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2008 manx grand prix
|
https://en.wikipedia.org/wiki/2008_Manx_Grand_Prix
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18649514-10.html.csv
|
ordinal
|
ross johnson had the 3rd fastest time in the 2008 manx grand prix .
|
{'row': '3', 'col': '5', 'order': '3', 'col_other': '2', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}
|
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'time', '3'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; time ; 3 }'}, 'rider'], 'result': 'ross johnson', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; time ; 3 } ; rider }'}, 'ross johnson'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; time ; 3 } ; rider } ; ross johnson } = true', 'tointer': 'select the row whose time record of all rows is 3rd minimum . the rider record of this row is ross johnson .'}
|
eq { hop { nth_argmin { all_rows ; time ; 3 } ; rider } ; ross johnson } = true
|
select the row whose time record of all rows is 3rd minimum . the rider record of this row is ross johnson .
|
3
|
3
|
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'time_5': 5, '3_6': 6, 'rider_7': 7, 'ross johnson_8': 8}
|
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmin_0': 'nth_argmin', 'all_rows_4': 'all_rows', 'time_5': 'time', '3_6': '3', 'rider_7': 'rider', 'ross johnson_8': 'ross johnson'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'time_5': [0], '3_6': [0], 'rider_7': [1], 'ross johnson_8': [2]}
|
['rank', 'rider', 'team', 'speed', 'time']
|
[['1', 'daniel kneen', '400cc honda', '106.619 mph', '1:03.41.86'], ['2', 'kirk farrow', '400cc honda', '105.905 mph', '1:04.07.62'], ['3', 'ross johnson', '400cc kawasaki', '105.161 mph', '1:04.34.85'], ['4', 'tim sayers', '400cc kawasaki', '105.009 mph', '1:04.40.47'], ['5', 'dan hobson', '400c honda', '104.574 mph', '1:04.56.60'], ['6', 'marie costello', '400cc honda', '103.668 mph', '1:05.30.66'], ['7', 'mike minns', '650cc kawasaki', '103.659 mph', '1:05.31.01'], ['8', 'anthony davies', '399cc yamaha', '103.389 mph', '1:05.41.28'], ['9', 'anthony redmond', '650cc kawasaki', '103.047 mph', '1:05.54.35'], ['10', 'alistair haworth', '400cc yamaha', '103.015 mph', '1:05.55.58']]
|
2008 brazilian grand prix
|
https://en.wikipedia.org/wiki/2008_Brazilian_Grand_Prix
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14270784-2.html.csv
|
majority
|
the majority of drivers in the 2008 brazilian grand prix completed 70 or more laps .
|
{'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'greater_than_eq', 'value': '70', 'subset': None}
|
{'func': 'most_greater_eq', 'args': ['all_rows', 'laps', '70'], 'result': True, 'ind': 0, 'tointer': 'for the laps records of all rows , most of them are greater than or equal to 70 .', 'tostr': 'most_greater_eq { all_rows ; laps ; 70 } = true'}
|
most_greater_eq { all_rows ; laps ; 70 } = true
|
for the laps records of all rows , most of them are greater than or equal to 70 .
|
1
|
1
|
{'most_greater_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'laps_3': 3, '70_4': 4}
|
{'most_greater_eq_0': 'most_greater_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'laps_3': 'laps', '70_4': '70'}
|
{'most_greater_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'laps_3': [0], '70_4': [0]}
|
['driver', 'constructor', 'laps', 'time / retired', 'grid']
|
[['felipe massa', 'ferrari', '71', '1:34:11.435', '1'], ['fernando alonso', 'renault', '71', '+ 13.298', '6'], ['kimi räikkönen', 'ferrari', '71', '+ 16.235', '3'], ['sebastian vettel', 'toro rosso - ferrari', '71', '+ 38.011', '7'], ['lewis hamilton', 'mclaren - mercedes', '71', '+ 38.907', '4'], ['timo glock', 'toyota', '71', '+ 44.368', '10'], ['heikki kovalainen', 'mclaren - mercedes', '71', '+ 55.074', '5'], ['jarno trulli', 'toyota', '71', '+ 1:08.433', '2'], ['mark webber', 'red bull - renault', '71', '+ 1:19.666', '12'], ['nick heidfeld', 'bmw sauber', '70', '+ 1 lap', '8'], ['robert kubica', 'bmw sauber', '70', '+ 1 lap', '13'], ['nico rosberg', 'williams - toyota', '70', '+ 1 lap', '18'], ['jenson button', 'honda', '70', '+ 1 lap', '17'], ['sébastien bourdais', 'toro rosso - ferrari', '70', '+ 1 lap', '9'], ['rubens barrichello', 'honda', '70', '+ 1 lap', '15'], ['adrian sutil', 'force india - ferrari', '69', '+ 2 laps', '20'], ['kazuki nakajima', 'williams - toyota', '69', '+ 2 laps', '16'], ['giancarlo fisichella', 'force india - ferrari', '69', '+ 2 laps', '19'], ['nelson piquet jr', 'renault', '0', 'accident', '11'], ['david coulthard', 'red bull - renault', '0', 'collision', '14']]
|
list of ngc objects ( 2001 - 3000 )
|
https://en.wikipedia.org/wiki/List_of_NGC_objects_%282001%E2%80%933000%29
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11097664-8.html.csv
|
comparative
|
ngc 2736 is a diffuse nebula whereas ngc 2770 is a spiral galaxy .
|
{'row_1': '2', 'row_2': '3', 'col': '2', 'col_other': '1', 'relation': 'not_equal', 'record_mentioned': 'yes', 'diff_result': None}
|
{'func': 'and', 'args': [{'func': 'not_str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'ngc number', '2736'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose ngc number record fuzzily matches to 2736 .', 'tostr': 'filter_eq { all_rows ; ngc number ; 2736 }'}, 'object type'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; ngc number ; 2736 } ; object type }', 'tointer': 'select the rows whose ngc number record fuzzily matches to 2736 . take the object type record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'ngc number', '2770'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose ngc number record fuzzily matches to 2770 .', 'tostr': 'filter_eq { all_rows ; ngc number ; 2770 }'}, 'object type'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; ngc number ; 2770 } ; object type }', 'tointer': 'select the rows whose ngc number record fuzzily matches to 2770 . take the object type record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'not_eq { hop { filter_eq { all_rows ; ngc number ; 2736 } ; object type } ; hop { filter_eq { all_rows ; ngc number ; 2770 } ; object type } }', 'tointer': 'select the rows whose ngc number record fuzzily matches to 2736 . take the object type record of this row . select the rows whose ngc number record fuzzily matches to 2770 . take the object type record of this row . the first record does not match to the second record .'}, {'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'ngc number', '2736'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose ngc number record fuzzily matches to 2736 .', 'tostr': 'filter_eq { all_rows ; ngc number ; 2736 }'}, 'object type'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; ngc number ; 2736 } ; object type }', 'tointer': 'select the rows whose ngc number record fuzzily matches to 2736 . take the object type record of this row .'}, 'diffuse nebula'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; ngc number ; 2736 } ; object type } ; diffuse nebula }', 'tointer': 'the object type record of the first row is diffuse nebula .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'ngc number', '2770'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose ngc number record fuzzily matches to 2770 .', 'tostr': 'filter_eq { all_rows ; ngc number ; 2770 }'}, 'object type'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; ngc number ; 2770 } ; object type }', 'tointer': 'select the rows whose ngc number record fuzzily matches to 2770 . take the object type record of this row .'}, 'spiral galaxy'], 'result': True, 'ind': 6, 'tostr': 'eq { hop { filter_eq { all_rows ; ngc number ; 2770 } ; object type } ; spiral galaxy }', 'tointer': 'the object type record of the second row is spiral galaxy .'}], 'result': True, 'ind': 7, 'tostr': 'and { eq { hop { filter_eq { all_rows ; ngc number ; 2736 } ; object type } ; diffuse nebula } ; eq { hop { filter_eq { all_rows ; ngc number ; 2770 } ; object type } ; spiral galaxy } }', 'tointer': 'the object type record of the first row is diffuse nebula . the object type record of the second row is spiral galaxy .'}], 'result': True, 'ind': 8, 'tostr': 'and { not_eq { hop { filter_eq { all_rows ; ngc number ; 2736 } ; object type } ; hop { filter_eq { all_rows ; ngc number ; 2770 } ; object type } } ; and { eq { hop { filter_eq { all_rows ; ngc number ; 2736 } ; object type } ; diffuse nebula } ; eq { hop { filter_eq { all_rows ; ngc number ; 2770 } ; object type } ; spiral galaxy } } } = true', 'tointer': 'select the rows whose ngc number record fuzzily matches to 2736 . take the object type record of this row . select the rows whose ngc number record fuzzily matches to 2770 . take the object type record of this row . the first record does not match to the second record . the object type record of the first row is diffuse nebula . the object type record of the second row is spiral galaxy .'}
|
and { not_eq { hop { filter_eq { all_rows ; ngc number ; 2736 } ; object type } ; hop { filter_eq { all_rows ; ngc number ; 2770 } ; object type } } ; and { eq { hop { filter_eq { all_rows ; ngc number ; 2736 } ; object type } ; diffuse nebula } ; eq { hop { filter_eq { all_rows ; ngc number ; 2770 } ; object type } ; spiral galaxy } } } = true
|
select the rows whose ngc number record fuzzily matches to 2736 . take the object type record of this row . select the rows whose ngc number record fuzzily matches to 2770 . take the object type record of this row . the first record does not match to the second record . the object type record of the first row is diffuse nebula . the object type record of the second row is spiral galaxy .
|
13
|
9
|
{'and_8': 8, 'result_9': 9, 'not_str_eq_4': 4, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_10': 10, 'ngc number_11': 11, '2736_12': 12, 'object type_13': 13, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_14': 14, 'ngc number_15': 15, '2770_16': 16, 'object type_17': 17, 'and_7': 7, 'str_eq_5': 5, 'diffuse nebula_18': 18, 'str_eq_6': 6, 'spiral galaxy_19': 19}
|
{'and_8': 'and', 'result_9': 'true', 'not_str_eq_4': 'not_str_eq', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_10': 'all_rows', 'ngc number_11': 'ngc number', '2736_12': '2736', 'object type_13': 'object type', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_14': 'all_rows', 'ngc number_15': 'ngc number', '2770_16': '2770', 'object type_17': 'object type', 'and_7': 'and', 'str_eq_5': 'str_eq', 'diffuse nebula_18': 'diffuse nebula', 'str_eq_6': 'str_eq', 'spiral galaxy_19': 'spiral galaxy'}
|
{'and_8': [9], 'result_9': [], 'not_str_eq_4': [8], 'str_hop_2': [4, 5], 'filter_str_eq_0': [2], 'all_rows_10': [0], 'ngc number_11': [0], '2736_12': [0], 'object type_13': [2], 'str_hop_3': [4, 6], 'filter_str_eq_1': [3], 'all_rows_14': [1], 'ngc number_15': [1], '2770_16': [1], 'object type_17': [3], 'and_7': [8], 'str_eq_5': [7], 'diffuse nebula_18': [5], 'str_eq_6': [7], 'spiral galaxy_19': [6]}
|
['ngc number', 'object type', 'constellation', 'right ascension ( j2000 )', 'declination ( j2000 )']
|
[['2715', 'spiral galaxy', 'camelopardalis', '09h08 m06 .1 s', 'degree05 ′ 07 ″'], ['2736', 'diffuse nebula', 'vela', '09h00 m', 'degree57 ′'], ['2770', 'spiral galaxy', 'lynx', '09h09 m33 .7 s', 'degree07 ′ 25 ″'], ['2775', 'spiral galaxy', 'cancer', '09h10 m20 .1 s', 'degree02 ′ 18 ″'], ['2787', 'lenticular galaxy', 'ursa major', '09h19 m18 .9 s', 'degree12 ′ 12 ″'], ['2798', 'spiral galaxy', 'lynx', '09h17 m23 .0 s', 'degree59 ′ 58 ″'], ['2799', 'irregular galaxy', 'lynx', '09h17 m31 .2 s', 'degree59 ′ 36 ″']]
|
tokushima vortis
|
https://en.wikipedia.org/wiki/Tokushima_Vortis
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1276456-1.html.csv
|
superlative
|
the 2007 season had a lower attendance than all other seasons .
|
{'scope': 'all', 'col_superlative': '5', 'row_superlative': '3', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '1', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'attendance / g'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; attendance / g }'}, 'season'], 'result': '2007', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; attendance / g } ; season }'}, '2007'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; attendance / g } ; season } ; 2007 } = true', 'tointer': 'select the row whose attendance / g record of all rows is minimum . the season record of this row is 2007 .'}
|
eq { hop { argmin { all_rows ; attendance / g } ; season } ; 2007 } = true
|
select the row whose attendance / g record of all rows is minimum . the season record of this row is 2007 .
|
3
|
3
|
{'eq_2': 2, 'result_3': 3, 'num_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'attendance / g_5': 5, 'season_6': 6, '2007_7': 7}
|
{'eq_2': 'eq', 'result_3': 'true', 'num_hop_1': 'num_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'attendance / g_5': 'attendance / g', 'season_6': 'season', '2007_7': '2007'}
|
{'eq_2': [3], 'result_3': [], 'num_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'attendance / g_5': [0], 'season_6': [1], '2007_7': [2]}
|
['season', 'div', 'tms', 'pos', 'attendance / g', 'j league cup', "emperor 's cup"]
|
[['2005', 'j2', '12', '9', '4366', '-', '4th round'], ['2006', 'j2', '13', '13', '3477', '-', '4th round'], ['2007', 'j2', '13', '13', '3289', '-', '4th round'], ['2008', 'j2', '15', '15', '3862', '-', '3rd round'], ['2009', 'j2', '18', '9', '4073', '-', '2nd round'], ['2010', 'j2', '19', '8', '4614', '-', '3rd round']]
|
list of 8 out of 10 cats episodes
|
https://en.wikipedia.org/wiki/List_of_8_Out_of_10_Cats_episodes
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-23292220-3.html.csv
|
ordinal
|
of the cats episodes , the episode with the second most recent first broadcast date was episode 3x07 .
|
{'row': '7', 'col': '2', 'order': '2', 'col_other': '1', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}
|
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'first broadcast', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; first broadcast ; 2 }'}, 'episode'], 'result': '3x07', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; first broadcast ; 2 } ; episode }'}, '3x07'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; first broadcast ; 2 } ; episode } ; 3x07 } = true', 'tointer': 'select the row whose first broadcast record of all rows is 2nd maximum . the episode record of this row is 3x07 .'}
|
eq { hop { nth_argmax { all_rows ; first broadcast ; 2 } ; episode } ; 3x07 } = true
|
select the row whose first broadcast record of all rows is 2nd maximum . the episode record of this row is 3x07 .
|
3
|
3
|
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'first broadcast_5': 5, '2_6': 6, 'episode_7': 7, '3x07_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', 'first broadcast_5': 'first broadcast', '2_6': '2', 'episode_7': 'episode', '3x07_8': '3x07'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'first broadcast_5': [0], '2_6': [0], 'episode_7': [1], '3x07_8': [2]}
|
['episode', 'first broadcast', 'seans team', 'daves team', 'scores']
|
[['3x01', '26 may 2006', 'david baddiel and ruth badger', 'alan carr and ulrika jonsson', '6 - 9'], ['3x02', '2 june 2006', 'debra stephenson and david walliams', 'frankie boyle and bez', '5 - 7'], ['3x03', '9 june 2006', 'peter serafinowicz and johnny vegas', 'reginald d hunter and jayne middlemiss', '4 - 8'], ['3x04', '16 june 2006', 'edith bowman and julian clary', 'dave johns and sally lindsay', '8 - 4'], ['3x05', '23 june 2006', 'krishnan guru - murthy and vic reeves', 'david walliams and louis walsh', '3 - 6'], ['3x06', '30 june 2006', 'germaine greer and phill jupitus', 'fiona allen and jason manford', '6 - 7'], ['3x07', '7 july 2006', 'emo philips and alex zane', 'trisha goddard and justin moorhouse', '3 - 6'], ['3x08', '14 july 2006', 'eamonn holmes and vic reeves', 'joan rivers and holly willoughby', '8 - 4']]
|
georgia in the eurovision song contest 2008
|
https://en.wikipedia.org/wiki/Georgia_in_the_Eurovision_Song_Contest_2008
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15427892-1.html.csv
|
count
|
according to the statistics of georgia results in the national final of eurovision song contest 2008 , among the artists earned places higher than 5 , 2 of them had results higher than 30.0 % .
|
{'scope': 'subset', 'criterion': 'greater_than', 'value': '30 %', 'result': '2', 'col': '4', 'subset': {'col': '5', 'criterion': 'less_than', 'value': '5'}}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_greater', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'place', '5'], 'result': None, 'ind': 0, 'tostr': 'filter_less { all_rows ; place ; 5 }', 'tointer': 'select the rows whose place record is less than 5 .'}, 'result', '30 %'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose place record is less than 5 . among these rows , select the rows whose result record is greater than 30 % .', 'tostr': 'filter_greater { filter_less { all_rows ; place ; 5 } ; result ; 30 % }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_greater { filter_less { all_rows ; place ; 5 } ; result ; 30 % } }', 'tointer': 'select the rows whose place record is less than 5 . among these rows , select the rows whose result record is greater than 30 % . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_greater { filter_less { all_rows ; place ; 5 } ; result ; 30 % } } ; 2 } = true', 'tointer': 'select the rows whose place record is less than 5 . among these rows , select the rows whose result record is greater than 30 % . the number of such rows is 2 .'}
|
eq { count { filter_greater { filter_less { all_rows ; place ; 5 } ; result ; 30 % } } ; 2 } = true
|
select the rows whose place record is less than 5 . among these rows , select the rows whose result record is greater than 30 % . the number of such rows is 2 .
|
4
|
4
|
{'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_greater_1': 1, 'filter_less_0': 0, 'all_rows_5': 5, 'place_6': 6, '5_7': 7, 'result_8': 8, '30%_9': 9, '2_10': 10}
|
{'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_greater_1': 'filter_greater', 'filter_less_0': 'filter_less', 'all_rows_5': 'all_rows', 'place_6': 'place', '5_7': '5', 'result_8': 'result', '30%_9': '30 %', '2_10': '2'}
|
{'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_greater_1': [2], 'filter_less_0': [1], 'all_rows_5': [0], 'place_6': [0], '5_7': [0], 'result_8': [1], '30%_9': [1], '2_10': [3]}
|
['draw', 'artist', 'song', 'result', 'place']
|
[['1', 'vivo', 'life', '1.3 %', '8'], ['2', 'aleko berdzenishvili', 'the beautiful girl', '1.3 %', '9'], ['3', 'salome gasviani', 'share your love', '32.9 %', '2'], ['4', 'irakli pirtskhalava', 'freedom', '8.3 %', '3'], ['5', 'diana gurtskaya', 'peace will come', '39.4 %', '1'], ['6', 'salome korkotashvili', 'captaine', '3.0 %', '6'], ['7', 'teatroni', 'sakartvelo itsvevs megobrebs', '2.3 %', '7'], ['8', 'tika patsatsia', 'never change', '4.0 %', '5'], ['9', '3 g', "i 'm free", '5.3 %', '4'], ['10', 'tako gachechiladze', 'me and my funky', '0.9 %', '10'], ['11', 'tamta chelidze', 'give me your love', '0.8 %', '11'], ['12', 'guga aptsiauri', "do n't look at me", '0.5 %', '12']]
|
dock jumping
|
https://en.wikipedia.org/wiki/Dock_jumping
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17371890-2.html.csv
|
count
|
the espn great outdoor games had 3 events from 2000 to 2011 .
|
{'scope': 'all', 'criterion': 'equal', 'value': 'espn great outdoor games', 'result': '3', 'col': '4', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'event', 'espn great outdoor games'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose event record fuzzily matches to espn great outdoor games .', 'tostr': 'filter_eq { all_rows ; event ; espn great outdoor games }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; event ; espn great outdoor games } }', 'tointer': 'select the rows whose event record fuzzily matches to espn great outdoor games . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; event ; espn great outdoor games } } ; 3 } = true', 'tointer': 'select the rows whose event record fuzzily matches to espn great outdoor games . the number of such rows is 3 .'}
|
eq { count { filter_eq { all_rows ; event ; espn great outdoor games } } ; 3 } = true
|
select the rows whose event record fuzzily matches to espn great outdoor games . 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, 'event_5': 5, 'espn great outdoor games_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', 'event_5': 'event', 'espn great outdoor games_6': 'espn great outdoor games', '3_7': '3'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'event_5': [0], 'espn great outdoor games_6': [0], '3_7': [2]}
|
['date', 'distance', 'handler', 'event', 'location']
|
[['7 july 2000', 'ft4in ( m )', 'beth gutteridge', 'espn great outdoor games', 'lake placid , ny'], ['8 july 2001', 'ft1in ( m )', 'mike wallace', 'espn great outdoor games', 'lake placid , ny'], ['20 february 2002', 'ft3in ( m )', 'mike jackson', 'indianapolis boat , sport & travel show', 'indianapolis , in'], ['1 may 2002', 'ft4in ( m )', 'john kline', 'espn2 super retriever series', 'northfield , mn'], ['6 july 2002', 'ft6in ( m )', 'mike jackson', 'espn great outdoor games', 'lake placid , ny'], ['7 august 2005', 'ft5in ( m )', 'kevin meese', 'bass pro shops', 'baltimore , md'], ['21 august 2005', 'ft7in ( m )', 'kevin meese', "big nickle time cabela 's", 'hamburg , pa'], ['9 october 2005', 'ft10in ( m )', 'kevin meese', 'bass pro shops', 'baltimore , md'], ['30 may 2010', 'ft11 .59 in ( m )', 'rande murphy', 'super retriever series crown championship', 'little rock , ar'], ['11 july 2010', 'ft1in ( m )', 'melissa ness', '2010 ukc premier', 'richmond , in'], ['29 may 2011', 'ft7in ( m )', 'tony lampert', 'super retriever series crown championship', 'little rock , ar'], ['07 oct 2011', 'ft11in ( m )', 'tony lampert', 'david letterman show , october 7 2011', 'new york , ny']]
|
1982 open championship
|
https://en.wikipedia.org/wiki/1982_Open_Championship
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18166348-7.html.csv
|
unique
|
nick price was the only player in the 1982 open championship from the country of zimbabwe .
|
{'scope': 'all', 'row': '3', 'col': '3', 'col_other': '2', 'criterion': 'equal', 'value': 'zimbabwe', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'zimbabwe'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose country record fuzzily matches to zimbabwe .', 'tostr': 'filter_eq { all_rows ; country ; zimbabwe }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; country ; zimbabwe } }', 'tointer': 'select the rows whose country record fuzzily matches to zimbabwe . 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', 'zimbabwe'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose country record fuzzily matches to zimbabwe .', 'tostr': 'filter_eq { all_rows ; country ; zimbabwe }'}, 'player'], 'result': 'nick price', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; country ; zimbabwe } ; player }'}, 'nick price'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; country ; zimbabwe } ; player } ; nick price }', 'tointer': 'the player record of this unqiue row is nick price .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; country ; zimbabwe } } ; eq { hop { filter_eq { all_rows ; country ; zimbabwe } ; player } ; nick price } } = true', 'tointer': 'select the rows whose country record fuzzily matches to zimbabwe . there is only one such row in the table . the player record of this unqiue row is nick price .'}
|
and { only { filter_eq { all_rows ; country ; zimbabwe } } ; eq { hop { filter_eq { all_rows ; country ; zimbabwe } ; player } ; nick price } } = true
|
select the rows whose country record fuzzily matches to zimbabwe . there is only one such row in the table . the player record of this unqiue row is nick price .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'country_7': 7, 'zimbabwe_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'player_9': 9, 'nick price_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', 'zimbabwe_8': 'zimbabwe', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'player_9': 'player', 'nick price_10': 'nick price'}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'country_7': [0], 'zimbabwe_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'player_9': [2], 'nick price_10': [3]}
|
['place', 'player', 'country', 'score', 'to par', 'money']
|
[['1', 'tom watson', 'united states', '69 + 71 + 74 + 70 = 284', '- 4', '32000'], ['t2', 'peter oosterhuis', 'england', '74 + 67 + 74 + 70 = 285', '- 3', '19300'], ['t2', 'nick price', 'zimbabwe', '69 + 69 + 74 + 73 = 285', '- 3', '19300'], ['t4', 'nick faldo', 'england', '73 + 73 + 71 + 69 = 286', '- 2', '11000'], ['t4', 'masahiro kuramoto', 'japan', '71 + 73 + 71 + 71 = 286', '- 2', '11000'], ['t4', 'tom purtzer', 'united states', '76 + 66 + 75 + 69 = 286', '- 2', '11000'], ['t4', 'des smyth', 'ireland', '70 + 69 + 74 + 73 = 286', '- 2', '11000'], ['t8', 'sandy lyle', 'scotland', '74 + 66 + 73 + 74 = 287', '- 1', '8750'], ['t8', 'fuzzy zoeller', 'united states', '73 + 71 + 73 + 70 = 287', '- 1', '8750'], ['t10', 'bobby clampett', 'united states', '67 + 66 + 78 + 77 = 288', 'e', '7350'], ['t10', 'jack nicklaus', 'united states', '77 + 70 + 72 + 69 = 288', 'e', '7350']]
|
2003 cfl draft
|
https://en.wikipedia.org/wiki/2003_CFL_Draft
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-21321804-3.html.csv
|
superlative
|
in round three of the 2003 cfl draft , patrick kabongo was the earliest pick .
|
{'scope': 'all', 'col_superlative': '1', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '3', 'subset': None}
|
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'pick'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; pick }'}, 'player'], 'result': 'patrick kabongo', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; pick } ; player }'}, 'patrick kabongo'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; pick } ; player } ; patrick kabongo } = true', 'tointer': 'select the row whose pick record of all rows is minimum . the player record of this row is patrick kabongo .'}
|
eq { hop { argmin { all_rows ; pick } ; player } ; patrick kabongo } = true
|
select the row whose pick record of all rows is minimum . the player record of this row is patrick kabongo .
|
3
|
3
|
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'pick_5': 5, 'player_6': 6, 'patrick kabongo_7': 7}
|
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'pick_5': 'pick', 'player_6': 'player', 'patrick kabongo_7': 'patrick kabongo'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'pick_5': [0], 'player_6': [1], 'patrick kabongo_7': [2]}
|
['pick', 'cfl team', 'player', 'position', 'college']
|
[['19', 'ottawa renegades', 'patrick kabongo', 'dt', 'nebraska'], ['20', 'edmonton eskimos', 'dounia whitehouse', 'cb', 'charleston southern'], ['21', 'hamilton tiger - cats', 'kevin scott', 'lb', 'california pa'], ['22', 'winnipeg blue bombers', 'todd krenbring', 'ol', 'regina'], ['23', 'saskatchewan roughriders', 'mike mccullough', 'lb', 'st francis xavier'], ['24', 'bc lions', 'carl gourgues', 'ol', 'laval'], ['25', 'calgary stampeders', 'mike labinjo', 'dl', 'michigan state'], ['26', 'edmonton eskimos', 'joseph bonaventura', 'lb', "saint mary 's"]]
|
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
|
aggregation
|
the average population of the local government areas in the outback region is 1759.42 .
|
{'scope': 'all', 'col': '5', 'type': 'average', 'result': '1759.42', 'subset': None}
|
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'pop 2006'], 'result': '1759.42', 'ind': 0, 'tostr': 'avg { all_rows ; pop 2006 }'}, '1759.42'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; pop 2006 } ; 1759.42 } = true', 'tointer': 'the average of the pop 2006 record of all rows is 1759.42 .'}
|
round_eq { avg { all_rows ; pop 2006 } ; 1759.42 } = true
|
the average of the pop 2006 record of all rows is 1759.42 .
|
2
|
2
|
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'pop 2006_4': 4, '1759.42_5': 5}
|
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'pop 2006_4': 'pop 2006', '1759.42_5': '1759.42'}
|
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'pop 2006_4': [0], '1759.42_5': [1]}
|
['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']]
|
northern state conference ( ihsaa )
|
https://en.wikipedia.org/wiki/Northern_State_Conference_%28IHSAA%29
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18936749-1.html.csv
|
superlative
|
bremen is the newest member school to join the northern state conference ( ihsaa ) .
|
{'scope': 'all', 'col_superlative': '7', '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', 'year joined'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; year joined }'}, 'school ( ihsaa id )'], 'result': 'bremen', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; year joined } ; school ( ihsaa id ) }'}, 'bremen'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; year joined } ; school ( ihsaa id ) } ; bremen } = true', 'tointer': 'select the row whose year joined record of all rows is maximum . the school ( ihsaa id ) record of this row is bremen .'}
|
eq { hop { argmax { all_rows ; year joined } ; school ( ihsaa id ) } ; bremen } = true
|
select the row whose year joined record of all rows is maximum . the school ( ihsaa id ) record of this row is bremen .
|
3
|
3
|
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'year joined_5': 5, 'school (ihsaa id)_6': 6, 'bremen_7': 7}
|
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'year joined_5': 'year joined', 'school (ihsaa id)_6': 'school ( ihsaa id )', 'bremen_7': 'bremen'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'year joined_5': [0], 'school (ihsaa id)_6': [1], 'bremen_7': [2]}
|
['school ( ihsaa id )', 'location', 'mascot', 'enrollment', 'ihsaa class', 'county', 'year joined']
|
[['bremen', 'bremen', 'lions', '505', 'aa', '50 marshall', '1989'], ['culver community', 'culver', 'cavaliers', '306', 'a', '50 marshall', '1977'], ['glenn', 'walkerton', 'falcons', '613', 'aaa', '71 st joseph', '1966'], ['jimtown', 'elkhart', 'jimmies', '642', 'aaa', '20 elkhart', '1966'], ['knox community', 'knox', 'redskins', '632', 'aaa', '75 starke', '1982'], ['laville', 'lakeville', 'lancers', '413', 'a', '71 st joseph', '1966'], ['new prairie', 'new carlisle', 'cougars', '859', 'aaaa', '46 laporte 71 st joseph', '1968'], ['triton', 'bourbon', 'trojans', '333', 'a', '50 marshall', '1980']]
|
1961 denver broncos season
|
https://en.wikipedia.org/wiki/1961_Denver_Broncos_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-17765888-1.html.csv
|
aggregation
|
the average attendance for the denver broncos in november 1961 is 13736 .
|
{'scope': 'subset', 'col': '7', 'type': 'average', 'result': '13736', 'subset': {'col': '2', 'criterion': 'fuzzily_match', 'value': 'november'}}
|
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'november'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; date ; november }', 'tointer': 'select the rows whose date record fuzzily matches to november .'}, 'attendance'], 'result': '13736', 'ind': 1, 'tostr': 'avg { filter_eq { all_rows ; date ; november } ; attendance }'}, '13736'], 'result': True, 'ind': 2, 'tostr': 'round_eq { avg { filter_eq { all_rows ; date ; november } ; attendance } ; 13736 } = true', 'tointer': 'select the rows whose date record fuzzily matches to november . the average of the attendance record of these rows is 13736 .'}
|
round_eq { avg { filter_eq { all_rows ; date ; november } ; attendance } ; 13736 } = true
|
select the rows whose date record fuzzily matches to november . the average of the attendance record of these rows is 13736 .
|
3
|
3
|
{'eq_2': 2, 'result_3': 3, 'avg_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'date_5': 5, 'november_6': 6, 'attendance_7': 7, '13736_8': 8}
|
{'eq_2': 'eq', 'result_3': 'true', 'avg_1': 'avg', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'date_5': 'date', 'november_6': 'november', 'attendance_7': 'attendance', '13736_8': '13736'}
|
{'eq_2': [3], 'result_3': [], 'avg_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'date_5': [0], 'november_6': [0], 'attendance_7': [1], '13736_8': [2]}
|
['week', 'date', 'opponent', 'result', 'game site', 'record', 'attendance']
|
[['1', 'september 10 , 1961', 'buffalo bills', 'w 22 - 10', 'war memorial stadium', '1 - 0', '16636'], ['2', 'september 16 , 1961', 'boston patriots', 'l 17 - 45', 'boston university field', '1 - 1', '14479'], ['3', 'september 24 , 1961', 'new york titans', 'l 28 - 35', 'polo grounds', '1 - 2', '14381'], ['4', 'october 1 , 1961', 'oakland raiders', 'l 19 - 33', 'candlestick park', '1 - 3', '8361'], ['5', 'october 8 , 1961', 'dallas texans', 'l 12 - 19', 'bears stadium', '1 - 4', '14500'], ['6', 'october 15 , 1961', 'oakland raiders', 'w 27 - 24', 'bears stadium', '2 - 4', '11129'], ['7', 'october 22 , 1961', 'new york titans', 'w 27 - 10', 'bears stadium', '3 - 4', '12508'], ['8', 'october 29 , 1961', 'san diego chargers', 'l 0 - 37', 'balboa stadium', '3 - 5', '32584'], ['9', 'november 5 , 1961', 'houston oilers', 'l 14 - 55', 'bears stadium', '3 - 6', '11564'], ['10', 'november 12 , 1961', 'san diego chargers', 'l 16 - 19', 'bears stadium', '3 - 7', '7859'], ['11', 'november 19 , 1961', 'buffalo bills', 'l 10 - 23', 'bears stadium', '3 - 8', '7645'], ['12', 'november 26 , 1961', 'houston oilers', 'l 14 - 45', 'jeppesen stadium', '3 - 9', '27874'], ['13', 'december 3 , 1961', 'boston patriots', 'l 24 - 28', 'bears stadium', '3 - 10', '9303']]
|
simon shirley
|
https://en.wikipedia.org/wiki/Simon_Shirley
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15170292-1.html.csv
|
comparative
|
the athlete placed higher in the commonwealth games than in the summer olympics .
|
{'row_1': '3', 'row_2': '1', 'col': '4', 'col_other': '2', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}
|
{'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'tournament', 'commonwealth games'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose tournament record fuzzily matches to commonwealth games .', 'tostr': 'filter_eq { all_rows ; tournament ; commonwealth games }'}, 'result'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; tournament ; commonwealth games } ; result }', 'tointer': 'select the rows whose tournament record fuzzily matches to commonwealth games . take the result record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'tournament', 'summer olympics'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose tournament record fuzzily matches to summer olympics .', 'tostr': 'filter_eq { all_rows ; tournament ; summer olympics }'}, 'result'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; tournament ; summer olympics } ; result }', 'tointer': 'select the rows whose tournament record fuzzily matches to summer olympics . take the result record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; tournament ; commonwealth games } ; result } ; hop { filter_eq { all_rows ; tournament ; summer olympics } ; result } } = true', 'tointer': 'select the rows whose tournament record fuzzily matches to commonwealth games . take the result record of this row . select the rows whose tournament record fuzzily matches to summer olympics . take the result record of this row . the first record is less than the second record .'}
|
less { hop { filter_eq { all_rows ; tournament ; commonwealth games } ; result } ; hop { filter_eq { all_rows ; tournament ; summer olympics } ; result } } = true
|
select the rows whose tournament record fuzzily matches to commonwealth games . take the result record of this row . select the rows whose tournament record fuzzily matches to summer olympics . take the result 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, 'commonwealth games_8': 8, 'result_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'tournament_11': 11, 'summer olympics_12': 12, 'result_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', 'commonwealth games_8': 'commonwealth games', 'result_9': 'result', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'tournament_11': 'tournament', 'summer olympics_12': 'summer olympics', 'result_13': 'result'}
|
{'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'tournament_7': [0], 'commonwealth games_8': [0], 'result_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'tournament_11': [1], 'summer olympics_12': [1], 'result_13': [3]}
|
['year', 'tournament', 'venue', 'result', 'event']
|
[['1988', 'summer olympics', 'seoul , south korea', '15th', 'decathlon'], ['1994', 'hypo - meeting', 'götzis , austria', '11th', 'decathlon'], ['1994', 'commonwealth games', 'victoria , canada', '2nd', 'decathlon'], ['1995', 'hypo - meeting', 'götzis , austria', '20th', 'decathlon'], ['1996', 'hypo - meeting', 'götzis , austria', '19th', 'decathlon']]
|
1989 open championship
|
https://en.wikipedia.org/wiki/1989_Open_Championship
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18135501-6.html.csv
|
comparative
|
of the players representing the united states in the open championship , payne stewart scored higher than fred couples .
|
{'row_1': '10', 'row_2': '6', 'col': '4', 'col_other': '2', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}
|
{'func': 'greater', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'payne stewart'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose player record fuzzily matches to payne stewart .', 'tostr': 'filter_eq { all_rows ; player ; payne stewart }'}, 'score'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; player ; payne stewart } ; score }', 'tointer': 'select the rows whose player record fuzzily matches to payne stewart . take the score record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'fred couples'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose player record fuzzily matches to fred couples .', 'tostr': 'filter_eq { all_rows ; player ; fred couples }'}, 'score'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; player ; fred couples } ; score }', 'tointer': 'select the rows whose player record fuzzily matches to fred couples . take the score record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; player ; payne stewart } ; score } ; hop { filter_eq { all_rows ; player ; fred couples } ; score } } = true', 'tointer': 'select the rows whose player record fuzzily matches to payne stewart . take the score record of this row . select the rows whose player record fuzzily matches to fred couples . take the score record of this row . the first record is greater than the second record .'}
|
greater { hop { filter_eq { all_rows ; player ; payne stewart } ; score } ; hop { filter_eq { all_rows ; player ; fred couples } ; score } } = true
|
select the rows whose player record fuzzily matches to payne stewart . take the score record of this row . select the rows whose player record fuzzily matches to fred couples . take the score record of this row . the first record is greater than the second record .
|
5
|
5
|
{'greater_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'player_7': 7, 'payne stewart_8': 8, 'score_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'player_11': 11, 'fred couples_12': 12, 'score_13': 13}
|
{'greater_4': 'greater', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'player_7': 'player', 'payne stewart_8': 'payne stewart', 'score_9': 'score', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'player_11': 'player', 'fred couples_12': 'fred couples', 'score_13': 'score'}
|
{'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'player_7': [0], 'payne stewart_8': [0], 'score_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'player_11': [1], 'fred couples_12': [1], 'score_13': [3]}
|
['place', 'player', 'country', 'score', 'to par', 'money']
|
[['t1', 'mark calcavecchia', 'united states', '71 + 68 + 68 + 68 = 275', '- 13', 'playoff'], ['t1', 'greg norman', 'australia', '69 + 70 + 72 + 64 = 275', '- 13', 'playoff'], ['t1', 'wayne grady', 'australia', '68 + 67 + 69 + 71 = 275', '- 13', 'playoff'], ['4', 'tom watson', 'united states', '69 + 68 + 68 + 72 = 277', '- 11', '40000'], ['5', 'jodie mudd', 'united states', '73 + 67 + 68 + 70 = 278', '- 10', '30000'], ['t6', 'fred couples', 'united states', '68 + 71 + 68 + 72 = 279', '- 9', '26000'], ['t6', 'david feherty', 'northern ireland', '71 + 67 + 69 + 72 = 279', '- 9', '26000'], ['t8', 'eduardo romero', 'argentina', '68 + 70 + 75 + 67 = 280', '- 8', '21000'], ['t8', 'paul azinger', 'united states', '68 + 73 + 67 + 72 = 280', '- 8', '21000'], ['t8', 'payne stewart', 'united states', '72 + 65 + 69 + 74 = 280', '- 8', '21000']]
|
luca badoer
|
https://en.wikipedia.org/wiki/Luca_Badoer
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1226492-2.html.csv
|
majority
|
luca badoer scored 0 championship points in all of the years of his racing career .
|
{'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'equal', 'value': '0', 'subset': None}
|
{'func': 'most_eq', 'args': ['all_rows', 'points', '0'], 'result': True, 'ind': 0, 'tointer': 'for the points records of all rows , most of them are equal to 0 .', 'tostr': 'most_eq { all_rows ; points ; 0 } = true'}
|
most_eq { all_rows ; points ; 0 } = true
|
for the points records of all rows , most of them are equal to 0 .
|
1
|
1
|
{'most_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'points_3': 3, '0_4': 4}
|
{'most_eq_0': 'most_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'points_3': 'points', '0_4': '0'}
|
{'most_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'points_3': [0], '0_4': [0]}
|
['year', 'entrant', 'chassis', 'engine', 'points']
|
[['1993', 'lola bms scuderia italia', 'lola t93 / 30', 'ferrari 040 3.5 v12', '0'], ['1995', 'minardi scuderia italia', 'minardi m195', 'ford edm 3.0 v8', '0'], ['1996', 'forti grand prix', 'forti fg01b', 'ford eca zetec - r 3.0 v10', '0'], ['1996', 'forti grand prix', 'forti fg03', 'ford eca zetec - r 3.0 v10', '0'], ['1999', 'fondmetal minardi ford', 'minardi m01', 'ford vjm1 / vjm2 zetec - r 3.0 v10', '0'], ['2009', 'scuderia ferrari marlboro', 'ferrari f60', 'ferrari 056 2.4 v8', '0']]
|
euroleague 2007 - 08 individual statistics
|
https://en.wikipedia.org/wiki/Euroleague_2007%E2%80%9308_Individual_Statistics
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16050349-2.html.csv
|
count
|
three of the top players in euroleague 2007 - 08 played in 14 games .
|
{'scope': 'all', 'criterion': 'equal', 'value': '14', 'result': '3', 'col': '4', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'games', '14'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose games record is equal to 14 .', 'tostr': 'filter_eq { all_rows ; games ; 14 }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; games ; 14 } }', 'tointer': 'select the rows whose games record is equal to 14 . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; games ; 14 } } ; 3 } = true', 'tointer': 'select the rows whose games record is equal to 14 . the number of such rows is 3 .'}
|
eq { count { filter_eq { all_rows ; games ; 14 } } ; 3 } = true
|
select the rows whose games record is equal to 14 . the number of such rows is 3 .
|
3
|
3
|
{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_eq_0': 0, 'all_rows_4': 4, 'games_5': 5, '14_6': 6, '3_7': 7}
|
{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'games_5': 'games', '14_6': '14', '3_7': '3'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'games_5': [0], '14_6': [0], '3_7': [2]}
|
['rank', 'name', 'team', 'games', 'rebounds']
|
[['1', 'travis watson', 'armani jeans milano', '14', '136'], ['2', 'mirsad türkcan', 'fenerbahçe', '11', '102'], ['3', 'jeremiah massey', 'aris thessaloniki', '14', '113'], ['4', 'nikola peković', 'partizan belgrade', '14', '112'], ['5', 'felipe reyes', 'real madrid', '13', '100']]
|
usa today all - usa high school basketball team
|
https://en.wikipedia.org/wiki/USA_Today_All-USA_high_school_basketball_team
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11677760-19.html.csv
|
count
|
there are two 6 ' 4 " players in usa today 's all-usa high school basketball team for boys ' '07 third team .
|
{'scope': 'all', 'criterion': 'fuzzily_match', 'value': '6 - 4', 'result': '2', 'col': '2', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'height', '6 - 4'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose height record fuzzily matches to 6 - 4 .', 'tostr': 'filter_eq { all_rows ; height ; 6 - 4 }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; height ; 6 - 4 } }', 'tointer': 'select the rows whose height record fuzzily matches to 6 - 4 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; height ; 6 - 4 } } ; 2 } = true', 'tointer': 'select the rows whose height record fuzzily matches to 6 - 4 . the number of such rows is 2 .'}
|
eq { count { filter_eq { all_rows ; height ; 6 - 4 } } ; 2 } = true
|
select the rows whose height record fuzzily matches to 6 - 4 . 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, 'height_5': 5, '6 - 4_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', 'height_5': 'height', '6 - 4_6': '6 - 4', '2_7': '2'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'height_5': [0], '6 - 4_6': [0], '2_7': [2]}
|
['player', 'height', 'school', 'hometown', 'college']
|
[['anthony randolph', '6 - 10', 'woodrow wilson high school', 'dallas , tx', 'lsu'], ['nolan smith', '6 - 3', 'oak hill academy', 'washington , dc', 'duke'], ['corey fisher', '6 - 0', 'st patrick high school', 'elizabeth , nj', 'villanova'], ['nick calathes', '6 - 4', 'lake howell high school', 'winter park , fl', 'florida'], ['austin freeman', '6 - 4', 'dematha catholic high school', 'hyattsville , md', 'georgetown']]
|
2002 - 03 san antonio spurs season
|
https://en.wikipedia.org/wiki/2002%E2%80%9303_San_Antonio_Spurs_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13667936-5.html.csv
|
majority
|
tim duncan was the leading scorer of most games played in december of the 2002 - 03 san antonio spurs season .
|
{'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'fuzzily_match', 'value': 'tim duncan', 'subset': None}
|
{'func': 'most_str_eq', 'args': ['all_rows', 'leading scorer', 'tim duncan'], 'result': True, 'ind': 0, 'tointer': 'for the leading scorer records of all rows , most of them fuzzily match to tim duncan .', 'tostr': 'most_eq { all_rows ; leading scorer ; tim duncan } = true'}
|
most_eq { all_rows ; leading scorer ; tim duncan } = true
|
for the leading scorer records of all rows , most of them fuzzily match to tim duncan .
|
1
|
1
|
{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'leading scorer_3': 3, 'tim duncan_4': 4}
|
{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'leading scorer_3': 'leading scorer', 'tim duncan_4': 'tim duncan'}
|
{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'leading scorer_3': [0], 'tim duncan_4': [0]}
|
['date', 'visitor', 'score', 'home', 'leading scorer', 'record']
|
[['3 december 2002', 'spurs', '75 - 89', 'rockets', 'tim duncan ( 25 )', '11 - 8'], ['6 december 2002', '76ers', '93 - 98', 'spurs', 'tim duncan ( 29 )', '12 - 8'], ['8 december 2002', 'kings', '104 - 80', 'spurs', 'tim duncan ( 16 )', '12 - 9'], ['11 december 2002', 'mavericks', '104 - 111', 'spurs', 'tony parker ( 32 )', '13 - 9'], ['13 december 2002', 'clippers', '84 - 97', 'spurs', 'tim duncan ( 25 )', '14 - 9'], ['16 december 2002', 'spurs', '79 - 91', 'clippers', 'tim duncan ( 32 )', '14 - 10'], ['18 december 2002', 'spurs', '91 - 88', 'supersonics', 'tony parker ( 22 )', '15 - 10'], ['19 december 2002', 'spurs', '83 - 81', 'kings', 'tim duncan ( 23 )', '16 - 10'], ['21 december 2002', 'wizards', '81 - 92', 'spurs', 'tony parker ( 21 )', '17 - 10'], ['23 december 2002', 'hornets', '94 - 99', 'spurs', 'tim duncan ( 23 )', '18 - 10'], ['27 december 2002', 'spurs', '79 - 81', 'hawks', 'tim duncan ( 27 )', '18 - 11'], ['28 december 2002', 'spurs', '109 - 95', 'bulls', 'tony parker ( 32 )', '19 - 11'], ['30 december 2002', 'spurs', '95 - 96', 'knicks', 'tim duncan ( 31 )', '19 - 12'], ['31 december 2002', 'spurs', '103 - 105', 'wizards', 'tim duncan ( 35 )', '19 - 13']]
|
list of prime ministers of albania
|
https://en.wikipedia.org/wiki/List_of_Prime_Ministers_of_Albania
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-167235-8.html.csv
|
comparative
|
vilson ahmeti started his term as prime minister of albania earlier than ilir meta did .
|
{'row_1': '4', 'row_2': '9', 'col': '3', 'col_other': '1', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}
|
{'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name', 'vilson ahmeti'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose name record fuzzily matches to vilson ahmeti .', 'tostr': 'filter_eq { all_rows ; name ; vilson ahmeti }'}, 'term start'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; name ; vilson ahmeti } ; term start }', 'tointer': 'select the rows whose name record fuzzily matches to vilson ahmeti . take the term start record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name', 'ilir meta'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose name record fuzzily matches to ilir meta .', 'tostr': 'filter_eq { all_rows ; name ; ilir meta }'}, 'term start'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; name ; ilir meta } ; term start }', 'tointer': 'select the rows whose name record fuzzily matches to ilir meta . take the term start record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; name ; vilson ahmeti } ; term start } ; hop { filter_eq { all_rows ; name ; ilir meta } ; term start } } = true', 'tointer': 'select the rows whose name record fuzzily matches to vilson ahmeti . take the term start record of this row . select the rows whose name record fuzzily matches to ilir meta . take the term start record of this row . the first record is less than the second record .'}
|
less { hop { filter_eq { all_rows ; name ; vilson ahmeti } ; term start } ; hop { filter_eq { all_rows ; name ; ilir meta } ; term start } } = true
|
select the rows whose name record fuzzily matches to vilson ahmeti . take the term start record of this row . select the rows whose name record fuzzily matches to ilir meta . take the term start record of this row . the first record is less than the second record .
|
5
|
5
|
{'less_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'name_7': 7, 'vilson ahmeti_8': 8, 'term start_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'name_11': 11, 'ilir meta_12': 12, 'term start_13': 13}
|
{'less_4': 'less', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'name_7': 'name', 'vilson ahmeti_8': 'vilson ahmeti', 'term start_9': 'term start', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'name_11': 'name', 'ilir meta_12': 'ilir meta', 'term start_13': 'term start'}
|
{'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'name_7': [0], 'vilson ahmeti_8': [0], 'term start_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'name_11': [1], 'ilir meta_12': [1], 'term start_13': [3]}
|
['name', 'born - died', 'term start', 'term end', 'political party']
|
[['prime ministers 1991 onwards', 'prime ministers 1991 onwards', 'prime ministers 1991 onwards', 'prime ministers 1991 onwards', 'prime ministers 1991 onwards'], ['fatos nano ( 1st time )', '1952 -', '22 february 1991', '5 june 1991', 'party of labour of albania'], ['ylli bufi', '1948 -', '5 june 1991', '10 december 1991', 'socialist party of albania'], ['vilson ahmeti', '1951 -', '10 december 1991', '13 april 1992', 'non - party'], ['aleksandër meksi', '1939 -', '13 april 1992', '11 march 1997', 'democratic party of albania'], ['bashkim fino', '1962 -', '11 march 1997', '24 july 1997', 'socialist party of albania'], ['fatos nano ( 2nd time )', '1952 -', '24 july 1997', '2 october 1998', 'socialist party of albania'], ['pandeli majko ( 1st time )', '1967 -', '2 october 1998', '29 october 1999', 'socialist party of albania'], ['ilir meta', '1969 -', '29 october 1999', '22 february 2002', 'socialist party of albania'], ['pandeli majko ( 2nd time )', '1967 -', '22 february 2002', '31 july 2002', 'socialist party of albania'], ['fatos nano ( 3rd time )', '1952 -', '31 july 2002', '11 september 2005', 'socialist party of albania'], ['sali berisha', '1944 -', '11 september 2005', '15 september 2013', 'democratic party of albania'], ['edi rama', '1964 -', '15 september 2013', 'incumbent', 'socialist party of albania']]
|
1908 michigan wolverines football team
|
https://en.wikipedia.org/wiki/1908_Michigan_Wolverines_football_team
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-25730460-2.html.csv
|
majority
|
most of the players in the 1908 michigan wolverines football team scored 0 extra points .
|
{'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'equal', 'value': '0', 'subset': None}
|
{'func': 'most_eq', 'args': ['all_rows', 'extra points', '0'], 'result': True, 'ind': 0, 'tointer': 'for the extra points records of all rows , most of them are equal to 0 .', 'tostr': 'most_eq { all_rows ; extra points ; 0 } = true'}
|
most_eq { all_rows ; extra points ; 0 } = true
|
for the extra points records of all rows , most of them are equal to 0 .
|
1
|
1
|
{'most_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'extra points_3': 3, '0_4': 4}
|
{'most_eq_0': 'most_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'extra points_3': 'extra points', '0_4': '0'}
|
{'most_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'extra points_3': [0], '0_4': [0]}
|
['player', 'touchdowns', 'extra points', 'field goals', 'points']
|
[['dave allerdice', '2', '14', '10', '64'], ['sam davison', '7', '0', '1', '39'], ['donald w greene', '2', '0', '0', '10'], ['william p edmunds', '1', '0', '0', '5'], ['maurice e crumpacker', '1', '0', '0', '5'], ['william j embs', '1', '0', '0', '5']]
|
1975 vfl season
|
https://en.wikipedia.org/wiki/1975_VFL_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10883333-6.html.csv
|
count
|
in the 1975 vfl season , among the games where home team scored above 15.00 , 2 of them had an attendance below 20,000 .
|
{'scope': 'subset', 'criterion': 'less_than', 'value': '20,000', 'result': '2', 'col': '6', 'subset': {'col': '2', 'criterion': 'greater_than', 'value': '15.0'}}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_less', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'home team score', '15.0'], 'result': None, 'ind': 0, 'tostr': 'filter_greater { all_rows ; home team score ; 15.0 }', 'tointer': 'select the rows whose home team score record is greater than 15.0 .'}, 'crowd', '20,000'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose home team score record is greater than 15.0 . among these rows , select the rows whose crowd record is less than 20,000 .', 'tostr': 'filter_less { filter_greater { all_rows ; home team score ; 15.0 } ; crowd ; 20,000 }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_less { filter_greater { all_rows ; home team score ; 15.0 } ; crowd ; 20,000 } }', 'tointer': 'select the rows whose home team score record is greater than 15.0 . among these rows , select the rows whose crowd record is less than 20,000 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_less { filter_greater { all_rows ; home team score ; 15.0 } ; crowd ; 20,000 } } ; 2 } = true', 'tointer': 'select the rows whose home team score record is greater than 15.0 . among these rows , select the rows whose crowd record is less than 20,000 . the number of such rows is 2 .'}
|
eq { count { filter_less { filter_greater { all_rows ; home team score ; 15.0 } ; crowd ; 20,000 } } ; 2 } = true
|
select the rows whose home team score record is greater than 15.0 . among these rows , select the rows whose crowd record is less than 20,000 . the number of such rows is 2 .
|
4
|
4
|
{'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_less_1': 1, 'filter_greater_0': 0, 'all_rows_5': 5, 'home team score_6': 6, '15.0_7': 7, 'crowd_8': 8, '20,000_9': 9, '2_10': 10}
|
{'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_less_1': 'filter_less', 'filter_greater_0': 'filter_greater', 'all_rows_5': 'all_rows', 'home team score_6': 'home team score', '15.0_7': '15.0', 'crowd_8': 'crowd', '20,000_9': '20,000', '2_10': '2'}
|
{'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_less_1': [2], 'filter_greater_0': [1], 'all_rows_5': [0], 'home team score_6': [0], '15.0_7': [0], 'crowd_8': [1], '20,000_9': [1], '2_10': [3]}
|
['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date']
|
[['melbourne', '22.23 ( 155 )', 'south melbourne', '13.12 ( 90 )', 'mcg', '15503', '10 may 1975'], ['essendon', '12.21 ( 93 )', 'geelong', '15.14 ( 104 )', 'windy hill', '17225', '10 may 1975'], ['collingwood', '14.12 ( 96 )', 'richmond', '22.14 ( 146 )', 'victoria park', '27729', '10 may 1975'], ['carlton', '16.15 ( 111 )', 'hawthorn', '12.13 ( 85 )', 'princes park', '27907', '10 may 1975'], ['north melbourne', '18.11 ( 119 )', 'footscray', '9.12 ( 66 )', 'arden street oval', '18875', '10 may 1975'], ['st kilda', '11.16 ( 82 )', 'fitzroy', '9.20 ( 74 )', 'vfl park', '18229', '10 may 1975']]
|
2008 - 09 nbl season
|
https://en.wikipedia.org/wiki/2008%E2%80%9309_NBL_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16653153-9.html.csv
|
comparative
|
the new zealand breakers played their game before the perth wildcats played theirs .
|
{'row_1': '2', 'row_2': '4', 'col': '1', 'col_other': '2', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}
|
{'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'home team', 'new zealand breakers'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose home team record fuzzily matches to new zealand breakers .', 'tostr': 'filter_eq { all_rows ; home team ; new zealand breakers }'}, 'date'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; home team ; new zealand breakers } ; date }', 'tointer': 'select the rows whose home team record fuzzily matches to new zealand breakers . take the date record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'home team', 'perth wildcats'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose home team record fuzzily matches to perth wildcats .', 'tostr': 'filter_eq { all_rows ; home team ; perth wildcats }'}, 'date'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; home team ; perth wildcats } ; date }', 'tointer': 'select the rows whose home team record fuzzily matches to perth wildcats . take the date record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; home team ; new zealand breakers } ; date } ; hop { filter_eq { all_rows ; home team ; perth wildcats } ; date } } = true', 'tointer': 'select the rows whose home team record fuzzily matches to new zealand breakers . take the date record of this row . select the rows whose home team record fuzzily matches to perth wildcats . take the date record of this row . the first record is less than the second record .'}
|
less { hop { filter_eq { all_rows ; home team ; new zealand breakers } ; date } ; hop { filter_eq { all_rows ; home team ; perth wildcats } ; date } } = true
|
select the rows whose home team record fuzzily matches to new zealand breakers . take the date record of this row . select the rows whose home team record fuzzily matches to perth wildcats . 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, 'home team_7': 7, 'new zealand breakers_8': 8, 'date_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'home team_11': 11, 'perth wildcats_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', 'home team_7': 'home team', 'new zealand breakers_8': 'new zealand breakers', 'date_9': 'date', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'home team_11': 'home team', 'perth wildcats_12': 'perth wildcats', 'date_13': 'date'}
|
{'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'home team_7': [0], 'new zealand breakers_8': [0], 'date_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'home team_11': [1], 'perth wildcats_12': [1], 'date_13': [3]}
|
['date', 'home team', 'score', 'away team', 'venue', 'box score', 'report']
|
[['24 september', 'adelaide 36ers', '99 - 89', 'sydney spirit', 'distinctive homes dome', 'box score', '-'], ['25 september', 'new zealand breakers', '120 - 111', 'melbourne tigers', 'north shore events centre', 'box score', '-'], ['25 september', 'south dragons', '89 - 91', 'townsville crocodiles', 'hisense arena', 'box score', '-'], ['26 september', 'perth wildcats', '94 - 80', 'gold coast blaze', 'challenge stadium', 'box score', '-'], ['27 september', 'townsville crocodiles', '100 - 96', 'cairns taipans', 'townsville entertainment centre', 'box score', '-'], ['27 september', 'sydney spirit', '112 - 105', 'adelaide 36ers', 'ais arena', 'box score', '-']]
|
telecommunications in moldova
|
https://en.wikipedia.org/wiki/Telecommunications_in_Moldova
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-19246-2.html.csv
|
majority
|
most of the telecommunications in moldova use umts hspa as a standard .
|
{'scope': 'all', 'col': '2', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'umts hspa', 'subset': None}
|
{'func': 'most_str_eq', 'args': ['all_rows', 'standard', 'umts hspa'], 'result': True, 'ind': 0, 'tointer': 'for the standard records of all rows , most of them fuzzily match to umts hspa .', 'tostr': 'most_eq { all_rows ; standard ; umts hspa } = true'}
|
most_eq { all_rows ; standard ; umts hspa } = true
|
for the standard records of all rows , most of them fuzzily match to umts hspa .
|
1
|
1
|
{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'standard_3': 3, 'umts hspa_4': 4}
|
{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'standard_3': 'standard', 'umts hspa_4': 'umts hspa'}
|
{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'standard_3': [0], 'umts hspa_4': [0]}
|
['carrier', 'standard', 'frequency', '( down )', '( up )', 'launch date ( ddmmyyyy )']
|
[['orange', 'umts hspa', '2100 mhz', '7.2 mbit / s', '2.0 mbit / s', '01.11.2008'], ['orange', 'umts hspa', '2100 mhz', '14.4 mbit / s', '5.76 mbit / s', '02.09.2009'], ['orange', 'umts hspa', '2100 mhz', '21.1 mbit / s', '5.76 mbit / s', '21.12.2009'], ['orange', 'umts hspa', '2100 mhz', '42 mbit / s', '5.76 mbit / s', '27.05.2011'], ['moldcell', 'umts hspa', '2100 mhz', '7.2 mbit / s', '2 mbit / s', '01.10.2008'], ['moldcell', 'umts hspa', '2100 mhz', '21.1 mbit / s', '5.76 mbit / s', '31.05.2011'], ['unité', 'cdma ev - do rev 0', '450 mhz', '2.4 mbit / s', '153 mbit / s', '01.03.2007'], ['unité', 'umts hspa', '2100 mhz', '14.4 mbit / s', '5.76 mbit / s', '01.03.2010'], ['idc', 'cdma ev - do rev 0', '800 mhz', '2.4 мmbit / s', '153 kbit / s', '01.03.2007'], ['idc', 'cdma ev - do rev 0', '450 mhz', '2.4 мmbit / s', '153 kbit / s', '01.03.2007'], ['idc', 'cdma ev - do rev a', '800 мгц', '3.1 mbit / s', '1.8 mbit / s', '21.03.2011']]
|
1977 atlanta falcons season
|
https://en.wikipedia.org/wiki/1977_Atlanta_Falcons_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17183877-2.html.csv
|
majority
|
all of the crowds were at least 25000 people .
|
{'scope': 'all', 'col': '5', 'most_or_all': 'all', 'criterion': 'greater_than', 'value': '25000', 'subset': None}
|
{'func': 'all_greater', 'args': ['all_rows', 'attendance', '25000'], 'result': True, 'ind': 0, 'tointer': 'for the attendance records of all rows , all of them are greater than 25000 .', 'tostr': 'all_greater { all_rows ; attendance ; 25000 } = true'}
|
all_greater { all_rows ; attendance ; 25000 } = true
|
for the attendance records of all rows , all of them are greater than 25000 .
|
1
|
1
|
{'all_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 'attendance_3': 3, '25000_4': 4}
|
{'all_greater_0': 'all_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 'attendance_3': 'attendance', '25000_4': '25000'}
|
{'all_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 'attendance_3': [0], '25000_4': [0]}
|
['week', 'date', 'opponent', 'result', 'attendance']
|
[['1', 'september 18 , 1977', 'los angeles rams', 'w 17 - 6', '55956'], ['2', 'september 25 , 1977', 'washington redskins', 'l 10 - 6', '55031'], ['3', 'october 2 , 1977', 'new york giants', 'w 17 - 3', '46374'], ['4', 'october 9 , 1977', 'san francisco 49ers', 'w 7 - 0', '38009'], ['5', 'october 16 , 1977', 'buffalo bills', 'l 3 - 0', '27348'], ['6', 'october 23 , 1977', 'chicago bears', 'w 16 - 10', '49407'], ['7', 'october 30 , 1977', 'minnesota vikings', 'l 14 - 7', '59257'], ['8', 'november 6 , 1977', 'san francisco 49ers', 'l 10 - 3', '46577'], ['9', 'november 13 , 1977', 'detroit lions', 'w 17 - 6', '47461'], ['10', 'november 20 , 1977', 'new orleans saints', 'l 21 - 20', '43135'], ['11', 'november 27 , 1977', 'tampa bay buccaneers', 'w 17 - 0', '43592'], ['12', 'december 4 , 1977', 'new england patriots', 'l 16 - 10', '57911'], ['13', 'december 11 , 1977', 'los angeles rams', 'l 23 - 7', '52574'], ['14', 'december 18 , 1977', 'new orleans saints', 'w 35 - 7', '36895']]
|
atlantic hurricane season
|
https://en.wikipedia.org/wiki/Atlantic_hurricane_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2930244-3.html.csv
|
superlative
|
the 1867 atlantic hurricane season has the largest number of recorded deaths .
|
{'scope': 'all', 'col_superlative': '5', 'row_superlative': '8', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'deaths'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; deaths }'}, 'year'], 'result': '1867', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; deaths } ; year }'}, '1867'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; deaths } ; year } ; 1867 } = true', 'tointer': 'select the row whose deaths record of all rows is maximum . the year record of this row is 1867 .'}
|
eq { hop { argmax { all_rows ; deaths } ; year } ; 1867 } = true
|
select the row whose deaths record of all rows is maximum . the year record of this row is 1867 .
|
3
|
3
|
{'eq_2': 2, 'result_3': 3, 'num_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'deaths_5': 5, 'year_6': 6, '1867_7': 7}
|
{'eq_2': 'eq', 'result_3': 'true', 'num_hop_1': 'num_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'deaths_5': 'deaths', 'year_6': 'year', '1867_7': '1867'}
|
{'eq_2': [3], 'result_3': [], 'num_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'deaths_5': [0], 'year_6': [1], '1867_7': [2]}
|
['year', 'number of tropical storms', 'number of hurricanes', 'number of major hurricanes', 'deaths', 'strongest storm']
|
[['1860', '1', '5', '1', '60 +', 'one'], ['1861', '2', '6', '0', '22 +', 'one and three'], ['1862', '3', '3', '0', '3', 'two and three'], ['1863', '4', '5', '0', '90', 'one , two , three & four'], ['1864', '2', '3', '0', 'none', 'one , three & five'], ['1865', '4', '3', '0', '326', 'four & seven'], ['1866', '1', '5', '1', '383', 'six'], ['1867', '2', '6', '0', '811', "' san narciso '"], ['1868', '1', '3', '0', '2', 'one , two & four']]
|
statistics relating to enlargement of the european union
|
https://en.wikipedia.org/wiki/Statistics_relating_to_enlargement_of_the_European_Union
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1307842-6.html.csv
|
count
|
only one of the joining european union countries have a square area of less than 100000 km square .
|
{'scope': 'all', 'criterion': 'less_than', 'value': '100000', 'result': '1', 'col': '3', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'area ( km square )', '100000'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose area ( km square ) record is less than 100000 .', 'tostr': 'filter_less { all_rows ; area ( km square ) ; 100000 }'}], 'result': '1', 'ind': 1, 'tostr': 'count { filter_less { all_rows ; area ( km square ) ; 100000 } }', 'tointer': 'select the rows whose area ( km square ) record is less than 100000 . the number of such rows is 1 .'}, '1'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_less { all_rows ; area ( km square ) ; 100000 } } ; 1 } = true', 'tointer': 'select the rows whose area ( km square ) record is less than 100000 . the number of such rows is 1 .'}
|
eq { count { filter_less { all_rows ; area ( km square ) ; 100000 } } ; 1 } = true
|
select the rows whose area ( km square ) record is less than 100000 . the number of such rows is 1 .
|
3
|
3
|
{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_less_0': 0, 'all_rows_4': 4, 'area (km square)_5': 5, '100000_6': 6, '1_7': 7}
|
{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_less_0': 'filter_less', 'all_rows_4': 'all_rows', 'area (km square)_5': 'area ( km square )', '100000_6': '100000', '1_7': '1'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_less_0': [1], 'all_rows_4': [0], 'area (km square)_5': [0], '100000_6': [0], '1_7': [2]}
|
['member countries', 'population', 'area ( km square )', 'gdp ( billion us )', 'gdp per capita ( us )']
|
[['austria', '8206524', '83871', '145.238', '18048'], ['finland', '5261008', '338145', '80.955', '15859'], ['sweden', '9047752', '449964', '156.640', '17644'], ['accession countries', '22029977', '871980', '382.833', '17378'], ['existing members ( 1995 )', '350909402', '2495174', '5894.232', '16797'], ['eu15 ( 1995 )', '372939379 ( + 6.28 % )', '3367154 ( + 34.95 % )', '6277.065 ( + 6.50 % )', '16831 ( + 0.20 % )']]
|
cleethorpes coast light railway
|
https://en.wikipedia.org/wiki/Cleethorpes_Coast_Light_Railway
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1158066-2.html.csv
|
majority
|
the majority of trains feature a 0-4 - configuration .
|
{'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'equal', 'value': '0-4', 'subset': None}
|
{'func': 'most_str_eq', 'args': ['all_rows', 'wheels', '0-4'], 'result': True, 'ind': 0, 'tointer': 'for the wheels records of all rows , most of them fuzzily match to 0-4 .', 'tostr': 'most_eq { all_rows ; wheels ; 0-4 } = true'}
|
most_eq { all_rows ; wheels ; 0-4 } = true
|
for the wheels records of all rows , most of them fuzzily match to 0-4 .
|
1
|
1
|
{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'wheels_3': 3, '0-4_4': 4}
|
{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'wheels_3': 'wheels', '0-4_4': '0-4'}
|
{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'wheels_3': [0], '0-4_4': [0]}
|
['name', 'built', 'wheels', 'fuel / trans', 'status', 'colour']
|
[['ted', 'lister 1944', '0 - 4 - 0', 'diesel - mechanical', 'under rebuild', 'brown'], ['the cub / john', 'minirail 1954', '0 - 4 - 0 bo', 'diesel - hydraulic', 'stored', 'grey undercoat'], ['battison', 'battison 1958', '2 - 6 - 4de', 'diesel - electric', 'out of service', 'lner black'], ['dudley', 'g & s light engineering 1946', 'bo - bo', '4 petrol - mechanical', 'on display', 'grey & red'], ['da1', 'bush mill railway 1986', '0 - 4 - 0', 'diesel - mechanical', 'in service', 'royal blue with white linings'], ['kd1', 'unknown', 'articulated', 'diesel electric', 'long term restoration , stored', 'ran in a red livery previously']]
|
united states house of representatives elections , 2000
|
https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_2000
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1341423-49.html.csv
|
majority
|
all of the re-elected republican won with a vote percentage above 60 % .
|
{'scope': 'subset', 'col': '6', 'most_or_all': 'all', 'criterion': 'greater_than', 'value': '60', 'subset': {'col': '3', 'criterion': 'equal', 'value': 'republican'}}
|
{'func': 'all_greater', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'party', 'republican'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; party ; republican }', 'tointer': 'select the rows whose party record fuzzily matches to republican .'}, 'candidates', '60'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose party record fuzzily matches to republican . for the candidates records of these rows , all of them are greater than 60 .', 'tostr': 'all_greater { filter_eq { all_rows ; party ; republican } ; candidates ; 60 } = true'}
|
all_greater { filter_eq { all_rows ; party ; republican } ; candidates ; 60 } = true
|
select the rows whose party record fuzzily matches to republican . for the candidates records of these rows , all of them are greater than 60 .
|
2
|
2
|
{'all_greater_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'party_4': 4, 'republican_5': 5, 'candidates_6': 6, '60_7': 7}
|
{'all_greater_1': 'all_greater', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'party_4': 'party', 'republican_5': 'republican', 'candidates_6': 'candidates', '60_7': '60'}
|
{'all_greater_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'party_4': [0], 'republican_5': [0], 'candidates_6': [1], '60_7': [1]}
|
['district', 'incumbent', 'party', 'first elected', 'results', 'candidates']
|
[['wisconsin 1', 'paul ryan', 'republican', '1998', 're - elected', 'paul ryan ( r ) 66 % jeffrey thomas ( d ) 34 %'], ['wisconsin 2', 'tammy baldwin', 'democratic', '1998', 're - elected', 'tammy baldwin ( d ) 51 % john sharpless ( r ) 49 %'], ['wisconsin 3', 'ron kind', 'democratic', '1996', 're - elected', 'ron kind ( d ) 64 % susan tully ( r ) 36 %'], ['wisconsin 5', 'tom barrett', 'democratic', '1992', 're - elected', 'tom barrett ( d ) 78 % jonathan smith ( r ) 22 %'], ['wisconsin 6', 'tom petri', 'republican', '1979', 're - elected', 'tom petri ( r ) 65 % dan flaherty ( d ) 35 %'], ['wisconsin 7', 'dave obey', 'democratic', '1969', 're - elected', 'dave obey ( d ) 63 % sean cronin ( r ) 37 %'], ['wisconsin 8', 'mark green', 'republican', '1998', 're - elected', 'mark green ( r ) 75 % dean reich ( d ) 25 %']]
|
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
|
majority
|
ted wass directed every episode of the 2009 tv series brothers .
|
{'scope': 'all', 'col': '3', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'ted wass', 'subset': None}
|
{'func': 'all_str_eq', 'args': ['all_rows', 'directed by', 'ted wass'], 'result': True, 'ind': 0, 'tointer': 'for the directed by records of all rows , all of them fuzzily match to ted wass .', 'tostr': 'all_eq { all_rows ; directed by ; ted wass } = true'}
|
all_eq { all_rows ; directed by ; ted wass } = true
|
for the directed by records of all rows , all of them fuzzily match to ted wass .
|
1
|
1
|
{'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'directed by_3': 3, 'ted wass_4': 4}
|
{'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'directed by_3': 'directed by', 'ted wass_4': 'ted wass'}
|
{'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'directed by_3': [0], 'ted wass_4': [0]}
|
['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']]
|
gastão elias
|
https://en.wikipedia.org/wiki/Gast%C3%A3o_Elias
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16741821-8.html.csv
|
comparative
|
during the 2008 davis cup europe and the 2013 davis cup europe , gastao elias played on a clay surface both times .
|
{'row_1': '3', 'row_2': '6', 'col': '5', 'col_other': '1', 'relation': 'equal', 'record_mentioned': 'yes', 'diff_result': None}
|
{'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'edition', '2008 davis cup europe / africa group ii'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose edition record fuzzily matches to 2008 davis cup europe / africa group ii .', 'tostr': 'filter_eq { all_rows ; edition ; 2008 davis cup europe / africa group ii }'}, 'surface'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; edition ; 2008 davis cup europe / africa group ii } ; surface }', 'tointer': 'select the rows whose edition record fuzzily matches to 2008 davis cup europe / africa group ii . take the surface record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'edition', '2013 davis cup europe / africa group ii'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose edition record fuzzily matches to 2013 davis cup europe / africa group ii .', 'tostr': 'filter_eq { all_rows ; edition ; 2013 davis cup europe / africa group ii }'}, 'surface'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; edition ; 2013 davis cup europe / africa group ii } ; surface }', 'tointer': 'select the rows whose edition record fuzzily matches to 2013 davis cup europe / africa group ii . take the surface record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_eq { all_rows ; edition ; 2008 davis cup europe / africa group ii } ; surface } ; hop { filter_eq { all_rows ; edition ; 2013 davis cup europe / africa group ii } ; surface } }', 'tointer': 'select the rows whose edition record fuzzily matches to 2008 davis cup europe / africa group ii . take the surface record of this row . select the rows whose edition record fuzzily matches to 2013 davis cup europe / africa group ii . take the surface record of this row . the first record fuzzily matches to the second record .'}, {'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'edition', '2008 davis cup europe / africa group ii'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose edition record fuzzily matches to 2008 davis cup europe / africa group ii .', 'tostr': 'filter_eq { all_rows ; edition ; 2008 davis cup europe / africa group ii }'}, 'surface'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; edition ; 2008 davis cup europe / africa group ii } ; surface }', 'tointer': 'select the rows whose edition record fuzzily matches to 2008 davis cup europe / africa group ii . take the surface record of this row .'}, 'clay'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; edition ; 2008 davis cup europe / africa group ii } ; surface } ; clay }', 'tointer': 'the surface record of the first row is clay .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'edition', '2013 davis cup europe / africa group ii'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose edition record fuzzily matches to 2013 davis cup europe / africa group ii .', 'tostr': 'filter_eq { all_rows ; edition ; 2013 davis cup europe / africa group ii }'}, 'surface'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; edition ; 2013 davis cup europe / africa group ii } ; surface }', 'tointer': 'select the rows whose edition record fuzzily matches to 2013 davis cup europe / africa group ii . take the surface record of this row .'}, 'clay'], 'result': True, 'ind': 6, 'tostr': 'eq { hop { filter_eq { all_rows ; edition ; 2013 davis cup europe / africa group ii } ; surface } ; clay }', 'tointer': 'the surface record of the second row is clay .'}], 'result': True, 'ind': 7, 'tostr': 'and { eq { hop { filter_eq { all_rows ; edition ; 2008 davis cup europe / africa group ii } ; surface } ; clay } ; eq { hop { filter_eq { all_rows ; edition ; 2013 davis cup europe / africa group ii } ; surface } ; clay } }', 'tointer': 'the surface record of the first row is clay . the surface record of the second row is clay .'}], 'result': True, 'ind': 8, 'tostr': 'and { eq { hop { filter_eq { all_rows ; edition ; 2008 davis cup europe / africa group ii } ; surface } ; hop { filter_eq { all_rows ; edition ; 2013 davis cup europe / africa group ii } ; surface } } ; and { eq { hop { filter_eq { all_rows ; edition ; 2008 davis cup europe / africa group ii } ; surface } ; clay } ; eq { hop { filter_eq { all_rows ; edition ; 2013 davis cup europe / africa group ii } ; surface } ; clay } } } = true', 'tointer': 'select the rows whose edition record fuzzily matches to 2008 davis cup europe / africa group ii . take the surface record of this row . select the rows whose edition record fuzzily matches to 2013 davis cup europe / africa group ii . take the surface record of this row . the first record fuzzily matches to the second record . the surface record of the first row is clay . the surface record of the second row is clay .'}
|
and { eq { hop { filter_eq { all_rows ; edition ; 2008 davis cup europe / africa group ii } ; surface } ; hop { filter_eq { all_rows ; edition ; 2013 davis cup europe / africa group ii } ; surface } } ; and { eq { hop { filter_eq { all_rows ; edition ; 2008 davis cup europe / africa group ii } ; surface } ; clay } ; eq { hop { filter_eq { all_rows ; edition ; 2013 davis cup europe / africa group ii } ; surface } ; clay } } } = true
|
select the rows whose edition record fuzzily matches to 2008 davis cup europe / africa group ii . take the surface record of this row . select the rows whose edition record fuzzily matches to 2013 davis cup europe / africa group ii . take the surface record of this row . the first record fuzzily matches to the second record . the surface record of the first row is clay . the surface record of the second row is clay .
|
13
|
9
|
{'and_8': 8, 'result_9': 9, 'str_eq_4': 4, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_10': 10, 'edition_11': 11, '2008 davis cup europe / africa group ii_12': 12, 'surface_13': 13, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_14': 14, 'edition_15': 15, '2013 davis cup europe / africa group ii_16': 16, 'surface_17': 17, 'and_7': 7, 'str_eq_5': 5, 'clay_18': 18, 'str_eq_6': 6, 'clay_19': 19}
|
{'and_8': 'and', 'result_9': 'true', 'str_eq_4': 'str_eq', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_10': 'all_rows', 'edition_11': 'edition', '2008 davis cup europe / africa group ii_12': '2008 davis cup europe / africa group ii', 'surface_13': 'surface', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_14': 'all_rows', 'edition_15': 'edition', '2013 davis cup europe / africa group ii_16': '2013 davis cup europe / africa group ii', 'surface_17': 'surface', 'and_7': 'and', 'str_eq_5': 'str_eq', 'clay_18': 'clay', 'str_eq_6': 'str_eq', 'clay_19': 'clay'}
|
{'and_8': [9], 'result_9': [], 'str_eq_4': [8], 'str_hop_2': [4, 5], 'filter_str_eq_0': [2], 'all_rows_10': [0], 'edition_11': [0], '2008 davis cup europe / africa group ii_12': [0], 'surface_13': [2], 'str_hop_3': [4, 6], 'filter_str_eq_1': [3], 'all_rows_14': [1], 'edition_15': [1], '2013 davis cup europe / africa group ii_16': [1], 'surface_17': [3], 'and_7': [8], 'str_eq_5': [7], 'clay_18': [5], 'str_eq_6': [7], 'clay_19': [6]}
|
['edition', 'round', 'date', 'against', 'surface', 'opponent', 'result']
|
[['2007 davis cup europe / africa group i', '1r', '9 - 11 february 2007', 'georgia', 'carpet', 'george khrikadze', '6 - 3 , 7 - 6 ( 7 - 5 )'], ['2007 davis cup europe / africa group i', 'gi po', '21 - 23 september 2007', 'netherlands', 'hard', 'robin haase', '1 - 6 , 1 - 6 , 6 - 2 , 7 - 5 , 2 - 6'], ['2008 davis cup europe / africa group ii', '1r', '11 - 13 april 2008', 'tunisia', 'clay', 'walid jallali', '5 - 7 , 2 - 6'], ['2008 davis cup europe / africa group ii', 'sf', '19 - 21 september 2008', 'ukraine', 'hard', 'sergiy stakhovsky', '4 - 6 , 6 - 7 ( 5 - 7 ) , 4 - 6'], ['2012 davis cup europe / africa group i', 'gi po', '14 - 16 september 2012', 'slovakia', 'hard', 'martin kližan', '6 - 3 , 2 - 6 , 6 - 7 ( 4 - 7 ) , 2 - 6'], ['2013 davis cup europe / africa group ii', '2r', '5 - 7 april 2013', 'lithuania', 'clay', 'lukas mugevicius', '6 - 0 , 6 - 1 , 6 - 2'], ['2013 davis cup europe / africa group ii', '3r', '13 - 15 february 2013', 'moldova', 'hard', 'radu albot', '3 - 6 , 6 - 2 , 4 - 6 , 4 - 6']]
|
thrust specific fuel consumption
|
https://en.wikipedia.org/wiki/Thrust_specific_fuel_consumption
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-172348-2.html.csv
|
unique
|
the ssme rocket engine engine was the only engine that was tested in a space shuttle vacuum scenario .
|
{'scope': 'all', 'row': '2', 'col': '2', 'col_other': '1', 'criterion': 'equal', 'value': 'space shuttle vacuum', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'scenario', 'space shuttle vacuum'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose scenario record fuzzily matches to space shuttle vacuum .', 'tostr': 'filter_eq { all_rows ; scenario ; space shuttle vacuum }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; scenario ; space shuttle vacuum } }', 'tointer': 'select the rows whose scenario record fuzzily matches to space shuttle vacuum . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'scenario', 'space shuttle vacuum'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose scenario record fuzzily matches to space shuttle vacuum .', 'tostr': 'filter_eq { all_rows ; scenario ; space shuttle vacuum }'}, 'engine type'], 'result': 'ssme rocket engine', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; scenario ; space shuttle vacuum } ; engine type }'}, 'ssme rocket engine'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; scenario ; space shuttle vacuum } ; engine type } ; ssme rocket engine }', 'tointer': 'the engine type record of this unqiue row is ssme rocket engine .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; scenario ; space shuttle vacuum } } ; eq { hop { filter_eq { all_rows ; scenario ; space shuttle vacuum } ; engine type } ; ssme rocket engine } } = true', 'tointer': 'select the rows whose scenario record fuzzily matches to space shuttle vacuum . there is only one such row in the table . the engine type record of this unqiue row is ssme rocket engine .'}
|
and { only { filter_eq { all_rows ; scenario ; space shuttle vacuum } } ; eq { hop { filter_eq { all_rows ; scenario ; space shuttle vacuum } ; engine type } ; ssme rocket engine } } = true
|
select the rows whose scenario record fuzzily matches to space shuttle vacuum . there is only one such row in the table . the engine type record of this unqiue row is ssme rocket engine .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'scenario_7': 7, 'space shuttle vacuum_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'engine type_9': 9, 'ssme rocket engine_10': 10}
|
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'scenario_7': 'scenario', 'space shuttle vacuum_8': 'space shuttle vacuum', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'engine type_9': 'engine type', 'ssme rocket engine_10': 'ssme rocket engine'}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'scenario_7': [0], 'space shuttle vacuum_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'engine type_9': [2], 'ssme rocket engine_10': [3]}
|
['engine type', 'scenario', 'sfc in lb / ( lbf h )', 'sfc in g / ( kn s )', 'specific impulse ( s )', 'effective exhaust velocity ( m / s )']
|
[['nk - 33 rocket engine', 'vacuum', '10.9', '309', '331', '3240'], ['ssme rocket engine', 'space shuttle vacuum', '7.95', '225', '453', '4423'], ['ramjet', 'mach 1', '4.5', '127', '800', '7877'], ['j - 58 turbojet', 'sr - 71 at mach 3.2 ( wet )', '1.9', '53.8', '1900', '18587'], ['rolls - royce / snecma olympus 593', 'concorde mach 2 cruise ( dry )', '1.195', '33.8', '3012', '29553'], ['cf6 - 80c2b1f turbofan', 'boeing 747 - 400 cruise', '0.605', '17.1', '5950', '58400'], ['general electric cf6 turbofan', 'sea level', '0.307', '8.696', '11700', '115000']]
|
2003 cfl draft
|
https://en.wikipedia.org/wiki/2003_CFL_Draft
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-21321804-5.html.csv
|
ordinal
|
blake machan was the second player picked among picks 36-43 in the 2003 cfl draft .
|
{'row': '2', 'col': '1', 'order': '2', 'col_other': '3', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}
|
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'pick', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; pick ; 2 }'}, 'player'], 'result': 'blake machan', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; pick ; 2 } ; player }'}, 'blake machan'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; pick ; 2 } ; player } ; blake machan } = true', 'tointer': 'select the row whose pick record of all rows is 2nd minimum . the player record of this row is blake machan .'}
|
eq { hop { nth_argmin { all_rows ; pick ; 2 } ; player } ; blake machan } = true
|
select the row whose pick record of all rows is 2nd minimum . the player record of this row is blake machan .
|
3
|
3
|
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'pick_5': 5, '2_6': 6, 'player_7': 7, 'blake machan_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', 'pick_5': 'pick', '2_6': '2', 'player_7': 'player', 'blake machan_8': 'blake machan'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'pick_5': [0], '2_6': [0], 'player_7': [1], 'blake machan_8': [2]}
|
['pick', 'cfl team', 'player', 'position', 'college']
|
[['36', 'ottawa renegades', 'marc parenteau', 'og', 'boston college'], ['37', 'calgary stampeders', 'blake machan', 'sb', 'calgary'], ['38', 'hamilton tiger - cats', 'david kasouf', 'wr', 'holy cross'], ['39', 'toronto argonauts', 'derik fury', 'lb', 'mount allison'], ['40', 'saskatchewan roughriders', 'mike thomas', 'wr', 'regina'], ['41', 'bc lions', 'nicholas hoffman', 'fb', 'mcgill'], ['42', 'winnipeg blue bombers', 'cory olynick', 'wr', 'regina'], ['43', 'calgary stampeders', 'travis arnold', 'ol', 'manitoba']]
|
marco barba
|
https://en.wikipedia.org/wiki/Marco_Barba
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16983879-1.html.csv
|
aggregation
|
for marco barba the total number of wins from 2003 to 2010 was 18 .
|
{'scope': 'all', 'col': '2', 'type': 'sum', 'result': '18', 'subset': None}
|
{'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'wins'], 'result': '18', 'ind': 0, 'tostr': 'sum { all_rows ; wins }'}, '18'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; wins } ; 18 } = true', 'tointer': 'the sum of the wins record of all rows is 18 .'}
|
round_eq { sum { all_rows ; wins } ; 18 } = true
|
the sum of the wins record of all rows is 18 .
|
2
|
2
|
{'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'wins_4': 4, '18_5': 5}
|
{'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'wins_4': 'wins', '18_5': '18'}
|
{'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'wins_4': [0], '18_5': [1]}
|
['season', 'wins', 'poles', 'podiums', 'position']
|
[['2003', '0', '2', '1', '12th'], ['2004', '6', '6', '8', '2nd'], ['2004', '0', '0', '0', 'nc'], ['2005', '2', '2', '9', '3rd'], ['2005', '0', '0', '0', '10th'], ['2006', '1', '0', '5', '7th'], ['2006', '0', '0', '0', '41st'], ['2007', '3', '0', '9', '2nd'], ['2008', '0', '0', '0', '14th'], ['2008', '0', '0', '0', 'nc'], ['2009', '0', '0', '2', '9th'], ['2010', '6', '3', '11', '1st'], ['2010', '0', '0', '0', '35th']]
|
1992 - 93 belarusian premier league
|
https://en.wikipedia.org/wiki/1992%E2%80%9393_Belarusian_Premier_League
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14744744-1.html.csv
|
count
|
in the 1992 - 93 belarusian premier league , there were 3 teams from minsk .
|
{'scope': 'all', 'criterion': 'equal', 'value': 'minsk', 'result': '3', 'col': '2', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'location', 'minsk'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose location record fuzzily matches to minsk .', 'tostr': 'filter_eq { all_rows ; location ; minsk }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; location ; minsk } }', 'tointer': 'select the rows whose location record fuzzily matches to minsk . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; location ; minsk } } ; 3 } = true', 'tointer': 'select the rows whose location record fuzzily matches to minsk . the number of such rows is 3 .'}
|
eq { count { filter_eq { all_rows ; location ; minsk } } ; 3 } = true
|
select the rows whose location record fuzzily matches to minsk . the number of such rows is 3 .
|
3
|
3
|
{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'location_5': 5, 'minsk_6': 6, '3_7': 7}
|
{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'location_5': 'location', 'minsk_6': 'minsk', '3_7': '3'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'location_5': [0], 'minsk_6': [0], '3_7': [2]}
|
['team', 'location', 'venue', 'capacity', 'position in 1992']
|
[['dinamo minsk', 'minsk', 'dinamo , minsk', '41040', '1'], ['dnepr', 'mogilev', 'spartak', '11200', '2'], ['dinamo brest', 'brest', 'dinamo , brest', '10080', '3'], ['fandok', 'bobruisk', 'spartak , bobruisk', '3550', '4'], ['neman', 'grodno', 'neman', '6300', '5'], ['kim', 'vitebsk', 'central , vitebsk', '8300', '6'], ['torpedo mogilev', 'mogilev', 'torpedo , mogilev', '3500', '7'], ['vedrich', 'rechytsa', 'central , rechytsa', '3550', '8'], ['molodechno', 'molodechno', 'city stadium , molodechno', '5500', '9'], ['torpedo minsk', 'minsk', 'torpedo , minsk', '5200', '10'], ['shakhtyor', 'soligorsk', 'stroitel', '5000', '11'], ['obuvshchik', 'lida', 'city stadium , lida', '4000', '12'], ['torpedo zhodino', 'zhodino', 'torpedo , zhodino', '3020', '13'], ['stroitel', 'starye dorogi', 'stroitel', '2000', '14'], ['lokomotiv', 'vitebsk', 'central , vitebsk', '8300', '15'], ['gomselmash', 'gomel', 'central , gomel', '11800', '16'], ['belarus', 'minsk', 'dinamo , minsk', '41040', 'first league , 1']]
|
list of australian test bowlers who have taken over 200 test wickets
|
https://en.wikipedia.org/wiki/List_of_Australian_Test_bowlers_who_have_taken_over_200_Test_wickets
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18914438-1.html.csv
|
ordinal
|
for the australian test bowlers who have taken over 200 test wickets , the person who had the 2nd highest number of matches is glenn mcgrath .
|
{'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', 'matches', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; matches ; 2 }'}, 'name'], 'result': 'glenn mcgrath', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; matches ; 2 } ; name }'}, 'glenn mcgrath'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; matches ; 2 } ; name } ; glenn mcgrath } = true', 'tointer': 'select the row whose matches record of all rows is 2nd maximum . the name record of this row is glenn mcgrath .'}
|
eq { hop { nth_argmax { all_rows ; matches ; 2 } ; name } ; glenn mcgrath } = true
|
select the row whose matches record of all rows is 2nd maximum . the name record of this row is glenn mcgrath .
|
3
|
3
|
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'matches_5': 5, '2_6': 6, 'name_7': 7, 'glenn mcgrath_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', 'matches_5': 'matches', '2_6': '2', 'name_7': 'name', 'glenn mcgrath_8': 'glenn mcgrath'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'matches_5': [0], '2_6': [0], 'name_7': [1], 'glenn mcgrath_8': [2]}
|
['name', 'career', 'matches', 'overs', 'maidens', 'runs', 'wickets', 'average', 'best']
|
[['shane warne', '1992 - 2007', '145', '6784.1', '1762', '17995', '708', '25.42', '8 / 71'], ['glenn mcgrath', '1993 - 2007', '124', '4874.4', '1470', '12186', '563', '21.64', '8 / 24'], ['dennis lillee', '1971 - 1984', '70', '2834.1', '652', '8493', '355', '23.92', '7 / 83'], ['brett lee', '1999 - 2010', '76', '2755.1', '547', '9555', '310', '30.82', '5 / 30'], ['craig mcdermott', '1984 - 1996', '71', '2764.2', '583', '8332', '291', '28.63', '8 / 97'], ['jason gillespie', '1996 - 2006', '71', '2372.2', '630', '6770', '259', '26.14', '7 / 37'], ['richie benaud', '1952 - 1964', '63', '2727.2', '805', '6704', '248', '27.03', '7 / 72'], ['graham mckenzie', '1961 - 1971', '60', '2629.5', '547', '7328', '246', '29.79', '8 / 71'], ['ray lindwall', '1946 - 1960', '61', '1970.2', '419', '5251', '228', '23.03', '7 / 38'], ['clarrie grimmett', '1925 - 1936', '37', '2408.3', '736', '5231', '216', '24.22', '7 / 40'], ['merv hughes', '1985 - 1994', '53', '2047.3', '499', '6017', '212', '28.38', '8 / 87'], ['stuart macgill', '1998 - 2008', '44', '1872.5', '365', '6037', '208', '29.02', '8 / 108'], ['mitchell johnson', '2007 -', '50', '1870', '331', '6281', '205', '30.64', '8 / 61'], ['jeff thomson', '1972 - 1985', '51', '1589.3', '300', '5601', '200', '29.01', '6 / 46']]
|
pasha kovalev
|
https://en.wikipedia.org/wiki/Pasha_Kovalev
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12051129-1.html.csv
|
unique
|
week 4 was the only week in which pasha kovalev did a west coast swing style dance .
|
{'scope': 'all', 'row': '4', 'col': '3', 'col_other': '1', 'criterion': 'equal', 'value': 'west coast swing', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'style', 'west coast swing'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose style record fuzzily matches to west coast swing .', 'tostr': 'filter_eq { all_rows ; style ; west coast swing }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; style ; west coast swing } }', 'tointer': 'select the rows whose style record fuzzily matches to west coast swing . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'style', 'west coast swing'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose style record fuzzily matches to west coast swing .', 'tostr': 'filter_eq { all_rows ; style ; west coast swing }'}, 'week'], 'result': '4', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; style ; west coast swing } ; week }'}, '4'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; style ; west coast swing } ; week } ; 4 }', 'tointer': 'the week record of this unqiue row is 4 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; style ; west coast swing } } ; eq { hop { filter_eq { all_rows ; style ; west coast swing } ; week } ; 4 } } = true', 'tointer': 'select the rows whose style record fuzzily matches to west coast swing . there is only one such row in the table . the week record of this unqiue row is 4 .'}
|
and { only { filter_eq { all_rows ; style ; west coast swing } } ; eq { hop { filter_eq { all_rows ; style ; west coast swing } ; week } ; 4 } } = true
|
select the rows whose style record fuzzily matches to west coast swing . there is only one such row in the table . the week record of this unqiue row is 4 .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'style_7': 7, 'west coast swing_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'week_9': 9, '4_10': 10}
|
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'style_7': 'style', 'west coast swing_8': 'west coast swing', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'week_9': 'week', '4_10': '4'}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'style_7': [0], 'west coast swing_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'week_9': [2], '4_10': [3]}
|
['week', 'partner', 'style', 'choreographer ( s )', 'results']
|
[['1', 'jessi peralta', 'smooth waltz', 'tony meredith', 'safe'], ['2', 'jessi peralta', 'jazz', 'tyce diorio', 'bottom three'], ['3', 'jessi peralta', 'cha cha', 'tony meredith melanie lapatin', 'safe'], ['4', 'sara von gillern', 'west coast swing', 'benji schwimmer heidi groskreutz', 'safe'], ['5', 'sara von gillern', 'jazz', 'mandy moore', 'safe'], ['6', 'lauren gottlieb', 'hip - hop', 'shane sparks', 'safe'], ['7', 'sabra johnson', 'broadway', 'tyce diorio', 'safe'], ['7', 'sabra johnson', 'quickstep', 'tony meredith melanie lapatin', 'safe'], ['8', 'lacey schwimmer', 'hip - hop', 'dave scott', 'top six'], ['8', 'lacey schwimmer', 'smooth waltz', 'hunter johnson', 'top six']]
|
2007 - 08 phoenix suns season
|
https://en.wikipedia.org/wiki/2007%E2%80%9308_Phoenix_Suns_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11965574-5.html.csv
|
comparative
|
the suns scored more points against the hornets than against the supersonics .
|
{'row_1': '3', 'row_2': '2', 'col': '3', 'col_other': '2', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}
|
{'func': 'greater', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'team', 'hornets'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose team record fuzzily matches to hornets .', 'tostr': 'filter_eq { all_rows ; team ; hornets }'}, 'score'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; team ; hornets } ; score }', 'tointer': 'select the rows whose team record fuzzily matches to hornets . take the score record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'team', 'supersonics'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose team record fuzzily matches to supersonics .', 'tostr': 'filter_eq { all_rows ; team ; supersonics }'}, 'score'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; team ; supersonics } ; score }', 'tointer': 'select the rows whose team record fuzzily matches to supersonics . take the score record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; team ; hornets } ; score } ; hop { filter_eq { all_rows ; team ; supersonics } ; score } } = true', 'tointer': 'select the rows whose team record fuzzily matches to hornets . take the score record of this row . select the rows whose team record fuzzily matches to supersonics . take the score record of this row . the first record is greater than the second record .'}
|
greater { hop { filter_eq { all_rows ; team ; hornets } ; score } ; hop { filter_eq { all_rows ; team ; supersonics } ; score } } = true
|
select the rows whose team record fuzzily matches to hornets . take the score record of this row . select the rows whose team record fuzzily matches to supersonics . take the score record of this row . the first record is greater than the second record .
|
5
|
5
|
{'greater_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'team_7': 7, 'hornets_8': 8, 'score_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'team_11': 11, 'supersonics_12': 12, 'score_13': 13}
|
{'greater_4': 'greater', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'team_7': 'team', 'hornets_8': 'hornets', 'score_9': 'score', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'team_11': 'team', 'supersonics_12': 'supersonics', 'score_13': 'score'}
|
{'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'team_7': [0], 'hornets_8': [0], 'score_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'team_11': [1], 'supersonics_12': [1], 'score_13': [3]}
|
['date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record']
|
[['date', 'visitor', 'score', 'home', 'ot', 'leading scorer', 'attendance', 'record'], ['3 january 2008', 'supersonics', '96 - 104', 'suns', 'na', "amar ' e stoudemire ( 34 )", '18422', '23 - 9'], ['5 january 2008', 'hornets', '118 - 113', 'suns', 'na', 'leandro barbosa ( 28 )', '18422', '23 - 10'], ['7 january 2008', 'nuggets', '115 - 137', 'suns', 'na', 'shawn marion ( 27 )', '18422', '24 - 10'], ['9 january 2008', 'pacers', '122 - 129', 'suns', '1', 'two - way tie ( 27 )', '18422', '25 - 10'], ['10 january 2008', 'suns', '86 - 108', 'jazz', 'na', 'leandro barbosa ( 25 )', '19911', '25 - 11'], ['12 january 2008', 'bucks', '114 - 122', 'suns', 'na', 'steve nash ( 35 )', '18422', '26 - 11'], ['15 january 2008', 'suns', '90 - 97', 'clippers', 'na', "amar ' e stoudemire ( 29 )", '16063', '26 - 12'], ['17 january 2008', 'suns', '106 - 98', 'lakers', 'na', 'leandro barbosa ( 22 )', '18997', '27 - 12'], ['18 january 2008', 'timberwolves', '95 - 115', 'suns', 'na', "amar ' e stoudemire ( 23 )", '18422', '28 - 12'], ['20 january 2008', 'nets', '92 - 116', 'suns', 'na', "amar ' e stoudemire ( 28 )", '18422', '29 - 12'], ['22 january 2008', 'suns', '114 - 105', 'bucks', 'na', 'steve nash ( 37 )', '14503', '30 - 12'], ['23 january 2008', 'suns', '107 - 117', 'timberwolves', 'na', "amar ' e stoudemire ( 33 )", '15101', '30 - 13'], ['25 january 2008', 'suns', '110 - 108', 'cavaliers', 'na', 'raja bell ( 27 )', '20562', '31 - 13'], ['27 january 2008', 'suns', '88 - 77', 'bulls', 'na', "amar ' e stoudemire ( 24 )", '22245', '32 - 13'], ['29 january 2008', 'hawks', '92 - 125', 'suns', 'na', "amar ' e stoudemire ( 24 )", '18422', '33 - 13'], ['31 january 2008', 'spurs', '84 - 81', 'suns', 'na', 'shawn marion ( 21 )', '18422', '33 - 14']]
|
2008 - 09 scottish third division
|
https://en.wikipedia.org/wiki/2008%E2%80%9309_Scottish_Third_Division
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-14003108-1.html.csv
|
superlative
|
the highest recorded attendance for a match in the 2008-9 scottish third division was at dumbarton , with a crowd of 1398 .
|
{'scope': 'all', 'col_superlative': '4', 'row_superlative': '2', 'value_mentioned': 'yes', 'max_or_min': 'max', 'other_col': '1', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'max', 'args': ['all_rows', 'highest'], 'result': '1398', 'ind': 0, 'tostr': 'max { all_rows ; highest }', 'tointer': 'the maximum highest record of all rows is 1398 .'}, '1398'], 'result': True, 'ind': 1, 'tostr': 'eq { max { all_rows ; highest } ; 1398 }', 'tointer': 'the maximum highest record of all rows is 1398 .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'highest'], 'result': None, 'ind': 2, 'tostr': 'argmax { all_rows ; highest }'}, 'team'], 'result': 'dumbarton', 'ind': 3, 'tostr': 'hop { argmax { all_rows ; highest } ; team }'}, 'dumbarton'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { argmax { all_rows ; highest } ; team } ; dumbarton }', 'tointer': 'the team record of the row with superlative highest record is dumbarton .'}], 'result': True, 'ind': 5, 'tostr': 'and { eq { max { all_rows ; highest } ; 1398 } ; eq { hop { argmax { all_rows ; highest } ; team } ; dumbarton } } = true', 'tointer': 'the maximum highest record of all rows is 1398 . the team record of the row with superlative highest record is dumbarton .'}
|
and { eq { max { all_rows ; highest } ; 1398 } ; eq { hop { argmax { all_rows ; highest } ; team } ; dumbarton } } = true
|
the maximum highest record of all rows is 1398 . the team record of the row with superlative highest record is dumbarton .
|
6
|
6
|
{'and_5': 5, 'result_6': 6, 'eq_1': 1, 'max_0': 0, 'all_rows_7': 7, 'highest_8': 8, '1398_9': 9, 'str_eq_4': 4, 'str_hop_3': 3, 'argmax_2': 2, 'all_rows_10': 10, 'highest_11': 11, 'team_12': 12, 'dumbarton_13': 13}
|
{'and_5': 'and', 'result_6': 'true', 'eq_1': 'eq', 'max_0': 'max', 'all_rows_7': 'all_rows', 'highest_8': 'highest', '1398_9': '1398', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'argmax_2': 'argmax', 'all_rows_10': 'all_rows', 'highest_11': 'highest', 'team_12': 'team', 'dumbarton_13': 'dumbarton'}
|
{'and_5': [6], 'result_6': [], 'eq_1': [5], 'max_0': [1], 'all_rows_7': [0], 'highest_8': [0], '1398_9': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'argmax_2': [3], 'all_rows_10': [2], 'highest_11': [2], 'team_12': [3], 'dumbarton_13': [4]}
|
['team', 'stadium', 'capacity', 'highest', 'lowest', 'average']
|
[['annan athletic', 'galabank stadium', '3500', '1343', '422', '734'], ['dumbarton', 'strathclyde homes stadium', '2025', '1398', '462', '716'], ['stenhousemuir', 'ochilview park', '2624', '805', '311', '496'], ['forfar athletic', 'station park', '5177', '621', '362', '460'], ['east stirlingshire', 'ochilview park', '2624', '812', '343', '450'], ['cowdenbeath', 'central park', '2000', '1181', '193', '415'], ['berwick rangers', 'shielfield park', '4131', '570', '288', '414'], ['elgin city', 'borough briggs', '3927', '537', '276', '392'], ['montrose', 'links park', '3292', '570', '294', '379']]
|
alberta senate nominee election , 2004
|
https://en.wikipedia.org/wiki/Alberta_Senate_nominee_election%2C_2004
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1202333-1.html.csv
|
unique
|
link byfield is the only elected candidate in the alberta senate nominee election of 2004 that resigned .
|
{'scope': 'all', 'row': '4', 'col': '7', 'col_other': '1', 'criterion': 'equal', 'value': 'resigned', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'appointed', 'resigned'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose appointed record fuzzily matches to resigned .', 'tostr': 'filter_eq { all_rows ; appointed ; resigned }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; appointed ; resigned } }', 'tointer': 'select the rows whose appointed record fuzzily matches to resigned . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'appointed', 'resigned'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose appointed record fuzzily matches to resigned .', 'tostr': 'filter_eq { all_rows ; appointed ; resigned }'}, 'candidate'], 'result': 'link byfield', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; appointed ; resigned } ; candidate }'}, 'link byfield'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; appointed ; resigned } ; candidate } ; link byfield }', 'tointer': 'the candidate record of this unqiue row is link byfield .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; appointed ; resigned } } ; eq { hop { filter_eq { all_rows ; appointed ; resigned } ; candidate } ; link byfield } } = true', 'tointer': 'select the rows whose appointed record fuzzily matches to resigned . there is only one such row in the table . the candidate record of this unqiue row is link byfield .'}
|
and { only { filter_eq { all_rows ; appointed ; resigned } } ; eq { hop { filter_eq { all_rows ; appointed ; resigned } ; candidate } ; link byfield } } = true
|
select the rows whose appointed record fuzzily matches to resigned . there is only one such row in the table . the candidate record of this unqiue row is link byfield .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'appointed_7': 7, 'resigned_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'candidate_9': 9, 'link byfield_10': 10}
|
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'appointed_7': 'appointed', 'resigned_8': 'resigned', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'candidate_9': 'candidate', 'link byfield_10': 'link byfield'}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'appointed_7': [0], 'resigned_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'candidate_9': [2], 'link byfield_10': [3]}
|
['candidate', 'party', 'votes', 'votes %', 'ballots %', 'elected', 'appointed']
|
[['bert brown', 'progressive conservative', '312041', '14.3 %', '43.7 %', 'x', 'july 10 , 2007'], ['betty unger', 'progressive conservative', '311964', '14.3 %', '43.6 %', 'x', 'january 6 , 2012'], ['cliff breitkreuz', 'progressive conservative', '241306', '11.1 %', '33.8 %', 'x', 'term ended march 26 , 2012'], ['link byfield', 'independent', '238751', '11.0 %', '33.4 %', 'x', 'resigned november 2010'], ['jim silye', 'progressive conservative', '217857', '10.0 %', '30.5 %', '30.5 %', '30.5 %'], ['david usherwood', 'progressive conservative', '193056', '8.9 %', '27.0 %', '27.0 %', '27.0 %'], ['michael roth', 'alberta alliance', '176339', '8.1 %', '24.7 %', '24.7 %', '24.7 %'], ['vance gough', 'alberta alliance', '167770', '7.7 %', '23.5 %', '23.5 %', '23.5 %'], ['tom sindlinger', 'independent', '161082', '7.4 %', '22.5 %', '22.5 %', '22.5 %'], ['gary horan', 'alberta alliance', '156175', '7.2 %', '21.9 %', '21.9 %', '21.9 %']]
|
2008 - 09 portland trail blazers season
|
https://en.wikipedia.org/wiki/2008%E2%80%9309_Portland_Trail_Blazers_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-17058178-8.html.csv
|
aggregation
|
the total attendance for portland trail blazers games at the rose garden in january of the 2008 - 09 season was 144197 .
|
{'scope': 'subset', 'col': '8', 'type': 'sum', 'result': '144197', 'subset': {'col': '8', 'criterion': 'equal', 'value': 'rose garden'}}
|
{'func': 'round_eq', 'args': [{'func': 'sum', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'location attendance', 'rose garden'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; location attendance ; rose garden }', 'tointer': 'select the rows whose location attendance record fuzzily matches to rose garden .'}, 'location attendance'], 'result': '144197', 'ind': 1, 'tostr': 'sum { filter_eq { all_rows ; location attendance ; rose garden } ; location attendance }'}, '144197'], 'result': True, 'ind': 2, 'tostr': 'round_eq { sum { filter_eq { all_rows ; location attendance ; rose garden } ; location attendance } ; 144197 } = true', 'tointer': 'select the rows whose location attendance record fuzzily matches to rose garden . the sum of the location attendance record of these rows is 144197 .'}
|
round_eq { sum { filter_eq { all_rows ; location attendance ; rose garden } ; location attendance } ; 144197 } = true
|
select the rows whose location attendance record fuzzily matches to rose garden . the sum of the location attendance record of these rows is 144197 .
|
3
|
3
|
{'eq_2': 2, 'result_3': 3, 'sum_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'location attendance_5': 5, 'rose garden_6': 6, 'location attendance_7': 7, '144197_8': 8}
|
{'eq_2': 'eq', 'result_3': 'true', 'sum_1': 'sum', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'location attendance_5': 'location attendance', 'rose garden_6': 'rose garden', 'location attendance_7': 'location attendance', '144197_8': '144197'}
|
{'eq_2': [3], 'result_3': [], 'sum_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'location attendance_5': [0], 'rose garden_6': [0], 'location attendance_7': [1], '144197_8': [2]}
|
['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record']
|
[['33', 'january 2', 'new orleans', 'l 77 - 92 ( ot )', 'rudy fernández ( 19 )', 'lamarcus aldridge ( 10 )', 'steve blake ( 6 )', 'rose garden 20708', '20 - 13'], ['35', 'january 7', 'detroit', 'w 84 - 83 ( ot )', 'lamarcus aldridge ( 26 )', 'joel przybilla ( 7 )', 'steve blake ( 10 )', 'rose garden 20644', '21 - 14'], ['36', 'january 10', 'golden state', 'w 113 - 100 ( ot )', 'lamarcus aldridge ( 26 )', 'greg oden ( 8 )', 'rudy fernández ( 6 )', 'rose garden 20687', '22 - 14'], ['37', 'january 12', 'chicago', 'w 109 - 95 ( ot )', 'travis outlaw ( 33 )', 'greg oden ( 13 )', 'steve blake ( 10 )', 'united center 18996', '23 - 14'], ['38', 'january 14', 'philadelphia', 'l 79 - 100 ( ot )', 'brandon roy ( 27 )', 'lamarcus aldridge , joel przybilla ( 9 )', 'brandon roy ( 6 )', 'wachovia center 14561', '23 - 15'], ['39', 'january 15', 'new jersey', 'w 105 - 99 ( ot )', 'brandon roy ( 29 )', 'joel przybilla ( 11 )', 'brandon roy ( 5 )', 'izod center 13824', '24 - 15'], ['40', 'january 17', 'charlotte', 'l 97 - 102 ( ot )', 'lamarcus aldridge ( 21 )', 'joel przybilla ( 10 )', 'brandon roy ( 6 )', 'time warner cable arena 17482', '24 - 16'], ['41', 'january 19', 'milwaukee', 'w 102 - 85 ( ot )', 'greg oden ( 24 )', 'greg oden ( 15 )', 'sergio rodríguez , brandon roy ( 7 )', 'rose garden 20580', '25 - 16'], ['42', 'january 21', 'cleveland', 'l 98 - 104 ( ot )', 'brandon roy ( 23 )', 'joel przybilla ( 15 )', 'sergio rodríguez ( 5 )', 'rose garden 20632', '25 - 17'], ['43', 'january 24', 'washington', 'w 100 - 87 ( ot )', 'brandon roy ( 22 )', 'greg oden ( 14 )', 'sergio rodríguez ( 8 )', 'rose garden 20566', '26 - 17'], ['44', 'january 26', 'la clippers', 'w 113 - 88 ( ot )', 'brandon roy ( 33 )', 'joel przybilla ( 8 )', 'jerryd bayless ( 6 )', 'staples center 16570', '27 - 17'], ['45', 'january 28', 'charlotte', 'w 88 - 74 ( ot )', 'lamarcus aldridge ( 25 )', 'greg oden ( 14 )', 'sergio rodríguez ( 7 )', 'rose garden 20380', '28 - 17']]
|
1995 - 96 toronto raptors season
|
https://en.wikipedia.org/wiki/1995%E2%80%9396_Toronto_Raptors_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-13464416-5.html.csv
|
majority
|
the toronto raptors lost the majority of their games from december 1 to december 26 .
|
{'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']
|
[['16', 'december 1', 'philadelphia', 'w 105 - 102 ( ot )', 'willie anderson ( 23 )', 'ed pinckney ( 16 )', 'damon stoudamire ( 10 )', 'skydome 19789', '6 - 10'], ['17', 'december 3', 'miami', 'l 94 - 112 ( ot )', 'oliver miller ( 29 )', 'ed pinckney ( 12 )', 'damon stoudamire ( 15 )', 'skydome 21238', '6 - 11'], ['18', 'december 5', 'seattle', 'l 89 - 119 ( ot )', 'tracy murray ( 23 )', 'oliver miller , alvin robertson , žan tabak ( 5 )', 'alvin robertson , damon stoudamire ( 5 )', 'keyarena 17072', '6 - 12'], ['19', 'december 7', 'portland', 'l 88 - 96 ( ot )', 'tracy murray ( 28 )', 'ed pinckney ( 15 )', 'damon stoudamire ( 10 )', 'rose garden 20039', '6 - 13'], ['20', 'december 8', 'la lakers', 'l 103 - 120 ( ot )', 'damon stoudamire ( 20 )', 'ed pinckney ( 8 )', 'damon stoudamire ( 10 )', 'great western forum 12982', '6 - 14'], ['21', 'december 10', 'vancouver', 'w 93 - 81 ( ot )', 'damon stoudamire ( 24 )', 'ed pinckney ( 16 )', 'damon stoudamire ( 8 )', 'general motors place 17438', '7 - 14'], ['22', 'december 12', 'boston', 'l 96 - 116 ( ot )', 'damon stoudamire ( 18 )', 'ed pinckney ( 8 )', 'damon stoudamire ( 9 )', 'skydome 21875', '7 - 15'], ['23', 'december 14', 'indiana', 'l 100 - 102 ( ot )', 'oliver miller ( 22 )', 'oliver miller ( 12 )', 'damon stoudamire ( 13 )', 'skydome 19763', '7 - 16'], ['24', 'december 15', 'boston', 'l 103 - 122 ( ot )', 'žan tabak ( 18 )', 'žan tabak ( 8 )', 'alvin robertson , damon stoudamire ( 7 )', 'fleetcenter 17580', '7 - 17'], ['25', 'december 17', 'orlando', 'w 110 - 93 ( ot )', 'damon stoudamire ( 21 )', 'ed pinckney ( 11 )', 'damon stoudamire ( 10 )', 'skydome 25820', '8 - 17'], ['26', 'december 19', 'detroit', 'l 82 - 94 ( ot )', 'damon stoudamire ( 19 )', 'oliver miller ( 11 )', 'damon stoudamire ( 8 )', 'skydome 21128', '8 - 18'], ['27', 'december 22', 'chicago', 'l 104 - 113 ( ot )', 'žan tabak ( 24 )', 'damon stoudamire , žan tabak ( 8 )', 'damon stoudamire ( 13 )', 'united center 22987', '8 - 19'], ['28', 'december 23', 'new york', 'l 91 - 103 ( ot )', 'damon stoudamire ( 25 )', 'ed pinckney ( 10 )', 'damon stoudamire ( 8 )', 'madison square garden 19763', '8 - 20'], ['29', 'december 26', 'milwaukee', 'w 93 - 87 ( ot )', 'damon stoudamire ( 21 )', 'ed pinckney ( 9 )', 'damon stoudamire ( 11 )', 'copps coliseum 17242', '9 - 20']]
|
bud tingelstad
|
https://en.wikipedia.org/wiki/Bud_Tingelstad
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1252053-1.html.csv
|
unique
|
1966 was the only year bud did not complete more than 16 laps .
|
{'scope': 'all', 'row': '6', 'col': '6', 'col_other': '1', 'criterion': 'less_than', 'value': '17', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'laps', '17'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose laps record is less than 17 .', 'tostr': 'filter_less { all_rows ; laps ; 17 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_less { all_rows ; laps ; 17 } }', 'tointer': 'select the rows whose laps record is less than 17 . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'laps', '17'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose laps record is less than 17 .', 'tostr': 'filter_less { all_rows ; laps ; 17 }'}, 'year'], 'result': '1966', 'ind': 2, 'tostr': 'hop { filter_less { all_rows ; laps ; 17 } ; year }'}, '1966'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_less { all_rows ; laps ; 17 } ; year } ; 1966 }', 'tointer': 'the year record of this unqiue row is 1966 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_less { all_rows ; laps ; 17 } } ; eq { hop { filter_less { all_rows ; laps ; 17 } ; year } ; 1966 } } = true', 'tointer': 'select the rows whose laps record is less than 17 . there is only one such row in the table . the year record of this unqiue row is 1966 .'}
|
and { only { filter_less { all_rows ; laps ; 17 } } ; eq { hop { filter_less { all_rows ; laps ; 17 } ; year } ; 1966 } } = true
|
select the rows whose laps record is less than 17 . there is only one such row in the table . the year record of this unqiue row is 1966 .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_less_0': 0, 'all_rows_6': 6, 'laps_7': 7, '17_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'year_9': 9, '1966_10': 10}
|
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_less_0': 'filter_less', 'all_rows_6': 'all_rows', 'laps_7': 'laps', '17_8': '17', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'year_9': 'year', '1966_10': '1966'}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_less_0': [1, 2], 'all_rows_6': [0], 'laps_7': [0], '17_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'year_9': [2], '1966_10': [3]}
|
['year', 'start', 'qual', 'rank', 'finish', 'laps']
|
[['1960', '28', '142.354', '29', '9', '200'], ['1962', '10', '147.753', '10', '15', '200'], ['1963', '25', '148.227', '27', '28', '46'], ['1964', '19', '151.210', '26', '6', '198'], ['1965', '24', '154.672', '23', '16', '115'], ['1966', '27', '159.144', '26', '21', '16'], ['1967', '25', '163.228', '22', '14', '182'], ['1968', '18', '164.444', '17', '16', '158'], ['1969', '18', '166.597', '18', '15', '155'], ['1971', '17', '170.156', '24', '7', '198']]
|
1971 vfl season
|
https://en.wikipedia.org/wiki/1971_VFL_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10826072-22.html.csv
|
superlative
|
the game at the mcg venue had the highest attendance .
|
{'scope': 'all', 'col_superlative': '6', 'row_superlative': '6', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '5', 'subset': None}
|
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'crowd'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; crowd }'}, 'venue'], 'result': 'mcg', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; crowd } ; venue }'}, 'mcg'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; crowd } ; venue } ; mcg } = true', 'tointer': 'select the row whose crowd record of all rows is maximum . the venue record of this row is mcg .'}
|
eq { hop { argmax { all_rows ; crowd } ; venue } ; mcg } = true
|
select the row whose crowd record of all rows is maximum . the venue record of this row is mcg .
|
3
|
3
|
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'crowd_5': 5, 'venue_6': 6, 'mcg_7': 7}
|
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'crowd_5': 'crowd', 'venue_6': 'venue', 'mcg_7': 'mcg'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'crowd_5': [0], 'venue_6': [1], 'mcg_7': [2]}
|
['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date']
|
[['hawthorn', '18.16 ( 124 )', 'melbourne', '8.17 ( 65 )', 'glenferrie oval', '14809', '28 august 1971'], ['footscray', '10.14 ( 74 )', 'st kilda', '12.18 ( 90 )', 'western oval', '16707', '28 august 1971'], ['essendon', '12.12 ( 84 )', 'fitzroy', '13.17 ( 95 )', 'windy hill', '12865', '28 august 1971'], ['carlton', '16.10 ( 106 )', 'collingwood', '13.9 ( 87 )', 'princes park', '32000', '28 august 1971'], ['south melbourne', '19.17 ( 131 )', 'north melbourne', '8.11 ( 59 )', 'lake oval', '9307', '28 august 1971'], ['richmond', '16.14 ( 110 )', 'geelong', '14.18 ( 102 )', 'mcg', '36423', '28 august 1971']]
|
comparison of intel graphics processing units
|
https://en.wikipedia.org/wiki/Comparison_of_Intel_graphics_processing_units
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-25839957-5.html.csv
|
majority
|
most of the devices have at least 17.1 gb in memory bandwidth available .
|
{'scope': 'all', 'col': '13', 'most_or_all': 'most', 'criterion': 'greater_than_eq', 'value': '17.1', 'subset': None}
|
{'func': 'most_greater_eq', 'args': ['all_rows', 'memory bandwidth ( gb / s )', '17.1'], 'result': True, 'ind': 0, 'tointer': 'for the memory bandwidth ( gb / s ) records of all rows , most of them are greater than or equal to 17.1 .', 'tostr': 'most_greater_eq { all_rows ; memory bandwidth ( gb / s ) ; 17.1 } = true'}
|
most_greater_eq { all_rows ; memory bandwidth ( gb / s ) ; 17.1 } = true
|
for the memory bandwidth ( gb / s ) records of all rows , most of them are greater than or equal to 17.1 .
|
1
|
1
|
{'most_greater_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'memory bandwidth ( gb / s )_3': 3, '17.1_4': 4}
|
{'most_greater_eq_0': 'most_greater_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'memory bandwidth ( gb / s )_3': 'memory bandwidth ( gb / s )', '17.1_4': '17.1'}
|
{'most_greater_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'memory bandwidth ( gb / s )_3': [0], '17.1_4': [0]}
|
['graphics', 'launch', 'market', 'cpu', 'code name', 'device id', 'core clock ( mhz )', 'execution units', 'shader model', 'directx', 'opengl', 'opencl', 'memory bandwidth ( gb / s )', 'dvmt ( mb )', 'cvt hd', 'qsv']
|
[['hd graphics', '2010', 'desktop', 'celeron g1101 pentiumg69xx', 'ironlake ( clarkdale )', '42', '533', '12', '4.0', '10.0', '2.1', 'no', '17', '1720', 'no', 'no'], ['hd graphics', '2010', 'desktop', 'core i3 - 5x0 core i5 - 6x0 core i5 - 655k', 'ironlake ( clarkdale )', '42', '733', '12', '4.0', '10.0', '2.1', 'no', '21.3', '1720', 'yes', 'no'], ['hd graphics', '2010', 'desktop', 'core i5 - 661', 'ironlake ( clarkdale )', '42', '900', '12', '4.0', '10.0', '2.1', 'no', '21.3', '1720', 'yes', 'no'], ['hd graphics', '2010', 'mobile', 'celeron u3xxx pentium u5xxx', 'ironlake ( arrandale )', '46', '166 - 500', '12', '4.0', '10.0', '2.1', 'no', '12.8', '1720', 'no', 'no'], ['hd graphics', '2010', 'mobile', 'core i7 - 620le core i7 - 6x0lm', 'ironlake ( arrandale )', '46', '266 - 566', '12', '4.0', '10.0', '2.1', 'no', '17.1', '1720', 'yes', 'no'], ['hd graphics', '2010', 'mobile', 'celeron p4xxx pentium p6xxx', 'ironlake ( arrandale )', '46', '500 - 667', '12', '4.0', '10.0', '2.1', 'no', '17.1', '1720', 'no', 'no']]
|
lisa bonder
|
https://en.wikipedia.org/wiki/Lisa_Bonder
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15057113-3.html.csv
|
unique
|
lisa bonder finished as a runner-up only once .
|
{'scope': 'all', 'row': '5', 'col': '1', 'col_other': 'n/a', 'criterion': 'equal', 'value': 'runner - up', 'subset': None}
|
{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'outcome', 'runner - up'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose outcome record fuzzily matches to runner - up .', 'tostr': 'filter_eq { all_rows ; outcome ; runner - up }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; outcome ; runner - up } } = true', 'tointer': 'select the rows whose outcome record fuzzily matches to runner - up . there is only one such row in the table .'}
|
only { filter_eq { all_rows ; outcome ; runner - up } } = true
|
select the rows whose outcome record fuzzily matches to runner - up . 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, 'outcome_4': 4, 'runner - up_5': 5}
|
{'only_1': 'only', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'outcome_4': 'outcome', 'runner - up_5': 'runner - up'}
|
{'only_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'outcome_4': [0], 'runner - up_5': [0]}
|
['outcome', 'date', 'tournament', 'surface', 'opponent', 'score']
|
[['winner', 'july 11 , 1982', 'hamburg', 'clay', 'renáta tomanová', '6 - 3 , 6 - 2'], ['winner', 'october 18 , 1982', 'tokyo', 'hard', 'shelley solomon', '2 - 6 , 6 - 0 , 6 - 3'], ['winner', 'september 18 , 1983', 'tokyo', 'carpet ( i )', 'andrea jaeger', '6 - 2 , 5 - 7 , 6 - 1'], ['winner', 'october 16 , 1983', 'tokyo', 'hard', 'laura arraya', '6 - 1 , 6 - 3'], ['runner - up', 'august 11 , 1984', 'indianapolis', 'clay', 'manuela maleeva', '6 - 4 , 6 - 3']]
|
rowing at the 2008 summer olympics - women 's single sculls
|
https://en.wikipedia.org/wiki/Rowing_at_the_2008_Summer_Olympics_%E2%80%93_Women%27s_single_sculls
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18662695-9.html.csv
|
ordinal
|
sophie balmary had the second fastest rowing time in the 2008 summer olympics - women 's single sculls .
|
{'row': '2', 'col': '4', 'order': '2', 'col_other': '2', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}
|
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'time', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; time ; 2 }'}, 'athlete'], 'result': 'sophie balmary', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; time ; 2 } ; athlete }'}, 'sophie balmary'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; time ; 2 } ; athlete } ; sophie balmary } = true', 'tointer': 'select the row whose time record of all rows is 2nd minimum . the athlete record of this row is sophie balmary .'}
|
eq { hop { nth_argmin { all_rows ; time ; 2 } ; athlete } ; sophie balmary } = true
|
select the row whose time record of all rows is 2nd minimum . the athlete record of this row is sophie balmary .
|
3
|
3
|
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'time_5': 5, '2_6': 6, 'athlete_7': 7, 'sophie balmary_8': 8}
|
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmin_0': 'nth_argmin', 'all_rows_4': 'all_rows', 'time_5': 'time', '2_6': '2', 'athlete_7': 'athlete', 'sophie balmary_8': 'sophie balmary'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'time_5': [0], '2_6': [0], 'athlete_7': [1], 'sophie balmary_8': [2]}
|
['rank', 'athlete', 'country', 'time', 'notes']
|
[['1', 'miroslava knapková', 'czech republic', '7:30.33', 'sa / b'], ['2', 'sophie balmary', 'france', '7:37.01', 'sa / b'], ['3', 'iva obradović', 'serbia', '7:39.16', 'sa / b'], ['4', 'mayra gonzález', 'cuba', '7:45.75', 'sc / d'], ['5', 'camila vargas', 'el salvador', '8:11.79', 'sc / d'], ['6', 'latt shwe zin', 'myanmar', '8:17.76', 'sc / d']]
|
stefano modena
|
https://en.wikipedia.org/wiki/Stefano_Modena
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1226330-2.html.csv
|
aggregation
|
on average , stefano modena scored about 2.7 points across all events he was an entrant in .
|
{'scope': 'all', 'col': '5', 'type': 'average', 'result': '2.7', 'subset': None}
|
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'points'], 'result': '2.7', 'ind': 0, 'tostr': 'avg { all_rows ; points }'}, '2.7'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; points } ; 2.7 } = true', 'tointer': 'the average of the points record of all rows is 2.7 .'}
|
round_eq { avg { all_rows ; points } ; 2.7 } = true
|
the average of the points record of all rows is 2.7 .
|
2
|
2
|
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'points_4': 4, '2.7_5': 5}
|
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'points_4': 'points', '2.7_5': '2.7'}
|
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'points_4': [0], '2.7_5': [1]}
|
['year', 'entrant', 'chassis', 'engine', 'points']
|
[['1987', 'motor racing developments', 'brabham bt56', 'bmw str - 4', '0'], ['1988', 'eurobrun racing', 'eurobrun er188', 'cosworth v8', '0'], ['1989', 'motor racing developments', 'brabham bt58', 'judd v8', '4'], ['1990', 'motor racing developments', 'brabham bt58', 'judd v8', '2'], ['1990', 'motor racing developments', 'brabham bt59', 'judd v8', '2'], ['1991', 'braun tyrrell honda', 'tyrrell 020', 'honda v10', '10'], ['1992', 'sasol jordan yamaha', 'jordan 192', 'yamaha v12', '1']]
|
cube ( film series )
|
https://en.wikipedia.org/wiki/Cube_%28film_series%29
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2933761-1.html.csv
|
majority
|
most of all male characters in the cube ( film series ) died .
|
{'scope': 'subset', 'col': '6', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'dead', 'subset': {'col': '3', 'criterion': 'equal', 'value': 'male'}}
|
{'func': 'most_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'gender', 'male'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; gender ; male }', 'tointer': 'select the rows whose gender record fuzzily matches to male .'}, 'status', 'dead'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose gender record fuzzily matches to male . for the status records of these rows , most of them fuzzily match to dead .', 'tostr': 'most_eq { filter_eq { all_rows ; gender ; male } ; status ; dead } = true'}
|
most_eq { filter_eq { all_rows ; gender ; male } ; status ; dead } = true
|
select the rows whose gender record fuzzily matches to male . for the status records of these rows , most of them fuzzily match to dead .
|
2
|
2
|
{'most_str_eq_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'gender_4': 4, 'male_5': 5, 'status_6': 6, 'dead_7': 7}
|
{'most_str_eq_1': 'most_str_eq', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'gender_4': 'gender', 'male_5': 'male', 'status_6': 'status', 'dead_7': 'dead'}
|
{'most_str_eq_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'gender_4': [0], 'male_5': [0], 'status_6': [1], 'dead_7': [1]}
|
['name', 'occupation', 'gender', 'prison connection', 'played by', 'status']
|
[['kazan', 'autistic savant', 'male', 'kazan prison ( russia )', 'andrew miller', 'alive after exiting the cube'], ['david worth', 'architect', 'male', 'leavenworth prison ( usa )', 'david hewlett', 'dead'], ['quentin', 'police officer', 'male', 'san quentin state prison ( usa )', 'maurice dean wint', 'dead'], ['joan leaven', 'mathematics student', 'female', 'leavenworth prison ( usa )', 'nicole de boer', 'dead'], ['dr helen holloway', 'free clinic doctor', 'female', "holloway women 's prison ( uk )", 'nicky guadagni', 'dead'], ['rennes', 'prison escapist', 'male', 'centre pãnitentiaire de rennes ( france )', 'wayne robson', 'dead']]
|
bucknell bison men 's basketball
|
https://en.wikipedia.org/wiki/Bucknell_Bison_men%27s_basketball
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17016075-1.html.csv
|
aggregation
|
the average seed for the bucknell bisons is 13.16 .
|
{'scope': 'all', 'col': '2', 'type': 'average', 'result': '13.16', 'subset': None}
|
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'seed'], 'result': '13.16', 'ind': 0, 'tostr': 'avg { all_rows ; seed }'}, '13.16'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; seed } ; 13.16 } = true', 'tointer': 'the average of the seed record of all rows is 13.16 .'}
|
round_eq { avg { all_rows ; seed } ; 13.16 } = true
|
the average of the seed record of all rows is 13.16 .
|
2
|
2
|
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'seed_4': 4, '13.16_5': 5}
|
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'seed_4': 'seed', '13.16_5': '13.16'}
|
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'seed_4': [0], '13.16_5': [1]}
|
['year', 'seed', 'round', 'opponent', 'result / score']
|
[['1987', '16', 'first round', '1 georgetown', 'l 75 - 53'], ['1989', '15', 'first round', '2 syracuse', 'l 104 - 81'], ['2005', '14', 'first round second round', '3 kansas 6 wisconsin', 'w 64 - 63 l 71 - 62'], ['2006', '9', 'first round second round', '8 arkansas 1 memphis', 'w 59 - 55 l 72 - 56'], ['2011', '14', 'first round', '3 uconn', 'l 81 - 52'], ['2013', '11', 'first round', '6 butler', 'l 68 - 56']]
|
linda wild
|
https://en.wikipedia.org/wiki/Linda_Wild
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15442936-2.html.csv
|
ordinal
|
the second time linda wild won a tournament in japan , she was playing on a carpet ( i ) surface .
|
{'scope': 'subset', 'row': '3', 'col': '1', 'order': '2', 'col_other': '3', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'subset': {'col': '2', 'criterion': 'fuzzily_match', 'value': 'japan'}}
|
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'tournament', 'japan'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; tournament ; japan }', 'tointer': 'select the rows whose tournament record fuzzily matches to japan .'}, 'date', '2'], 'result': None, 'ind': 1, 'tostr': 'nth_argmin { filter_eq { all_rows ; tournament ; japan } ; date ; 2 }'}, 'surface'], 'result': 'carpet ( i )', 'ind': 2, 'tostr': 'hop { nth_argmin { filter_eq { all_rows ; tournament ; japan } ; date ; 2 } ; surface }'}, 'carpet ( i )'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { nth_argmin { filter_eq { all_rows ; tournament ; japan } ; date ; 2 } ; surface } ; carpet ( i ) } = true', 'tointer': 'select the rows whose tournament record fuzzily matches to japan . select the row whose date record of these rows is 2nd minimum . the surface record of this row is carpet ( i ) .'}
|
eq { hop { nth_argmin { filter_eq { all_rows ; tournament ; japan } ; date ; 2 } ; surface } ; carpet ( i ) } = true
|
select the rows whose tournament record fuzzily matches to japan . select the row whose date record of these rows is 2nd minimum . the surface record of this row is carpet ( i ) .
|
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, 'tournament_6': 6, 'japan_7': 7, 'date_8': 8, '2_9': 9, 'surface_10': 10, 'carpet (i)_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', 'tournament_6': 'tournament', 'japan_7': 'japan', 'date_8': 'date', '2_9': '2', 'surface_10': 'surface', 'carpet (i)_11': 'carpet ( i )'}
|
{'str_eq_3': [4], 'result_4': [], 'str_hop_2': [3], 'nth_argmin_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'tournament_6': [0], 'japan_7': [0], 'date_8': [1], '2_9': [1], 'surface_10': [2], 'carpet (i)_11': [3]}
|
['date', 'tournament', 'surface', 'opponent in the final', 'score']
|
[['july 26 , 1993', 'san juan , puerto rico', 'hard', 'ann grossman', '6 - 3 , 5 - 7 , 6 - 3'], ['september 27 , 1993', 'sapporo , japan', 'carpet ( i )', 'irina spîrlea', '6 - 4 , 6 - 3'], ['september 11 , 1995', 'nagoya , japan', 'carpet ( i )', 'sandra kleinová', '6 - 4 , 6 - 2'], ['september 25 , 1995', 'beijing , china', 'hard', 'wang shi - ting', '7 - 5 , 6 - 2'], ['april 8 , 1996', 'jakarta , indonesia', 'hard', 'yayuk basuki', 'walkover']]
|
2005 - 06 north carolina tar heels men 's basketball team
|
https://en.wikipedia.org/wiki/2005%E2%80%9306_North_Carolina_Tar_Heels_men%27s_basketball_team
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-20785990-2.html.csv
|
comparative
|
north carolina tar heels player tyler hansbrough is taller than wes miller .
|
{'row_1': '6', 'row_2': '7', 'col': '3', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}
|
{'func': 'greater', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name', 'tyler hansbrough'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose name record fuzzily matches to tyler hansbrough .', 'tostr': 'filter_eq { all_rows ; name ; tyler hansbrough }'}, 'height'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; name ; tyler hansbrough } ; height }', 'tointer': 'select the rows whose name record fuzzily matches to tyler hansbrough . take the height record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name', 'wes miller'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose name record fuzzily matches to wes miller .', 'tostr': 'filter_eq { all_rows ; name ; wes miller }'}, 'height'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; name ; wes miller } ; height }', 'tointer': 'select the rows whose name record fuzzily matches to wes miller . take the height record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; name ; tyler hansbrough } ; height } ; hop { filter_eq { all_rows ; name ; wes miller } ; height } } = true', 'tointer': 'select the rows whose name record fuzzily matches to tyler hansbrough . take the height record of this row . select the rows whose name record fuzzily matches to wes miller . take the height record of this row . the first record is greater than the second record .'}
|
greater { hop { filter_eq { all_rows ; name ; tyler hansbrough } ; height } ; hop { filter_eq { all_rows ; name ; wes miller } ; height } } = true
|
select the rows whose name record fuzzily matches to tyler hansbrough . take the height record of this row . select the rows whose name record fuzzily matches to wes miller . take the height 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, 'name_7': 7, 'tyler hansbrough_8': 8, 'height_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'name_11': 11, 'wes miller_12': 12, 'height_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', 'name_7': 'name', 'tyler hansbrough_8': 'tyler hansbrough', 'height_9': 'height', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'name_11': 'name', 'wes miller_12': 'wes miller', 'height_13': 'height'}
|
{'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'name_7': [0], 'tyler hansbrough_8': [0], 'height_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'name_11': [1], 'wes miller_12': [1], 'height_13': [3]}
|
['name', '-', 'height', 'weight', 'position', 'year', 'home town', 'high school']
|
[['dewey burke', '15', '6 - 0', '185', 'guard', 'junior', 'philadelphia , pa', 'conestoga'], ['mike copeland', '40', '6 - 7', '225', 'forward', 'freshman', 'winston - salem , nc', 'r j reynolds'], ['bobby frasor', '4', '6 - 3', '208', 'guard', 'freshman', 'blue island , il', 'brother rice'], ['marcus ginyard', '1', '6 - 5', '218', 'guard - forward', 'freshman', 'alexandria , va', "bishop o'connell"], ['danny green', '14', '6 - 5', '210', 'guard', 'freshman', 'north babylon , ny', "st mary 's"], ['tyler hansbrough', '50', '6 - 9', '245', 'center', 'freshman', 'poplar bluff , mo', 'poplar bluff'], ['wes miller', '22', '5 - 11', '190', 'guard', 'junior', 'charlotte , nc', 'new hampton prep ( n h )'], ['david noel', '34', '6 - 6', '232', 'forward', 'senior', 'durham , nc', 'southern durham'], ['will robinson', '30', '6 - 6', '220', 'forward', 'senior', 'chapel hill , nc', 'chapel hill'], ['byron sanders', '41', '6 - 9', '238', 'forward', 'senior', 'gulfport , ms', 'harrison central'], ['reyshawn terry', '3', '6 - 8', '232', 'forward', 'junior', 'winston - salem , nc', 'r j reynolds'], ['quentin thomas', '11', '6 - 3', '185', 'guard', 'sophomore', 'oakland , ca', 'oakland technical senior'], ['thomas wilkins', '31', '5 - 8', '175', 'guard', 'senior', 'cary , nc', 'glen hope']]
|
2008 - 09 cardiff city f.c. season
|
https://en.wikipedia.org/wiki/2008%E2%80%9309_Cardiff_City_F.C._season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-17596418-4.html.csv
|
count
|
1 player moving to cardiff city f.c. in the 2008 - 09 season on a free transfer was moving from everton .
|
{'scope': 'subset', 'criterion': 'equal', 'value': 'everton', 'result': '1', 'col': '8', 'subset': {'col': '11', 'criterion': 'equal', 'value': 'free'}}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'transfer fee', 'free'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; transfer fee ; free }', 'tointer': 'select the rows whose transfer fee record fuzzily matches to free .'}, 'moving from', 'everton'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose transfer fee record fuzzily matches to free . among these rows , select the rows whose moving from record fuzzily matches to everton .', 'tostr': 'filter_eq { filter_eq { all_rows ; transfer fee ; free } ; moving from ; everton }'}], 'result': '1', 'ind': 2, 'tostr': 'count { filter_eq { filter_eq { all_rows ; transfer fee ; free } ; moving from ; everton } }', 'tointer': 'select the rows whose transfer fee record fuzzily matches to free . among these rows , select the rows whose moving from record fuzzily matches to everton . the number of such rows is 1 .'}, '1'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_eq { all_rows ; transfer fee ; free } ; moving from ; everton } } ; 1 } = true', 'tointer': 'select the rows whose transfer fee record fuzzily matches to free . among these rows , select the rows whose moving from record fuzzily matches to everton . the number of such rows is 1 .'}
|
eq { count { filter_eq { filter_eq { all_rows ; transfer fee ; free } ; moving from ; everton } } ; 1 } = true
|
select the rows whose transfer fee record fuzzily matches to free . among these rows , select the rows whose moving from record fuzzily matches to everton . 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, 'transfer fee_6': 6, 'free_7': 7, 'moving from_8': 8, 'everton_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', 'transfer fee_6': 'transfer fee', 'free_7': 'free', 'moving from_8': 'moving from', 'everton_9': 'everton', '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], 'transfer fee_6': [0], 'free_7': [0], 'moving from_8': [1], 'everton_9': [1], '1_10': [3]}
|
['n', 'p', 'name', 'eu', 'country', 'age', 'type', 'moving from', 'transfer window', 'ends', 'transfer fee', 'source']
|
[['15', 'df', 'comminges', 'eu', 'gpe', '26', 'free transfer', 'swindon town', 'summer', '2010', 'free', 'bbc sport'], ['21', 'mf', 'kennedy', 'eu', 'irl', '32', 'free transfer', 'crystal palace', 'summer', '2010', 'free', 'bbc sport'], ['1', 'gk', 'enckelman', 'eu', 'fin', '31', 'free transfer', 'blackburn rovers', 'summer', '2010', 'free', 'bbc sport'], ['17', 'df', 'dennehy', 'eu', 'irl', '19', 'free transfer', 'everton', 'summer', '2010', 'free', 'bbc sport'], ['44', 'fw', 'mccormack', 'eu', 'sco', '21', 'transfer', 'motherwell', 'summer', '2010', '120000', 'bbc sport'], ['8', 'fw', 'bothroyd', 'eu', 'eng', '26', 'transfer', 'wolverhampton wanderers', 'summer', '2011', '350000', 'bbc sport'], ['6', 'df', 'gyepes', 'eu', 'hun', '27', 'transfer', 'northampton town', 'summer', '2010', '200000', 'bbc sport']]
|
kurt maschler award
|
https://en.wikipedia.org/wiki/Kurt_Maschler_Award
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15641996-1.html.csv
|
count
|
walker was the publisher of the kurt maschler award winner a total of six times .
|
{'scope': 'all', 'criterion': 'equal', 'value': 'walker', 'result': '6', 'col': '5', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'publisher', 'walker'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose publisher record fuzzily matches to walker .', 'tostr': 'filter_eq { all_rows ; publisher ; walker }'}], 'result': '6', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; publisher ; walker } }', 'tointer': 'select the rows whose publisher record fuzzily matches to walker . the number of such rows is 6 .'}, '6'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; publisher ; walker } } ; 6 } = true', 'tointer': 'select the rows whose publisher record fuzzily matches to walker . the number of such rows is 6 .'}
|
eq { count { filter_eq { all_rows ; publisher ; walker } } ; 6 } = true
|
select the rows whose publisher record fuzzily matches to walker . 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, 'publisher_5': 5, 'walker_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', 'publisher_5': 'publisher', 'walker_6': 'walker', '6_7': '6'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'publisher_5': [0], 'walker_6': [0], '6_7': [2]}
|
['year', 'author', 'illustrator', 'title', 'publisher']
|
[['1982', 'angela carter ( ed and translator )', 'michael foreman', 'sleeping beauty and other favourite fairy tales', 'v gollancz'], ['1983', 'anthony browne', 'browne', 'gorilla', 'julia macrae'], ['1984', 'john burningham', 'burningham', 'granpa', 'j cape'], ['1985', 'ted hughes ( 1968 )', 'andrew davidson', 'the iron man', 'faber'], ['1986', 'allan ahlberg', 'janet ahlberg', 'the jolly postman', 'heinemann'], ['1987', 'charles causley', 'charles keeping', 'jack the treacle eater', 'macmillan'], ['1988', 'lewis carroll ( 1865 )', 'anthony browne', "alice 's adventures in wonderland", 'julia macrae'], ['1989', 'martin waddell', 'barbara firth', 'the park in the dark', 'walker'], ['1990', 'quentin blake', 'blake', 'all join in', 'j cape'], ['1991', 'colin mcnaughton', 'mcnaughton', "have you seen who 's just moved in next door to us", 'walker'], ['1992', 'raymond briggs', 'briggs', 'the man', 'julia macrae'], ['1993', 'karen wallace', 'mike bostock', 'think of an eel', 'walker'], ['1994', 'trish cooke', 'helen oxenbury', 'so much', 'walker'], ['1995', 'kathy henderson', 'patrick benson', 'the little boat', 'walker'], ['1996', 'babette cole', 'cole', 'drop dead', 'j cape'], ['1997', 'william mayne', 'jonathan heale', 'lady muck', 'heinemann'], ['1998', 'anthony browne', 'browne', 'voices in the park', 'doubleday'], ['1999', 'lewis carroll ( 1865 )', 'helen oxenbury', "alice 's adventures in wonderland", 'walker']]
|
1969 oakland raiders season
|
https://en.wikipedia.org/wiki/1969_Oakland_Raiders_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12828987-1.html.csv
|
superlative
|
the oakland raiders got the highest points against the buffalo bills .
|
{'scope': 'all', 'col_superlative': '4', 'row_superlative': '6', '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', 'result'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; result }'}, 'opponent'], 'result': 'buffalo bills', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; result } ; opponent }'}, 'buffalo bills'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; result } ; opponent } ; buffalo bills } = true', 'tointer': 'select the row whose result record of all rows is maximum . the opponent record of this row is buffalo bills .'}
|
eq { hop { argmax { all_rows ; result } ; opponent } ; buffalo bills } = true
|
select the row whose result record of all rows is maximum . the opponent record of this row is buffalo bills .
|
3
|
3
|
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'result_5': 5, 'opponent_6': 6, 'buffalo bills_7': 7}
|
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'result_5': 'result', 'opponent_6': 'opponent', 'buffalo bills_7': 'buffalo bills'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'result_5': [0], 'opponent_6': [1], 'buffalo bills_7': [2]}
|
['week', 'date', 'opponent', 'result', 'attendance']
|
[['1', 'september 14 , 1969', 'houston oilers', 'w 21 - 17', '49361'], ['2', 'september 20 , 1969', 'miami dolphins', 'w 20 - 17', '50277'], ['3', 'september 28 , 1969', 'boston patriots', 'w 38 - 23', '19069'], ['4', 'october 4 , 1969', 'miami dolphins', 't 20 - 20', '35614'], ['5', 'october 12 , 1969', 'denver broncos', 'w 24 - 14', '49511'], ['6', 'october 19 , 1969', 'buffalo bills', 'w 50 - 21', '54418'], ['7', 'october 26 , 1969', 'san diego chargers', 'w 24 - 12', '54008'], ['8', 'november 2 , 1969', 'cincinnati bengals', 'l 31 - 17', '27927'], ['9', 'november 9 , 1969', 'denver broncos', 'w 41 - 10', '54416'], ['10', 'november 16 , 1969', 'san diego chargers', 'w 21 - 16', '54372'], ['11', 'november 23 , 1969', 'kansas city chiefs', 'w 27 - 24', '51982'], ['12', 'november 30 , 1969', 'new york jets', 'w 27 - 14', '63865'], ['13', 'december 7 , 1969', 'cincinnati bengals', 'w 37 - 17', '54427'], ['14', 'december 13 , 1969', 'kansas city chiefs', 'w 10 - 6', '54443']]
|
indiana high school athletics conferences : mid - eastern - northwestern
|
https://en.wikipedia.org/wiki/Indiana_High_School_Athletics_Conferences%3A_Mid-Eastern_%E2%80%93_Northwestern
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18942405-7.html.csv
|
majority
|
most of the teams with over 350 students in the indiana high school athletic conference were in the ihsaa class a.
|
{'scope': 'subset', 'col': '6', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'a', 'subset': {'col': '4', 'criterion': 'greater_than', 'value': '350'}}
|
{'func': 'most_str_eq', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'enrollment', '350'], 'result': None, 'ind': 0, 'tostr': 'filter_greater { all_rows ; enrollment ; 350 }', 'tointer': 'select the rows whose enrollment record is greater than 350 .'}, 'ihsaa football class', 'a'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose enrollment record is greater than 350 . for the ihsaa football class records of these rows , most of them fuzzily match to a .', 'tostr': 'most_eq { filter_greater { all_rows ; enrollment ; 350 } ; ihsaa football class ; a } = true'}
|
most_eq { filter_greater { all_rows ; enrollment ; 350 } ; ihsaa football class ; a } = true
|
select the rows whose enrollment record is greater than 350 . for the ihsaa football class records of these rows , most of them fuzzily match to a .
|
2
|
2
|
{'most_str_eq_1': 1, 'result_2': 2, 'filter_greater_0': 0, 'all_rows_3': 3, 'enrollment_4': 4, '350_5': 5, 'ihsaa football class_6': 6, 'a_7': 7}
|
{'most_str_eq_1': 'most_str_eq', 'result_2': 'true', 'filter_greater_0': 'filter_greater', 'all_rows_3': 'all_rows', 'enrollment_4': 'enrollment', '350_5': '350', 'ihsaa football class_6': 'ihsaa football class', 'a_7': 'a'}
|
{'most_str_eq_1': [2], 'result_2': [], 'filter_greater_0': [1], 'all_rows_3': [0], 'enrollment_4': [0], '350_5': [0], 'ihsaa football class_6': [1], 'a_7': [1]}
|
['school', 'location', 'mascot', 'enrollment', 'ihsaa class', 'ihsaa football class', 'county']
|
[['caston', 'fulton', 'comets', '240', 'a', 'a', '25 fulton'], ['frontier', 'chalmers', 'falcons', '258', 'a', 'a', '91 white'], ['north white', 'monon', 'vikings', '278', 'a', 'a', '91 white'], ['pioneer', 'royal center', 'panthers', '316', 'a', 'a', '09 cass'], ['south newton', 'kentland', 'rebels', '270', 'a', 'a', '56 newton'], ['tri - county', 'wolcott', 'cavaliers', '227', 'a', 'a', '91 white 1'], ['west central', 'medaryville', 'trojans', '264', 'a', 'a', '66 pulaski'], ['winamac community', 'winamac', 'warriors', '395', 'aa', 'a', '66 pulaski']]
|
bolt thrust
|
https://en.wikipedia.org/wiki/Bolt_thrust
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-26967904-1.html.csv
|
ordinal
|
the .45 acp chambering cartridge has the second highest p1 diameter in millimeters .
|
{'row': '7', 'col': '2', 'order': '2', 'col_other': '1', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}
|
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'p1 diameter ( mm )', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; p1 diameter ( mm ) ; 2 }'}, 'chambering'], 'result': '.45 acp', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; p1 diameter ( mm ) ; 2 } ; chambering }'}, '.45 acp'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; p1 diameter ( mm ) ; 2 } ; chambering } ; .45 acp } = true', 'tointer': 'select the row whose p1 diameter ( mm ) record of all rows is 2nd maximum . the chambering record of this row is .45 acp .'}
|
eq { hop { nth_argmax { all_rows ; p1 diameter ( mm ) ; 2 } ; chambering } ; .45 acp } = true
|
select the row whose p1 diameter ( mm ) record of all rows is 2nd maximum . the chambering record of this row is .45 acp .
|
3
|
3
|
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'p1 diameter (mm)_5': 5, '2_6': 6, 'chambering_7': 7, '.45 acp_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', 'p1 diameter (mm)_5': 'p1 diameter ( mm )', '2_6': '2', 'chambering_7': 'chambering', '.45 acp_8': '.45 acp'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'p1 diameter (mm)_5': [0], '2_6': [0], 'chambering_7': [1], '.45 acp_8': [2]}
|
['chambering', 'p1 diameter ( mm )', 'a external ( cm 2 )', 'p max ( bar )', 'f bolt ( kgf )', 'f bolt']
|
[['.22 long rifle', '5.74', '0.2587', '1650', '435', 'n ( lbf )'], ['9x19 mm parabellum', '9.93', '0.7744', '2350', '1820', 'n ( lbf )'], ['.357 sig', '10.77', '0.9110', '3050', '2779', 'n ( lbf )'], ['.380 acp', '9.70', '0.7390', '1500', '1130', 'n ( lbf )'], ['.40 s & w', '10.77', '0.9110', '2250', '2050', 'n ( lbf )'], ['10 mm auto', '10.81', '0.9178', '2300', '2111', 'n ( lbf )'], ['.45 acp', '12.09', '1.1671', '1300', '1517', 'n ( lbf )'], ['.454 casull', '12.13', '1.1556', '3900', '4507', 'n ( lbf )']]
|
1982 all - ireland senior hurling championship
|
https://en.wikipedia.org/wiki/1982_All-Ireland_Senior_Hurling_Championship
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10577744-2.html.csv
|
majority
|
most of the players played in four matches .
|
{'scope': 'all', 'col': '6', 'most_or_all': 'most', 'criterion': 'equal', 'value': '4', 'subset': None}
|
{'func': 'most_eq', 'args': ['all_rows', 'matches', '4'], 'result': True, 'ind': 0, 'tointer': 'for the matches records of all rows , most of them are equal to 4 .', 'tostr': 'most_eq { all_rows ; matches ; 4 } = true'}
|
most_eq { all_rows ; matches ; 4 } = true
|
for the matches records of all rows , most of them are equal to 4 .
|
1
|
1
|
{'most_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'matches_3': 3, '4_4': 4}
|
{'most_eq_0': 'most_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'matches_3': 'matches', '4_4': '4'}
|
{'most_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'matches_3': [0], '4_4': [0]}
|
['rank', 'player', 'county', 'tally', 'total', 'matches', 'average']
|
[['1', 'pádraig horan', 'offaly', '5 - 17', '32', '4', '8.00'], ['2', 'billy fitzpatrick', 'kilkenny', '2 - 24', '30', '4', '7.50'], ['3', "tony o ' sullivan", 'cork', '0 - 28', '28', '4', '7.00'], ['4', 'p j molloy', 'galway', '3 - 11', '20', '2', '10.00'], ['5', 'christy heffernan', 'kilkenny', '3 - 9', '18', '4', '4.50'], ['5', 'pat horgan', 'cork', '0 - 18', '18', '4', '4.50']]
|
fiba eurobasket 2007 squads
|
https://en.wikipedia.org/wiki/FIBA_EuroBasket_2007_squads
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12962773-16.html.csv
|
ordinal
|
robert witka was the third tallest player in the fiba eurobasket 2007 squads .
|
{'row': '4', 'col': '2', 'order': '3', 'col_other': '1', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}
|
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'height', '3'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; height ; 3 }'}, 'player'], 'result': 'robert witka', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; height ; 3 } ; player }'}, 'robert witka'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; height ; 3 } ; player } ; robert witka } = true', 'tointer': 'select the row whose height record of all rows is 3rd maximum . the player record of this row is robert witka .'}
|
eq { hop { nth_argmax { all_rows ; height ; 3 } ; player } ; robert witka } = true
|
select the row whose height record of all rows is 3rd maximum . the player record of this row is robert witka .
|
3
|
3
|
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'height_5': 5, '3_6': 6, 'player_7': 7, 'robert witka_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', 'height_5': 'height', '3_6': '3', 'player_7': 'player', 'robert witka_8': 'robert witka'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'height_5': [0], '3_6': [0], 'player_7': [1], 'robert witka_8': [2]}
|
['player', 'height', 'position', 'year born', 'current club']
|
[['bartłomiej wołoszyn', '1.97', 'forward', '1986', 'anwil wloclawek'], ['andrzej pluta', '1.81', 'guard', '1974', 'anwil wloclawek'], ['robert skibniewski', '1.82', 'guard', '1983', 'bot turów'], ['robert witka', '2.06', 'forward', '1981', 'bot turów'], ['filip dylewicz', '2.02', 'forward', '1980', 'prokom trefl sopot'], ['radosław hyży', '2.00', 'forward', '1977', 'śląsk wrocław'], ['adam wójcik', '2.08', 'forward', '1970', "upea capo d'orlando"], ['kamil pietras', '2.04', 'forward', '1988', 'olimpija ljubljana'], ['szymon szewczyk', '2.09', 'center', '1982', 'lokomotiv rostov'], ['iwo kitzinger', '1.88', 'guard', '1985', 'bot turów'], ['przemysław frasunkiewicz', '2.01', 'forward', '1979', 'energa czarni'], ['łukasz koszarek', '1.87', 'guard', '1984', 'anwil wloclawek']]
|
1983 central american and caribbean championships in athletics
|
https://en.wikipedia.org/wiki/1983_Central_American_and_Caribbean_Championships_in_Athletics
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14420686-3.html.csv
|
comparative
|
puerto rico was awarded more total medals than the dominican republic at the 1983 central american and caribbean championships in athletics .
|
{'row_1': '4', 'row_2': '5', 'col': '6', 'col_other': '2', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}
|
{'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nation', 'puerto rico'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose nation record fuzzily matches to puerto rico .', 'tostr': 'filter_eq { all_rows ; nation ; puerto rico }'}, 'total'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; nation ; puerto rico } ; total }', 'tointer': 'select the rows whose nation record fuzzily matches to puerto rico . take the total record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nation', 'dominican republic'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose nation record fuzzily matches to dominican republic .', 'tostr': 'filter_eq { all_rows ; nation ; dominican republic }'}, 'total'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; nation ; dominican republic } ; total }', 'tointer': 'select the rows whose nation record fuzzily matches to dominican republic . take the total record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; nation ; puerto rico } ; total } ; hop { filter_eq { all_rows ; nation ; dominican republic } ; total } } = true', 'tointer': 'select the rows whose nation record fuzzily matches to puerto rico . take the total record of this row . select the rows whose nation record fuzzily matches to dominican republic . take the total record of this row . the first record is greater than the second record .'}
|
greater { hop { filter_eq { all_rows ; nation ; puerto rico } ; total } ; hop { filter_eq { all_rows ; nation ; dominican republic } ; total } } = true
|
select the rows whose nation record fuzzily matches to puerto rico . take the total record of this row . select the rows whose nation record fuzzily matches to dominican republic . take the total record of this row . the first record is greater than the second record .
|
5
|
5
|
{'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'nation_7': 7, 'puerto rico_8': 8, 'total_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'nation_11': 11, 'dominican republic_12': 12, 'total_13': 13}
|
{'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'nation_7': 'nation', 'puerto rico_8': 'puerto rico', 'total_9': 'total', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'nation_11': 'nation', 'dominican republic_12': 'dominican republic', 'total_13': 'total'}
|
{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'nation_7': [0], 'puerto rico_8': [0], 'total_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'nation_11': [1], 'dominican republic_12': [1], 'total_13': [3]}
|
['rank', 'nation', 'gold', 'silver', 'bronze', 'total']
|
[['1', 'cuba', '26', '15', '16', '57'], ['2', 'mexico', '5', '2', '2', '9'], ['3', 'bahamas', '4', '6', '4', '14'], ['4', 'puerto rico', '2', '4', '5', '11'], ['5', 'dominican republic', '2', '3', '0', '5'], ['6', 'colombia', '1', '4', '2', '7'], ['7', 'venezuela', '0', '2', '9', '11'], ['8', 'barbados', '0', '1', '0', '1'], ['8', 'guyana', '0', '1', '0', '1'], ['8', 'us virgin islands', '0', '1', '0', '1'], ['8', 'bermuda', '0', '1', '0', '1'], ['12', 'panama', '0', '0', '1', '1']]
|
2007 - 08 four hills tournament
|
https://en.wikipedia.org/wiki/2007%E2%80%9308_Four_Hills_Tournament
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14948647-5.html.csv
|
unique
|
janne ahonen was the only participant representing the nation of finland .
|
{'scope': 'all', 'row': '1', 'col': '3', 'col_other': '2', 'criterion': 'equal', 'value': 'fin', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nationality', 'fin'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose nationality record fuzzily matches to fin .', 'tostr': 'filter_eq { all_rows ; nationality ; fin }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; nationality ; fin } }', 'tointer': 'select the rows whose nationality record fuzzily matches to fin . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nationality', 'fin'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose nationality record fuzzily matches to fin .', 'tostr': 'filter_eq { all_rows ; nationality ; fin }'}, 'name'], 'result': 'janne ahonen', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; nationality ; fin } ; name }'}, 'janne ahonen'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; nationality ; fin } ; name } ; janne ahonen }', 'tointer': 'the name record of this unqiue row is janne ahonen .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; nationality ; fin } } ; eq { hop { filter_eq { all_rows ; nationality ; fin } ; name } ; janne ahonen } } = true', 'tointer': 'select the rows whose nationality record fuzzily matches to fin . there is only one such row in the table . the name record of this unqiue row is janne ahonen .'}
|
and { only { filter_eq { all_rows ; nationality ; fin } } ; eq { hop { filter_eq { all_rows ; nationality ; fin } ; name } ; janne ahonen } } = true
|
select the rows whose nationality record fuzzily matches to fin . there is only one such row in the table . the name record of this unqiue row is janne ahonen .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'nationality_7': 7, 'fin_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'name_9': 9, 'janne ahonen_10': 10}
|
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'nationality_7': 'nationality', 'fin_8': 'fin', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'name_9': 'name', 'janne ahonen_10': 'janne ahonen'}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'nationality_7': [0], 'fin_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'name_9': [2], 'janne ahonen_10': [3]}
|
['rank', 'name', 'nationality', '1st ( m )', '2nd ( m )', 'points', 'overall fht points', 'overall wc points ( rank )']
|
[['1', 'janne ahonen', 'fin', '126.0', '136.0', '251.6', '1085.8 ( 1 )', '615 ( 3 )'], ['2', 'anders bardal', 'nor', '132.5', '124.5', '243.6', '958.7 ( 7 )', '245 ( 13 )'], ['3', 'thomas morgenstern', 'aut', '121.0', '135.5', '242.7', '1066.0 ( 2 )', '940 ( 1 )'], ['4', 'martin schmitt', 'ger', '121.5', '132.5', '235.7', '955.9 ( 8 )', '115 ( 20 )'], ['5', 'denis kornilov', 'rus', '120.0', '132.0', '232.6', '685.0 ( 24 )', '140 ( 18 )']]
|
t.o.p ( entertainer )
|
https://en.wikipedia.org/wiki/T.O.P_%28entertainer%29
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18180883-6.html.csv
|
majority
|
the majority of t.o.p. 's wins and nominations are for 71 : into the fire .
|
{'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'equal', 'value': '71 : into the fire', 'subset': None}
|
{'func': 'most_str_eq', 'args': ['all_rows', 'nominated work', '71 : into the fire'], 'result': True, 'ind': 0, 'tointer': 'for the nominated work records of all rows , most of them fuzzily match to 71 : into the fire .', 'tostr': 'most_eq { all_rows ; nominated work ; 71 : into the fire } = true'}
|
most_eq { all_rows ; nominated work ; 71 : into the fire } = true
|
for the nominated work records of all rows , most of them fuzzily match to 71 : into the fire .
|
1
|
1
|
{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'nominated work_3': 3, '71: into the fire_4': 4}
|
{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'nominated work_3': 'nominated work', '71: into the fire_4': '71 : into the fire'}
|
{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'nominated work_3': [0], '71: into the fire_4': [0]}
|
['year', 'event', 'category', 'nominated work', 'result']
|
[['2010', '47th grand bell awards', 'hallyu popularity', '71 : into the fire', 'won'], ['2010', '47th grand bell awards', 'best new actor', '71 : into the fire', 'nominated'], ['2010', '8th korea film awards', 'best new actor', '71 : into the fire', 'nominated'], ['2010', 'style icon awards', 'new icon ( movie ) ( korean )', '71 : into the fire', 'won'], ['2010', '31st blue dragon film awards', 'best new actor', '71 : into the fire', 'won'], ['2010', '31st blue dragon film awards', 'popularity', '71 : into the fire', 'won'], ['2010', 'max movie award', 'best new actor', '71 : into the fire', 'won'], ['2010', '5th asian film awards', 'best new actor', '71 : into the fire', 'nominated'], ['2010', '47th paeksang arts award', 'best new actor', '71 : into the fire', 'won'], ['2010', '47th paeksang arts award', 'popularity award ( actor in a motion picture )', '71 : into the fire', 'won'], ['2013', '17th biff asia star awards', 'rookie awards', 'commitment', 'won']]
|
list of top association football goal scorers by country
|
https://en.wikipedia.org/wiki/List_of_top_association_football_goal_scorers_by_country
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1590321-78.html.csv
|
aggregation
|
of the top association football goal scorers , the average number of goals was 120.8 .
|
{'scope': 'all', 'col': '4', 'type': 'average', 'result': '120.8', 'subset': None}
|
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'goals'], 'result': '120.8', 'ind': 0, 'tostr': 'avg { all_rows ; goals }'}, '120.8'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; goals } ; 120.8 } = true', 'tointer': 'the average of the goals record of all rows is 120.8 .'}
|
round_eq { avg { all_rows ; goals } ; 120.8 } = true
|
the average of the goals record of all rows is 120.8 .
|
2
|
2
|
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'goals_4': 4, '120.8_5': 5}
|
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'goals_4': 'goals', '120.8_5': '120.8'}
|
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'goals_4': [0], '120.8_5': [1]}
|
['rank', 'player', 'country', 'goals', 'years']
|
[['1', 'ali al - nono', 'yemen', '146', "'99 -"], ['2', 'adel al - salimi', 'yemen', '136', "'97 - ' 11"], ['3', 'sharaf mahfood', 'yemen', '121', "'85 - ' 05"], ['4', 'fathi jabir', 'yemen', '108', "'97 - ' 08"], ['5', 'yordanos abay', 'ethiopia', '93', "'03 -"]]
|
bitburger open
|
https://en.wikipedia.org/wiki/Bitburger_Open
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-12275654-1.html.csv
|
comparative
|
in the bitburger open , li xuerui won the women 's singles one year before juliane schenk .
|
{'row_1': '16', 'row_2': '17', 'col': '1', 'col_other': '3', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}
|
{'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'womens singles', 'li xuerui'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose womens singles record fuzzily matches to li xuerui .', 'tostr': 'filter_eq { all_rows ; womens singles ; li xuerui }'}, 'year'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; womens singles ; li xuerui } ; year }', 'tointer': 'select the rows whose womens singles record fuzzily matches to li xuerui . take the year record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'womens singles', 'juliane schenk'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose womens singles record fuzzily matches to juliane schenk .', 'tostr': 'filter_eq { all_rows ; womens singles ; juliane schenk }'}, 'year'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; womens singles ; juliane schenk } ; year }', 'tointer': 'select the rows whose womens singles record fuzzily matches to juliane schenk . take the year record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; womens singles ; li xuerui } ; year } ; hop { filter_eq { all_rows ; womens singles ; juliane schenk } ; year } } = true', 'tointer': 'select the rows whose womens singles record fuzzily matches to li xuerui . take the year record of this row . select the rows whose womens singles record fuzzily matches to juliane schenk . take the year record of this row . the first record is greater than the second record .'}
|
greater { hop { filter_eq { all_rows ; womens singles ; li xuerui } ; year } ; hop { filter_eq { all_rows ; womens singles ; juliane schenk } ; year } } = true
|
select the rows whose womens singles record fuzzily matches to li xuerui . take the year record of this row . select the rows whose womens singles record fuzzily matches to juliane schenk . take the year record of this row . the first record is greater than the second record .
|
5
|
5
|
{'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'womens singles_7': 7, 'li xuerui_8': 8, 'year_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'womens singles_11': 11, 'juliane schenk_12': 12, 'year_13': 13}
|
{'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'womens singles_7': 'womens singles', 'li xuerui_8': 'li xuerui', 'year_9': 'year', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'womens singles_11': 'womens singles', 'juliane schenk_12': 'juliane schenk', 'year_13': 'year'}
|
{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'womens singles_7': [0], 'li xuerui_8': [0], 'year_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'womens singles_11': [1], 'juliane schenk_12': [1], 'year_13': [3]}
|
['year', 'mens singles', 'womens singles', 'mens doubles', 'womens doubles', 'mixed doubles']
|
[['1988', 'kim brodersen', 'katrin schmidt', 'markus keck robert neumann', 'katrin schmidt nicole baldewein', 'markus keck katrin schmidt'], ['1989', 'sörgard', 'katrin schmidt', 'stefan frey robert neumann', 'birgitta lehnert monica halim', 'chen jin katrin schmidt'], ['1998', 'yong yudianto', 'karolina ericsson', 'michael keck christian mohr', 'erica van den heuvel judith meulendijks', 'michael keck nicol pitro'], ['1999', 'oliver pongratz', 'zheng yaqiong', 'quinten van dalm dennis lens', 'britta andersen lene mork', 'chris bruil erica van den heuvel'], ['2000', 'xie yangchun', 'xu huaiwen', 'michael søgaard joachim fischer nielsen', 'claudia vogelgsang xu huaiwen', 'michael keck erica van den heuvel'], ['2001', 'niels christian kaldau', 'pi hongyan', 'michael søgaard michael lamp', 'neli boteva elena nozdran', 'chris bruil lotte bruil - jonathans'], ['2002', 'chen gang', 'pi hongyan', 'simon archer flandy limpele', 'mia audina lotte bruil - jonathans', 'nathan robertson gail emms'], ['2003', 'dicky palyama', 'xu huaiwen', 'michał łogosz robert mateusiak', 'nicole grether juliane schenk', 'frederik bergström johanna persson'], ['2004', 'niels christian kaldau', 'xu huaiwen', 'simon archer anthony clark', 'kamila augustyn nadieżda kostiuczyk', 'rasmus mangor andersen britta andersen'], ['2005', 'kasper ødum', 'xu huaiwen', 'tony gunawan halim haryanto', 'nicole grether juliane schenk', 'vladislav druzhchenko johanna persson'], ['2006', 'ronald susilo', 'xu huaiwen', 'michał łogosz robert mateusiak', 'jiang yanmei li yujia', 'robert mateusiak nadieżda kostiuczyk'], ['2007', 'lu yi', 'wang yihan', 'mathias boe carsten mogensen', 'yang wei zhang jiewen', 'kristof hopp birgit overzier'], ['2008', 'chetan anand', 'maria febe kusumastuti', 'mathias boe carsten mogensen', 'helle nielsen marie roepke', 'diju valiyaveetil jwala gutta'], ['2009', 'jan ø jørgensen', 'juliane schenk', 'rupesh kumar sanave thomas', 'helle nielsen marie roepke', 'mikkel delbo larsen mie schjoett - kristensen'], ['2010', 'chen long', 'liu xin', 'mathias boe carsten mogensen', 'pan pan tian qing', 'zhang nan zhao yunlei'], ['2011', 'hans - kristian vittinghus', 'li xuerui', 'bodin isara maneepong jongjit', 'mizuki fujii reika kakiiwa', 'chan peng soon goh liu ying'], ['2012', 'chou tien - chen', 'juliane schenk', 'ingo kindervater johannes schoettler', 'wang rong zhang zhibo', 'anders kristiansen julie houmann']]
|
gulf coast league orioles
|
https://en.wikipedia.org/wiki/Gulf_Coast_League_Orioles
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13076944-1.html.csv
|
unique
|
in the only season where they were managed by tommy shields , the orioles finished in 4th place .
|
{'scope': 'all', 'row': '7', 'col': '4', 'col_other': '3', 'criterion': 'equal', 'value': 'tommy shields', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'manager', 'tommy shields'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose manager record fuzzily matches to tommy shields .', 'tostr': 'filter_eq { all_rows ; manager ; tommy shields }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; manager ; tommy shields } }', 'tointer': 'select the rows whose manager record fuzzily matches to tommy shields . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'manager', 'tommy shields'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose manager record fuzzily matches to tommy shields .', 'tostr': 'filter_eq { all_rows ; manager ; tommy shields }'}, 'finish'], 'result': '4th', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; manager ; tommy shields } ; finish }'}, '4th'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; manager ; tommy shields } ; finish } ; 4th }', 'tointer': 'the finish record of this unqiue row is 4th .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; manager ; tommy shields } } ; eq { hop { filter_eq { all_rows ; manager ; tommy shields } ; finish } ; 4th } } = true', 'tointer': 'select the rows whose manager record fuzzily matches to tommy shields . there is only one such row in the table . the finish record of this unqiue row is 4th .'}
|
and { only { filter_eq { all_rows ; manager ; tommy shields } } ; eq { hop { filter_eq { all_rows ; manager ; tommy shields } ; finish } ; 4th } } = true
|
select the rows whose manager record fuzzily matches to tommy shields . there is only one such row in the table . the finish record of this unqiue row is 4th .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'manager_7': 7, 'tommy shields_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'finish_9': 9, '4th_10': 10}
|
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'manager_7': 'manager', 'tommy shields_8': 'tommy shields', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'finish_9': 'finish', '4th_10': '4th'}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'manager_7': [0], 'tommy shields_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'finish_9': [2], '4th_10': [3]}
|
['year', 'record', 'finish', 'manager', 'playoffs']
|
[['gcl orioles ( 1991 - 2003 )', 'gcl orioles ( 1991 - 2003 )', 'gcl orioles ( 1991 - 2003 )', 'gcl orioles ( 1991 - 2003 )', 'gcl orioles ( 1991 - 2003 )'], ['1991', '35 - 24', '1st', 'ed napoleon', 'lost league finals'], ['1992', '29 - 29', '9th', 'phillip wellman', 'missed'], ['1993', '30 - 28', '8th', 'oneri fleita', 'missed'], ['1994', '23 - 36', '12th', 'oneri fleita', 'missed'], ['1995', '34 - 25', '7th', 'julio garcia', 'missed'], ['1996', '36 - 24', '4th', 'tommy shields', 'missed'], ['1997', '27 - 33', '10th', 'butch davis', 'missed'], ['1998', '28 - 32', '8th ( t )', 'butch davis', 'missed'], ['1999', '31 - 28', '6th', 'jesus alfaro', 'missed'], ['2000', '25 - 31', '9th', 'jesus alfaro', 'missed'], ['2001', '22 - 34', '11th ( t )', 'jesus alfaro', 'missed'], ['2002', '24 - 36', '12th', 'jesus alfaro', 'missed'], ['2003', '32 - 28', '4th', 'jesus alfaro', 'missed'], ['gcl orioles ( 2007 - present )', 'gcl orioles ( 2007 - present )', 'gcl orioles ( 2007 - present )', 'gcl orioles ( 2007 - present )', 'gcl orioles ( 2007 - present )'], ['2007', '32 - 24', '2nd east', 'orlando gomez', 'missed']]
|
jacksonville jaguars draft history
|
https://en.wikipedia.org/wiki/Jacksonville_Jaguars_draft_history
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15100419-8.html.csv
|
unique
|
clenton ballard was the only player from southwest texas state college drafted by the jacksonville jaguars .
|
{'scope': 'all', 'row': '6', 'col': '6', 'col_other': '4', 'criterion': 'equal', 'value': 'southwest texas state', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'college', 'southwest texas state'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose college record fuzzily matches to southwest texas state .', 'tostr': 'filter_eq { all_rows ; college ; southwest texas state }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; college ; southwest texas state } }', 'tointer': 'select the rows whose college record fuzzily matches to southwest texas state . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'college', 'southwest texas state'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose college record fuzzily matches to southwest texas state .', 'tostr': 'filter_eq { all_rows ; college ; southwest texas state }'}, 'name'], 'result': 'clenton ballard', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; college ; southwest texas state } ; name }'}, 'clenton ballard'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; college ; southwest texas state } ; name } ; clenton ballard }', 'tointer': 'the name record of this unqiue row is clenton ballard .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; college ; southwest texas state } } ; eq { hop { filter_eq { all_rows ; college ; southwest texas state } ; name } ; clenton ballard } } = true', 'tointer': 'select the rows whose college record fuzzily matches to southwest texas state . there is only one such row in the table . the name record of this unqiue row is clenton ballard .'}
|
and { only { filter_eq { all_rows ; college ; southwest texas state } } ; eq { hop { filter_eq { all_rows ; college ; southwest texas state } ; name } ; clenton ballard } } = true
|
select the rows whose college record fuzzily matches to southwest texas state . there is only one such row in the table . the name record of this unqiue row is clenton ballard .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'college_7': 7, 'southwest texas state_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'name_9': 9, 'clenton ballard_10': 10}
|
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'college_7': 'college', 'southwest texas state_8': 'southwest texas state', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'name_9': 'name', 'clenton ballard_10': 'clenton ballard'}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'college_7': [0], 'southwest texas state_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'name_9': [2], 'clenton ballard_10': [3]}
|
['round', 'pick', 'overall', 'name', 'position', 'college']
|
[['1', '9', '9', 'john henderson', 'defensive tackle', 'tennessee'], ['2', '8', '40', 'mike pearson', 'offensive tackle', 'florida'], ['3', '24', '89', 'akin ayodele', 'linebacker', 'purdue'], ['4', '10', '108', 'david garrard', 'quarterback', 'east carolina'], ['4', '20', '118', 'chris luzar', 'tight end', 'virginia'], ['6', '8', '180', 'clenton ballard', 'defensive tackle', 'southwest texas state'], ['7', '11', '222', 'kendall newson', 'wide receiver', 'middle tennessee state'], ['7', '36', '247', 'steve smith', 'defensive back', 'oregon'], ['7', '37', '248', 'hayden epstein', 'kicker', 'michigan']]
|
2009 - 10 cleveland cavaliers season
|
https://en.wikipedia.org/wiki/2009%E2%80%9310_Cleveland_Cavaliers_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-22654073-6.html.csv
|
count
|
mo williams had the high assists 3 times totals .
|
{'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'mo williams', 'result': '5', 'col': '7', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'high assists', 'mo williams'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose high assists record fuzzily matches to mo williams .', 'tostr': 'filter_eq { all_rows ; high assists ; mo williams }'}], 'result': '5', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; high assists ; mo williams } }', 'tointer': 'select the rows whose high assists record fuzzily matches to mo williams . the number of such rows is 5 .'}, '5'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; high assists ; mo williams } } ; 5 } = true', 'tointer': 'select the rows whose high assists record fuzzily matches to mo williams . the number of such rows is 5 .'}
|
eq { count { filter_eq { all_rows ; high assists ; mo williams } } ; 5 } = true
|
select the rows whose high assists record fuzzily matches to mo williams . 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, 'high assists_5': 5, 'mo williams_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', 'high assists_5': 'high assists', 'mo williams_6': 'mo williams', '5_7': '5'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'high assists_5': [0], 'mo williams_6': [0], '5_7': [2]}
|
['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record']
|
[['5', 'november 3', 'washington wizards', 'w 102 - 90 ( ot )', 'lebron james ( 27 )', 'anderson varejao ( 10 )', 'lebron james , mo williams ( 6 )', 'quicken loans arena 20562', '3 - 2'], ['6', 'november 5', 'chicago bulls', 'l 85 - 86 ( ot )', 'lebron james ( 25 )', 'anderson varejao ( 13 )', 'lebron james , mo williams ( 6 )', 'quicken loans arena 20562', '3 - 3'], ['7', 'november 6', 'new york knicks', 'w 100 - 91 ( ot )', 'lebron james ( 33 )', 'anderson varejao ( 14 )', 'lebron james ( 9 )', 'madison square garden 19763', '4 - 3'], ['8', 'november 11', 'orlando magic', 'w 102 - 93 ( ot )', 'lebron james ( 36 )', 'lebron james ( 8 )', 'mo williams ( 6 )', 'amway arena 17461', '5 - 3'], ['9', 'november 12', 'miami heat', 'w 111 - 104 ( ot )', 'lebron james ( 34 )', 'jamario moon ( 6 )', 'lebron james ( 7 )', 'american airlines arena 19600', '6 - 3'], ['10', 'november 14', 'utah jazz', 'w 107 - 103 ( ot )', 'lebron james , mo williams ( 21 )', 'lebron james , zydrunas ilgauskas ( 6 )', 'lebron james ( 9 )', 'quicken loans arena 20562', '7 - 3'], ['11', 'november 17', 'golden state warriors', 'w 114 - 108 ( ot )', 'lebron james ( 31 )', 'jj hickson ( 9 )', 'lebron james ( 12 )', 'quicken loans arena 20562', '8 - 3'], ['12', 'november 18', 'washington wizards', 'l 91 - 108 ( ot )', 'lebron james ( 34 )', 'zydrunas ilgauskas ( 9 )', 'lebron james ( 8 )', 'verizon center 20173', '8 - 4'], ['13', 'november 20', 'indiana pacers', 'w 105 - 95 ( ot )', 'lebron james ( 40 )', 'zydrunas ilgauskas ( 11 )', 'lebron james ( 7 )', 'conseco fieldhouse 18165', '9 - 4'], ['14', 'november 21', 'philadelphia 76ers', 'w 97 - 91 ( ot )', 'lebron james ( 32 )', 'zydrunas ilgauskas ( 8 )', 'lebron james ( 9 )', 'quicken loans arena 20562', '10 - 4'], ['15', 'november 25', 'detroit pistons', 'w 98 - 88 ( ot )', 'lebron james ( 34 )', 'lebron james , anderson varejão ( 8 )', 'mo williams ( 8 )', 'the palace of auburn hills 22076', '11 - 4'], ['16', 'november 27', 'charlotte bobcats', 'l 87 - 94 ( ot )', 'lebron james ( 25 )', 'anderson varejão ( 11 )', 'mo williams ( 6 )', 'time warner cable arena 19168', '11 - 5']]
|
2008 baltimore ravens season
|
https://en.wikipedia.org/wiki/2008_Baltimore_Ravens_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15013564-4.html.csv
|
superlative
|
the most points that the ravens scored in the 2008 season was in week ten .
|
{'scope': 'all', 'col_superlative': '8', 'row_superlative': '10', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'results'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; results }'}, 'week'], 'result': '10', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; results } ; week }'}, '10'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; results } ; week } ; 10 } = true', 'tointer': 'select the row whose results record of all rows is maximum . the week record of this row is 10 .'}
|
eq { hop { argmax { all_rows ; results } ; week } ; 10 } = true
|
select the row whose results record of all rows is maximum . the week record of this row is 10 .
|
3
|
3
|
{'eq_2': 2, 'result_3': 3, 'num_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'results_5': 5, 'week_6': 6, '10_7': 7}
|
{'eq_2': 'eq', 'result_3': 'true', 'num_hop_1': 'num_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'results_5': 'results', 'week_6': 'week', '10_7': '10'}
|
{'eq_2': [3], 'result_3': [], 'num_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'results_5': [0], 'week_6': [1], '10_7': [2]}
|
['week', 'opponent', 'date', 'tv network', 'time ( et )', 'stadium', 'location', 'results', 'record']
|
[['1', 'cincinnati bengals', 'sunday , september 7 , 2008', 'cbs', '1:00 pm', 'm & t bank stadium', 'baltimore , maryland', 'w 17 - 10', '1 - 0'], ['2', 'bye week', 'bye week', 'bye week', 'bye week', 'bye week', 'bye week', 'bye week', 'bye week'], ['3', 'cleveland browns', 'sunday , september 21 , 2008', 'cbs', '4:15 pm', 'm & t bank stadium', 'baltimore , maryland', 'w 28 - 10', '2 - 0'], ['4', 'pittsburgh steelers', 'monday , september 29 , 2008', 'espn', '8:30 pm', 'heinz field', 'pittsburgh , pennsylvania', 'l 20 - 23 ot', '2 - 1'], ['5', 'tennessee titans', 'sunday , october 5 , 2008', 'cbs', '1:00 pm', 'm & t bank stadium', 'baltimore , maryland', 'l 10 - 13', '2 - 2'], ['6', 'indianapolis colts', 'sunday , october 12 , 2008', 'cbs', '1:00 pm', 'lucas oil stadium', 'indianapolis , indiana', 'l 3 - 31', '2 - 3'], ['7', 'miami dolphins', 'sunday , october 19 , 2008', 'cbs', '1:00 pm', 'dolphin stadium', 'miami , florida', 'w 27 - 13', '3 - 3'], ['8', 'oakland raiders', 'sunday , october 26 , 2008', 'cbs', '1:00 pm', 'm & t bank stadium', 'baltimore , maryland', 'w 29 - 10', '4 - 3'], ['9', 'cleveland browns', 'sunday , november 2 , 2008', 'cbs', '1:00 pm', 'cleveland browns stadium', 'cleveland , ohio', 'w 37 - 27', '5 - 3'], ['10', 'houston texans', 'sunday , november 9 , 2008', 'cbs', '1:00 pm', 'reliant stadium', 'houston , texas', 'w 41 - 13', '6 - 3'], ['11', 'new york giants', 'sunday , november 16 , 2008', 'cbs', '1:00 pm', 'giants stadium', 'east rutherford , new jersey', 'l 10 - 30', '6 - 4'], ['12', 'philadelphia eagles', 'sunday , november 23 , 2008', 'fox', '1:00 pm', 'm & t bank stadium', 'baltimore , maryland', 'w 36 - 7', '7 - 4'], ['13', 'cincinnati bengals', 'sunday , november 30 , 2008', 'cbs', '1:00 pm', 'paul brown stadium', 'cincinnati , ohio', 'w 34 - 3', '8 - 4'], ['14', 'washington redskins', 'sunday , december 7 , 2008', 'nbc', '8:15 pm', 'm & t bank stadium', 'baltimore , maryland', 'w 24 - 10', '9 - 4'], ['15', 'pittsburgh steelers', 'sunday , december 14 , 2008', 'cbs', '4:15 pm', 'm & t bank stadium', 'baltimore , maryland', 'l 9 - 13', '9 - 5'], ['16', 'dallas cowboys', 'saturday , december 20 , 2008', 'nfl network', '8:00 pm', 'texas stadium', 'irving , texas', 'w 33 - 24', '10 - 5'], ['17', 'jacksonville jaguars', 'sunday , december 28 , 2008', 'cbs', '4:15 pm', 'm & t bank stadium', 'baltimore , maryland', 'w 27 - 7', '11 - 5']]
|
1977 vfl season
|
https://en.wikipedia.org/wiki/1977_VFL_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10887379-12.html.csv
|
ordinal
|
lake oval venue recorded the highest crowd participation during the 1977 vfl season .
|
{'row': '2', 'col': '6', 'order': '1', 'col_other': '5', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}
|
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'crowd', '1'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; crowd ; 1 }'}, 'venue'], 'result': 'lake oval', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; crowd ; 1 } ; venue }'}, 'lake oval'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; venue } ; lake oval } = true', 'tointer': 'select the row whose crowd record of all rows is 1st maximum . the venue record of this row is lake oval .'}
|
eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; venue } ; lake oval } = true
|
select the row whose crowd record of all rows is 1st maximum . the venue record of this row is lake oval .
|
3
|
3
|
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'crowd_5': 5, '1_6': 6, 'venue_7': 7, 'lake oval_8': 8}
|
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'crowd_5': 'crowd', '1_6': '1', 'venue_7': 'venue', 'lake oval_8': 'lake oval'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'crowd_5': [0], '1_6': [0], 'venue_7': [1], 'lake oval_8': [2]}
|
['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date']
|
[['carlton', '3.13 ( 31 )', 'geelong', '2.12 ( 24 )', 'princes park', '11009', '18 june 1977'], ['south melbourne', '8.22 ( 70 )', 'melbourne', '9.10 ( 64 )', 'lake oval', '20785', '18 june 1977'], ['north melbourne', '6.11 ( 47 )', 'hawthorn', '6.12 ( 48 )', 'arden street oval', '9027', '18 june 1977'], ['richmond', '13.15 ( 93 )', 'fitzroy', '6.5 ( 41 )', 'mcg', '12877', '18 june 1977'], ['footscray', '5.6 ( 36 )', 'collingwood', '9.15 ( 69 )', 'western oval', '11921', '18 june 1977'], ['essendon', '5.16 ( 46 )', 'st kilda', '5.12 ( 42 )', 'vfl park', '14337', '18 june 1977']]
|
list of intel pentium iii microprocessors
|
https://en.wikipedia.org/wiki/List_of_Intel_Pentium_III_microprocessors
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-16400024-1.html.csv
|
majority
|
all the intel pentium iii microprocessors with fsb 100mt/s has mult more than 4 .
|
{'scope': 'subset', 'col': '6', 'most_or_all': 'all', 'criterion': 'greater_than', 'value': '4', 'subset': {'col': '5', 'criterion': 'equal', 'value': '100 mt/s'}}
|
{'func': 'all_greater', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'fsb', '100 mt/s'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; fsb ; 100 mt/s }', 'tointer': 'select the rows whose fsb record fuzzily matches to 100 mt/s .'}, 'mult', '4'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose fsb record fuzzily matches to 100 mt/s . for the mult records of these rows , all of them are greater than 4 .', 'tostr': 'all_greater { filter_eq { all_rows ; fsb ; 100 mt/s } ; mult ; 4 } = true'}
|
all_greater { filter_eq { all_rows ; fsb ; 100 mt/s } ; mult ; 4 } = true
|
select the rows whose fsb record fuzzily matches to 100 mt/s . for the mult records of these rows , all of them are greater than 4 .
|
2
|
2
|
{'all_greater_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'fsb_4': 4, '100 mt/s_5': 5, 'mult_6': 6, '4_7': 7}
|
{'all_greater_1': 'all_greater', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'fsb_4': 'fsb', '100 mt/s_5': '100 mt/s', 'mult_6': 'mult', '4_7': '4'}
|
{'all_greater_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'fsb_4': [0], '100 mt/s_5': [0], 'mult_6': [1], '4_7': [1]}
|
['model number', 'sspec number', 'frequency', 'l2 cache', 'fsb', 'mult', 'voltage', 'tdp', 'socket', 'release date', 'part number ( s )', 'release price ( usd )']
|
[['pentium iii 450', 'sl364 ( kb0 ) sl38e ( kb0 ) sl3cc ( kb0 ) sl35d ( kc0 ) sl37c ( kc0 )', '450 mhz', '512 kb', '100 mt / s', '4.5', '1.93 - 2.07 v', '33.76 w', 'slot 1', 'february 26 , 1999', '80525py450512bx80525u450512bx80525u450512e', '496'], ['pentium iii 500', 'sl365 ( kb0 ) sl38f ( kb0 ) sl3cd ( kb0 ) sl35e ( kc0 ) sl37d ( kc0 )', '500 mhz', '512 kb', '100 mt / s', '5', '1.93 - 2.07 v', '37.52 w', 'slot 1', 'february 26 , 1999', '80525py500512bx80525u500512bx80525u500512e', '696'], ['pentium iii 533b', 'sl3bn ( kc0 ) sl3e9 ( kc0 )', '533 mhz', '512 kb', '133 mt / s', '4', '1.93 - 2.07 v', '39.04 w', 'slot 1', 'september 27 , 1999', '80525pz533512bx80525u533512bx80525u533512e', '369'], ['pentium iii 550', 'sl3f7 ( kc0 ) sl3fj ( kc0 )', '550 mhz', '512 kb', '100 mt / s', '5.5', '1.93 - 2.07 v', '39.8 w', 'slot 1', 'may 17 , 1999', '80525py550512bx80525u550512', '700'], ['pentium iii 600', 'sl3jm ( kc0 ) sl3jt ( kc0 )', '600 mhz', '512 kb', '100 mt / s', '6', '1.98 - 2.12 v', '42.76 w', 'slot 1', 'august 2 , 1999', '80525py600512bx80525u600512', '669']]
|
2007 preakness stakes
|
https://en.wikipedia.org/wiki/2007_Preakness_Stakes
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11306889-2.html.csv
|
comparative
|
the horse cp west finished 1 1/2 lengths behind hard spun in the 2007 preakness stakes .
|
{'row_1': '4', 'row_2': '3', 'col': '2', 'col_other': '4', 'relation': 'diff', 'record_mentioned': 'no', 'diff_result': {'diff_value': '1\xa01/2', 'bigger': 'row1'}}
|
{'func': 'eq', 'args': [{'func': 'diff', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'horse name', 'c p west'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose horse name record fuzzily matches to c p west .', 'tostr': 'filter_eq { all_rows ; horse name ; c p west }'}, 'lengths behind'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; horse name ; c p west } ; lengths behind }', 'tointer': 'select the rows whose horse name record fuzzily matches to c p west . take the lengths behind record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'horse name', 'hard spun'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose horse name record fuzzily matches to hard spun .', 'tostr': 'filter_eq { all_rows ; horse name ; hard spun }'}, 'lengths behind'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; horse name ; hard spun } ; lengths behind }', 'tointer': 'select the rows whose horse name record fuzzily matches to hard spun . take the lengths behind record of this row .'}], 'result': '1\xa01/2', 'ind': 4, 'tostr': 'diff { hop { filter_eq { all_rows ; horse name ; c p west } ; lengths behind } ; hop { filter_eq { all_rows ; horse name ; hard spun } ; lengths behind } }'}, '1\xa01/2'], 'result': True, 'ind': 5, 'tostr': 'eq { diff { hop { filter_eq { all_rows ; horse name ; c p west } ; lengths behind } ; hop { filter_eq { all_rows ; horse name ; hard spun } ; lengths behind } } ; 1\xa01/2 } = true', 'tointer': 'select the rows whose horse name record fuzzily matches to c p west . take the lengths behind record of this row . select the rows whose horse name record fuzzily matches to hard spun . take the lengths behind record of this row . the first record is 1\xa01/2 larger than the second record .'}
|
eq { diff { hop { filter_eq { all_rows ; horse name ; c p west } ; lengths behind } ; hop { filter_eq { all_rows ; horse name ; hard spun } ; lengths behind } } ; 1 1/2 } = true
|
select the rows whose horse name record fuzzily matches to c p west . take the lengths behind record of this row . select the rows whose horse name record fuzzily matches to hard spun . take the lengths behind record of this row . the first record is 1 1/2 larger than the second record .
|
6
|
6
|
{'str_eq_5': 5, 'result_6': 6, 'diff_4': 4, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'horse name_8': 8, 'c p west_9': 9, 'lengths behind_10': 10, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_11': 11, 'horse name_12': 12, 'hard spun_13': 13, 'lengths behind_14': 14, '1 1/2_15': 15}
|
{'str_eq_5': 'str_eq', 'result_6': 'true', 'diff_4': 'diff', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'horse name_8': 'horse name', 'c p west_9': 'c p west', 'lengths behind_10': 'lengths behind', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_11': 'all_rows', 'horse name_12': 'horse name', 'hard spun_13': 'hard spun', 'lengths behind_14': 'lengths behind', '1 1/2_15': '1\xa01/2'}
|
{'str_eq_5': [6], 'result_6': [], 'diff_4': [5], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_7': [0], 'horse name_8': [0], 'c p west_9': [0], 'lengths behind_10': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_11': [1], 'horse name_12': [1], 'hard spun_13': [1], 'lengths behind_14': [3], '1 1/2_15': [5]}
|
['finish position', 'lengths behind', 'post position', 'horse name', 'trainer', 'jockey', 'owner', 'post time odds']
|
[['1 st', '0', '4', 'curlin', 'robby albarado', 'steve asmussen', 'stonestreet stables', '3.40 - 1'], ['2 nd', 'head', '8', 'street sense', 'calvin borel', 'carl nafzger', 'jim tafel', '1.30 - 1 favorite'], ['3 rd', '4', '7', 'hard spun', 'mario g pino', 'j larry jones', 'fox hill farms', '4.10 - 1'], ['4 th', '5 ½', '9', 'c p west', 'edgar prado', 'nick zito', 'robert lapenta , v', '24.90 - 1'], ['5 th', '6 ¾', '3', 'circular quay', 'john r velazquez', 'todd pletcher', 'michael tabor', '6.00 - 1'], ['6 th', '10 ½', '5', 'king of the roxy', 'garrett gomez', 'todd pletcher', 'team valor', '14.20 - 1'], ['7 th', '17', '1', 'mint slewlep', 'alan garcia', 'larry jones', 'marshall e dowell', '40.10 - 1'], ['8 th', '25 ½', '2', 'xchanger', 'ramon dominguez', 'carl nafzger', 'circle z stables', '23.00 - 1'], ['9 th', '29 ¾', '6', 'flying first class', 'mark guidry', 'd wayne lukas', 'ellwood w johnston', '16.60 - 1']]
|
eastern indiana athletic conference
|
https://en.wikipedia.org/wiki/Eastern_Indiana_Athletic_Conference
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18773999-1.html.csv
|
aggregation
|
the 2008-2009 average enrollment for the eastern indiana athletic conference was 923.5 .
|
{'scope': 'all', 'col': '4', 'type': 'average', 'result': '923.5', 'subset': None}
|
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'enrollment 08 - 09'], 'result': '923.5', 'ind': 0, 'tostr': 'avg { all_rows ; enrollment 08 - 09 }'}, '923.5'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; enrollment 08 - 09 } ; 923.5 } = true', 'tointer': 'the average of the enrollment 08 - 09 record of all rows is 923.5 .'}
|
round_eq { avg { all_rows ; enrollment 08 - 09 } ; 923.5 } = true
|
the average of the enrollment 08 - 09 record of all rows is 923.5 .
|
2
|
2
|
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'enrollment 08 - 09_4': 4, '923.5_5': 5}
|
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'enrollment 08 - 09_4': 'enrollment 08 - 09', '923.5_5': '923.5'}
|
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'enrollment 08 - 09_4': [0], '923.5_5': [1]}
|
['school', 'city', 'team name', 'enrollment 08 - 09', 'ihsaa class', 'ihsaa class football', 'county', 'year joined ( or joining )', 'previous conference']
|
[['batesville', 'batesville', 'bulldogs', '677', 'aaa', 'aaa', '69 ripley', '1956', 'southeastern indiana'], ['connersville', 'connersville', 'spartans', '1337', 'aaaa', 'aaaa', '21 fayette', '2013', 'independents'], ['east central', 'st leon', 'trojans', '1440', 'aaaa', 'aaaa', '15 dearborn', '1973', 'none ( new school )'], ['franklin county', 'brookville', 'wildcats', '969', 'aaa', 'aaaa', '24 franklin', '1989', 'none ( new school )'], ['greensburg', 'greensburg', 'pirates', '643', 'aaa', 'aaa', '16 decatur', '1978', 'south central'], ['lawrenceburg', 'lawrenceburg', 'tigers', '528', 'aaa', 'aaa', '15 dearborn', '1956', 'southeastern indiana'], ['rushville', 'rushville', 'lions', '821', 'aaa', 'aaa', '70 rush', '2013', 'hoosier heritage'], ['south dearborn', 'aurora', 'knights', '973', 'aaa', 'aaaa', '15 dearborn', '1978', 'none ( new school )']]
|
somerset county cricket club in 2010
|
https://en.wikipedia.org/wiki/Somerset_County_Cricket_Club_in_2010
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-28846752-4.html.csv
|
unique
|
marcus trescothick was the only team member to play in over 27 innings .
|
{'scope': 'all', 'row': '2', 'col': '3', 'col_other': '1', 'criterion': 'greater_than', 'value': '27', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'innings', '27'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose innings record is greater than 27 .', 'tostr': 'filter_greater { all_rows ; innings ; 27 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_greater { all_rows ; innings ; 27 } }', 'tointer': 'select the rows whose innings record is greater than 27 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'innings', '27'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose innings record is greater than 27 .', 'tostr': 'filter_greater { all_rows ; innings ; 27 }'}, 'player'], 'result': 'marcus trescothick', 'ind': 2, 'tostr': 'hop { filter_greater { all_rows ; innings ; 27 } ; player }'}, 'marcus trescothick'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_greater { all_rows ; innings ; 27 } ; player } ; marcus trescothick }', 'tointer': 'the player record of this unqiue row is marcus trescothick .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_greater { all_rows ; innings ; 27 } } ; eq { hop { filter_greater { all_rows ; innings ; 27 } ; player } ; marcus trescothick } } = true', 'tointer': 'select the rows whose innings record is greater than 27 . there is only one such row in the table . the player record of this unqiue row is marcus trescothick .'}
|
and { only { filter_greater { all_rows ; innings ; 27 } } ; eq { hop { filter_greater { all_rows ; innings ; 27 } ; player } ; marcus trescothick } } = true
|
select the rows whose innings record is greater than 27 . there is only one such row in the table . the player record of this unqiue row is marcus trescothick .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_greater_0': 0, 'all_rows_6': 6, 'innings_7': 7, '27_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'player_9': 9, 'marcus trescothick_10': 10}
|
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_greater_0': 'filter_greater', 'all_rows_6': 'all_rows', 'innings_7': 'innings', '27_8': '27', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'player_9': 'player', 'marcus trescothick_10': 'marcus trescothick'}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_greater_0': [1, 2], 'all_rows_6': [0], 'innings_7': [0], '27_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'player_9': [2], 'marcus trescothick_10': [3]}
|
['player', 'matches', 'innings', 'runs', 'average', 'highest score', '100s', '50s']
|
[['james hildreth', '16', '23', '1440', '65.45', '151', '7', '5'], ['marcus trescothick', '16', '28', '1397', '58.20', '228', '4', '6'], ['zander de bruyn', '14', '21', '814', '38.76', '95', '0', '5'], ['arul suppiah', '16', '26', '771', '33.52', '125', '1', '4'], ['jos buttler', '13', '20', '569', '33.47', '144', '1', '2'], ['nick compton', '11', '17', '465', '33.21', '72', '0', '2'], ['peter trego', '16', '23', '693', '33.00', '108', '1', '5'], ['craig kieswetter', '12', '18', '467', '27.47', '84', '0', '4']]
|
1989 pga championship
|
https://en.wikipedia.org/wiki/1989_PGA_Championship
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18135029-1.html.csv
|
comparative
|
hubert green had a lower to par score than dave stockton .
|
{'row_1': '5', 'row_2': '6', 'col': '5', 'col_other': '1', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}
|
{'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'hubert green'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose player record fuzzily matches to hubert green .', 'tostr': 'filter_eq { all_rows ; player ; hubert green }'}, 'to par'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; player ; hubert green } ; to par }', 'tointer': 'select the rows whose player record fuzzily matches to hubert green . take the to par record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'dave stockton'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose player record fuzzily matches to dave stockton .', 'tostr': 'filter_eq { all_rows ; player ; dave stockton }'}, 'to par'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; player ; dave stockton } ; to par }', 'tointer': 'select the rows whose player record fuzzily matches to dave stockton . take the to par record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; player ; hubert green } ; to par } ; hop { filter_eq { all_rows ; player ; dave stockton } ; to par } } = true', 'tointer': 'select the rows whose player record fuzzily matches to hubert green . take the to par record of this row . select the rows whose player record fuzzily matches to dave stockton . take the to par record of this row . the first record is less than the second record .'}
|
less { hop { filter_eq { all_rows ; player ; hubert green } ; to par } ; hop { filter_eq { all_rows ; player ; dave stockton } ; to par } } = true
|
select the rows whose player record fuzzily matches to hubert green . take the to par record of this row . select the rows whose player record fuzzily matches to dave stockton . take the to par 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, 'player_7': 7, 'hubert green_8': 8, 'to par_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'player_11': 11, 'dave stockton_12': 12, 'to par_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', 'player_7': 'player', 'hubert green_8': 'hubert green', 'to par_9': 'to par', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'player_11': 'player', 'dave stockton_12': 'dave stockton', 'to par_13': 'to par'}
|
{'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'player_7': [0], 'hubert green_8': [0], 'to par_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'player_11': [1], 'dave stockton_12': [1], 'to par_13': [3]}
|
['player', 'country', 'year ( s ) won', 'total', 'to par', 'finish']
|
[['jeff sluman', 'united states', '1988', '284', '- 4', 't24'], ['jack nicklaus', 'united states', '1963 , 1971 , 1973 1975 , 1980', '285', '- 3', 't27'], ['larry nelson', 'united states', '1981 , 1987', '288', 'e', 't46'], ['raymond floyd', 'united states', '1969 , 1982', '288', 'e', 't46'], ['hubert green', 'united states', '1985', '295', '+ 7', '66'], ['dave stockton', 'united states', '1970 , 1976', '297', '+ 9', '68']]
|
1960 buffalo bills season
|
https://en.wikipedia.org/wiki/1960_Buffalo_Bills_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16351892-4.html.csv
|
ordinal
|
the game in week 8 had the second highest attendance among all the games .
|
{'row': '7', 'col': '5', 'order': '2', 'col_other': '1', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'attendance', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; attendance ; 2 }'}, 'week'], 'result': '8', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; attendance ; 2 } ; week }'}, '8'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; attendance ; 2 } ; week } ; 8 } = true', 'tointer': 'select the row whose attendance record of all rows is 2nd maximum . the week record of this row is 8 .'}
|
eq { hop { nth_argmax { all_rows ; attendance ; 2 } ; week } ; 8 } = true
|
select the row whose attendance record of all rows is 2nd maximum . the week record of this row is 8 .
|
3
|
3
|
{'eq_2': 2, 'result_3': 3, 'num_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'attendance_5': 5, '2_6': 6, 'week_7': 7, '8_8': 8}
|
{'eq_2': 'eq', 'result_3': 'true', 'num_hop_1': 'num_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'attendance_5': 'attendance', '2_6': '2', 'week_7': 'week', '8_8': '8'}
|
{'eq_2': [3], 'result_3': [], 'num_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'attendance_5': [0], '2_6': [0], 'week_7': [1], '8_8': [2]}
|
['week', 'date', 'opponent', 'result', 'attendance']
|
[['1', 'september 11 , 1960', 'new york titans', 'l 27 - 3', '10200'], ['2', 'september 18 , 1960', 'denver broncos', 'l 27 - 21', '15229'], ['3', 'september 23 , 1960', 'boston patriots', 'w 13 - 0', '20732'], ['4', 'october 2 , 1960', 'los angeles chargers', 'l 24 - 10', '15821'], ['6', 'october 16 , 1960', 'new york titans', 'l 17 - 13', '14988'], ['7', 'october 23 , 1960', 'oakland raiders', 'w 38 - 9', '8876'], ['8', 'october 30 , 1960', 'houston oilers', 'w 25 - 24', '23001'], ['9', 'november 6 , 1960', 'dallas texans', 'l 45 - 28', '19610'], ['10', 'november 13 , 1960', 'oakland raiders', 'l 20 - 7', '8800'], ['11', 'november 20 , 1960', 'los angeles chargers', 'w 32 - 3', '16161'], ['12', 'november 27 , 1960', 'denver broncos', 't 38 - 38', '7785'], ['13', 'december 4 , 1960', 'boston patriots', 'w 38 - 14', '14335'], ['14', 'december 11 , 1960', 'houston oilers', 'l 31 - 23', '25243'], ['15', 'december 18 , 1960', 'dallas texans', 'l 24 - 7', '18000']]
|
tiny lund
|
https://en.wikipedia.org/wiki/Tiny_Lund
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1777959-1.html.csv
|
ordinal
|
tiny lund experienced his second-highest finish at his race in 1967 .
|
{'row': '6', 'col': '4', 'order': '2', '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', 'finish', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; finish ; 2 }'}, 'year'], 'result': '1967', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; finish ; 2 } ; year }'}, '1967'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; finish ; 2 } ; year } ; 1967 } = true', 'tointer': 'select the row whose finish record of all rows is 2nd minimum . the year record of this row is 1967 .'}
|
eq { hop { nth_argmin { all_rows ; finish ; 2 } ; year } ; 1967 } = true
|
select the row whose finish record of all rows is 2nd minimum . the year record of this row is 1967 .
|
3
|
3
|
{'eq_2': 2, 'result_3': 3, 'num_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'finish_5': 5, '2_6': 6, 'year_7': 7, '1967_8': 8}
|
{'eq_2': 'eq', 'result_3': 'true', 'num_hop_1': 'num_hop', 'nth_argmin_0': 'nth_argmin', 'all_rows_4': 'all_rows', 'finish_5': 'finish', '2_6': '2', 'year_7': 'year', '1967_8': '1967'}
|
{'eq_2': [3], 'result_3': [], 'num_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'finish_5': [0], '2_6': [0], 'year_7': [1], '1967_8': [2]}
|
['year', 'manufacturer', 'start', 'finish', 'team']
|
[['1959', 'chevrolet', '13', '40', 'buck baker'], ['1960', 'oldsmobile', '64', '51', 'gazaway'], ['1963', 'ford', '12', '1', 'wood'], ['1964', 'ford', '13', '11', 'graham shaw'], ['1965', 'ford', '24', '29', 'lyle stelter'], ['1967', 'plymouth', '11', '4', 'petty'], ['1968', 'mercury', '5', '9', 'moore'], ['1970', 'dodge', '8', '13', 'john mcconnell'], ['1971', 'dodge', '23', '39', 'john mcconnell'], ['1973', 'chevrolet', '19', '36', 'carl price']]
|
nrn
|
https://en.wikipedia.org/wiki/NRN
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1213811-1.html.csv
|
comparative
|
the newcastle channel has a higher erp than the lismore channel has .
|
{'row_1': '3', 'row_2': '4', 'col': '5', 'col_other': '2', 'relation': 'greater', 'record_mentioned': 'yes', 'diff_result': None}
|
{'func': 'and', 'args': [{'func': 'greater', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'city', 'newcastle'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose city record fuzzily matches to newcastle .', 'tostr': 'filter_eq { all_rows ; city ; newcastle }'}, 'erp ( analog / digital )'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; city ; newcastle } ; erp ( analog / digital ) }', 'tointer': 'select the rows whose city record fuzzily matches to newcastle . take the erp ( analog / digital ) record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'city', 'lismore'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose city record fuzzily matches to lismore .', 'tostr': 'filter_eq { all_rows ; city ; lismore }'}, 'erp ( analog / digital )'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; city ; lismore } ; erp ( analog / digital ) }', 'tointer': 'select the rows whose city record fuzzily matches to lismore . take the erp ( analog / digital ) record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; city ; newcastle } ; erp ( analog / digital ) } ; hop { filter_eq { all_rows ; city ; lismore } ; erp ( analog / digital ) } }', 'tointer': 'select the rows whose city record fuzzily matches to newcastle . take the erp ( analog / digital ) record of this row . select the rows whose city record fuzzily matches to lismore . take the erp ( analog / digital ) record of this row . the first record is greater than the second record .'}, {'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'city', 'newcastle'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose city record fuzzily matches to newcastle .', 'tostr': 'filter_eq { all_rows ; city ; newcastle }'}, 'erp ( analog / digital )'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; city ; newcastle } ; erp ( analog / digital ) }', 'tointer': 'select the rows whose city record fuzzily matches to newcastle . take the erp ( analog / digital ) record of this row .'}, '1200 kw 500 kw'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; city ; newcastle } ; erp ( analog / digital ) } ; 1200 kw 500 kw }', 'tointer': 'the erp ( analog / digital ) record of the first row is 1200 kw 500 kw .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'city', 'lismore'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose city record fuzzily matches to lismore .', 'tostr': 'filter_eq { all_rows ; city ; lismore }'}, 'erp ( analog / digital )'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; city ; lismore } ; erp ( analog / digital ) }', 'tointer': 'select the rows whose city record fuzzily matches to lismore . take the erp ( analog / digital ) record of this row .'}, '200 kw 200 kw'], 'result': True, 'ind': 6, 'tostr': 'eq { hop { filter_eq { all_rows ; city ; lismore } ; erp ( analog / digital ) } ; 200 kw 200 kw }', 'tointer': 'the erp ( analog / digital ) record of the second row is 200 kw 200 kw .'}], 'result': True, 'ind': 7, 'tostr': 'and { eq { hop { filter_eq { all_rows ; city ; newcastle } ; erp ( analog / digital ) } ; 1200 kw 500 kw } ; eq { hop { filter_eq { all_rows ; city ; lismore } ; erp ( analog / digital ) } ; 200 kw 200 kw } }', 'tointer': 'the erp ( analog / digital ) record of the first row is 1200 kw 500 kw . the erp ( analog / digital ) record of the second row is 200 kw 200 kw .'}], 'result': True, 'ind': 8, 'tostr': 'and { greater { hop { filter_eq { all_rows ; city ; newcastle } ; erp ( analog / digital ) } ; hop { filter_eq { all_rows ; city ; lismore } ; erp ( analog / digital ) } } ; and { eq { hop { filter_eq { all_rows ; city ; newcastle } ; erp ( analog / digital ) } ; 1200 kw 500 kw } ; eq { hop { filter_eq { all_rows ; city ; lismore } ; erp ( analog / digital ) } ; 200 kw 200 kw } } } = true', 'tointer': 'select the rows whose city record fuzzily matches to newcastle . take the erp ( analog / digital ) record of this row . select the rows whose city record fuzzily matches to lismore . take the erp ( analog / digital ) record of this row . the first record is greater than the second record . the erp ( analog / digital ) record of the first row is 1200 kw 500 kw . the erp ( analog / digital ) record of the second row is 200 kw 200 kw .'}
|
and { greater { hop { filter_eq { all_rows ; city ; newcastle } ; erp ( analog / digital ) } ; hop { filter_eq { all_rows ; city ; lismore } ; erp ( analog / digital ) } } ; and { eq { hop { filter_eq { all_rows ; city ; newcastle } ; erp ( analog / digital ) } ; 1200 kw 500 kw } ; eq { hop { filter_eq { all_rows ; city ; lismore } ; erp ( analog / digital ) } ; 200 kw 200 kw } } } = true
|
select the rows whose city record fuzzily matches to newcastle . take the erp ( analog / digital ) record of this row . select the rows whose city record fuzzily matches to lismore . take the erp ( analog / digital ) record of this row . the first record is greater than the second record . the erp ( analog / digital ) record of the first row is 1200 kw 500 kw . the erp ( analog / digital ) record of the second row is 200 kw 200 kw .
|
13
|
9
|
{'and_8': 8, 'result_9': 9, 'greater_4': 4, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_10': 10, 'city_11': 11, 'newcastle_12': 12, 'erp (analog / digital)_13': 13, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_14': 14, 'city_15': 15, 'lismore_16': 16, 'erp (analog / digital)_17': 17, 'and_7': 7, 'str_eq_5': 5, '1200 kw 500 kw_18': 18, 'str_eq_6': 6, '200 kw 200 kw_19': 19}
|
{'and_8': 'and', 'result_9': 'true', 'greater_4': 'greater', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_10': 'all_rows', 'city_11': 'city', 'newcastle_12': 'newcastle', 'erp (analog / digital)_13': 'erp ( analog / digital )', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_14': 'all_rows', 'city_15': 'city', 'lismore_16': 'lismore', 'erp (analog / digital)_17': 'erp ( analog / digital )', 'and_7': 'and', 'str_eq_5': 'str_eq', '1200 kw 500 kw_18': '1200 kw 500 kw', 'str_eq_6': 'str_eq', '200 kw 200 kw_19': '200 kw 200 kw'}
|
{'and_8': [9], 'result_9': [], 'greater_4': [8], 'str_hop_2': [4, 5], 'filter_str_eq_0': [2], 'all_rows_10': [0], 'city_11': [0], 'newcastle_12': [0], 'erp (analog / digital)_13': [2], 'str_hop_3': [4, 6], 'filter_str_eq_1': [3], 'all_rows_14': [1], 'city_15': [1], 'lismore_16': [1], 'erp (analog / digital)_17': [3], 'and_7': [8], 'str_eq_5': [7], '1200 kw 500 kw_18': [5], 'str_eq_6': [7], '200 kw 200 kw_19': [6]}
|
['region served', 'city', 'channels ( analog / digital )', 'first air date', 'erp ( analog / digital )', 'haat ( analog / digital ) 1', 'transmitter location']
|
[['grafton / kempsey', 'coffs harbour', '11 ( vhf ) 3 38 ( uhf )', '23 january 1965', '250 kw 250 kw', '706 m 730 m', 'mount moombil'], ['manning river', 'taree', '65 ( uhf ) 3 44 ( uhf )', '31 december 1991', '600 kw 320 kw', '633 m 633 m', 'middle brother'], ['newcastle / hunter river', 'newcastle', '57 ( uhf ) 3 51 ( uhf )', '31 december 1991', '1200 kw 500 kw', '439 m 439 m', 'mount sugarloaf'], ['richmond and tweed 2', 'lismore', '8 ( vhf ) 3 32 ( uhf )', '12 may 1962', '200 kw 200 kw', '612 m 648 m', 'mount nardi'], ['upper namoi', 'tamworth', '34 ( uhf ) 3 40 ( uhf )', '31 december 1991', '600 kw 330 kw', '844 m 874 m', 'mount dowe']]
|
olga govortsova
|
https://en.wikipedia.org/wiki/Olga_Govortsova
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12472200-5.html.csv
|
count
|
olga govortsova partnered with tatiana poutchek a total of two times .
|
{'scope': 'all', 'criterion': 'equal', 'value': 'tatiana poutchek', 'result': '2', 'col': '4', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'partner', 'tatiana poutchek'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose partner record fuzzily matches to tatiana poutchek .', 'tostr': 'filter_eq { all_rows ; partner ; tatiana poutchek }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; partner ; tatiana poutchek } }', 'tointer': 'select the rows whose partner record fuzzily matches to tatiana poutchek . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; partner ; tatiana poutchek } } ; 2 } = true', 'tointer': 'select the rows whose partner record fuzzily matches to tatiana poutchek . the number of such rows is 2 .'}
|
eq { count { filter_eq { all_rows ; partner ; tatiana poutchek } } ; 2 } = true
|
select the rows whose partner record fuzzily matches to tatiana poutchek . 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, 'tatiana poutchek_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', 'tatiana poutchek_6': 'tatiana poutchek', '2_7': '2'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'partner_5': [0], 'tatiana poutchek_6': [0], '2_7': [2]}
|
['outcome', 'date', 'surface', 'partner', 'opponents', 'score']
|
[['runner - up', '14 april 2008', 'clay', 'edina gallovits', 'katarina srebotnik ai sugiyama', '2 - 6 , 2 - 6'], ['winner', '19 may 2008', 'clay', 'jill craybas', 'marina erakovic polona hercog', '6 - 1 , 6 - 2'], ['winner', '20 september 2009', 'hard', 'tatiana poutchek', 'kimiko date - krumm sun tiantian', '3 - 6 , 6 - 2 ,'], ['winner', '26 september 2009', 'hard', 'tatiana poutchek', 'vitalia diatchenko ekaterina dzehalevich', '6 - 2 , 6 - 7 ( 1 - 7 ) ,'], ['winner', '9 october 2010', 'hard', 'chuang chia - jung', 'gisela dulko flavia pennetta', '7 - 6 ( 7 - 2 ) , 1 - 6 ,'], ['winner', '19 february 2011', 'hard ( i )', 'alla kudryavtseva', 'andrea hlaváčková lucie hradecká', '6 - 3 , 4 - 6 ,'], ['winner', '12 june 2011', 'grass', 'alla kudryavtseva', 'sara errani roberta vinci', '1 - 6 , 6 - 1 ,'], ['runner - up', '31 july 2011', 'hard', 'alla kudryavtseva', 'sania mirza yaroslava shvedova', '3 - 6 , 3 - 6'], ['winner', '27 august 2011', 'hard', 'chuang chia - jung', 'sara errani roberta vinci', '7 - 5 , 6 - 2'], ['runner - up', '25 february 2012', 'hard ( i )', 'vera dushevina', 'andrea hlaváčková lucie hradecká', '3 - 6 , 4 - 6'], ['winner', '26 may 2012', 'clay', 'klaudia jans - ignacik', 'natalie grandin vladimíra uhlířová', '6 - 7 ( 4 - 7 ) , 6 - 3 ,'], ['runner - up', '14 september 2013', 'hard', 'mandy minella', 'timea babos yaroslava shvedova', '3 - 6 , 3 - 6']]
|
sweeney todd : the demon barber of fleet street
|
https://en.wikipedia.org/wiki/Sweeney_Todd%3A_The_Demon_Barber_of_Fleet_Street
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1794747-7.html.csv
|
majority
|
the majority of award wins and nominations for sweeney todd were for drama desk awards .
|
{'scope': 'all', 'col': '2', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'drama desk award', 'subset': None}
|
{'func': 'most_str_eq', 'args': ['all_rows', 'award ceremony', 'drama desk award'], 'result': True, 'ind': 0, 'tointer': 'for the award ceremony records of all rows , most of them fuzzily match to drama desk award .', 'tostr': 'most_eq { all_rows ; award ceremony ; drama desk award } = true'}
|
most_eq { all_rows ; award ceremony ; drama desk award } = true
|
for the award ceremony records of all rows , most of them fuzzily match to drama desk award .
|
1
|
1
|
{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'award ceremony_3': 3, 'drama desk award_4': 4}
|
{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'award ceremony_3': 'award ceremony', 'drama desk award_4': 'drama desk award'}
|
{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'award ceremony_3': [0], 'drama desk award_4': [0]}
|
['year', 'award ceremony', 'category', 'nominee', 'result']
|
[['2006', 'tony award', 'best revival of a musical', 'best revival of a musical', 'nominated'], ['2006', 'tony award', 'best performance by a leading actor in a musical', 'michael cerveris', 'nominated'], ['2006', 'tony award', 'best performance by a leading actress in a musical', 'patti lupone', 'nominated'], ['2006', 'tony award', 'best performance by a featured actor in a musical', 'manoel felciano', 'nominated'], ['2006', 'tony award', 'best direction of a musical', 'john doyle', 'won'], ['2006', 'tony award', 'best orchestrations', 'sarah travis', 'won'], ['2006', 'drama desk award', 'outstanding revival of a musical', 'outstanding revival of a musical', 'won'], ['2006', 'drama desk award', 'outstanding actor in a musical', 'michael cerveris', 'nominated'], ['2006', 'drama desk award', 'outstanding actress in a musical', 'patti lupone', 'nominated'], ['2006', 'drama desk award', 'outstanding featured actor in a musical', 'alexander gemignani', 'nominated'], ['2006', 'drama desk award', 'outstanding orchestrations', 'sarah travis', 'won'], ['2006', 'drama desk award', 'outstanding director of a musical', 'john doyle', 'won'], ['2006', 'drama desk award', 'outstanding set design', 'john doyle', 'nominated'], ['2006', 'drama desk award', 'outstanding lighting design', 'richard g jones', 'won'], ['2006', 'drama desk award', 'outstanding sound design', 'dan moses schreier', 'nominated']]
|
2003 cleveland browns season
|
https://en.wikipedia.org/wiki/2003_Cleveland_Browns_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10652497-2.html.csv
|
majority
|
in the 2003 cleveland browns season , when the game was in december , most of the games were losses for the browns .
|
{'scope': 'subset', 'col': '4', 'most_or_all': 'most', 'criterion': 'fuzzily_match', 'value': 'l', 'subset': {'col': '2', 'criterion': 'fuzzily_match', 'value': 'december'}}
|
{'func': 'most_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'december'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; date ; december }', 'tointer': 'select the rows whose date record fuzzily matches to december .'}, 'result', 'l'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to december . for the result records of these rows , most of them fuzzily match to l .', 'tostr': 'most_eq { filter_eq { all_rows ; date ; december } ; result ; l } = true'}
|
most_eq { filter_eq { all_rows ; date ; december } ; result ; l } = true
|
select the rows whose date record fuzzily matches to december . 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, 'december_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', 'december_5': 'december', '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], 'december_5': [0], 'result_6': [1], 'l_7': [1]}
|
['week', 'date', 'opponent', 'result', 'kickoff time', 'attendance']
|
[['1', 'september 7 , 2003', 'indianapolis colts', 'l 9 - 6', 'cbs 1:00 pm', '73358'], ['2', 'september 14 , 2003', 'baltimore ravens', 'l 33 - 13', 'cbs 1:00 pm', '69473'], ['3', 'september 21 , 2003', 'san francisco 49ers', 'w 13 - 12', 'cbs 4:05 pm', '67412'], ['4', 'september 28 , 2003', 'cincinnati bengals', 'l 21 - 14', 'cbs 1:00 pm', '73428'], ['5', 'october 5 , 2003', 'pittsburgh steelers', 'w 33 - 13', 'espn 8:30 pm', '64595'], ['6', 'october 12 , 2003', 'oakland raiders', 'w 13 - 7', 'cbs 1:00 pm', '73318'], ['7', 'october 19 , 2003', 'san diego chargers', 'l 26 - 20', 'cbs 1:00 pm', '73238'], ['8', 'october 26 , 2003', 'new england patriots', 'l 9 - 3', 'cbs 1:00 pm', '68436'], ['10', 'november 9 , 2003', 'kansas city chiefs', 'l 41 - 20', 'cbs 1:00 pm', '78560'], ['11', 'november 16 , 2003', 'arizona cardinals', 'w 44 - 6', 'fox 1:00 pm', '72908'], ['12', 'november 23 , 2003', 'pittsburgh steelers', 'l 13 - 6', 'cbs 1:00 pm', '73658'], ['13', 'november 30 , 2003', 'seattle seahawks', 'l 34 - 7', 'cbs 4:15 pm', '64680'], ['14', 'december 8 , 2003', 'st louis rams', 'l 26 - 20', 'abc 9:00 pm', '73108'], ['15', 'december 14 , 2003', 'denver broncos', 'l 23 - 20', 'cbs 4:05 pm', '75358'], ['16', 'december 21 , 2003', 'baltimore ravens', 'l 35 - 0', 'cbs 1:00 pm', '72548'], ['17', 'december 28 , 2003', 'cincinnati bengals', 'w 22 - 14', 'cbs 1:00 pm', '65362']]
|
sweeney todd : the demon barber of fleet street
|
https://en.wikipedia.org/wiki/Sweeney_Todd%3A_The_Demon_Barber_of_Fleet_Street
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1794747-7.html.csv
|
count
|
sweeney todd actor michael cerveris was nominated for a total of two awards .
|
{'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'michael cerveris', 'result': '2', 'col': '4', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nominee', 'michael cerveris'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose nominee record fuzzily matches to michael cerveris .', 'tostr': 'filter_eq { all_rows ; nominee ; michael cerveris }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; nominee ; michael cerveris } }', 'tointer': 'select the rows whose nominee record fuzzily matches to michael cerveris . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; nominee ; michael cerveris } } ; 2 } = true', 'tointer': 'select the rows whose nominee record fuzzily matches to michael cerveris . the number of such rows is 2 .'}
|
eq { count { filter_eq { all_rows ; nominee ; michael cerveris } } ; 2 } = true
|
select the rows whose nominee record fuzzily matches to michael cerveris . 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, 'nominee_5': 5, 'michael cerveris_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', 'nominee_5': 'nominee', 'michael cerveris_6': 'michael cerveris', '2_7': '2'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'nominee_5': [0], 'michael cerveris_6': [0], '2_7': [2]}
|
['year', 'award ceremony', 'category', 'nominee', 'result']
|
[['2006', 'tony award', 'best revival of a musical', 'best revival of a musical', 'nominated'], ['2006', 'tony award', 'best performance by a leading actor in a musical', 'michael cerveris', 'nominated'], ['2006', 'tony award', 'best performance by a leading actress in a musical', 'patti lupone', 'nominated'], ['2006', 'tony award', 'best performance by a featured actor in a musical', 'manoel felciano', 'nominated'], ['2006', 'tony award', 'best direction of a musical', 'john doyle', 'won'], ['2006', 'tony award', 'best orchestrations', 'sarah travis', 'won'], ['2006', 'drama desk award', 'outstanding revival of a musical', 'outstanding revival of a musical', 'won'], ['2006', 'drama desk award', 'outstanding actor in a musical', 'michael cerveris', 'nominated'], ['2006', 'drama desk award', 'outstanding actress in a musical', 'patti lupone', 'nominated'], ['2006', 'drama desk award', 'outstanding featured actor in a musical', 'alexander gemignani', 'nominated'], ['2006', 'drama desk award', 'outstanding orchestrations', 'sarah travis', 'won'], ['2006', 'drama desk award', 'outstanding director of a musical', 'john doyle', 'won'], ['2006', 'drama desk award', 'outstanding set design', 'john doyle', 'nominated'], ['2006', 'drama desk award', 'outstanding lighting design', 'richard g jones', 'won'], ['2006', 'drama desk award', 'outstanding sound design', 'dan moses schreier', 'nominated']]
|
list of baden locomotives and railbuses
|
https://en.wikipedia.org/wiki/List_of_Baden_locomotives_and_railbuses
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18914503-3.html.csv
|
superlative
|
of the baden locomotives and railbuses , the highest quantity is for i b.
|
{'scope': 'all', 'col_superlative': '3', 'row_superlative': '2', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None}
|
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'quantity'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; quantity }'}, 'class ( old ) to 1868'], 'result': 'i b', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; quantity } ; class ( old ) to 1868 }'}, 'i b'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; quantity } ; class ( old ) to 1868 } ; i b } = true', 'tointer': 'select the row whose quantity record of all rows is maximum . the class ( old ) to 1868 record of this row is i b .'}
|
eq { hop { argmax { all_rows ; quantity } ; class ( old ) to 1868 } ; i b } = true
|
select the row whose quantity record of all rows is maximum . the class ( old ) to 1868 record of this row is i b .
|
3
|
3
|
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'quantity_5': 5, 'class (old) to 1868_6': 6, 'i b_7': 7}
|
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'quantity_5': 'quantity', 'class (old) to 1868_6': 'class ( old ) to 1868', 'i b_7': 'i b'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'quantity_5': [0], 'class (old) to 1868_6': [1], 'i b_7': [2]}
|
['class ( old ) to 1868', 'railway number ( s )', 'quantity', 'year ( s ) of manufacture', 'type']
|
[['i a ( de )', '1 - 6', '6', '1839 - 1843', '1a1 n2'], ['i b', '7 - 15', '9', '1842 - 1843', '1a1 n2'], ['ii ( de )', '16 - 19', '4', '1843 - 1844', '1a1 n2'], ['iii a ( de )', '20 - 24', '5', '1844', '1a1 n2'], ['iii b ( de )', '25 - 28', '4', '1844', '1a1 n2'], ['iii c ( de )', '29 - 36', '8', '1845', '1a1 n2'], ['iv ( de )', '37 - 41', '5', '1845', '1a1 n2'], ['v ( de )', '42 - 46', '5', '1845', '1b n2'], ['vi ( de )', '47 - 54', '8', '1845', 'c n2'], ['vii ( de )', '55 - 58', '4', '1846', '2 ′ b n2'], ['viii ( de )', '59 - 65', '7', '1847', '1a1 n2'], ['viii ( de )', '66', '1', '1846', '1a1 n2']]
|
yorkshire county cricket club in 2008
|
https://en.wikipedia.org/wiki/Yorkshire_County_Cricket_Club_in_2008
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-15893020-2.html.csv
|
aggregation
|
the players in the yorkshire county cricket club in 2008 played in an average of eight matches apiece .
|
{'scope': 'all', 'col': '2', 'type': 'average', 'result': '8', 'subset': None}
|
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'matches'], 'result': '8', 'ind': 0, 'tostr': 'avg { all_rows ; matches }'}, '8'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; matches } ; 8 } = true', 'tointer': 'the average of the matches record of all rows is 8 .'}
|
round_eq { avg { all_rows ; matches } ; 8 } = true
|
the average of the matches record of all rows is 8 .
|
2
|
2
|
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'matches_4': 4, '8_5': 5}
|
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'matches_4': 'matches', '8_5': '8'}
|
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'matches_4': [0], '8_5': [1]}
|
['player', 'matches', 'overs', 'maidens', 'runs', 'wickets', 'average', 'economy', '5w', '10w', 'best bowling']
|
[['ajmal shahzad', '1', '24.0', '6', '64', '3', '21.33', '2.67', '0', '0', '2 / 43'], ['steven patterson', '4', '99.1', '23', '280', '12', '23.33', '2.82', '0', '0', '3 / 19'], ['matthew hoggard', '13', '342.5', '66', '1037', '42', '24.69', '3.02', '1', '0', '6 / 57'], ['tim bresnan', '14', '419.0', '726', '1267', '44', '28.80', '3.02', '1', '0', '5 / 94'], ['adil rashid', '16', '590.1', '64', '1886', '62', '30.42', '3.20', '4', '0', '7 / 107'], ['david wainwright', '4', '85.1', '18', '246', '8', '30.75', '2.89', '0', '0', '3 / 9'], ['anthony mcgrath', '14', '99.1', '16', '282', '9', '31.33', '2.84', '0', '0', '2 / 27'], ['mornã morkel', '1', '15.2', '4', '33', '1', '33.00', '2.15', '0', '0', '1 / 33'], ['rana naved - ul - hasan', '7', '153.1', '21', '604', '16', '37.75', '3.94', '0', '0', '4 / 86'], ['deon kruis', '10', '295.3', '68', '903', '22', '41.05', '3.06', '1', '0', '5 / 47'], ['darren gough', '8', '149.0', '25', '528', '9', '58.67', '3.54', '0', '0', '2 / 34'], ['jacques rudolph', '16', '21.2', '2', '74', '1', '74.00', '3.47', '0', '0', '1 / 13'], ['adam lyth', '14', '30.1', '5', '105', '1', '105.00', '3.48', '0', '0', '1 / 20'], ['oliver hannon - dalby', '1', '29.0', '5', '114', '1', '114.00', '3.93', '0', '0', '1 / 58'], ['ben sanderson', '2', '37.0', '7', '140', '1', '140.00', '3.78', '0', '0', '1 / 87'], ['richard pyrah', '5', '56.0', '11', '201', '1', '201.00', '3.59', '0', '0', '1 / 14'], ['andrew gale', '15', '1.0', '0', '3', '0', 'n / a', '3.00', '0', '0', '0 / 3'], ['michael vaughan', '6', '6.0', '0', '47', '0', 'n / a', '7.83', '0', '0', '0 / 47']]
|
2000 - 01 prva hnl
|
https://en.wikipedia.org/wiki/2000%E2%80%9301_Prva_HNL
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17573976-1.html.csv
|
superlative
|
the home stadium of dinamo zagreb has the highest capacity among all stadiums .
|
{'scope': 'all', 'col_superlative': '5', 'row_superlative': '3', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None}
|
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'capacity'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; capacity }'}, 'team'], 'result': 'dinamo zagreb', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; capacity } ; team }'}, 'dinamo zagreb'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; capacity } ; team } ; dinamo zagreb } = true', 'tointer': 'select the row whose capacity record of all rows is maximum . the team record of this row is dinamo zagreb .'}
|
eq { hop { argmax { all_rows ; capacity } ; team } ; dinamo zagreb } = true
|
select the row whose capacity record of all rows is maximum . the team record of this row is dinamo zagreb .
|
3
|
3
|
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'capacity_5': 5, 'team_6': 6, 'dinamo zagreb_7': 7}
|
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'capacity_5': 'capacity', 'team_6': 'team', 'dinamo zagreb_7': 'dinamo zagreb'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'capacity_5': [0], 'team_6': [1], 'dinamo zagreb_7': [2]}
|
['team', 'manager', 'home city', 'stadium', 'capacity']
|
[['cibalia', 'davor mladina', 'vinkovci', 'stadion hnk cibalia', '9920'], ['čakovec', 'ilija lončarević', 'čakovec', 'stadion src mladost', '8000'], ['dinamo zagreb', 'marijan vlak', 'zagreb', 'stadion maksimir', '37168'], ['hajduk split', 'petar nadoveza', 'split', 'gradski stadion u poljudu', '35000'], ['hrvatski dragovoljac', 'milivoj bračun', 'zagreb', 'stadion nšc stjepan spajić', '5000'], ['marsonia', 'stjepan deverić', 'slavonski brod', 'gradski stadion uz savu', '10000'], ['osijek', 'stanko mršić', 'osijek', 'stadion gradski vrt', '19500'], ['rijeka', 'nenad gračan', 'rijeka', 'stadion na kantridi', '10275'], ['slaven belupo', 'mladen frančić', 'koprivnica', 'gradski stadion u koprivnici', '4000'], ['šibenik', 'milo nižetić', 'šibenik', 'stadion šubićevac', '8000'], ['varteks', 'ivan katalinić', 'varaždin', 'stadion nk varteks', '10800'], ['nk zagreb', 'branko karačić', 'zagreb', 'stadion u kranjčevićevoj ulici', '8850']]
|
sandro cortese
|
https://en.wikipedia.org/wiki/Sandro_Cortese
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-16710541-2.html.csv
|
aggregation
|
the average number of podiums for sandro cortese was 2.89 .
|
{'scope': 'all', 'col': '8', 'type': 'average', 'result': '2.89', 'subset': None}
|
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'podiums'], 'result': '2.89', 'ind': 0, 'tostr': 'avg { all_rows ; podiums }'}, '2.89'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; podiums } ; 2.89 } = true', 'tointer': 'the average of the podiums record of all rows is 2.89 .'}
|
round_eq { avg { all_rows ; podiums } ; 2.89 } = true
|
the average of the podiums record of all rows is 2.89 .
|
2
|
2
|
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'podiums_4': 4, '2.89_5': 5}
|
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'podiums_4': 'podiums', '2.89_5': '2.89'}
|
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'podiums_4': [0], '2.89_5': [1]}
|
['season', 'class', 'team', 'motorcycle', 'type', 'races', 'wins', 'podiums', 'poles', 'fastest laps', 'pts', 'position']
|
[['2005', '125cc', 'kiefer - bos - castrol honda', 'honda', 'honda rs125r', '16', '0', '0', '0', '0', '8', '26th'], ['2006', '125cc', 'elit - caffè latte', 'honda', 'honda rs125r', '16', '0', '0', '0', '0', '23', '17th'], ['2007', '125cc', 'emmi - caffè latte', 'aprilia', 'aprilia rs 125', '17', '0', '0', '0', '0', '66', '14th'], ['2008', '125cc', 'emmi - caffè latte', 'aprilia', 'aprilia rsa 125', '17', '0', '0', '0', '1', '141', '8th'], ['2009', '125cc', 'ajo interwetten', 'derbi', 'derbi rsa 125', '16', '0', '3', '1', '2', '130', '6th'], ['2010', '125cc', 'ajo motorsport', 'derbi', 'derbi rs 125 r', '17', '0', '2', '1', '2', '143', '7th'], ['2011', '125cc', 'intact - racing team germany', 'aprilia', 'aprilia rsa 125', '17', '2', '6', '1', '2', '225', '4th'], ['2012', 'moto3', 'red bull ktm ajo', 'ktm', 'ktm m32', '17', '5', '15', '7', '4', '325', '1st'], ['2013', 'moto2', 'dynavolt intact gp', 'kalex', 'kalex moto2', '16', '0', '0', '0', '0', '19', '20th']]
|
tasmania cricket team first - class records
|
https://en.wikipedia.org/wiki/Tasmania_cricket_team_first-class_records
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14412861-4.html.csv
|
unique
|
the only team that tasmania has scored over sixty runs against is queensland .
|
{'scope': 'all', 'row': '5', 'col': '2', 'col_other': '3', 'criterion': 'greater_than', 'value': '60', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'runs', '60'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose runs record is greater than 60 .', 'tostr': 'filter_greater { all_rows ; runs ; 60 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_greater { all_rows ; runs ; 60 } }', 'tointer': 'select the rows whose runs record is greater than 60 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'runs', '60'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose runs record is greater than 60 .', 'tostr': 'filter_greater { all_rows ; runs ; 60 }'}, 'opponent'], 'result': 'queensland', 'ind': 2, 'tostr': 'hop { filter_greater { all_rows ; runs ; 60 } ; opponent }'}, 'queensland'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_greater { all_rows ; runs ; 60 } ; opponent } ; queensland }', 'tointer': 'the opponent record of this unqiue row is queensland .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_greater { all_rows ; runs ; 60 } } ; eq { hop { filter_greater { all_rows ; runs ; 60 } ; opponent } ; queensland } } = true', 'tointer': 'select the rows whose runs record is greater than 60 . there is only one such row in the table . the opponent record of this unqiue row is queensland .'}
|
and { only { filter_greater { all_rows ; runs ; 60 } } ; eq { hop { filter_greater { all_rows ; runs ; 60 } ; opponent } ; queensland } } = true
|
select the rows whose runs record is greater than 60 . there is only one such row in the table . the opponent record of this unqiue row is queensland .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_greater_0': 0, 'all_rows_6': 6, 'runs_7': 7, '60_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'opponent_9': 9, 'queensland_10': 10}
|
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_greater_0': 'filter_greater', 'all_rows_6': 'all_rows', 'runs_7': 'runs', '60_8': '60', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'opponent_9': 'opponent', 'queensland_10': 'queensland'}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_greater_0': [1, 2], 'all_rows_6': [0], 'runs_7': [0], '60_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'opponent_9': [2], 'queensland_10': [3]}
|
['rank', 'runs', 'opponent', 'venue', 'season']
|
[['1', '50', 'victoria', 'launceston cricket club ground , launceston', '1853 / 54'], ['2', '53', 'new south wales', 'bellerive oval , hobart', '2006 / 07'], ['3', '55', 'south australia', 'bellerive oval , hobart', '2010 / 11'], ['4', '57', 'victoria', 'launceston cricket club ground , launceston', '1850 / 51'], ['5', '62', 'queensland', 'gabba , brisbane', '2008 / 09']]
|
2008 - 09 guildford flames season
|
https://en.wikipedia.org/wiki/2008%E2%80%9309_Guildford_Flames_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-17120964-5.html.csv
|
aggregation
|
during the ’ 08 - ’09 guildford flames season , the average attendance for away games was 1,692 .
|
{'scope': 'subset', 'col': '5', 'type': 'average', 'result': '1,692', 'subset': {'col': '3', 'criterion': 'equal', 'value': 'away'}}
|
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'venue', 'away'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; venue ; away }', 'tointer': 'select the rows whose venue record fuzzily matches to away .'}, 'attendance'], 'result': '1,692', 'ind': 1, 'tostr': 'avg { filter_eq { all_rows ; venue ; away } ; attendance }'}, '1,692'], 'result': True, 'ind': 2, 'tostr': 'round_eq { avg { filter_eq { all_rows ; venue ; away } ; attendance } ; 1,692 } = true', 'tointer': 'select the rows whose venue record fuzzily matches to away . the average of the attendance record of these rows is 1,692 .'}
|
round_eq { avg { filter_eq { all_rows ; venue ; away } ; attendance } ; 1,692 } = true
|
select the rows whose venue record fuzzily matches to away . the average of the attendance record of these rows is 1,692 .
|
3
|
3
|
{'eq_2': 2, 'result_3': 3, 'avg_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'venue_5': 5, 'away_6': 6, 'attendance_7': 7, '1,692_8': 8}
|
{'eq_2': 'eq', 'result_3': 'true', 'avg_1': 'avg', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'venue_5': 'venue', 'away_6': 'away', 'attendance_7': 'attendance', '1,692_8': '1,692'}
|
{'eq_2': [3], 'result_3': [], 'avg_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'venue_5': [0], 'away_6': [0], 'attendance_7': [1], '1,692_8': [2]}
|
['date', 'opponent', 'venue', 'result', 'attendance', 'competition', 'man of the match']
|
[['4th', 'milton keynes lightning', 'away', 'lost 6 - 5 ( ot )', '1093', 'league / cup', 'lukas smital'], ['5th', 'telford tigers', 'home', 'lost 3 - 4', '1538', 'league / cup', 'lukas smital'], ['11th', 'sheffield scimitars', 'home', 'won 3 - 2 ( so )', '1249', 'league / cup', 'joe watkins'], ['12th', 'peterborough phantoms', 'away', 'won 7 - 3', '526', 'league / cup', 'joe watkins'], ['15th', 'peterborough phantoms', 'home', 'won 4 - 2', '945', 'league / cup', 'joe watkins'], ['18th', 'slough jets', 'away', 'won 6 - 5', '649', 'league / cup', 'lukas smital'], ['19th', 'wightlink raiders', 'home', 'lost 4 - 5', '1424', 'league', 'stephen lee'], ['24th', 'hk aquacity å kp poprad', 'away', 'lost 6 - 5', '4500', 'exhibition', 'n / a'], ['25th', 'bracknell bees', 'home', 'won 1 - 0', '1542', 'league', 'nick cross']]
|
2008 - 09 football league trophy
|
https://en.wikipedia.org/wiki/2008%E2%80%9309_Football_League_Trophy
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18720697-4.html.csv
|
majority
|
a majority of crowd attendance in the 2008-09 season was over 1500 attended .
|
{'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '1500', 'subset': None}
|
{'func': 'most_greater', 'args': ['all_rows', 'attendance', '1500'], 'result': True, 'ind': 0, 'tointer': 'for the attendance records of all rows , most of them are greater than 1500 .', 'tostr': 'most_greater { all_rows ; attendance ; 1500 } = true'}
|
most_greater { all_rows ; attendance ; 1500 } = true
|
for the attendance records of all rows , most of them are greater than 1500 .
|
1
|
1
|
{'most_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 'attendance_3': 3, '1500_4': 4}
|
{'most_greater_0': 'most_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 'attendance_3': 'attendance', '1500_4': '1500'}
|
{'most_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 'attendance_3': [0], '1500_4': [0]}
|
['tie no', 'home team', 'score', 'away team', 'attendance']
|
[['1', 'cheltenham town', '1 - 2', 'walsall', '1741'], ['2', 'hereford united', '1 - 2', 'swindon town', '1458'], ['3', 'wycombe wanderers', '0 - 7', 'shrewsbury town', '1730'], ['4', 'milton keynes dons', '0 - 1', 'bournemouth', '4329'], ['5', 'peterborough united', '0 - 1', 'dagenham & redbridge', '2644'], ['6', 'brighton & hove albion', '2 - 2', 'leyton orient', '2157'], ['brighton & hove albion won 5 - 4 on penalties', 'brighton & hove albion won 5 - 4 on penalties', 'brighton & hove albion won 5 - 4 on penalties', 'brighton & hove albion won 5 - 4 on penalties', 'brighton & hove albion won 5 - 4 on penalties'], ['7', 'gillingham', '0 - 1', 'colchester united', '1557'], ['8', 'luton town', '2 - 2', 'brentford', '2029'], ['luton town won 4 - 3 on penalties', 'luton town won 4 - 3 on penalties', 'luton town won 4 - 3 on penalties', 'luton town won 4 - 3 on penalties', 'luton town won 4 - 3 on penalties']]
|
billy casper
|
https://en.wikipedia.org/wiki/Billy_Casper
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1544891-3.html.csv
|
superlative
|
the highest winning score that billy casper had was at the us senior open .
|
{'scope': 'all', 'col_superlative': '3', 'row_superlative': '3', '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', 'winning score'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; winning score }'}, 'tournament'], 'result': 'us senior open', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; winning score } ; tournament }'}, 'us senior open'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; winning score } ; tournament } ; us senior open } = true', 'tointer': 'select the row whose winning score record of all rows is maximum . the tournament record of this row is us senior open .'}
|
eq { hop { argmax { all_rows ; winning score } ; tournament } ; us senior open } = true
|
select the row whose winning score record of all rows is maximum . the tournament record of this row is us senior open .
|
3
|
3
|
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'winning score_5': 5, 'tournament_6': 6, 'us senior open_7': 7}
|
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'winning score_5': 'winning score', 'tournament_6': 'tournament', 'us senior open_7': 'us senior open'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'winning score_5': [0], 'tournament_6': [1], 'us senior open_7': [2]}
|
['date', 'tournament', 'winning score', 'margin of victory', 'runner ( s ) - up']
|
[['aug 28 , 1982', 'shootout at jeremy ranch', '- 9 ( 74 + 71 + 69 + 65 = 279 )', '1 stroke', 'miller barber , don january'], ['sep 19 , 1982', 'merrill lynch / golf digest commemorative pro - am', '- 10 ( 68 + 7 + 68 = 206 )', 'playoff', 'bob toski'], ['jul 25 , 1983', 'us senior open', '+ 4 ( 73 + 73 + 69 + 73 = 288 )', 'playoff', 'rod funseth'], ['apr 22 , 1984', 'senior pga tour roundup', '- 14 ( 68 + 69 + 65 = 202 )', '2 strokes', 'bob stone'], ['mar 15 , 1987', 'del e webb arizona classic', '- 15 ( 68 + 65 + 68 = 201 )', '5 strokes', 'bob charles , dale douglass'], ['jun 28 , 1987', 'greater grand rapids open', '- 13 ( 69 + 68 + 63 = 200 )', '3 strokes', 'miller barber'], ['may 8 , 1988', 'vantage at the dominion', '- 14 ( 70 + 68 + 67 = 205 )', '1 stroke', 'chi - chi rodríguez'], ['jun 12 , 1988', 'mazda senior tournament players championship', '- 10 ( 69 + 68 + 74 + 67 = 278 )', '2 strokes', 'al geiberger'], ['oct 22 , 1989', 'transamerica senior golf championship', '- 9 ( 69 + 70 + 68 = 207 )', '3 strokes', 'al geiberger']]
|
united states house of representatives elections , 1802
|
https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1802
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2668393-10.html.csv
|
aggregation
|
all districts in the 1822 house elections have winning candidates with an average percentage ratio of 60 .
|
{'scope': 'all', 'col': '6', 'type': 'average', 'result': '60', 'subset': None}
|
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'candidates'], 'result': '60', 'ind': 0, 'tostr': 'avg { all_rows ; candidates }'}, '60'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; candidates } ; 60 } = true', 'tointer': 'the average of the candidates record of all rows is 60 .'}
|
round_eq { avg { all_rows ; candidates } ; 60 } = true
|
the average of the candidates record of all rows is 60 .
|
2
|
2
|
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'candidates_4': 4, '60_5': 5}
|
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'candidates_4': 'candidates', '60_5': '60'}
|
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'candidates_4': [0], '60_5': [1]}
|
['district', 'incumbent', 'party', 'first elected', 'result', 'candidates']
|
[['new york 1', 'john smith', 'democratic - republican', '1799 ( special )', 're - elected', 'john smith ( dr ) 100 %'], ['new york 2', 'none ( district created )', 'none ( district created )', 'none ( district created )', 'new seat federalist gain', 'joshua sands ( f ) 51.3 % john broome ( dr ) 48.7 %'], ['new york 5', 'none ( district created )', 'none ( district created )', 'none ( district created )', 'new seat democratic - republican gain', 'andrew mccord ( dr ) 84.4 % john hathorn ( f ) 15.6 %'], ['new york 6', 'none ( district created )', 'none ( district created )', 'none ( district created )', 'new seat democratic - republican gain', 'isaac bloom ( dr ) 55.4 % samuel mott ( f ) 44.6 %'], ['new york 10', 'none ( district created )', 'none ( district created )', 'none ( district created )', 'new seat federalist gain', 'george tibbits ( f ) 51.2 % josiah masters ( dr ) 48.8 %'], ['new york 12', 'david thomas redistricted from the 7th district', 'democratic - republican', '1800', 're - elected', 'david thomas ( dr ) 64.1 % john williams 35.9 %'], ['new york 14', 'none ( district created )', 'none ( district created )', 'none ( district created )', 'new seat democratic - republican gain', 'erastus root ( dr ) 57.4 % benjamin gilbert ( f ) 42.8 %']]
|
list of england national rugby union team results 1990 - 99
|
https://en.wikipedia.org/wiki/List_of_England_national_rugby_union_team_results_1990%E2%80%9399
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18178534-3.html.csv
|
majority
|
most of the venues used were located in london .
|
{'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'fuzzily_match', 'value': 'london', 'subset': None}
|
{'func': 'most_str_eq', 'args': ['all_rows', 'venue', 'london'], 'result': True, 'ind': 0, 'tointer': 'for the venue records of all rows , most of them fuzzily match to london .', 'tostr': 'most_eq { all_rows ; venue ; london } = true'}
|
most_eq { all_rows ; venue ; london } = true
|
for the venue records of all rows , most of them fuzzily match to london .
|
1
|
1
|
{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'venue_3': 3, 'london_4': 4}
|
{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'venue_3': 'venue', 'london_4': 'london'}
|
{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'venue_3': [0], 'london_4': [0]}
|
['opposing teams', 'against', 'date', 'venue', 'status']
|
[['scotland', '7', '18 / 01 / 1992', 'murrayfield , edinburgh', 'five nations'], ['ireland', '9', '01 / 02 / 1992', 'twickenham , london', 'five nations'], ['france', '13', '15 / 02 / 1992', 'parc des princes , paris', 'five nations'], ['wales', '0', '07 / 03 / 1992', 'twickenham , london', 'five nations'], ['canada', '13', '17 / 10 / 1992', 'wembley stadium , london', 'test match'], ['south africa', '16', '14 / 11 / 1992', 'twickenham , london', 'test match']]
|
2001 new orleans saints season
|
https://en.wikipedia.org/wiki/2001_New_Orleans_Saints_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16882035-1.html.csv
|
majority
|
in the 2001 new orleans saints season , most of the games held before november had an attendance over 70000 .
|
{'scope': 'subset', 'col': '5', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '70000', 'subset': {'col': '2', 'criterion': 'less_than', 'value': 'november 1'}}
|
{'func': 'most_greater', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'date', 'november 1'], 'result': None, 'ind': 0, 'tostr': 'filter_less { all_rows ; date ; november 1 }', 'tointer': 'select the rows whose date record is less than november 1 .'}, 'attendance', '70000'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose date record is less than november 1 . for the attendance records of these rows , most of them are greater than 70000 .', 'tostr': 'most_greater { filter_less { all_rows ; date ; november 1 } ; attendance ; 70000 } = true'}
|
most_greater { filter_less { all_rows ; date ; november 1 } ; attendance ; 70000 } = true
|
select the rows whose date record is less than november 1 . for the attendance records of these rows , most of them are greater than 70000 .
|
2
|
2
|
{'most_greater_1': 1, 'result_2': 2, 'filter_less_0': 0, 'all_rows_3': 3, 'date_4': 4, 'november 1_5': 5, 'attendance_6': 6, '70000_7': 7}
|
{'most_greater_1': 'most_greater', 'result_2': 'true', 'filter_less_0': 'filter_less', 'all_rows_3': 'all_rows', 'date_4': 'date', 'november 1_5': 'november 1', 'attendance_6': 'attendance', '70000_7': '70000'}
|
{'most_greater_1': [2], 'result_2': [], 'filter_less_0': [1], 'all_rows_3': [0], 'date_4': [0], 'november 1_5': [0], 'attendance_6': [1], '70000_7': [1]}
|
['week', 'date', 'opponent', 'result', 'attendance']
|
[['1', 'september 9 , 2001', 'buffalo bills', 'w 24 - 6', '71447'], ['3', 'september 30 , 2001', 'new york giants', 'l 21 - 13', '78451'], ['4', 'october 7 , 2001', 'minnesota vikings', 'w 28 - 15', '70020'], ['5', 'october 14 , 2001', 'carolina panthers', 'w 27 - 25', '72049'], ['6', 'october 21 , 2001', 'atlanta falcons', 'l 20 - 13', '70020'], ['7', 'october 28 , 2001', 'st louis rams', 'w 34 - 31', '66189'], ['8', 'november 4 , 2001', 'new york jets', 'l 16 - 9', '70020'], ['9', 'november 11 , 2001', 'san francisco 49ers', 'l 28 - 27', '68063'], ['10', 'november 18 , 2001', 'indianapolis colts', 'w 34 - 20', '70020'], ['11', 'november 25 , 2001', 'new england patriots', 'l 34 - 17', '60292'], ['12', 'december 2 , 2001', 'carolina panthers', 'w 27 - 23', '70020'], ['13', 'december 9 , 2001', 'atlanta falcons', 'w 28 - 10', '68826'], ['14', 'december 17 , 2001', 'st louis rams', 'l 34 - 21', '70332'], ['15', 'december 23 , 2001', 'tampa bay buccaneers', 'l 48 - 21', '65526'], ['16', 'december 30 , 2001', 'washington redskins', 'l 40 - 10', '70020'], ['17', 'january 6 , 2002', 'san francisco 49ers', 'l 38 - 0', '70020']]
|
kevin curren
|
https://en.wikipedia.org/wiki/Kevin_Curren
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1717013-4.html.csv
|
unique
|
1985 was the only year that kevin curren played against boris becker .
|
{'scope': 'all', 'row': '9', 'col': '5', 'col_other': '2', 'criterion': 'equal', 'value': 'boris becker', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'boris becker'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opponent record fuzzily matches to boris becker .', 'tostr': 'filter_eq { all_rows ; opponent ; boris becker }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; opponent ; boris becker } }', 'tointer': 'select the rows whose opponent record fuzzily matches to boris becker . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'boris becker'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opponent record fuzzily matches to boris becker .', 'tostr': 'filter_eq { all_rows ; opponent ; boris becker }'}, 'date'], 'result': '1985', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; opponent ; boris becker } ; date }'}, '1985'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; opponent ; boris becker } ; date } ; 1985 }', 'tointer': 'the date record of this unqiue row is 1985 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; opponent ; boris becker } } ; eq { hop { filter_eq { all_rows ; opponent ; boris becker } ; date } ; 1985 } } = true', 'tointer': 'select the rows whose opponent record fuzzily matches to boris becker . there is only one such row in the table . the date record of this unqiue row is 1985 .'}
|
and { only { filter_eq { all_rows ; opponent ; boris becker } } ; eq { hop { filter_eq { all_rows ; opponent ; boris becker } ; date } ; 1985 } } = true
|
select the rows whose opponent record fuzzily matches to boris becker . there is only one such row in the table . the date record of this unqiue row is 1985 .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'opponent_7': 7, 'boris becker_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'date_9': 9, '1985_10': 10}
|
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'opponent_7': 'opponent', 'boris becker_8': 'boris becker', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'date_9': 'date', '1985_10': '1985'}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'opponent_7': [0], 'boris becker_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'date_9': [2], '1985_10': [3]}
|
['outcome', 'date', 'championship', 'surface', 'opponent', 'score']
|
[['winner', '1981', 'johannesburg , south africa', 'hard', 'bernard mitton', '6 - 4 , 6 - 4'], ['runner - up', '1982', 'los angeles , us', 'carpet', 'ivan lendl', '6 - 7 ( 5 - 7 ) , 5 - 7 , 1 - 6'], ['runner - up', '1982', 'amsterdam , netherlands', 'carpet', 'wojtek fibak', '5 - 7 , 6 - 3 , 4 - 6 , 3 - 6'], ['winner', '1982', 'cologne , germany', 'hard ( i )', 'shlomo glickstein', '2 - 6 , 6 - 2 , 6 - 3'], ['runner - up', '1983', 'milan , italy', 'carpet', 'ivan lendl', '7 - 5 , 3 - 6 , 6 - 7'], ['runner - up', '1984', 'australian open', 'grass', 'mats wilander', '7 - 6 ( 7 - 5 ) , 4 - 6 , 6 - 7 ( 3 - 7 ) , 2 - 6'], ['winner', '1985', 'toronto , canada', 'carpet', 'anders järryd', '7 - 6 ( 8 - 6 ) , 6 - 3'], ['runner - up', '1985', 'houston , us', 'carpet', 'john mcenroe', '5 - 7 , 1 - 6 , 6 - 7 ( 4 - 7 )'], ['runner - up', '1985', 'wimbledon', 'grass', 'boris becker', '3 - 6 , 7 - 6 ( 7 - 4 ) , 6 - 7 ( 3 - 7 ) , 4 - 6'], ['winner', '1986', 'atlanta , us', 'carpet', 'tim wilkison', '7 - 6 ( 7 - 5 ) , 7 - 6 ( 7 - 2 )'], ['runner - up', '1986', 'scottsdale , us', 'hard', 'john mcenroe', '3 - 6 , 6 - 3 , 2 - 6'], ['runner - up', '1988', 'toronto , canada', 'hard', 'ivan lendl', '6 - 7 ( 10 - 12 ) , 2 - 6'], ['winner', '1989', 'frankfurt , germany', 'carpet', 'petr korda', '6 - 2 , 7 - 5']]
|
1926 vfl season
|
https://en.wikipedia.org/wiki/1926_VFL_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10746808-11.html.csv
|
aggregation
|
on july 10 , 1926 , vfl total attendance between venues was 91,651 .
|
{'scope': 'all', 'col': '6', 'type': 'sum', 'result': '91651', 'subset': None}
|
{'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'crowd'], 'result': '91651', 'ind': 0, 'tostr': 'sum { all_rows ; crowd }'}, '91651'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; crowd } ; 91651 } = true', 'tointer': 'the sum of the crowd record of all rows is 91651 .'}
|
round_eq { sum { all_rows ; crowd } ; 91651 } = true
|
the sum of the crowd record of all rows is 91651 .
|
2
|
2
|
{'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'crowd_4': 4, '91651_5': 5}
|
{'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'crowd_4': 'crowd', '91651_5': '91651'}
|
{'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'crowd_4': [0], '91651_5': [1]}
|
['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date']
|
[['footscray', '14.9 ( 93 )', 'north melbourne', '10.8 ( 68 )', 'western oval', '12000', '10 july 1926'], ['essendon', '11.8 ( 74 )', 'hawthorn', '7.5 ( 47 )', 'windy hill', '10000', '10 july 1926'], ['collingwood', '14.15 ( 99 )', 'richmond', '5.11 ( 41 )', 'victoria park', '23000', '10 july 1926'], ['carlton', '11.14 ( 80 )', 'st kilda', '9.9 ( 63 )', 'princes park', '15000', '10 july 1926'], ['south melbourne', '9.13 ( 67 )', 'geelong', '8.8 ( 56 )', 'lake oval', '20000', '10 july 1926'], ['melbourne', '14.15 ( 99 )', 'fitzroy', '8.12 ( 60 )', 'mcg', '11651', '10 july 1926']]
|
2007 cologne centurions season
|
https://en.wikipedia.org/wiki/2007_Cologne_Centurions_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-24396664-2.html.csv
|
majority
|
all of the games the 2007 cologne centurions played at the rheinenergiestadion had a 6:00 pm kickoff .
|
{'scope': 'subset', 'col': '3', 'most_or_all': 'all', 'criterion': 'equal', 'value': '6:00 pm', 'subset': {'col': '7', 'criterion': 'equal', 'value': 'rheinenergiestadion'}}
|
{'func': 'all_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'game site', 'rheinenergiestadion'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; game site ; rheinenergiestadion }', 'tointer': 'select the rows whose game site record fuzzily matches to rheinenergiestadion .'}, 'kickoff', '6:00 pm'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose game site record fuzzily matches to rheinenergiestadion . for the kickoff records of these rows , all of them fuzzily match to 6:00 pm .', 'tostr': 'all_eq { filter_eq { all_rows ; game site ; rheinenergiestadion } ; kickoff ; 6:00 pm } = true'}
|
all_eq { filter_eq { all_rows ; game site ; rheinenergiestadion } ; kickoff ; 6:00 pm } = true
|
select the rows whose game site record fuzzily matches to rheinenergiestadion . for the kickoff records of these rows , all of them fuzzily match to 6:00 pm .
|
2
|
2
|
{'all_str_eq_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'game site_4': 4, 'rheinenergiestadion_5': 5, 'kickoff_6': 6, '6:00pm_7': 7}
|
{'all_str_eq_1': 'all_str_eq', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'game site_4': 'game site', 'rheinenergiestadion_5': 'rheinenergiestadion', 'kickoff_6': 'kickoff', '6:00pm_7': '6:00 pm'}
|
{'all_str_eq_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'game site_4': [0], 'rheinenergiestadion_5': [0], 'kickoff_6': [1], '6:00pm_7': [1]}
|
['week', 'date', 'kickoff', 'opponent', 'final score', 'team record', 'game site', 'attendance']
|
[['1', 'saturday , april 14', '6:00 pm', 'hamburg sea devils', 'w 24 - 18', '1 - 0', 'aol arena', '20887'], ['2', 'saturday , april 21', '6:00 pm', 'frankfurt galaxy', 'l 13 - 18', '1 - 1', 'rheinenergiestadion', '16422'], ['3', 'saturday , april 28', '7:00 pm', 'rhein fire', 'w 14 - 6', '2 - 1', 'ltu arena', '21347'], ['4', 'saturday , may 5', '6:00 pm', 'berlin thunder', 'l 28 - 31', '2 - 2', 'rheinenergiestadion', '10084'], ['5', 'sunday , may 13', '4:00 pm', 'berlin thunder', 'w 24 - 10', '3 - 2', 'olympic stadium', '11995'], ['6', 'saturday , may 19', '6:00 pm', 'rhein fire', 'w 20 - 17', '4 - 2', 'rheinenergiestadion', '22154'], ['7', 'friday , may 25', '8:00 pm', 'amsterdam admirals', 'w 30 - 7', '5 - 2', 'amsterdam arena', '11714'], ['8', 'saturday , june 2', '6:00 pm', 'hamburg sea devils', 'l 7 - 21', '5 - 3', 'rheinenergiestadion', '10221'], ['9', 'saturday , june 9', '6:00 pm', 'amsterdam admirals', 'w 31 - 13', '6 - 3', 'rheinenergiestadion', '12878']]
|
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
|
aggregation
|
the average crowd attendance during the 1929 vfl season was around 13800 .
|
{'scope': 'all', 'col': '6', 'type': 'average', 'result': '13841', 'subset': None}
|
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'crowd'], 'result': '13841', 'ind': 0, 'tostr': 'avg { all_rows ; crowd }'}, '13841'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; crowd } ; 13841 } = true', 'tointer': 'the average of the crowd record of all rows is 13841 .'}
|
round_eq { avg { all_rows ; crowd } ; 13841 } = true
|
the average of the crowd record of all rows is 13841 .
|
2
|
2
|
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'crowd_4': 4, '13841_5': 5}
|
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'crowd_4': 'crowd', '13841_5': '13841'}
|
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'crowd_4': [0], '13841_5': [1]}
|
['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']]
|
1912 summer olympics
|
https://en.wikipedia.org/wiki/1912_Summer_Olympics
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-180200-1.html.csv
|
aggregation
|
in total , 94 gold medals were awarded during the 1912 summer olympics .
|
{'scope': 'all', 'col': '3', 'type': 'sum', 'result': '94', 'subset': None}
|
{'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'gold'], 'result': '94', 'ind': 0, 'tostr': 'sum { all_rows ; gold }'}, '94'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; gold } ; 94 } = true', 'tointer': 'the sum of the gold record of all rows is 94 .'}
|
round_eq { sum { all_rows ; gold } ; 94 } = true
|
the sum of the gold record of all rows is 94 .
|
2
|
2
|
{'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'gold_4': 4, '94_5': 5}
|
{'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'gold_4': 'gold', '94_5': '94'}
|
{'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'gold_4': [0], '94_5': [1]}
|
['rank', 'nation', 'gold', 'silver', 'bronze', 'total']
|
[['1', 'united states', '25', '19', '19', '63'], ['2', 'sweden ( host nation )', '24', '24', '17', '65'], ['3', 'great britain', '10', '15', '16', '41'], ['4', 'finland', '9', '8', '9', '26'], ['5', 'france', '7', '4', '3', '14'], ['6', 'germany', '5', '13', '7', '25'], ['7', 'south africa', '4', '2', '0', '6'], ['8', 'norway', '4', '1', '4', '9'], ['9', 'canada', '3', '2', '3', '8'], ['9', 'hungary', '3', '2', '3', '8']]
|
2003 tennessee titans season
|
https://en.wikipedia.org/wiki/2003_Tennessee_Titans_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18817998-1.html.csv
|
count
|
in the 2003 season , the tennessee titans won eight of the games that were aired on cbs at 12:00 pm .
|
{'scope': 'subset', 'criterion': 'fuzzily_match', 'value': 'w', 'result': '8', 'col': '4', 'subset': {'col': '5', 'criterion': 'equal', 'value': 'cbs 12:00 pm'}}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'tv time', 'cbs 12:00 pm'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; tv time ; cbs 12:00 pm }', 'tointer': 'select the rows whose tv time record fuzzily matches to cbs 12:00 pm .'}, 'result', 'w'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose tv time record fuzzily matches to cbs 12:00 pm . among these rows , select the rows whose result record fuzzily matches to w .', 'tostr': 'filter_eq { filter_eq { all_rows ; tv time ; cbs 12:00 pm } ; result ; w }'}], 'result': '8', 'ind': 2, 'tostr': 'count { filter_eq { filter_eq { all_rows ; tv time ; cbs 12:00 pm } ; result ; w } }', 'tointer': 'select the rows whose tv time record fuzzily matches to cbs 12:00 pm . among these rows , select the rows whose result record fuzzily matches to w . the number of such rows is 8 .'}, '8'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_eq { all_rows ; tv time ; cbs 12:00 pm } ; result ; w } } ; 8 } = true', 'tointer': 'select the rows whose tv time record fuzzily matches to cbs 12:00 pm . among these rows , select the rows whose result record fuzzily matches to w . the number of such rows is 8 .'}
|
eq { count { filter_eq { filter_eq { all_rows ; tv time ; cbs 12:00 pm } ; result ; w } } ; 8 } = true
|
select the rows whose tv time record fuzzily matches to cbs 12:00 pm . among these rows , select the rows whose result record fuzzily matches to w . the number of such rows is 8 .
|
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, 'tv time_6': 6, 'cbs 12:00 pm_7': 7, 'result_8': 8, 'w_9': 9, '8_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', 'tv time_6': 'tv time', 'cbs 12:00 pm_7': 'cbs 12:00 pm', 'result_8': 'result', 'w_9': 'w', '8_10': '8'}
|
{'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_str_eq_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'tv time_6': [0], 'cbs 12:00 pm_7': [0], 'result_8': [1], 'w_9': [1], '8_10': [3]}
|
['week', 'date', 'opponent', 'result', 'tv time', 'attendance']
|
[['1', 'september 7 , 2003', 'oakland raiders', 'w 25 - 20', 'espn 7:30 pm', '68809'], ['2', 'september 14 , 2003', 'indianapolis colts', 'l 7 - 33', 'cbs 12:00 pm', '56999'], ['3', 'september 21 , 2003', 'new orleans saints', 'w 27 - 12', 'fox 12:00 pm', '68809'], ['4', 'september 28 , 2003', 'pittsburgh steelers', 'w 30 - 13', 'cbs 12:00 pm', '63244'], ['5', 'october 5 , 2003', 'new england patriots', 'l 30 - 38', 'cbs 12:00 pm', '68436'], ['6', 'october 12 , 2003', 'houston texans', 'w 38 - 17', 'cbs 12:00 pm', '68809'], ['7', 'october 19 , 2003', 'carolina panthers', 'w 37 - 17', 'cbs 12:00 pm', '72851'], ['8', 'october 26 , 2003', 'jacksonville jaguars', 'w 30 - 17', 'cbs 12:00 pm', '55918'], ['10', 'november 9 , 2003', 'miami dolphins', 'w 31 - 7', 'cbs 12:00 pm', '68809'], ['11', 'november 16 , 2003', 'jacksonville jaguars', 'w 10 - 3', 'cbs 12:00 pm', '68809'], ['12', 'november 23 , 2003', 'atlanta falcons', 'w 38 - 31', 'cbs 3:05 pm', '70891'], ['13', 'december 1 , 2003', 'new york jets', 'l 17 - 24', 'abc 8:00 pm', '77710'], ['14', 'december 7 , 2003', 'indianapolis colts', 'l 27 - 29', 'cbs 12:00 pm', '68809'], ['15', 'december 14 , 2003', 'buffalo bills', 'w 28 - 26', 'cbs 12:00 pm', '68809'], ['16', 'december 21 , 2003', 'houston texans', 'w 27 - 24', 'cbs 12:00 pm', '70758'], ['17', 'december 28 , 2003', 'tampa bay buccaneers', 'w 33 - 13', 'fox 12:00 pm', '68809']]
|
1984 seattle seahawks season
|
https://en.wikipedia.org/wiki/1984_Seattle_Seahawks_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-13258851-2.html.csv
|
aggregation
|
the average crowd attendance for games in the 1984 seattle seahawks season was 58252 .
|
{'scope': 'all', 'col': '7', 'type': 'average', 'result': '58252', 'subset': None}
|
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'attendance'], 'result': '58252', 'ind': 0, 'tostr': 'avg { all_rows ; attendance }'}, '58252'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; attendance } ; 58252 } = true', 'tointer': 'the average of the attendance record of all rows is 58252 .'}
|
round_eq { avg { all_rows ; attendance } ; 58252 } = true
|
the average of the attendance record of all rows is 58252 .
|
2
|
2
|
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'attendance_4': 4, '58252_5': 5}
|
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'attendance_4': 'attendance', '58252_5': '58252'}
|
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'attendance_4': [0], '58252_5': [1]}
|
['week', 'date', 'opponent', 'result', 'game site', 'record', 'attendance']
|
[['1', 'september 3 , 1984', 'cleveland browns', 'w 33 - 0', 'kingdome', '1 - 0', '59540'], ['2', 'september 9 , 1984', 'san diego chargers', 'w 31 - 17', 'kingdome', '2 - 0', '61314'], ['3', 'september 16 , 1984', 'new england patriots', 'l 23 - 38', 'sullivan stadium', '2 - 1', '43140'], ['4', 'september 23 , 1984', 'chicago bears', 'w 38 - 9', 'kingdome', '3 - 1', '61520'], ['5', 'september 30 , 1984', 'minnesota vikings', 'w 20 - 12', 'hubert h humphrey metrodome', '4 - 1', '57171'], ['6', 'october 7 , 1984', 'los angeles raiders', 'l 14 - 28', 'los angeles memorial coliseum', '4 - 2', '77904'], ['7', 'october 14 , 1984', 'buffalo bills', 'w 31 - 28', 'kingdome', '5 - 2', '59034'], ['8', 'october 21 , 1984', 'green bay packers', 'w 30 - 24', 'lambeau field', '6 - 2', '52286'], ['9', 'october 29 , 1984', 'san diego chargers', 'w 24 - 0', 'jack murphy stadium', '7 - 2', '53974'], ['10', 'november 4 , 1984', 'kansas city chiefs', 'w 45 - 0', 'kingdome', '8 - 2', '61396'], ['11', 'november 12 , 1984', 'los angeles raiders', 'w 17 - 14', 'kingdome', '9 - 2', '64001'], ['12', 'november 18 , 1984', 'cincinnati bengals', 'w 26 - 6', 'riverfront stadium', '10 - 2', '50280'], ['13', 'november 25 , 1984', 'denver broncos', 'w 27 - 24', 'mile high stadium', '11 - 2', '74922'], ['14', 'december 2 , 1984', 'detroit lions', 'w 38 - 17', 'kingdome', '12 - 2', '62441'], ['15', 'december 9 , 1984', 'kansas city chiefs', 'l 7 - 34', 'arrowhead stadium', '12 - 3', '34855']]
|
1973 - 74 segunda división
|
https://en.wikipedia.org/wiki/1973%E2%80%9374_Segunda_Divisi%C3%B3n
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12252458-2.html.csv
|
comparative
|
in the 1973-4 segunda division , real valladolid scored more goals than sevilla fc .
|
{'row_1': '7', 'row_2': '9', 'col': '8', 'col_other': '2', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}
|
{'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'club', 'real valladolid'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose club record fuzzily matches to real valladolid .', 'tostr': 'filter_eq { all_rows ; club ; real valladolid }'}, 'goals for'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; club ; real valladolid } ; goals for }', 'tointer': 'select the rows whose club record fuzzily matches to real valladolid . take the goals for record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'club', 'sevilla fc'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose club record fuzzily matches to sevilla fc .', 'tostr': 'filter_eq { all_rows ; club ; sevilla fc }'}, 'goals for'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; club ; sevilla fc } ; goals for }', 'tointer': 'select the rows whose club record fuzzily matches to sevilla fc . take the goals for record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; club ; real valladolid } ; goals for } ; hop { filter_eq { all_rows ; club ; sevilla fc } ; goals for } } = true', 'tointer': 'select the rows whose club record fuzzily matches to real valladolid . take the goals for record of this row . select the rows whose club record fuzzily matches to sevilla fc . take the goals for record of this row . the first record is greater than the second record .'}
|
greater { hop { filter_eq { all_rows ; club ; real valladolid } ; goals for } ; hop { filter_eq { all_rows ; club ; sevilla fc } ; goals for } } = true
|
select the rows whose club record fuzzily matches to real valladolid . take the goals for record of this row . select the rows whose club record fuzzily matches to sevilla fc . take the goals for 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, 'club_7': 7, 'real valladolid_8': 8, 'goals for_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'club_11': 11, 'sevilla fc_12': 12, 'goals for_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', 'club_7': 'club', 'real valladolid_8': 'real valladolid', 'goals for_9': 'goals for', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'club_11': 'club', 'sevilla fc_12': 'sevilla fc', 'goals for_13': 'goals for'}
|
{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'club_7': [0], 'real valladolid_8': [0], 'goals for_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'club_11': [1], 'sevilla fc_12': [1], 'goals for_13': [3]}
|
['position', 'club', 'played', 'points', 'wins', 'draws', 'losses', 'goals for', 'goals against', 'goal difference']
|
[['1', 'real betis', '38', '51 + 13', '19', '13', '6', '69', '31', '+ 38'], ['2', 'hércules cf', '38', '49 + 11', '20', '9', '9', '51', '34', '+ 17'], ['3', 'ud salamanca', '38', '48 + 10', '20', '8', '10', '53', '39', '+ 14'], ['4', 'cd tenerife', '38', '46 + 8', '19', '8', '11', '57', '41', '+ 16'], ['5', 'cádiz cf', '38', '46 + 8', '18', '10', '10', '52', '37', '+ 15'], ['6', 'gimnástico de tarragona', '38', '41 + 3', '16', '9', '13', '46', '40', '+ 6'], ['7', 'real valladolid', '38', '41 + 3', '16', '9', '13', '61', '50', '+ 11'], ['8', 'cd san andrés', '38', '39 + 1', '16', '7', '15', '47', '38', '+ 9'], ['9', 'sevilla fc', '38', '39 + 1', '15', '9', '14', '48', '40', '+ 8'], ['10', 'baracaldo cf', '38', '39 + 1', '14', '11', '13', '49', '52', '- 3'], ['11', 'rcd mallorca', '38', '39 + 1', '12', '15', '11', '36', '32', '+ 4'], ['12', 'cd orense', '38', '38', '13', '12', '13', '43', '45', '- 2'], ['13', 'córdoba cf', '38', '38', '16', '6', '16', '58', '58', '0'], ['14', 'rayo vallecano', '38', '33 - 5', '14', '5', '19', '39', '51', '- 12'], ['15', 'cd sabadell', '38', '33 - 5', '11', '11', '16', '35', '52', '- 17'], ['16', 'burgos cf', '38', '32 - 6', '13', '6', '19', '34', '44', '- 10'], ['17', 'ca osasuna', '38', '28 - 10', '10', '8', '20', '36', '63', '- 27'], ['18', 'deportivo de la coruña', '38', '28 - 10', '11', '6', '21', '30', '56', '- 26'], ['19', 'levante ud', '38', '27 - 11', '10', '7', '21', '37', '48', '- 11'], ['20', 'linares cf', '38', '25 - 13', '8', '9', '21', '29', '59', '- 30']]
|
somerset county cricket club in 2009
|
https://en.wikipedia.org/wiki/Somerset_County_Cricket_Club_in_2009
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27922491-11.html.csv
|
comparative
|
craig kieswetter had seven more runs than zander be bruyn in the 2009 somerset county cricket club season .
|
{'row_1': '2', 'row_2': '1', 'col': '4', 'col_other': '1', 'relation': 'diff', 'record_mentioned': 'no', 'diff_result': {'diff_value': '7', 'bigger': 'row1'}}
|
{'func': 'eq', 'args': [{'func': 'diff', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'craig kieswetter'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose player record fuzzily matches to craig kieswetter .', 'tostr': 'filter_eq { all_rows ; player ; craig kieswetter }'}, 'runs'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; player ; craig kieswetter } ; runs }', 'tointer': 'select the rows whose player record fuzzily matches to craig kieswetter . take the runs record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'zander de bruyn'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose player record fuzzily matches to zander de bruyn .', 'tostr': 'filter_eq { all_rows ; player ; zander de bruyn }'}, 'runs'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; player ; zander de bruyn } ; runs }', 'tointer': 'select the rows whose player record fuzzily matches to zander de bruyn . take the runs record of this row .'}], 'result': '7', 'ind': 4, 'tostr': 'diff { hop { filter_eq { all_rows ; player ; craig kieswetter } ; runs } ; hop { filter_eq { all_rows ; player ; zander de bruyn } ; runs } }'}, '7'], 'result': True, 'ind': 5, 'tostr': 'eq { diff { hop { filter_eq { all_rows ; player ; craig kieswetter } ; runs } ; hop { filter_eq { all_rows ; player ; zander de bruyn } ; runs } } ; 7 } = true', 'tointer': 'select the rows whose player record fuzzily matches to craig kieswetter . take the runs record of this row . select the rows whose player record fuzzily matches to zander de bruyn . take the runs record of this row . the first record is 7 larger than the second record .'}
|
eq { diff { hop { filter_eq { all_rows ; player ; craig kieswetter } ; runs } ; hop { filter_eq { all_rows ; player ; zander de bruyn } ; runs } } ; 7 } = true
|
select the rows whose player record fuzzily matches to craig kieswetter . take the runs record of this row . select the rows whose player record fuzzily matches to zander de bruyn . take the runs record of this row . the first record is 7 larger than the second record .
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6
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6
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{'eq_5': 5, 'result_6': 6, 'diff_4': 4, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'player_8': 8, 'craig kieswetter_9': 9, 'runs_10': 10, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_11': 11, 'player_12': 12, 'zander de bruyn_13': 13, 'runs_14': 14, '7_15': 15}
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{'eq_5': 'eq', 'result_6': 'true', 'diff_4': 'diff', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'player_8': 'player', 'craig kieswetter_9': 'craig kieswetter', 'runs_10': 'runs', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_11': 'all_rows', 'player_12': 'player', 'zander de bruyn_13': 'zander de bruyn', 'runs_14': 'runs', '7_15': '7'}
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{'eq_5': [6], 'result_6': [], 'diff_4': [5], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_7': [0], 'player_8': [0], 'craig kieswetter_9': [0], 'runs_10': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_11': [1], 'player_12': [1], 'zander de bruyn_13': [1], 'runs_14': [3], '7_15': [5]}
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['player', 'matches', 'innings', 'runs', 'average', 'highest score', '100s', '50s']
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[['zander de bruyn', '9', '6', '388', '97.00', '96', '0', '5'], ['craig kieswetter', '8', '8', '395', '65.83', '138', '2', '0'], ['justin langer', '8', '4', '195', '65.00', '78', '0', '2'], ['marcus trescothick', '9', '9', '476', '59.50', '144', '1', '4'], ['peter trego', '9', '6', '171', '57.00', '74', '0', '2'], ['james hildreth', '9', '9', '313', '34.77', '151', '1', '1'], ['arul suppiah', '9', '5', '101', '33.66', '48', '0', '0']]
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