unqiue row is geoffrey fitton . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'theatre_7': 7, 'royale theatre_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'role_9': 9, 'geoffrey fitton_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'theatre_7': 'theatre', 'royale theatre_8': 'royale theatre', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'role_9': 'role', 'geoffrey fitton_10': 'geoffrey fitton'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'theatre_7': [0], 'royale theatre_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'role_9': [2], 'geoffrey fitton_10': [3]} | ['opening date', 'closing date', 'performances', 'role', 'theatre'] | [['mar 10 , 1959', 'jan 30 , 1960', '375', 'tom junior - replacement', 'martin beck theatre'], ['oct 29 , 1960', 'feb 4 , 1961', '113', 'aaron jablonski schuyler grogan', 'music box theatre'], ['nov 11 , 1963', 'nov 16 , 1963', '8', 'shorty ensemble', 'lunt - fontanne theatre'], ['jan 1 , 1964', 'jan 4 , 1964', '5', 'stage manager', 'brooks atkinson theatre'], ['feb 18 , 1965', 'mar 27 , 1965', '44', 'geoffrey fitton', 'royale theatre'], ['nov 1 , 1965', 'nov 6 , 1965', '8', 'unknown', 'brooks atkinson theatre'], ['march 05 , 1966', 'april 17 , 1966', '49', 'joe ( monopoly ) mr stein ( suburban tragedy )', 'stage 73']] |
toronto raptors all - time roster | https://en.wikipedia.org/wiki/Toronto_Raptors_all-time_roster | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10015132-11.html.csv | unique | antonio lang is the only player on the toronto raptors all - time roster that attended duke . | {'scope': 'all', 'row': '1', 'col': '5', 'col_other': '1', 'criterion': 'equal', 'value': 'duke', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'school / club team', 'duke'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose school / club team record fuzzily matches to duke .', 'tostr': 'filter_eq { all_rows ; school / club team ; duke }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; school / club team ; duke } }', 'tointer': 'select the rows whose school / club team record fuzzily matches to duke . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'school / club team', 'duke'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose school / club team record fuzzily matches to duke .', 'tostr': 'filter_eq { all_rows ; school / club team ; duke }'}, 'player'], 'result': 'antonio lang', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; school / club team ; duke } ; player }'}, 'antonio lang'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; school / club team ; duke } ; player } ; antonio lang }', 'tointer': 'the player record of this unqiue row is antonio lang .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; school / club team ; duke } } ; eq { hop { filter_eq { all_rows ; school / club team ; duke } ; player } ; antonio lang } } = true', 'tointer': 'select the rows whose school / club team record fuzzily matches to duke . there is only one such row in the table . the player record of this unqiue row is antonio lang .'} | and { only { filter_eq { all_rows ; school / club team ; duke } } ; eq { hop { filter_eq { all_rows ; school / club team ; duke } ; player } ; antonio lang } } = true | select the rows whose school / club team record fuzzily matches to duke . there is only one such row in the table . the player record of this unqiue row is antonio lang . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'school / club team_7': 7, 'duke_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'player_9': 9, 'antonio lang_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'school / club team_7': 'school / club team', 'duke_8': 'duke', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'player_9': 'player', 'antonio lang_10': 'antonio lang'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'school / club team_7': [0], 'duke_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'player_9': [2], 'antonio lang_10': [3]} | ['player', 'nationality', 'position', 'years in toronto', 'school / club team'] | [['antonio lang', 'united states', 'guard - forward', '1999 - 2000', 'duke'], ['voshon lenard', 'united states', 'guard', '2002 - 03', 'minnesota'], ['martin lewis', 'united states', 'guard - forward', '1996 - 97', 'butler cc ( ks )'], ['brad lohaus', 'united states', 'forward - center', '1996', 'iowa'], ['art long', 'united states', 'forward - center', '2002 - 03', 'cincinnati'], ['john long', 'united states', 'guard', '1996 - 97', 'detroit'], ['kyle lowry', 'united states', 'guard', '2012 - present', 'villanova'], ['john lucas iii', 'united states', 'guard', '2012 - 2013', 'oklahoma state']] |
2000 tennessee titans season | https://en.wikipedia.org/wiki/2000_Tennessee_Titans_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16025613-2.html.csv | unique | the tennessee titans did not play during week 3 of the 2000 nfl season . | {'scope': 'all', 'row': '3', 'col': '5', 'col_other': '1', 'criterion': 'equal', 'value': 'none', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 'none'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to none .', 'tostr': 'filter_eq { all_rows ; result ; none }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; result ; none } }', 'tointer': 'select the rows whose result record fuzzily matches to none . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 'none'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to none .', 'tostr': 'filter_eq { all_rows ; result ; none }'}, 'week'], 'result': '3', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; result ; none } ; week }'}, '3'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; result ; none } ; week } ; 3 }', 'tointer': 'the week record of this unqiue row is 3 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; result ; none } } ; eq { hop { filter_eq { all_rows ; result ; none } ; week } ; 3 } } = true', 'tointer': 'select the rows whose result record fuzzily matches to none . there is only one such row in the table . the week record of this unqiue row is 3 .'} | and { only { filter_eq { all_rows ; result ; none } } ; eq { hop { filter_eq { all_rows ; result ; none } ; week } ; 3 } } = true | select the rows whose result record fuzzily matches to none . there is only one such row in the table . the week record of this unqiue row is 3 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'result_7': 7, 'None_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'week_9': 9, '3_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'result_7': 'result', 'None_8': 'none', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'week_9': 'week', '3_10': '3'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'result_7': [0], 'None_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'week_9': [2], '3_10': [3]} | ['week', 'date', 'tv time', 'opponent', 'result'] | [['1', 'september 3 , 2000', 'espn 7:30 pm cdt', 'buffalo bills', 'l 13 - 16'], ['2', 'september 10 , 2000', 'cbs 12:00 pm cdt', 'kansas city chiefs', 'w 17 - 14'], ['3', '-', '-', '-', 'none'], ['4', 'september 24 , 2000', 'cbs 12:00 pm cdt', 'pittsburgh steelers', 'w 23 - 20'], ['5', 'october 1 , 2000', 'fox 12:00 pm cdt', 'new york giants', 'w 28 - 14'], ['6', 'october 8 , 2000', 'cbs 12:00 pm cdt', 'cincinnati bengals', 'w 23 - 14'], ['7', 'october 16 , 2000', 'abc 8:00 pm cdt', 'jacksonville jaguars', 'w 27 - 13'], ['8', 'october 22 , 2000', 'cbs 12:00 pm cdt', 'baltimore ravens', 'w 14 - 6'], ['9', 'october 30 , 2000', 'abc 8:00 pm cdt', 'washington redskins', 'w 27 - 21'], ['10', 'november 5 , 2000', 'cbs 12:00 pm cdt', 'pittsburgh steelers', 'w 9 - 7'], ['11', 'november 12 , 2000', 'cbs 12:00 pm cdt', 'baltimore ravens', 'l 23 - 24'], ['12', 'november 19 , 2000', 'cbs 12:00 pm cdt', 'cleveland browns', 'w 24 - 10'], ['13', 'november 26 , 2000', 'cbs 3:15 pm cdt', 'jacksonville jaguars', 'l 13 - 16'], ['14', 'december 3 , 2000', 'cbs 12:00 pm cdt', 'philadelphia eagles', 'w 15 - 13'], ['15', 'december 10 , 2000', 'cbs 12:00 pm cdt', 'cincinnati bengals', 'w 35 - 3'], ['16', 'december 17 , 2000', 'cbs 12:00 pm cdt', 'cleveland browns', 'w 24 - 0'], ['17', 'december 25 , 2000', 'abc 8:00 pm cdt', 'dallas cowboys', 'w 31 - 0']] |
denni avdić | https://en.wikipedia.org/wiki/Denni_Avdi%C4%87 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12471124-1.html.csv | superlative | the highest number of goals were scored when playing in the 2009-2010 season . | {'scope': 'all', 'col_superlative': '6', 'row_superlative': '4', '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', 'goals'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; goals }'}, 'season'], 'result': '2009 - 10', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; goals } ; season }'}, '2009 - 10'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; goals } ; season } ; 2009 - 10 } = true', 'tointer': 'select the row whose goals record of all rows is maximum . the season record of this row is 2009 - 10 .'} | eq { hop { argmax { all_rows ; goals } ; season } ; 2009 - 10 } = true | select the row whose goals record of all rows is maximum . the season record of this row is 2009 - 10 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'goals_5': 5, 'season_6': 6, '2009 - 10_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'goals_5': 'goals', 'season_6': 'season', '2009 - 10_7': '2009 - 10'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'goals_5': [0], 'season_6': [1], '2009 - 10_7': [2]} | ['season', 'club', 'country', 'competition', 'apps', 'goals'] | [['2006 - 07', 'if elfsborg', 'sweden', 'allsvenskan', '19', '0'], ['2007 - 08', 'if elfsborg', 'sweden', 'allsvenskan', '26', '4'], ['2008 - 09', 'if elfsborg', 'sweden', 'allsvenskan', '30', '3'], ['2009 - 10', 'if elfsborg', 'sweden', 'allsvenskan', '29', '19'], ['2010 - 11', 'werder bremen', 'germany', 'bundesliga', '7', '0'], ['2011 - 12', 'werder bremen ii', 'germany', 'regionalliga nord', '12', '0'], ['2012 - 13', 'pec zwolle', 'netherlands', 'eredivisie', '24', '8']] |
2007 - 08 utah jazz season | https://en.wikipedia.org/wiki/2007%E2%80%9308_Utah_Jazz_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11964263-8.html.csv | count | utah jazz won four of the games in the 2007 - 2008 season when carlos boozer was the leading scorer . | {'scope': 'subset', 'criterion': 'fuzzily_match', 'value': 'w', 'result': '4', 'col': '3', 'subset': {'col': '5', 'criterion': 'fuzzily_match', 'value': 'carlos boozer'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'leading scorer', 'carlos boozer'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; leading scorer ; carlos boozer }', 'tointer': 'select the rows whose leading scorer record fuzzily matches to carlos boozer .'}, 'score', 'w'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose leading scorer record fuzzily matches to carlos boozer . among these rows , select the rows whose score record fuzzily matches to w .', 'tostr': 'filter_eq { filter_eq { all_rows ; leading scorer ; carlos boozer } ; score ; w }'}], 'result': '4', 'ind': 2, 'tostr': 'count { filter_eq { filter_eq { all_rows ; leading scorer ; carlos boozer } ; score ; w } }', 'tointer': 'select the rows whose leading scorer record fuzzily matches to carlos boozer . among these rows , select the rows whose score record fuzzily matches to w . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_eq { all_rows ; leading scorer ; carlos boozer } ; score ; w } } ; 4 } = true', 'tointer': 'select the rows whose leading scorer record fuzzily matches to carlos boozer . among these rows , select the rows whose score record fuzzily matches to w . the number of such rows is 4 .'} | eq { count { filter_eq { filter_eq { all_rows ; leading scorer ; carlos boozer } ; score ; w } } ; 4 } = true | select the rows whose leading scorer record fuzzily matches to carlos boozer . among these rows , select the rows whose score record fuzzily matches to w . the number of such rows is 4 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_str_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'leading scorer_6': 6, 'carlos boozer_7': 7, 'score_8': 8, 'w_9': 9, '4_10': 10} | {'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_str_eq_1': 'filter_str_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'leading scorer_6': 'leading scorer', 'carlos boozer_7': 'carlos boozer', 'score_8': 'score', 'w_9': 'w', '4_10': '4'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_str_eq_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'leading scorer_6': [0], 'carlos boozer_7': [0], 'score_8': [1], 'w_9': [1], '4_10': [3]} | ['date', 'visitor', 'score', 'home', 'leading scorer', 'record'] | [['february 1', 'jazz', 'w 96 - 87 ( ot )', 'wizards', 'mehmet okur ( 27 )', '29 - 18'], ['february 2', 'jazz', 'w 110 - 91 ( ot )', 'grizzlies', 'carlos boozer ( 19 )', '30 - 18'], ['february 4', 'hornets', 'w 110 - 88 ( ot )', 'jazz', 'deron williams ( 29 )', '31 - 18'], ['february 6', 'jazz', 'w 118 - 115 ( ot )', 'nuggets', 'deron williams ( 29 )', '32 - 18'], ['february 8', 'jazz', 'l 104 - 117 ( ot )', 'kings', 'carlos boozer ( 23 )', '32 - 19'], ['february 9', 'bulls', 'w 97 - 87 ( ot )', 'jazz', 'carlos boozer ( 22 )', '33 - 19'], ['february 13', 'jazz', 'w 112 - 93 ( ot )', 'supersonics', 'carlos boozer ( 22 )', '34 - 19'], ['february 19', 'warriors', 'w 119 - 109 ( ot )', 'jazz', 'deron williams ( 29 )', '35 - 19'], ['february 22', 'jazz', 'l 104 - 114 ( ot )', 'clippers', 'deron williams ( 26 )', '35 - 20'], ['february 23', 'hawks', 'w 100 - 94 ( ot )', 'jazz', 'carlos boozer ( 21 )', '36 - 20'], ['february 26', 'jazz', 'l 100 - 111 ( ot )', 'timberwolves', 'carlos boozer ( 34 )', '36 - 21'], ['february 27', 'pistons', 'w 103 - 95 ( ot )', 'jazz', 'mehmet okur ( 24 )', '37 - 21'], ['february 29', 'jazz', 'l 98 - 110 ( ot )', 'hornets', 'mehmet okur ( 23 )', '37 - 22']] |
kazunari murakami | https://en.wikipedia.org/wiki/Kazunari_Murakami | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12302903-2.html.csv | unique | the only match that kazunari murakami had that lasted over six minutes was against masaaki satake . | {'scope': 'all', 'row': '3', 'col': '7', 'col_other': '3', 'criterion': 'greater_than', 'value': '6:00', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'time', '6:00'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose time record is greater than 6:00 .', 'tostr': 'filter_greater { all_rows ; time ; 6:00 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_greater { all_rows ; time ; 6:00 } }', 'tointer': 'select the rows whose time record is greater than 6:00 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'time', '6:00'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose time record is greater than 6:00 .', 'tostr': 'filter_greater { all_rows ; time ; 6:00 }'}, 'opponent'], 'result': 'masaaki satake', 'ind': 2, 'tostr': 'hop { filter_greater { all_rows ; time ; 6:00 } ; opponent }'}, 'masaaki satake'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_greater { all_rows ; time ; 6:00 } ; opponent } ; masaaki satake }', 'tointer': 'the opponent record of this unqiue row is masaaki satake .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_greater { all_rows ; time ; 6:00 } } ; eq { hop { filter_greater { all_rows ; time ; 6:00 } ; opponent } ; masaaki satake } } = true', 'tointer': 'select the rows whose time record is greater than 6:00 . there is only one such row in the table . the opponent record of this unqiue row is masaaki satake .'} | and { only { filter_greater { all_rows ; time ; 6:00 } } ; eq { hop { filter_greater { all_rows ; time ; 6:00 } ; opponent } ; masaaki satake } } = true | select the rows whose time record is greater than 6:00 . there is only one such row in the table . the opponent record of this unqiue row is masaaki satake . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_greater_0': 0, 'all_rows_6': 6, 'time_7': 7, '6:00_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'opponent_9': 9, 'masaaki satake_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_greater_0': 'filter_greater', 'all_rows_6': 'all_rows', 'time_7': 'time', '6:00_8': '6:00', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'opponent_9': 'opponent', 'masaaki satake_10': 'masaaki satake'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_greater_0': [1, 2], 'all_rows_6': [0], 'time_7': [0], '6:00_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'opponent_9': [2], 'masaaki satake_10': [3]} | ['res', 'record', 'opponent', 'method', 'event', 'round', 'time', 'location'] | [['win', '5 - 5', 'lee young gun', 'verbal submission ( armbar )', 'jungle fight 1', '1', '1:09', 'manaus , amazonas , brazil'], ['loss', '4 - 5', 'wallid ismail', 'tko ( punches )', 'ufo : legend', '2', '3:03', 'tokyo , japan'], ['loss', '4 - 4', 'masaaki satake', 'tko ( punches )', 'pride 10', '1', '6:58', 'tokorozawa , saitama , japan'], ['win', '4 - 3', 'john dixson', 'submission ( armbar )', 'pride 1', '1', '1:34', 'tokyo , japan'], ['loss', '3 - 3', 'maurice smith', 'ko ( punch )', 'extreme fighting 4', '1', '4:23', 'des moines , iowa , united states'], ['win', '3 - 2', 'bart vale', 'tko ( punches )', 'extreme fighting 3', '1', '4:37', 'tulsa , oklahoma , united states'], ['loss', '2 - 2', 'masanori suda', 'submission ( armbar )', "lumax cup : tournament of j ' 96", '2', '1:38', 'japan'], ['win', '2 - 1', 'akihiro gono', 'decision', "lumax cup : tournament of j ' 96", '2', '3:00', 'japan'], ['win', '1 - 1', 'isamu osugi', 'submission ( armlock )', "lumax cup : tournament of j ' 96", '1', '4:10', 'japan'], ['loss', '0 - 1', 'akihiro gono', 'ko ( kick )', "lumax cup : tournament of j ' 95", '1', '2:25', 'japan']] |
raleigh - durham skyhawks | https://en.wikipedia.org/wiki/Raleigh%E2%80%93Durham_Skyhawks | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1639689-2.html.csv | majority | most of the games had an attendance of over 10,000 . | {'scope': 'all', 'col': '8', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '10,000', 'subset': None} | {'func': 'most_greater', 'args': ['all_rows', 'attendance', '10,000'], 'result': True, 'ind': 0, 'tointer': 'for the attendance records of all rows , most of them are greater than 10,000 .', 'tostr': 'most_greater { all_rows ; attendance ; 10,000 } = true'} | most_greater { all_rows ; attendance ; 10,000 } = true | for the attendance records of all rows , most of them are greater than 10,000 . | 1 | 1 | {'most_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 'attendance_3': 3, '10,000_4': 4} | {'most_greater_0': 'most_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 'attendance_3': 'attendance', '10,000_4': '10,000'} | {'most_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 'attendance_3': [0], '10,000_4': [0]} | ['week', 'date', 'kickoff', 'opponent', 'final score', 'team record', 'game site', 'attendance'] | [['1', 'saturday , march 23', '4:00 pm', 'sacramento surge', 'l 3 - 9', '0 - 1', 'hughes stadium', '15126'], ['2', 'saturday , march 30', '8:00 pm', 'orlando thunder', 'l 20 - 58', '0 - 2', 'florida citrus bowl', '20811'], ['3', 'saturday , april 6', '8:00 pm', 'barcelona dragons', 'l 14 - 26', '0 - 3', 'carter - finley stadium', '17900'], ['4', 'monday , april 15', '8:00 pm', 'san antonio riders', 'l 15 - 37', '0 - 4', 'carter - finley stadium', '11818'], ['5', 'saturday , april 20', '8:00 pm', 'frankfurt galaxy', 'l 28 - 30', '0 - 5', 'waldstadion', '21065'], ['6', 'sunday , april 28', '6:00 pm', 'london monarchs', 'l 10 - 35', '0 - 6', 'wembley stadium', '33997'], ['7', 'sunday , may 5', '1:00 pm', 'new york / new jersey knights', 'l 6 - 42', '0 - 7', 'carter - finley stadium', '10069'], ['8', 'monday , may 13', '8:00 pm', 'montreal machine', 'l 6 - 15', '0 - 8', 'olympic stadium', '20123'], ['9', 'monday , may 20', '8:00 pm', 'orlando thunder', 'l 14 - 20', '0 - 9', 'carter - finley stadium', '4207']] |
list of nascar teams | https://en.wikipedia.org/wiki/List_of_NASCAR_teams | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1266602-2.html.csv | majority | most of the participating teams had at least one primary sponsor . | {'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'not_equal', 'value': 'n / a', 'subset': None} | {'func': 'most_str_not_eq', 'args': ['all_rows', 'primary sponsor ( s )', 'n / a'], 'result': True, 'ind': 0, 'tointer': 'for the primary sponsor ( s ) records of all rows , most of them do not match to n / a .', 'tostr': 'most_not_eq { all_rows ; primary sponsor ( s ) ; n / a } = true'} | most_not_eq { all_rows ; primary sponsor ( s ) ; n / a } = true | for the primary sponsor ( s ) records of all rows , most of them do not match to n / a . | 1 | 1 | {'most_str_not_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'primary sponsor (s)_3': 3, 'n / a_4': 4} | {'most_str_not_eq_0': 'most_str_not_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'primary sponsor (s)_3': 'primary sponsor ( s )', 'n / a_4': 'n / a'} | {'most_str_not_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'primary sponsor (s)_3': [0], 'n / a_4': [0]} | ['team', 'car ( s )', 'driver ( s )', 'primary sponsor ( s )', 'owner ( s )', 'crew chief', 'rounds'] | [['circle sport', 'chevrolet ss', 'tony raines', "little joe 's auto", 'joe falk', 'john rahlf', '25'], ['go green racing', 'ford fusion', 'brian keselowski', 'my 3 sons vending', 'bob keselowski', 'ben leslie', '7'], ['hillman racing', 'chevrolet ss', 'landon cassill', 'crc brakleen', 'mike hillman', 'mike abner', '9'], ['humphrey smith racing', 'chevrolet ss', 'mike bliss', 'n / a', 'randy humphrey', 'peter sospenzo', '22'], ['leavine family racing', 'ford fusion', 'reed sorenson', 'n / a', 'bob leavine', 'wally rogers', '16'], ['phil parsons racing', 'ford fusion', 'michael mcdowell', 'k - love / curb records', 'mike curb', 'gene nead', '26'], ['richard childress racing', 'chevrolet ss', 'austin dillon', 'advocare', 'richard childress', 'scott naset', '4'], ['wood brothers racing', 'ford fusion', 'trevor bayne', 'motorcraft / quick lane', 'glen wood', 'donnie wingo', '8'], ['xxxtreme motorsport', 'ford fusion', 'scott riggs', 'no label watches', 'john cohen', 'walter giles', '10']] |
2006 in paleontology | https://en.wikipedia.org/wiki/2006_in_paleontology | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15577542-12.html.csv | count | 3 of the named species in paleontology in the year 2006 are located in china . | {'scope': 'all', 'criterion': 'equal', 'value': 'china', 'result': '3', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'location', 'china'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose location record fuzzily matches to china .', 'tostr': 'filter_eq { all_rows ; location ; china }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; location ; china } }', 'tointer': 'select the rows whose location record fuzzily matches to china . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; location ; china } } ; 3 } = true', 'tointer': 'select the rows whose location record fuzzily matches to china . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; location ; china } } ; 3 } = true | select the rows whose location record fuzzily matches to china . 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, 'location_5': 5, 'china_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', 'location_5': 'location', 'china_6': 'china', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'location_5': [0], 'china_6': [0], '3_7': [2]} | ['name', 'status', 'authors', 'unit', 'location'] | [['cathayopterus', 'valid', 'wang zhou', 'yixian formation', 'china'], ['caviramus', 'valid', 'fröbisch fröbisch', 'kössen formation', 'switzerland'], ['longchengpterus', 'valid', 'wang li duan cheng', 'jiufotang formation', 'china'], ['muzquizopteryx', 'valid', 'frey buchy stinnesbeck gonzalez stefano', 'austin group', 'mexico'], ['yixianopterus', 'valid', 'lü ji yuan gao sun ji', 'yixian formation', 'china']] |
teo fabi | https://en.wikipedia.org/wiki/Teo_Fabi | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1218368-3.html.csv | unique | 1980 was the only year that teo fabi did not complete more than 6 laps in the race . | {'scope': 'all', 'row': '1', 'col': '6', 'col_other': '1', 'criterion': 'less_than', 'value': '7', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'laps', '7'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose laps record is less than 7 .', 'tostr': 'filter_less { all_rows ; laps ; 7 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_less { all_rows ; laps ; 7 } }', 'tointer': 'select the rows whose laps record is less than 7 . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'laps', '7'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose laps record is less than 7 .', 'tostr': 'filter_less { all_rows ; laps ; 7 }'}, 'year'], 'result': '1980', 'ind': 2, 'tostr': 'hop { filter_less { all_rows ; laps ; 7 } ; year }'}, '1980'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_less { all_rows ; laps ; 7 } ; year } ; 1980 }', 'tointer': 'the year record of this unqiue row is 1980 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_less { all_rows ; laps ; 7 } } ; eq { hop { filter_less { all_rows ; laps ; 7 } ; year } ; 1980 } } = true', 'tointer': 'select the rows whose laps record is less than 7 . there is only one such row in the table . the year record of this unqiue row is 1980 .'} | and { only { filter_less { all_rows ; laps ; 7 } } ; eq { hop { filter_less { all_rows ; laps ; 7 } ; year } ; 1980 } } = true | select the rows whose laps record is less than 7 . there is only one such row in the table . the year record of this unqiue row is 1980 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_less_0': 0, 'all_rows_6': 6, 'laps_7': 7, '7_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'year_9': 9, '1980_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_less_0': 'filter_less', 'all_rows_6': 'all_rows', 'laps_7': 'laps', '7_8': '7', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'year_9': 'year', '1980_10': '1980'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_less_0': [1, 2], 'all_rows_6': [0], 'laps_7': [0], '7_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'year_9': [2], '1980_10': [3]} | ['year', 'class', 'tyres', 'team', 'co - drivers', 'laps', 'pos', 'class pos'] | [['1980', 'gr5', 'p', 'scuderia lancia corse', 'hans heyer bernard darniche', '6', 'dnf', 'dnf'], ['1982', 'gr6', 'p', 'martini racing', 'michele alboreto rolf stommelen', '92', 'dnf', 'dnf'], ['1983', 'c', 'd', 'martini lancia', 'michele alboreto alessandro nannini', '27', 'dnf', 'dnf'], ['1991', 'c2', 'g', 'silk cut jaguar tom walkinshaw racing', 'bob wollek kenny acheson', '358', '3rd', '3rd'], ['1992', 'c1', 'g', "toyota team tom 's", 'jan lammers andy wallace', '331', '8th', '5th'], ['1993', 'c1', 'm', 'peugeot talbot sport', 'thierry boutsen yannick dalmas', '374', '2nd', '2nd']] |
fibt world championships 2008 | https://en.wikipedia.org/wiki/FIBT_World_Championships_2008 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13566976-7.html.csv | unique | germany was the only country that one more than one gold medal in the 2008 fibt world championships . | {'scope': 'all', 'row': '1', 'col': '3', 'col_other': '2', 'criterion': 'greater_than', 'value': '1', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'gold', '1'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose gold record is greater than 1 .', 'tostr': 'filter_greater { all_rows ; gold ; 1 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_greater { all_rows ; gold ; 1 } }', 'tointer': 'select the rows whose gold record is greater than 1 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'gold', '1'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose gold record is greater than 1 .', 'tostr': 'filter_greater { all_rows ; gold ; 1 }'}, 'nation'], 'result': 'germany', 'ind': 2, 'tostr': 'hop { filter_greater { all_rows ; gold ; 1 } ; nation }'}, 'germany'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_greater { all_rows ; gold ; 1 } ; nation } ; germany }', 'tointer': 'the nation record of this unqiue row is germany .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_greater { all_rows ; gold ; 1 } } ; eq { hop { filter_greater { all_rows ; gold ; 1 } ; nation } ; germany } } = true', 'tointer': 'select the rows whose gold record is greater than 1 . there is only one such row in the table . the nation record of this unqiue row is germany .'} | and { only { filter_greater { all_rows ; gold ; 1 } } ; eq { hop { filter_greater { all_rows ; gold ; 1 } ; nation } ; germany } } = true | select the rows whose gold record is greater than 1 . there is only one such row in the table . the nation record of this unqiue row is germany . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_greater_0': 0, 'all_rows_6': 6, 'gold_7': 7, '1_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'nation_9': 9, 'germany_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_greater_0': 'filter_greater', 'all_rows_6': 'all_rows', 'gold_7': 'gold', '1_8': '1', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'nation_9': 'nation', 'germany_10': 'germany'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_greater_0': [1, 2], 'all_rows_6': [0], 'gold_7': [0], '1_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'nation_9': [2], 'germany_10': [3]} | ['rank', 'nation', 'gold', 'silver', 'bronze', 'total'] | [['1', 'germany', '5', '2', '4', '11'], ['2', 'canada', '0', '2', '0', '2'], ['3', 'united states', '0', '1', '1', '2'], ['4', 'russia', '0', '1', '1', '2'], ['5', 'united kingdom', '1', '0', '0', '1']] |
2007 kansas lottery indy 300 | https://en.wikipedia.org/wiki/2007_Kansas_Lottery_Indy_300 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-17693171-1.html.csv | count | there were 5 drivers with a +2 laps completion time during the 2007 kansas lottery indy 300 . | {'scope': 'all', 'criterion': 'equal', 'value': '+ 2 laps', 'result': '5', 'col': '6', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'time / retired', '+ 2 laps'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose time / retired record fuzzily matches to + 2 laps .', 'tostr': 'filter_eq { all_rows ; time / retired ; + 2 laps }'}], 'result': '5', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; time / retired ; + 2 laps } }', 'tointer': 'select the rows whose time / retired record fuzzily matches to + 2 laps . the number of such rows is 5 .'}, '5'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; time / retired ; + 2 laps } } ; 5 } = true', 'tointer': 'select the rows whose time / retired record fuzzily matches to + 2 laps . the number of such rows is 5 .'} | eq { count { filter_eq { all_rows ; time / retired ; + 2 laps } } ; 5 } = true | select the rows whose time / retired record fuzzily matches to + 2 laps . 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, 'time / retired_5': 5, '+ 2 laps_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', 'time / retired_5': 'time / retired', '+ 2 laps_6': '+ 2 laps', '5_7': '5'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'time / retired_5': [0], '+ 2 laps_6': [0], '5_7': [2]} | ['fin pos', 'car no', 'driver', 'team', 'laps', 'time / retired', 'grid', 'laps led', 'points'] | [['1', '10', 'dan wheldon', 'target chip ganassi', '200', '1:36:56.0586', '4', '177', '50 + 3'], ['2', '27', 'dario franchitti', 'andretti green', '200', '+ 18.4830', '6', '0', '40'], ['3', '3', 'hãlio castroneves', 'team penske', '200', '+ 33.2280', '3', '0', '35'], ['4', '9', 'scott dixon', 'target chip ganassi', '200', '+ 34.4208', '5', '16', '32'], ['5', '2', 'tomas scheckter', 'vision racing', '199', '+ 1 lap', '7', '0', '30'], ['6', '6', 'sam hornish , jr', 'team penske', '199', '+ 1 lap', '2', '0', '28'], ['7', '7', 'danica patrick', 'andretti green', '198', '+ 2 laps', '10', '0', '26'], ['8', '4', 'vitor meira', 'panther racing', '198', '+ 2 laps', '8', '0', '24'], ['9', '22', 'a j foyt iv', 'vision racing', '198', '+ 2 laps', '15', '0', '22'], ['10', '17', 'jeff simmons', 'rahal letterman', '198', '+ 2 laps', '16', '0', '20'], ['11', '14', 'darren manning', 'aj foyt racing', '198', '+ 2 laps', '11', '0', '19'], ['12', '5', 'sarah fisher', 'dreyer & reinbold racing', '196', '+ 4 laps', '17', '0', '18'], ['13', '8', 'scott sharp', 'rahal letterman', '195', 'accident', '14', '0', '17'], ['14', '23', 'milka duno', 'samax motorsport', '194', '+ 6 laps', '21', '0', '16'], ['15', '11', 'tony kanaan', 'andretti green racing', '192', '+ 8 laps', '1', '7', '15'], ['16', '98', 'alex barron', 'curb / agajanian / beck', '191', '+ 9 laps', '20', '0', '14'], ['17', '20', 'ed carpenter', 'vision racing', '99', 'accident', '13', '0', '13'], ['18', '55', 'kosuke matsuura', 'super aguri panther racing', '57', 'mechanical', '12', '0', '12'], ['19', '26', 'marco andretti', 'andretti green racing', '43', 'mechanical', '9', '0', '12'], ['20', '15', 'buddy rice', 'dreyer & reinbold racing', '37', 'mechanical', '18', '0', '12']] |
2001 masters tournament | https://en.wikipedia.org/wiki/2001_Masters_Tournament | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16514667-3.html.csv | superlative | in the 2001 masters tournament , chris dimarco ranks the highest . | {'scope': 'all', 'col_superlative': '1', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '2', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'place'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; place }'}, 'player'], 'result': 'chris dimarco', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; place } ; player }'}, 'chris dimarco'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; place } ; player } ; chris dimarco } = true', 'tointer': 'select the row whose place record of all rows is minimum . the player record of this row is chris dimarco .'} | eq { hop { argmin { all_rows ; place } ; player } ; chris dimarco } = true | select the row whose place record of all rows is minimum . the player record of this row is chris dimarco . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'place_5': 5, 'player_6': 6, 'chris dimarco_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'place_5': 'place', 'player_6': 'player', 'chris dimarco_7': 'chris dimarco'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'place_5': [0], 'player_6': [1], 'chris dimarco_7': [2]} | ['place', 'player', 'country', 'score', 'to par'] | [['1', 'chris dimarco', 'united states', '65 + 69 = 134', '- 10'], ['t2', 'phil mickelson', 'united states', '67 + 69 = 136', '- 8'], ['t2', 'tiger woods', 'united states', '70 + 66 = 136', '- 8'], ['t4', 'ángel cabrera', 'argentina', '66 + 71 = 137', '- 7'], ['t4', 'david duval', 'united states', '71 + 66 = 137', '- 7'], ['t4', 'toshimitsu izawa', 'japan', '71 + 66 = 137', '- 7'], ['t4', 'lee janzen', 'united states', '67 + 70 = 137', '- 7'], ['t4', 'steve stricker', 'united states', '66 + 71 = 137', '- 7'], ['t9', 'mark calcavecchia', 'united states', '72 + 66 = 138', '- 6'], ['t9', 'josé maría olazábal', 'spain', '70 + 68 = 138', '- 6'], ['t9', 'kirk triplett', 'united states', '68 + 70 = 138', '- 6']] |
alpert awards in the arts | https://en.wikipedia.org/wiki/Alpert_Awards_in_the_Arts | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10996831-1.html.csv | count | in the visual arts category , there were two people named catherine . | {'scope': 'all', 'criterion': 'equal', 'value': 'catherine', 'result': '2', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'visual arts', 'catherine'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose visual arts record fuzzily matches to catherine .', 'tostr': 'filter_eq { all_rows ; visual arts ; catherine }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; visual arts ; catherine } }', 'tointer': 'select the rows whose visual arts record fuzzily matches to catherine . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; visual arts ; catherine } } ; 2 } = true', 'tointer': 'select the rows whose visual arts record fuzzily matches to catherine . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; visual arts ; catherine } } ; 2 } = true | select the rows whose visual arts record fuzzily matches to catherine . 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, 'visual arts_5': 5, 'catherine_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', 'visual arts_5': 'visual arts', 'catherine_6': 'catherine', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'visual arts_5': [0], 'catherine_6': [0], '2_7': [2]} | ['year', 'film / video', 'visual arts', 'theatre', 'dance', 'music'] | [['1995', 'leslie thornton', 'mel chin', 'reza abdoh', 'ann carlson', 'james carter'], ['1996', 'su friedrich', 'carrie mae weems', 'suzan - lori parks', 'david rousseve', 'anne lebaron'], ['1997', 'craig baldwin', 'kerry james marshall', 'lisa kron', 'victoria marks', 'chen yi'], ['1998', 'jeanne c finley', 'roni horn', 'danny hoch', 'joanna haigood', 'pamela z'], ['1999', 'lourdes portillo', 'pepon osorio', 'brian freeman', 'ralph lemon', 'george lewis'], ['2000', 'peggy ahwesh', 'shirin neshat', 'w david hancock', 'mark dendy', 'steve coleman'], ['2001', 'ellen bruno', 'cai guo - qiang', 'erik ehn', 'john kelly', 'zhou long'], ['2002', '® tm', 'christian marclay', 'david greenspan', 'lisa nelson', 'laetitia sonami'], ['2003', 'coco fusco', 'catherine opie', 'carl hancock rux', 'rennie harris', 'vijay iyer'], ['2004', 'renee tajima - peña', 'catherine sullivan', 'dan hurlin', 'stephan koplowitz', 'miya masaoka'], ['2005', 'jem cohen', 'harrell fletcher', 'naomi iizuka', 'donna uchizono', 'david dunn'], ['2006', 'bill morrison', 'jim hodges', 'daniel alexander jones', 'sarah michelson', 'lawrence d morris'], ['2007', 'jacqueline goss', 'walid raad', 'cynthia hopkins', 'jeanine durning', 'mark feldman'], ['2008', 'bruce mcclure', 'bryon kim', "lisa d'amour", 'pat graney', 'derek bermel'], ['2009', 'paul chan', 'paul pfeiffer', 'rinde eckert', 'reggie wilson', 'john king'], ['2010', 'jim trainor', 'rachel harrison', 'bill talen', 'susan rethorst', 'lukas ligeti'], ['2011', 'natalia almada', 'emily jacir', 'marc bamuthi joseph', 'jess curtis', 'nicole mitchell'], ['2012', 'kevin everson', 'michael smith', 'eisa davis', 'nora chipaumire', 'myra melford'], ['2013', 'lucien castaing - taylor', 'sharon hayes', 'pavol liska & kelly copper', 'julia rhoads', 'alex mincek']] |
carlos andino | https://en.wikipedia.org/wiki/Carlos_Andino | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16055831-2.html.csv | comparative | carlos andino fought against larry reynolds earlier than he did against junior pitbull . | {'row_1': '2', 'row_2': '6', 'col': '5', 'col_other': '3', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'larry reynolds'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opponent record fuzzily matches to larry reynolds .', 'tostr': 'filter_eq { all_rows ; opponent ; larry reynolds }'}, 'date'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; opponent ; larry reynolds } ; date }', 'tointer': 'select the rows whose opponent record fuzzily matches to larry reynolds . take the date record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'junior pitbull'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose opponent record fuzzily matches to junior pitbull .', 'tostr': 'filter_eq { all_rows ; opponent ; junior pitbull }'}, 'date'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; opponent ; junior pitbull } ; date }', 'tointer': 'select the rows whose opponent record fuzzily matches to junior pitbull . take the date record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; opponent ; larry reynolds } ; date } ; hop { filter_eq { all_rows ; opponent ; junior pitbull } ; date } } = true', 'tointer': 'select the rows whose opponent record fuzzily matches to larry reynolds . take the date record of this row . select the rows whose opponent record fuzzily matches to junior pitbull . take the date record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; opponent ; larry reynolds } ; date } ; hop { filter_eq { all_rows ; opponent ; junior pitbull } ; date } } = true | select the rows whose opponent record fuzzily matches to larry reynolds . take the date record of this row . select the rows whose opponent record fuzzily matches to junior pitbull . take the date record of this row . the first record is less than the second record . | 5 | 5 | {'less_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'opponent_7': 7, 'larry reynolds_8': 8, 'date_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'opponent_11': 11, 'junior pitbull_12': 12, 'date_13': 13} | {'less_4': 'less', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'opponent_7': 'opponent', 'larry reynolds_8': 'larry reynolds', 'date_9': 'date', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'opponent_11': 'opponent', 'junior pitbull_12': 'junior pitbull', 'date_13': 'date'} | {'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'opponent_7': [0], 'larry reynolds_8': [0], 'date_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'opponent_11': [1], 'junior pitbull_12': [1], 'date_13': [3]} | ['result', 'record', 'opponent', 'method', 'date', 'round', 'location', 'notes'] | [['win', '1 - 0 - 0', 'joa mendes', 'knockout ( strikes )', '1995', '1', 'itapeua , brazil', 'vale tudo'], ['win', '2 - 0 - 0', 'larry reynolds', 'knockout ( strikes )', '1995', '1', 'itapeua , brazil', 'vale tudo'], ['win', '3 - 0 - 0', 'héctor rodríguez', 'knockout ( strikes )', '1995', '1', 'itapeua , brazil', 'vale tudo'], ['win', '4 - 0 - 0', 'larry reynolds', 'knockout ( strikes )', '1996', '1', 'itapeua , brazil', 'vale tudo'], ['win', '5 - 0 - 0', 'luigi maiolini', 'knockout ( strikes )', '1999', '1', 'itapeua , brazil', 'vale tudo'], ['loss', '5 - 1 - 0', 'junior pitbull', 'knockout ( strikes )', '2003', '1', 'itapeua , brazil', 'vale tudo'], ['loss', '5 - 2 - 0', 'zuluzinho', 'knockout ( strikes )', '4 november 2003', '1', 'itapeua , brazil', 'vale tudo'], ['loss', '5 - 3 - 0', 'osvaldo castuera', 'submission ( armbar )', '2007', '1', 'itapeua , brazil', 'vale tudo']] |
united states house of representatives elections , 1890 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1890 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1431450-5.html.csv | comparative | john j hemphill has a first elected year which is earlier than that of william h perry . | {'row_1': '5', 'row_2': '4', 'col': '4', 'col_other': '2', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'incumbent', 'john j hemphill'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose incumbent record fuzzily matches to john j hemphill .', 'tostr': 'filter_eq { all_rows ; incumbent ; john j hemphill }'}, 'first elected'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; incumbent ; john j hemphill } ; first elected }', 'tointer': 'select the rows whose incumbent record fuzzily matches to john j hemphill . take the first elected record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'incumbent', 'william h perry'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose incumbent record fuzzily matches to william h perry .', 'tostr': 'filter_eq { all_rows ; incumbent ; william h perry }'}, 'first elected'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; incumbent ; william h perry } ; first elected }', 'tointer': 'select the rows whose incumbent record fuzzily matches to william h perry . take the first elected record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; incumbent ; john j hemphill } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; william h perry } ; first elected } } = true', 'tointer': 'select the rows whose incumbent record fuzzily matches to john j hemphill . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to william h perry . take the first elected record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; incumbent ; john j hemphill } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; william h perry } ; first elected } } = true | select the rows whose incumbent record fuzzily matches to john j hemphill . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to william h perry . take the first elected record of this row . the first record is less than the second record . | 5 | 5 | {'less_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'incumbent_7': 7, 'john j hemphill_8': 8, 'first elected_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'incumbent_11': 11, 'william h perry_12': 12, 'first elected_13': 13} | {'less_4': 'less', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'incumbent_7': 'incumbent', 'john j hemphill_8': 'john j hemphill', 'first elected_9': 'first elected', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'incumbent_11': 'incumbent', 'william h perry_12': 'william h perry', 'first elected_13': 'first elected'} | {'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'incumbent_7': [0], 'john j hemphill_8': [0], 'first elected_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'incumbent_11': [1], 'william h perry_12': [1], 'first elected_13': [3]} | ['district', 'incumbent', 'party', 'first elected', 'result'] | [['south carolina 1', 'samuel dibble', 'democratic', '1882', 'retired democratic hold'], ['south carolina 2', 'george d tillman', 'democratic', '1878', 're - elected'], ['south carolina 3', 'james s cothran', 'democratic', '1886', 'retired democratic hold'], ['south carolina 4', 'william h perry', 'democratic', '1884', 'retired democratic hold'], ['south carolina 5', 'john j hemphill', 'democratic', '1882', 're - elected'], ['south carolina 6', 'george w dargan', 'democratic', '1882', 'retired democratic hold'], ['south carolina 7', 'thomas e miller', 'republican', '1888', 'lost re - election democratic gain']] |
list of townships in north dakota | https://en.wikipedia.org/wiki/List_of_townships_in_North_Dakota | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-18600760-10.html.csv | aggregation | the average population of townships in north dakota in 2010 was 44 . | {'scope': 'all', 'col': '3', 'type': 'average', 'result': '44', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'pop ( 2010 )'], 'result': '44', 'ind': 0, 'tostr': 'avg { all_rows ; pop ( 2010 ) }'}, '44'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; pop ( 2010 ) } ; 44 } = true', 'tointer': 'the average of the pop ( 2010 ) record of all rows is 44 .'} | round_eq { avg { all_rows ; pop ( 2010 ) } ; 44 } = true | the average of the pop ( 2010 ) record of all rows is 44 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'pop (2010)_4': 4, '44_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'pop (2010)_4': 'pop ( 2010 )', '44_5': '44'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'pop (2010)_4': [0], '44_5': [1]} | ['township', 'county', 'pop ( 2010 )', 'land ( sqmi )', 'water ( sqmi )', 'latitude', 'longitude', 'geo id', 'ansi code'] | [['jackson', 'sargent', '33', '35.809', '0.000', '46.066276', '- 97.945530', '3808140460', '1036797'], ['james hill', 'mountrail', '32', '31.820', '4.243', '48.423125', '- 102.429934', '3806140500', '1037048'], ['james river valley', 'dickey', '40', '28.597', '0.000', '46.246641', '- 98.188329', '3802140540', '1036767'], ['janke', 'logan', '28', '35.995', '0.163', '46.415512', '- 99.131701', '3804740620', '1037193'], ['jefferson', 'pierce', '45', '35.069', '1.125', '48.232149', '- 100.182370', '3806940700', '1759556'], ['jim river valley', 'stutsman', '38', '34.134', '1.746', '47.112388', '- 98.778478', '3809340780', '1036484'], ['johnson', 'wells', '36', '35.299', '0.908', '47.377745', '- 99.458677', '3810340820', '1037137'], ['johnstown', 'grand forks', '79', '36.199', '0.000', '48.151362', '- 97.449033', '3803540940', '1036624'], ['joliette', 'pembina', '67', '70.044', '0.771', '48.796545', '- 97.217227', '3806741020', '1036723']] |
1957 vfl season | https://en.wikipedia.org/wiki/1957_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10774891-18.html.csv | count | two of the matches played in the 1957 vfl season drew a crowd of 10000 . | {'scope': 'all', 'criterion': 'equal', 'value': '10000', 'result': '2', 'col': '6', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'crowd', '10000'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose crowd record is equal to 10000 .', 'tostr': 'filter_eq { all_rows ; crowd ; 10000 }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; crowd ; 10000 } }', 'tointer': 'select the rows whose crowd record is equal to 10000 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; crowd ; 10000 } } ; 2 } = true', 'tointer': 'select the rows whose crowd record is equal to 10000 . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; crowd ; 10000 } } ; 2 } = true | select the rows whose crowd record is equal to 10000 . the number of such rows is 2 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_eq_0': 0, 'all_rows_4': 4, 'crowd_5': 5, '10000_6': 6, '2_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'crowd_5': 'crowd', '10000_6': '10000', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'crowd_5': [0], '10000_6': [0], '2_7': [2]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['north melbourne', '10.20 ( 80 )', 'south melbourne', '17.11 ( 113 )', 'arden street oval', '10000', '24 august 1957'], ['melbourne', '18.12 ( 120 )', 'richmond', '10.11 ( 71 )', 'mcg', '35751', '24 august 1957'], ['footscray', '8.11 ( 59 )', 'hawthorn', '7.15 ( 57 )', 'western oval', '25436', '24 august 1957'], ['fitzroy', '15.14 ( 104 )', 'geelong', '10.20 ( 80 )', 'brunswick street oval', '10000', '24 august 1957'], ['st kilda', '14.12 ( 96 )', 'collingwood', '7.14 ( 56 )', 'junction oval', '29300', '24 august 1957'], ['essendon', '17.21 ( 123 )', 'carlton', '9.8 ( 62 )', 'windy hill', '35000', '24 august 1957']] |
1973 vfl season | https://en.wikipedia.org/wiki/1973_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10869537-16.html.csv | aggregation | the total crowd for games on 21st july in the 1973 vfl season was 116129 . | {'scope': 'all', 'col': '6', 'type': 'sum', 'result': '116129', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'crowd'], 'result': '116129', 'ind': 0, 'tostr': 'sum { all_rows ; crowd }'}, '116129'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; crowd } ; 116129 } = true', 'tointer': 'the sum of the crowd record of all rows is 116129 .'} | round_eq { sum { all_rows ; crowd } ; 116129 } = true | the sum of the crowd record of all rows is 116129 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'crowd_4': 4, '116129_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'crowd_4': 'crowd', '116129_5': '116129'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'crowd_4': [0], '116129_5': [1]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['st kilda', '14.16 ( 100 )', 'south melbourne', '13.15 ( 93 )', 'moorabbin oval', '17454', '21 july 1973'], ['footscray', '6.9 ( 45 )', 'hawthorn', '9.11 ( 65 )', 'western oval', '10987', '21 july 1973'], ['richmond', '16.14 ( 110 )', 'melbourne', '9.19 ( 73 )', 'mcg', '30492', '21 july 1973'], ['geelong', '12.10 ( 82 )', 'essendon', '11.15 ( 81 )', 'kardinia park', '16746', '21 july 1973'], ['fitzroy', '12.19 ( 91 )', 'carlton', '11.14 ( 80 )', 'junction oval', '14800', '21 july 1973'], ['north melbourne', '11.10 ( 76 )', 'collingwood', '10.11 ( 71 )', 'vfl park', '25650', '21 july 1973']] |
1982 - 83 fa cup | https://en.wikipedia.org/wiki/1982%E2%80%9383_FA_Cup | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17751846-5.html.csv | count | in the 1982-83 fa cup , when at least one team had 0 points , there were 4 times the game was on february 19th . | {'scope': 'subset', 'criterion': 'equal', 'value': '19 february', 'result': '4', 'col': '5', 'subset': {'col': '3', 'criterion': 'fuzzily_match', 'value': '0'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'score', '0'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; score ; 0 }', 'tointer': 'select the rows whose score record fuzzily matches to 0 .'}, 'date', '19 february'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose score record fuzzily matches to 0 . among these rows , select the rows whose date record fuzzily matches to 19 february .', 'tostr': 'filter_eq { filter_eq { all_rows ; score ; 0 } ; date ; 19 february }'}], 'result': '4', 'ind': 2, 'tostr': 'count { filter_eq { filter_eq { all_rows ; score ; 0 } ; date ; 19 february } }', 'tointer': 'select the rows whose score record fuzzily matches to 0 . among these rows , select the rows whose date record fuzzily matches to 19 february . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_eq { all_rows ; score ; 0 } ; date ; 19 february } } ; 4 } = true', 'tointer': 'select the rows whose score record fuzzily matches to 0 . among these rows , select the rows whose date record fuzzily matches to 19 february . the number of such rows is 4 .'} | eq { count { filter_eq { filter_eq { all_rows ; score ; 0 } ; date ; 19 february } } ; 4 } = true | select the rows whose score record fuzzily matches to 0 . among these rows , select the rows whose date record fuzzily matches to 19 february . the number of such rows is 4 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_str_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'score_6': 6, '0_7': 7, 'date_8': 8, '19 february_9': 9, '4_10': 10} | {'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_str_eq_1': 'filter_str_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'score_6': 'score', '0_7': '0', 'date_8': 'date', '19 february_9': '19 february', '4_10': '4'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_str_eq_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'score_6': [0], '0_7': [0], 'date_8': [1], '19 february_9': [1], '4_10': [3]} | ['tie no', 'home team', 'score', 'away team', 'date'] | [['1', 'liverpool', '1 - 2', 'brighton & hove albion', '20 february 1983'], ['2', 'aston villa', '4 - 1', 'watford', '19 february 1983'], ['3', 'middlesbrough', '1 - 1', 'arsenal', '19 february 1983'], ['replay', 'arsenal', '3 - 2', 'middlesbrough', '28 february 1983'], ['4', 'derby county', '0 - 1', 'manchester united', '19 february 1983'], ['5', 'everton', '2 - 0', 'tottenham hotspur', '19 february 1983'], ['6', 'norwich city', '1 - 0', 'ipswich town', '19 february 1983'], ['7', 'crystal palace', '0 - 0', 'burnley', '19 february 1983'], ['replay', 'burnley', '1 - 0', 'crystal palace', '28 february 1983'], ['8', 'cambridge united', '1 - 2', 'sheffield wednesday', '19 february 1983']] |
canadian university field lacrosse association | https://en.wikipedia.org/wiki/Canadian_University_Field_Lacrosse_Association | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-18042409-1.html.csv | count | three players are alumni of the university of guelph . | {'scope': 'all', 'criterion': 'equal', 'value': 'university of guelph', 'result': '3', 'col': '2', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'alma mater', 'university of guelph'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose alma mater record fuzzily matches to university of guelph .', 'tostr': 'filter_eq { all_rows ; alma mater ; university of guelph }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; alma mater ; university of guelph } }', 'tointer': 'select the rows whose alma mater record fuzzily matches to university of guelph . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; alma mater ; university of guelph } } ; 3 } = true', 'tointer': 'select the rows whose alma mater record fuzzily matches to university of guelph . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; alma mater ; university of guelph } } ; 3 } = true | select the rows whose alma mater record fuzzily matches to university of guelph . 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, 'alma mater_5': 5, 'university of guelph_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', 'alma mater_5': 'alma mater', 'university of guelph_6': 'university of guelph', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'alma mater_5': [0], 'university of guelph_6': [0], '3_7': [2]} | ['player', 'alma mater', 'national lacrosse league', 'major league lacrosse', 'international competition'] | [['colin doyle', 'wilfrid laurier university', 'ontario raiders / toronto rock , san jose stealth', 'toronto nationals', 'team canada'], ['steve hoar', 'university of toronto', 'toronto rock', 'toronto nationals', 'team canada'], ['creighton reid', 'university of toronto ( practice squad )', 'toronto rock , colorado mammoth', 'none', 'none'], ['jay thorimbert', 'university of guelph', 'buffalo bandits , boston blazers , minnesota swarm', 'none', 'none'], ['sean thomson', 'university of guelph', 'philadelphia wings , minnesota swarm', 'none', 'none'], ['greg harnett', "bishop 's university", 'calgary roughnecks', 'none', 'none'], ['jon harnett', 'university of guelph', 'boston blazers', 'none', 'none'], ['josh wasson', 'trent university', 'chicago shamrox , toronto rock', 'none', 'none'], ['casey zaph', 'university of toronto', 'rochester knighthawks', 'none', 'none']] |
2005 world weightlifting championships | https://en.wikipedia.org/wiki/2005_World_Weightlifting_Championships | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10929638-3.html.csv | ordinal | in the 2005 world weightlifting championships , china was first , with the highest number of total medals . | {'row': '1', 'col': '6', 'order': '1', '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', 'total', '1'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; total ; 1 }'}, 'nation'], 'result': 'china', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; total ; 1 } ; nation }'}, 'china'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; total ; 1 } ; nation } ; china } = true', 'tointer': 'select the row whose total record of all rows is 1st maximum . the nation record of this row is china .'} | eq { hop { nth_argmax { all_rows ; total ; 1 } ; nation } ; china } = true | select the row whose total record of all rows is 1st maximum . the nation record of this row is china . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'total_5': 5, '1_6': 6, 'nation_7': 7, 'china_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', 'total_5': 'total', '1_6': '1', 'nation_7': 'nation', 'china_8': 'china'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'total_5': [0], '1_6': [0], 'nation_7': [1], 'china_8': [2]} | ['rank', 'nation', 'gold', 'silver', 'bronze', 'total'] | [['1', 'china', '7', '4', '1', '12'], ['2', 'russia', '2', '4', '4', '10'], ['3', 'thailand', '1', '3', '1', '5'], ['4', 'south korea', '1', '2', '0', '2'], ['5', 'azerbaijan', '1', '0', '0', '1'], ['5', 'chinese taipei', '1', '0', '0', '1'], ['5', 'iran', '1', '0', '0', '1'], ['5', 'kazakhstan', '1', '0', '0', '1'], ['9', 'romania', '0', '1', '1', '2'], ['10', 'moldova', '0', '1', '0', '1'], ['11', 'qatar', '0', '0', '2', '2'], ['12', 'bulgaria', '0', '0', '1', '1'], ['12', 'dominican republic', '0', '0', '1', '1'], ['12', 'france', '0', '0', '1', '1'], ['12', 'slovakia', '0', '0', '1', '1'], ['12', 'united states', '0', '0', '1', '1'], ['12', 'vietnam', '0', '0', '1', '1'], ['total', 'total', '15', '15', '15', '45']] |
jack brabham | https://en.wikipedia.org/wiki/Jack_Brabham | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16564-3.html.csv | majority | most of jack brabham 's racing years in the indy 500 had a qualifying points above 150 . | {'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '150', 'subset': None} | {'func': 'most_greater', 'args': ['all_rows', 'qual', '150'], 'result': True, 'ind': 0, 'tointer': 'for the qual records of all rows , most of them are greater than 150 .', 'tostr': 'most_greater { all_rows ; qual ; 150 } = true'} | most_greater { all_rows ; qual ; 150 } = true | for the qual records of all rows , most of them are greater than 150 . | 1 | 1 | {'most_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 'qual_3': 3, '150_4': 4} | {'most_greater_0': 'most_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 'qual_3': 'qual', '150_4': '150'} | {'most_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 'qual_3': [0], '150_4': [0]} | ['year', 'start', 'qual', 'rank', 'finish', 'laps'] | [['1961', '13', '145.144', '17', '9', '200'], ['1964', '25', '152.504', '15', '20', '77'], ['1969', '29', '163.875', '29', '24', '58'], ['1970', '26', '166.397', '22', '13', '175']] |
northumberland county , new brunswick | https://en.wikipedia.org/wiki/Northumberland_County%2C_New_Brunswick | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-171354-1.html.csv | count | four of the communities in northumberland county , new brunswick are villages . | {'scope': 'all', 'criterion': 'equal', 'value': 'village', 'result': '4', 'col': '2', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'status', 'village'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose status record fuzzily matches to village .', 'tostr': 'filter_eq { all_rows ; status ; village }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; status ; village } }', 'tointer': 'select the rows whose status record fuzzily matches to village . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; status ; village } } ; 4 } = true', 'tointer': 'select the rows whose status record fuzzily matches to village . the number of such rows is 4 .'} | eq { count { filter_eq { all_rows ; status ; village } } ; 4 } = true | select the rows whose status record fuzzily matches to village . 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, 'status_5': 5, 'village_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', 'status_5': 'status', 'village_6': 'village', '4_7': '4'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'status_5': [0], 'village_6': [0], '4_7': [2]} | ['official name', 'status', 'area km 2', 'population', 'census ranking'] | [['miramichi', 'city', '179.84', '17811', '232 of 5008'], ['neguac', 'village', '26.69', '1678', '1500 of 5008'], ['rogersville', 'village', '7.23', '1170', '1875 of 5008'], ['blackville', 'village', '21.73', '990', '2086 of 5008'], ['doaktown', 'village', '28.74', '793', '2387 of 5008']] |
royal canadian mint numismatic coins ( 2000s ) | https://en.wikipedia.org/wiki/Royal_Canadian_Mint_numismatic_coins_%282000s%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11916083-49.html.csv | majority | the majority of the coins issued from 2007 to 2011 had a price of less than $ 100 . | {'scope': 'all', 'col': '6', 'most_or_all': 'most', 'criterion': 'less_than', 'value': '100.0', 'subset': None} | {'func': 'most_less', 'args': ['all_rows', 'issue price', '100.0'], 'result': True, 'ind': 0, 'tointer': 'for the issue price records of all rows , most of them are less than 100.0 .', 'tostr': 'most_less { all_rows ; issue price ; 100.0 } = true'} | most_less { all_rows ; issue price ; 100.0 } = true | for the issue price records of all rows , most of them are less than 100.0 . | 1 | 1 | {'most_less_0': 0, 'result_1': 1, 'all_rows_2': 2, 'issue price_3': 3, '100.0_4': 4} | {'most_less_0': 'most_less', 'result_1': 'true', 'all_rows_2': 'all_rows', 'issue price_3': 'issue price', '100.0_4': '100.0'} | {'most_less_0': [1], 'result_1': [], 'all_rows_2': [0], 'issue price_3': [0], '100.0_4': [0]} | ['year', 'theme', 'artist', 'composition', 'mintage', 'issue price'] | [['2007', 'blue crystal - piedfort', 'konrad wachelko', '92.5 % silver , 7.5 % copper', '5000', '94.95'], ['2007', 'iridescent crystal - piedfort', 'konrad wachelko', '92.5 % silver , 7.5 % copper', '5000', '94.95'], ['2008', 'amethyst crystal', 'konrad wachelko', '99.99 % silver', '7500', '94.95'], ['2008', 'sapphire crystal', 'konrad wachelko', '99.99 % silver', '7500', '94.95'], ['2009', 'blue crystal', 'konrad wachelko', '99.99 % silver', '7500', '94.95'], ['2009', 'pink crystal', 'konrad wachelko', '99.99 % silver', '7500', '94.95'], ['2010', 'blue crystal', 'konrad wachelko', '99.99 % silver', '7500', '99.95'], ['2010', 'tanzanite crystal', 'konrad wachelko', '99.99 % silver', '7500', '99.95'], ['2011', 'emerald crystal', 'konrad wachelko', '99.99 % silver', '15000', '114.95'], ['2011', 'topaz crystal', 'konrad wachelko', '99.99 % silver', '15000', '114.95'], ['2011', 'hyacinth red small crystal', 'konrad wachelko', '99.99 % silver', '15000', '114.95'], ['2011', 'montana blue small crystal', 'konrad wachelko', '99.99 % silver', '15000', '114.95']] |
vivian girls | https://en.wikipedia.org/wiki/Vivian_Girls | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18710512-3.html.csv | comparative | the single wild eyes had more copies than the single tell the world . | {'row_1': '1', 'row_2': '2', 'col': '6', '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', 'single', 'wild eyes'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose single record fuzzily matches to wild eyes .', 'tostr': 'filter_eq { all_rows ; single ; wild eyes }'}, 'other details'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; single ; wild eyes } ; other details }', 'tointer': 'select the rows whose single record fuzzily matches to wild eyes . take the other details record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'single', 'tell the world'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose single record fuzzily matches to tell the world .', 'tostr': 'filter_eq { all_rows ; single ; tell the world }'}, 'other details'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; single ; tell the world } ; other details }', 'tointer': 'select the rows whose single record fuzzily matches to tell the world . take the other details record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; single ; wild eyes } ; other details } ; hop { filter_eq { all_rows ; single ; tell the world } ; other details } } = true', 'tointer': 'select the rows whose single record fuzzily matches to wild eyes . take the other details record of this row . select the rows whose single record fuzzily matches to tell the world . take the other details record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; single ; wild eyes } ; other details } ; hop { filter_eq { all_rows ; single ; tell the world } ; other details } } = true | select the rows whose single record fuzzily matches to wild eyes . take the other details record of this row . select the rows whose single record fuzzily matches to tell the world . take the other details 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, 'single_7': 7, 'wild eyes_8': 8, 'other details_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'single_11': 11, 'tell the world_12': 12, 'other details_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', 'single_7': 'single', 'wild eyes_8': 'wild eyes', 'other details_9': 'other details', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'single_11': 'single', 'tell the world_12': 'tell the world', 'other details_13': 'other details'} | {'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'single_7': [0], 'wild eyes_8': [0], 'other details_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'single_11': [1], 'tell the world_12': [1], 'other details_13': [3]} | ['date', 'single', 'backed with', 'record label', 'format', 'other details'] | [['2008', 'wild eyes', 'my baby wants me dead', 'plays with dolls / wild world', '7 single', '4000 copies'], ['2008', 'tell the world', 'i believe in nothing & damaged', 'woodsist', '7 single', '3000 copies'], ['2008', "i ca n't stay", 'blind spot', 'in the red', '7 single', '2000 copies'], ['2008', 'surfin away & second date', "girl do n't tell me ( wilson )", 'wild world', '7 single', '1000 copies'], ['2009', 'moped girls', 'death', 'for us', '7 single', '1500 copies'], ['2010', 'my love will follow me', "he 's gone ( the chantels cover )", 'wild world', '7 single', '2000 copies'], ['2011', 'i heard you say', "i wo n't be long", 'polyvinyl', '7 single', 'rsd 2000 copies']] |
wreh | https://en.wikipedia.org/wiki/WREH | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11540543-2.html.csv | aggregation | wreh in the d class has an average effective radiated power ( erp w ) of 37.5 watts . | {'scope': 'all', 'col': '4', 'type': 'average', 'result': '37.5 watts', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'erp w'], 'result': '37.5 watts', 'ind': 0, 'tostr': 'avg { all_rows ; erp w }'}, '37.5 watts'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; erp w } ; 37.5 watts } = true', 'tointer': 'the average of the erp w record of all rows is 37.5 watts .'} | round_eq { avg { all_rows ; erp w } ; 37.5 watts } = true | the average of the erp w record of all rows is 37.5 watts . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'erp w_4': 4, '37.5 watts_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'erp w_4': 'erp w', '37.5 watts_5': '37.5 watts'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'erp w_4': [0], '37.5 watts_5': [1]} | ['call sign', 'frequency mhz', 'city of license', 'erp w', 'class', 'fcc info'] | [['w247aq', '97.3', 'tropical gulf acres , florida', '25', 'd', 'fcc'], ['w244bk', '96.7', 'twentymile bend , florida', '80', 'd', 'fcc'], ['w252bb', '98.3', 'vero beach , florida', '13', 'd', 'fcc'], ['w220dz', '91.9', 'wesley chapel , florida', '8', 'd', 'fcc'], ['w220du', '91.9', 'west deerfield beach , florida', '80', 'd', 'fcc'], ['w299au', '107.7', 'zolfo springs , florida', '19', 'd', 'fcc']] |
1989 open championship | https://en.wikipedia.org/wiki/1989_Open_Championship | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18135501-5.html.csv | count | eight of the players in the 1989 open championships are from the united states . | {'scope': 'all', 'criterion': 'equal', 'value': 'united states', 'result': '8', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'united states'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose country record fuzzily matches to united states .', 'tostr': 'filter_eq { all_rows ; country ; united states }'}], 'result': '8', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; country ; united states } }', 'tointer': 'select the rows whose country record fuzzily matches to united states . the number of such rows is 8 .'}, '8'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; country ; united states } } ; 8 } = true', 'tointer': 'select the rows whose country record fuzzily matches to united states . the number of such rows is 8 .'} | eq { count { filter_eq { all_rows ; country ; united states } } ; 8 } = true | select the rows whose country record fuzzily matches to united states . the number of such rows is 8 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'country_5': 5, 'united states_6': 6, '8_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'country_5': 'country', 'united states_6': 'united states', '8_7': '8'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'country_5': [0], 'united states_6': [0], '8_7': [2]} | ['place', 'player', 'country', 'score', 'to par'] | [['1', 'wayne grady', 'australia', '68 + 67 + 69 = 204', '- 12'], ['2', 'tom watson', 'united states', '69 + 68 + 68 = 205', '- 11'], ['3', 'payne stewart', 'united states', '72 + 65 + 69 = 206', '- 10'], ['t4', 'mark calcavecchia', 'united states', '71 + 68 + 68 = 207', '- 9'], ['t4', 'fred couples', 'united states', '68 + 71 + 68 = 207', '- 9'], ['t4', 'david feherty', 'northern ireland', '71 + 67 + 69 = 207', '- 9'], ['t7', 'paul azinger', 'united states', '68 + 73 + 67 = 208', '- 8'], ['t7', 'jodie mudd', 'united states', '73 + 67 + 68 = 208', '- 8'], ['t9', 'mark mccumber', 'united states', '71 + 68 + 70 = 209', '- 7'], ['t9', 'josé maría olazábal', 'spain', '68 + 72 + 69 = 209', '- 7'], ['t9', 'steve pate', 'united states', '69 + 70 + 70 = 209', '- 7']] |
list of ottawa senators draft picks | https://en.wikipedia.org/wiki/List_of_Ottawa_Senators_draft_picks | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11803648-22.html.csv | superlative | quentin shore is the player with the highest overall points in the ottawa senators draft pick . | {'scope': 'all', 'col_superlative': '2', 'row_superlative': '7', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '3', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'overall'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; overall }'}, 'player'], 'result': 'quentin shore', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; overall } ; player }'}, 'quentin shore'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; overall } ; player } ; quentin shore } = true', 'tointer': 'select the row whose overall record of all rows is maximum . the player record of this row is quentin shore .'} | eq { hop { argmax { all_rows ; overall } ; player } ; quentin shore } = true | select the row whose overall record of all rows is maximum . the player record of this row is quentin shore . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'overall_5': 5, 'player_6': 6, 'quentin shore_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'overall_5': 'overall', 'player_6': 'player', 'quentin shore_7': 'quentin shore'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'overall_5': [0], 'player_6': [1], 'quentin shore_7': [2]} | ['round', 'overall', 'player', 'position', 'nationality', 'club team'] | [['1', '17', 'curtis lazar', 'centre / right wing', 'canada', 'edmonton oil kings ( whl )'], ['3', '78', 'marcus hogberg', 'goalie', 'sweden', 'linköpings hc ( se )'], ['4', '102 ( from philadelphia via tampa bay )', 'tobias lindberg', 'right wing', 'sweden', 'djurgårdens if ( se )'], ['4', '108', 'ben harpur', 'defense', 'canada', 'guelph storm ( ohl )'], ['5', '138', 'vincent dunn', 'centre', 'canada', "val - d'or foreurs ( qmjhl )"], ['6', '161 ( from dallas )', 'chris leblanc', 'right wing', 'united states', 'south shore kings ( ejhl )'], ['6', '168', 'quentin shore', 'centre', 'united states', 'denver pioneers ( wcha )']] |
2003 tennessee titans season | https://en.wikipedia.org/wiki/2003_Tennessee_Titans_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18817998-1.html.csv | ordinal | in the 2003 tennessee titans season , the 2nd game against the jacksonville jaguars had an attendance of 68809 . | {'scope': 'subset', 'row': '10', 'col': '2', 'order': '2', 'col_other': '6', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'subset': {'col': '3', 'criterion': 'equal', 'value': 'jacksonville jaguars'}} | {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'nth_argmin', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'jacksonville jaguars'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; opponent ; jacksonville jaguars }', 'tointer': 'select the rows whose opponent record fuzzily matches to jacksonville jaguars .'}, 'date', '2'], 'result': None, 'ind': 1, 'tostr': 'nth_argmin { filter_eq { all_rows ; opponent ; jacksonville jaguars } ; date ; 2 }'}, 'attendance'], 'result': '68809', 'ind': 2, 'tostr': 'hop { nth_argmin { filter_eq { all_rows ; opponent ; jacksonville jaguars } ; date ; 2 } ; attendance }'}, '68809'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { nth_argmin { filter_eq { all_rows ; opponent ; jacksonville jaguars } ; date ; 2 } ; attendance } ; 68809 } = true', 'tointer': 'select the rows whose opponent record fuzzily matches to jacksonville jaguars . select the row whose date record of these rows is 2nd minimum . the attendance record of this row is 68809 .'} | eq { hop { nth_argmin { filter_eq { all_rows ; opponent ; jacksonville jaguars } ; date ; 2 } ; attendance } ; 68809 } = true | select the rows whose opponent record fuzzily matches to jacksonville jaguars . select the row whose date record of these rows is 2nd minimum . the attendance record of this row is 68809 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'num_hop_2': 2, 'nth_argmin_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'opponent_6': 6, 'jacksonville jaguars_7': 7, 'date_8': 8, '2_9': 9, 'attendance_10': 10, '68809_11': 11} | {'eq_3': 'eq', 'result_4': 'true', 'num_hop_2': 'num_hop', 'nth_argmin_1': 'nth_argmin', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'opponent_6': 'opponent', 'jacksonville jaguars_7': 'jacksonville jaguars', 'date_8': 'date', '2_9': '2', 'attendance_10': 'attendance', '68809_11': '68809'} | {'eq_3': [4], 'result_4': [], 'num_hop_2': [3], 'nth_argmin_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'opponent_6': [0], 'jacksonville jaguars_7': [0], 'date_8': [1], '2_9': [1], 'attendance_10': [2], '68809_11': [3]} | ['week', 'date', 'opponent', 'result', 'tv time', 'attendance'] | [['1', 'september 7 , 2003', 'oakland raiders', 'w 25 - 20', 'espn 7:30 pm', '68809'], ['2', 'september 14 , 2003', 'indianapolis colts', 'l 7 - 33', 'cbs 12:00 pm', '56999'], ['3', 'september 21 , 2003', 'new orleans saints', 'w 27 - 12', 'fox 12:00 pm', '68809'], ['4', 'september 28 , 2003', 'pittsburgh steelers', 'w 30 - 13', 'cbs 12:00 pm', '63244'], ['5', 'october 5 , 2003', 'new england patriots', 'l 30 - 38', 'cbs 12:00 pm', '68436'], ['6', 'october 12 , 2003', 'houston texans', 'w 38 - 17', 'cbs 12:00 pm', '68809'], ['7', 'october 19 , 2003', 'carolina panthers', 'w 37 - 17', 'cbs 12:00 pm', '72851'], ['8', 'october 26 , 2003', 'jacksonville jaguars', 'w 30 - 17', 'cbs 12:00 pm', '55918'], ['10', 'november 9 , 2003', 'miami dolphins', 'w 31 - 7', 'cbs 12:00 pm', '68809'], ['11', 'november 16 , 2003', 'jacksonville jaguars', 'w 10 - 3', 'cbs 12:00 pm', '68809'], ['12', 'november 23 , 2003', 'atlanta falcons', 'w 38 - 31', 'cbs 3:05 pm', '70891'], ['13', 'december 1 , 2003', 'new york jets', 'l 17 - 24', 'abc 8:00 pm', '77710'], ['14', 'december 7 , 2003', 'indianapolis colts', 'l 27 - 29', 'cbs 12:00 pm', '68809'], ['15', 'december 14 , 2003', 'buffalo bills', 'w 28 - 26', 'cbs 12:00 pm', '68809'], ['16', 'december 21 , 2003', 'houston texans', 'w 27 - 24', 'cbs 12:00 pm', '70758'], ['17', 'december 28 , 2003', 'tampa bay buccaneers', 'w 33 - 13', 'fox 12:00 pm', '68809']] |
list of ottawa senators draft picks | https://en.wikipedia.org/wiki/List_of_Ottawa_Senators_draft_picks | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11803648-1.html.csv | count | of the ottawa senators draft picks , three are from czechoslovakia . | {'scope': 'all', 'criterion': 'equal', 'value': 'czechoslovakia', 'result': '3', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nationality', 'czechoslovakia'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose nationality record fuzzily matches to czechoslovakia .', 'tostr': 'filter_eq { all_rows ; nationality ; czechoslovakia }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; nationality ; czechoslovakia } }', 'tointer': 'select the rows whose nationality record fuzzily matches to czechoslovakia . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; nationality ; czechoslovakia } } ; 3 } = true', 'tointer': 'select the rows whose nationality record fuzzily matches to czechoslovakia . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; nationality ; czechoslovakia } } ; 3 } = true | select the rows whose nationality record fuzzily matches to czechoslovakia . 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, 'nationality_5': 5, 'czechoslovakia_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', 'nationality_5': 'nationality', 'czechoslovakia_6': 'czechoslovakia', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'nationality_5': [0], 'czechoslovakia_6': [0], '3_7': [2]} | ['round', 'overall', 'player', 'nationality', 'club team'] | [['1', '2', 'alexei yashin', 'russia', 'hc dynamo moscow ( russia )'], ['2', '25', 'chad penney', 'canada', 'north bay centennials ( ohl )'], ['3', '50', 'patrick traverse', 'canada', 'shawinigan cataractes ( qmjhl )'], ['4', '73', 'radek hamr', 'czechoslovakia', 'hc sparta praha ( czech )'], ['5', '98', 'daniel guerard', 'canada', 'victoriaville tigres ( qmjhl )'], ['6', '121', 'alan sinclair', 'united states', 'university of michigan ( ncaa )'], ['7', '146', 'jaroslav miklenda', 'czechoslovakia', 'ds olomuc ( czech )'], ['8', '169', 'jay kenney', 'united states', 'new milford canterbury school ( us hs )'], ['9', '194', 'claude savoie', 'canada', 'victoriaville tigres ( qmjhl )'], ['10', '217', 'jack grimes', 'canada', 'belleville bulls ( ohl )'], ['11', '242', 'tomas jelinek', 'czechoslovakia', 'hpk ( finland )'], ['11', '264', 'petter ronnquist', 'sweden', 'nacka hk ( sweden )']] |
1996 - 97 toronto raptors season | https://en.wikipedia.org/wiki/1996%E2%80%9397_Toronto_Raptors_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-13557843-8.html.csv | unique | in the 1996 - 97 toronto raptors season , in games where damon stoudamire has the high points , the only time the opponent was miami was on april 5 . | {'scope': 'subset', 'row': '2', 'col': '3', 'col_other': '2', 'criterion': 'equal', 'value': 'miami', 'subset': {'col': '5', 'criterion': 'fuzzily_match', 'value': 'damon stoudamire'}} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'high points', 'damon stoudamire'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; high points ; damon stoudamire }', 'tointer': 'select the rows whose high points record fuzzily matches to damon stoudamire .'}, 'team', 'miami'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose high points record fuzzily matches to damon stoudamire . among these rows , select the rows whose team record fuzzily matches to miami .', 'tostr': 'filter_eq { filter_eq { all_rows ; high points ; damon stoudamire } ; team ; miami }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_eq { filter_eq { all_rows ; high points ; damon stoudamire } ; team ; miami } }', 'tointer': 'select the rows whose high points record fuzzily matches to damon stoudamire . among these rows , select the rows whose team record fuzzily matches to miami . 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', 'high points', 'damon stoudamire'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; high points ; damon stoudamire }', 'tointer': 'select the rows whose high points record fuzzily matches to damon stoudamire .'}, 'team', 'miami'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose high points record fuzzily matches to damon stoudamire . among these rows , select the rows whose team record fuzzily matches to miami .', 'tostr': 'filter_eq { filter_eq { all_rows ; high points ; damon stoudamire } ; team ; miami }'}, 'date'], 'result': 'april 5', 'ind': 3, 'tostr': 'hop { filter_eq { filter_eq { all_rows ; high points ; damon stoudamire } ; team ; miami } ; date }'}, 'april 5'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_eq { filter_eq { all_rows ; high points ; damon stoudamire } ; team ; miami } ; date } ; april 5 }', 'tointer': 'the date record of this unqiue row is april 5 .'}], 'result': True, 'ind': 5, 'tostr': 'and { only { filter_eq { filter_eq { all_rows ; high points ; damon stoudamire } ; team ; miami } } ; eq { hop { filter_eq { filter_eq { all_rows ; high points ; damon stoudamire } ; team ; miami } ; date } ; april 5 } } = true', 'tointer': 'select the rows whose high points record fuzzily matches to damon stoudamire . among these rows , select the rows whose team record fuzzily matches to miami . there is only one such row in the table . the date record of this unqiue row is april 5 .'} | and { only { filter_eq { filter_eq { all_rows ; high points ; damon stoudamire } ; team ; miami } } ; eq { hop { filter_eq { filter_eq { all_rows ; high points ; damon stoudamire } ; team ; miami } ; date } ; april 5 } } = true | select the rows whose high points record fuzzily matches to damon stoudamire . among these rows , select the rows whose team record fuzzily matches to miami . there is only one such row in the table . the date record of this unqiue row is april 5 . | 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, 'high points_8': 8, 'damon stoudamire_9': 9, 'team_10': 10, 'miami_11': 11, 'str_eq_4': 4, 'str_hop_3': 3, 'date_12': 12, 'april 5_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', 'high points_8': 'high points', 'damon stoudamire_9': 'damon stoudamire', 'team_10': 'team', 'miami_11': 'miami', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'date_12': 'date', 'april 5_13': 'april 5'} | {'and_5': [6], 'result_6': [], 'only_2': [5], 'filter_str_eq_1': [2, 3], 'filter_str_eq_0': [1], 'all_rows_7': [0], 'high points_8': [0], 'damon stoudamire_9': [0], 'team_10': [1], 'miami_11': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'date_12': [3], 'april 5_13': [4]} | ['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record'] | [['74', 'april 2', 'philadelphia', 'w 112 - 90 ( ot )', 'doug christie ( 29 )', 'doug christie ( 15 )', 'damon stoudamire ( 15 )', 'corestates center 13769', '27 - 47'], ['75', 'april 5', 'miami', 'l 84 - 98 ( ot )', 'damon stoudamire ( 25 )', 'marcus camby , clifford rozier ( 6 )', 'damon stoudamire ( 7 )', 'miami arena 15200', '27 - 48'], ['76', 'april 8', 'washington', 'w 100 - 94 ( ot )', 'damon stoudamire ( 29 )', 'clifford rozier ( 10 )', 'damon stoudamire ( 13 )', 'skydome 17159', '28 - 48'], ['77', 'april 10', 'orlando', 'l 69 - 105 ( ot )', 'sharone wright ( 17 )', 'popeye jones ( 12 )', 'damon stoudamire ( 5 )', 'skydome 20280', '28 - 49'], ['78', 'april 12', 'indiana', 'l 89 - 100 ( ot )', 'damon stoudamire ( 22 )', 'popeye jones , clifford rozier ( 11 )', 'damon stoudamire ( 11 )', 'skydome 21832', '28 - 50'], ['79', 'april 14', 'chicago', 'l 100 - 117 ( ot )', 'damon stoudamire ( 29 )', 'carlos rogers ( 12 )', 'damon stoudamire ( 12 )', 'united center 23896', '28 - 51'], ['80', 'april 15', 'milwaukee', 'l 85 - 92 ( ot )', 'reggie slater ( 19 )', 'clifford rozier ( 13 )', 'damon stoudamire ( 11 )', 'bradley center 14652', '28 - 52'], ['81', 'april 18', 'charlotte', 'w 108 - 100 ( ot )', 'damon stoudamire ( 28 )', 'marcus camby , popeye jones ( 8 )', 'damon stoudamire ( 9 )', 'charlotte coliseum 24042', '29 - 52']] |
2008 - 09 west ham united f.c. season | https://en.wikipedia.org/wiki/2008%E2%80%9309_West_Ham_United_F.C._season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18539546-8.html.csv | majority | the majority of players leaving in the 2008 - 09 west ham united f.c. season were loaned out . | {'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'loaned', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'status', 'loaned'], 'result': True, 'ind': 0, 'tointer': 'for the status records of all rows , most of them fuzzily match to loaned .', 'tostr': 'most_eq { all_rows ; status ; loaned } = true'} | most_eq { all_rows ; status ; loaned } = true | for the status records of all rows , most of them fuzzily match to loaned . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'status_3': 3, 'loaned_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'status_3': 'status', 'loaned_4': 'loaned'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'status_3': [0], 'loaned_4': [0]} | ['name', 'country', 'status', 'moving to', 'transfer fee'] | [['solano', 'per', 'transferred', 'released', 'free'], ['zamora', 'eng', 'transferred', 'fulham', '4.8 m'], ['paintsil', 'gha', 'transferred', 'fulham', '1.5 m'], ['wright', 'eng', 'transferred', 'ipswich town', '0.5 m'], ['ljungberg', 'swe', 'transferred', 'released', 'free'], ['ferdinand', 'eng', 'transferred', 'sunderland', '8 m'], ['mccartney', 'nir', 'transferred', 'sunderland', '6 m'], ['blackmore', 'eng', 'loaned', 'thurrock', 'n / a'], ['jeffery', 'eng', 'loaned', 'leyton orient', 'n / a'], ['payne', 'eng', 'loaned', 'cheltenham town', 'n / a'], ['quashie', 'sco', 'loaned', 'birmingham city', 'n / a'], ['miller', 'eng', 'loaned', "bishop 's stortford", 'n / a'], ["n'gala", 'eng', 'loaned', 'mk dons', 'n / a'], ['spence', 'eng', 'loaned', 'leyton orient', 'n / a'], ['reid', 'eng', 'loaned', 'blackpool', 'n / a'], ['walker', 'eng', 'loaned', 'colchester united', 'n / a'], ['tomkins', 'eng', 'loaned', 'derby county', 'n / a'], ['stanislas', 'eng', 'loaned', 'southend', 'n / a'], ['etherington', 'eng', 'transferred', 'stoke', 'undisclosed']] |
2008 - 09 new york knicks season | https://en.wikipedia.org/wiki/2008%E2%80%9309_New_York_Knicks_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-17060277-10.html.csv | ordinal | the new york knicks ' game against chicago recorded their highest attendance in the 2008 - 09 season . | {'row': '3', 'col': '8', 'order': '1', 'col_other': '3', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'location attendance', '1'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; location attendance ; 1 }'}, 'team'], 'result': 'chicago', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; location attendance ; 1 } ; team }'}, 'chicago'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; location attendance ; 1 } ; team } ; chicago } = true', 'tointer': 'select the row whose location attendance record of all rows is 1st maximum . the team record of this row is chicago .'} | eq { hop { nth_argmax { all_rows ; location attendance ; 1 } ; team } ; chicago } = true | select the row whose location attendance record of all rows is 1st maximum . the team record of this row is chicago . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'location attendance_5': 5, '1_6': 6, 'team_7': 7, 'chicago_8': 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'location attendance_5': 'location attendance', '1_6': '1', 'team_7': 'team', 'chicago_8': 'chicago'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'location attendance_5': [0], '1_6': [0], 'team_7': [1], 'chicago_8': [2]} | ['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record'] | [['76', 'april 4', 'toronto', 'l 95 - 102 ( ot )', 'al harrington , chris duhon ( 22 )', 'al harrington , david lee ( 7 )', 'david lee ( 6 )', 'madison square garden 19763', '29 - 47'], ['77', 'april 5', 'toronto', 'w 112 - 103 ( ot )', 'wilson chandler ( 17 )', 'david lee , al harrington ( 10 )', 'nate robinson ( 7 )', 'air canada centre 18879', '30 - 47'], ['78', 'april 7', 'chicago', 'l 103 - 110 ( ot )', 'wilson chandler ( 26 )', 'david lee ( 13 )', 'david lee , chris duhon ( 6 )', 'united center 20764', '30 - 48'], ['79', 'april 8', 'detroit', 'l 86 - 113 ( ot )', 'al harrington ( 26 )', 'wilson chandler ( 8 )', 'nate robinson , chris duhon ( 4 )', 'madison square garden 19763', '30 - 49'], ['80', 'april 10', 'orlando', 'w 105 - 95 ( ot )', 'al harrington ( 27 )', 'david lee ( 16 )', 'chris duhon , al harrington , david lee ( 4 )', 'amway arena 17461', '31 - 49'], ['81', 'april 12', 'miami', 'l 105 - 122 ( ot )', 'wilson chandler , al harrington ( 21 )', 'david lee ( 11 )', 'larry hughes , nate robinson ( 4 )', 'american airlines arena 19600', '31 - 50']] |
1943 vfl season | https://en.wikipedia.org/wiki/1943_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10808346-8.html.csv | comparative | during the 1943 vfl season , carlton had a much higher scoring game than flootscray . | {'row_1': '3', 'row_2': '1', 'col': '2', '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', 'home team', 'carlton'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose home team record fuzzily matches to carlton .', 'tostr': 'filter_eq { all_rows ; home team ; carlton }'}, 'home team score'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; home team ; carlton } ; home team score }', 'tointer': 'select the rows whose home team record fuzzily matches to carlton . take the home team score record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'home team', 'footscray'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose home team record fuzzily matches to footscray .', 'tostr': 'filter_eq { all_rows ; home team ; footscray }'}, 'home team score'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; home team ; footscray } ; home team score }', 'tointer': 'select the rows whose home team record fuzzily matches to footscray . take the home team score record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; home team ; carlton } ; home team score } ; hop { filter_eq { all_rows ; home team ; footscray } ; home team score } } = true', 'tointer': 'select the rows whose home team record fuzzily matches to carlton . take the home team score record of this row . select the rows whose home team record fuzzily matches to footscray . take the home team score record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; home team ; carlton } ; home team score } ; hop { filter_eq { all_rows ; home team ; footscray } ; home team score } } = true | select the rows whose home team record fuzzily matches to carlton . take the home team score record of this row . select the rows whose home team record fuzzily matches to footscray . take the home team score record of this row . the first record is greater than the second record . | 5 | 5 | {'greater_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'home team_7': 7, 'carlton_8': 8, 'home team score_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'home team_11': 11, 'footscray_12': 12, 'home team score_13': 13} | {'greater_4': 'greater', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'home team_7': 'home team', 'carlton_8': 'carlton', 'home team score_9': 'home team score', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'home team_11': 'home team', 'footscray_12': 'footscray', 'home team score_13': 'home team score'} | {'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'home team_7': [0], 'carlton_8': [0], 'home team score_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'home team_11': [1], 'footscray_12': [1], 'home team score_13': [3]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['footscray', '10.11 ( 71 )', 'south melbourne', '6.14 ( 50 )', 'western oval', '7500', '26 june 1943'], ['collingwood', '10.21 ( 81 )', 'melbourne', '13.9 ( 87 )', 'victoria park', '5000', '26 june 1943'], ['carlton', '15.16 ( 106 )', 'fitzroy', '9.13 ( 67 )', 'princes park', '12000', '26 june 1943'], ['richmond', '15.16 ( 106 )', 'hawthorn', '8.14 ( 62 )', 'punt road oval', '16000', '26 june 1943'], ['st kilda', '15.8 ( 98 )', 'essendon', '20.19 ( 139 )', 'toorak park', '6000', '26 june 1943']] |
1998 cfl draft | https://en.wikipedia.org/wiki/1998_CFL_Draft | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16441561-6.html.csv | majority | the majority of players drafted from 36-44 played the lb position . | {'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'lb', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'position', 'lb'], 'result': True, 'ind': 0, 'tointer': 'for the position records of all rows , most of them fuzzily match to lb .', 'tostr': 'most_eq { all_rows ; position ; lb } = true'} | most_eq { all_rows ; position ; lb } = true | for the position records of all rows , most of them fuzzily match to lb . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'position_3': 3, 'lb_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'position_3': 'position', 'lb_4': 'lb'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'position_3': [0], 'lb_4': [0]} | ['pick', 'cfl team', 'player', 'position', 'college'] | [['36', 'hamilton', 'benjie hutchison', 'dl', 'british columbia'], ['37', 'winnipeg', 'john baunemann', 'k', 'manitoba'], ['38', 'winnipeg', 'chad vath', 'lb', 'manitoba'], ['39', 'calgary', 'jodi bednarek', 'lb', 'calgary'], ['40', 'edmonton', 'adam kossack', 'ol', 'hastings college'], ['41', 'montreal', 'kelly ireland', 'ol', "saint mary 's"], ['42', 'saskatchewan', 'james rapesse', 'lb', 'saskatchewan'], ['43', 'hamilton', 'robert yelenich', 'lb', 'york'], ['44', 'toronto', 'bill mitoulas', 'lb', 'notre dame']] |
2008 - 09 ottawa senators season | https://en.wikipedia.org/wiki/2008%E2%80%9309_Ottawa_Senators_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17040191-6.html.csv | count | the ottawa senators won four of the games they played at scotiabank place , during the month of december in the 2008-2009 season . | {'scope': 'subset', 'criterion': 'fuzzily_match', 'value': 'w', 'result': '4', 'col': '4', 'subset': {'col': '5', 'criterion': 'equal', 'value': 'scotiabank place'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'location', 'scotiabank place'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; location ; scotiabank place }', 'tointer': 'select the rows whose location record fuzzily matches to scotiabank place .'}, 'score', 'w'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose location record fuzzily matches to scotiabank place . among these rows , select the rows whose score record fuzzily matches to w .', 'tostr': 'filter_eq { filter_eq { all_rows ; location ; scotiabank place } ; score ; w }'}], 'result': '4', 'ind': 2, 'tostr': 'count { filter_eq { filter_eq { all_rows ; location ; scotiabank place } ; score ; w } }', 'tointer': 'select the rows whose location record fuzzily matches to scotiabank place . among these rows , select the rows whose score record fuzzily matches to w . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_eq { all_rows ; location ; scotiabank place } ; score ; w } } ; 4 } = true', 'tointer': 'select the rows whose location record fuzzily matches to scotiabank place . among these rows , select the rows whose score record fuzzily matches to w . the number of such rows is 4 .'} | eq { count { filter_eq { filter_eq { all_rows ; location ; scotiabank place } ; score ; w } } ; 4 } = true | select the rows whose location record fuzzily matches to scotiabank place . among these rows , select the rows whose score record fuzzily matches to w . the number of such rows is 4 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_str_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'location_6': 6, 'scotiabank place_7': 7, 'score_8': 8, 'w_9': 9, '4_10': 10} | {'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_str_eq_1': 'filter_str_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'location_6': 'location', 'scotiabank place_7': 'scotiabank place', 'score_8': 'score', 'w_9': 'w', '4_10': '4'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_str_eq_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'location_6': [0], 'scotiabank place_7': [0], 'score_8': [1], 'w_9': [1], '4_10': [3]} | ['game', 'date', 'opponent', 'score', 'location', 'attendance', 'record', 'points'] | [['23', 'december 3', 'atlanta thrashers', '5 - 1 ( w )', 'scotiabank place', '17215', '9 - 10 - 4', '22'], ['24', 'december 6', 'pittsburgh penguins', '3 - 2 ( w )', 'scotiabank place', '19561', '10 - 10 - 4', '24'], ['25', 'december 8', 'florida panthers', '4 - 3 ( ot )', 'scotiabank place', '17947', '10 - 10 - 5', '25'], ['26', 'december 10', 'chicago blackhawks', '2 - 0 ( l )', 'chicago', '21614', '10 - 11 - 5', '25'], ['27', 'december 12', 'washington capitals', '5 - 1 ( l )', 'washington', '17973', '10 - 12 - 5', '25'], ['28', 'december 13', 'tampa bay lightning', '2 - 0 ( w )', 'scotiabank place', '18446', '11 - 12 - 5', '27'], ['29', 'december 16', 'atlanta thrashers', '4 - 1 ( l )', 'scotiabank place', '18746', '11 - 13 - 5', '27'], ['30', 'december 19', 'new jersey devils', '5 - 1 ( l )', 'newark', '13242', '11 - 14 - 5', '27'], ['31', 'december 20', 'dallas stars', '5 - 4 ( w )', 'scotiabank place', '19486', '12 - 14 - 5', '29'], ['32', 'december 23', 'philadelphia flyers', '6 - 4 ( l )', 'philadelphia', '19578', '12 - 15 - 5', '29'], ['33', 'december 27', 'calgary flames', '6 - 3 ( l )', 'calgary', '19289', '12 - 16 - 5', '29'], ['34', 'december 28', 'vancouver canucks', '3 - 0 ( l )', 'vancouver', '18630', '12 - 17 - 5', '29'], ['35', 'december 30', 'edmonton oilers', '3 - 2 ( w )', 'edmonton', '16839', '13 - 17 - 5', '31']] |
list of tallest buildings in quebec city | https://en.wikipedia.org/wiki/List_of_tallest_buildings_in_Quebec_City | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11111275-1.html.csv | aggregation | tallest buildings in quebec city have an average of 24.8 floors each . | {'scope': 'all', 'col': '4', 'type': 'average', 'result': '24.8', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'floors'], 'result': '24.8', 'ind': 0, 'tostr': 'avg { all_rows ; floors }'}, '24.8'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; floors } ; 24.8 } = true', 'tointer': 'the average of the floors record of all rows is 24.8 .'} | round_eq { avg { all_rows ; floors } ; 24.8 } = true | the average of the floors record of all rows is 24.8 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'floors_4': 4, '24.8_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'floors_4': 'floors', '24.8_5': '24.8'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'floors_4': [0], '24.8_5': [1]} | ['rank', 'name', 'height m ( ft )', 'floors', 'year'] | [['1', 'édifice marie - guyart', '-', '33', '1972'], ['2', 'complexe jules dallaire ii', '-', '28', '2013'], ['3', 'place hauteville', '-', '34', '1974'], ['4', 'hôtel loews le concorde', '-', '31', '1974'], ['5', 'hôtel hilton québec', '-', '28', '1974'], ['6', 'édifice price', '-', '18', '1930'], ['7', 'place de la capitale', '-', '21', '1974'], ['8', 'le samuel - holland i', '-', '24', '1981'], ['9', 'chteau frontenac', '-', '18', '1893'], ['10', "édifice d'youville", '-', '21', '1969'], ['11', 'complexe jules - dallaire i', '-', '17', '2010']] |
1984 - 85 fa cup | https://en.wikipedia.org/wiki/1984%E2%80%9385_FA_Cup | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17438338-4.html.csv | majority | the majority of the 1984 - 85 fa cup matches were played on 26 january 1985 . | {'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'equal', 'value': '26 january 1985', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'date', '26 january 1985'], 'result': True, 'ind': 0, 'tointer': 'for the date records of all rows , most of them fuzzily match to 26 january 1985 .', 'tostr': 'most_eq { all_rows ; date ; 26 january 1985 } = true'} | most_eq { all_rows ; date ; 26 january 1985 } = true | for the date records of all rows , most of them fuzzily match to 26 january 1985 . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'date_3': 3, '26 january 1985_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'date_3': 'date', '26 january 1985_4': '26 january 1985'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'date_3': [0], '26 january 1985_4': [0]} | ['tie no', 'home team', 'score', 'away team', 'date'] | [['1', 'darlington', '1 - 1', 'telford united', '29 january 1985'], ['replay', 'telford united', '3 - 0', 'darlington', '4 february 1985'], ['2', 'liverpool', '1 - 0', 'tottenham hotspur', '27 january 1985'], ['3', 'leicester city', '1 - 0', 'carlisle united', '26 january 1985'], ['4', 'nottingham forest', '0 - 0', 'wimbledon', '26 january 1985'], ['replay', 'wimbledon', '1 - 0', 'nottingham forest', '30 january 1985'], ['5', 'sheffield wednesday', '5 - 1', 'oldham athletic', '26 january 1985'], ['6', 'grimsby town', '1 - 3', 'watford', '26 january 1985'], ['7', 'luton town', '2 - 0', 'huddersfield town', '26 january 1985'], ['8', 'everton', '2 - 0', 'doncaster rovers', '26 january 1985'], ['9', 'ipswich town', '3 - 2', 'gillingham', '26 january 1985'], ['10', 'barnsley', '2 - 1', 'brighton & hove albion', '26 january 1985'], ['11', 'west ham united', '2 - 1', 'norwich city', '4 february 1985'], ['12', 'manchester united', '2 - 1', 'coventry city', '26 january 1985'], ['13', 'chelsea', '2 - 3', 'millwall', '4 february 1985'], ['14', 'york city', '1 - 0', 'arsenal', '26 january 1985'], ['15', 'oxford united', '0 - 1', 'blackburn rovers', '30 january 1985'], ['16', 'orient', '0 - 2', 'southampton', '26 january 1985']] |
henry cejudo | https://en.wikipedia.org/wiki/Henry_Cejudo | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18931507-2.html.csv | comparative | henry cejudo 's fourth fight was finished with a time of 1:43 while cejudo 's fifth fight took longer with a time of 5:00 . | {'row_1': '2', 'row_2': '1', 'col': '7', 'col_other': '2', 'relation': 'less', 'record_mentioned': 'yes', 'diff_result': None} | {'func': 'and', 'args': [{'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'record', '4 - 0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose record record fuzzily matches to 4 - 0 .', 'tostr': 'filter_eq { all_rows ; record ; 4 - 0 }'}, 'time'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; record ; 4 - 0 } ; time }', 'tointer': 'select the rows whose record record fuzzily matches to 4 - 0 . take the time record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'record', '5 - 0'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose record record fuzzily matches to 5 - 0 .', 'tostr': 'filter_eq { all_rows ; record ; 5 - 0 }'}, 'time'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; record ; 5 - 0 } ; time }', 'tointer': 'select the rows whose record record fuzzily matches to 5 - 0 . take the time record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; record ; 4 - 0 } ; time } ; hop { filter_eq { all_rows ; record ; 5 - 0 } ; time } }', 'tointer': 'select the rows whose record record fuzzily matches to 4 - 0 . take the time record of this row . select the rows whose record record fuzzily matches to 5 - 0 . take the time record of this row . the first record is less than the second record .'}, {'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'record', '4 - 0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose record record fuzzily matches to 4 - 0 .', 'tostr': 'filter_eq { all_rows ; record ; 4 - 0 }'}, 'time'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; record ; 4 - 0 } ; time }', 'tointer': 'select the rows whose record record fuzzily matches to 4 - 0 . take the time record of this row .'}, '1:43'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; record ; 4 - 0 } ; time } ; 1:43 }', 'tointer': 'the time record of the first row is 1:43 .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'record', '5 - 0'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose record record fuzzily matches to 5 - 0 .', 'tostr': 'filter_eq { all_rows ; record ; 5 - 0 }'}, 'time'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; record ; 5 - 0 } ; time }', 'tointer': 'select the rows whose record record fuzzily matches to 5 - 0 . take the time record of this row .'}, '5:00'], 'result': True, 'ind': 6, 'tostr': 'eq { hop { filter_eq { all_rows ; record ; 5 - 0 } ; time } ; 5:00 }', 'tointer': 'the time record of the second row is 5:00 .'}], 'result': True, 'ind': 7, 'tostr': 'and { eq { hop { filter_eq { all_rows ; record ; 4 - 0 } ; time } ; 1:43 } ; eq { hop { filter_eq { all_rows ; record ; 5 - 0 } ; time } ; 5:00 } }', 'tointer': 'the time record of the first row is 1:43 . the time record of the second row is 5:00 .'}], 'result': True, 'ind': 8, 'tostr': 'and { less { hop { filter_eq { all_rows ; record ; 4 - 0 } ; time } ; hop { filter_eq { all_rows ; record ; 5 - 0 } ; time } } ; and { eq { hop { filter_eq { all_rows ; record ; 4 - 0 } ; time } ; 1:43 } ; eq { hop { filter_eq { all_rows ; record ; 5 - 0 } ; time } ; 5:00 } } } = true', 'tointer': 'select the rows whose record record fuzzily matches to 4 - 0 . take the time record of this row . select the rows whose record record fuzzily matches to 5 - 0 . take the time record of this row . the first record is less than the second record . the time record of the first row is 1:43 . the time record of the second row is 5:00 .'} | and { less { hop { filter_eq { all_rows ; record ; 4 - 0 } ; time } ; hop { filter_eq { all_rows ; record ; 5 - 0 } ; time } } ; and { eq { hop { filter_eq { all_rows ; record ; 4 - 0 } ; time } ; 1:43 } ; eq { hop { filter_eq { all_rows ; record ; 5 - 0 } ; time } ; 5:00 } } } = true | select the rows whose record record fuzzily matches to 4 - 0 . take the time record of this row . select the rows whose record record fuzzily matches to 5 - 0 . take the time record of this row . the first record is less than the second record . the time record of the first row is 1:43 . the time record of the second row is 5:00 . | 13 | 9 | {'and_8': 8, 'result_9': 9, 'less_4': 4, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_10': 10, 'record_11': 11, '4 - 0_12': 12, 'time_13': 13, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_14': 14, 'record_15': 15, '5 - 0_16': 16, 'time_17': 17, 'and_7': 7, 'str_eq_5': 5, '1:43_18': 18, 'str_eq_6': 6, '5:00_19': 19} | {'and_8': 'and', 'result_9': 'true', 'less_4': 'less', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_10': 'all_rows', 'record_11': 'record', '4 - 0_12': '4 - 0', 'time_13': 'time', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_14': 'all_rows', 'record_15': 'record', '5 - 0_16': '5 - 0', 'time_17': 'time', 'and_7': 'and', 'str_eq_5': 'str_eq', '1:43_18': '1:43', 'str_eq_6': 'str_eq', '5:00_19': '5:00'} | {'and_8': [9], 'result_9': [], 'less_4': [8], 'str_hop_2': [4, 5], 'filter_str_eq_0': [2], 'all_rows_10': [0], 'record_11': [0], '4 - 0_12': [0], 'time_13': [2], 'str_hop_3': [4, 6], 'filter_str_eq_1': [3], 'all_rows_14': [1], 'record_15': [1], '5 - 0_16': [1], 'time_17': [3], 'and_7': [8], 'str_eq_5': [7], '1:43_18': [5], 'str_eq_6': [7], '5:00_19': [6]} | ['res', 'record', 'opponent', 'method', 'event', 'round', 'time', 'location'] | [['win', '5 - 0', 'ryan hollis', 'decision ( unanimous )', 'lfc 24 - legacy fighting championship 24', '3', '5:00', 'dallas , texas , united states'], ['win', '4 - 0', 'miguelito marti', 'tko ( punches )', 'gladiator challenge : american dream', '1', '1:43', 'lincoln , california , united states'], ['win', '3 - 0', 'anthony sessions', 'tko ( punches )', 'wff 10 : cejudo v sessions', '1', '4:23', 'chandler , arizona , united states'], ['win', '2 - 0', 'sean henry barnett', 'tko ( punches )', 'gladiator challenge : battleground', '1', '4:55', 'san jacinto , california , united states'], ['win', '1 - 0', 'michael poe', 'submission ( punches )', 'wff mma : pascua yaqui fights 4', '1', '1:25', 'tucson , arizona , united states']] |
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 | majority | in the 1928 vfl season , when the away team is from somewhere in melbourne , all of the crowds were under 25000 . | {'scope': 'subset', 'col': '6', 'most_or_all': 'all', 'criterion': 'less_than', 'value': '25000', 'subset': {'col': '3', 'criterion': 'fuzzily_match', 'value': 'melbourne'}} | {'func': 'all_less', '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 .'}, 'crowd', '25000'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose away team record fuzzily matches to melbourne . for the crowd records of these rows , all of them are less than 25000 .', 'tostr': 'all_less { filter_eq { all_rows ; away team ; melbourne } ; crowd ; 25000 } = true'} | all_less { filter_eq { all_rows ; away team ; melbourne } ; crowd ; 25000 } = true | select the rows whose away team record fuzzily matches to melbourne . for the crowd records of these rows , all of them are less than 25000 . | 2 | 2 | {'all_less_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'away team_4': 4, 'melbourne_5': 5, 'crowd_6': 6, '25000_7': 7} | {'all_less_1': 'all_less', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'away team_4': 'away team', 'melbourne_5': 'melbourne', 'crowd_6': 'crowd', '25000_7': '25000'} | {'all_less_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'away team_4': [0], 'melbourne_5': [0], 'crowd_6': [1], '25000_7': [1]} | ['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']] |
mauro baldi | https://en.wikipedia.org/wiki/Mauro_Baldi | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1226503-1.html.csv | unique | mauro baldi used a car with an alfa romeo chassis in only one of his five formula one races from 1982 to 1985 . | {'scope': 'all', 'row': '3', 'col': '3', 'col_other': '1', 'criterion': 'fuzzily_match', 'value': 'alfa romeo', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'chassis', 'alfa romeo'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose chassis record fuzzily matches to alfa romeo .', 'tostr': 'filter_eq { all_rows ; chassis ; alfa romeo }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; chassis ; alfa romeo } }', 'tointer': 'select the rows whose chassis record fuzzily matches to alfa romeo . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'chassis', 'alfa romeo'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose chassis record fuzzily matches to alfa romeo .', 'tostr': 'filter_eq { all_rows ; chassis ; alfa romeo }'}, 'year'], 'result': '1983', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; chassis ; alfa romeo } ; year }'}, '1983'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; chassis ; alfa romeo } ; year } ; 1983 }', 'tointer': 'the year record of this unqiue row is 1983 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; chassis ; alfa romeo } } ; eq { hop { filter_eq { all_rows ; chassis ; alfa romeo } ; year } ; 1983 } } = true', 'tointer': 'select the rows whose chassis record fuzzily matches to alfa romeo . there is only one such row in the table . the year record of this unqiue row is 1983 .'} | and { only { filter_eq { all_rows ; chassis ; alfa romeo } } ; eq { hop { filter_eq { all_rows ; chassis ; alfa romeo } ; year } ; 1983 } } = true | select the rows whose chassis record fuzzily matches to alfa romeo . there is only one such row in the table . the year record of this unqiue row is 1983 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'chassis_7': 7, 'alfa romeo_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'year_9': 9, '1983_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'chassis_7': 'chassis', 'alfa romeo_8': 'alfa romeo', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'year_9': 'year', '1983_10': '1983'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'chassis_7': [0], 'alfa romeo_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'year_9': [2], '1983_10': [3]} | ['year', 'entrant', 'chassis', 'engine', 'points'] | [['1982', 'arrows racing team', 'arrows a4', 'cosworth v8', '2'], ['1982', 'arrows racing team', 'arrows a5', 'cosworth v8', '2'], ['1983', 'marlboro team alfa romeo', 'alfa romeo 183t', 'alfa romeo v8', '3'], ['1984', 'spirit racing', 'spirit 101', 'hart straight - 4', '0'], ['1985', 'spirit enterprises ltd', 'spirit 101d', 'hart straight - 4', '0']] |
1951 - 52 segunda división | https://en.wikipedia.org/wiki/1951%E2%80%9352_Segunda_Divisi%C3%B3n | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17298923-2.html.csv | majority | all of the football clubs in the 1951 - 52 segunda división played a total of 30 matches . | {'scope': 'all', 'col': '2', 'most_or_all': 'all', 'criterion': 'equal', 'value': '30', 'subset': None} | {'func': 'all_eq', 'args': ['all_rows', 'played', '30'], 'result': True, 'ind': 0, 'tointer': 'for the played records of all rows , all of them are equal to 30 .', 'tostr': 'all_eq { all_rows ; played ; 30 } = true'} | all_eq { all_rows ; played ; 30 } = true | for the played records of all rows , all of them are equal to 30 . | 1 | 1 | {'all_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'played_3': 3, '30_4': 4} | {'all_eq_0': 'all_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'played_3': 'played', '30_4': '30'} | {'all_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'played_3': [0], '30_4': [0]} | ['position', 'played', 'points', 'wins', 'draws', 'losses', 'goals for', 'goals against', 'goal difference'] | [['1', '30', '39', '16', '7', '7', '66', '29', '+ 37'], ['2', '30', '36', '15', '6', '9', '48', '40', '+ 8'], ['3', '30', '33', '11', '11', '8', '55', '44', '+ 11'], ['4', '30', '33', '13', '7', '10', '58', '44', '+ 14'], ['5', '30', '33', '13', '7', '10', '61', '32', '+ 29'], ['6', '30', '33', '12', '9', '9', '49', '41', '+ 8'], ['7', '30', '32', '14', '4', '12', '41', '52', '- 11'], ['8', '30', '32', '13', '6', '11', '55', '45', '+ 10'], ['9', '30', '30', '10', '10', '10', '51', '50', '+ 1'], ['10', '30', '29', '12', '5', '13', '49', '60', '- 11'], ['11', '30', '29', '13', '3', '14', '56', '64', '- 8'], ['12', '30', '28', '12', '4', '14', '51', '57', '- 6'], ['13', '30', '28', '9', '10', '11', '51', '71', '- 20'], ['14', '30', '24', '9', '6', '15', '40', '56', '- 16'], ['15', '30', '21', '7', '7', '16', '49', '64', '- 15'], ['16', '30', '20', '5', '10', '15', '32', '63', '- 31']] |
fort lauderdale strikers ( 1988 - 94 ) | https://en.wikipedia.org/wiki/Fort_Lauderdale_Strikers_%281988%E2%80%9394%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-12002388-1.html.csv | count | the fort lauderdale strikers made it to the finals in the playoffs twice between 1988 and 1994 . | {'scope': 'all', 'criterion': 'equal', 'value': 'finals', 'result': '2', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'playoffs', 'finals'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose playoffs record fuzzily matches to finals .', 'tostr': 'filter_eq { all_rows ; playoffs ; finals }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; playoffs ; finals } }', 'tointer': 'select the rows whose playoffs record fuzzily matches to finals . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; playoffs ; finals } } ; 2 } = true', 'tointer': 'select the rows whose playoffs record fuzzily matches to finals . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; playoffs ; finals } } ; 2 } = true | select the rows whose playoffs record fuzzily matches to finals . 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, 'playoffs_5': 5, 'finals_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', 'playoffs_5': 'playoffs', 'finals_6': 'finals', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'playoffs_5': [0], 'finals_6': [0], '2_7': [2]} | ['year', 'division', 'league', 'reg season', 'playoffs', 'open cup'] | [['1988', 'n / a', 'asl', '1st , southern', 'final', 'did not enter'], ['1989', 'n / a', 'asl', '2nd , southern', 'champion', 'did not enter'], ['1990', 'n / a', 'apsl', '1st , asl south', 'final', 'did not enter'], ['1991', 'n / a', 'apsl', '1st , american', 'semifinals', 'did not enter'], ['1992', 'n / a', 'apsl', '4th', 'semifinals', 'did not enter'], ['1993', 'n / a', 'apsl', '6th', 'did not qualify', 'did not enter']] |
utah jazz all - time roster | https://en.wikipedia.org/wiki/Utah_Jazz_all-time_roster | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-11545282-6.html.csv | comparative | jim farmer had a higher jersery number than derek fisher in the utah jazz all-time roster . | {'row_1': '1', 'row_2': '4', 'col': '2', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'jim farmer'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose player record fuzzily matches to jim farmer .', 'tostr': 'filter_eq { all_rows ; player ; jim farmer }'}, 'no'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; player ; jim farmer } ; no }', 'tointer': 'select the rows whose player record fuzzily matches to jim farmer . take the no record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'derek fisher'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose player record fuzzily matches to derek fisher .', 'tostr': 'filter_eq { all_rows ; player ; derek fisher }'}, 'no'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; player ; derek fisher } ; no }', 'tointer': 'select the rows whose player record fuzzily matches to derek fisher . take the no record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; player ; jim farmer } ; no } ; hop { filter_eq { all_rows ; player ; derek fisher } ; no } } = true', 'tointer': 'select the rows whose player record fuzzily matches to jim farmer . take the no record of this row . select the rows whose player record fuzzily matches to derek fisher . take the no record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; player ; jim farmer } ; no } ; hop { filter_eq { all_rows ; player ; derek fisher } ; no } } = true | select the rows whose player record fuzzily matches to jim farmer . take the no record of this row . select the rows whose player record fuzzily matches to derek fisher . take the no record of this row . the first record is greater than the second record . | 5 | 5 | {'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'player_7': 7, 'jim farmer_8': 8, 'no_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'player_11': 11, 'derek fisher_12': 12, 'no_13': 13} | {'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'player_7': 'player', 'jim farmer_8': 'jim farmer', 'no_9': 'no', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'player_11': 'player', 'derek fisher_12': 'derek fisher', 'no_13': 'no'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'player_7': [0], 'jim farmer_8': [0], 'no_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'player_11': [1], 'derek fisher_12': [1], 'no_13': [3]} | ['player', 'no', 'nationality', 'position', 'years for jazz', 'school / club team'] | [['jim farmer', '30', 'united states', 'guard', '1988 - 89', 'alabama'], ['derrick favors', '15', 'united states', 'forward', '2011 - present', 'georgia tech'], ['kyrylo fesenko', '44', 'ukraine', 'center', '2007 - 11', 'cherkasy monkeys ( ukraine )'], ['derek fisher', '2', 'united states', 'guard', '2006 - 2007', 'arkansas - little rock'], ['greg foster', '44', 'united states', 'center / forward', '1995 - 99', 'utep'], ['bernie fryer', '25', 'united states', 'guard', '1975 - 76', 'byu'], ['todd fuller', '52', 'united states', 'center', '1998 - 99', 'north carolina state']] |
united states house of representatives elections , 1928 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1928 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1342370-12.html.csv | unique | in the 1928 united states house of representatives elections , district 21 in illinois was the only district in that state that lost the re-election resulting in a republican gain . | {'scope': 'all', 'row': '7', 'col': '5', 'col_other': '1', 'criterion': 'equal', 'value': 'lost re - election republican gain', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 'lost re - election republican gain'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to lost re - election republican gain .', 'tostr': 'filter_eq { all_rows ; result ; lost re - election republican gain }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; result ; lost re - election republican gain } }', 'tointer': 'select the rows whose result record fuzzily matches to lost re - election republican gain . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 'lost re - election republican gain'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to lost re - election republican gain .', 'tostr': 'filter_eq { all_rows ; result ; lost re - election republican gain }'}, 'district'], 'result': 'illinois 21', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; result ; lost re - election republican gain } ; district }'}, 'illinois 21'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; result ; lost re - election republican gain } ; district } ; illinois 21 }', 'tointer': 'the district record of this unqiue row is illinois 21 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; result ; lost re - election republican gain } } ; eq { hop { filter_eq { all_rows ; result ; lost re - election republican gain } ; district } ; illinois 21 } } = true', 'tointer': 'select the rows whose result record fuzzily matches to lost re - election republican gain . there is only one such row in the table . the district record of this unqiue row is illinois 21 .'} | and { only { filter_eq { all_rows ; result ; lost re - election republican gain } } ; eq { hop { filter_eq { all_rows ; result ; lost re - election republican gain } ; district } ; illinois 21 } } = true | select the rows whose result record fuzzily matches to lost re - election republican gain . there is only one such row in the table . the district record of this unqiue row is illinois 21 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'result_7': 7, 'lost re - election republican gain_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'district_9': 9, 'illinois 21_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'result_7': 'result', 'lost re - election republican gain_8': 'lost re - election republican gain', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'district_9': 'district', 'illinois 21_10': 'illinois 21'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'result_7': [0], 'lost re - election republican gain_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'district_9': [2], 'illinois 21_10': [3]} | ['district', 'incumbent', 'party', 'first elected', 'result', 'candidates'] | [['illinois 6', 'james t igoe', 'democratic', '1926', 're - elected', 'james t igoe ( d ) 60.3 % samuel l golan ( r ) 39.7 %'], ['illinois 8', 'stanley h kunz', 'democratic', '1920', 're - elected', 'stanley h kunz ( d ) 70.8 % edward walz ( r ) 29.2 %'], ['illinois 11', 'frank r reid', 'republican', '1922', 're - elected', 'frank r reid ( r ) 68.9 % edwin l wilson ( d ) 31.1 %'], ['illinois 12', 'john t buckbee', 'republican', '1926', 're - elected', 'john t buckbee ( r ) 73.8 % jules vallatt ( d ) 26.2 %'], ['illinois 17', 'homer w hall', 'republican', '1926', 're - elected', 'homer w hall ( r ) 65.0 % frank gillespie ( d ) 35.0 %'], ['illinois 19', 'charles adkins', 'republican', '1924', 're - elected', 'charles adkins ( r ) 66.2 % w w reeves ( d ) 33.8 %'], ['illinois 21', 'j earl major', 'democratic', '1926', 'lost re - election republican gain', 'frank m ramey ( r ) 50.1 % j earl major ( d ) 49.9 %']] |
holby city ( series 3 ) | https://en.wikipedia.org/wiki/Holby_City_%28series_3%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-26139405-1.html.csv | aggregation | the third season of the television series " holby city " had an average viewer count of 7.76 million viewers per episode . | {'scope': 'all', 'col': '7', 'type': 'average', 'result': '7.76', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'viewers ( in millions )'], 'result': '7.76', 'ind': 0, 'tostr': 'avg { all_rows ; viewers ( in millions ) }'}, '7.76'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; viewers ( in millions ) } ; 7.76 } = true', 'tointer': 'the average of the viewers ( in millions ) record of all rows is 7.76 .'} | round_eq { avg { all_rows ; viewers ( in millions ) } ; 7.76 } = true | the average of the viewers ( in millions ) record of all rows is 7.76 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'viewers (in millions)_4': 4, '7.76_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'viewers (in millions)_4': 'viewers ( in millions )', '7.76_5': '7.76'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'viewers (in millions)_4': [0], '7.76_5': [1]} | ['episode', 'series', 'title', 'director', 'writer', 'original airdate', 'viewers ( in millions )'] | [['26', '1', 'the deep end', 'julie edwards', 'peter jukes', '5 2000', '7.79'], ['29', '4', 'first impressions', 'brett fallis', 'steve lawson', '9 2000', '6.47'], ['39', '14', 'night shift', 'indra bhose', 'leslie stewart', '6 2001', '8.51'], ['43', '18', 'borrowed time', 'kim flitcroft', 'tony lindsay', '6 2001', '8.38'], ['46', '21', 'snakes and ladders', 'mike cocker', 'colin bytheway', '3 2001', '7.90'], ['50', '25', "i 'm not in love", 'jim shields', 'leslie stewart', '1 2001', '8.14'], ['51', '26', 'getting even', 'jim shields', 'andrew holden', '8 2001', '7.14']] |
peter arundell | https://en.wikipedia.org/wiki/Peter_Arundell | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1235866-1.html.csv | comparative | peter arundell scored more points for team lotus in 1964 than in 1963 . | {'row_1': '2', 'row_2': '1', 'col': '5', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'year', '1964'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose year record fuzzily matches to 1964 .', 'tostr': 'filter_eq { all_rows ; year ; 1964 }'}, 'points'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; year ; 1964 } ; points }', 'tointer': 'select the rows whose year record fuzzily matches to 1964 . take the points record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'year', '1963'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose year record fuzzily matches to 1963 .', 'tostr': 'filter_eq { all_rows ; year ; 1963 }'}, 'points'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; year ; 1963 } ; points }', 'tointer': 'select the rows whose year record fuzzily matches to 1963 . take the points record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; year ; 1964 } ; points } ; hop { filter_eq { all_rows ; year ; 1963 } ; points } } = true', 'tointer': 'select the rows whose year record fuzzily matches to 1964 . take the points record of this row . select the rows whose year record fuzzily matches to 1963 . take the points record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; year ; 1964 } ; points } ; hop { filter_eq { all_rows ; year ; 1963 } ; points } } = true | select the rows whose year record fuzzily matches to 1964 . take the points record of this row . select the rows whose year record fuzzily matches to 1963 . take the points record of this row . the first record is greater than the second record . | 5 | 5 | {'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'year_7': 7, '1964_8': 8, 'points_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'year_11': 11, '1963_12': 12, 'points_13': 13} | {'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'year_7': 'year', '1964_8': '1964', 'points_9': 'points', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'year_11': 'year', '1963_12': '1963', 'points_13': 'points'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'year_7': [0], '1964_8': [0], 'points_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'year_11': [1], '1963_12': [1], 'points_13': [3]} | ['year', 'entrant', 'chassis', 'engine', 'points'] | [['1963', 'team lotus', 'lotus 25', 'climax v8', '0'], ['1964', 'team lotus', 'lotus 25', 'climax v8', '11'], ['1966', 'team lotus', 'lotus 43', 'brm h16', '1'], ['1966', 'team lotus', 'lotus 33', 'brm v8', '1'], ['1966', 'team lotus', 'lotus 33', 'climax v8', '1']] |
1973 england rugby union tour of fiji and new zealand | https://en.wikipedia.org/wiki/1973_England_rugby_union_tour_of_Fiji_and_New_Zealand | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17020789-1.html.csv | comparative | fiji scored more points against england than taranaki in the 1973 england rugby union tour of fiji and new zealand . | {'row_1': '1', 'row_2': '2', 'col': '2', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opposing team', 'fiji'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opposing team record fuzzily matches to fiji .', 'tostr': 'filter_eq { all_rows ; opposing team ; fiji }'}, 'against'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; opposing team ; fiji } ; against }', 'tointer': 'select the rows whose opposing team record fuzzily matches to fiji . take the against record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opposing team', 'taranaki'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose opposing team record fuzzily matches to taranaki .', 'tostr': 'filter_eq { all_rows ; opposing team ; taranaki }'}, 'against'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; opposing team ; taranaki } ; against }', 'tointer': 'select the rows whose opposing team record fuzzily matches to taranaki . take the against record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; opposing team ; fiji } ; against } ; hop { filter_eq { all_rows ; opposing team ; taranaki } ; against } } = true', 'tointer': 'select the rows whose opposing team record fuzzily matches to fiji . take the against record of this row . select the rows whose opposing team record fuzzily matches to taranaki . take the against record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; opposing team ; fiji } ; against } ; hop { filter_eq { all_rows ; opposing team ; taranaki } ; against } } = true | select the rows whose opposing team record fuzzily matches to fiji . take the against record of this row . select the rows whose opposing team record fuzzily matches to taranaki . take the against 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, 'opposing team_7': 7, 'fiji_8': 8, 'against_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'opposing team_11': 11, 'taranaki_12': 12, 'against_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', 'opposing team_7': 'opposing team', 'fiji_8': 'fiji', 'against_9': 'against', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'opposing team_11': 'opposing team', 'taranaki_12': 'taranaki', 'against_13': 'against'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'opposing team_7': [0], 'fiji_8': [0], 'against_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'opposing team_11': [1], 'taranaki_12': [1], 'against_13': [3]} | ['opposing team', 'against', 'date', 'venue', 'status'] | [['fiji', '12', '28 / 08 / 1973', 'buckhurst park , suva', 'tour match'], ['taranaki', '6', '01 / 09 / 1973', 'rugby park , new plymouth', 'tour match'], ['wellington', '25', '05 / 09 / 1973', 'athletic park , wellington', 'tour match'], ['canterbury', '19', '08 / 09 / 1973', 'lancaster park , christchurch', 'tour match'], ['new zealand', '10', '15 / 09 / 1973', 'eden park , auckland', 'test match']] |
2000 san diego chargers season | https://en.wikipedia.org/wiki/2000_San_Diego_Chargers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15331726-1.html.csv | comparative | in the 2000 season the san diego chargers scored more points against the buffalo bills than against the miami dolphins . | {'row_1': '7', 'row_2': '11', 'col': '4', 'col_other': '3', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'buffalo bills'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opponent record fuzzily matches to buffalo bills .', 'tostr': 'filter_eq { all_rows ; opponent ; buffalo bills }'}, 'result'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; opponent ; buffalo bills } ; result }', 'tointer': 'select the rows whose opponent record fuzzily matches to buffalo bills . take the result record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'miami dolphins'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose opponent record fuzzily matches to miami dolphins .', 'tostr': 'filter_eq { all_rows ; opponent ; miami dolphins }'}, 'result'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; opponent ; miami dolphins } ; result }', 'tointer': 'select the rows whose opponent record fuzzily matches to miami dolphins . take the result record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; opponent ; buffalo bills } ; result } ; hop { filter_eq { all_rows ; opponent ; miami dolphins } ; result } } = true', 'tointer': 'select the rows whose opponent record fuzzily matches to buffalo bills . take the result record of this row . select the rows whose opponent record fuzzily matches to miami dolphins . take the result record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; opponent ; buffalo bills } ; result } ; hop { filter_eq { all_rows ; opponent ; miami dolphins } ; result } } = true | select the rows whose opponent record fuzzily matches to buffalo bills . take the result record of this row . select the rows whose opponent record fuzzily matches to miami dolphins . take the result 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, 'buffalo bills_8': 8, 'result_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'opponent_11': 11, 'miami dolphins_12': 12, 'result_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', 'buffalo bills_8': 'buffalo bills', 'result_9': 'result', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'opponent_11': 'opponent', 'miami dolphins_12': 'miami dolphins', 'result_13': 'result'} | {'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'opponent_7': [0], 'buffalo bills_8': [0], 'result_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'opponent_11': [1], 'miami dolphins_12': [1], 'result_13': [3]} | ['week', 'date', 'opponent', 'result', 'game site', 'record', 'attendance'] | [['1', 'september 3 , 2000', 'oakland raiders', 'l 6 - 9', 'network associates coliseum', '0 - 1', '56373'], ['2', 'september 10 , 2000', 'new orleans saints', 'l 27 - 28', 'qualcomm stadium', '0 - 2', '51300'], ['3', 'september 17 , 2000', 'kansas city chiefs', 'l 10 - 42', 'arrowhead stadium', '0 - 3', '77604'], ['4', 'september 24 , 2000', 'seattle seahawks', 'l 12 - 20', 'qualcomm stadium', '0 - 4', '47233'], ['5', 'october 1 , 2000', 'st louis rams', 'l 31 - 57', 'trans world dome', '0 - 5', '66010'], ['6', 'october 8 , 2000', 'denver broncos', 'l 7 - 21', 'qualcomm stadium', '0 - 6', '56079'], ['7', 'october 15 , 2000', 'buffalo bills', 'l 24 - 27', 'ralph wilson stadium', '0 - 7', '72351'], ['8', 'october 22 , 2000', '-', '-', '-', '-', ''], ['9', 'october 29 , 2000', 'oakland raiders', 'l 13 - 15', 'qualcomm stadium', '0 - 8', '66659'], ['10', 'november 5 , 2000', 'seattle seahawks', 'l 15 - 17', 'husky stadium', '0 - 9', '59884'], ['11', 'november 12 , 2000', 'miami dolphins', 'l 7 - 17', 'qualcomm stadium', '0 - 10', '56896'], ['12', 'november 19 , 2000', 'denver broncos', 'l 37 - 38', 'mile high stadium', '0 - 11', '75218'], ['13', 'november 26 , 2000', 'kansas city chiefs', 'w 17 - 16', 'qualcomm stadium', '1 - 11', '47228'], ['14', 'december 3 , 2000', 'san francisco 49ers', 'l 17 - 45', 'qualcomm stadium', '1 - 12', '57255'], ['15', 'december 10 , 2000', 'baltimore ravens', 'l 3 - 24', 'psinet stadium', '1 - 13', '68805'], ['16', 'december 17 , 2000', 'carolina panthers', 'l 22 - 30', 'ericsson stadium', '1 - 14', '72159'], ['17', 'december 24 , 2000', 'pittsburgh steelers', 'l 21 - 34', 'qualcomm stadium', '1 - 15', '50809']] |
south wales derby | https://en.wikipedia.org/wiki/South_Wales_derby | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15473253-4.html.csv | aggregation | the average number of draws for south wales derby was approximately 4.5 . | {'scope': 'all', 'col': '4', 'type': 'average', 'result': '4.5', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'draw'], 'result': '4.5', 'ind': 0, 'tostr': 'avg { all_rows ; draw }'}, '4.5'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; draw } ; 4.5 } = true', 'tointer': 'the average of the draw record of all rows is 4.5 .'} | round_eq { avg { all_rows ; draw } ; 4.5 } = true | the average of the draw record of all rows is 4.5 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'draw_4': 4, '4.5_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'draw_4': 'draw', '4.5_5': '4.5'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'draw_4': [0], '4.5_5': [1]} | ['competition', 'total matches', 'cardiff win', 'draw', 'swansea win'] | [['league', '55', '19', '16', '20'], ['fa cup', '2', '0', '0', '2'], ['league cup', '5', '2', '0', '3'], ['associate members cup', '4', '1', '1', '2'], ['welsh cup / faw premier cup', '36', '21', '8', '7'], ['southern league', '4', '1', '2', '1'], ['total', '106', '44', '27', '35']] |
agriculture in bolivia | https://en.wikipedia.org/wiki/Agriculture_in_Bolivia | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-21249915-1.html.csv | superlative | potosi has the highest micro out of all the departments of bolivia . | {'scope': 'all', 'col_superlative': '2', 'row_superlative': '5', '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', 'micro ( 10ha )'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; micro ( 10ha ) }'}, 'department'], 'result': 'potosi', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; micro ( 10ha ) } ; department }'}, 'potosi'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; micro ( 10ha ) } ; department } ; potosi } = true', 'tointer': 'select the row whose micro ( 10ha ) record of all rows is maximum . the department record of this row is potosi .'} | eq { hop { argmax { all_rows ; micro ( 10ha ) } ; department } ; potosi } = true | select the row whose micro ( 10ha ) record of all rows is maximum . the department record of this row is potosi . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'micro (10ha)_5': 5, 'department_6': 6, 'potosi_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'micro (10ha)_5': 'micro ( 10ha )', 'department_6': 'department', 'potosi_7': 'potosi'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'micro (10ha)_5': [0], 'department_6': [1], 'potosi_7': [2]} | ['department', 'micro ( 10ha )', 'small ( 100ha )', 'medium ( 500ha )', 'big ( > 500ha )', 'total'] | [['chuquisaca', '1653', '11370', '4261', '3884', '21168'], ['cochabamba', '1938', '22225', '27403', '35968', '81925'], ['la paz', '1703', '21047', '6052', '7192', '35994'], ['oruro', '940', '3638', '440', '9021', '14039'], ['potosi', '3240', '10146', '2254', '600', '16240'], ['santa cruz', '269', '5456', '8434', '1080', '15239'], ['tarija', '785', '12755', '17101', '5710', '36351']] |
camarines norte | https://en.wikipedia.org/wiki/Camarines_Norte | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-255885-1.html.csv | ordinal | in 2007 , labo had the 2nd highest population of any municipality in camarines norte . | {'row': '5', 'col': '4', 'order': '2', 'col_other': '1', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'population ( 2007 )', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; population ( 2007 ) ; 2 }'}, 'municipality'], 'result': 'labo', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; population ( 2007 ) ; 2 } ; municipality }'}, 'labo'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; population ( 2007 ) ; 2 } ; municipality } ; labo } = true', 'tointer': 'select the row whose population ( 2007 ) record of all rows is 2nd maximum . the municipality record of this row is labo .'} | eq { hop { nth_argmax { all_rows ; population ( 2007 ) ; 2 } ; municipality } ; labo } = true | select the row whose population ( 2007 ) record of all rows is 2nd maximum . the municipality record of this row is labo . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'population (2007)_5': 5, '2_6': 6, 'municipality_7': 7, 'labo_8': 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'population (2007)_5': 'population ( 2007 )', '2_6': '2', 'municipality_7': 'municipality', 'labo_8': 'labo'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'population (2007)_5': [0], '2_6': [0], 'municipality_7': [1], 'labo_8': [2]} | ['municipality', 'no of s barangay', 'area ( km square )', 'population ( 2007 )', 'population ( 2010 )'] | [['basud', '29', '260.28', '36763', '38176'], ['capalonga', '22', '290.00', '29683', '31299'], ['daet ( capital town )', '25', '46.00', '94184', '95572'], ['jose panganiban', '27', '214.44', '49028', '55557'], ['labo', '52', '589.36', '88087', '92041'], ['mercedes', '26', '173.69', '44375', '47674'], ['paracale', '27', '197.90', '46856', '53243'], ['san lorenzo ruiz', '12', '119.37', '12299', '12592'], ['san vicente', '9', '57.49', '9615', '10114'], ['santa elena', '19', '199.35', '40300', '40828'], ['talisay', '15', '30.76', '22942', '23904']] |
indiana high school athletics conferences : mid - eastern - northwestern | https://en.wikipedia.org/wiki/Indiana_High_School_Athletics_Conferences%3A_Mid-Eastern_%E2%80%93_Northwestern | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18942405-13.html.csv | count | a total of four indiana high schools are in the aaa ihsaa class . | {'scope': 'all', 'criterion': 'equal', 'value': 'aaa', 'result': '4', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'ihsaa class', 'aaa'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose ihsaa class record fuzzily matches to aaa .', 'tostr': 'filter_eq { all_rows ; ihsaa class ; aaa }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; ihsaa class ; aaa } }', 'tointer': 'select the rows whose ihsaa class record fuzzily matches to aaa . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; ihsaa class ; aaa } } ; 4 } = true', 'tointer': 'select the rows whose ihsaa class record fuzzily matches to aaa . the number of such rows is 4 .'} | eq { count { filter_eq { all_rows ; ihsaa class ; aaa } } ; 4 } = true | select the rows whose ihsaa class record fuzzily matches to aaa . 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, 'ihsaa class_5': 5, 'aaa_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', 'ihsaa class_5': 'ihsaa class', 'aaa_6': 'aaa', '4_7': '4'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'ihsaa class_5': [0], 'aaa_6': [0], '4_7': [2]} | ['school', 'location', 'mascot', 'enrollment', 'ihsaa class', 'ihsaa football class', 'county'] | [['bremen', 'bremen', 'lions', '495', 'aa', 'aa', '50 marshall'], ['culver community', 'culver', 'cavaliers', '287', 'a', 'a', '50 marshall'], ['glenn', 'walkerton', 'falcons', '605', 'aaa', 'aaa', '71 st joseph'], ['jimtown', 'elkhart', 'jimmies', '601', 'aaa', 'aaa', '20 elkhart'], ['knox community', 'knox', 'redskins', '620', 'aaa', 'aaa', '75 starke'], ['laville', 'lakeville', 'lancers', '379', 'aa', 'a', '71 st joseph'], ['new prairie 1', 'new carlisle', 'cougars', '852', 'aaa', 'aaaa', '46 laporte 71 st joseph'], ['triton', 'bourbon', 'trojans', '316', 'a', 'a', '50 marshall']] |
2008 - 09 montreal canadiens season | https://en.wikipedia.org/wiki/2008%E2%80%9309_Montreal_Canadiens_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17206737-10.html.csv | majority | all of the games took place in april . | {'scope': 'all', 'col': '1', 'most_or_all': 'all', 'criterion': 'fuzzily_match', 'value': 'april', 'subset': None} | {'func': 'all_str_eq', 'args': ['all_rows', 'date', 'april'], 'result': True, 'ind': 0, 'tointer': 'for the date records of all rows , all of them fuzzily match to april .', 'tostr': 'all_eq { all_rows ; date ; april } = true'} | all_eq { all_rows ; date ; april } = true | for the date records of all rows , all of them fuzzily match to april . | 1 | 1 | {'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'date_3': 3, 'april_4': 4} | {'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'date_3': 'date', 'april_4': 'april'} | {'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'date_3': [0], 'april_4': [0]} | ['date', 'visitor', 'score', 'home', 'decision', 'attendance', 'record', 'points'] | [['april 2', 'montreal canadiens', '5 - 1', 'new york islanders', 'halak', '15255', '40 - 27 - 10', '90'], ['april 4', 'montreal canadiens', '6 - 2', 'toronto maple leafs', 'halak', '19516', '41 - 27 - 10', '92'], ['april 6', 'ottawa senators', '3 - 2', 'montreal canadiens', 'halak', '21273', '41 - 28 - 10', '92'], ['april 7', 'montreal canadiens', '3 - 1', 'new york rangers', 'price', '18200', '41 - 29 - 10', '92'], ['april 9', 'montreal canadiens', '4 - 5 ot', 'boston bruins', 'price', '17565', '41 - 29 - 11', '93'], ['april 11', 'pittsburgh penguins', '3 - 1', 'montreal canadiens', 'price', '21273', '41 - 30 - 11', '93']] |
1980 vfl season | https://en.wikipedia.org/wiki/1980_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10809823-18.html.csv | majority | all games of the 1980 vfl season was played on the 2nd of august . | {'scope': 'all', 'col': '7', 'most_or_all': 'all', 'criterion': 'equal', 'value': '2 august 1980', 'subset': None} | {'func': 'all_str_eq', 'args': ['all_rows', 'date', '2 august 1980'], 'result': True, 'ind': 0, 'tointer': 'for the date records of all rows , all of them fuzzily match to 2 august 1980 .', 'tostr': 'all_eq { all_rows ; date ; 2 august 1980 } = true'} | all_eq { all_rows ; date ; 2 august 1980 } = true | for the date records of all rows , all of them fuzzily match to 2 august 1980 . | 1 | 1 | {'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'date_3': 3, '2 august 1980_4': 4} | {'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'date_3': 'date', '2 august 1980_4': '2 august 1980'} | {'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'date_3': [0], '2 august 1980_4': [0]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['fitzroy', '15.14 ( 104 )', 'st kilda', '12.8 ( 80 )', 'junction oval', '6836', '2 august 1980'], ['collingwood', '11.24 ( 90 )', 'south melbourne', '8.9 ( 57 )', 'victoria park', '24739', '2 august 1980'], ['north melbourne', '12.15 ( 87 )', 'melbourne', '7.15 ( 57 )', 'arden street oval', '7544', '2 august 1980'], ['geelong', '15.14 ( 104 )', 'essendon', '13.12 ( 90 )', 'kardinia park', '24738', '2 august 1980'], ['hawthorn', '9.15 ( 69 )', 'carlton', '16.17 ( 113 )', 'princes park', '15046', '2 august 1980'], ['richmond', '23.18 ( 156 )', 'footscray', '6.5 ( 41 )', 'vfl park', '18282', '2 august 1980']] |
list of england national rugby union team results 1980 - 89 | https://en.wikipedia.org/wiki/List_of_England_national_rugby_union_team_results_1980%E2%80%9389 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18178608-9.html.csv | comparative | fiji scored more points against the england national rugby union team than france . | {'row_1': '8', 'row_2': '1', 'col': '2', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opposing teams', 'fiji'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opposing teams record fuzzily matches to fiji .', 'tostr': 'filter_eq { all_rows ; opposing teams ; fiji }'}, 'against'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; opposing teams ; fiji } ; against }', 'tointer': 'select the rows whose opposing teams record fuzzily matches to fiji . take the against record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opposing teams', 'france'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose opposing teams record fuzzily matches to france .', 'tostr': 'filter_eq { all_rows ; opposing teams ; france }'}, 'against'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; opposing teams ; france } ; against }', 'tointer': 'select the rows whose opposing teams record fuzzily matches to france . take the against record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; opposing teams ; fiji } ; against } ; hop { filter_eq { all_rows ; opposing teams ; france } ; against } } = true', 'tointer': 'select the rows whose opposing teams record fuzzily matches to fiji . take the against record of this row . select the rows whose opposing teams record fuzzily matches to france . take the against record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; opposing teams ; fiji } ; against } ; hop { filter_eq { all_rows ; opposing teams ; france } ; against } } = true | select the rows whose opposing teams record fuzzily matches to fiji . take the against record of this row . select the rows whose opposing teams record fuzzily matches to france . take the against 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, 'opposing teams_7': 7, 'fiji_8': 8, 'against_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'opposing teams_11': 11, 'france_12': 12, 'against_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', 'opposing teams_7': 'opposing teams', 'fiji_8': 'fiji', 'against_9': 'against', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'opposing teams_11': 'opposing teams', 'france_12': 'france', 'against_13': 'against'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'opposing teams_7': [0], 'fiji_8': [0], 'against_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'opposing teams_11': [1], 'france_12': [1], 'against_13': [3]} | ['opposing teams', 'against', 'date', 'venue', 'status'] | [['france', '10', '16 / 01 / 1988', 'parc des princes , paris', 'five nations'], ['wales', '11', '06 / 02 / 1988', 'twickenham , london', 'five nations'], ['scotland', '6', '05 / 03 / 1988', 'murrayfield , edinburgh', 'five nations'], ['ireland', '3', '19 / 03 / 1988', 'twickenham , london', 'five nations'], ['ireland', '10', '23 / 04 / 1988', 'lansdowne road , dublin', 'millennium trophy match'], ['australia', '22', '29 / 05 / 1988', 'ballymore , brisbane', 'first test'], ['australia', '28', '12 / 06 / 1988', 'concord oval , sydney', 'second test'], ['fiji', '12', '16 / 06 / 1988', 'national stadium , suva', 'test match'], ['australia', '19', '05 / 11 / 1988', 'twickenham , london', 'test match']] |
list of tvb series ( 2006 ) | https://en.wikipedia.org/wiki/List_of_TVB_series_%282006%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10942714-1.html.csv | ordinal | the 2006 tvb series that drew the second highest peak viewership is la femme desperado . | {'row': '1', 'col': '5', 'order': '2', 'col_other': '2', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'peak', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; peak ; 2 }'}, 'english title'], 'result': 'la femme desperado', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; peak ; 2 } ; english title }'}, 'la femme desperado'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; peak ; 2 } ; english title } ; la femme desperado } = true', 'tointer': 'select the row whose peak record of all rows is 2nd maximum . the english title record of this row is la femme desperado .'} | eq { hop { nth_argmax { all_rows ; peak ; 2 } ; english title } ; la femme desperado } = true | select the row whose peak record of all rows is 2nd maximum . the english title record of this row is la femme desperado . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'peak_5': 5, '2_6': 6, 'english title_7': 7, 'la femme desperado_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', 'peak_5': 'peak', '2_6': '2', 'english title_7': 'english title', 'la femme desperado_8': 'la femme desperado'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'peak_5': [0], '2_6': [0], 'english title_7': [1], 'la femme desperado_8': [2]} | ['rank', 'english title', 'chinese title', 'average', 'peak', 'premiere', 'finale', 'hk viewers'] | [['1', 'la femme desperado', '女人唔易做', '33', '41', '31', '34', '2.14 million'], ['2', 'forensic heroes', '法證先鋒', '33', '43', '28', '37', '2.11 million'], ['3', 'the saviour of the soul', '神鵰俠侶', '32', '40', '32', '35', '2.07 million'], ['4', 'love guaranteed', '愛情全保', '32', '36', '30', '34', '2.07 million'], ['5', 'bar bender', '潮爆大狀', '32', '38', '31', '34', '2.06 million'], ['6', 'the dance of passion', '火舞黃沙', '32', '38', '34', '35', '2.05 million'], ['7', "maiden 's vow", '鳳凰四重奏', '32', '37', '32', '29', '2.05 million'], ['8', 'to grow with love', '肥田囍事', '32', '35', '32', '32', '2.04 million'], ['9', 'men in pain', '男人之苦', '32', '39', '28', '33', '2.03 million'], ['10', 'under the canopy of love', '天幕下的戀人', '31', '37', '28', '33', '2.02 million']] |
virginia wade | https://en.wikipedia.org/wiki/Virginia_Wade | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-177273-2.html.csv | count | virginia wade was partnered with margaret court for a total of six tournaments . | {'scope': 'all', 'criterion': 'equal', 'value': 'margaret court', 'result': '6', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'partner', 'margaret court'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose partner record fuzzily matches to margaret court .', 'tostr': 'filter_eq { all_rows ; partner ; margaret court }'}], 'result': '6', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; partner ; margaret court } }', 'tointer': 'select the rows whose partner record fuzzily matches to margaret court . the number of such rows is 6 .'}, '6'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; partner ; margaret court } } ; 6 } = true', 'tointer': 'select the rows whose partner record fuzzily matches to margaret court . the number of such rows is 6 .'} | eq { count { filter_eq { all_rows ; partner ; margaret court } } ; 6 } = true | select the rows whose partner record fuzzily matches to margaret court . 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, 'partner_5': 5, 'margaret court_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', 'partner_5': 'partner', 'margaret court_6': 'margaret court', '6_7': '6'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'partner_5': [0], 'margaret court_6': [0], '6_7': [2]} | ['outcome', 'year', 'championship', 'surface', 'partner', 'opponents', 'score'] | [['runner - up', '1969', 'us open', 'grass', 'margaret court', 'françoise dürr darlene hard', '0 - 6 , 6 - 4 , 6 - 4'], ['runner - up', '1970', 'wimbledon', 'grass', 'françoise dürr', 'rosie casals billie jean king', '6 - 2 , 6 - 3'], ['runner - up', '1970', 'us open', 'grass', 'rosie casals', 'margaret court judy tegart dalton', '6 - 3 , 6 - 4'], ['runner - up', '1972', 'us open', 'grass', 'margaret court', 'françoise dürr betty stöve', '6 - 3 , 1 - 6 , 6 - 3'], ['winner', '1973', 'australian open', 'grass', 'margaret court', 'kerry harris kerry melville', '6 - 4 , 6 - 4'], ['winner', '1973', 'french open', 'clay', 'margaret court', 'françoise dürr betty stöve', '6 - 2 , 6 - 3'], ['winner', '1973', 'us open', 'grass', 'margaret court', 'rosie casals billie jean king', '2 - 6 , 6 - 3 , 7 - 5'], ['winner', '1975', 'us open', 'clay', 'margaret court', 'rosie casals billie jean king', '7 - 5 , 2 - 6 , 7 - 6 ( 5 )'], ['runner - up', '1976', 'us open', 'clay', 'olga morozova', 'linky boshoff ilana kloss', '6 - 1 , 6 - 4']] |
1972 vfl season | https://en.wikipedia.org/wiki/1972_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10826385-12.html.csv | aggregation | in the 1972 vfl season , the average score for home teams was 12.09 . | {'scope': 'all', 'col': '2', 'type': 'average', 'result': '12.09', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'home team score'], 'result': '12.09', 'ind': 0, 'tostr': 'avg { all_rows ; home team score }'}, '12.09'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; home team score } ; 12.09 } = true', 'tointer': 'the average of the home team score record of all rows is 12.09 .'} | round_eq { avg { all_rows ; home team score } ; 12.09 } = true | the average of the home team score record of all rows is 12.09 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'home team score_4': 4, '12.09_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'home team score_4': 'home team score', '12.09_5': '12.09'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'home team score_4': [0], '12.09_5': [1]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['north melbourne', '7.8 ( 50 )', 'st kilda', '12.19 ( 91 )', 'arden street oval', '10681', '17 june 1972'], ['collingwood', '11.24 ( 90 )', 'richmond', '12.13 ( 85 )', 'victoria park', '28188', '17 june 1972'], ['melbourne', '11.10 ( 76 )', 'hawthorn', '11.9 ( 75 )', 'mcg', '31314', '17 june 1972'], ['geelong', '15.14 ( 104 )', 'south melbourne', '10.13 ( 73 )', 'kardinia park', '14426', '24 june 1972'], ['essendon', '14.15 ( 99 )', 'footscray', '19.19 ( 133 )', 'windy hill', '23903', '24 june 1972'], ['carlton', '13.13 ( 91 )', 'fitzroy', '8.7 ( 55 )', 'vfl park', '29380', '24 june 1972']] |
l'amour n'est rien | https://en.wikipedia.org/wiki/L%27amour_n%27est_rien... | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14773149-2.html.csv | count | laurent boutonnat remixed two versions of the track l'amour n'est rien . | {'scope': 'all', 'criterion': 'equal', 'value': 'laurent boutonnat', 'result': '2', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'remixed by', 'laurent boutonnat'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose remixed by record fuzzily matches to laurent boutonnat .', 'tostr': 'filter_eq { all_rows ; remixed by ; laurent boutonnat }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; remixed by ; laurent boutonnat } }', 'tointer': 'select the rows whose remixed by record fuzzily matches to laurent boutonnat . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; remixed by ; laurent boutonnat } } ; 2 } = true', 'tointer': 'select the rows whose remixed by record fuzzily matches to laurent boutonnat . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; remixed by ; laurent boutonnat } } ; 2 } = true | select the rows whose remixed by record fuzzily matches to laurent boutonnat . 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, 'remixed by_5': 5, 'laurent boutonnat_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', 'remixed by_5': 'remixed by', 'laurent boutonnat_6': 'laurent boutonnat', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'remixed by_5': [0], 'laurent boutonnat_6': [0], '2_7': [2]} | ['version', 'length', 'album', 'remixed by', 'year'] | [['single / album version', '5:03', "avant que l'ombre", '-', '2005'], ['radio edit', '3:40', '-', 'laurent boutonnat', '2006'], ['instrumental', '5:03', '-', 'laurent boutonnat', '2006'], ['the sexually no remix', '3:30', '-', 'the bionix', '2006'], ['obsessed club mix', '5:47', '-', 'fat phaze', '2006'], ['music video', '3:40', 'music videos iv', '-', '2006'], ['patrice strike & teo moss remix', '5:04', '-', 'patrice strike and teo moss', '2006'], ['live version ( recorded in 2006 )', '4:59 ( video ) 5:05 ( audio )', "avant que l'ombre à bercy", '-', '2006']] |
1999 u.s. open ( golf ) | https://en.wikipedia.org/wiki/1999_U.S._Open_%28golf%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17162128-2.html.csv | majority | all of the players in the 1999 u.s. open 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'] | [['payne stewart', 'united states', '1991', '279', '1', '1'], ['corey pavin', 'united states', '1995', '296', '+ 16', 't34'], ['lee janzen', 'united states', '1993 , 1998', '298', '+ 18', 't46'], ['tom watson', 'united states', '1982', '301', '+ 21', 't57'], ['tom kite', 'united states', '1992', '302', '+ 22', 't60']] |
eurovision song contest 2008 | https://en.wikipedia.org/wiki/Eurovision_Song_Contest_2008 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11173692-2.html.csv | ordinal | in the eurovision song contest of 2008 , the artist with the 2nd highest number of points was sirusho . | {'row': '14', 'col': '6', 'order': '2', 'col_other': '3', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'points', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; points ; 2 }'}, 'artist'], 'result': 'sirusho', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; points ; 2 } ; artist }'}, 'sirusho'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; points ; 2 } ; artist } ; sirusho } = true', 'tointer': 'select the row whose points record of all rows is 2nd maximum . the artist record of this row is sirusho .'} | eq { hop { nth_argmax { all_rows ; points ; 2 } ; artist } ; sirusho } = true | select the row whose points record of all rows is 2nd maximum . the artist record of this row is sirusho . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'points_5': 5, '2_6': 6, 'artist_7': 7, 'sirusho_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', 'points_5': 'points', '2_6': '2', 'artist_7': 'artist', 'sirusho_8': 'sirusho'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'points_5': [0], '2_6': [0], 'artist_7': [1], 'sirusho_8': [2]} | ['draw', 'language', 'artist', 'song', 'place', 'points'] | [['01', 'montenegrin', 'stefan filipović', 'zauvijek volim te', '14', '23'], ['02', 'hebrew , english', "boaz ma'uda", 'the fire in your eyes', '5', '104'], ['03', 'serbian , german , finnish', 'kreisiraadio', 'leto svet', '18', '8'], ['04', 'english', 'geta burlacu', 'a century of love', '12', '36'], ['05', 'italian', 'miodio', 'complice', '19', '5'], ['06', 'imaginary', 'ishtar', 'o julissi', '17', '16'], ['07', 'english', 'elnur and samir', 'day after day', '6', '96'], ['08', 'slovene', 'rebeka dremelj', 'vrag naj vzame', '11', '36'], ['09', 'english', 'maria haukaas storeng', 'hold on be strong', '4', '106'], ['10', 'english', 'isis gee', 'for life', '10', '42'], ['11', 'english , french', 'dustin the turkey', 'irelande douze pointe', '15', '22'], ['12', 'english , catalan', 'gisela', 'casanova', '16', '22'], ['13', 'bosnian', 'laka', 'pokušaj', '9', '72'], ['14', 'english , armenian', 'sirusho', 'qélé , qélé ( քելե քելե )', '2', '139'], ['15', 'english', 'hind', 'your heart belongs to me', '13', '27'], ['16', 'finnish', 'teräsbetoni', 'missä miehet ratsastaa', '8', '79'], ['17', 'romanian , italian', 'nico and vlad', 'pe - o margine de lume', '7', '94'], ['18', 'english', 'dima bilan', 'believe', '3', '135'], ['19', 'english', 'kalomira', 'secret combination', '1', '156']] |
indiana high school athletics conferences : ohio river valley - western indiana | https://en.wikipedia.org/wiki/Indiana_High_School_Athletics_Conferences%3A_Ohio_River_Valley_%E2%80%93_Western_Indiana | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18974097-12.html.csv | aggregation | the average enrollment at all of the indiana high school athletics conferences was 476 . | {'scope': 'all', 'col': '4', 'type': 'average', 'result': '476', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'enrollment'], 'result': '476', 'ind': 0, 'tostr': 'avg { all_rows ; enrollment }'}, '476'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; enrollment } ; 476 } = true', 'tointer': 'the average of the enrollment record of all rows is 476 .'} | round_eq { avg { all_rows ; enrollment } ; 476 } = true | the average of the enrollment record of all rows is 476 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'enrollment_4': 4, '476_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'enrollment_4': 'enrollment', '476_5': '476'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'enrollment_4': [0], '476_5': [1]} | ['school', 'location', 'mascot', 'enrollment', 'ihsaa class', 'ihsaa football class', 'county'] | [['manchester', 'north manchester', 'squires', '432', 'aa', 'aa', '85 wabash'], ['northfield', 'wabash', 'norsemen', '389', 'aa', 'a', '85 wabash'], ['north miami', 'denver', 'warriors', '349', 'aa', 'a', '52 miami'], ['rochester community', 'rochester', 'zebras', '565', 'aa', 'aa', '25 fulton'], ['southwood', 'wabash', 'knights', '413', 'aa', 'a', '85 wabash'], ['tippecanoe valley', 'akron', 'vikings', '618', 'aaa', 'aaa', '43 kosciusko'], ['wabash', 'wabash', 'apachees', '447', 'aa', 'aa', '85 wabash'], ['whitko', 'south whitley', 'wildcats', '595', 'aaa', 'aaa', '92 whitley']] |
lukáš melich | https://en.wikipedia.org/wiki/Luk%C3%A1%C5%A1_Melich | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12582968-1.html.csv | count | lukas melich competed in a total of 3 world championships . | {'scope': 'all', 'criterion': 'equal', 'value': 'world championships', 'result': '3', 'col': '2', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'competition', 'world championships'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose competition record fuzzily matches to world championships .', 'tostr': 'filter_eq { all_rows ; competition ; world championships }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; competition ; world championships } }', 'tointer': 'select the rows whose competition record fuzzily matches to world championships . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; competition ; world championships } } ; 3 } = true', 'tointer': 'select the rows whose competition record fuzzily matches to world championships . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; competition ; world championships } } ; 3 } = true | select the rows whose competition record fuzzily matches to world championships . the number of such rows is 3 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'competition_5': 5, 'world championships_6': 6, '3_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'competition_5': 'competition', 'world championships_6': 'world championships', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'competition_5': [0], 'world championships_6': [0], '3_7': [2]} | ['year', 'competition', 'venue', 'position', 'notes'] | [['1998', 'world junior championships', 'annecy , france', '10th', '61.51 m'], ['1999', 'european junior championships', 'riga , latvia', '5th', '64.20 m'], ['2001', 'european u23 championships', 'amsterdam , netherlands', '11th', '66.41 m'], ['2003', 'universiade', 'daegu , south korea', '4th', '71.26 m'], ['2005', 'world championships', 'helsinki , finland', '14th', '74.53 m'], ['2006', 'european championships', 'gothenburg , sweden', '15th', '73.77 m'], ['2008', 'olympic games', 'beijing , pr china', '29th', '70.56 m'], ['2009', 'world championships', 'berlin , germany', '14th', '74.47 m'], ['2012', 'olympic games', 'london , great britain', '6th', '77.17 m'], ['2013', 'world championships', 'moscow , russia', '3rd', '79.36 m']] |
nero wolfe ( 1981 tv series ) | https://en.wikipedia.org/wiki/Nero_Wolfe_%281981_TV_series%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17189526-1.html.csv | unique | the episode " the blue ribbon hostage " was the only one directed by ron satlof . | {'scope': 'all', 'row': '13', 'col': '3', 'col_other': '1', 'criterion': 'equal', 'value': 'ron satlof', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'director', 'ron satlof'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose director record fuzzily matches to ron satlof .', 'tostr': 'filter_eq { all_rows ; director ; ron satlof }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; director ; ron satlof } }', 'tointer': 'select the rows whose director record fuzzily matches to ron satlof . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'director', 'ron satlof'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose director record fuzzily matches to ron satlof .', 'tostr': 'filter_eq { all_rows ; director ; ron satlof }'}, 'title'], 'result': 'the blue ribbon hostage', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; director ; ron satlof } ; title }'}, 'the blue ribbon hostage'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; director ; ron satlof } ; title } ; the blue ribbon hostage }', 'tointer': 'the title record of this unqiue row is the blue ribbon hostage .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; director ; ron satlof } } ; eq { hop { filter_eq { all_rows ; director ; ron satlof } ; title } ; the blue ribbon hostage } } = true', 'tointer': 'select the rows whose director record fuzzily matches to ron satlof . there is only one such row in the table . the title record of this unqiue row is the blue ribbon hostage .'} | and { only { filter_eq { all_rows ; director ; ron satlof } } ; eq { hop { filter_eq { all_rows ; director ; ron satlof } ; title } ; the blue ribbon hostage } } = true | select the rows whose director record fuzzily matches to ron satlof . there is only one such row in the table . the title record of this unqiue row is the blue ribbon hostage . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'director_7': 7, 'ron satlof_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'title_9': 9, 'the blue ribbon hostage_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'director_7': 'director', 'ron satlof_8': 'ron satlof', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'title_9': 'title', 'the blue ribbon hostage_10': 'the blue ribbon hostage'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'director_7': [0], 'ron satlof_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'title_9': [2], 'the blue ribbon hostage_10': [3]} | ['title', 'season', 'director', 'teleplay', 'first broadcast'] | [['the golden spiders', '1.1', "michael o'herlihy", 'wallace ware + peter nasco', 'january 16 , 1981'], ['death on the doorstep', '1.2', 'george mccowan', 'stephen downing', 'january 23 , 1981'], ['before i die', '1.3', 'edward m abroms', 'alfred hayes', 'january 30 , 1981'], ['wolfe at the door', '1.4', 'herbert hirschman', 'lee sheldon', 'february 6 , 1981'], ['might as well be dead', '1.5', 'george mccowan', 'seeleg lester', 'february 13 , 1981'], ['to catch a dead man', '1.6', 'edward m abroms', 'john meredyth lucas', 'february 20 , 1981'], ['in the best families', '1.7', 'george mccowan', 'alfred hayes', 'march 6 , 1981'], ['murder by the book', '1.8', 'bob kelljan', 'wallace ware', 'march 13 , 1981'], ['what happened to april', '1.9', 'edward m abroms', 'stephen downing', 'march 20 , 1981'], ['gambit', '1.10', 'george mccowan', 'stephen kandel', 'april 3 , 1981'], ['death and the dolls', '1.11', 'gerald mayer', 'gerald sanford', 'april 10 , 1981'], ['the murder in question', '1.12', 'george mccowan', 'merwin gerard', 'april 17 , 1981'], ['the blue ribbon hostage', '1.13', 'ron satlof', 'dick nelson', 'may 5 , 1981'], ['sweet revenge', '1.14', 'george mccowan', 'ben roberts', 'june 2 , 1981']] |
world team chess championship | https://en.wikipedia.org/wiki/World_Team_Chess_Championship | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15669776-3.html.csv | majority | the majority of countries achieved at least one second place finish in the world team chess championship . | {'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'greater_than_eq', 'value': '1', 'subset': None} | {'func': 'most_greater_eq', 'args': ['all_rows', '2nd place', '1'], 'result': True, 'ind': 0, 'tointer': 'for the 2nd place records of all rows , most of them are greater than or equal to 1 .', 'tostr': 'most_greater_eq { all_rows ; 2nd place ; 1 } = true'} | most_greater_eq { all_rows ; 2nd place ; 1 } = true | for the 2nd place records of all rows , most of them are greater than or equal to 1 . | 1 | 1 | {'most_greater_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, '2nd place_3': 3, '1_4': 4} | {'most_greater_eq_0': 'most_greater_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', '2nd place_3': '2nd place', '1_4': '1'} | {'most_greater_eq_0': [1], 'result_1': [], 'all_rows_2': [0], '2nd place_3': [0], '1_4': [0]} | ['rank', 'country', '1st place', '2nd place', '3rd place', 'total'] | [['1', 'russia', '3', '1', '1', '5'], ['2', 'soviet union', '2', '0', '0', '2'], ['3', 'united states', '1', '2', '0', '3'], ['4', 'ukraine', '1', '1', '1', '3'], ['5', 'armenia', '1', '0', '3', '4'], ['6', 'china', '0', '2', '0', '2'], ['7', 'hungary', '0', '1', '0', '1'], ['7', 'yugoslavia', '0', '1', '0', '1'], ['9', 'england', '0', '0', '2', '2'], ['10', 'india', '0', '0', '1', '1']] |
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