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wmbj | https://en.wikipedia.org/wiki/WMBJ | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14996829-1.html.csv | aggregation | the average erp of these wmbj frequencies is roughly 33.2 . | {'scope': 'all', 'col': '4', 'type': 'average', 'result': '33.2', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'erp w'], 'result': '33.2', 'ind': 0, 'tostr': 'avg { all_rows ; erp w }'}, '33.2'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; erp w } ; 33.2 } = true', 'tointer': 'the average of the erp w record of all rows is 33.2 .'} | round_eq { avg { all_rows ; erp w } ; 33.2 } = true | the average of the erp w record of all rows is 33.2 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'erp w_4': 4, '33.2_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'erp w_4': 'erp w', '33.2_5': '33.2'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'erp w_4': [0], '33.2_5': [1]} | ['call sign', 'frequency mhz', 'city of license', 'erp w', 'class', 'fcc info'] | [['w203bq', '88.5 fm', 'walterboro , sc', '30', 'd', 'fcc'], ['w298aj', '107.5 fm', 'boone , nc', '10', 'd', 'fcc'], ['w227bk', '93.3 fm', 'surfside beach , sc', '27', 'd', 'fcc'], ['w238bi', '95.5 fm', 'georgetown , sc', '10', 'd', 'fcc'], ['w283av', '104.5 fm', 'little river , sc', '5', 'd', 'fcc'], ['w286ay', '105.1 fm', 'charleston , sc', '117', 'd', 'fcc']] |
sandra cecchini | https://en.wikipedia.org/wiki/Sandra_Cecchini | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14979843-3.html.csv | comparative | of the tournaments that sandra cecchini participated in , the nice tournament was 1 year before the estoril tournament . | {'row_1': '10', 'row_2': '11', 'col': '2', 'col_other': '3', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'tournament', 'nice'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose tournament record fuzzily matches to nice .', 'tostr': 'filter_eq { all_rows ; tournament ; nice }'}, 'date'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; tournament ; nice } ; date }', 'tointer': 'select the rows whose tournament record fuzzily matches to nice . take the date record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'tournament', 'estoril'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose tournament record fuzzily matches to estoril .', 'tostr': 'filter_eq { all_rows ; tournament ; estoril }'}, 'date'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; tournament ; estoril } ; date }', 'tointer': 'select the rows whose tournament record fuzzily matches to estoril . take the date record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; tournament ; nice } ; date } ; hop { filter_eq { all_rows ; tournament ; estoril } ; date } } = true', 'tointer': 'select the rows whose tournament record fuzzily matches to nice . take the date record of this row . select the rows whose tournament record fuzzily matches to estoril . take the date record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; tournament ; nice } ; date } ; hop { filter_eq { all_rows ; tournament ; estoril } ; date } } = true | select the rows whose tournament record fuzzily matches to nice . take the date record of this row . select the rows whose tournament record fuzzily matches to estoril . 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, 'tournament_7': 7, 'nice_8': 8, 'date_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'tournament_11': 11, 'estoril_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', 'tournament_7': 'tournament', 'nice_8': 'nice', 'date_9': 'date', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'tournament_11': 'tournament', 'estoril_12': 'estoril', 'date_13': 'date'} | {'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'tournament_7': [0], 'nice_8': [0], 'date_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'tournament_11': [1], 'estoril_12': [1], 'date_13': [3]} | ['outcome', 'date', 'tournament', 'surface', 'opponent', 'score'] | [['winner', '23 april 1984', 'taranto', 'clay', 'sabrina goleš', '6 - 3 , 7 - 5'], ['winner', '9 july 1984', 'rio de janeiro', 'hard', 'adriana villagran', '6 - 3 , 6 - 3'], ['winner', '6 may 1985', 'barcelona', 'clay', 'raffaella reggi', '6 - 3 , 6 - 4'], ['winner', '14 july 1986', 'bregenz', 'clay', 'mariana pérez - roldán', '6 - 4 , 6 - 0'], ['runner - up', '18 may 1987', 'strasbourg', 'clay', 'carling bassett', '3 - 6 , 4 - 6'], ['winner', '6 july 1987', 'båstad', 'clay', 'catarina lindqvist', '6 - 4 , 6 - 4'], ['winner', '2 november 1987', 'little rock', 'hard', 'natalia zvereva', '6 - 0 , 1 - 6 , 3 - 6'], ['winner', '16 may 1988', 'strasbourg', 'clay', 'judith wiesner', '6 - 3 , 6 - 0'], ['runner - up', '4 july 1988', 'båstad', 'clay', 'isabel cueto', '5 - 7 , 1 - 6'], ['winner', '11 july 1988', 'nice', 'clay', 'nathalie tauziat', '7 - 5 , 6 - 4'], ['runner - up', '17 july 1989', 'estoril', 'clay', 'isabel cueto', '6 - 7 ( 3 - 7 ) , 2 - 6'], ['winner', '18 september 1989', 'paris', 'clay', 'regina rajchrtová', '6 - 4 , 6 - 7 ( 5 - 7 ) , 6 - 1'], ['winner', '9 july 1990', 'båstad', 'clay', 'csilla bartos', '6 - 1 , 6 - 2'], ['winner', '22 april 1991', 'bol', 'clay', 'magdalena maleeva', '6 - 4 , 3 - 6 , 7 - 5'], ['runner - up', '8 july 1991', 'palermo', 'clay', 'mary pierce', '0 - 6 , 3 - 6'], ['winner', '14 september 1992', 'paris', 'clay', 'emanuela zardo', '6 - 2 , 6 - 1'], ['runner - up', '19 september 1994', 'moscow', 'carpet ( i )', 'magdalena maleeva', '5 - 7 , 1 - 6'], ['runner - up', '5 august 1996', 'maria lankowitz', 'clay', 'barbara paulus', 'w / o']] |
2006 - 07 manchester united f.c. season | https://en.wikipedia.org/wiki/2006%E2%80%9307_Manchester_United_F.C._season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11115098-4.html.csv | superlative | the 19 may 2007 final against chelsea drew the highest attendance . | {'scope': 'all', 'col_superlative': '6', 'row_superlative': '8', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'attendance'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; attendance }'}, 'date'], 'result': '19 may 2007', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; attendance } ; date }'}, '19 may 2007'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; attendance } ; date } ; 19 may 2007 } = true', 'tointer': 'select the row whose attendance record of all rows is maximum . the date record of this row is 19 may 2007 .'} | eq { hop { argmax { all_rows ; attendance } ; date } ; 19 may 2007 } = true | select the row whose attendance record of all rows is maximum . the date record of this row is 19 may 2007 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'attendance_5': 5, 'date_6': 6, '19 may 2007_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'attendance_5': 'attendance', 'date_6': 'date', '19 may 2007_7': '19 may 2007'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'attendance_5': [0], 'date_6': [1], '19 may 2007_7': [2]} | ['date', 'round', 'opponents', 'h / a', 'result f - a', 'attendance'] | [['7 january 2007', 'round 3', 'aston villa', 'h', '2 - 1', '74924'], ['27 january 2007', 'round 4', 'portsmouth', 'h', '2 - 1', '71137'], ['17 february 2007', 'round 5', 'reading', 'h', '1 - 1', '70608'], ['27 february 2007', 'round 5 replay', 'reading', 'a', '3 - 2', '23821'], ['10 march 2007', 'round 6', 'middlesbrough', 'a', '2 - 2', '33308'], ['19 march 2007', 'round 6 replay', 'middlesbrough', 'h', '1 - 0', '61325'], ['14 april 2007', 'semi - final', 'watford', 'n', '4 - 1', '37425'], ['19 may 2007', 'final', 'chelsea', 'n', '0 - 1 ( aet )', '89826']] |
australian cricket team in 2007 - 08 | https://en.wikipedia.org/wiki/Australian_cricket_team_in_2007%E2%80%9308 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13136868-11.html.csv | aggregation | for the australian cricket team in 2007-08 , the average number of wkts was 12.91 . | {'scope': 'all', 'col': '2', 'type': 'average', 'result': '12.91', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'wkts'], 'result': '12.91', 'ind': 0, 'tostr': 'avg { all_rows ; wkts }'}, '12.91'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; wkts } ; 12.91 } = true', 'tointer': 'the average of the wkts record of all rows is 12.91 .'} | round_eq { avg { all_rows ; wkts } ; 12.91 } = true | the average of the wkts record of all rows is 12.91 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'wkts_4': 4, '12.91_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'wkts_4': 'wkts', '12.91_5': '12.91'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'wkts_4': [0], '12.91_5': [1]} | ['player', 'wkts', 'runs', 'econ', 'ovrs'] | [['brett lee', '29', '703', '4.90', '143.2'], ['nathan bracken', '26', '629', '4.47', '140.3'], ['mitchell johnson', '25', '571', '4.26', '134.0'], ['brad hogg', '23', '583', '4.66', '125.0'], ['james hopes', '17', '450', '3.88', '115.5'], ['stuart clark', '9', '212', '3.95', '53.4'], ['michael clarke', '6', '204', '5.10', '40.0'], ['shaun tait', '5', '89', '4.95', '18.0'], ['ashley noffke', '1', '46', '5.11', '9.0'], ['andrew symonds', '1', '137', '5.30', '25.5'], ['brad hodge', '0', '18', '9.00', '2.0']] |
mike hezemans | https://en.wikipedia.org/wiki/Mike_Hezemans | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16514457-1.html.csv | aggregation | mike hezemans has completed 1279 laps during his racing career . | {'scope': 'all', 'col': '5', 'type': 'sum', 'result': '1279', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'laps'], 'result': '1279', 'ind': 0, 'tostr': 'sum { all_rows ; laps }'}, '1279'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; laps } ; 1279 } = true', 'tointer': 'the sum of the laps record of all rows is 1279 .'} | round_eq { sum { all_rows ; laps } ; 1279 } = true | the sum of the laps record of all rows is 1279 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'laps_4': 4, '1279_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'laps_4': 'laps', '1279_5': '1279'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'laps_4': [0], '1279_5': [1]} | ['year', 'team', 'co - drivers', 'class', 'laps', 'pos', 'class pos'] | [['1997', 'gt1 lotus racing', 'jan lammers alexander grau', 'gt1', '121', 'dnf', 'dnf'], ['2000', 'carsport holland', 'david hart hans hugenholtz', 'gts', '317', '13th', '6th'], ['2002', 'team carsport holland racing box', 'gabriele matteuzzi anthony kumpen', 'gts', '93', 'dnf', 'dnf'], ['2003', 'carsport america', 'anthony kumpen david hart', 'gts', '10', 'dnf', 'dnf'], ['2004', 'barron connor racing', 'ange barde jean - denis délétraz', 'gts', '200', 'dnf', 'dnf'], ['2006', 'spyker squadron', 'jeroen bleekemolen jonny kane', 'gt2', '202', 'dnf', 'dnf'], ['2007', 'spyker squadron', 'jaroslav janiš jonny kane', 'gt2', '70', 'dnf', 'dnf'], ['2008', 'ipb spartak racing reiter engineering', 'peter kox roman rusinov', 'gt1', '266', 'nc', 'nc']] |
blue ridge hockey conference | https://en.wikipedia.org/wiki/Blue_Ridge_Hockey_Conference | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16404837-5.html.csv | ordinal | in the blue ridge hockey conference , the school with the 2nd most recent founding date is coastal carolina university . | {'row': '2', 'col': '3', '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', 'founded', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; founded ; 2 }'}, 'school'], 'result': 'coastal carolina university', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; founded ; 2 } ; school }'}, 'coastal carolina university'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; founded ; 2 } ; school } ; coastal carolina university } = true', 'tointer': 'select the row whose founded record of all rows is 2nd maximum . the school record of this row is coastal carolina university .'} | eq { hop { nth_argmax { all_rows ; founded ; 2 } ; school } ; coastal carolina university } = true | select the row whose founded record of all rows is 2nd maximum . the school record of this row is coastal carolina university . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'founded_5': 5, '2_6': 6, 'school_7': 7, 'coastal carolina university_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', 'founded_5': 'founded', '2_6': '2', 'school_7': 'school', 'coastal carolina university_8': 'coastal carolina university'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'founded_5': [0], '2_6': [0], 'school_7': [1], 'coastal carolina university_8': [2]} | ['school', 'location', 'founded', 'affiliation', 'nickname'] | [['appalachian state university', 'boone , nc', '1899', 'public ( university of north carolina system )', 'mountaineers'], ['coastal carolina university', 'conway , sc', '1954', 'public', 'chanticleers'], ['high point university', 'high point , nc', '1924', 'private / methodist', 'panthers'], ['johnson & wales university', 'charlotte , nc', '2004', 'private / non - profit', 'wildcats'], ['virginia military institute', 'lexington , va', '1839', 'public military college', 'keydets']] |
politics of sicily | https://en.wikipedia.org/wiki/Politics_of_Sicily | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16841178-2.html.csv | majority | all of municipalities in sicily that had an election in 2013 had at least 100000 inhabitants . | {'scope': 'subset', 'col': '2', 'most_or_all': 'all', 'criterion': 'greater_than_eq', 'value': '100000', 'subset': {'col': '5', 'criterion': 'equal', 'value': '2013'}} | {'func': 'all_greater_eq', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'election', '2013'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; election ; 2013 }', 'tointer': 'select the rows whose election record is equal to 2013 .'}, 'inhabitants', '100000'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose election record is equal to 2013 . for the inhabitants records of these rows , all of them are greater than or equal to 100000 .', 'tostr': 'all_greater_eq { filter_eq { all_rows ; election ; 2013 } ; inhabitants ; 100000 } = true'} | all_greater_eq { filter_eq { all_rows ; election ; 2013 } ; inhabitants ; 100000 } = true | select the rows whose election record is equal to 2013 . for the inhabitants records of these rows , all of them are greater than or equal to 100000 . | 2 | 2 | {'all_greater_eq_1': 1, 'result_2': 2, 'filter_eq_0': 0, 'all_rows_3': 3, 'election_4': 4, '2013_5': 5, 'inhabitants_6': 6, '100000_7': 7} | {'all_greater_eq_1': 'all_greater_eq', 'result_2': 'true', 'filter_eq_0': 'filter_eq', 'all_rows_3': 'all_rows', 'election_4': 'election', '2013_5': '2013', 'inhabitants_6': 'inhabitants', '100000_7': '100000'} | {'all_greater_eq_1': [2], 'result_2': [], 'filter_eq_0': [1], 'all_rows_3': [0], 'election_4': [0], '2013_5': [0], 'inhabitants_6': [1], '100000_7': [1]} | ['municipality', 'inhabitants', 'mayor', 'party', 'election'] | [['palermo', '654121', 'leoluca orlando', 'italy of values', '2012'], ['catania', '291274', 'enzo bianco', 'democratic party', '2013'], ['messina', '241310', 'renato accorinti', 'independent', '2013'], ['siracusa', '123376', 'giancarlo garrozzo', 'democratic party', '2013'], ['marsala', '82933', 'giulia adamo', 'union of the centre', '2012'], ['gela', '77335', 'angelo fasulo', 'democratic party', '2009']] |
nickelodeon movies | https://en.wikipedia.org/wiki/Nickelodeon_Movies | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1305286-7.html.csv | unique | the rugrats movie was the only one to have no winner/nominee . | {'scope': 'all', 'row': '2', 'col': '4', 'col_other': '3', 'criterion': 'equal', 'value': 'n/a', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'winner / nominee ( s )', 'n/a'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose winner / nominee ( s ) record fuzzily matches to n/a .', 'tostr': 'filter_eq { all_rows ; winner / nominee ( s ) ; n/a }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; winner / nominee ( s ) ; n/a } }', 'tointer': 'select the rows whose winner / nominee ( s ) record fuzzily matches to n/a . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'winner / nominee ( s )', 'n/a'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose winner / nominee ( s ) record fuzzily matches to n/a .', 'tostr': 'filter_eq { all_rows ; winner / nominee ( s ) ; n/a }'}, 'film'], 'result': 'the rugrats movie', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; winner / nominee ( s ) ; n/a } ; film }'}, 'the rugrats movie'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; winner / nominee ( s ) ; n/a } ; film } ; the rugrats movie }', 'tointer': 'the film record of this unqiue row is the rugrats movie .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; winner / nominee ( s ) ; n/a } } ; eq { hop { filter_eq { all_rows ; winner / nominee ( s ) ; n/a } ; film } ; the rugrats movie } } = true', 'tointer': 'select the rows whose winner / nominee ( s ) record fuzzily matches to n/a . there is only one such row in the table . the film record of this unqiue row is the rugrats movie .'} | and { only { filter_eq { all_rows ; winner / nominee ( s ) ; n/a } } ; eq { hop { filter_eq { all_rows ; winner / nominee ( s ) ; n/a } ; film } ; the rugrats movie } } = true | select the rows whose winner / nominee ( s ) record fuzzily matches to n/a . there is only one such row in the table . the film record of this unqiue row is the rugrats movie . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'winner / nominee (s)_7': 7, 'n/a_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'film_9': 9, 'the rugrats movie_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'winner / nominee (s)_7': 'winner / nominee ( s )', 'n/a_8': 'n/a', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'film_9': 'film', 'the rugrats movie_10': 'the rugrats movie'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'winner / nominee (s)_7': [0], 'n/a_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'film_9': [2], 'the rugrats movie_10': [3]} | ['year', 'category', 'film', 'winner / nominee ( s )', 'result'] | [['1997', 'favorite movie actress', 'harriet the spy', "rosie o'donnell", 'nominated'], ['1999', 'favorite movie', 'the rugrats movie', 'n / a', 'won'], ['2001', 'favorite voice from an animated movie', 'rugrats in paris : the movie', 'susan sarandon', 'won'], ['2004', 'favorite voice from an animated movie', 'rugrats go wild', 'bruce willis', 'nominated'], ['2005', 'favorite movie actor', "lemony snicket 's a series of unfortunate events", 'jim carrey', 'nominated'], ['2007', 'favorite movie actor', 'nacho libre', 'jack black', 'nominated'], ['2007', 'favorite movie actress', "charlotte 's web", 'dakota fanning', 'won'], ['2012', 'favorite voice from an animated movie', 'rango', 'johnny depp', 'nominated']] |
2002 miami dolphins season | https://en.wikipedia.org/wiki/2002_Miami_Dolphins_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18925638-1.html.csv | ordinal | the match on november 4 , 2002 had the second lowest attendance of any match . | {'row': '8', 'col': '6', 'order': '2', 'col_other': '2', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'attendance', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; attendance ; 2 }'}, 'date'], 'result': 'november 4 , 2002', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; attendance ; 2 } ; date }'}, 'november 4 , 2002'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; attendance ; 2 } ; date } ; november 4 , 2002 } = true', 'tointer': 'select the row whose attendance record of all rows is 2nd minimum . the date record of this row is november 4 , 2002 .'} | eq { hop { nth_argmin { all_rows ; attendance ; 2 } ; date } ; november 4 , 2002 } = true | select the row whose attendance record of all rows is 2nd minimum . the date record of this row is november 4 , 2002 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'attendance_5': 5, '2_6': 6, 'date_7': 7, 'november 4 , 2002_8': 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmin_0': 'nth_argmin', 'all_rows_4': 'all_rows', 'attendance_5': 'attendance', '2_6': '2', 'date_7': 'date', 'november 4 , 2002_8': 'november 4 , 2002'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'attendance_5': [0], '2_6': [0], 'date_7': [1], 'november 4 , 2002_8': [2]} | ['week', 'date', 'opponent', 'result', 'tv time', 'attendance'] | [['1', 'september 8 , 2002', 'detroit lions', 'w 49 - 21', 'fox 1:00 pm', '72216'], ['2', 'september 15 , 2002', 'indianapolis colts', 'w 21 - 13', 'cbs 1:00 pm', '56650'], ['3', 'september 22 , 2002', 'new york jets', 'w 30 - 3', 'cbs 1:00 pm', '73426'], ['4', 'september 29 , 2002', 'kansas city chiefs', 'l 48 - 30', 'cbs 1:00 pm', '78178'], ['5', 'october 6 , 2002', 'new england patriots', 'w 26 - 13', 'cbs 1:00 pm', '73369'], ['6', 'october 13 , 2002', 'denver broncos', 'w 24 - 22', 'espn 8:30 pm', '75941'], ['7', 'october 20 , 2002', 'buffalo bills', 'l 23 - 10', 'cbs 1:00 pm', '73180'], ['9', 'november 4 , 2002', 'green bay packers', 'l 24 - 10', 'abc 9:00 pm', '63284'], ['10', 'november 10 , 2002', 'new york jets', 'l 13 - 10', 'espn 8:30 pm', '78920'], ['11', 'november 17 , 2002', 'baltimore ravens', 'w 26 - 7', 'cbs 4:15 pm', '73013'], ['12', 'november 24 , 2002', 'san diego chargers', 'w 30 - 3', 'cbs 1:00 pm', '73138'], ['13', 'december 1 , 2002', 'buffalo bills', 'l 38 - 21', 'cbs 1:00 pm', '73287'], ['14', 'december 9 , 2002', 'chicago bears', 'w 27 - 9', 'abc 9:00 pm', '73609'], ['15', 'december 15 , 2002', 'oakland raiders', 'w 23 - 17', 'cbs 1:00 pm', '73572'], ['16', 'december 21 , 2002', 'minnesota vikings', 'l 20 - 17', 'cbs 12:30 pm', '64285'], ['17', 'december 29 , 2002', 'new england patriots', 'l 27 - 24', 'cbs 1:00 pm', '68436']] |
1986 - 87 segunda división | https://en.wikipedia.org/wiki/1986%E2%80%9387_Segunda_Divisi%C3%B3n | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12109851-6.html.csv | ordinal | real oviedo had the 3rd best goal difference in the 1986-86 segunda division . | {'row': '4', 'col': '10', 'order': '3', 'col_other': '2', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'goal difference', '3'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; goal difference ; 3 }'}, 'club'], 'result': 'real oviedo', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; goal difference ; 3 } ; club }'}, 'real oviedo'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; goal difference ; 3 } ; club } ; real oviedo } = true', 'tointer': 'select the row whose goal difference record of all rows is 3rd maximum . the club record of this row is real oviedo .'} | eq { hop { nth_argmax { all_rows ; goal difference ; 3 } ; club } ; real oviedo } = true | select the row whose goal difference record of all rows is 3rd maximum . the club record of this row is real oviedo . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'goal difference_5': 5, '3_6': 6, 'club_7': 7, 'real oviedo_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', 'goal difference_5': 'goal difference', '3_6': '3', 'club_7': 'club', 'real oviedo_8': 'real oviedo'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'goal difference_5': [0], '3_6': [0], 'club_7': [1], 'real oviedo_8': [2]} | ['position', 'club', 'played', 'points', 'wins', 'draws', 'losses', 'goals for', 'goals against', 'goal difference'] | [['1', 'barcelona atlã ¨ tic', '44', '42 - 2', '16', '10', '18', '56', '58', '- 2'], ['2', 'ue figueres', '44', '42 - 2', '15', '12', '17', '59', '53', '+ 6'], ['3', 'cartagena fc', '44', '42 - 2', '14', '14', '16', '52', '67', '- 15'], ['4', 'real oviedo', '44', '40 - 4', '13', '14', '17', '50', '64', '- 14'], ['5', 'castilla cf', '44', '33 - 11', '11', '11', '22', '49', '71', '- 22'], ['6', 'jerez deportivo', '44', '22 - 22', '5', '12', '27', '32', '78', '- 46']] |
florida board of governors | https://en.wikipedia.org/wiki/Florida_Board_of_Governors | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1641054-2.html.csv | count | 6 of the universities do n't have kiplinger 's top 100 values available . | {'scope': 'all', 'criterion': 'equal', 'value': 'n / a', 'result': '6', 'col': '6', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', "kiplinger 's top 100 values", 'n / a'], 'result': None, 'ind': 0, 'tointer': "select the rows whose kiplinger 's top 100 values record fuzzily matches to n / a .", 'tostr': "filter_eq { all_rows ; kiplinger 's top 100 values ; n / a }"}], 'result': '6', 'ind': 1, 'tostr': "count { filter_eq { all_rows ; kiplinger 's top 100 values ; n / a } }", 'tointer': "select the rows whose kiplinger 's top 100 values record fuzzily matches to n / a . the number of such rows is 6 ."}, '6'], 'result': True, 'ind': 2, 'tostr': "eq { count { filter_eq { all_rows ; kiplinger 's top 100 values ; n / a } } ; 6 } = true", 'tointer': "select the rows whose kiplinger 's top 100 values record fuzzily matches to n / a . the number of such rows is 6 ."} | eq { count { filter_eq { all_rows ; kiplinger 's top 100 values ; n / a } } ; 6 } = true | select the rows whose kiplinger 's top 100 values record fuzzily matches to n / a . 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, "kiplinger 's top 100 values_5": 5, 'n / a_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', "kiplinger 's top 100 values_5": "kiplinger 's top 100 values", 'n / a_6': 'n / a', '6_7': '6'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], "kiplinger 's top 100 values_5": [0], 'n / a_6': [0], '6_7': [2]} | ['university', 'location', 'established', 'endowment as of 2008', 'campus area ( acres )', "kiplinger 's top 100 values", 'enrollment as of 2008'] | [['florida a & m university', 'tallahassee , florida', '1887', '119 million', '419', 'n / a', '11567'], ['florida atlantic university', 'boca raton , florida', '1961', '182 million', '850', 'n / a', '26525'], ['florida gulf coast university', 'fort myers , florida', '1991', '39 million', '760', 'n / a', '9387'], ['florida international university', 'miami , florida', '1965', '97 million', '573', 'n / a', '38614'], ['florida state university', 'tallahassee , florida', '1851', '570 million', '1200', '17th overall in the united states', '41002'], ['new college of florida', 'sarasota , florida', '1960', '33 million', '144', '8th overall in the united states', '769'], ['university of central florida', 'orlando , florida', '1963', '114 million', '1415', '42nd overall in the united states', '48699'], ['university of florida', 'gainesville , florida', '1853', '1.3 billion', '2000', '2nd overall in the united states', '52084'], ['university of north florida', 'jacksonville , florida', '1969', '95 million', '1300', 'n / a', '16570'], ['university of south florida', 'tampa , florida', '1956', '360 million', '1913', '75th overall in the united states', '45524'], ['university of west florida', 'pensacola , florida', '1963', '61 million', '1600', 'n / a', '10394']] |
1984 - 85 north west counties football league | https://en.wikipedia.org/wiki/1984%E2%80%9385_North_West_Counties_Football_League | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17775406-3.html.csv | aggregation | for the 1984-85 north west counties football league the average number of games lost was 13.3 . | {'scope': 'all', 'col': '5', 'type': 'average', 'result': '13.3', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'lost'], 'result': '13.3', 'ind': 0, 'tostr': 'avg { all_rows ; lost }'}, '13.3'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; lost } ; 13.3 } = true', 'tointer': 'the average of the lost record of all rows is 13.3 .'} | round_eq { avg { all_rows ; lost } ; 13.3 } = true | the average of the lost record of all rows is 13.3 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'lost_4': 4, '13.3_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'lost_4': 'lost', '13.3_5': '13.3'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'lost_4': [0], '13.3_5': [1]} | ['position', 'team', 'played', 'drawn', 'lost', 'goals for', 'goals against', 'goal difference', 'points 1'] | [['1', 'kirkby town', '34', '5', '3', '83', '30', '+ 53', '57'], ['2', 'colwyn bay', '34', '10', '2', '75', '32', '+ 43', '54'], ['3', 'newton', '34', '10', '8', '56', '33', '+ 23', '42'], ['4', 'urmston town', '34', '10', '10', '42', '39', '+ 3', '38'], ['5', 'blackpool mechanics', '34', '7', '12', '61', '48', '+ 13', '37'], ['6', 'lytham', '34', '7', '13', '54', '45', '+ 9', '35'], ['7', 'atherton collieries', '34', '8', '13', '44', '44', '0', '34'], ['8', 'ashton town', '34', '7', '14', '62', '56', '+ 6', '33'], ['9', 'oldham dew', '34', '10', '13', '51', '44', '+ 7', '32'], ['10', 'bolton st', '34', '8', '14', '55', '73', '18', '32'], ['11', 'maghull', '34', '7', '15', '56', '51', '+ 5', '31'], ['12', 'cheadle town', '34', '7', '15', '46', '62', '16', '31'], ['13', 'bacup borough', '34', '8', '15', '54', '59', '5', '30'], ['14', 'ashton athletic', '34', '6', '16', '45', '61', '16', '30'], ['15', 'daisy hill', '34', '9', '15', '51', '61', '10', '27 2'], ['16', 'whitworth valley', '34', '7', '17', '51', '71', '20', '27'], ['17', 'nelson', '34', '4', '21', '43', '80', '37', '22'], ['18', 'prestwich heys', '34', '4', '23', '35', '75', '40', '16 2']] |
2007 - 08 fis ski jumping world cup | https://en.wikipedia.org/wiki/2007%E2%80%9308_FIS_Ski_Jumping_World_Cup | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14407512-6.html.csv | majority | the majority of players in the 2007 - 08 fis ski jumping world cup have austrian nationality . | {'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'aut', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'nationality', 'aut'], 'result': True, 'ind': 0, 'tointer': 'for the nationality records of all rows , most of them fuzzily match to aut .', 'tostr': 'most_eq { all_rows ; nationality ; aut } = true'} | most_eq { all_rows ; nationality ; aut } = true | for the nationality records of all rows , most of them fuzzily match to aut . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'nationality_3': 3, 'aut_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'nationality_3': 'nationality', 'aut_4': 'aut'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'nationality_3': [0], 'aut_4': [0]} | ['rank', 'name', 'nationality', '1st ( m )', '2nd ( m )', 'points', 'overall wc points ( rank )'] | [['1', 'thomas morgenstern', 'aut', '132.5', '133.0', '260.4', '600 ( 1 )'], ['2', 'andreas kofler', 'aut', '134.5', '128.5', '254.4', '248 ( 6 )'], ['3', 'tom hilde', 'nor', '133.5', '129.5', '252.9', '256 ( 4 )'], ['4', 'gregor schlierenzauer', 'aut', '126.5', '134.5', '249.8', '349 ( 2 )'], ['5', 'wolfgang loitzl', 'aut', '130.5', '126.5', '242.6', '250 ( 5 )']] |
switzerland at the 2008 summer olympics | https://en.wikipedia.org/wiki/Switzerland_at_the_2008_Summer_Olympics | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17085947-32.html.csv | aggregation | at the 2008 summer olympics in switzerland , the average time for men to swim 1.5 km was 18 minutes and 41 seconds . | {'scope': 'subset', 'col': '3', 'type': 'average', 'result': '18:41', 'subset': {'col': '2', 'criterion': 'equal', 'value': "men 's"}} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'event', "men 's"], 'result': None, 'ind': 0, 'tostr': "filter_eq { all_rows ; event ; men 's }", 'tointer': "select the rows whose event record fuzzily matches to men 's ."}, 'swim ( 1.5 km )'], 'result': '18:41', 'ind': 1, 'tostr': "avg { filter_eq { all_rows ; event ; men 's } ; swim ( 1.5 km ) }"}, '18:41'], 'result': True, 'ind': 2, 'tostr': "round_eq { avg { filter_eq { all_rows ; event ; men 's } ; swim ( 1.5 km ) } ; 18:41 } = true", 'tointer': "select the rows whose event record fuzzily matches to men 's . the average of the swim ( 1.5 km ) record of these rows is 18:41 ."} | round_eq { avg { filter_eq { all_rows ; event ; men 's } ; swim ( 1.5 km ) } ; 18:41 } = true | select the rows whose event record fuzzily matches to men 's . the average of the swim ( 1.5 km ) record of these rows is 18:41 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'avg_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'event_5': 5, "men's_6": 6, 'swim (1.5 km)_7': 7, '18:41_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'avg_1': 'avg', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'event_5': 'event', "men's_6": "men 's", 'swim (1.5 km)_7': 'swim ( 1.5 km )', '18:41_8': '18:41'} | {'eq_2': [3], 'result_3': [], 'avg_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'event_5': [0], "men's_6": [0], 'swim (1.5 km)_7': [1], '18:41_8': [2]} | ['athlete', 'event', 'swim ( 1.5 km )', 'trans 1', 'bike ( 40 km )', 'trans 2', 'run ( 10 km )', 'total time', 'rank'] | [['reto hug', "men 's", '18:55', '0:27', '58:20', '0:29', '33:53', '1:52:04.93', '29'], ['olivier marceau', "men 's", '18:55', '0:29', '58:18', '0:31', '32:37', '1:50:50.07', '19'], ['sven riederer', "men 's", '18:14', '0:34', '58:52', '0:28', '33:11', '1:51:19.45', '23'], ['magali chopard di marco', "women 's", '19:50', '0:30', '1:04:22', '0:29', '36:39', '2:01:50.74', '13'], ['daniela ryf', "women 's", '19:56', '0:26', '1:04:17', '0:30', '35:31', '2:00:40.20', '7'], ['nicola spirig', "women 's", '20:17', '0:28', '1:03:54', '0:31', '35:20', '2:00:30.48', '6']] |
united states house of representatives elections , 2006 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_2006 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1805191-24.html.csv | ordinal | for the 2006 election for the united states house of representatives , the incumbent with the 2nd to earliest date of first election was martin sabo . | {'row': '5', 'col': '4', 'order': '2', 'col_other': '2', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'first elected', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; first elected ; 2 }'}, 'incumbent'], 'result': 'martin sabo', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; first elected ; 2 } ; incumbent }'}, 'martin sabo'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; first elected ; 2 } ; incumbent } ; martin sabo } = true', 'tointer': 'select the row whose first elected record of all rows is 2nd minimum . the incumbent record of this row is martin sabo .'} | eq { hop { nth_argmin { all_rows ; first elected ; 2 } ; incumbent } ; martin sabo } = true | select the row whose first elected record of all rows is 2nd minimum . the incumbent record of this row is martin sabo . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'first elected_5': 5, '2_6': 6, 'incumbent_7': 7, 'martin sabo_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', 'first elected_5': 'first elected', '2_6': '2', 'incumbent_7': 'incumbent', 'martin sabo_8': 'martin sabo'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'first elected_5': [0], '2_6': [0], 'incumbent_7': [1], 'martin sabo_8': [2]} | ['district', 'incumbent', 'party', 'first elected', 'results'] | [['minnesota 1', 'gil gutknecht', 'republican', '1994', 'lost re - election democratic gain'], ['minnesota 2', 'john kline', 'republican', '2002', 're - elected'], ['minnesota 3', 'jim ramstad', 'republican', '1990', 're - elected'], ['minnesota 4', 'betty mccollum', 'democratic', '2000', 're - elected'], ['minnesota 5', 'martin sabo', 'democratic', '1978', 'retired democratic hold'], ['minnesota 6', 'mark kennedy', 'republican', '2000', 'retired to run for us senate republican hold'], ['minnesota 7', 'collin peterson', 'democratic', '1990', 're - elected'], ['minnesota 8', 'jim oberstar', 'democratic', '1974', 're - elected']] |
1949 giro d'italia | https://en.wikipedia.org/wiki/1949_Giro_d%27Italia | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12582434-1.html.csv | count | in 1949 giro d'italia , ten races were plain sage . | {'scope': 'all', 'criterion': 'equal', 'value': 'plain stage', 'result': '10', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'type', 'plain stage'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose type record fuzzily matches to plain stage .', 'tostr': 'filter_eq { all_rows ; type ; plain stage }'}], 'result': '10', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; type ; plain stage } }', 'tointer': 'select the rows whose type record fuzzily matches to plain stage . the number of such rows is 10 .'}, '10'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; type ; plain stage } } ; 10 } = true', 'tointer': 'select the rows whose type record fuzzily matches to plain stage . the number of such rows is 10 .'} | eq { count { filter_eq { all_rows ; type ; plain stage } } ; 10 } = true | select the rows whose type record fuzzily matches to plain stage . the number of such rows is 10 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'type_5': 5, 'plain stage_6': 6, '10_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'type_5': 'type', 'plain stage_6': 'plain stage', '10_7': '10'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'type_5': [0], 'plain stage_6': [0], '10_7': [2]} | ['date', 'course', 'distance', 'type', 'winner'] | [['21 may', 'palermo to catania', '100 km', 'stage with mountain ( s )', 'mario fazio ( ita )'], ['22 may', 'catania to messina', '100 km', 'plain stage', 'sergio maggini ( ita )'], ['23 may', 'villa san giovanni to cosenza', '100 km', 'stage with mountain ( s )', 'guido de santi ( ita )'], ['24 may', 'cosenza to salerno', '100 km', 'plain stage', 'fausto coppi ( ita )'], ['26 may', 'salerno to naples', '100 km', 'plain stage', 'serafino biagioni ( ita )'], ['27 may', 'naples to rome', '100 km', 'plain stage', 'mario ricci ( ita )'], ['28 may', 'rome to pesaro', '100 km', 'plain stage', 'adolfo leoni ( ita )'], ['29 may', 'pesaro to venezia', '100 km', 'plain stage', 'luigi casola ( ita )'], ['31 may', 'venezia to udine', '100 km', 'plain stage', 'adolfo leoni ( ita )'], ['1 june', 'udine to bassano del grappa', '100 km', 'plain stage', 'giovanni corrieri ( ita )'], ['2 june', 'bassano del grappa to bolzano', '100 km', 'stage with mountain ( s )', 'fausto coppi ( ita )'], ['4 june', 'bolzano to modena', '100 km', 'plain stage', 'oreste conte ( ita )'], ['5 june', 'modena to montecatini terme', '100 km', 'stage with mountain ( s )', 'adolfo leoni ( ita )'], ['6 june', 'montecatini terme to genoa', '100 km', 'stage with mountain ( s )', 'vincenzo rossello ( ita )'], ['7 june', 'genoa to sanremo', '100 km', 'plain stage', 'luciano maggini ( ita )'], ['9 june', 'sanremo to cuneo', '100 km', 'stage with mountain ( s )', 'oreste conte ( ita )'], ['10 june', 'cuneo to pinerolo', '100 km', 'stage with mountain ( s )', 'fausto coppi ( ita )'], ['11 june', 'pinerolo to turin', '100 km', 'individual time trial', 'antonio bevilacqua ( ita )'], ['12 june', 'turin to monza', '100 km', 'stage with mountain ( s )', 'giovanni corrieri ( ita )']] |
united states house of representatives elections , 2006 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_2006 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1805191-26.html.csv | count | 5 incumbents have been first elected in 2000 or later . | {'scope': 'all', 'criterion': 'greater_than_eq', 'value': '2000', 'result': '5', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_greater_eq', 'args': ['all_rows', 'first elected', '2000'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose first elected record is greater than or equal to 2000 .', 'tostr': 'filter_greater_eq { all_rows ; first elected ; 2000 }'}], 'result': '5', 'ind': 1, 'tostr': 'count { filter_greater_eq { all_rows ; first elected ; 2000 } }', 'tointer': 'select the rows whose first elected record is greater than or equal to 2000 . the number of such rows is 5 .'}, '5'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_greater_eq { all_rows ; first elected ; 2000 } } ; 5 } = true', 'tointer': 'select the rows whose first elected record is greater than or equal to 2000 . the number of such rows is 5 .'} | eq { count { filter_greater_eq { all_rows ; first elected ; 2000 } } ; 5 } = true | select the rows whose first elected record is greater than or equal to 2000 . the number of such rows is 5 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_greater_eq_0': 0, 'all_rows_4': 4, 'first elected_5': 5, '2000_6': 6, '5_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_greater_eq_0': 'filter_greater_eq', 'all_rows_4': 'all_rows', 'first elected_5': 'first elected', '2000_6': '2000', '5_7': '5'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_greater_eq_0': [1], 'all_rows_4': [0], 'first elected_5': [0], '2000_6': [0], '5_7': [2]} | ['district', 'incumbent', 'party', 'first elected', 'results'] | [['missouri 1', 'william lacy clay jr', 'democratic', '2000', 're - elected'], ['missouri 2', 'todd akin', 'republican', '2000', 're - elected'], ['missouri 3', 'russ carnahan', 'democratic', '2004', 're - elected'], ['missouri 4', 'ike skelton', 'democratic', '1976', 're - elected'], ['missouri 5', 'emanuel cleaver', 'democratic', '2004', 're - elected'], ['missouri 6', 'sam graves', 'republican', '2000', 're - elected'], ['missouri 7', 'roy blunt', 'republican', '1996', 're - elected'], ['missouri 8', 'jo ann emerson', 'republican', '1996', 're - elected'], ['missouri 9', 'kenny hulshof', 'republican', '1996', 're - elected']] |
2006 tampa bay storm season | https://en.wikipedia.org/wiki/2006_Tampa_Bay_Storm_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11866255-1.html.csv | ordinal | tampa bay storm played their second game of the 2006 season on february 3 . | {'row': '2', 'col': '1', 'order': '2', 'col_other': '2', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'week', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; week ; 2 }'}, 'date'], 'result': 'february 3', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; week ; 2 } ; date }'}, 'february 3'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; week ; 2 } ; date } ; february 3 } = true', 'tointer': 'select the row whose week record of all rows is 2nd minimum . the date record of this row is february 3 .'} | eq { hop { nth_argmin { all_rows ; week ; 2 } ; date } ; february 3 } = true | select the row whose week record of all rows is 2nd minimum . the date record of this row is february 3 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'week_5': 5, '2_6': 6, 'date_7': 7, 'february 3_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', 'week_5': 'week', '2_6': '2', 'date_7': 'date', 'february 3_8': 'february 3'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'week_5': [0], '2_6': [0], 'date_7': [1], 'february 3_8': [2]} | ['week', 'date', 'opponent', 'home / away', 'result'] | [['1', 'january 29', 'philadelphia soul', 'away', 'l 52 - 34'], ['2', 'february 3', 'grand rapids rampage', 'away', 'w 51 - 43'], ['3', 'february 10', 'georgia force', 'home', 'w 61 - 60'], ['4', 'february 19', 'orlando predators', 'home', 'l 67 - 64 ( ot )'], ['5', 'february 25', 'austin wranglers', 'home', 'w 58 - 48'], ['6', 'march 5', 'kansas city brigade', 'away', 'w 69 - 59'], ['7', 'march 12', 'dallas desperados', 'home', 'l 64 - 35'], ['8', 'march 18', 'new york dragons', 'home', 'w 60 - 44'], ['9', 'march 26', 'georgia force', 'away', 'l 61 - 51'], ['10', 'april 1', 'utah blaze', 'home', 'w 56 - 41'], ['11', 'april 7', 'san jose sabercats', 'home', 'l 52 - 43'], ['12', 'april 15', 'austin wranglers', 'away', 'l 60 - 59'], ['13', 'april 22', 'orlando predators', 'away', 'l 52 - 13'], ['14', 'april 29', 'kansas city brigade', 'home', 'w 58 - 42'], ['15', 'may 6', 'columbus destroyers', 'away', 'l 51 - 48'], ['16', 'may 13', 'nashville kats', 'away', 'l 66 - 50']] |
list of highways in webb county , texas | https://en.wikipedia.org/wiki/List_of_highways_in_Webb_County%2C_Texas | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11336756-5.html.csv | count | in the list of highways in webb county , texas , the sh 359 us 59 junctions , the termini of one of them is aguilares , texas us 59 . | {'scope': 'subset', 'criterion': 'equal', 'value': 'aguilares , texas us 59', 'result': '1', 'col': '3', 'subset': {'col': '4', 'criterion': 'equal', 'value': 'sh 359 us 59'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'junctions', 'sh 359 us 59'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; junctions ; sh 359 us 59 }', 'tointer': 'select the rows whose junctions record fuzzily matches to sh 359 us 59 .'}, 'termini', 'aguilares , texas us 59'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose junctions record fuzzily matches to sh 359 us 59 . among these rows , select the rows whose termini record fuzzily matches to aguilares , texas us 59 .', 'tostr': 'filter_eq { filter_eq { all_rows ; junctions ; sh 359 us 59 } ; termini ; aguilares , texas us 59 }'}], 'result': '1', 'ind': 2, 'tostr': 'count { filter_eq { filter_eq { all_rows ; junctions ; sh 359 us 59 } ; termini ; aguilares , texas us 59 } }', 'tointer': 'select the rows whose junctions record fuzzily matches to sh 359 us 59 . among these rows , select the rows whose termini record fuzzily matches to aguilares , texas us 59 . the number of such rows is 1 .'}, '1'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_eq { all_rows ; junctions ; sh 359 us 59 } ; termini ; aguilares , texas us 59 } } ; 1 } = true', 'tointer': 'select the rows whose junctions record fuzzily matches to sh 359 us 59 . among these rows , select the rows whose termini record fuzzily matches to aguilares , texas us 59 . the number of such rows is 1 .'} | eq { count { filter_eq { filter_eq { all_rows ; junctions ; sh 359 us 59 } ; termini ; aguilares , texas us 59 } } ; 1 } = true | select the rows whose junctions record fuzzily matches to sh 359 us 59 . among these rows , select the rows whose termini record fuzzily matches to aguilares , texas us 59 . the number of such rows is 1 . | 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, 'junctions_6': 6, 'sh 359 us 59_7': 7, 'termini_8': 8, 'aguilares , texas us 59_9': 9, '1_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', 'junctions_6': 'junctions', 'sh 359 us 59_7': 'sh 359 us 59', 'termini_8': 'termini', 'aguilares , texas us 59_9': 'aguilares , texas us 59', '1_10': '1'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_str_eq_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'junctions_6': [0], 'sh 359 us 59_7': [0], 'termini_8': [1], 'aguilares , texas us 59_9': [1], '1_10': [3]} | ['route name', 'direction', 'termini', 'junctions', 'length', 'population area'] | [['fm 649', 'south north', 'zapata county sh 359', 'sh 359', '-', 'mirando city'], ['fm 1472', 'south north', 'i - 35 a point miles ( km ) northwest of sh 255', 'i - 35 fm 3338 sh 255', '-', 'laredo'], ['fm 2050', 'south north', 'bruni , texas us 59', 'sh 359 us 59', '-', 'bruni'], ['fm 2895', 'south north', 'aguilares , texas us 59', 'sh 359 us 59', '-', 'aguilares'], ['fm 3338', 'south north', 'fm 1472 sh 255', 'ur 1472 sh 255', '-', 'laredo ranchos penitas west']] |
ka commuter jabodetabek | https://en.wikipedia.org/wiki/KA_Commuter_Jabodetabek | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15039992-1.html.csv | superlative | of the train lines of the ka commuter jabodetabek , the orange line is the longest . | {'scope': 'all', 'col_superlative': '5', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'length'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; length }'}, 'line color'], 'result': 'orange', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; length } ; line color }'}, 'orange'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; length } ; line color } ; orange } = true', 'tointer': 'select the row whose length record of all rows is maximum . the line color record of this row is orange .'} | eq { hop { argmax { all_rows ; length } ; line color } ; orange } = true | select the row whose length record of all rows is maximum . the line color record of this row is orange . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'length_5': 5, 'line color_6': 6, 'orange_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'length_5': 'length', 'line color_6': 'line color', 'orange_7': 'orange'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'length_5': [0], 'line color_6': [1], 'orange_7': [2]} | ['line color', 'line', 'route', 'stations served', 'length'] | [['orange', 'jakarta loopline', 'jatinegara to depok / bogor', '30', '71.8 km'], ['red', 'jakarta - bogor', 'jakarta kota to depok / bogor', '25', '54.6 km'], ['green', 'jakarta - south tangerang', 'tanah abang to serpong / parung panjang / maja', '19', '55.7 km'], ['blue', 'jakarta - bekasi', 'jakarta kota to bekasi', '18', '27.4 km'], ['brown', 'jakarta - tangerang', 'duri to tangerang', '9', '18.9 km'], ['pink', 'tanjung priok line', 'jakarta kota to tanjung priok', '4', '7.9 km ( total ) 1.6 km ( operated )']] |
obsolete russian units of measurement | https://en.wikipedia.org/wiki/Obsolete_Russian_units_of_measurement | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1375378-4.html.csv | comparative | the obsolete russian unit berkovets has a higher metric value than pood . | {'row_1': '6', 'row_2': '5', 'col': '4', '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', 'unit', 'berkovets'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose unit record fuzzily matches to berkovets .', 'tostr': 'filter_eq { all_rows ; unit ; berkovets }'}, 'metric value'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; unit ; berkovets } ; metric value }', 'tointer': 'select the rows whose unit record fuzzily matches to berkovets . take the metric value record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'unit', 'pood'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose unit record fuzzily matches to pood .', 'tostr': 'filter_eq { all_rows ; unit ; pood }'}, 'metric value'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; unit ; pood } ; metric value }', 'tointer': 'select the rows whose unit record fuzzily matches to pood . take the metric value record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; unit ; berkovets } ; metric value } ; hop { filter_eq { all_rows ; unit ; pood } ; metric value } } = true', 'tointer': 'select the rows whose unit record fuzzily matches to berkovets . take the metric value record of this row . select the rows whose unit record fuzzily matches to pood . take the metric value record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; unit ; berkovets } ; metric value } ; hop { filter_eq { all_rows ; unit ; pood } ; metric value } } = true | select the rows whose unit record fuzzily matches to berkovets . take the metric value record of this row . select the rows whose unit record fuzzily matches to pood . take the metric value 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, 'unit_7': 7, 'berkovets_8': 8, 'metric value_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'unit_11': 11, 'pood_12': 12, 'metric value_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', 'unit_7': 'unit', 'berkovets_8': 'berkovets', 'metric value_9': 'metric value', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'unit_11': 'unit', 'pood_12': 'pood', 'metric value_13': 'metric value'} | {'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'unit_7': [0], 'berkovets_8': [0], 'metric value_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'unit_11': [1], 'pood_12': [1], 'metric value_13': [3]} | ['unit', 'russian', 'ratio', 'metric value', 'avoirdupois value'] | [['dolia', 'до́ля', '1 / 9216 = 1 / 96 2', '44.435 mg', '0.686 gr'], ['zolotnik', 'золотни́к', '1 / 96', '4.26580 g', '65.831 gr ( 0.152 oz )'], ['lot', 'лот', '1 / 32', '12.7974 g', '0.451 oz'], ['funt', 'фунт', '1', '409.51718 g', '14.445 oz ( 0.903 lb )'], ['pood', 'пуд', '40', '16.3807 kg', '36.121 lb'], ['berkovets', 'берковец', '400', '163.807 kg', '361.206 lb ( 25.8 st )']] |
1972 - 73 philadelphia flyers season | https://en.wikipedia.org/wiki/1972%E2%80%9373_Philadelphia_Flyers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14294324-2.html.csv | majority | in the 1972-78 philadelphia flyers season , most of the games had under 10 points . | {'scope': 'all', 'col': '6', 'most_or_all': 'most', 'criterion': 'less_than', 'value': '10', 'subset': None} | {'func': 'most_less', 'args': ['all_rows', 'points', '10'], 'result': True, 'ind': 0, 'tointer': 'for the points records of all rows , most of them are less than 10 .', 'tostr': 'most_less { all_rows ; points ; 10 } = true'} | most_less { all_rows ; points ; 10 } = true | for the points records of all rows , most of them are less than 10 . | 1 | 1 | {'most_less_0': 0, 'result_1': 1, 'all_rows_2': 2, 'points_3': 3, '10_4': 4} | {'most_less_0': 'most_less', 'result_1': 'true', 'all_rows_2': 'all_rows', 'points_3': 'points', '10_4': '10'} | {'most_less_0': [1], 'result_1': [], 'all_rows_2': [0], 'points_3': [0], '10_4': [0]} | ['game', 'october', 'opponent', 'score', 'record', 'points'] | [['1', '7', 'st louis blues', '4 - 4', '0 - 0 - 1', '1'], ['2', '12', 'vancouver canucks', '7 - 3', '1 - 0 - 1', '3'], ['3', '14', 'detroit red wings', '0 - 5', '1 - 1 - 1', '3'], ['4', '15', 'california golden seals', '1 - 4', '1 - 2 - 1', '3'], ['5', '18', 'los angeles kings', '4 - 3', '2 - 2 - 1', '5'], ['6', '20', 'california golden seals', '3 - 3', '2 - 2 - 2', '6'], ['7', '25', 'new york rangers', '1 - 6', '2 - 3 - 2', '6'], ['8', '26', 'detroit red wings', '2 - 1', '3 - 3 - 2', '8'], ['9', '28', 'minnesota north stars', '1 - 2', '3 - 4 - 2', '8'], ['10', '29', 'toronto maple leafs', '5 - 2', '4 - 4 - 2', '10']] |
miss international | https://en.wikipedia.org/wiki/Miss_International | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1816311-1.html.csv | count | in the miss international contest , there are 8 occasions where the location is in japan . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'japan', 'result': '8', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'location', 'japan'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose location record fuzzily matches to japan .', 'tostr': 'filter_eq { all_rows ; location ; japan }'}], 'result': '8', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; location ; japan } }', 'tointer': 'select the rows whose location record fuzzily matches to japan . the number of such rows is 8 .'}, '8'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; location ; japan } } ; 8 } = true', 'tointer': 'select the rows whose location record fuzzily matches to japan . the number of such rows is 8 .'} | eq { count { filter_eq { all_rows ; location ; japan } } ; 8 } = true | select the rows whose location record fuzzily matches to japan . 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, 'location_5': 5, 'japan_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', 'location_5': 'location', 'japan_6': 'japan', '8_7': '8'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'location_5': [0], 'japan_6': [0], '8_7': [2]} | ['year', 'country / territory', 'miss international', 'national title', 'location'] | [['2013', 'tba', 'tba', 'tba', 'tokyo , japan'], ['2012', 'japan', 'ikumi yoshimatsu', 'miss japan', 'okinawa , japan'], ['2011', 'ecuador', 'fernanda cornejo', 'miss ecuador', 'chengdu , china'], ['2010', 'venezuela', 'elizabeth mosquera', 'miss venezuela', 'chengdu , china'], ['2009', 'mexico', 'anagabriela espinoza', 'nuestra belleza méxico', 'chengdu , china'], ['2008', 'spain', 'alejandra andreu', 'miss spain', 'macau , china'], ['2007', 'mexico', 'priscila perales', 'nuestra belleza méxico', 'tokyo , japan'], ['2006', 'venezuela', 'daniela di giacomo', 'miss venezuela', 'beijing , china'], ['2005', 'philippines', 'lara quigaman', 'binibining pilipinas', 'tokyo , japan'], ['2004', 'colombia', 'jeymmy vargas', 'miss colombia', 'beijing , china'], ['2003', 'venezuela', 'goizeder azúa', 'miss venezuela', 'tokyo , japan'], ['2002', 'lebanon', 'christina sawaya', 'miss lebanon', 'tokyo , japan'], ['2001', 'poland', 'małgorzata rożniecka', 'miss polonia', 'tokyo , japan'], ['2000', 'venezuela', 'vivian urdaneta', 'miss venezuela', 'tokyo , japan']] |
list of the busiest airports in africa | https://en.wikipedia.org/wiki/List_of_the_busiest_airports_in_Africa | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18600121-1.html.csv | unique | kotoka international airport is the only airport from ghana on the busiest airports in africa list . | {'scope': 'all', 'row': '13', 'col': '1', 'col_other': '2', 'criterion': 'equal', 'value': 'ghana', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'ghana'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose country record fuzzily matches to ghana .', 'tostr': 'filter_eq { all_rows ; country ; ghana }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; country ; ghana } }', 'tointer': 'select the rows whose country record fuzzily matches to ghana . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'ghana'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose country record fuzzily matches to ghana .', 'tostr': 'filter_eq { all_rows ; country ; ghana }'}, 'airport'], 'result': 'kotoka international airport', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; country ; ghana } ; airport }'}, 'kotoka international airport'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; country ; ghana } ; airport } ; kotoka international airport }', 'tointer': 'the airport record of this unqiue row is kotoka international airport .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; country ; ghana } } ; eq { hop { filter_eq { all_rows ; country ; ghana } ; airport } ; kotoka international airport } } = true', 'tointer': 'select the rows whose country record fuzzily matches to ghana . there is only one such row in the table . the airport record of this unqiue row is kotoka international airport .'} | and { only { filter_eq { all_rows ; country ; ghana } } ; eq { hop { filter_eq { all_rows ; country ; ghana } ; airport } ; kotoka international airport } } = true | select the rows whose country record fuzzily matches to ghana . there is only one such row in the table . the airport record of this unqiue row is kotoka international airport . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'country_7': 7, 'ghana_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'airport_9': 9, 'kotoka international airport_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'country_7': 'country', 'ghana_8': 'ghana', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'airport_9': 'airport', 'kotoka international airport_10': 'kotoka international airport'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'country_7': [0], 'ghana_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'airport_9': [2], 'kotoka international airport_10': [3]} | ['country', 'airport', 'city', '2012', 'change ( 12 / 11 )'] | [['south africa', 'or tambo international airport', 'johannesburg', '18681458', '0 1.2 %'], ['spain', 'gran canaria airport', 'las palmas de gran canaria', '9892067', '0 6.1 %'], ['spain', 'tenerife sur', 'granadilla de abona', '8530729', '0 1.5 %'], ['south africa', 'cape town international airport', 'cape town', '8505563', '0 0.8 %'], ['morocco', 'mohammed v international airport', 'casablanca', '7186331', '0 1.4 %'], ['spain', 'lanzarote airport', 'san bartolomé , las palmas', '5168775', '0 6.8 %'], ['south africa', 'king shaka international airport', 'durban', '4747224', '0 5.8 %'], ['spain', 'fuerteventura airport', 'puerto del rosario', '4399023', '0 11.1 %'], ['spain', 'tenerife norte', 'san cristóbal de la laguna', '3717944', '0 9.2 %'], ['morocco', 'marrakesh menara airport', 'marrakesh', '3373475', '0 1.7 %'], ['mauritius', 'sir seewoosagur ramgoolam international airport', 'mauritius', '2490862', '0 3.7 %'], ['france', 'la réunion roland garros airport', 'saint - denis', '1997800', '0 4.2 %'], ['ghana', 'kotoka international airport', 'accra', '1726051', '0 8.9 %'], ['morocco', 'agadir - al massira airport', 'agadir', '1384931', '0 8.7 %'], ['south africa', 'port elizabeth airport', 'port elizabeth', '1316063', '0 3.7 %'], ['tunisia', 'monastir international airport', 'monastir', '1238757', '0 23.9 %']] |
1949 vfl season | https://en.wikipedia.org/wiki/1949_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10809351-5.html.csv | ordinal | princes park venue recorded the highest crowd participation during the 1949 vfl season . | {'row': '4', 'col': '6', 'order': '1', 'col_other': '5', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'crowd', '1'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; crowd ; 1 }'}, 'venue'], 'result': 'princes park', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; crowd ; 1 } ; venue }'}, 'princes park'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; venue } ; princes park } = true', 'tointer': 'select the row whose crowd record of all rows is 1st maximum . the venue record of this row is princes park .'} | eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; venue } ; princes park } = true | select the row whose crowd record of all rows is 1st maximum . the venue record of this row is princes park . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'crowd_5': 5, '1_6': 6, 'venue_7': 7, 'princes park_8': 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'crowd_5': 'crowd', '1_6': '1', 'venue_7': 'venue', 'princes park_8': 'princes park'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'crowd_5': [0], '1_6': [0], 'venue_7': [1], 'princes park_8': [2]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['melbourne', '5.16 ( 46 )', 'north melbourne', '6.12 ( 48 )', 'mcg', '19000', '14 may 1949'], ['geelong', '15.13 ( 103 )', 'st kilda', '10.11 ( 71 )', 'kardinia park', '15500', '14 may 1949'], ['essendon', '11.18 ( 84 )', 'richmond', '9.15 ( 69 )', 'windy hill', '21000', '14 may 1949'], ['carlton', '16.8 ( 104 )', 'collingwood', '10.14 ( 74 )', 'princes park', '33000', '14 may 1949'], ['south melbourne', '10.8 ( 68 )', 'footscray', '6.14 ( 50 )', 'lake oval', '11000', '14 may 1949'], ['hawthorn', '9.13 ( 67 )', 'fitzroy', '14.21 ( 105 )', 'glenferrie oval', '7500', '14 may 1949']] |
sorana cîrstea | https://en.wikipedia.org/wiki/Sorana_C%C3%AErstea | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11057284-2.html.csv | count | sorana cîrstea reached the second round of 2 grand slam tournaments in 2011 . | {'scope': 'all', 'criterion': 'equal', 'value': '2r', 'result': '2', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', '2011', '2r'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose 2011 record fuzzily matches to 2r .', 'tostr': 'filter_eq { all_rows ; 2011 ; 2r }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; 2011 ; 2r } }', 'tointer': 'select the rows whose 2011 record fuzzily matches to 2r . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; 2011 ; 2r } } ; 2 } = true', 'tointer': 'select the rows whose 2011 record fuzzily matches to 2r . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; 2011 ; 2r } } ; 2 } = true | select the rows whose 2011 record fuzzily matches to 2r . 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, '2011_5': 5, '2r_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', '2011_5': '2011', '2r_6': '2r', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], '2011_5': [0], '2r_6': [0], '2_7': [2]} | ['tournament', '2009', '2011', '2012', '2013'] | [['grand slam tournaments', 'grand slam tournaments', 'grand slam tournaments', 'grand slam tournaments', 'grand slam tournaments'], ['australian open', '2r', '2r', '1r', '1r'], ['french open', '1r', '1r', '1r', '2r'], ['wimbledon', '2r', '3r', '1r', '1r'], ['us open', '3r', '2r', '1r', '1r'], ['win - loss', '4 - 4', '4 - 4', '0 - 4', '1 - 4']] |
list of manly - warringah sea eagles honours | https://en.wikipedia.org/wiki/List_of_Manly-Warringah_Sea_Eagles_honours | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12573519-7.html.csv | superlative | 2011 was the only year that there were more than 81000 people attending the manly - warringah sea eagles honours . | {'scope': 'all', 'col_superlative': '6', 'row_superlative': '8', 'value_mentioned': 'yes', 'max_or_min': 'max', 'other_col': '1', 'subset': None} | {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'max', 'args': ['all_rows', 'attendance'], 'result': '81988', 'ind': 0, 'tostr': 'max { all_rows ; attendance }', 'tointer': 'the maximum attendance record of all rows is 81988 .'}, '81988'], 'result': True, 'ind': 1, 'tostr': 'eq { max { all_rows ; attendance } ; 81988 }', 'tointer': 'the maximum attendance record of all rows is 81988 .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'attendance'], 'result': None, 'ind': 2, 'tostr': 'argmax { all_rows ; attendance }'}, 'year'], 'result': '2011', 'ind': 3, 'tostr': 'hop { argmax { all_rows ; attendance } ; year }'}, '2011'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { argmax { all_rows ; attendance } ; year } ; 2011 }', 'tointer': 'the year record of the row with superlative attendance record is 2011 .'}], 'result': True, 'ind': 5, 'tostr': 'and { eq { max { all_rows ; attendance } ; 81988 } ; eq { hop { argmax { all_rows ; attendance } ; year } ; 2011 } } = true', 'tointer': 'the maximum attendance record of all rows is 81988 . the year record of the row with superlative attendance record is 2011 .'} | and { eq { max { all_rows ; attendance } ; 81988 } ; eq { hop { argmax { all_rows ; attendance } ; year } ; 2011 } } = true | the maximum attendance record of all rows is 81988 . the year record of the row with superlative attendance record is 2011 . | 6 | 6 | {'and_5': 5, 'result_6': 6, 'eq_1': 1, 'max_0': 0, 'all_rows_7': 7, 'attendance_8': 8, '81988_9': 9, 'eq_4': 4, 'num_hop_3': 3, 'argmax_2': 2, 'all_rows_10': 10, 'attendance_11': 11, 'year_12': 12, '2011_13': 13} | {'and_5': 'and', 'result_6': 'true', 'eq_1': 'eq', 'max_0': 'max', 'all_rows_7': 'all_rows', 'attendance_8': 'attendance', '81988_9': '81988', 'eq_4': 'eq', 'num_hop_3': 'num_hop', 'argmax_2': 'argmax', 'all_rows_10': 'all_rows', 'attendance_11': 'attendance', 'year_12': 'year', '2011_13': '2011'} | {'and_5': [6], 'result_6': [], 'eq_1': [5], 'max_0': [1], 'all_rows_7': [0], 'attendance_8': [0], '81988_9': [1], 'eq_4': [5], 'num_hop_3': [4], 'argmax_2': [3], 'all_rows_10': [2], 'attendance_11': [2], 'year_12': [3], '2011_13': [4]} | ['year', 'opponent', 'competition', 'score', 'venue', 'attendance'] | [['1972', 'eastern suburbs roosters', 'nswrfl', '19 - 14', 'sydney cricket ground', '54537'], ['1973', 'cronulla - sutherland sharks', 'nswrfl', '10 - 7', 'sydney cricket ground', '52044'], ['1976', 'parramatta eels', 'nswrfl', '13 - 10', 'sydney cricket ground', '57343'], ['1978', 'cronulla - sutherland sharks', 'nswrfl', '16 - 0', 'sydney cricket ground', '33552'], ['1987', 'canberra raiders', 'nswrl', '18 - 8', 'sydney cricket ground', '50201'], ['1996', 'st george dragons', 'arl', '20 - 8', 'sydney football stadium', '40985'], ['2008', 'melbourne storm', 'nrl', '40 - 0', 'anz stadium', '80388'], ['2011', 'new zealand warriors', 'nrl', '24 - 10', 'anz stadium', '81988']] |
list of town tramway systems in the netherlands | https://en.wikipedia.org/wiki/List_of_town_tramway_systems_in_the_Netherlands | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12562214-1.html.csv | count | in the list of town tramway systems in the netherlands only two town had a system power by steam . | {'scope': 'all', 'criterion': 'equal', 'value': 'steam', 'result': '2', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'traction type', 'steam'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose traction type record fuzzily matches to steam .', 'tostr': 'filter_eq { all_rows ; traction type ; steam }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; traction type ; steam } }', 'tointer': 'select the rows whose traction type record fuzzily matches to steam . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; traction type ; steam } } ; 2 } = true', 'tointer': 'select the rows whose traction type record fuzzily matches to steam . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; traction type ; steam } } ; 2 } = true | select the rows whose traction type record fuzzily matches to steam . 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, 'traction type_5': 5, 'steam_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', 'traction type_5': 'traction type', 'steam_6': 'steam', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'traction type_5': [0], 'steam_6': [0], '2_7': [2]} | ['name of system', 'location', 'traction type', 'date ( from )', 'date ( to )'] | [['atm ( 1897 - 1917 ) gta ( 1919 - 1922 )', 'apeldoorn', 'horse', '12 august 1897', '11 november 1917'], ['atm ( 1897 - 1917 ) gta ( 1919 - 1922 )', 'apeldoorn', 'petrol ( gasoline )', '5 june 1919', '8 october 1922'], ['atm ( 1880 - 1911 ) geta ( 1911 - 1944 )', 'arnhem', 'horse', '3 may 1880', '12 june 1912'], ['atm ( 1880 - 1911 ) geta ( 1911 - 1944 )', 'arnhem', 'electric', '21 may 1911', '17 september 1944'], ['hsm ( 1883 - 1910 ) gt ( 1915 - 1922 )', 'groenlo', 'steam', '29 may 1883', '31 december 1910'], ['hsm ( 1883 - 1910 ) gt ( 1915 - 1922 )', 'groenlo', 'petrol ( gasoline )', '6 august 1915', 'oct 1922'], ['hsm ( 1883 - 1910 ) gt ( 1915 - 1922 )', 'groenlo', 'horse', '1917', '1919'], ['ntm ( 1889 - 1912 ) m & w ( 1912 - 1921 ) gtn ( 1911 - 1955 )', 'nijmegen', 'horse', '1889', '1911'], ['ntm ( 1889 - 1912 ) m & w ( 1912 - 1921 ) gtn ( 1911 - 1955 )', 'nijmegen', 'steam', '30 june 1889', '31 december 1921'], ['ntm ( 1889 - 1912 ) m & w ( 1912 - 1921 ) gtn ( 1911 - 1955 )', 'nijmegen', 'electric', '4 june 1911', '20 november 1955'], ['gtz', 'zaltbommel', 'horse', '14 march 1910', '31 august 1923'], ['ztm', 'zutphen', 'horse', '16 may 1889', '29 january 1904']] |
mercedes - benz r230 | https://en.wikipedia.org/wiki/Mercedes-Benz_R230 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1867831-2.html.csv | superlative | the sl65 amg model of the mercedes - benz r230 is the one that makes its peak power at the lowest rpm range . | {'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', 'power rpm'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; power rpm }'}, 'model'], 'result': 'sl 65 amg', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; power rpm } ; model }'}, 'sl 65 amg'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; power rpm } ; model } ; sl 65 amg } = true', 'tointer': 'select the row whose power rpm record of all rows is minimum . the model record of this row is sl 65 amg .'} | eq { hop { argmin { all_rows ; power rpm } ; model } ; sl 65 amg } = true | select the row whose power rpm record of all rows is minimum . the model record of this row is sl 65 amg . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'power rpm_5': 5, 'model_6': 6, 'sl 65 amg_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'power rpm_5': 'power rpm', 'model_6': 'model', 'sl 65 amg_7': 'sl 65 amg'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'power rpm_5': [0], 'model_6': [1], 'sl 65 amg_7': [2]} | ['model', 'years', 'type / code', 'power rpm', 'torque rpm'] | [['sl 350', '2006 - 2008', 'cubic centimetres ( cuin ) v6 ( m272 )', '6000', '2400 - 5000'], ['sl 500 , sl 550', '2006 - 2008', 'cubic centimetres ( cuin ) v8 ( m273 )', '6000', '2800 - 4800'], ['sl 55 amg', '2006 - 2008', 'cubic centimetres ( cuin ) v8 supercharged ( m113 )', '6100', '2600 - 4000'], ['sl 600', '2006 - 2008', 'cubic centimetres ( cuin ) v12 biturbo ( m275 )', '5000', '1900 - 3500'], ['sl 65 amg', '2004 -', 'cubic centimetres ( cuin ) v12 biturbo ( m275 amg )', '4800 - 5100', '2000 - 4000']] |
yen plus | https://en.wikipedia.org/wiki/Yen_Plus | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18685750-2.html.csv | majority | most of the manwha in yen plus began in august 2008 . | {'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'august 2008', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'first issue', 'august 2008'], 'result': True, 'ind': 0, 'tointer': 'for the first issue records of all rows , most of them fuzzily match to august 2008 .', 'tostr': 'most_eq { all_rows ; first issue ; august 2008 } = true'} | most_eq { all_rows ; first issue ; august 2008 } = true | for the first issue records of all rows , most of them fuzzily match to august 2008 . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'first issue_3': 3, 'august 2008_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'first issue_3': 'first issue', 'august 2008_4': 'august 2008'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'first issue_3': [0], 'august 2008_4': [0]} | ['title', 'author', 'first issue', 'last issue', 'completed'] | [["aron 's absurd armada", 'misun kim', 'august 2010', 'ongoing', 'no'], ['jack frost', 'jinho ko', 'august 2008', 'ongoing', 'no'], ['one fine day', 'sirial', 'august 2008', 'july 2010', 'yes'], ['pig bride', 'kookhwa huh ( author ) , sujin kim ( artist )', 'august 2008', 'july 2010', 'yes'], ['sarasah', 'ryang ruy', 'august 2008', 'june 2009', 'no'], ['time and again', 'jiun yun', 'february 2009', 'ongoing', 'no']] |
flavio cipolla | https://en.wikipedia.org/wiki/Flavio_Cipolla | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16474033-9.html.csv | ordinal | flavio cipolla 's tennis tournament in genoa , italy was the second earliest tournament that was played . | {'row': '2', 'col': '1', 'order': '2', 'col_other': '2', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'date', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; date ; 2 }'}, 'tournament'], 'result': 'genoa , italy', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; date ; 2 } ; tournament }'}, 'genoa , italy'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; date ; 2 } ; tournament } ; genoa , italy } = true', 'tointer': 'select the row whose date record of all rows is 2nd minimum . the tournament record of this row is genoa , italy .'} | eq { hop { nth_argmin { all_rows ; date ; 2 } ; tournament } ; genoa , italy } = true | select the row whose date record of all rows is 2nd minimum . the tournament record of this row is genoa , italy . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'date_5': 5, '2_6': 6, 'tournament_7': 7, 'genoa , italy_8': 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmin_0': 'nth_argmin', 'all_rows_4': 'all_rows', 'date_5': 'date', '2_6': '2', 'tournament_7': 'tournament', 'genoa , italy_8': 'genoa , italy'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'date_5': [0], '2_6': [0], 'tournament_7': [1], 'genoa , italy_8': [2]} | ['date', 'tournament', 'surface', 'opponent', 'score'] | [['29 august 2005', 'freudenstadt , germany', 'clay', 'sergio roitman', '7 - 5 , 6 - 4'], ['6 september 2005', 'genoa , italy', 'clay', 'potito starace', '6 - 3 , 7 - 6 ( 7 - 3 )'], ['3 april 2006', 'monza , italy', 'clay', 'nicolas devilder', '6 - 2 , 7 - 5'], ['28 july 2008', 'tampere , finland', 'clay', 'mathieu montcourt', '6 - 2 , 6 - 2'], ['9 january 2010', 'nouméa , new caledonia', 'hard', 'florian mayer', '6 - 3 , 6 - 0'], ['4 juny 2011', 'prostějov , czech republic', 'clay', 'yuri schukin', '6 - 4 , 4 - 6 , 6 - 0'], ['13 november 2011', 'loughborough , uk', 'hard', 'tobias kamke', '2 - 6 , 5 - 7'], ['9 september 2012', 'saint - rémy - de - provence , france', 'hard', 'josselin ouanna', '4 - 6 , 5 - 7']] |
swimming at the 2000 summer olympics - men 's 200 metre butterfly | https://en.wikipedia.org/wiki/Swimming_at_the_2000_Summer_Olympics_%E2%80%93_Men%27s_200_metre_butterfly | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12446425-5.html.csv | superlative | tom malchow recorded the fastest time at the 2000 summer olympics - men 's 200 metre butterfly . | {'scope': 'all', 'col_superlative': '5', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '3', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'time'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; time }'}, 'name'], 'result': 'tom malchow', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; time } ; name }'}, 'tom malchow'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; time } ; name } ; tom malchow } = true', 'tointer': 'select the row whose time record of all rows is minimum . the name record of this row is tom malchow .'} | eq { hop { argmin { all_rows ; time } ; name } ; tom malchow } = true | select the row whose time record of all rows is minimum . the name record of this row is tom malchow . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'time_5': 5, 'name_6': 6, 'tom malchow_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'time_5': 'time', 'name_6': 'name', 'tom malchow_7': 'tom malchow'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'time_5': [0], 'name_6': [1], 'tom malchow_7': [2]} | ['rank', 'lane', 'name', 'nationality', 'time'] | [['1', '4', 'tom malchow', 'united states', '1:56.02'], ['2', '3', 'anatoly polyakov', 'russia', '1:56.78'], ['3', '5', 'michael phelps', 'united states', '1:57.00'], ['4', '6', 'franck esposito', 'france', '1:57.04'], ['5', '2', 'denis pankratov', 'russia', '1:57.24'], ['6', '8', 'andrew livingston', 'puerto rico', '1:58.63'], ['7', '1', 'stefan aartsen', 'netherlands', '1:58.66'], ['8', '7', 'thomas rupprath', 'germany', '1:58.96']] |
1983 - 84 north west counties football league | https://en.wikipedia.org/wiki/1983%E2%80%9384_North_West_Counties_Football_League | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17718005-3.html.csv | majority | all teams which participated in the 1983 - 84 north west counties football league each played 34 games . | {'scope': 'all', 'col': '3', 'most_or_all': 'all', 'criterion': 'equal', 'value': '34', 'subset': None} | {'func': 'all_eq', 'args': ['all_rows', 'played', '34'], 'result': True, 'ind': 0, 'tointer': 'for the played records of all rows , all of them are equal to 34 .', 'tostr': 'all_eq { all_rows ; played ; 34 } = true'} | all_eq { all_rows ; played ; 34 } = true | for the played records of all rows , all of them are equal to 34 . | 1 | 1 | {'all_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'played_3': 3, '34_4': 4} | {'all_eq_0': 'all_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'played_3': 'played', '34_4': '34'} | {'all_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'played_3': [0], '34_4': [0]} | ['position', 'team', 'played', 'drawn', 'lost', 'goals for', 'goals against', 'goal difference', 'points 1'] | [['1', 'clitheroe', '34', '7', '5', '79', '29', '+ 50', '51'], ['2', 'padiham', '34', '8', '7', '58', '34', '+ 24', '46'], ['3', 'ashton town', '34', '7', '8', '54', '42', '+ 12', '45'], ['4', 'oldham dew', '34', '9', '8', '63', '37', '+ 26', '43'], ['5', 'daisy hill', '34', '3', '12', '54', '40', '+ 14', '41'], ['6', 'maghull', '34', '8', '10', '60', '50', '+ 10', '40'], ['7', 'blackpool mechanics', '34', '5', '12', '70', '49', '+ 21', '39'], ['8', 'atherton collieries', '34', '9', '11', '54', '50', '+ 4', '37'], ['9', 'vulcan newton', '34', '8', '11', '64', '54', '+ 10', '36 2'], ['10', 'prestwich heys', '34', '5', '14', '61', '59', '+ 2', '33 2'], ['11', 'whitworth valley', '34', '8', '15', '45', '53', '8', '30'], ['12', 'bolton st', '34', '10', '14', '49', '64', '15', '30'], ['13', 'bacup borough', '34', '9', '14', '65', '60', '+ 5', '27 3'], ['14', 'nelson', '34', '10', '16', '49', '55', '6', '26'], ['15', 'cheadle town', '34', '8', '17', '39', '67', '28', '26'], ['16', 'urmston town', '34', '9', '18', '35', '67', '32', '23'], ['17', 'newton', '34', '4', '22', '33', '63', '30', '20'], ['18', 'ashton athletic', '34', '3', '27', '30', '89', '59', '11']] |
2008 indiana fever season | https://en.wikipedia.org/wiki/2008_Indiana_Fever_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17104539-12.html.csv | superlative | during this period of the 2008 indiana fever season , the indiana fever had their highest attendance during their september 5th game against detroit . | {'scope': 'all', 'col_superlative': '8', 'row_superlative': '2', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '2', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'location / attendance'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; location / attendance }'}, 'date'], 'result': 'september 5', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; location / attendance } ; date }'}, 'september 5'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; location / attendance } ; date } ; september 5 } = true', 'tointer': 'select the row whose location / attendance record of all rows is maximum . the date record of this row is september 5 .'} | eq { hop { argmax { all_rows ; location / attendance } ; date } ; september 5 } = true | select the row whose location / attendance record of all rows is maximum . the date record of this row is september 5 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'location / attendance_5': 5, 'date_6': 6, 'september 5_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'location / attendance_5': 'location / attendance', 'date_6': 'date', 'september 5_7': 'september 5'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'location / attendance_5': [0], 'date_6': [1], 'september 5_7': [2]} | ['game', 'date', 'opponent', 'score', 'high points', 'high rebounds', 'high assists', 'location / attendance', 'record'] | [['29', 'september 2', 'washington', 'w 79 - 68', 'catchings ( 26 )', 'catchings ( 9 )', 'douglas ( 4 )', 'verizon center 7244', '14 - 15'], ['30', 'september 5', 'detroit', 'l 68 - 90', 'catchings ( 20 )', 'catchings ( 10 )', 'bevilaqua ( 4 )', 'the palace of auburn hills 9287', '14 - 16'], ['31', 'september 8', 'atlanta', 'w 81 - 77', 'white ( 24 )', 'catchings ( 10 )', 'catchings ( 6 )', 'philips arena 7706', '15 - 16'], ['32', 'september 9', 'minnesota', 'l 86 - 76', 'white ( 21 )', 'sutton - brown ( 11 )', 'catchings ( 7 )', 'target center 6706', '15 - 17'], ['33', 'september 11', 'new york', 'w 74 - 59', 'sutton - brown ( 16 )', 'catchings ( 8 )', 'douglas ( 5 )', 'conseco fieldhouse 7062', '16 - 17'], ['34', 'september 14', 'phoenix', 'w 103 - 89', 'sutton - brown ( 26 )', 'catchings ( 9 )', 'douglas ( 6 )', 'conseco fieldhouse 8776', '17 - 17']] |
deputy minister handicap | https://en.wikipedia.org/wiki/Deputy_Minister_Handicap | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16828302-1.html.csv | superlative | deep gold recorded the first win of the deputy minister handicap race . | {'scope': 'all', 'col_superlative': '1', 'row_superlative': '8', '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', 'year'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; year }'}, 'winner'], 'result': 'deep gold', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; year } ; winner }'}, 'deep gold'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; year } ; winner } ; deep gold } = true', 'tointer': 'select the row whose year record of all rows is minimum . the winner record of this row is deep gold .'} | eq { hop { argmin { all_rows ; year } ; winner } ; deep gold } = true | select the row whose year record of all rows is minimum . the winner record of this row is deep gold . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'year_5': 5, 'winner_6': 6, 'deep gold_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'year_5': 'year', 'winner_6': 'winner', 'deep gold_7': 'deep gold'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'year_5': [0], 'winner_6': [1], 'deep gold_7': [2]} | ['year', 'winner', 'jockey', 'trainer', 'owner', 'time'] | [['2007', 'keyed entry', 'john velazquez', 'anthony j sciametta , jr', 'starlight stable et al', '1:15:72'], ['2006', 'universal form', 'manoel cruz', 'elliston rolle', 'universal xperience', '1:16.48'], ['2005', 'medallist', 'jose santos', 'h allen jerkens', 'robert clay', '1:15.62'], ['2004', 'alke', 'john velazquez', 'todd a pletcher', 'k d english / a braun', '1:15.80'], ['2003', 'native heir', 'cornelio velasquez', 'mark shuman', 'michael gill', '1:15.17'], ['2002', 'fappies notebook', 'jorge chavez', 'manny tortora', 'irving & marjorie cowan', '1:16.19'], ['2001', 'istintaj', 'jerry bailey', 'mark hennig', 'shadwell racing', '1:16.08'], ['2000', 'deep gold', 'john velazquez', 'bernie flint', 'n chowhan & j l millan', '1:15.89']] |
1995 pga championship | https://en.wikipedia.org/wiki/1995_PGA_Championship | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18100823-2.html.csv | aggregation | the average total shots taken by australian players at the 1995 pga championships is 145.5 . | {'scope': 'all', 'col': '4', 'type': 'average', 'result': '145.5', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'total'], 'result': '145.5', 'ind': 0, 'tostr': 'avg { all_rows ; total }'}, '145.5'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; total } ; 145.5 } = true', 'tointer': 'the average of the total record of all rows is 145.5 .'} | round_eq { avg { all_rows ; total } ; 145.5 } = true | the average of the total record of all rows is 145.5 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'total_4': 4, '145.5_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'total_4': 'total', '145.5_5': '145.5'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'total_4': [0], '145.5_5': [1]} | ['player', 'country', 'year ( s ) won', 'total', 'to par'] | [['bob tway', 'united states', '1986', '143', '+ 1'], ['hal sutton', 'united states', '1983', '144', '+ 2'], ['hubert green', 'united states', '1985', '145', '+ 3'], ['wayne grady', 'australia', '1990', '145', '+ 3'], ['larry nelson', 'united states', '1981 , 1987', '145', '+ 3'], ['david graham', 'australia', '1979', '146', '+ 4'], ['john mahaffey', 'united states', '1978', '147', '+ 5'], ['john daly', 'united states', '1991', '149', '+ 7']] |
thomaz bellucci | https://en.wikipedia.org/wiki/Thomaz_Bellucci | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17436425-4.html.csv | count | thomaz bellucci won three of the finals in which he competed . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'winner', 'result': '3', 'col': '1', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'outcome', 'winner'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose outcome record fuzzily matches to winner .', 'tostr': 'filter_eq { all_rows ; outcome ; winner }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; outcome ; winner } }', 'tointer': 'select the rows whose outcome record fuzzily matches to winner . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; outcome ; winner } } ; 3 } = true', 'tointer': 'select the rows whose outcome record fuzzily matches to winner . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; outcome ; winner } } ; 3 } = true | select the rows whose outcome record fuzzily matches to winner . 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, 'outcome_5': 5, 'winner_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', 'outcome_5': 'outcome', 'winner_6': 'winner', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'outcome_5': [0], 'winner_6': [0], '3_7': [2]} | ['outcome', 'date', 'tournament', 'surface', 'opponent', 'score'] | [['runner - up', '14 february 2009', 'brasil open , costa do sauípe , brazil', 'clay', 'tommy robredo', '3 - 6 , 6 - 3 , 4 - 6'], ['winner', '2 august 2009', 'swiss open , gstaad , switzerland', 'clay', 'andreas beck', '6 - 4 , 7 - 6 ( 7 - 2 )'], ['winner', '7 february 2010', 'movistar open , santiago , chile', 'clay', 'juan mónaco', '6 - 2 , 0 - 6 , 6 - 4'], ['winner', '22 july 2012', 'swiss open , gstaad , switzerland ( 2 )', 'clay', 'janko tipsarević', '6 - 7 ( 6 - 8 ) , 6 - 4 , 6 - 2'], ['runner - up', '21 october 2012', 'kremlin cup , moscow , russia', 'hard ( i )', 'andreas seppi', '6 - 3 , 6 - 7 ( 3 - 7 ) , 3 - 6']] |
spain in the eurovision song contest 2009 | https://en.wikipedia.org/wiki/Spain_in_the_Eurovision_Song_Contest_2009 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-19763199-4.html.csv | superlative | in the 2008 eurovison song contest , electronikboy received the fewest votes among spanish artists . | {'scope': 'all', 'col_superlative': '6', 'row_superlative': '9', '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', 'total votes'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; total votes }'}, 'artist'], 'result': 'electronikboy', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; total votes } ; artist }'}, 'electronikboy'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; total votes } ; artist } ; electronikboy } = true', 'tointer': 'select the row whose total votes record of all rows is minimum . the artist record of this row is electronikboy .'} | eq { hop { argmin { all_rows ; total votes } ; artist } ; electronikboy } = true | select the row whose total votes record of all rows is minimum . the artist record of this row is electronikboy . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'total votes_5': 5, 'artist_6': 6, 'electronikboy_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'total votes_5': 'total votes', 'artist_6': 'artist', 'electronikboy_7': 'electronikboy'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'total votes_5': [0], 'artist_6': [1], 'electronikboy_7': [2]} | ['draw', 'artist', 'song', 'jury votes', 'televotes', 'total votes', 'result'] | [['1', 'diqesi', 'subiré', '5', '4', '9', 'out'], ['2', 'roel', 'y ahora dices', '6', '3', '9', 'out'], ['3', 'salva ortega', 'lujuria', '7', '7', '14', 'second chance > final'], ['4', 'soraya', 'la noche es para mí', '12', '12', '24', 'final'], ['5', 'virginia', 'true love', '10', '10', '20', 'final'], ['6', 'calipop', 'burbuja', '2', '2', '4', 'out'], ['7', 'ángeles vela', 'vístete de primavera', '4', '5', '9', 'out'], ['8', 'jorge gonzález', 'si yo vengo a enamorarte', '8', '8', '16', 'final'], ['9', 'electronikboy', 'mon petit oiseau', '1', '1', '2', 'out']] |
swimming at the 2000 summer olympics - women 's 100 metre backstroke | https://en.wikipedia.org/wiki/Swimming_at_the_2000_Summer_Olympics_%E2%80%93_Women%27s_100_metre_backstroke | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12427181-5.html.csv | superlative | noriko inada was the slowest janapese swimmer at the 2000 olympics - women 's 100 metre backstroke . | {'scope': 'subset', 'col_superlative': '5', 'row_superlative': '2', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '3,4', 'subset': {'col': '4', 'criterion': 'equal', 'value': 'japan'}} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nationality', 'japan'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; nationality ; japan }', 'tointer': 'select the rows whose nationality record fuzzily matches to japan .'}, 'time'], 'result': None, 'ind': 1, 'tostr': 'argmax { filter_eq { all_rows ; nationality ; japan } ; time }'}, 'name'], 'result': 'noriko inada', 'ind': 2, 'tostr': 'hop { argmax { filter_eq { all_rows ; nationality ; japan } ; time } ; name }'}, 'noriko inada'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { argmax { filter_eq { all_rows ; nationality ; japan } ; time } ; name } ; noriko inada } = true', 'tointer': 'select the rows whose nationality record fuzzily matches to japan . select the row whose time record of these rows is maximum . the name record of this row is noriko inada .'} | eq { hop { argmax { filter_eq { all_rows ; nationality ; japan } ; time } ; name } ; noriko inada } = true | select the rows whose nationality record fuzzily matches to japan . select the row whose time record of these rows is maximum . the name record of this row is noriko inada . | 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, 'nationality_6': 6, 'japan_7': 7, 'time_8': 8, 'name_9': 9, 'noriko inada_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', 'nationality_6': 'nationality', 'japan_7': 'japan', 'time_8': 'time', 'name_9': 'name', 'noriko inada_10': 'noriko inada'} | {'str_eq_3': [4], 'result_4': [], 'str_hop_2': [3], 'argmax_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'nationality_6': [0], 'japan_7': [0], 'time_8': [1], 'name_9': [2], 'noriko inada_10': [3]} | ['rank', 'lane', 'name', 'nationality', 'time'] | [['1', '4', 'mai nakamura', 'japan', '1:01.07'], ['2', '6', 'noriko inada', 'japan', '1:01.25'], ['3', '3', 'nina zhivanevskaya', 'spain', '1:01.41'], ['4', '5', 'roxana maracineanu', 'france', '1:01.61'], ['5', '2', 'antje buschschulte', 'germany', '1:01.91'], ['6', '7', 'katy sexton', 'great britain', '1:02.35'], ['7', '1', 'sandra vãlker', 'germany', '1:03.01'], ['8', '8', 'lu donghua', 'china', '1:03.31']] |
italian army | https://en.wikipedia.org/wiki/Italian_Army | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1392092-5.html.csv | comparative | the italian army had more bell 212 helicopters in service than bell 412 helicopters . | {'row_1': '4', 'row_2': '5', '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', 'aircraft', 'bell 212'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose aircraft record fuzzily matches to bell 212 .', 'tostr': 'filter_eq { all_rows ; aircraft ; bell 212 }'}, 'in service'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; aircraft ; bell 212 } ; in service }', 'tointer': 'select the rows whose aircraft record fuzzily matches to bell 212 . take the in service record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'aircraft', 'bell 412'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose aircraft record fuzzily matches to bell 412 .', 'tostr': 'filter_eq { all_rows ; aircraft ; bell 412 }'}, 'in service'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; aircraft ; bell 412 } ; in service }', 'tointer': 'select the rows whose aircraft record fuzzily matches to bell 412 . take the in service record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; aircraft ; bell 212 } ; in service } ; hop { filter_eq { all_rows ; aircraft ; bell 412 } ; in service } } = true', 'tointer': 'select the rows whose aircraft record fuzzily matches to bell 212 . take the in service record of this row . select the rows whose aircraft record fuzzily matches to bell 412 . take the in service record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; aircraft ; bell 212 } ; in service } ; hop { filter_eq { all_rows ; aircraft ; bell 412 } ; in service } } = true | select the rows whose aircraft record fuzzily matches to bell 212 . take the in service record of this row . select the rows whose aircraft record fuzzily matches to bell 412 . take the in service 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, 'aircraft_7': 7, 'bell 212_8': 8, 'in service_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'aircraft_11': 11, 'bell 412_12': 12, 'in service_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', 'aircraft_7': 'aircraft', 'bell 212_8': 'bell 212', 'in service_9': 'in service', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'aircraft_11': 'aircraft', 'bell 412_12': 'bell 412', 'in service_13': 'in service'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'aircraft_7': [0], 'bell 212_8': [0], 'in service_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'aircraft_11': [1], 'bell 412_12': [1], 'in service_13': [3]} | ['aircraft', 'origin', 'type', 'versions', 'in service'] | [['agusta a109', 'italy', 'recce helicopter', 'a109eoa - 2', '12'], ['agusta a129 mangusta', 'italy', 'attack helicopter', 'cbt', '56'], ['bell uh - 1 iroquois', 'united states', 'transport helicopter', 'ab 205', '42'], ['bell 212', 'united states', 'transport helicopter', 'ab 212', '39'], ['bell 412', 'united states', 'transport helicopter', 'ab 412', '31'], ['nhi nh90', 'european union', 'transport helicopter', 'tth', '21'], ['boeing ch - 47 chinook', 'united states', 'transport helicopter', 'ch - 47c', '14'], ['boeing ch - 47 chinook', 'united states', 'transport helicopter', 'ch - 47f', '0'], ['dornier do 228', 'germany', 'utility transport', 'do 228 - 200', '3'], ['piaggio p180 avanti', 'italy', 'utility transport', 'p180 m', '3']] |
1971 vfl season | https://en.wikipedia.org/wiki/1971_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10826072-5.html.csv | majority | most of the away team 's score in 1971 vfl season was above 10.0 . | {'scope': 'subset', 'col': '4', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '10.0', 'subset': {'col': '4', 'criterion': 'greater_than', 'value': '10.0'}} | {'func': 'most_greater', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'away team score', '10.0'], 'result': None, 'ind': 0, 'tostr': 'filter_greater { all_rows ; away team score ; 10.0 }', 'tointer': 'select the rows whose away team score record is greater than 10.0 .'}, 'away team score', '10.0'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose away team score record is greater than 10.0 . for the away team score records of these rows , most of them are greater than 10.0 .', 'tostr': 'most_greater { filter_greater { all_rows ; away team score ; 10.0 } ; away team score ; 10.0 } = true'} | most_greater { filter_greater { all_rows ; away team score ; 10.0 } ; away team score ; 10.0 } = true | select the rows whose away team score record is greater than 10.0 . for the away team score records of these rows , most of them are greater than 10.0 . | 2 | 2 | {'most_greater_1': 1, 'result_2': 2, 'filter_greater_0': 0, 'all_rows_3': 3, 'away team score_4': 4, '10.0_5': 5, 'away team score_6': 6, '10.0_7': 7} | {'most_greater_1': 'most_greater', 'result_2': 'true', 'filter_greater_0': 'filter_greater', 'all_rows_3': 'all_rows', 'away team score_4': 'away team score', '10.0_5': '10.0', 'away team score_6': 'away team score', '10.0_7': '10.0'} | {'most_greater_1': [2], 'result_2': [], 'filter_greater_0': [1], 'all_rows_3': [0], 'away team score_4': [0], '10.0_5': [0], 'away team score_6': [1], '10.0_7': [1]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['fitzroy', '14.8 ( 92 )', 'melbourne', '17.18 ( 120 )', 'junction oval', '12363', '1 may 1971'], ['essendon', '21.15 ( 141 )', 'geelong', '16.12 ( 108 )', 'windy hill', '16435', '1 may 1971'], ['collingwood', '16.15 ( 111 )', 'north melbourne', '10.11 ( 71 )', 'victoria park', '22546', '1 may 1971'], ['carlton', '13.18 ( 96 )', 'st kilda', '11.17 ( 83 )', 'princes park', '24027', '1 may 1971'], ['richmond', '14.17 ( 101 )', 'footscray', '17.19 ( 121 )', 'mcg', '23758', '1 may 1971'], ['hawthorn', '12.21 ( 93 )', 'south melbourne', '6.9 ( 45 )', 'vfl park', '16206', '1 may 1971']] |
2010 - 11 washington wizards season | https://en.wikipedia.org/wiki/2010%E2%80%9311_Washington_Wizards_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27721131-2.html.csv | superlative | in the 2010-11 washington wizards season , the highest score in their winning games was 107 points . | {'scope': 'subset', 'col_superlative': '4', 'row_superlative': '4', 'value_mentioned': 'yes', 'max_or_min': 'max', 'other_col': 'n/a', 'subset': {'col': '4', 'criterion': 'fuzzily_match', 'value': 'w'}} | {'func': 'eq', 'args': [{'func': 'max', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'score', 'w'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; score ; w }', 'tointer': 'select the rows whose score record fuzzily matches to w .'}, 'score'], 'result': 'w 107 - 92 ( ot )', 'ind': 1, 'tostr': 'max { filter_eq { all_rows ; score ; w } ; score }', 'tointer': 'select the rows whose score record fuzzily matches to w . the maximum score record of these rows is w 107 - 92 ( ot ) .'}, 'w 107 - 92 ( ot )'], 'result': True, 'ind': 2, 'tostr': 'eq { max { filter_eq { all_rows ; score ; w } ; score } ; w 107 - 92 ( ot ) } = true', 'tointer': 'select the rows whose score record fuzzily matches to w . the maximum score record of these rows is w 107 - 92 ( ot ) .'} | eq { max { filter_eq { all_rows ; score ; w } ; score } ; w 107 - 92 ( ot ) } = true | select the rows whose score record fuzzily matches to w . the maximum score record of these rows is w 107 - 92 ( ot ) . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'max_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'score_5': 5, 'w_6': 6, 'score_7': 7, 'w 107 - 92 (ot)_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'max_1': 'max', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'score_5': 'score', 'w_6': 'w', 'score_7': 'score', 'w 107 - 92 (ot)_8': 'w 107 - 92 ( ot )'} | {'eq_2': [3], 'result_3': [], 'max_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'score_5': [0], 'w_6': [0], 'score_7': [1], 'w 107 - 92 (ot)_8': [2]} | ['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record'] | [['1', 'october 5', 'dallas', 'w 97 - 94 ( ot )', 'andray blatche ( 22 )', 'yi jianlian ( 10 )', 'john wall ( 9 )', 'american airlines center 15546', '1 - 0'], ['2', 'october 7', 'cleveland', 'w 97 - 83 ( ot )', 'yi jianlian ( 16 )', 'yi jianlian , andray blatche ( 7 )', 'john wall ( 9 )', 'quicken loans arena 19124', '2 - 0'], ['3', 'october 8', 'chicago', 'l 96 - 107 ( ot )', 'nick young ( 18 )', 'javale mcgee ( 5 )', 'john wall ( 6 )', 'united center 20898', '2 - 1'], ['4', 'october 12', 'atlanta', 'w 107 - 92 ( ot )', 'nick young ( 24 )', 'javale mcgee ( 11 )', 'kirk hinrich ( 8 )', 'verizon center 9230', '3 - 1'], ['5', 'october 14', 'milwaukee', 'l 88 - 96 ( ot )', 'andray blatche ( 17 )', 'andray blatche ( 9 )', 'john wall ( 11 )', 'verizon center 9263', '3 - 2'], ['6', 'october 17', 'new york', 'w 92 - 90 ( ot )', 'john wall ( 19 )', 'kirk hinrich ( 9 )', 'john wall ( 6 )', 'madison square garden 18792', '3 - 3']] |
patty schnyder | https://en.wikipedia.org/wiki/Patty_Schnyder | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1547798-4.html.csv | majority | most of patty schnyder 's matches were played on carpeted surfaces . | {'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'carpet', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'surface', 'carpet'], 'result': True, 'ind': 0, 'tointer': 'for the surface records of all rows , most of them fuzzily match to carpet .', 'tostr': 'most_eq { all_rows ; surface ; carpet } = true'} | most_eq { all_rows ; surface ; carpet } = true | for the surface records of all rows , most of them fuzzily match to carpet . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'surface_3': 3, 'carpet_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'surface_3': 'surface', 'carpet_4': 'carpet'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'surface_3': [0], 'carpet_4': [0]} | ['date', 'tournament', 'surface', 'partner', 'opponent in the final', 'score'] | [['3 may 1998', 'hamburg , germany', 'clay', 'barbara schett', 'martina hingis jana novotná', '7 - 6 , 3 - 6 , 6 - 3'], ['17 february 2002', 'antwerp , belgium', 'carpet', 'magdalena maleeva', 'nathalie dechy meilen tu', '6 - 3 , 6 - 7 , 6 - 3'], ['9 february 2003', 'paris , france', 'carpet', 'barbara schett', 'marion bartoli stéphanie cohen - aloro', '2 - 6 , 6 - 2 , 7 - 6'], ['15 february 2004', 'paris , france', 'carpet', 'barbara schett', 'silvia farina elia francesca schiavone', '6 - 3 , 6 - 2'], ['5 october 2008', 'stuttgart , germany', 'hard', 'anna - lena grönefeld', 'květa peschke rennae stubbs', '6 - 2 , 6 - 4']] |
wbfj - fm | https://en.wikipedia.org/wiki/WBFJ-FM | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10711725-1.html.csv | count | a total of five wbfj-fm radio channels use an erp w of 10 . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': '10', 'result': '5', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'erp w', '10'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose erp w record fuzzily matches to 10 .', 'tostr': 'filter_eq { all_rows ; erp w ; 10 }'}], 'result': '5', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; erp w ; 10 } }', 'tointer': 'select the rows whose erp w record fuzzily matches to 10 . the number of such rows is 5 .'}, '5'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; erp w ; 10 } } ; 5 } = true', 'tointer': 'select the rows whose erp w record fuzzily matches to 10 . the number of such rows is 5 .'} | eq { count { filter_eq { all_rows ; erp w ; 10 } } ; 5 } = true | select the rows whose erp w record fuzzily matches to 10 . 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, 'erp w_5': 5, '10_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', 'erp w_5': 'erp w', '10_6': '10', '5_7': '5'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'erp w_5': [0], '10_6': [0], '5_7': [2]} | ['call sign', 'frequency mhz', 'city of license', 'facility id', 'erp w', 'height m ( ft )', 'class', 'fcc info'] | [['w267ag', '101.3', 'salisbury , north carolina', '67830', '38', '-', 'd', 'fcc'], ['w267 am', '101.3', 'mocksville , north carolina', '87027', '33', '-', 'd', 'fcc'], ['w267an', '101.3', 'wilkesboro , north carolina', '87078', '10', '-', 'd', 'fcc'], ['w274al', '102.7', 'high point , north carolina', '87044', '10', '-', 'd', 'fcc'], ['w276ba', '103.1', 'fancy gap , virginia', '87029', '10', '-', 'd', 'fcc'], ['w278 am', '103.5', 'sedalia , north carolina', '87023', '10', '-', 'd', 'fcc'], ['w285dj', '104.9', 'mount airy , north carolina', '67829', '10', '-', 'd', 'fcc']] |
2006 connecticut sun season | https://en.wikipedia.org/wiki/2006_Connecticut_Sun_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18894744-5.html.csv | majority | whalen had the majority of high assists performances . | {'scope': 'all', 'col': '7', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'whalen', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'high assists', 'whalen'], 'result': True, 'ind': 0, 'tointer': 'for the high assists records of all rows , most of them fuzzily match to whalen .', 'tostr': 'most_eq { all_rows ; high assists ; whalen } = true'} | most_eq { all_rows ; high assists ; whalen } = true | for the high assists records of all rows , most of them fuzzily match to whalen . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'high assists_3': 3, 'whalen_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'high assists_3': 'high assists', 'whalen_4': 'whalen'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'high assists_3': [0], 'whalen_4': [0]} | ['game', 'date', 'opponent', 'score', 'high points', 'high rebounds', 'high assists', 'location / attendance', 'record'] | [['4', 'june 1', 'charlotte', 'w 89 - 65', 'sales ( 18 )', 'dydek ( 15 )', 'douglas ( 5 )', 'charlotte bobcats arena 3632', '3 - 1'], ['5', 'june 3', 'charlotte', 'w 89 - 71', 'dydek ( 17 )', 'mcwilliams - franklin ( 15 )', 'sales ( 5 )', 'mohegan sun arena 7318', '4 - 1'], ['6', 'june 7', 'new york', 'w 75 - 60', 'douglas ( 17 )', 'mcwilliams - franklin ( 7 )', 'sales , phillips ( 4 )', 'madison square garden 10180', '5 - 1'], ['7', 'june 9', 'seattle', 'w 85 - 81', 'douglas ( 18 )', 'dydek ( 12 )', 'whalen ( 9 )', 'mohegan sun arena 7138', '6 - 1'], ['8', 'june 13', 'washington', 'w 85 - 71', 'douglas ( 26 )', 'douglas ( 7 )', 'whalen , jones ( 4 )', 'mohegan sun arena 6339', '7 - 1'], ['9', 'june 16', 'phoenix', 'l 86 - 91', 'douglas ( 27 )', 'mcwilliams - franklin ( 17 )', 'whalen ( 4 )', 'us airways center 6378', '7 - 2'], ['10', 'june 17', 'los angeles', 'l 70 - 82', 'jones ( 16 )', 'sales , dydek , jones ( 5 )', 'whalen ( 5 )', 'staples center 7991', '7 - 3'], ['11', 'june 20', 'charlotte', 'w 90 - 66', 'sales ( 15 )', 'mcwilliams - franklin ( 9 )', 'whalen ( 6 )', 'charlotte bobcats arena 4243', '8 - 3'], ['12', 'june 22', 'minnesota', 'w 79 - 62', 'whalen , dydek ( 16 )', 'jones ( 11 )', 'sales ( 4 )', 'mohegan sun arena 6573', '9 - 3'], ['13', 'june 23', 'chicago', 'w 84 - 79', 'sales ( 23 )', 'mcwilliams - franklin ( 14 )', 'whalen ( 6 )', 'uic pavilion 2818', '10 - 3'], ['14', 'june 25', 'washington', 'l 80 - 87', 'mcwilliams - franklin , sales , jones ( 15 )', 'mcwilliams - franklin ( 11 )', 'whalen ( 6 )', 'mci center 7216', '10 - 4'], ['15', 'june 27', 'houston', 'w 73 - 57', 'sales ( 19 )', 'dydek ( 13 )', 'whalen ( 6 )', 'mohegan sun arena 6220', '11 - 4'], ['16', 'june 30', 'detroit', 'l 64 - 70', 'jones ( 16 )', 'mcwilliams - franklin ( 10 )', 'whalen ( 5 )', 'mohegan sun arena 7003', '11 - 5']] |
2007 - 08 fis ski jumping world cup | https://en.wikipedia.org/wiki/2007%E2%80%9308_FIS_Ski_Jumping_World_Cup | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14407512-14.html.csv | count | two of the people in the 2007-08 fis ski jumping world cup are from norway . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'nor', 'result': '2', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nationality', 'nor'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose nationality record fuzzily matches to nor .', 'tostr': 'filter_eq { all_rows ; nationality ; nor }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; nationality ; nor } }', 'tointer': 'select the rows whose nationality record fuzzily matches to nor . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; nationality ; nor } } ; 2 } = true', 'tointer': 'select the rows whose nationality record fuzzily matches to nor . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; nationality ; nor } } ; 2 } = true | select the rows whose nationality record fuzzily matches to nor . the number of such rows is 2 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'nationality_5': 5, 'nor_6': 6, '2_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'nationality_5': 'nationality', 'nor_6': 'nor', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'nationality_5': [0], 'nor_6': [0], '2_7': [2]} | ['rank', 'name', 'nationality', '1st ( m )', 'points', 'overall wc points ( rank )'] | [['1', 'janne ahonen', 'fin', '199.5', '187.9', '810 ( 2 )'], ['2', 'tom hilde', 'nor', '193.0', '185.6', '682 ( 4 )'], ['3', 'anders jacobsen', 'nor', '191.0', '181.2', '283 ( 13 )'], ['4', 'dmitry vassiliev', 'rus', '191.0', '178.2', '236 ( 16 )'], ['5', 'thomas morgenstern', 'aut', '188.0', '177.6', '1115 ( 1 )']] |
list of intel core i7 microprocessors | https://en.wikipedia.org/wiki/List_of_Intel_Core_i7_microprocessors | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18823880-2.html.csv | ordinal | the core i7 - 870 has the 2nd highest release price . | {'row': '3', 'col': '15', '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', 'release price ( usd )', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; release price ( usd ) ; 2 }'}, 'model number'], 'result': 'core i7 - 870', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; release price ( usd ) ; 2 } ; model number }'}, 'core i7 - 870'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; release price ( usd ) ; 2 } ; model number } ; core i7 - 870 } = true', 'tointer': 'select the row whose release price ( usd ) record of all rows is 2nd maximum . the model number record of this row is core i7 - 870 .'} | eq { hop { nth_argmax { all_rows ; release price ( usd ) ; 2 } ; model number } ; core i7 - 870 } = true | select the row whose release price ( usd ) record of all rows is 2nd maximum . the model number record of this row is core i7 - 870 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'release price ( usd )_5': 5, '2_6': 6, 'model number_7': 7, 'core i7 - 870_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', 'release price ( usd )_5': 'release price ( usd )', '2_6': '2', 'model number_7': 'model number', 'core i7 - 870_8': 'core i7 - 870'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'release price ( usd )_5': [0], '2_6': [0], 'model number_7': [1], 'core i7 - 870_8': [2]} | ['model number', 'sspec number', 'frequency', 'turbo', 'cores', 'l2 cache', 'l3 cache', 'i / o bus', 'mult', 'memory', 'voltage', 'socket', 'release date', 'part number ( s )', 'release price ( usd )'] | [['standard power', 'standard power', 'standard power', 'standard power', 'standard power', 'standard power', 'standard power', 'standard power', 'standard power', 'standard power', 'standard power', 'standard power', 'standard power', 'standard power', 'standard power'], ['core i7 - 860', 'slbjj ( b1 )', '2.8 ghz', '1 / 1 / 4 / 5', '4', '4 256 kb', '8 mb', 'dmi', '21', '2 ddr3 - 1333', '0.65 - 1.4 v', 'lga 1156', 'september 2009', 'bv80605001908akbx80605i7860', '284'], ['core i7 - 870', 'slbjg ( b1 )', '2.93 ghz', '2 / 2 / 4 / 5', '4', '4 256 kb', '8 mb', 'dmi', '22', '2 ddr3 - 1333', '0.65 - 1.4 v', 'lga 1156', 'september 2009', 'bv80605001905aibx80605i7870', '562'], ['core i7 - 875k', 'slbs2 ( b1 )', '2.93 ghz', '2 / 2 / 4 / 5', '4', '4 256 kb', '8 mb', 'dmi', '22', '2 ddr3 - 1333', '0.65 - 1.4 v', 'lga 1156', 'may 2010', 'bv80605001905 ambx80605i7875k', '342'], ['core i7 - 880', 'slbps ( b1 )', '3.07 ghz', '2 / 2 / 4 / 5', '4', '4 256 kb', '8 mb', 'dmi', '23', '2 ddr3 - 1333', '0.65 - 1.4 v', 'lga 1156', 'may 2010', 'bv80605002505ag', '583'], ['low power', 'low power', 'low power', 'low power', 'low power', 'low power', 'low power', 'low power', 'low power', 'low power', 'low power', 'low power', 'low power', 'low power', 'low power'], ['core i7 - 860s', 'slblg ( b1 )', '2.53 ghz', '0 / 0 / 6 / 7', '4', '4 256 kb', '8 mb', 'dmi', '19', '2 ddr3 - 1333', '0.65 - 1.4 v', 'lga 1156', 'january 2010', 'bv80605003210adbx80605i7860s', '337'], ['core i7 - 870s', 'slbq7 ( b1 )', '2.67 ghz', '0 / 0 / 6 / 7', '4', '4 256 kb', '8 mb', 'dmi', '20', '2 ddr3 - 1333', '0.65 - 1.4 v', 'lga 1156', 'july 2010', 'bx80605i7870sbv80605004494ab', '351']] |
list of largest nordic companies | https://en.wikipedia.org/wiki/List_of_largest_Nordic_companies | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12794433-3.html.csv | unique | securitas is the only top ranked nordic company in the security services industry . | {'scope': 'all', 'row': '2', 'col': '4', 'col_other': '2', 'criterion': 'equal', 'value': 'security services', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'industry', 'security services'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose industry record fuzzily matches to security services .', 'tostr': 'filter_eq { all_rows ; industry ; security services }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; industry ; security services } }', 'tointer': 'select the rows whose industry record fuzzily matches to security services . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'industry', 'security services'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose industry record fuzzily matches to security services .', 'tostr': 'filter_eq { all_rows ; industry ; security services }'}, 'company'], 'result': 'securitas', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; industry ; security services } ; company }'}, 'securitas'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; industry ; security services } ; company } ; securitas }', 'tointer': 'the company record of this unqiue row is securitas .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; industry ; security services } } ; eq { hop { filter_eq { all_rows ; industry ; security services } ; company } ; securitas } } = true', 'tointer': 'select the rows whose industry record fuzzily matches to security services . there is only one such row in the table . the company record of this unqiue row is securitas .'} | and { only { filter_eq { all_rows ; industry ; security services } } ; eq { hop { filter_eq { all_rows ; industry ; security services } ; company } ; securitas } } = true | select the rows whose industry record fuzzily matches to security services . there is only one such row in the table . the company record of this unqiue row is securitas . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'industry_7': 7, 'security services_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'company_9': 9, 'securitas_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'industry_7': 'industry', 'security services_8': 'security services', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'company_9': 'company', 'securitas_10': 'securitas'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'industry_7': [0], 'security services_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'company_9': [2], 'securitas_10': [3]} | ['rank', 'company', 'headquarters', 'industry', 'employees', 'reference date'] | [['1', 'iss', 'copenhagen , denmark', 'facility management', '534500', '2011'], ['2', 'securitas', 'stockholm , sweden', 'security services', '272425', '2011'], ['3', 'nokia', 'espoo , finland', 'technology', '130050', '2011'], ['4', 'ap mãller - maersk', 'copenhagen , denmark', 'transportation', '117080', '2011'], ['5', 'ericsson', 'stockholm , sweden', 'telecommunication', '104525', '2011'], ['6', 'volvo', 'gothenburg , sweden', 'automotive', '98162', '2011'], ['7', 'h & m', 'stockholm , sweden', 'retailing', '64874', '2011'], ['8', 'electrolux', 'stockholm , sweden', 'manufacturing', '52916', '2011'], ['9', 'skanska', 'stockholm , sweden', 'construction', '52557', '2011'], ['10', 'sandvik', 'sandviken , sweden', 'capital goods', '50030', '2011']] |
fred stolle | https://en.wikipedia.org/wiki/Fred_Stolle | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2201724-2.html.csv | majority | most of the tournaments that fred stolle played at had a grass surface . | {'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'grass', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'surface', 'grass'], 'result': True, 'ind': 0, 'tointer': 'for the surface records of all rows , most of them fuzzily match to grass .', 'tostr': 'most_eq { all_rows ; surface ; grass } = true'} | most_eq { all_rows ; surface ; grass } = true | for the surface records of all rows , most of them fuzzily match to grass . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'surface_3': 3, 'grass_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'surface_3': 'surface', 'grass_4': 'grass'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'surface_3': [0], 'grass_4': [0]} | ['outcome', 'year', 'championship', 'surface', 'partner', 'opponents in the final', 'score in the final'] | [['runner - up', '1961', 'wimbledon', 'grass', 'bob hewitt', 'roy emerson neale fraser', '4 - 6 , 8 - 6 , 4 - 6 , 8 - 6 , 6 - 8'], ['runner - up', '1962', 'australian championships', 'grass', 'bob hewitt', 'roy emerson neale fraser', '6 - 4 , 6 - 4 , 1 - 6 , 4 - 6 , 9 - 11'], ['winner', '1962', 'wimbledon', 'grass', 'bob hewitt', 'boro jovanović nikola pilić', '6 - 2 , 5 - 7 , 6 - 2 , 6 - 4'], ['winner', '1963', 'australian championships', 'grass', 'bob hewitt', 'ken fletcher john newcombe', '6 - 2 , 3 - 6 , 6 - 3 , 3 - 6 , 6 - 3'], ['winner', '1964', 'australian championships', 'grass', 'bob hewitt', 'roy emerson ken fletcher', '6 - 4 , 7 - 5 , 3 - 6 , 4 - 6 , 14 - 12'], ['winner', '1964', 'wimbledon', 'grass', 'bob hewitt', 'roy emerson ken fletcher', '7 - 5 , 11 - 9 , 6 - 4'], ['runner - up', '1965', 'australian championships', 'grass', 'roy emerson', 'john newcombe tony roche', '6 - 3 , 6 - 4 , 11 - 13 , 3 - 6 , 4 - 6'], ['winner', '1965', 'french championships', 'clay', 'roy emerson', 'ken fletcher bob hewitt', '6 - 8 , 6 - 3 , 8 - 6 , 6 - 2'], ['winner', '1965', 'us championships', 'grass', 'roy emerson', 'frank froehling charles pasarell', '6 - 4 , 10 - 12 , 7 - 5 , 6 - 3'], ['winner', '1966', 'australian championships', 'grass', 'roy emerson', 'john newcombe tony roche', '7 - 9 , 6 - 3 , 6 - 8 , 14 - 12 , 12 - 10'], ['winner', '1966', 'us championships', 'grass', 'roy emerson', 'clark graebner dennis ralston', '6 - 4 , 6 - 4 , 6 - 4'], ['winner', '1968', 'french open', 'clay', 'ken rosewall', 'roy emerson rod laver', '6 - 3 , 6 - 4 , 6 - 3'], ['runner - up', '1968', 'wimbledon', 'grass', 'ken rosewall', 'john newcombe tony roche', '6 - 3 , 6 - 8 , 7 - 5 , 12 - 14 , 3 - 6'], ['runner - up', '1969', 'australian open', 'grass', 'ken rosewall', 'rod laver roy emerson', '4 - 6 , 4 - 6'], ['winner', '1969', 'us open', 'grass', 'ken rosewall', 'charles pasarell dennis ralston', '2 - 6 , 7 - 5 , 13 - 11 , 6 - 3']] |
the paul mccartney world tour | https://en.wikipedia.org/wiki/The_Paul_McCartney_World_Tour | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14936656-2.html.csv | count | paul mccartney played the acoustic guitar in two songs of his world tour . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'acoustic guitar', 'result': '2', 'col': '1', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'paul mccartney', 'acoustic guitar'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose paul mccartney record fuzzily matches to acoustic guitar .', 'tostr': 'filter_eq { all_rows ; paul mccartney ; acoustic guitar }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; paul mccartney ; acoustic guitar } }', 'tointer': 'select the rows whose paul mccartney record fuzzily matches to acoustic guitar . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; paul mccartney ; acoustic guitar } } ; 2 } = true', 'tointer': 'select the rows whose paul mccartney record fuzzily matches to acoustic guitar . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; paul mccartney ; acoustic guitar } } ; 2 } = true | select the rows whose paul mccartney record fuzzily matches to acoustic guitar . 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, 'paul mccartney_5': 5, 'acoustic guitar_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', 'paul mccartney_5': 'paul mccartney', 'acoustic guitar_6': 'acoustic guitar', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'paul mccartney_5': [0], 'acoustic guitar_6': [0], '2_7': [2]} | ['paul mccartney', 'stuart', 'mcintosh', 'whitten', 'linda mccartney'] | [['bass', 'electric guitar', 'electric guitar', 'drums', 'tambourine'], ['bass', 'electric guitar', 'electric guitar', 'drums', 'keyboards'], ['bass', 'acoustic guitar', 'electric guitar', 'drums', 'keyboards'], ['piano', 'bass', 'electric guitar', 'drums', 'keyboards or drum'], ['piano', 'bass', 'electric guitar', 'drums', 'keyboards'], ['electric guitar', 'bass', 'electric guitar', 'drums', 'keyboards'], ['acoustic guitar', 'bass', 'electric guitar', 'drums', 'keyboards'], ['acoustic guitar', 'none', 'none', 'none', 'none'], ['piano', 'bass', 'electric guitar', 'drums', 'tambourine / keyboards'], ['none', 'bass', 'electric guitar', 'drums', 'keyboards'], ['keyboards / electric guitar', 'bass / electric guitar', 'electric guitar', 'drums', 'keyboards']] |
brothers ( 2009 tv series ) | https://en.wikipedia.org/wiki/Brothers_%282009_TV_series%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-22815870-1.html.csv | ordinal | the third episode of the tv series " brothers " was written by adrienne carter . | {'row': '3', 'col': '1', 'order': '3', 'col_other': '4', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'series', '3'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; series ; 3 }'}, 'written by'], 'result': 'adrienne carter', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; series ; 3 } ; written by }'}, 'adrienne carter'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; series ; 3 } ; written by } ; adrienne carter } = true', 'tointer': 'select the row whose series record of all rows is 3rd minimum . the written by record of this row is adrienne carter .'} | eq { hop { nth_argmin { all_rows ; series ; 3 } ; written by } ; adrienne carter } = true | select the row whose series record of all rows is 3rd minimum . the written by record of this row is adrienne carter . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'series_5': 5, '3_6': 6, 'written by_7': 7, 'adrienne carter_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', 'series_5': 'series', '3_6': '3', 'written by_7': 'written by', 'adrienne carter_8': 'adrienne carter'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'series_5': [0], '3_6': [0], 'written by_7': [1], 'adrienne carter_8': [2]} | ['series', 'title', 'directed by', 'written by', 'original air date', 'prod code'] | [['1', 'pilot', 'ted wass', 'don reo', 'september 25 , 2009', '101'], ['2', 'house rules / anniversary', 'ted wass', 'don reo', 'september 25 , 2009', '103'], ['3', 'mom at the bar / train buddy', 'ted wass', 'adrienne carter', 'october 2 , 2009', '106'], ['4', 'snoop / fat kid', 'ted wass', 'kevin rooney', 'october 9 , 2009', '107'], ['5', 'lenny', 'ted wass', 'don reo', 'october 11 , 2009', '102'], ['6', 'commercial / coach dmv', 'ted wass', 'don reo', 'october 18 , 2009', '108'], ['7', 'meet mike trainor / assistant coach', 'ted wass', 'alyson fouse', 'october 23 , 2009', '104'], ['8', "mike 's comeback", 'ted wass', 'adrienne carter', 'november 8 , 2009', '105'], ['9', 'week in chair', 'ted wass', 'jj wall', 'november 22 , 2009', '109'], ['10', 'snoop returns', 'ted wass', 'sassi darling', 'december 13 , 2009', '110'], ['11', 'christmas', 'ted wass', 'dean lorey', 'december 13 , 2009', '112'], ['12', 'girls , girls , girls', 'ted wass', 'don reo', 'december 27 , 2009', '113']] |
forbes global 2000 | https://en.wikipedia.org/wiki/Forbes_Global_2000 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1682026-3.html.csv | aggregation | as presented in the forbes global list in 2000 , companies in the oil and gas industry totaled 151.9 billion in profits . | {'scope': 'subset', 'col': '6', 'type': 'sum', 'result': '151.9', 'subset': {'col': '4', 'criterion': 'equal', 'value': 'oil and gas'}} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'industry', 'oil and gas'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; industry ; oil and gas }', 'tointer': 'select the rows whose industry record fuzzily matches to oil and gas .'}, 'profits ( billion )'], 'result': '151.9', 'ind': 1, 'tostr': 'sum { filter_eq { all_rows ; industry ; oil and gas } ; profits ( billion ) }'}, '151.9'], 'result': True, 'ind': 2, 'tostr': 'round_eq { sum { filter_eq { all_rows ; industry ; oil and gas } ; profits ( billion ) } ; 151.9 } = true', 'tointer': 'select the rows whose industry record fuzzily matches to oil and gas . the sum of the profits ( billion ) record of these rows is 151.9 .'} | round_eq { sum { filter_eq { all_rows ; industry ; oil and gas } ; profits ( billion ) } ; 151.9 } = true | select the rows whose industry record fuzzily matches to oil and gas . the sum of the profits ( billion ) record of these rows is 151.9 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'sum_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'industry_5': 5, 'oil and gas_6': 6, 'profits (billion )_7': 7, '151.9_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'sum_1': 'sum', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'industry_5': 'industry', 'oil and gas_6': 'oil and gas', 'profits (billion )_7': 'profits ( billion )', '151.9_8': '151.9'} | {'eq_2': [3], 'result_3': [], 'sum_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'industry_5': [0], 'oil and gas_6': [0], 'profits (billion )_7': [1], '151.9_8': [2]} | ['rank', 'company', 'headquarters', 'industry', 'sales ( billion )', 'profits ( billion )', 'assets ( billion )', 'market value ( billion )'] | [['1', 'jpmorgan chase', 'usa', 'banking', '115.5', '17.4', '2117.6', '182.2'], ['2', 'hsbc', 'uk', 'banking', '103.3', '13.3', '2467.9', '186.5'], ['3', 'general electric', 'usa', 'conglomerate', '156.2', '11.6', '751.2', '216.2'], ['4', 'exxonmobil', 'usa', 'oil and gas', '341.6', '30.5', '302.5', '407.2'], ['5', 'royal dutch shell', 'netherlands', 'oil and gas', '369.1', '20.1', '317.2', '212.9'], ['6', 'petrochina', 'china', 'oil and gas', '222.3', '21.2', '251.3', '320.8'], ['7', 'industrial and commercial bank of china', 'china', 'banking', '69.2', '18.8', '1723.5', '239.5'], ['8', 'berkshire hathaway', 'usa', 'conglomerate', '136.2', '13', '372.2', '211'], ['8', 'petrobras', 'brazil', 'oil and gas', '121.3', '21.2', '313.2', '238.8'], ['10', 'citigroup', 'usa', 'banking', '111.5', '10.6', '1913.9', '132.8'], ['11', 'bnp paribas', 'france', 'banking', '130.4', '10.5', '2680.7', '88'], ['11', 'wells fargo', 'usa', 'banking', '93.2', '12.4', '1258.1', '170.6'], ['13', 'santander group', 'spain', 'banking', '109.7', '12.8', '1570.6', '94.7'], ['14', 'at & t inc', 'usa', 'telecommunications', '124.3', '19.9', '268.5', '168.2'], ['15', 'gazprom', 'russia', 'oil and gas', '98.7', '25.7', '275.9', '172.9'], ['16', 'chevron', 'usa', 'oil and gas', '189.6', '19', '184.8', '200.6'], ['17', 'china construction bank', 'china', 'banking', '58.2', '15.6', '1408', '224.8'], ['18', 'walmart', 'usa', 'retailing', '421.8', '16.4', '180.7', '187.3'], ['19', 'total', 'france', 'oil and gas', '188.1', '14.2', '192.8', '138']] |
european parliament election , 1989 ( ireland ) | https://en.wikipedia.org/wiki/European_Parliament_election%2C_1989_%28Ireland%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13564557-2.html.csv | superlative | in the 1989 european parliament election in ireland the constituency munster had the highest turnout . | {'scope': 'all', 'col_superlative': '3', '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', 'turnout'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; turnout }'}, 'constituency'], 'result': 'munster', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; turnout } ; constituency }'}, 'munster'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; turnout } ; constituency } ; munster } = true', 'tointer': 'select the row whose turnout record of all rows is maximum . the constituency record of this row is munster .'} | eq { hop { argmax { all_rows ; turnout } ; constituency } ; munster } = true | select the row whose turnout record of all rows is maximum . the constituency record of this row is munster . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'turnout_5': 5, 'constituency_6': 6, 'munster_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'turnout_5': 'turnout', 'constituency_6': 'constituency', 'munster_7': 'munster'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'turnout_5': [0], 'constituency_6': [1], 'munster_7': [2]} | ['constituency', 'electorate', 'turnout', 'spoilt', 'valid poll', 'quota', 'seats', 'candidates'] | [['connachtulster', '464661', '322664 ( 69.4 % )', '10362 ( 3.2 % )', '312302', '78076', '3', '13'], ['dublin', '711416', '455539 ( 64.0 % )', '7137 ( 1.5 % )', '448402', '89681', '4', '11'], ['leinster', '571694', '391697 ( 68.5 % )', '14106 ( 3.6 % )', '377591', '94398', '3', '15'], ['munster', '703913', '505219 ( 71.7 % )', '10786 ( 2.2 % )', '494433', '82406', '5', '15'], ['total', '2451684', '1675119 ( 68.3 % )', '42391 ( 2.6 % )', '1632728', 'n / a', '15', '44']] |
list of palatine locomotives and railbuses | https://en.wikipedia.org/wiki/List_of_Palatine_locomotives_and_railbuses | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18843924-6.html.csv | aggregation | for the palatine locomotives and railbuses the total quantity was 10 . | {'scope': 'all', 'col': '3', 'type': 'sum', 'result': '10', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'quantity'], 'result': '10', 'ind': 0, 'tostr': 'sum { all_rows ; quantity }'}, '10'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; quantity } ; 10 } = true', 'tointer': 'the sum of the quantity record of all rows is 10 .'} | round_eq { sum { all_rows ; quantity } ; 10 } = true | the sum of the quantity record of all rows is 10 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'quantity_4': 4, '10_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'quantity_4': 'quantity', '10_5': '10'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'quantity_4': [0], '10_5': [1]} | ['class', 'railway number ( s )', 'quantity', 'year ( s ) of manufacture', 'axle arrangement ( uic )'] | [['none', 'i - ii', '2', '1898', 'bo ′ 2 ′ g2t'], ['none', 'iii', '1', '1900', 'bo g2t'], ['mc', '3050 , 5130', '2', '1900', 'a1a g2t'], ['mbcc', '8856 - 8859', '4', '1900 - 1902', 'bo ′ 2 ′ g2t'], ['mbcl', 'i', '1', '1903', 'a1 n2v']] |
édouard roger - vasselin | https://en.wikipedia.org/wiki/%C3%89douard_Roger-Vasselin | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11511365-4.html.csv | count | édouard roger - vasselin partnered with igor sijsling for a total of two tournaments . | {'scope': 'all', 'criterion': 'equal', 'value': 'igor sijsling', 'result': '2', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'partner', 'igor sijsling'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose partner record fuzzily matches to igor sijsling .', 'tostr': 'filter_eq { all_rows ; partner ; igor sijsling }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; partner ; igor sijsling } }', 'tointer': 'select the rows whose partner record fuzzily matches to igor sijsling . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; partner ; igor sijsling } } ; 2 } = true', 'tointer': 'select the rows whose partner record fuzzily matches to igor sijsling . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; partner ; igor sijsling } } ; 2 } = true | select the rows whose partner record fuzzily matches to igor sijsling . the number of such rows is 2 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'partner_5': 5, 'igor sijsling_6': 6, '2_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'partner_5': 'partner', 'igor sijsling_6': 'igor sijsling', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'partner_5': [0], 'igor sijsling_6': [0], '2_7': [2]} | ['outcome', 'date', 'surface', 'partner', 'opponents', 'score'] | [['winner', '5 february 2012', 'hard ( i )', 'nicolas mahut', 'paul hanley jamie murray', '6 - 4 , 7 - 6 ( 7 - 4 )'], ['winner', '20 february 2012', 'hard ( i )', 'nicolas mahut', 'dustin brown jo - wilfried tsonga', '3 - 6 , 6 - 3 ,'], ['winner', '17 september 2012', 'hard ( i )', 'nicolas mahut', 'johan brunström frederik nielsen', '7 - 6 ( 7 - 3 ) , 6 - 4'], ['winner', '15 july 2013', 'grass', 'nicolas mahut', 'tim smyczek rhyne williams', '6 - 7 ( 4 - 7 ) , 6 - 2 ,'], ['runner - up', '20 july 2013', 'hard', 'igor sijsling', 'purav raja divij sharan', '6 - 7 ( 4 - 7 ) , 6 - 7 ( 3 - 7 )'], ['winner', '29 july 2013', 'hard', 'igor sijsling', 'colin fleming jonathan marray', '7 - 6 ( 8 - 6 ) , 6 - 3'], ['winner', '6 october 2013', 'hard', 'rohan bopanna', 'jamie murray john peers', '7 - 6 ( 7 - 5 ) , 6 - 4']] |
2010 - 11 new jersey nets season | https://en.wikipedia.org/wiki/2010%E2%80%9311_New_Jersey_Nets_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27700375-8.html.csv | unique | the january 8 game against milwaukee was the only time devin harris did not have the high assists performance for the new jersey nets . | {'scope': 'all', 'row': '4', 'col': '7', 'col_other': '2', 'criterion': 'not_equal', 'value': 'devin harris', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_not_eq', 'args': ['all_rows', 'high assists', 'devin harris'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose high assists record does not match to devin harris .', 'tostr': 'filter_not_eq { all_rows ; high assists ; devin harris }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_not_eq { all_rows ; high assists ; devin harris } }', 'tointer': 'select the rows whose high assists record does not match to devin harris . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_not_eq', 'args': ['all_rows', 'high assists', 'devin harris'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose high assists record does not match to devin harris .', 'tostr': 'filter_not_eq { all_rows ; high assists ; devin harris }'}, 'date'], 'result': 'january 8', 'ind': 2, 'tostr': 'hop { filter_not_eq { all_rows ; high assists ; devin harris } ; date }'}, 'january 8'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_not_eq { all_rows ; high assists ; devin harris } ; date } ; january 8 }', 'tointer': 'the date record of this unqiue row is january 8 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_not_eq { all_rows ; high assists ; devin harris } } ; eq { hop { filter_not_eq { all_rows ; high assists ; devin harris } ; date } ; january 8 } } = true', 'tointer': 'select the rows whose high assists record does not match to devin harris . there is only one such row in the table . the date record of this unqiue row is january 8 .'} | and { only { filter_not_eq { all_rows ; high assists ; devin harris } } ; eq { hop { filter_not_eq { all_rows ; high assists ; devin harris } ; date } ; january 8 } } = true | select the rows whose high assists record does not match to devin harris . there is only one such row in the table . the date record of this unqiue row is january 8 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_not_eq_0': 0, 'all_rows_6': 6, 'high assists_7': 7, 'devin harris_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'date_9': 9, 'january 8_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_not_eq_0': 'filter_str_not_eq', 'all_rows_6': 'all_rows', 'high assists_7': 'high assists', 'devin harris_8': 'devin harris', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'date_9': 'date', 'january 8_10': 'january 8'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_not_eq_0': [1, 2], 'all_rows_6': [0], 'high assists_7': [0], 'devin harris_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'date_9': [2], 'january 8_10': [3]} | ['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record'] | [['34', 'january 1', 'minnesota', 'l 88 - 103 ( ot )', 'sasha vujačić ( 22 )', 'kris humphries ( 14 )', 'devin harris ( 8 )', 'target center 12665', '9 - 25'], ['35', 'january 5', 'chicago', 'w 96 - 94 ( ot )', 'kris humphries ( 20 )', 'kris humphries ( 11 )', 'devin harris ( 11 )', 'prudential center 15025', '10 - 25'], ['36', 'january 7', 'washington', 'l 77 - 97 ( ot )', 'jordan farmar , brook lopez ( 14 )', 'stephen graham ( 9 )', 'devin harris ( 3 )', 'verizon center 16017', '10 - 26'], ['37', 'january 8', 'milwaukee', 'l 92 - 115 ( ot )', 'kris humphries ( 22 )', 'kris humphries ( 8 )', 'jordan farmar ( 10 )', 'prudential center 12898', '10 - 27'], ['38', 'january 12', 'phoenix', 'l 109 - 118 ( ot )', 'sasha vujačić ( 19 )', 'travis outlaw ( 11 )', 'devin harris ( 15 )', 'us airways center 16334', '10 - 28'], ['39', 'january 14', 'la lakers', 'l 88 - 100 ( ot )', 'brook lopez ( 35 )', 'kris humphries ( 15 )', 'devin harris ( 8 )', 'staples center 18997', '10 - 29'], ['40', 'january 15', 'portland', 'l 89 - 96 ( ot )', 'brook lopez ( 32 )', 'kris humphries ( 10 )', 'devin harris ( 6 )', 'rose garden 20441', '10 - 30'], ['41', 'january 17', 'golden state', 'l 100 - 109 ( ot )', 'brook lopez ( 20 )', 'kris humphries ( 10 )', 'devin harris ( 8 )', 'oracle arena 18563', '10 - 31'], ['42', 'january 19', 'utah', 'w 103 - 95 ( ot )', 'brook lopez ( 20 )', 'travis outlaw ( 8 )', 'devin harris ( 8 )', 'prudential center 13251', '11 - 31'], ['43', 'january 21', 'detroit', 'w 89 - 74 ( ot )', 'brook lopez ( 15 )', 'kris humphries ( 12 )', 'devin harris ( 9 )', 'prudential center 13316', '12 - 31'], ['44', 'january 22', 'dallas', 'l 86 - 87 ( ot )', 'brook lopez ( 24 )', 'kris humphries ( 15 )', 'devin harris ( 11 )', 'prudential center 14051', '12 - 32'], ['45', 'january 24', 'cleveland', 'w 103 - 101 ( ot )', 'brook lopez ( 28 )', 'kris humphries ( 11 )', 'devin harris ( 10 )', 'prudential center 10197', '13 - 32'], ['46', 'january 26', 'memphis', 'w 93 - 88 ( ot )', 'anthony morrow ( 19 )', 'derrick favors ( 9 )', 'devin harris ( 9 )', 'prudential center 8866', '14 - 32'], ['47', 'january 28', 'indiana', 'l 92 - 124 ( ot )', 'brook lopez ( 28 )', 'travis outlaw ( 6 )', 'devin harris ( 9 )', 'conseco fieldhouse 11337', '14 - 33'], ['48', 'january 29', 'milwaukee', 'l 81 - 91 ( ot )', 'brook lopez ( 26 )', 'kris humphries ( 10 )', 'devin harris ( 16 )', 'bradley center 17173', '14 - 34']] |
fox television stations | https://en.wikipedia.org/wiki/Fox_Television_Stations | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1353096-2.html.csv | count | a total of four fox television stations were owned for the period of 1995 - 2008 . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': '1995 - 2008', 'result': '4', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'years owned', '1995 - 2008'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose years owned record fuzzily matches to 1995 - 2008 .', 'tostr': 'filter_eq { all_rows ; years owned ; 1995 - 2008 }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; years owned ; 1995 - 2008 } }', 'tointer': 'select the rows whose years owned record fuzzily matches to 1995 - 2008 . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; years owned ; 1995 - 2008 } } ; 4 } = true', 'tointer': 'select the rows whose years owned record fuzzily matches to 1995 - 2008 . the number of such rows is 4 .'} | eq { count { filter_eq { all_rows ; years owned ; 1995 - 2008 } } ; 4 } = true | select the rows whose years owned record fuzzily matches to 1995 - 2008 . 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, 'years owned_5': 5, '1995 - 2008_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', 'years owned_5': 'years owned', '1995 - 2008_6': '1995 - 2008', '4_7': '4'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'years owned_5': [0], '1995 - 2008_6': [0], '4_7': [2]} | ['city of license / market', 'station', 'channel tv ( dt )', 'years owned', 'current status'] | [['birmingham - tuscaloosa - anniston', 'wbrc - tv', '6 ( 50 )', '1995 - 2008', 'fox affiliate owned by raycom media'], ['san francisco - oakland - san jose', 'kbhk - tv ¤ ¤ ( now kbcw )', '44 ( 45 )', '2001 - 2002', 'cw affiliate owned by cbs corporation'], ['denver', 'kdvr', '31 ( 32 )', '1995 - 2008', 'fox affiliate owned by local tv'], ['fort collins , colorado', 'kfct ( satellite of kdvr )', '22 ( 21 )', '1995 - 2008', 'fox affiliate owned by local tv'], ['atlanta', 'watl - tv', '36 ( 25 )', '1993 - 1995', 'mynetworktv affiliate owned by gannett company'], ['boston', 'wcvb - tv 1', '5 ( 20 )', '1986', 'abc affiliate owned by hearst television'], ['kansas city , missouri', 'wdaf - tv + +', '4 ( 34 )', '1997 - 2008', 'fox affiliate owned by local tv'], ['saint louis', 'ktvi + +', '2 ( 43 )', '1997 - 2008', 'fox affiliate owned by local tv'], ['high point - greensboro - winston - salem', 'wghp', '8 ( 35 )', '1995 - 2008', 'fox affiliate owned by local tv'], ['cleveland - akron', 'wjw - tv + +', '8 ( 8 )', '1997 - 2008', 'fox affiliate owned by local tv'], ['portland , oregon', 'kptv ¤ ¤', '12 ( 12 )', '2001 - 2002', 'fox affiliate owned by meredith corporation'], ['dallas - fort worth', 'kdaf', '33 ( 32 )', '1986 - 1995', 'cw affiliate owned by tribune broadcasting'], ['san antonio', 'kmol - tv ¤ ¤ ( now woai - tv )', '4 ( 48 )', '2001', 'nbc affiliate owned by sinclair broadcast group'], ['salt lake city', 'kstu', '13 ( 28 )', '1990 - 2008', 'fox affiliate owned by local tv'], ['salt lake city', 'ktvx ¤ ¤', '4 ( 40 )', '2001', 'abc affiliate owned by nexstar broadcasting group']] |
rowing at the 2008 summer olympics - men 's single sculls | https://en.wikipedia.org/wiki/Rowing_at_the_2008_Summer_Olympics_%E2%80%93_Men%27s_single_sculls | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18662643-10.html.csv | ordinal | ondřej synek of the czech republic finished with the fastest time in the men 's single sculls rowing event at the 2008 summer olympics . | {'row': '1', 'col': '4', 'order': '1', 'col_other': '1', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'time', '1'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; time ; 1 }'}, 'rank'], 'result': '1', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; time ; 1 } ; rank }'}, '1'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; time ; 1 } ; rank } ; 1 } = true', 'tointer': 'select the row whose time record of all rows is 1st minimum . the rank record of this row is 1 .'} | eq { hop { nth_argmin { all_rows ; time ; 1 } ; rank } ; 1 } = true | select the row whose time record of all rows is 1st minimum . the rank record of this row is 1 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'num_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'time_5': 5, '1_6': 6, 'rank_7': 7, '1_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'num_hop_1': 'num_hop', 'nth_argmin_0': 'nth_argmin', 'all_rows_4': 'all_rows', 'time_5': 'time', '1_6': '1', 'rank_7': 'rank', '1_8': '1'} | {'eq_2': [3], 'result_3': [], 'num_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'time_5': [0], '1_6': [0], 'rank_7': [1], '1_8': [2]} | ['rank', 'athlete', 'country', 'time', 'notes'] | [['1', 'ondřej synek', 'czech republic', '6:50.23', 'sa / b'], ['2', 'tim maeyens', 'belgium', '6:52.70', 'sa / b'], ['3', 'peter hardcastle', 'australia', '7:00.09', 'sa / b'], ['4', 'andrei jämsä', 'estonia', '7:05.48', 'sc / d'], ['5', 'wang ming - hui', 'chinese taipei', '7:17.08', 'sc / d'], ['6', 'law hiu fung', 'hong kong', '7:29.21', 'sc / d']] |
1972 england rugby union tour of south africa | https://en.wikipedia.org/wiki/1972_England_rugby_union_tour_of_South_Africa | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17020783-1.html.csv | superlative | the highest against in the 1972 england rugby union tour of south africa , was when the opposing team was giqualand west . | {'scope': 'all', 'col_superlative': '2', 'row_superlative': '6', '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', 'against'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; against }'}, 'opposing team'], 'result': 'giqualand west', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; against } ; opposing team }'}, 'giqualand west'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; against } ; opposing team } ; giqualand west } = true', 'tointer': 'select the row whose against record of all rows is maximum . the opposing team record of this row is giqualand west .'} | eq { hop { argmax { all_rows ; against } ; opposing team } ; giqualand west } = true | select the row whose against record of all rows is maximum . the opposing team record of this row is giqualand west . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'against_5': 5, 'opposing team_6': 6, 'giqualand west_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'against_5': 'against', 'opposing team_6': 'opposing team', 'giqualand west_7': 'giqualand west'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'against_5': [0], 'opposing team_6': [1], 'giqualand west_7': [2]} | ['opposing team', 'against', 'date', 'venue', 'status'] | [['natal', '0', 'may 17 , 1972', 'durban', 'tour match'], ['western province', '6', 'may 20 , 1972', 'cape town', 'tour match'], ['sa rugby fed xv', '6', 'may 22 , 1972', 'cape town', 'tour match'], ['sa leopards', '3', 'may 24 , 1972', 'port elizabeth', 'tour match'], ['northern transvaal', '13', 'may 27 , 1972', 'pretoria', 'tour match'], ['giqualand west', '21', 'may 30 , 1972', 'kimberley', 'tour match'], ['south africa', '9', 'june 3 , 1972', 'ellis park , johannesburg', 'test match']] |
1989 senior pga tour | https://en.wikipedia.org/wiki/1989_Senior_PGA_Tour | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11622496-4.html.csv | aggregation | the average number of wins of players in the 1989 senior pga tour is 15 . | {'scope': 'all', 'col': '5', 'type': 'average', 'result': '15', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'wins'], 'result': '15', 'ind': 0, 'tostr': 'avg { all_rows ; wins }'}, '15'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; wins } ; 15 } = true', 'tointer': 'the average of the wins record of all rows is 15 .'} | round_eq { avg { all_rows ; wins } ; 15 } = true | the average of the wins record of all rows is 15 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'wins_4': 4, '15_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'wins_4': 'wins', '15_5': '15'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'wins_4': [0], '15_5': [1]} | ['rank', 'player', 'country', 'earnings', 'wins'] | [['1', 'miller barber', 'united states', '2214603', '24'], ['2', 'bob charles', 'new zealand', '1910413', '13'], ['3', 'orville moody', 'united states', '1862956', '9'], ['4', 'bruce crampton', 'australia', '1682961', '15'], ['5', 'gary player', 'south africa', '1604659', '14']] |
all - time saint louis athletica roster | https://en.wikipedia.org/wiki/All-time_Saint_Louis_Athletica_roster | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-23963781-3.html.csv | comparative | niki cross played 60 minutes less than amanda cinalli in the all-time saint louis athletica roster . | {'row_1': '3', 'row_2': '2', 'col': '6', 'col_other': '1', 'relation': 'diff', 'record_mentioned': 'no', 'diff_result': {'diff_value': '60', 'bigger': 'row2'}} | {'func': 'eq', 'args': [{'func': 'diff', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name', 'niki cross'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose name record fuzzily matches to niki cross .', 'tostr': 'filter_eq { all_rows ; name ; niki cross }'}, 'minutes'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; name ; niki cross } ; minutes }', 'tointer': 'select the rows whose name record fuzzily matches to niki cross . take the minutes record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name', 'amanda cinalli'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose name record fuzzily matches to amanda cinalli .', 'tostr': 'filter_eq { all_rows ; name ; amanda cinalli }'}, 'minutes'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; name ; amanda cinalli } ; minutes }', 'tointer': 'select the rows whose name record fuzzily matches to amanda cinalli . take the minutes record of this row .'}], 'result': '-60', 'ind': 4, 'tostr': 'diff { hop { filter_eq { all_rows ; name ; niki cross } ; minutes } ; hop { filter_eq { all_rows ; name ; amanda cinalli } ; minutes } }'}, '-60'], 'result': True, 'ind': 5, 'tostr': 'eq { diff { hop { filter_eq { all_rows ; name ; niki cross } ; minutes } ; hop { filter_eq { all_rows ; name ; amanda cinalli } ; minutes } } ; -60 } = true', 'tointer': 'select the rows whose name record fuzzily matches to niki cross . take the minutes record of this row . select the rows whose name record fuzzily matches to amanda cinalli . take the minutes record of this row . the second record is 60 larger than the first record .'} | eq { diff { hop { filter_eq { all_rows ; name ; niki cross } ; minutes } ; hop { filter_eq { all_rows ; name ; amanda cinalli } ; minutes } } ; -60 } = true | select the rows whose name record fuzzily matches to niki cross . take the minutes record of this row . select the rows whose name record fuzzily matches to amanda cinalli . take the minutes record of this row . the second record is 60 larger than the first record . | 6 | 6 | {'eq_5': 5, 'result_6': 6, 'diff_4': 4, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'name_8': 8, 'niki cross_9': 9, 'minutes_10': 10, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_11': 11, 'name_12': 12, 'amanda cinalli_13': 13, 'minutes_14': 14, '-60_15': 15} | {'eq_5': 'eq', 'result_6': 'true', 'diff_4': 'diff', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'name_8': 'name', 'niki cross_9': 'niki cross', 'minutes_10': 'minutes', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_11': 'all_rows', 'name_12': 'name', 'amanda cinalli_13': 'amanda cinalli', 'minutes_14': 'minutes', '-60_15': '-60'} | {'eq_5': [6], 'result_6': [], 'diff_4': [5], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_7': [0], 'name_8': [0], 'niki cross_9': [0], 'minutes_10': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_11': [1], 'name_12': [1], 'amanda cinalli_13': [1], 'minutes_14': [3], '-60_15': [5]} | ['name', 'nationality', 'position', 'appearances', 'starts', 'minutes', 'goals'] | [['lori chalupny', 'united states', 'mf', '1', '1', '90', '0'], ['amanda cinalli', 'united states', 'fw', '1', '1', '90', '0'], ['niki cross', 'united states', 'df', '1', '0', '30', '0'], ['tina ellertson', 'united states', 'df', '1', '1', '90', '0'], ['kendall fletcher', 'united states', 'df', '1', '1', '90', '0'], ['stephanie logterman', 'united states', 'df', '1', '0', '26', '0'], ['kia mcneill', 'united states', 'df', '1', '1', '64', '0'], ['ashlee pistorius', 'united states', 'fw', '1', '0', '19', '0'], ['hope solo', 'united states', 'gk', '1', '1', '90', '0'], ['melissa tancredi', 'canada', 'fw', '1', '1', '90', '0'], ['sarah walsh', 'australia', 'fw', '1', '1', '90', '0'], ['elise weber', 'united states', 'df', '1', '1', '90', '0'], ['christie welsh', 'united states', 'fw', '1', '1', '71', '0']] |
1969 - 70 phoenix suns season | https://en.wikipedia.org/wiki/1969%E2%80%9370_Phoenix_Suns_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-30049462-4.html.csv | majority | the majority of game results were losses for the suns in the 1969 - 70 phoenix suns season . | {'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'fuzzily_match', 'value': 'l', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'score', 'l'], 'result': True, 'ind': 0, 'tointer': 'for the score records of all rows , most of them fuzzily match to l .', 'tostr': 'most_eq { all_rows ; score ; l } = true'} | most_eq { all_rows ; score ; l } = true | for the score records of all rows , most of them fuzzily match to l . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'score_3': 3, 'l_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'score_3': 'score', 'l_4': 'l'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'score_3': [0], 'l_4': [0]} | ['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record'] | [['9', 'november 4', 'new york', 'l 99 - 116', 'connie hawkins ( 39 )', 'paul silas ( 9 )', 'gail goodrich , connie hawkins ( 4 )', 'arizona veterans memorial coliseum 10552', '3 - 6'], ['10', 'november 7', 'los angeles', 'w 122 - 120', 'gail goodrich ( 37 )', 'paul silas ( 14 )', 'dick van arsdale ( 7 )', 'the forum 10557', '4 - 6'], ['11', 'november 9', 'detroit', 'w 140 - 129', 'connie hawkins ( 35 )', 'paul silas ( 14 )', 'gail goodrich ( 12 )', 'arizona veterans memorial coliseum 9500', '5 - 6'], ['12', 'november 13', 'philadelphia', 'l 110 - 124', 'dick van arsdale ( 30 )', 'jim fox ( 14 )', 'gail goodrich ( 7 )', 'arizona veterans memorial coliseum 5440', '5 - 7'], ['13', 'november 14', 'los angeles', 'l 112 - 127', 'jim fox ( 39 )', 'jim fox ( 23 )', 'connie hawkins ( 7 )', 'the forum 8902', '5 - 8'], ['14', 'november 15', 'los angeles', 'w 114 - 111', 'gail goodrich ( 31 )', 'connie hawkins ( 18 )', 'gail goodrich ( 6 )', 'arizona veterans memorial coliseum 6318', '6 - 8'], ['15', 'november 16', 'atlanta', 'w 139 - 118', 'connie hawkins ( 29 )', 'paul silas ( 15 )', 'gail goodrich , connie hawkins ( 8 )', 'university arena 5094', '7 - 8'], ['16', 'november 18', 'boston', 'l 119 - 120 ( ot )', 'dick van arsdale ( 32 )', 'connie hawkins ( 17 )', 'connie hawkins , dick van arsdale ( 6 )', 'baltimore civic center 6028', '7 - 9'], ['17', 'november 19', 'baltimore', 'l 118 - 133', 'gail goodrich ( 29 )', 'lamar green ( 16 )', 'dick van arsdale ( 11 )', 'the spectrum 9287', '7 - 10'], ['19', 'november 22', 'new york', 'l 114 - 128', 'dick van arsdale ( 24 )', 'paul silas ( 10 )', 'gail goodrich ( 12 )', 'madison square garden 19401', '7 - 12'], ['20', 'november 23', 'cincinnati', 'l 123 - 137', 'jerry chambers ( 31 )', 'connie hawkins ( 9 )', 'gail goodrich ( 9 )', 'cincinnati gardens 2866', '7 - 13'], ['21', 'november 25', 'baltimore', 'l 124 - 134', 'dick van arsdale ( 30 )', 'connie hawkins ( 16 )', 'gail goodrich ( 10 )', 'arizona veterans memorial coliseum 5776', '7 - 14'], ['22', 'november 29', 'seattle', 'l 129 - 130', 'jim fox ( 31 )', 'jim fox ( 13 )', 'dick van arsdale ( 9 )', 'seattle center coliseum 9418', '7 - 15']] |
list of schools in the wellington region | https://en.wikipedia.org/wiki/List_of_schools_in_the_Wellington_Region | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12214488-8.html.csv | unique | st teresa 's school is the only one among those with years 1-8 that has integrated authority in the list of schools in the wellington region . | {'scope': 'subset', 'row': '8', 'col': '5', 'col_other': '1', 'criterion': 'equal', 'value': 'integrated', 'subset': {'col': '2', 'criterion': 'equal', 'value': '1 - 8'}} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'years', '1 - 8'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; years ; 1 - 8 }', 'tointer': 'select the rows whose years record fuzzily matches to 1 - 8 .'}, 'authority', 'integrated'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose years record fuzzily matches to 1 - 8 . among these rows , select the rows whose authority record fuzzily matches to integrated .', 'tostr': 'filter_eq { filter_eq { all_rows ; years ; 1 - 8 } ; authority ; integrated }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_eq { filter_eq { all_rows ; years ; 1 - 8 } ; authority ; integrated } }', 'tointer': 'select the rows whose years record fuzzily matches to 1 - 8 . among these rows , select the rows whose authority record fuzzily matches to integrated . 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', 'years', '1 - 8'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; years ; 1 - 8 }', 'tointer': 'select the rows whose years record fuzzily matches to 1 - 8 .'}, 'authority', 'integrated'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose years record fuzzily matches to 1 - 8 . among these rows , select the rows whose authority record fuzzily matches to integrated .', 'tostr': 'filter_eq { filter_eq { all_rows ; years ; 1 - 8 } ; authority ; integrated }'}, 'name'], 'result': "st teresa 's school", 'ind': 3, 'tostr': 'hop { filter_eq { filter_eq { all_rows ; years ; 1 - 8 } ; authority ; integrated } ; name }'}, "st teresa 's school"], 'result': True, 'ind': 4, 'tostr': "eq { hop { filter_eq { filter_eq { all_rows ; years ; 1 - 8 } ; authority ; integrated } ; name } ; st teresa 's school }", 'tointer': "the name record of this unqiue row is st teresa 's school ."}], 'result': True, 'ind': 5, 'tostr': "and { only { filter_eq { filter_eq { all_rows ; years ; 1 - 8 } ; authority ; integrated } } ; eq { hop { filter_eq { filter_eq { all_rows ; years ; 1 - 8 } ; authority ; integrated } ; name } ; st teresa 's school } } = true", 'tointer': "select the rows whose years record fuzzily matches to 1 - 8 . among these rows , select the rows whose authority record fuzzily matches to integrated . there is only one such row in the table . the name record of this unqiue row is st teresa 's school ."} | and { only { filter_eq { filter_eq { all_rows ; years ; 1 - 8 } ; authority ; integrated } } ; eq { hop { filter_eq { filter_eq { all_rows ; years ; 1 - 8 } ; authority ; integrated } ; name } ; st teresa 's school } } = true | select the rows whose years record fuzzily matches to 1 - 8 . among these rows , select the rows whose authority record fuzzily matches to integrated . there is only one such row in the table . the name record of this unqiue row is st teresa 's school . | 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, 'years_8': 8, '1 - 8_9': 9, 'authority_10': 10, 'integrated_11': 11, 'str_eq_4': 4, 'str_hop_3': 3, 'name_12': 12, "st teresa 's school_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', 'years_8': 'years', '1 - 8_9': '1 - 8', 'authority_10': 'authority', 'integrated_11': 'integrated', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'name_12': 'name', "st teresa 's school_13": "st teresa 's school"} | {'and_5': [6], 'result_6': [], 'only_2': [5], 'filter_str_eq_1': [2, 3], 'filter_str_eq_0': [1], 'all_rows_7': [0], 'years_8': [0], '1 - 8_9': [0], 'authority_10': [1], 'integrated_11': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'name_12': [3], "st teresa 's school_13": [4]} | ['name', 'years', 'gender', 'area', 'authority', 'decile', 'roll'] | [['featherston school', '1 - 8', 'coed', 'featherston', 'state', '3', '64'], ['greytown school', '1 - 8', 'coed', 'greytown', 'state', '6', '348'], ['kahutara school', '1 - 8', 'coed', 'kahutara', 'state', '7', '100'], ['kuranui college', '9 - 13', 'coed', 'greytown', 'state', '5', '488'], ['martinborough school', '1 - 8', 'coed', 'martinborough', 'state', '7', '251'], ['pirinoa school', '1 - 8', 'coed', 'pirinoa', 'state', '6', '26'], ['south featherston school', '1 - 8', 'coed', 'featherston', 'state', '5', '65'], ["st teresa 's school", '1 - 8', 'coed', 'featherston', 'integrated', '7', '111'], ['tuturumuri school', '1 - 8', 'coed', 'martinborough', 'state', '7', '17']] |
1929 vfl season | https://en.wikipedia.org/wiki/1929_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10767118-10.html.csv | superlative | south melbourne had the highest scoring game in the 1929 vfl season . | {'scope': 'all', 'col_superlative': '4', 'row_superlative': '3', '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', 'away team score'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; away team score }'}, 'away team'], 'result': 'south melbourne', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; away team score } ; away team }'}, 'south melbourne'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; away team score } ; away team } ; south melbourne } = true', 'tointer': 'select the row whose away team score record of all rows is maximum . the away team record of this row is south melbourne .'} | eq { hop { argmax { all_rows ; away team score } ; away team } ; south melbourne } = true | select the row whose away team score record of all rows is maximum . the away team record of this row is south melbourne . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'away team score_5': 5, 'away team_6': 6, 'south melbourne_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'away team score_5': 'away team score', 'away team_6': 'away team', 'south melbourne_7': 'south melbourne'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'away team score_5': [0], 'away team_6': [1], 'south melbourne_7': [2]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['melbourne', '10.8 ( 68 )', 'richmond', '9.11 ( 65 )', 'mcg', '18048', '6 july 1929'], ['footscray', '9.13 ( 67 )', 'geelong', '9.5 ( 59 )', 'western oval', '9000', '6 july 1929'], ['fitzroy', '12.14 ( 86 )', 'south melbourne', '17.8 ( 110 )', 'brunswick street oval', '6000', '6 july 1929'], ['north melbourne', '8.6 ( 54 )', 'hawthorn', '8.18 ( 66 )', 'arden street oval', '4500', '6 july 1929'], ['st kilda', '9.9 ( 63 )', 'essendon', '7.6 ( 48 )', 'junction oval', '12500', '6 july 1929'], ['collingwood', '15.15 ( 105 )', 'carlton', '11.10 ( 76 )', 'victoria park', '33000', '6 july 1929']] |
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 | count | there are seven different departments in the country of bolivia . | {'scope': 'all', 'criterion': 'all', 'value': 'n/a', 'result': '7', 'col': '1', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_all', 'args': ['all_rows', 'department'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose department record is arbitrary .', 'tostr': 'filter_all { all_rows ; department }'}], 'result': '7', 'ind': 1, 'tostr': 'count { filter_all { all_rows ; department } }', 'tointer': 'select the rows whose department record is arbitrary . the number of such rows is 7 .'}, '7'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_all { all_rows ; department } } ; 7 } = true', 'tointer': 'select the rows whose department record is arbitrary . the number of such rows is 7 .'} | eq { count { filter_all { all_rows ; department } } ; 7 } = true | select the rows whose department record is arbitrary . the number of such rows is 7 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_all_0': 0, 'all_rows_4': 4, 'department_5': 5, '7_6': 6} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_all_0': 'filter_all', 'all_rows_4': 'all_rows', 'department_5': 'department', '7_6': '7'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_all_0': [1], 'all_rows_4': [0], 'department_5': [0], '7_6': [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']] |
fairfax connector | https://en.wikipedia.org/wiki/Fairfax_Connector | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1816795-1.html.csv | unique | only in the period from 2011 to 2013 are there 2 different sizes of fairfax connector buses concurrently . | {'scope': 'all', 'row': '11', 'col': '4', 'col_other': '1', 'criterion': 'fuzzily_match', 'value': '40 / 35', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'length ( ft )', '40 / 35'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose length ( ft ) record fuzzily matches to 40 / 35 .', 'tostr': 'filter_eq { all_rows ; length ( ft ) ; 40 / 35 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; length ( ft ) ; 40 / 35 } }', 'tointer': 'select the rows whose length ( ft ) record fuzzily matches to 40 / 35 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'length ( ft )', '40 / 35'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose length ( ft ) record fuzzily matches to 40 / 35 .', 'tostr': 'filter_eq { all_rows ; length ( ft ) ; 40 / 35 }'}, 'order year'], 'result': '20112013', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; length ( ft ) ; 40 / 35 } ; order year }'}, '20112013'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; length ( ft ) ; 40 / 35 } ; order year } ; 20112013 }', 'tointer': 'the order year record of this unqiue row is 20112013 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; length ( ft ) ; 40 / 35 } } ; eq { hop { filter_eq { all_rows ; length ( ft ) ; 40 / 35 } ; order year } ; 20112013 } } = true', 'tointer': 'select the rows whose length ( ft ) record fuzzily matches to 40 / 35 . there is only one such row in the table . the order year record of this unqiue row is 20112013 .'} | and { only { filter_eq { all_rows ; length ( ft ) ; 40 / 35 } } ; eq { hop { filter_eq { all_rows ; length ( ft ) ; 40 / 35 } ; order year } ; 20112013 } } = true | select the rows whose length ( ft ) record fuzzily matches to 40 / 35 . there is only one such row in the table . the order year record of this unqiue row is 20112013 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'length (ft)_7': 7, '40 / 35_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'order year_9': 9, '20112013_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'length (ft)_7': 'length ( ft )', '40 / 35_8': '40 / 35', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'order year_9': 'order year', '20112013_10': '20112013'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'length (ft)_7': [0], '40 / 35_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'order year_9': [2], '20112013_10': [3]} | ['order year', 'builder', 'model', 'length ( ft )', 'engine / transmission'] | [['1998', 'obi', '05.503', '30', 'dds 50 / allison wb - 400r6'], ['1999', 'obi', '05.505', '30', 'dds 50 / allison wb - 400r6'], ['2000', 'obi', '05.501', '40', 'dds 50 / allison wb - 400r6'], ['2001', 'obi', '05.505', '30', 'dds 50 / allison wb - 400r6'], ['2001 - 02', 'obi', '05.502', '35', 'dds 50 / allison wb - 400r6'], ['2007', 'nfi', 'd40lfr', '40', 'cummins isl / allison b400r'], ['2007', 'nfi', 'd35lfr', '35', 'cummins isl / allison b400r'], ['2009', 'nfi', 'd40lfr', '40', 'cummins isl / allison b400r'], ['2009', 'obi', '07.502 ng', '30', 'cummins isl / allison b400r'], ['2010', 'nfi', 'd40lfr', '40', 'cummins isl / allison b400r'], ['20112013', 'nfi', 'xd40 / xd35', '40 / 35', 'cummins isl9 / allison b400r']] |
economy of europe | https://en.wikipedia.org/wiki/Economy_of_Europe | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1069072-1.html.csv | majority | most of the cities in europe are in areas that use the euro , or are in the eurozone . | {'scope': 'all', 'col': '7', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'y', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'eurozone', 'y'], 'result': True, 'ind': 0, 'tointer': 'for the eurozone records of all rows , most of them fuzzily match to y .', 'tostr': 'most_eq { all_rows ; eurozone ; y } = true'} | most_eq { all_rows ; eurozone ; y } = true | for the eurozone records of all rows , most of them fuzzily match to y . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'eurozone_3': 3, 'y_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'eurozone_3': 'eurozone', 'y_4': 'y'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'eurozone_3': [0], 'y_4': [0]} | ['rank', 'city', 'state', 'gdp in id b', 'population m ( luz )', 'gdp per capita id k', 'eurozone'] | [['1', 'paris', 'france', '731', '11.5', '62.4', 'y'], ['2', 'london', 'united kingdom', '565', '11.9', '49.4', 'n'], ['3', 'moscow', 'russia', '321', '10.5', '30.6', 'n'], ['4', 'madrid', 'spain', '230', '5.80', '39.7', 'y'], ['5', 'istanbul', 'turkey', '187', '13.2', '14.2', 'n'], ['6', 'barcelona', 'spain', '177', '4.97', '35.6', 'y'], ['7', 'rome', 'italy', '144', '3.46', '41.6', 'y'], ['8', 'milan', 'italy', '136', '3.08', '44.2', 'y'], ['9', 'vienna', 'austria', '122', '2.18', '56.0', 'y'], ['10', 'lisbon', 'portugal', '98', '2.44', '40.2', 'y'], ['11', 'athens', 'greece', '96', '4.01', '23.9', 'y'], ['12', 'berlin', 'germany', '95', '4.97', '19.1', 'y']] |
outback ( region ) | https://en.wikipedia.org/wiki/Outback_%28region%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-23685890-2.html.csv | superlative | outback areas community development trust was established earlier than any other local government area in the outback region . | {'scope': 'all', 'col_superlative': '8', 'row_superlative': '7', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '1', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'est'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; est }'}, 'local government area'], 'result': 'outback areas community development trust', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; est } ; local government area }'}, 'outback areas community development trust'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; est } ; local government area } ; outback areas community development trust } = true', 'tointer': 'select the row whose est record of all rows is minimum . the local government area record of this row is outback areas community development trust .'} | eq { hop { argmin { all_rows ; est } ; local government area } ; outback areas community development trust } = true | select the row whose est record of all rows is minimum . the local government area record of this row is outback areas community development trust . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'est_5': 5, 'local government area_6': 6, 'outback areas community development trust_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'est_5': 'est', 'local government area_6': 'local government area', 'outback areas community development trust_7': 'outback areas community development trust'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'est_5': [0], 'local government area_6': [1], 'outback areas community development trust_7': [2]} | ['local government area', 'type', 'major town', 'land area ( km square )', 'pop 2006', 'density km 2', 'towns', 'est'] | [['roxby downs', 'municipal council', 'roxby downs', '110', '4292', '39018', '2', '1982'], ['coober pedy', 'district council', 'coober pedy', '77 , 8', '1996', '25656', '1', '1987'], ['anangu pitjantjatjara yankunytjatjara', 'aboriginal council', 'umuwa', '102650', '2204', '21', '18', '1981'], ['maralinga tjarutja 1 )', 'aboriginal council', 'oak valley', '102863 , 6', '105', '1', '1', '1984'], ['yalata', 'aboriginal council', 'yalata', '4563', '100', '22', '1', '1994'], ['nepabunna', 'aboriginal council', 'nepabunna , south australia', '76 , 4', '49', '641', '1', '1998'], ['outback areas community development trust', 'unincorporated area', 'leigh creek', '624339.0', '3750', '6', '36', '1978']] |
89th united states congress | https://en.wikipedia.org/wiki/89th_United_States_Congress | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1847180-4.html.csv | count | of all the members of the 89th united states congress , 9 resigned on december 30 , 1966 . | {'scope': 'all', 'criterion': 'equal', 'value': 'resigned december 30 , 1966', 'result': '9', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'reason for change', 'resigned december 30 , 1966'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose reason for change record fuzzily matches to resigned december 30 , 1966 .', 'tostr': 'filter_eq { all_rows ; reason for change ; resigned december 30 , 1966 }'}], 'result': '9', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; reason for change ; resigned december 30 , 1966 } }', 'tointer': 'select the rows whose reason for change record fuzzily matches to resigned december 30 , 1966 . the number of such rows is 9 .'}, '9'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; reason for change ; resigned december 30 , 1966 } } ; 9 } = true', 'tointer': 'select the rows whose reason for change record fuzzily matches to resigned december 30 , 1966 . the number of such rows is 9 .'} | eq { count { filter_eq { all_rows ; reason for change ; resigned december 30 , 1966 } } ; 9 } = true | select the rows whose reason for change record fuzzily matches to resigned december 30 , 1966 . the number of such rows is 9 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'reason for change_5': 5, 'resigned december 30 , 1966_6': 6, '9_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'reason for change_5': 'reason for change', 'resigned december 30 , 1966_6': 'resigned december 30 , 1966', '9_7': '9'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'reason for change_5': [0], 'resigned december 30 , 1966_6': [0], '9_7': [2]} | ['district', 'vacator', 'reason for change', 'successor', 'date successor seated'] | [['louisiana 7th', 't ashton thompson ( d )', 'died july 1 , 1965', 'edwin edwards ( d )', 'october 2 , 1965'], ['ohio 7th', 'clarence j brown ( r )', 'died august 23 , 1965', 'bud brown ( r )', 'november 2 , 1965'], ['north carolina 1st', 'herbert c bonner ( d )', 'died november 7 , 1965', 'walter b jones , sr ( d )', 'february 5 , 1966'], ['texas 8th', 'albert r thomas ( d )', 'died february 15 , 1966', 'lera m thomas ( d )', 'march 26 , 1966'], ['california 14th', 'john f baldwin , jr ( r )', 'died march 9 , 1966', 'jerome r waldie ( d )', 'june 7 , 1966'], ['alaska at - large', 'ralph j rivers ( d )', 'resigned december 30 , 1966', 'vacant', 'not filled this term'], ['indiana 8th', 'winfield k denton ( d )', 'resigned december 30 , 1966', 'vacant', 'not filled this term'], ['indiana 10th', 'ralph harvey ( r )', 'resigned december 30 , 1966', 'vacant', 'not filled this term'], ['new york 29th', "leo w o'brien ( d )", 'resigned december 30 , 1966', 'vacant', 'not filled this term'], ['north carolina 4th', 'harold d cooley ( d )', 'resigned december 30 , 1966', 'vacant', 'not filled this term'], ['ohio 15th', 'robert t secrest ( d )', 'resigned december 30 , 1966', 'vacant', 'not filled this term'], ['pennsylvania 9th', 'paul b dague ( r )', 'resigned december 30 , 1966', 'vacant', 'not filled this term'], ['pennsylvania 16th', 'john c kunkel ( r )', 'resigned december 30 , 1966', 'vacant', 'not filled this term'], ['tennessee 7th', 'tom j murray ( d )', 'resigned december 30 , 1966', 'vacant', 'not filled this term']] |
vasek pospisil | https://en.wikipedia.org/wiki/Vasek_Pospisil | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13181492-2.html.csv | majority | vasek pospisil was the winner in most tournaments played on a hard court . | {'scope': 'subset', 'col': '1', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'winner', 'subset': {'col': '4', 'criterion': 'fuzzily_match', 'value': 'hard'}} | {'func': 'most_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'surface', 'hard'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; surface ; hard }', 'tointer': 'select the rows whose surface record fuzzily matches to hard .'}, 'outcome', 'winner'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose surface record fuzzily matches to hard . for the outcome records of these rows , most of them fuzzily match to winner .', 'tostr': 'most_eq { filter_eq { all_rows ; surface ; hard } ; outcome ; winner } = true'} | most_eq { filter_eq { all_rows ; surface ; hard } ; outcome ; winner } = true | select the rows whose surface record fuzzily matches to hard . for the outcome records of these rows , most of them fuzzily match to winner . | 2 | 2 | {'most_str_eq_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'surface_4': 4, 'hard_5': 5, 'outcome_6': 6, 'winner_7': 7} | {'most_str_eq_1': 'most_str_eq', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'surface_4': 'surface', 'hard_5': 'hard', 'outcome_6': 'outcome', 'winner_7': 'winner'} | {'most_str_eq_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'surface_4': [0], 'hard_5': [0], 'outcome_6': [1], 'winner_7': [1]} | ['outcome', 'date', 'tournament', 'surface', 'opponent', 'score'] | [['runner - up', 'july 13 , 2009', 'usa f17 , peoria', 'clay', 'michael venus', '7 - 6 ( 7 - 4 ) , 4 - 6 , 4 - 6'], ['winner', 'september 26 , 2009', 'italy f29 , alghero', 'hard', 'francesco piccari', '6 - 3 , 6 - 7 ( 5 - 7 ) , 6 - 3'], ['winner', 'october 3 , 2009', "italy f30 , quartu sant ' elena", 'hard', 'matteo viola', '6 - 1 , 6 - 2'], ['winner', 'november 1 , 2009', 'mexico f12 , obregón', 'hard', 'daniel garza', '7 - 6 ( 7 - 0 ) , 6 - 3'], ['winner', 'november 8 , 2009', 'mexico f14 , guadalajara', 'clay', 'césar ramírez', '6 - 2 , 6 - 2'], ['runner - up', 'february 22 , 2010', 'usa f5 , brownsville', 'hard', 'víctor estrella', '4 - 6 , 3 - 6'], ['winner', 'march 21 , 2010', 'canada f3 , sherbrooke', 'hard ( i )', 'milos raonic', '6 - 4 , 4 - 6 , 6 - 3'], ['winner', 'september 5 , 2010', 'mexico f6 , león', 'hard', 'david rice', '6 - 1 , 6 - 2'], ['winner', 'september 12 , 2010', 'mexico f7 , guadalajara', 'hard', 'adam el mihdawy', '6 - 0 , 6 - 1'], ['winner', 'october 3 , 2010', 'canada f5 , markham', 'hard ( i )', 'nicholas monroe', '6 - 3 , 6 - 2'], ['winner', 'may 29 , 2011', 'korea f2 , changwon', 'hard', 'lim yong - kyu', '7 - 5 , 6 - 4'], ['winner', 'july 31 , 2011', 'canada f4 , saskatoon', 'hard', 'érik chvojka', '7 - 5 , 6 - 2'], ['winner', 'march 25 , 2012', 'rimouski , canada', 'hard ( i )', 'maxime authom', '7 - 6 ( 8 - 6 ) , 6 - 4'], ['winner', 'july 22 , 2012', 'granby , canada', 'hard', 'igor sijsling', '7 - 6 ( 7 - 2 ) , 6 - 4'], ['runner - up', 'march 18 , 2013', 'rimouski , canada', 'hard ( i )', 'rik de voest', '6 - 7 ( 6 - 8 ) , 4 - 6'], ['winner', 'may 4 , 2013', 'johannesburg , south africa', 'hard', 'michał przysiężny', '6 - 7 ( 7 - 9 ) , 6 - 0 , 4 - 1 ret'], ['winner', 'august 4 , 2013', 'vancouver , canada', 'hard', 'daniel evans', '6 - 0 , 1 - 6 , 7 - 5']] |
2008 - 09 big ten conference men 's basketball season | https://en.wikipedia.org/wiki/2008%E2%80%9309_Big_Ten_Conference_men%27s_basketball_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-21330550-2.html.csv | aggregation | in the 2008 - 09 big ten conference men 's basketball season , games aired on espn2 had a total of 44,912 people in attendance . | {'scope': 'subset', 'col': '7', 'type': 'sum', 'result': '44912', 'subset': {'col': '6', 'criterion': 'equal', 'value': 'espn2'}} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'television', 'espn2'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; television ; espn2 }', 'tointer': 'select the rows whose television record fuzzily matches to espn2 .'}, 'attendance'], 'result': '44912', 'ind': 1, 'tostr': 'sum { filter_eq { all_rows ; television ; espn2 } ; attendance }'}, '44912'], 'result': True, 'ind': 2, 'tostr': 'round_eq { sum { filter_eq { all_rows ; television ; espn2 } ; attendance } ; 44912 } = true', 'tointer': 'select the rows whose television record fuzzily matches to espn2 . the sum of the attendance record of these rows is 44912 .'} | round_eq { sum { filter_eq { all_rows ; television ; espn2 } ; attendance } ; 44912 } = true | select the rows whose television record fuzzily matches to espn2 . the sum of the attendance record of these rows is 44912 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'sum_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'television_5': 5, 'espn2_6': 6, 'attendance_7': 7, '44912_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'sum_1': 'sum', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'television_5': 'television', 'espn2_6': 'espn2', 'attendance_7': 'attendance', '44912_8': '44912'} | {'eq_2': [3], 'result_3': [], 'sum_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'television_5': [0], 'espn2_6': [0], 'attendance_7': [1], '44912_8': [2]} | ['date', 'time', 'acc team', 'big ten team', 'location', 'television', 'attendance', 'winner', 'challenge leader'] | [['mon , dec 1', '7:00 pm', 'virginia tech', '22 wisconsin', 'cassell coliseum blacksburg , va', 'espn2', '9847', 'wisconsin ( 74 - 72 )', 'bigten ( 1 - 0 )'], ['tue , dec 2', '7:00 pm', 'boston college', 'iowa', 'conte forum chestnut hill , ma', 'espnu', '4084', 'boston college ( 57 - 55 )', 'tied ( 1 - 1 )'], ['tue , dec 2', '7:00 pm', '22 miami ( fl )', 'ohio state', 'bankunited center coral gables , fl', 'espn', '5870', 'ohio state ( 73 - 68 )', 'bigten ( 2 - 1 )'], ['tue , dec 2', '7:30 pm', 'clemson', 'illinois', 'assembly hall champaign , il', 'espn2', '14741', 'clemson ( 76 - 74 )', 'tied ( 2 - 2 )'], ['tue , dec 2', '9:00 pm', '4 duke', '10 purdue', 'mackey arena west lafayette , in', 'espn', '14123', 'duke ( 76 - 60 )', 'acc ( 3 - 2 )'], ['tue , dec 2', '9:30 pm', 'virginia', 'minnesota', 'williams arena minneapolis , mn', 'espn2', '12424', 'minnesota ( 66 - 56 )', 'tied ( 3 - 3 )'], ['wed , dec 3', '7:15 pm', '17 wake forest', 'indiana', 'ljvm coliseum winston - salem , nc', 'espn', '12445', 'wake forest ( 83 - 58 )', 'acc ( 4 - 3 )'], ['wed , dec 3', '7:30 pm', 'maryland', 'michigan', 'comcast center college park , md', 'espnu', '17950', 'maryland ( 75 - 70 )', 'acc ( 5 - 3 )'], ['wed , dec 3', '7:30 pm', 'georgia tech', 'penn state', 'alexander memorial coliseum atlanta , ga', 'espn2', '7900', 'penn state ( 85 - 83 )', 'acc ( 5 - 4 )'], ['wed , dec 3', '9:15 pm', '1 north carolina', '12 michigan state', 'ford field detroit , mi ( basketbowl ii )', 'espn', '25267', 'north carolina ( 98 - 63 )', 'acc ( 6 - 4 )']] |
daren kagasoff | https://en.wikipedia.org/wiki/Daren_Kagasoff | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18381900-1.html.csv | unique | choice summer tv star : male was the only category that daren kagasoff won . | {'scope': 'all', 'row': '1', 'col': '5', 'col_other': '4', 'criterion': 'equal', 'value': 'won', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 'won'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to won .', 'tostr': 'filter_eq { all_rows ; result ; won }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; result ; won } }', 'tointer': 'select the rows whose result record fuzzily matches to won . 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', 'won'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to won .', 'tostr': 'filter_eq { all_rows ; result ; won }'}, 'category'], 'result': 'choice summer tv star : male', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; result ; won } ; category }'}, 'choice summer tv star : male'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; result ; won } ; category } ; choice summer tv star : male }', 'tointer': 'the category record of this unqiue row is choice summer tv star : male .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; result ; won } } ; eq { hop { filter_eq { all_rows ; result ; won } ; category } ; choice summer tv star : male } } = true', 'tointer': 'select the rows whose result record fuzzily matches to won . there is only one such row in the table . the category record of this unqiue row is choice summer tv star : male .'} | and { only { filter_eq { all_rows ; result ; won } } ; eq { hop { filter_eq { all_rows ; result ; won } ; category } ; choice summer tv star : male } } = true | select the rows whose result record fuzzily matches to won . there is only one such row in the table . the category record of this unqiue row is choice summer tv star : male . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'result_7': 7, 'won_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'category_9': 9, 'choice summer tv star : male_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', 'won_8': 'won', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'category_9': 'category', 'choice summer tv star : male_10': 'choice summer tv star : male'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'result_7': [0], 'won_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'category_9': [2], 'choice summer tv star : male_10': [3]} | ['year', 'award', 'work', 'category', 'result'] | [['2009', 'teen choice awards', 'the secret life of the american teenager', 'choice summer tv star : male', 'won'], ['2009', 'teen choice awards', 'the secret life of the american teenager', 'choice tv breakout star : male', 'nominated'], ['2010', 'teen choice awards', 'the secret life of the american teenager', 'choice tv actor : drama', 'nominated'], ['2010', 'teen choice awards', 'the secret life of the american teenager', 'choice summer tv star : male', 'nominated'], ['2011', 'teen choice awards', 'the secret life of the american teenager', 'choice tv actor : drama', 'nominated'], ['2012', 'teen choice awards', 'the secret life of the american teenager', 'choice summer tv star : male', 'nominated']] |
2007 - 08 florida panthers season | https://en.wikipedia.org/wiki/2007%E2%80%9308_Florida_Panthers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11771022-8.html.csv | aggregation | the total attendance at florida panthers games in march 2008 was 203,967 . | {'scope': 'all', 'col': '6', 'type': 'sum', 'result': '203,967', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'attendance'], 'result': '203,967', 'ind': 0, 'tostr': 'sum { all_rows ; attendance }'}, '203,967'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; attendance } ; 203,967 } = true', 'tointer': 'the sum of the attendance record of all rows is 203,967 .'} | round_eq { sum { all_rows ; attendance } ; 203,967 } = true | the sum of the attendance record of all rows is 203,967 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'attendance_4': 4, '203,967_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'attendance_4': 'attendance', '203,967_5': '203,967'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'attendance_4': [0], '203,967_5': [1]} | ['date', 'visitor', 'score', 'home', 'decision', 'attendance', 'record'] | [['march 2', 'florida', '1 - 0', 'ny islanders', 'anderson', '15314', '29 - 31 - 8'], ['march 4', 'florida', '1 - 0', 'boston', 'anderson', '16478', '30 - 31 - 8'], ['march 6', 'pittsburgh', '2 - 5', 'florida', 'anderson', '17012', '31 - 31 - 8'], ['march 8', 'atlanta', '2 - 3', 'florida', 'vokoun', '16614', '32 - 31 - 8'], ['march 12', 'ny islanders', '2 - 4', 'florida', 'vokoun', '15233', '33 - 31 - 8'], ['march 14', 'ny rangers', '2 - 3', 'florida', 'vokoun', '19321', '34 - 31 - 8'], ['march 16', 'atlanta', '1 - 3', 'florida', 'vokoun', '15704', '35 - 31 - 8'], ['march 20', 'carolina', '2 - 1', 'florida', 'vokoun', '18546', '35 - 31 - 9'], ['march 22', 'tampa bay', '2 - 4', 'florida', 'vokoun', '18502', '36 - 31 - 9'], ['march 25', 'florida', '1 - 3', 'tampa bay', 'vokoun', '16110', '36 - 32 - 9'], ['march 27', 'atlanta', '3 - 2', 'florida', 'vokoun', '17301', '36 - 33 - 9'], ['march 29', 'washington', '3 - 2', 'florida', 'vokoun', '17832', '36 - 34 - 9']] |
1971 vfl season | https://en.wikipedia.org/wiki/1971_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10826072-3.html.csv | aggregation | the average crowd for the season was about 22,800 . | {'scope': 'all', 'col': '6', 'type': 'average', 'result': '22,800', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'crowd'], 'result': '22,800', 'ind': 0, 'tostr': 'avg { all_rows ; crowd }'}, '22,800'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; crowd } ; 22,800 } = true', 'tointer': 'the average of the crowd record of all rows is 22,800 .'} | round_eq { avg { all_rows ; crowd } ; 22,800 } = true | the average of the crowd record of all rows is 22,800 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'crowd_4': 4, '22,800_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'crowd_4': 'crowd', '22,800_5': '22,800'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'crowd_4': [0], '22,800_5': [1]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['footscray', '14.15 ( 99 )', 'south melbourne', '14.9 ( 93 )', 'western oval', '15003', '17 april 1971'], ['fitzroy', '17.26 ( 128 )', 'north melbourne', '14.10 ( 94 )', 'junction oval', '8917', '17 april 1971'], ['hawthorn', '16.19 ( 115 )', 'geelong', '16.11 ( 107 )', 'glenferrie oval', '14090', '17 april 1971'], ['essendon', '9.12 ( 66 )', 'collingwood', '9.12 ( 66 )', 'windy hill', '22421', '17 april 1971'], ['melbourne', '19.13 ( 127 )', 'carlton', '15.10 ( 100 )', 'mcg', '42885', '17 april 1971'], ['richmond', '10.8 ( 68 )', 'st kilda', '6.13 ( 49 )', 'vfl park', '33489', '17 april 1971']] |
larry perkins | https://en.wikipedia.org/wiki/Larry_Perkins | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1203364-2.html.csv | aggregation | for all races participated in , larry perkins had an average score of 0 . | {'scope': 'all', 'col': '5', 'type': 'average', 'result': '0', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'points'], 'result': '0', 'ind': 0, 'tostr': 'avg { all_rows ; points }'}, '0'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; points } ; 0 } = true', 'tointer': 'the average of the points record of all rows is 0 .'} | round_eq { avg { all_rows ; points } ; 0 } = true | the average of the points record of all rows is 0 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'points_4': 4, '0_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'points_4': 'points', '0_5': '0'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'points_4': [0], '0_5': [1]} | ['year', 'entrant', 'chassis', 'engine', 'points'] | [['1974', 'dalton - amon international', 'amon af101', 'cosworth v8', '0'], ['1976', 'hb bewaking alarm systems', 'boro ensign n175', 'cosworth v8', '0'], ['1976', 'martini racing', 'brabham bt45', 'alfa romeo flat 12', '0'], ['1977', 'rotary watches stanley brm', 'brm p207', 'brm v12', '0'], ['1977', 'rotary watches stanley brm', 'brm p201b / 204', 'brm v12', '0'], ['1977', 'team surtees', 'surtees ts19', 'cosworth v8', '0']] |
supreme court of puerto rico | https://en.wikipedia.org/wiki/Supreme_Court_of_Puerto_Rico | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1097299-1.html.csv | unique | federico hernández denton is the only chief justice in the puerto rico supreme court . | {'scope': 'all', 'row': '4', 'col': '2', 'col_other': '1', 'criterion': 'equal', 'value': 'chief justice', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'rank', 'chief justice'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose rank record fuzzily matches to chief justice .', 'tostr': 'filter_eq { all_rows ; rank ; chief justice }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; rank ; chief justice } }', 'tointer': 'select the rows whose rank record fuzzily matches to chief justice . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'rank', 'chief justice'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose rank record fuzzily matches to chief justice .', 'tostr': 'filter_eq { all_rows ; rank ; chief justice }'}, 'name'], 'result': 'federico hernández denton', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; rank ; chief justice } ; name }'}, 'federico hernández denton'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; rank ; chief justice } ; name } ; federico hernández denton }', 'tointer': 'the name record of this unqiue row is federico hernández denton .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; rank ; chief justice } } ; eq { hop { filter_eq { all_rows ; rank ; chief justice } ; name } ; federico hernández denton } } = true', 'tointer': 'select the rows whose rank record fuzzily matches to chief justice . there is only one such row in the table . the name record of this unqiue row is federico hernández denton .'} | and { only { filter_eq { all_rows ; rank ; chief justice } } ; eq { hop { filter_eq { all_rows ; rank ; chief justice } ; name } ; federico hernández denton } } = true | select the rows whose rank record fuzzily matches to chief justice . there is only one such row in the table . the name record of this unqiue row is federico hernández denton . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'rank_7': 7, 'chief justice_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'name_9': 9, 'federico hernández denton_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'rank_7': 'rank', 'chief justice_8': 'chief justice', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'name_9': 'name', 'federico hernández denton_10': 'federico hernández denton'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'rank_7': [0], 'chief justice_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'name_9': [2], 'federico hernández denton_10': [3]} | ['name', 'rank', 'years until mandatory retirement', 'appointed by', 'year appointed'] | [['anabelle rodríguez', 'associate justice', '6 years', 'sila maría calderón', '2004'], ['edgardo rivera garcia', 'associate justice', '11 years', 'luis fortuño', '2010'], ['erick kolthoff caraballo', 'associate justice', '17 years', 'luis fortuño', '2009'], ['federico hernández denton', 'chief justice', '0 year', 'sila maría calderón', '2004'], ['liana fiol matta', 'associate justice', '2 years', 'sila maría calderón', '2004'], ['luis estrella martínez', 'associate justice', '27 years', 'luis fortuño', '2011'], ['mildred pabón charneco', 'associate justice', '13 years', 'luis fortuño', '2009'], ['rafael martínez torres', 'associate justice', '15 years', 'luis fortuño', '2009'], ['roberto feliberti cintrón', 'associate justice', '19 years', 'luis fortuño', '2011']] |
2007 - 08 cleveland cavaliers season | https://en.wikipedia.org/wiki/2007%E2%80%9308_Cleveland_Cavaliers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11960713-5.html.csv | majority | lebron james was the leading scorer in the majority of games in the 2007 - 08 cleveland cavaliers season . | {'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'fuzzily_match', 'value': 'lebron james', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'leading scorer', 'lebron james'], 'result': True, 'ind': 0, 'tointer': 'for the leading scorer records of all rows , most of them fuzzily match to lebron james .', 'tostr': 'most_eq { all_rows ; leading scorer ; lebron james } = true'} | most_eq { all_rows ; leading scorer ; lebron james } = true | for the leading scorer records of all rows , most of them fuzzily match to lebron james . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'leading scorer_3': 3, 'lebron james_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'leading scorer_3': 'leading scorer', 'lebron james_4': 'lebron james'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'leading scorer_3': [0], 'lebron james_4': [0]} | ['date', 'visitor', 'score', 'home', 'leading scorer', 'attendance', 'record'] | [['march 2', 'chicago', '95 - 86', 'cleveland', 'lebron james ( 37 )', '20562', '34 - 26'], ['march 5', 'cleveland', '119 - 105', 'ny knicks', 'lebron james ( 50 )', '18760', '35 - 26'], ['march 6', 'cleveland', '96 - 107', 'chicago', 'lebron james ( 39 )', '22097', '35 - 27'], ['march 8', 'indiana', '103 - 95', 'cleveland', 'lebron james ( 38 )', '20562', '36 - 27'], ['march 10', 'portland', '88 - 80', 'cleveland', 'lebron james ( 24 )', '20213', '37 - 27'], ['march 12', 'cleveland', '99 - 104', 'new jersey', 'lebron james ( 42 )', '18287', '37 - 28'], ['march 13', 'cleveland', '99 - 101', 'washington', 'lebron james ( 25 )', '20173', '37 - 29'], ['march 16', 'charlotte', '98 - 91', 'cleveland', 'lebron james ( 33 )', '20562', '38 - 29'], ['march 17', 'cleveland', '90 - 104', 'orlando', 'lebron james ( 30 )', '17519', '38 - 30'], ['march 19', 'detroit', '89 - 73', 'cleveland', 'lebron james ( 30 )', '20562', '39 - 30'], ['march 21', 'toronto', '90 - 83', 'cleveland', 'lebron james ( 29 )', '20562', '40 - 30'], ['march 22', 'cleveland', '98 - 108', 'milwaukee', 'lebron james ( 29 )', '15337', '40 - 31'], ['march 26', 'new orleans', '99 - 100', 'cleveland', 'žydrūnas ilgauskas ( 29 )', '20562', '40 - 32'], ['march 29', 'cleveland', '71 - 85', 'detroit', 'lebron james ( 13 )', '22076', '40 - 33'], ['march 30', 'philadelphia', '91 - 88', 'cleveland', 'lebron james ( 26 )', '20562', '41 - 33']] |
1898 - 99 rangers f.c. season | https://en.wikipedia.org/wiki/1898%E2%80%9399_Rangers_F.C._season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16834484-2.html.csv | ordinal | the 2nd away game in the 1898 - 99 rangers f.c. season attracted and attendance of 10000 . | {'scope': 'subset', 'row': '4', 'col': '1', 'order': '2', 'col_other': '6', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'subset': {'col': '4', 'criterion': 'fuzzily_match', 'value': 'a'}} | {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'nth_argmin', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'venue', 'a'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; venue ; a }', 'tointer': 'select the rows whose venue record fuzzily matches to a .'}, 'date', '2'], 'result': None, 'ind': 1, 'tostr': 'nth_argmin { filter_eq { all_rows ; venue ; a } ; date ; 2 }'}, 'attendance'], 'result': '10000', 'ind': 2, 'tostr': 'hop { nth_argmin { filter_eq { all_rows ; venue ; a } ; date ; 2 } ; attendance }'}, '10000'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { nth_argmin { filter_eq { all_rows ; venue ; a } ; date ; 2 } ; attendance } ; 10000 } = true', 'tointer': 'select the rows whose venue record fuzzily matches to a . select the row whose date record of these rows is 2nd minimum . the attendance record of this row is 10000 .'} | eq { hop { nth_argmin { filter_eq { all_rows ; venue ; a } ; date ; 2 } ; attendance } ; 10000 } = true | select the rows whose venue record fuzzily matches to a . select the row whose date record of these rows is 2nd minimum . the attendance record of this row is 10000 . | 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, 'venue_6': 6, 'a_7': 7, 'date_8': 8, '2_9': 9, 'attendance_10': 10, '10000_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', 'venue_6': 'venue', 'a_7': 'a', 'date_8': 'date', '2_9': '2', 'attendance_10': 'attendance', '10000_11': '10000'} | {'eq_3': [4], 'result_4': [], 'num_hop_2': [3], 'nth_argmin_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'venue_6': [0], 'a_7': [0], 'date_8': [1], '2_9': [1], 'attendance_10': [2], '10000_11': [3]} | ['date', 'round', 'opponent', 'venue', 'result', 'attendance'] | [['14 january 1899', 'r1', 'heart of midlothian', 'h', '4 - 1', '25612'], ['11 february 1899', 'r2', 'ayr parkhouse', 'a', '4 - 1', '5000'], ['18 february 1899', 'qf', 'clyde', 'h', '4 - 0', '6000'], ['15 april 1899', 'sf', 'st mirren', 'a', '2 - 1', '10000'], ['22 april 1899', 'f', 'celtic', 'n', '0 - 2', '25000']] |
1975 cleveland browns season | https://en.wikipedia.org/wiki/1975_Cleveland_Browns_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10651104-2.html.csv | majority | the cleveland browns lost most games in the month of august in the 1975 season . | {'scope': 'subset', 'col': '4', 'most_or_all': 'most', 'criterion': 'fuzzily_match', 'value': 'l', 'subset': {'col': '2', 'criterion': 'fuzzily_match', 'value': 'august'}} | {'func': 'most_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'august'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; date ; august }', 'tointer': 'select the rows whose date record fuzzily matches to august .'}, 'result', 'l'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to august . for the result records of these rows , most of them fuzzily match to l .', 'tostr': 'most_eq { filter_eq { all_rows ; date ; august } ; result ; l } = true'} | most_eq { filter_eq { all_rows ; date ; august } ; result ; l } = true | select the rows whose date record fuzzily matches to august . for the result records of these rows , most of them fuzzily match to l . | 2 | 2 | {'most_str_eq_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'date_4': 4, 'august_5': 5, 'result_6': 6, 'l_7': 7} | {'most_str_eq_1': 'most_str_eq', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'date_4': 'date', 'august_5': 'august', 'result_6': 'result', 'l_7': 'l'} | {'most_str_eq_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'date_4': [0], 'august_5': [0], 'result_6': [1], 'l_7': [1]} | ['week', 'date', 'opponent', 'result', 'attendance'] | [['1', 'august 10 , 1975', 'san francisco 49ers', 'l 17 - 13', '45560'], ['2', 'august 16 , 1975', 'philadelphia eagles', 'w 14 - 6', '35769'], ['3', 'august 22 , 1975', 'washington redskins', 'l 23 - 14', '15513'], ['4', 'september 1 , 1975', 'buffalo bills', 'l 34 - 20', '31155'], ['5', 'september 7 , 1975', 'new york giants at seattle', 'w 24 - 20', '20000'], ['6', 'september 13 , 1975', 'detroit lions', 'l 27 - 24', '32341']] |
1971 icf canoe sprint world championships | https://en.wikipedia.org/wiki/1971_ICF_Canoe_Sprint_World_Championships | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18567469-4.html.csv | ordinal | bulgaria ranked seventh in the 1971 icf canoe sprint world championships . | {'row': '7', 'col': '1', 'order': '7', 'col_other': '2', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'rank', '7'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; rank ; 7 }'}, 'nation'], 'result': 'bulgaria', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; rank ; 7 } ; nation }'}, 'bulgaria'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; rank ; 7 } ; nation } ; bulgaria } = true', 'tointer': 'select the row whose rank record of all rows is 7th minimum . the nation record of this row is bulgaria .'} | eq { hop { nth_argmin { all_rows ; rank ; 7 } ; nation } ; bulgaria } = true | select the row whose rank record of all rows is 7th minimum . the nation record of this row is bulgaria . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'rank_5': 5, '7_6': 6, 'nation_7': 7, 'bulgaria_8': 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmin_0': 'nth_argmin', 'all_rows_4': 'all_rows', 'rank_5': 'rank', '7_6': '7', 'nation_7': 'nation', 'bulgaria_8': 'bulgaria'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'rank_5': [0], '7_6': [0], 'nation_7': [1], 'bulgaria_8': [2]} | ['rank', 'nation', 'gold', 'silver', 'bronze', 'total'] | [['1', 'soviet union', '7', '2', '6', '15'], ['2', 'hungary', '4', '5', '2', '11'], ['3', 'romania', '2', '2', '5', '9'], ['4', 'west germany', '2', '2', '1', '5'], ['5', 'east germany', '1', '1', '2', '4'], ['6', 'sweden', '1', '1', '0', '2'], ['7', 'bulgaria', '0', '0', '2', '2'], ['8', 'poland', '1', '0', '0', '1'], ['9', 'austria', '0', '1', '0', '1'], ['10', 'belgium', '0', '1', '0', '1'], ['11', 'czechoslovakia', '0', '1', '0', '1'], ['12', 'netherlands', '0', '1', '0', '1'], ['13', 'norway', '0', '1', '0', '1'], ['total', 'total', '18', '18', '18', '54']] |
1995 miami dolphins season | https://en.wikipedia.org/wiki/1995_Miami_Dolphins_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16023832-2.html.csv | comparative | week 17 saw a higher scoring game than the one in week 16 . | {'row_1': '16', 'row_2': '15', 'col': '4', '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', 'week', '17'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose week record fuzzily matches to 17 .', 'tostr': 'filter_eq { all_rows ; week ; 17 }'}, 'result'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; week ; 17 } ; result }', 'tointer': 'select the rows whose week record fuzzily matches to 17 . take the result record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'week', '16'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose week record fuzzily matches to 16 .', 'tostr': 'filter_eq { all_rows ; week ; 16 }'}, 'result'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; week ; 16 } ; result }', 'tointer': 'select the rows whose week record fuzzily matches to 16 . take the result record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; week ; 17 } ; result } ; hop { filter_eq { all_rows ; week ; 16 } ; result } } = true', 'tointer': 'select the rows whose week record fuzzily matches to 17 . take the result record of this row . select the rows whose week record fuzzily matches to 16 . take the result record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; week ; 17 } ; result } ; hop { filter_eq { all_rows ; week ; 16 } ; result } } = true | select the rows whose week record fuzzily matches to 17 . take the result record of this row . select the rows whose week record fuzzily matches to 16 . 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, 'week_7': 7, '17_8': 8, 'result_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'week_11': 11, '16_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', 'week_7': 'week', '17_8': '17', 'result_9': 'result', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'week_11': 'week', '16_12': '16', 'result_13': 'result'} | {'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'week_7': [0], '17_8': [0], 'result_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'week_11': [1], '16_12': [1], 'result_13': [3]} | ['week', 'date', 'opponent', 'result', 'attendance'] | [['1', 'september 3 , 1995', 'new york jets', 'w 52 - 14', '71317'], ['2', 'september 10 , 1995', 'new england patriots', 'w 20 - 3', '60239'], ['3', 'september 18 , 1995', 'pittsburgh steelers', 'w 23 - 10', '72874'], ['5', 'october 1 , 1995', 'cincinnati bengals', 'w 26 - 23', '52671'], ['6', 'october 8 , 1995', 'indianapolis colts', 'l 27 - 24', '68471'], ['7', 'october 15 , 1995', 'new orleans saints', 'l 33 - 30', '55628'], ['8', 'october 22 , 1995', 'new york jets', 'l 17 - 16', '67228'], ['9', 'october 29 , 1995', 'buffalo bills', 'w 23 - 6', '71060'], ['10', 'november 6 , 1995', 'san diego chargers', 'w 24 - 14', '61966'], ['11', 'november 12 , 1995', 'new england patriots', 'l 34 - 17', '70399'], ['12', 'november 20 , 1995', 'san francisco 49ers', 'l 44 - 20', '73080'], ['13', 'november 26 , 1995', 'indianapolis colts', 'l 36 - 28', '60414'], ['14', 'december 3 , 1995', 'atlanta falcons', 'w 21 - 20', '63395'], ['15', 'december 11 , 1995', 'kansas city chiefs', 'w 13 - 6', '70321'], ['16', 'december 17 , 1995', 'buffalo bills', 'l 23 - 20', '79531'], ['17', 'december 24 , 1995', 'st louis rams', 'w 41 - 22', '63876']] |
colbie caillat | https://en.wikipedia.org/wiki/Colbie_Caillat | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12416709-3.html.csv | count | colbie caillat won three awards in total in 2008 and 2009 . | {'scope': 'all', 'criterion': 'equal', 'value': 'won', 'result': '3', 'col': '2', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 'won'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to won .', 'tostr': 'filter_eq { all_rows ; result ; won }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; result ; won } }', 'tointer': 'select the rows whose result record fuzzily matches to won . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; result ; won } } ; 3 } = true', 'tointer': 'select the rows whose result record fuzzily matches to won . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; result ; won } } ; 3 } = true | select the rows whose result record fuzzily matches to won . 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, 'result_5': 5, 'won_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', 'result_5': 'result', 'won_6': 'won', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'result_5': [0], 'won_6': [0], '3_7': [2]} | ['year', 'result', 'award', 'category', 'nominated work'] | [['2008', 'nominated', 'american music awards', 't - mobile breakthrough artist', 'general'], ['2008', 'nominated', 'teen choice awards', 'choice breakthrough artist', 'general'], ['2008', 'nominated', 'teen choice awards', 'choice love song', 'bubbly'], ['2008', 'won', 'billboard music awards', 'rising star', 'general'], ['2009', 'won', 'bmi pop awards', 'songwriter of the year', 'colbie caillat'], ['2009', 'won', 'bmi pop awards', 'song of the year', 'bubbly'], ['2009', 'nominated', 'teen choice awards', 'choice music : hook up', 'lucky'], ['2010', 'nominated', "people 's choice awards", 'favorite music collaboration', 'lucky']] |
list of whose line is it anyway ? uk episodes | https://en.wikipedia.org/wiki/List_of_Whose_Line_Is_It_Anyway%3F_UK_episodes | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-14934885-1.html.csv | majority | john sessions was performer 1 in all of the episodes of whose line is it anyway ? uk . | {'scope': 'all', 'col': '3', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'john sessions', 'subset': None} | {'func': 'all_str_eq', 'args': ['all_rows', 'performer 1', 'john sessions'], 'result': True, 'ind': 0, 'tointer': 'for the performer 1 records of all rows , all of them fuzzily match to john sessions .', 'tostr': 'all_eq { all_rows ; performer 1 ; john sessions } = true'} | all_eq { all_rows ; performer 1 ; john sessions } = true | for the performer 1 records of all rows , all of them fuzzily match to john sessions . | 1 | 1 | {'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'performer 1_3': 3, 'john sessions_4': 4} | {'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'performer 1_3': 'performer 1', 'john sessions_4': 'john sessions'} | {'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'performer 1_3': [0], 'john sessions_4': [0]} | ['date', 'episode', 'performer 1', 'performer 2', 'performer 3', 'performer 4'] | [['2 january 1988', '1', 'john sessions', 'stephen fry', 'dawn french', 'lenny henry'], ['9 january 1988', '2', 'john sessions', 'stephen fry', 'hugh laurie', 'enn reitel'], ['16 january 1988', '3', 'john sessions', 'stephen fry', 'nonny williams', 'jimmy mulville'], ['23 january 1988', '4', 'john sessions', 'stephen fry', 'kate robbins', 'griff rhys jones'], ['30 january 1988', '5', 'john sessions', 'stephen fry', 'jimmy mulville', 'john bird']] |
81st united states congress | https://en.wikipedia.org/wiki/81st_United_States_Congress | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1694492-2.html.csv | majority | the majority of vacators from the 81st us congress were due to the reason of death . | {'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'fuzzily_match', 'value': 'died', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'reason for change', 'died'], 'result': True, 'ind': 0, 'tointer': 'for the reason for change records of all rows , most of them fuzzily match to died .', 'tostr': 'most_eq { all_rows ; reason for change ; died } = true'} | most_eq { all_rows ; reason for change ; died } = true | for the reason for change records of all rows , most of them fuzzily match to died . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'reason for change_3': 3, 'died_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'reason for change_3': 'reason for change', 'died_4': 'died'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'reason for change_3': [0], 'died_4': [0]} | ['district', 'vacator', 'reason for change', 'successor', 'date successor seated'] | [['new york 7th', 'vacant', 'rep john j delaney died during previous congress', 'louis b heller ( d )', 'february 15 , 1949'], ['new york 20th', 'sol bloom ( d )', 'died march 7 , 1949', 'franklin delano roosevelt , jr ( lib )', 'may 17 , 1949'], ['new york 10th', 'andrew l somers ( d )', 'died april 6 , 1949', 'edna f kelly ( d )', 'november 8 , 1949'], ['pennsylvania 26th', 'robert l coffey ( d )', 'died april 20 , 1949', 'john p saylor ( r )', 'september 13 , 1949'], ['california 5th', 'richard j welch ( r )', 'died september 10 , 1949', 'john shelley ( d )', 'november 8 , 1949'], ['massachusetts 6th', 'george j bates ( r )', 'died november 1 , 1949', 'william h bates ( r )', 'february 14 , 1950'], ['illinois 5th', 'martin gorski ( d )', 'died december 4 , 1949', 'vacant', 'not filled for the remainder of this term'], ['virginia 1st', 's otis bland ( d )', 'died february 16 , 1950', 'edward j robeson , jr ( d )', 'may 2 , 1950'], ['illinois 13th', 'ralph e church ( r )', 'died march 21 , 1950', 'vacant', 'not filled for the remainder of this term'], ['michigan 16th', 'john lesinski , sr ( d )', 'died may 27 , 1950', 'vacant', 'not filled for the remainder of this term'], ['north dakota at - large', 'william lemke ( r )', 'died may 30 , 1950', 'vacant', 'not filled for the remainder of this term'], ['north carolina 11th', 'alfred l bulwinkle ( d )', 'died august 31 , 1950', 'woodrow w jones ( d )', 'november 7 , 1950']] |
1962 - 63 segunda división | https://en.wikipedia.org/wiki/1962%E2%80%9363_Segunda_Divisi%C3%B3n | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17724929-2.html.csv | comparative | in 1962 - 63 segunda división , the club rcd español had one more win than the club pontevedra cf. | {'row_1': '2', 'row_2': '1', 'col': '5', 'col_other': '2', 'relation': 'diff', 'record_mentioned': 'no', 'diff_result': {'diff_value': '1', 'bigger': 'row1'}} | {'func': 'eq', 'args': [{'func': 'diff', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'club', 'rcd español'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose club record fuzzily matches to rcd español .', 'tostr': 'filter_eq { all_rows ; club ; rcd español }'}, 'wins'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; club ; rcd español } ; wins }', 'tointer': 'select the rows whose club record fuzzily matches to rcd español . take the wins record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'club', 'pontevedra cf'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose club record fuzzily matches to pontevedra cf .', 'tostr': 'filter_eq { all_rows ; club ; pontevedra cf }'}, 'wins'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; club ; pontevedra cf } ; wins }', 'tointer': 'select the rows whose club record fuzzily matches to pontevedra cf . take the wins record of this row .'}], 'result': '1', 'ind': 4, 'tostr': 'diff { hop { filter_eq { all_rows ; club ; rcd español } ; wins } ; hop { filter_eq { all_rows ; club ; pontevedra cf } ; wins } }'}, '1'], 'result': True, 'ind': 5, 'tostr': 'eq { diff { hop { filter_eq { all_rows ; club ; rcd español } ; wins } ; hop { filter_eq { all_rows ; club ; pontevedra cf } ; wins } } ; 1 } = true', 'tointer': 'select the rows whose club record fuzzily matches to rcd español . take the wins record of this row . select the rows whose club record fuzzily matches to pontevedra cf . take the wins record of this row . the first record is 1 larger than the second record .'} | eq { diff { hop { filter_eq { all_rows ; club ; rcd español } ; wins } ; hop { filter_eq { all_rows ; club ; pontevedra cf } ; wins } } ; 1 } = true | select the rows whose club record fuzzily matches to rcd español . take the wins record of this row . select the rows whose club record fuzzily matches to pontevedra cf . take the wins record of this row . the first record is 1 larger than the second record . | 6 | 6 | {'eq_5': 5, 'result_6': 6, 'diff_4': 4, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'club_8': 8, 'rcd español_9': 9, 'wins_10': 10, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_11': 11, 'club_12': 12, 'pontevedra cf_13': 13, 'wins_14': 14, '1_15': 15} | {'eq_5': 'eq', 'result_6': 'true', 'diff_4': 'diff', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'club_8': 'club', 'rcd español_9': 'rcd español', 'wins_10': 'wins', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_11': 'all_rows', 'club_12': 'club', 'pontevedra cf_13': 'pontevedra cf', 'wins_14': 'wins', '1_15': '1'} | {'eq_5': [6], 'result_6': [], 'diff_4': [5], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_7': [0], 'club_8': [0], 'rcd español_9': [0], 'wins_10': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_11': [1], 'club_12': [1], 'pontevedra cf_13': [1], 'wins_14': [3], '1_15': [5]} | ['position', 'club', 'played', 'points', 'wins', 'draws', 'losses', 'goals for', 'goals against', 'goal difference'] | [['1', 'pontevedra cf', '30', '41', '16', '9', '5', '44', '31', '+ 13'], ['2', 'rcd español', '30', '39', '17', '5', '8', '40', '24', '+ 16'], ['3', 'real santander', '30', '37', '15', '7', '8', '53', '39', '+ 14'], ['4', 'real sociedad', '30', '35', '14', '7', '9', '77', '44', '+ 33'], ['5', 'real gijón', '30', '34', '16', '2', '12', '50', '46', '+ 4'], ['6', 'rc celta de vigo', '30', '32', '13', '6', '11', '47', '31', '+ 16'], ['7', 'cd orense', '30', '31', '14', '3', '13', '43', '37', '+ 6'], ['8', 'deportivo alavés', '30', '30', '12', '6', '12', '43', '46', '- 3'], ['9', 'sd indauchu', '30', '30', '11', '8', '11', '46', '42', '+ 4'], ['10', 'burgos cf', '30', '29', '12', '5', '13', '39', '47', '- 8'], ['11', 'ud salamanca', '30', '27', '10', '7', '13', '40', '46', '- 6'], ['12', 'cd constancia', '30', '26', '11', '4', '15', '42', '51', '- 9'], ['13', 'up langreo', '30', '25', '8', '9', '13', '33', '42', '- 9'], ['14', 'atlético baleares', '30', '23', '9', '5', '16', '37', '51', '- 14'], ['15', 'cd basconia', '30', '21', '9', '3', '18', '31', '65', '- 34'], ['16', 'cd sabadell cf', '30', '20', '8', '4', '18', '43', '66', '- 23']] |
minnesota golden gophers football under bernie bierman | https://en.wikipedia.org/wiki/Minnesota_Golden_Gophers_football_under_Bernie_Bierman | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16518708-1.html.csv | aggregation | between october 1st and november 11th 1932 , the minnesota golden gophers , coached by bernie bierman , had an average attendance of around 21,596 people . | {'scope': 'all', 'col': '5', 'type': 'average', 'result': '21,596', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'attendance'], 'result': '21,596', 'ind': 0, 'tostr': 'avg { all_rows ; attendance }'}, '21,596'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; attendance } ; 21,596 } = true', 'tointer': 'the average of the attendance record of all rows is 21,596 .'} | round_eq { avg { all_rows ; attendance } ; 21,596 } = true | the average of the attendance record of all rows is 21,596 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'attendance_4': 4, '21,596_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'attendance_4': 'attendance', '21,596_5': '21,596'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'attendance_4': [0], '21,596_5': [1]} | ['date', 'opponent', 'site', 'result', 'attendance'] | [['10 / 01 / 1932', 'south dakota state', 'memorial stadium minneapolis , mn', 'w12 - 0', '20000'], ['10 / 08 / 1932', 'purdue', 'memorial stadium minneapolis , mn', 'l0 - 7', '20000'], ['10 / 15 / 1932', 'nebraska', 'memorial stadium minneapolis , mn', 'w7 - 6', '18000'], ['10 / 22 / 1932', 'iowa', 'iowa stadium iowa city , ia', 'w21 - 6', '12000'], ['10 / 29 / 1932', 'northwestern', 'memorial stadium minneapolis , mn', 'w7 - 0', '35000'], ['11 / 05 / 1932', 'ole miss', 'memorial stadium minneapolis , mn', 'w26 - 0', '12000'], ['11 / 12 / 1932', 'wisconsin', 'camp randall stadium madison , wi', 'l13 - 20', '31000'], ['11 / 19 / 1932', 'michigan', 'memorial stadium minneapolis , mn', 'l0 - 3', '24766']] |
2008 washington redskins season | https://en.wikipedia.org/wiki/2008_Washington_Redskins_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-10966926-2.html.csv | ordinal | during the 2008 washington redskins season , the player with a height of 6 ' 2 with the lowest weight went to college in hawaii . | {'scope': 'subset', 'row': '8', 'col': '6', 'order': '1', 'col_other': '7', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'subset': {'col': '5', 'criterion': 'equal', 'value': "6 ' 2"}} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'height', "6 ' 2"], 'result': None, 'ind': 0, 'tostr': "filter_eq { all_rows ; height ; 6 ' 2 }", 'tointer': "select the rows whose height record fuzzily matches to 6 ' 2 ."}, 'weight', '1'], 'result': None, 'ind': 1, 'tostr': "nth_argmin { filter_eq { all_rows ; height ; 6 ' 2 } ; weight ; 1 }"}, 'college'], 'result': 'hawaii', 'ind': 2, 'tostr': "hop { nth_argmin { filter_eq { all_rows ; height ; 6 ' 2 } ; weight ; 1 } ; college }"}, 'hawaii'], 'result': True, 'ind': 3, 'tostr': "eq { hop { nth_argmin { filter_eq { all_rows ; height ; 6 ' 2 } ; weight ; 1 } ; college } ; hawaii } = true", 'tointer': "select the rows whose height record fuzzily matches to 6 ' 2 . select the row whose weight record of these rows is 1st minimum . the college record of this row is hawaii ."} | eq { hop { nth_argmin { filter_eq { all_rows ; height ; 6 ' 2 } ; weight ; 1 } ; college } ; hawaii } = true | select the rows whose height record fuzzily matches to 6 ' 2 . select the row whose weight record of these rows is 1st minimum . the college record of this row is hawaii . | 4 | 4 | {'str_eq_3': 3, 'result_4': 4, 'str_hop_2': 2, 'nth_argmin_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'height_6': 6, "6'2_7": 7, 'weight_8': 8, '1_9': 9, 'college_10': 10, 'hawaii_11': 11} | {'str_eq_3': 'str_eq', 'result_4': 'true', 'str_hop_2': 'str_hop', 'nth_argmin_1': 'nth_argmin', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'height_6': 'height', "6'2_7": "6 ' 2", 'weight_8': 'weight', '1_9': '1', 'college_10': 'college', 'hawaii_11': 'hawaii'} | {'str_eq_3': [4], 'result_4': [], 'str_hop_2': [3], 'nth_argmin_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'height_6': [0], "6'2_7": [0], 'weight_8': [1], '1_9': [1], 'college_10': [2], 'hawaii_11': [3]} | ['round', 'choice', 'player name', 'position', 'height', 'weight', 'college'] | [['2', '34', 'devin thomas', 'wide receiver', "6 ' 2", '215', 'michigan state'], ['2', '48', 'fred davis', 'tight end', "6 ' 4", '250', 'southern cal'], ['2', '51', 'malcolm kelly', 'wide receiver', "6 ' 4", '219', 'oklahoma'], ['3', '96', 'chad rinehart', 'offensive guard', "6 ' 5", '320', 'northern iowa'], ['4', '124', 'justin tryon', 'cornerback', "5 ' 9", '180', 'arizona state'], ['6', '168', 'durant brooks', 'punter', "6 ' 0", '204', 'georgia tech'], ['6', '180', 'kareem moore', 'safety', "5 ' 11", '213', 'nicholls state'], ['6', '186', 'colt brennan', 'quarterback', "6 ' 2", '205', 'hawaii'], ['7', '242', 'rob jackson', 'defensive end', "6 ' 4", '257', 'kansas state']] |
2008 french road cycling cup | https://en.wikipedia.org/wiki/2008_French_Road_Cycling_Cup | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15930479-1.html.csv | unique | davide rebellin was the only italian winner for all events . | {'scope': 'all', 'row': '1', 'col': '3', 'col_other': 'n/a', 'criterion': 'fuzzily_match', 'value': 'ita', 'subset': None} | {'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'winner', 'ita'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose winner record fuzzily matches to ita .', 'tostr': 'filter_eq { all_rows ; winner ; ita }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; winner ; ita } } = true', 'tointer': 'select the rows whose winner record fuzzily matches to ita . there is only one such row in the table .'} | only { filter_eq { all_rows ; winner ; ita } } = true | select the rows whose winner record fuzzily matches to ita . there is only one such row in the table . | 2 | 2 | {'only_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'winner_4': 4, 'ita_5': 5} | {'only_1': 'only', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'winner_4': 'winner', 'ita_5': 'ita'} | {'only_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'winner_4': [0], 'ita_5': [0]} | ['date', 'event', 'winner', 'team', 'series leader'] | [['february 24', 'tour du haut var', 'davide rebellin ( ita )', 'gerolsteiner', 'rinaldo nocentini ( ita )'], ['march 23', 'cholet - pays de loire', 'janek tombak ( est )', 'mitsubishi - jartazi', 'rinaldo nocentini ( ita )'], ['april 6', 'grand prix de rennes', 'mikhaylo khalilov ( ukr )', 'ceramica flaminia - bossini docce', 'jimmy casper ( fra )'], ['april 15', 'paris - camembert', 'alejandro valverde ( esp )', "caisse d'epargne", 'jérôme pineau ( fra )'], ['april 17', 'grand prix de denain', 'edvald boasson hagen ( nor )', 'team high road', 'jimmy casper ( fra )'], ['april 19', 'tour du finistère', 'david lelay ( fra )', 'bretagne - armor lux', 'jimmy casper ( fra )'], ['april 20', 'tro - bro léon', 'frédéric guesdon ( fra )', 'française des jeux', 'jimmy casper ( fra )'], ['may 4', 'trophée des grimpeurs', 'david lelay ( fra )', 'bretagne - armor lux', 'david lelay ( fra )'], ['may 31', 'grand prix de plumelec - morbihan', 'thomas voeckler ( fra )', 'bouygues télécom', 'david lelay ( fra )'], ['august 3', 'polynormande', 'arnaud gérard ( fra )', 'française des jeux', 'jérôme pineau ( fra )'], ['august 31', 'chteauroux classic', 'anthony ravard ( fra )', 'agritubel', 'jérôme pineau ( fra )'], ['september 21', "grand prix d'isbergues", 'william bonnet ( fra )', 'crédit agricole', 'jérôme pineau ( fra )'], ['october 5', 'tour de vendée', 'koldo fernández ( esp )', 'euskaltel - euskadi', 'jérôme pineau ( fra )'], ['october 9', 'paris - bourges', 'bernhard eisel ( aut )', 'team columbia', 'jérôme pineau ( fra )']] |
2008 iaaf world indoor championships - women 's 400 metres | https://en.wikipedia.org/wiki/2008_IAAF_World_Indoor_Championships_%E2%80%93_Women%27s_400_metres | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16196238-4.html.csv | comparative | shareese woods had a lower react score than natalya nazarova . | {'row_1': '3', 'row_2': '2', 'col': '5', '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', 'name', 'shareese woods'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose name record fuzzily matches to shareese woods .', 'tostr': 'filter_eq { all_rows ; name ; shareese woods }'}, 'react'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; name ; shareese woods } ; react }', 'tointer': 'select the rows whose name record fuzzily matches to shareese woods . take the react record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name', 'natalya nazarova'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose name record fuzzily matches to natalya nazarova .', 'tostr': 'filter_eq { all_rows ; name ; natalya nazarova }'}, 'react'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; name ; natalya nazarova } ; react }', 'tointer': 'select the rows whose name record fuzzily matches to natalya nazarova . take the react record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; name ; shareese woods } ; react } ; hop { filter_eq { all_rows ; name ; natalya nazarova } ; react } } = true', 'tointer': 'select the rows whose name record fuzzily matches to shareese woods . take the react record of this row . select the rows whose name record fuzzily matches to natalya nazarova . take the react record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; name ; shareese woods } ; react } ; hop { filter_eq { all_rows ; name ; natalya nazarova } ; react } } = true | select the rows whose name record fuzzily matches to shareese woods . take the react record of this row . select the rows whose name record fuzzily matches to natalya nazarova . take the react 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, 'name_7': 7, 'shareese woods_8': 8, 'react_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'name_11': 11, 'natalya nazarova_12': 12, 'react_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', 'name_7': 'name', 'shareese woods_8': 'shareese woods', 'react_9': 'react', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'name_11': 'name', 'natalya nazarova_12': 'natalya nazarova', 'react_13': 'react'} | {'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'name_7': [0], 'shareese woods_8': [0], 'react_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'name_11': [1], 'natalya nazarova_12': [1], 'react_13': [3]} | ['lane', 'name', 'country', 'mark', 'react'] | [['6', 'olesya zykina', 'russia', '51.09 wl', '0.297'], ['5', 'natalya nazarova', 'russia', '51.10 sb', '0.247'], ['3', 'shareese woods', 'united states', '51.41 pb', '0.237'], ['4', 'antonina yefremova', 'ukraine', '51.53 pb', '0.147'], ['2', 'angela morosanu', 'romania', '53.07', '0.269'], ['1', 'moushaumi robinson', 'united states', '53.10', '0.257']] |
1997 australian touring car championship | https://en.wikipedia.org/wiki/1997_Australian_Touring_Car_Championship | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15374407-2.html.csv | count | there were 10 circuits used during the 1997 australian touring car championship . | {'scope': 'all', 'criterion': 'all', 'value': 'n/a', 'result': '10', 'col': '2', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_all', 'args': ['all_rows', 'circuit'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose circuit record is arbitrary .', 'tostr': 'filter_all { all_rows ; circuit }'}], 'result': '10', 'ind': 1, 'tostr': 'count { filter_all { all_rows ; circuit } }', 'tointer': 'select the rows whose circuit record is arbitrary . the number of such rows is 10 .'}, '10'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_all { all_rows ; circuit } } ; 10 } = true', 'tointer': 'select the rows whose circuit record is arbitrary . the number of such rows is 10 .'} | eq { count { filter_all { all_rows ; circuit } } ; 10 } = true | select the rows whose circuit record is arbitrary . the number of such rows is 10 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_all_0': 0, 'all_rows_4': 4, 'circuit_5': 5, '10_6': 6} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_all_0': 'filter_all', 'all_rows_4': 'all_rows', 'circuit_5': 'circuit', '10_6': '10'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_all_0': [1], 'all_rows_4': [0], 'circuit_5': [0], '10_6': [2]} | ['race title', 'circuit', 'city / state', 'date', 'winner', 'team'] | [['calder', 'calder park raceway', 'melbourne , victoria', '13 - 15 mar', 'wayne gardner', 'wayne gardner racing'], ['phillip island', 'phillip island grand prix circuit', 'phillip island , victoria', '11 - 13 apr', 'russell ingall', 'castrol perkins motorsport'], ['sandown', 'sandown international raceway', 'melbourne , victoria', '25 - 27 apr', 'glenn seton', 'glenn seton racing'], ['symmons plains', 'symmons plains raceway', 'launceston , tasmania', '2 - 4 may', 'greg murphy', 'holden racing team'], ['winton', 'winton motor raceway', 'benalla , victoria', '16 - 18 may', 'russell ingall', 'castrol perkins motorsport'], ['eastern creek', 'eastern creek raceway', 'sydney , new south wales', '23 - 25 may', 'glenn seton', 'glenn seton racing'], ['lakeside', 'lakeside international raceway', 'brisbane , queensland', '13 - 15 jun', 'john bowe', 'dick johnson racing'], ['wanneroo', 'barbagallo raceway', 'perth , western australia', '4 - 6 jul', 'peter brock', 'holden racing team'], ['mallala', 'mallala motor sport park', 'adelaide , south australia', '11 - 13 jul', 'greg murphy', 'holden racing team'], ['oran park', 'oran park raceway', 'sydney , new south wales', '1 - 3 aug', 'greg murphy', 'holden racing team']] |
gulf coast athletic conference | https://en.wikipedia.org/wiki/Gulf_Coast_Athletic_Conference | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10577579-3.html.csv | aggregation | of colleges formerly in the gulf coast athletic conference , average enrollment of those who joined after 1981 is 2767 . | {'scope': 'subset', 'col': '7', 'type': 'average', 'result': '2767', 'subset': {'col': '8', 'criterion': 'greater_than', 'value': '1981'}} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'joined', '1981'], 'result': None, 'ind': 0, 'tostr': 'filter_greater { all_rows ; joined ; 1981 }', 'tointer': 'select the rows whose joined record is greater than 1981 .'}, 'enrollment'], 'result': '2767', 'ind': 1, 'tostr': 'avg { filter_greater { all_rows ; joined ; 1981 } ; enrollment }'}, '2767'], 'result': True, 'ind': 2, 'tostr': 'round_eq { avg { filter_greater { all_rows ; joined ; 1981 } ; enrollment } ; 2767 } = true', 'tointer': 'select the rows whose joined record is greater than 1981 . the average of the enrollment record of these rows is 2767 .'} | round_eq { avg { filter_greater { all_rows ; joined ; 1981 } ; enrollment } ; 2767 } = true | select the rows whose joined record is greater than 1981 . the average of the enrollment record of these rows is 2767 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'avg_1': 1, 'filter_greater_0': 0, 'all_rows_4': 4, 'joined_5': 5, '1981_6': 6, 'enrollment_7': 7, '2767_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'avg_1': 'avg', 'filter_greater_0': 'filter_greater', 'all_rows_4': 'all_rows', 'joined_5': 'joined', '1981_6': '1981', 'enrollment_7': 'enrollment', '2767_8': '2767'} | {'eq_2': [3], 'result_3': [], 'avg_1': [2], 'filter_greater_0': [1], 'all_rows_4': [0], 'joined_5': [0], '1981_6': [0], 'enrollment_7': [1], '2767_8': [2]} | ['institution', 'location', 'mens nickname', 'womens nickname', 'founded', 'type', 'enrollment', 'joined', 'left', 'current conference', 'classification'] | [['belhaven college', 'jackson , mississippi', 'blazers', 'blazers', '1883', 'private / ( presbyterian church usa )', '1500', '1981 2002', '2000 2010', 'ssac', 'naia division i'], ['louisiana college', 'pineville , louisiana', 'wildcats', 'lady wildcats', '1906', 'private / ( louisiana baptist convention )', '1000', '1981', '2000', 'american southwest', 'ncaa division iii'], ['louisiana state university in shreveport', 'shreveport , louisiana', 'pilots', 'lady pilots', '1967', 'public', '4200', '2000', '2010', 'rrac', 'naia division i'], ['loyola university new orleans', 'new orleans , louisiana', 'wolfpack', 'wolfpack', '1904', 'private / ( catholic )', '2600', '1995', '2010', 'ssac', 'naia division i'], ['spring hill college', 'mobile , alabama', 'badgers', 'lady badgers', '1830', 'private / ( catholic )', '1300', '1981', '2010', 'ssac', 'naia division i'], ['university of mobile', 'mobile , alabama', 'rams', 'lady rams', '1961', 'private / ( alabama baptist state convention )', '1500', '1985', '2010', 'ssac', 'naia division i'], ['william carey university', 'hattiesburg , mississippi', 'crusaders', 'lady crusaders', '1906', 'private / ( mississippi baptist convention )', '4418', '1981', '2010', 'ssac', 'naia division i']] |
2009 beach volleyball world championships | https://en.wikipedia.org/wiki/2009_Beach_Volleyball_World_Championships | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18246956-16.html.csv | superlative | 45:40 is the highest total in 2009 beach volleyball world championships . | {'scope': 'all', 'col_superlative': '5', 'row_superlative': '5', 'value_mentioned': 'yes', 'max_or_min': 'max', 'other_col': 'n/a', 'subset': None} | {'func': 'eq', 'args': [{'func': 'max', 'args': ['all_rows', 'total'], 'result': '45:40', 'ind': 0, 'tostr': 'max { all_rows ; total }', 'tointer': 'the maximum total record of all rows is 45:40 .'}, '45:40'], 'result': True, 'ind': 1, 'tostr': 'eq { max { all_rows ; total } ; 45:40 } = true', 'tointer': 'the maximum total record of all rows is 45:40 .'} | eq { max { all_rows ; total } ; 45:40 } = true | the maximum total record of all rows is 45:40 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'max_0': 0, 'all_rows_3': 3, 'total_4': 4, '45:40_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'max_0': 'max', 'all_rows_3': 'all_rows', 'total_4': 'total', '45:40_5': '45:40'} | {'eq_1': [2], 'result_2': [], 'max_0': [1], 'all_rows_3': [0], 'total_4': [0], '45:40_5': [1]} | ['date', 'score', 'set 1', 'set 2', 'total'] | [['3 july', '0 - 2', '11:21', '11:21', '22:42'], ['3 july', '0 - 2', '11:21', '12:21', '23:42'], ['3 july', '0 - 2', '24:26', '13:21', '37:47'], ['3 july', '2 - 0', '21:18', '21:16', '42:34'], ['3 july', '2 - 0', '21:18', '24:22', '45:40'], ['3 july', '2 - 0', '21:12', '21:18', '42:30'], ['3 july', '0 - 2', '10:21', '13:21', '23:42'], ['3 july', '2 - 0', '21:17', '21:12', '42:29']] |
2010 - 11 miami heat season | https://en.wikipedia.org/wiki/2010%E2%80%9311_Miami_Heat_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27713030-8.html.csv | majority | most of the high assists per game was by lebron james in december during the 2010 - 11 miami heat season . | {'scope': 'all', 'col': '7', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'lebron james', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'high assists', 'lebron james'], 'result': True, 'ind': 0, 'tointer': 'for the high assists records of all rows , most of them fuzzily match to lebron james .', 'tostr': 'most_eq { all_rows ; high assists ; lebron james } = true'} | most_eq { all_rows ; high assists ; lebron james } = true | for the high assists records of all rows , most of them fuzzily match to lebron james . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'high assists_3': 3, 'lebron james_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'high assists_3': 'high assists', 'lebron james_4': 'lebron james'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'high assists_3': [0], 'lebron james_4': [0]} | ['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record'] | [['19', 'december 1', 'detroit', 'w 97 - 72 ( ot )', 'lebron james ( 18 )', 'chris bosh , james jones ( 7 )', 'mario chalmers ( 6 )', 'american airlines arena 19600', '11 - 8'], ['20', 'december 2', 'cleveland', 'w 118 - 90 ( ot )', 'lebron james ( 38 )', 'dwyane wade ( 9 )', 'dwyane wade ( 9 )', 'quicken loans arena 20562', '12 - 8'], ['21', 'december 4', 'atlanta', 'w 89 - 77 ( ot )', 'chris bosh ( 27 )', 'chris bosh , dwyane wade ( 10 )', 'mario chalmers , lebron james ( 4 )', 'american airlines arena 19600', '13 - 8'], ['22', 'december 6', 'milwaukee', 'w 88 - 78 ( ot )', 'dwyane wade ( 25 )', 'dwyane wade ( 14 )', 'lebron james ( 6 )', 'bradley center 17167', '14 - 8'], ['23', 'december 8', 'utah', 'w 111 - 98 ( ot )', 'lebron james ( 33 )', 'žydrūnas ilgauskas ( 10 )', 'lebron james ( 9 )', 'energysolutions arena 19911', '15 - 8'], ['24', 'december 10', 'golden state', 'w 106 - 84 ( ot )', 'dwyane wade ( 34 )', 'dwyane wade ( 9 )', 'lebron james ( 9 )', 'oracle arena 20036', '16 - 8'], ['25', 'december 11', 'sacramento', 'w 104 - 83 ( ot )', 'dwyane wade ( 36 )', 'chris bosh ( 17 )', 'dwyane wade ( 6 )', 'arco arena 16396', '17 - 8'], ['26', 'december 13', 'new orleans', 'w 96 - 84 ( ot )', 'dwyane wade ( 32 )', 'chris bosh ( 11 )', 'lebron james ( 7 )', 'american airlines arena 19600', '18 - 8'], ['27', 'december 15', 'cleveland', 'w 101 - 95 ( ot )', 'dwyane wade ( 28 )', 'lebron james ( 13 )', 'lebron james ( 5 )', 'american airlines arena 19899', '19 - 8'], ['28', 'december 17', 'new york', 'w 113 - 91 ( ot )', 'lebron james ( 32 )', 'lebron james ( 11 )', 'lebron james ( 10 )', 'madison square garden 19763', '20 - 8'], ['29', 'december 18', 'washington', 'w 95 - 94 ( ot )', 'lebron james ( 32 )', 'chris bosh ( 9 )', 'lebron james ( 6 )', 'verizon center 20278', '21 - 8'], ['30', 'december 20', 'dallas', 'l 96 - 98 ( ot )', 'dwyane wade ( 22 )', 'lebron james ( 10 )', 'lebron james , dwyane wade ( 7 )', 'american airlines arena 20178', '21 - 9'], ['31', 'december 23', 'phoenix', 'w 95 - 83 ( ot )', 'lebron james ( 36 )', 'chris bosh ( 11 )', 'lebron james ( 4 )', 'us airways center 18422', '22 - 9'], ['32', 'december 25', 'la lakers', 'w 96 - 80 ( ot )', 'lebron james ( 27 )', 'chris bosh ( 13 )', 'lebron james ( 10 )', 'staples center 18997', '23 - 9']] |
1970 - 71 cleveland cavaliers season | https://en.wikipedia.org/wiki/1970%E2%80%9371_Cleveland_Cavaliers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16275352-7.html.csv | count | in the 1970 - 71 season , the cleveland cavaliers played agains the buffalo braves 3 times . | {'scope': 'all', 'criterion': 'equal', 'value': 'buffalo braves', 'result': '3', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'buffalo braves'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opponent record fuzzily matches to buffalo braves .', 'tostr': 'filter_eq { all_rows ; opponent ; buffalo braves }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; opponent ; buffalo braves } }', 'tointer': 'select the rows whose opponent record fuzzily matches to buffalo braves . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; opponent ; buffalo braves } } ; 3 } = true', 'tointer': 'select the rows whose opponent record fuzzily matches to buffalo braves . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; opponent ; buffalo braves } } ; 3 } = true | select the rows whose opponent record fuzzily matches to buffalo braves . 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, 'opponent_5': 5, 'buffalo braves_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', 'opponent_5': 'opponent', 'buffalo braves_6': 'buffalo braves', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'opponent_5': [0], 'buffalo braves_6': [0], '3_7': [2]} | ['date', 'h / a / n', 'opponent', 'score', 'record'] | [['january 1', 'h', 'baltimore bullets', '105 - 128', '5 - 40'], ['january 2', 'a', 'milwaukee bucks', '73 - 118', '5 - 41'], ['january 4', 'h', 'portland trail blazers', '106 - 119', '5 - 42'], ['january 6', 'h', 'new york knicks', '94 - 127', '5 - 43'], ['january 7', 'h', 'los angeles lakers', '105 - 110', '5 - 44'], ['january 9', 'h', 'buffalo braves', '111 - 89', '6 - 44'], ['january 14', 'a', 'detroit pistons', '106 - 108', '6 - 45'], ['january 16', 'a', 'philadelphia 76ers', '96 - 115', '6 - 46'], ['january 19', 'n', 'buffalo braves', '111 - 79', '7 - 46'], ['january 24', 'a', 'boston celtics', '110 - 121', '7 - 47'], ['january 25', 'h', 'boston celtics', '117 - 116', '8 - 47'], ['january 27', 'h', 'portland trail blazers', '118 - 104', '9 - 47'], ['january 29', 'a', 'atlanta hawks', '111 - 119', '9 - 48'], ['january 31', 'h', 'buffalo braves', '117 - 108', '10 - 48']] |
primera división de fútbol profesional apertura 2008 | https://en.wikipedia.org/wiki/Primera_Divisi%C3%B3n_de_F%C3%BAtbol_Profesional_Apertura_2008 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18522916-4.html.csv | majority | the majority of matches in the primera división de fútbol profesional apertura 2008 took place at night . | {'scope': 'all', 'col': '9', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'night', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'time of day', 'night'], 'result': True, 'ind': 0, 'tointer': 'for the time of day records of all rows , most of them fuzzily match to night .', 'tostr': 'most_eq { all_rows ; time of day ; night } = true'} | most_eq { all_rows ; time of day ; night } = true | for the time of day records of all rows , most of them fuzzily match to night . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'time of day_3': 3, 'night_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'time of day_3': 'time of day', 'night_4': 'night'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'time of day_3': [0], 'night_4': [0]} | ['attendance', 'round', 'date', 'home', 'score', 'away', 'venue', 'weekday', 'time of day'] | [['14403', 'final', '21 december 2008', 'chalatenango', '3 - 3', 'metapán', 'estadio cuscatlán', 'sunday', 'afternoon'], ['11463', 'semifinal - 2nd leg', '13 december 2008', 'fas', '1 - 3', 'metapán', 'estadio oscar quiteño', 'saturday', 'night'], ['7690', 'round 2', '6 august 2008', 'águila', '3 - 1', 'fas', 'estadio juan francisco barraza', 'wednesday', 'night'], ['6997', 'round 16', '12 november 2008', 'fas', '1 - 0', 'águila', 'estadio oscar quiteño', 'wednesday', 'night'], ['6156', 'round 8', '20 september 2008', 'águila', '1 - 0', 'alianza', 'estadio juan francisco barraza', 'saturday', 'twilight'], ['5815', 'round 15', '15 november 2008', 'fas', '1 - 1', 'metapán', 'estadio oscar quiteño', 'saturday', 'night'], ['5307', 'round 2', '6 august 2008', 'alianza', '3 - 1', 'independiente', 'estadio cuscatlán', 'wednesday', 'afternoon'], ['5122', 'semifinal - 2nd leg', '13 december 2008', 'águila', '1 - 0', 'chalatenango', 'estadio juan francisco barraza', 'saturday', 'night'], ['4800', 'semifinal - 1st leg', '7 december 2008', 'chalatenango', '3 - 0', 'águila', 'estadio josé gregorio martínez', 'sunday', 'afternoon'], ['4722', 'round 13', '5 november 2008', 'águila', '3 - 2', 'firpo', 'estadio juan francisco barraza', 'wednesday', 'night'], ['4510', 'round 3', '9 august 2008', 'firpo', '1 - 2', 'alianza', 'estadio sergio torres', 'saturday', 'night']] |
united states house of representatives elections , 1962 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1962 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1341884-12.html.csv | aggregation | the median of the first-elected year of the georgia-representing incumbents in the united states house of representatives elections of '62 is 1953.5 . | {'scope': 'all', 'col': '4', 'type': 'average', 'result': '1953.5', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'first elected'], 'result': '1953.5', 'ind': 0, 'tostr': 'avg { all_rows ; first elected }'}, '1953.5'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; first elected } ; 1953.5 } = true', 'tointer': 'the average of the first elected record of all rows is 1953.5 .'} | round_eq { avg { all_rows ; first elected } ; 1953.5 } = true | the average of the first elected record of all rows is 1953.5 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'first elected_4': 4, '1953.5_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'first elected_4': 'first elected', '1953.5_5': '1953.5'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'first elected_4': [0], '1953.5_5': [1]} | ['district', 'incumbent', 'party', 'first elected', 'result', 'candidates'] | [['georgia 1', 'george elliott hagan', 'democratic', '1960', 're - elected', 'george elliott hagan ( d ) unopposed'], ['georgia 2', 'j l pilcher', 'democratic', '1953', 're - elected', 'j l pilcher ( d ) unopposed'], ['georgia 3', 'tic forrester', 'democratic', '1950', 're - elected', 'tic forrester ( d ) unopposed'], ['georgia 4', 'john james flynt , jr', 'democratic', '1954', 're - elected', 'john james flynt , jr ( d ) unopposed'], ['georgia 6', 'carl vinson', 'democratic', '1914', 're - elected', 'carl vinson ( d ) unopposed'], ['georgia 7', 'john w davis', 'democratic', '1960', 're - elected', 'john w davis ( d ) 72.4 % e ralph ivey ( r ) 27.6 %'], ['georgia 8', 'iris faircloth blitch', 'democratic', '1954', 'retired democratic hold', 'j russell tuten ( d ) unopposed'], ['georgia 9', 'phillip m landrum', 'democratic', '1952', 're - elected', 'phillip m landrum ( d ) unopposed']] |
the great british bake off | https://en.wikipedia.org/wiki/The_Great_British_Bake_Off | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-28962227-1.html.csv | comparative | edd kimber won the series before joanne wheatley won . | {'row_1': '1', 'row_2': '3', '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', 'winner', 'edd kimber'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose winner record fuzzily matches to edd kimber .', 'tostr': 'filter_eq { all_rows ; winner ; edd kimber }'}, 'premiere'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; winner ; edd kimber } ; premiere }', 'tointer': 'select the rows whose winner record fuzzily matches to edd kimber . take the premiere record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'winner', 'joanne wheatley'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose winner record fuzzily matches to joanne wheatley .', 'tostr': 'filter_eq { all_rows ; winner ; joanne wheatley }'}, 'premiere'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; winner ; joanne wheatley } ; premiere }', 'tointer': 'select the rows whose winner record fuzzily matches to joanne wheatley . take the premiere record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; winner ; edd kimber } ; premiere } ; hop { filter_eq { all_rows ; winner ; joanne wheatley } ; premiere } } = true', 'tointer': 'select the rows whose winner record fuzzily matches to edd kimber . take the premiere record of this row . select the rows whose winner record fuzzily matches to joanne wheatley . take the premiere record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; winner ; edd kimber } ; premiere } ; hop { filter_eq { all_rows ; winner ; joanne wheatley } ; premiere } } = true | select the rows whose winner record fuzzily matches to edd kimber . take the premiere record of this row . select the rows whose winner record fuzzily matches to joanne wheatley . take the premiere 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, 'winner_7': 7, 'edd kimber_8': 8, 'premiere_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'winner_11': 11, 'joanne wheatley_12': 12, 'premiere_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', 'winner_7': 'winner', 'edd kimber_8': 'edd kimber', 'premiere_9': 'premiere', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'winner_11': 'winner', 'joanne wheatley_12': 'joanne wheatley', 'premiere_13': 'premiere'} | {'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'winner_7': [0], 'edd kimber_8': [0], 'premiere_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'winner_11': [1], 'joanne wheatley_12': [1], 'premiere_13': [3]} | ['series', 'premiere', 'finale', 'runners - up', 'winner'] | [['1', '17 august 2010', '21 september 2010', 'miranda gore browne', 'edd kimber'], ['1', '17 august 2010', '21 september 2010', 'ruth clemens', 'edd kimber'], ['2', '14 august 2011', '4 october 2011', 'holly bell', 'joanne wheatley'], ['2', '14 august 2011', '4 october 2011', 'mary - anne boermans', 'joanne wheatley'], ['3', '14 august 2012', '16 october 2012', 'brendan lynch', 'john whaite'], ['3', '14 august 2012', '16 october 2012', 'james morton', 'john whaite'], ['4', '20 august 2013', '22 october 2013', 'kimberley wilson', 'frances quinn']] |
1988 - 89 philadelphia flyers season | https://en.wikipedia.org/wiki/1988%E2%80%9389_Philadelphia_Flyers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14323142-3.html.csv | aggregation | in the 1988-89 philadelphia flyers season , the average number of points was 16.88 . | {'scope': 'all', 'col': '6', 'type': 'average', 'result': '16.88', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'points'], 'result': '16.88', 'ind': 0, 'tostr': 'avg { all_rows ; points }'}, '16.88'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; points } ; 16.88 } = true', 'tointer': 'the average of the points record of all rows is 16.88 .'} | round_eq { avg { all_rows ; points } ; 16.88 } = true | the average of the points record of all rows is 16.88 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'points_4': 4, '16.88_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'points_4': 'points', '16.88_5': '16.88'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'points_4': [0], '16.88_5': [1]} | ['game', 'november', 'opponent', 'score', 'record', 'points'] | [['12', '1', 'new jersey devils', '2 - 3', '6 - 6 - 0', '12'], ['13', '3', 'vancouver canucks', '2 - 5', '6 - 7 - 0', '12'], ['14', '4', 'detroit red wings', '4 - 3', '7 - 7 - 0', '14'], ['15', '6', 'pittsburgh penguins', '5 - 4', '8 - 7 - 0', '16'], ['16', '9', 'new york rangers', '3 - 5', '8 - 8 - 0', '16'], ['17', '10', 'calgary flames', '2 - 3 ot', '8 - 9 - 0', '16'], ['18', '12', 'detroit red wings', '4 - 5', '8 - 10 - 0', '16'], ['19', '15', 'new york rangers', '3 - 3 ot', '8 - 10 - 1', '17'], ['20', '17', 'st louis blues', '1 - 3', '8 - 11 - 1', '17'], ['21', '19', 'quebec nordiques', '5 - 6', '8 - 12 - 1', '17'], ['22', '20', 'new jersey devils', '7 - 1', '9 - 12 - 1', '19'], ['23', '22', 'los angeles kings', '1 - 6', '9 - 13 - 1', '19'], ['24', '24', 'boston bruins', '1 - 2 ot', '9 - 14 - 1', '19'], ['25', '26', 'pittsburgh penguins', '3 - 4', '9 - 15 - 1', '19'], ['26', '27', 'buffalo sabres', '3 - 7', '9 - 16 - 1', '19'], ['27', '29', 'boston bruins', '5 - 1', '10 - 16 - 1', '21']] |
rupert keegan | https://en.wikipedia.org/wiki/Rupert_Keegan | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1226565-1.html.csv | aggregation | the average amount of points rupert keegan scored was 0 . | {'scope': 'all', 'col': '5', 'type': 'average', 'result': '0', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'pts'], 'result': '0', 'ind': 0, 'tostr': 'avg { all_rows ; pts }'}, '0'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; pts } ; 0 } = true', 'tointer': 'the average of the pts record of all rows is 0 .'} | round_eq { avg { all_rows ; pts } ; 0 } = true | the average of the pts record of all rows is 0 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'pts_4': 4, '0_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'pts_4': 'pts', '0_5': '0'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'pts_4': [0], '0_5': [1]} | ['year', 'entrant', 'chassis', 'engine', 'pts'] | [['1977', 'penthouse rizla racing', 'hesketh 308e', 'ford v8', '0'], ['1978', 'team surtees', 'surtees ts19', 'ford v8', '0'], ['1978', 'team surtees', 'surtees ts20', 'ford v8', '0'], ['1980', 'ram penthouse rizla racing', 'williams fw07', 'ford v8', '0'], ['1982', 'rothmans march grand prix team', 'march 821', 'ford v8', '0']] |
united states house of representatives elections , 2012 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_2012 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-25030512-36.html.csv | ordinal | mel watt had the highest percentage ratio among all candidates of the 2012 house of representatives elections . | {'row': '8', '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', 'candidates', '1'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; candidates ; 1 }'}, 'incumbent'], 'result': 'mel watt', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; candidates ; 1 } ; incumbent }'}, 'mel watt'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; candidates ; 1 } ; incumbent } ; mel watt } = true', 'tointer': 'select the row whose candidates record of all rows is 1st maximum . the incumbent record of this row is mel watt .'} | eq { hop { nth_argmax { all_rows ; candidates ; 1 } ; incumbent } ; mel watt } = true | select the row whose candidates record of all rows is 1st maximum . the incumbent record of this row is mel watt . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'candidates_5': 5, '1_6': 6, 'incumbent_7': 7, 'mel watt_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', 'candidates_5': 'candidates', '1_6': '1', 'incumbent_7': 'incumbent', 'mel watt_8': 'mel watt'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'candidates_5': [0], '1_6': [0], 'incumbent_7': [1], 'mel watt_8': [2]} | ['district', 'incumbent', 'party', 'first elected', 'result', 'candidates'] | [['north carolina 3', 'walter jones jr', 'republican', '1994', 're - elected', 'walter jones jr ( r ) 63.2 % erik anderson ( d ) 36.8 %'], ['north carolina 4', 'david price', 'democratic', '1996', 're - elected', "david price ( d ) 74.4 % tim d'annunzio ( r ) 25.6 %"], ['north carolina 6', 'howard coble', 'republican', '1984', 're - elected', 'howard coble ( r ) 60.9 % tony foriest ( d ) 39.1 %'], ['north carolina 7', 'mike mcintyre', 'democratic', '1996', 're - elected', 'mike mcintyre ( d ) 50.1 % david rouzer ( r ) 49.9 %'], ['north carolina 8', 'larry kissell', 'democratic', '2008', 'lost re - election republican gain', 'richard hudson ( r ) 54.1 % larry kissell ( d ) 45.9 %'], ['north carolina 10', 'patrick mchenry', 'republican', '2004', 're - elected', 'patrick mchenry ( r ) 57.0 % patsy keever ( d ) 43.0 %'], ['north carolina 11', 'heath shuler', 'democratic', '2006', 'retired republican gain', 'mark meadows ( r ) 57.4 % hayden rogers ( d ) 42.6 %'], ['north carolina 12', 'mel watt', 'democratic', '1992', 're - elected', 'mel watt ( d ) 79.7 % jack brosch ( r ) 20.3 %']] |
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