niryuu/sni-each-converted huggingface dataset:
select the rows whose total record is equal to 5 . there is only one such row in the table . the nation record of this unqiue row is germany .
Output: and { only { filter_eq { all_rows ; total ; 5 } } ; eq { hop { filter_eq { all_rows ; total ; 5 } ; nation } ; germany } }
select the rows whose result record fuzzily matches to retired to run for us senate . there is only one such row in the table . the incumbent record of this unqiue row is roman c pucinski .
Output: and { only { filter_eq { all_rows ; result ; retired to run for us senate } } ; eq { hop { filter_eq { all_rows ; result ; retired to run for us senate } ; incumbent } ; roman c pucinski } }
select the rows whose silver record is equal to 3 . there is only one such row in the table . the nation record of this unqiue row is soviet union .
Output: and { only { filter_eq { all_rows ; silver ; 3 } } ; eq { hop { filter_eq { all_rows ; silver ; 3 } ; nation } ; soviet union } }
select the rows whose year record fuzzily matches to 1993 . take the laps record of this row . select the rows whose year record fuzzily matches to 1997 . take the laps record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; year ; 1993 } ; laps } ; hop { filter_eq { all_rows ; year ; 1997 } ; laps } }
select the rows whose director record fuzzily matches to brian kelly . take the title record of this row . select the rows whose director record fuzzily matches to andrew lincoln . take the title record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; director ; brian kelly } ; title } ; hop { filter_eq { all_rows ; director ; andrew lincoln } ; title } }
select the rows whose event record fuzzily matches to championship test . among these rows , select the rows whose horse record fuzzily matches to donna dm . there is only one such row in the table . the athlete record of this unqiue row is sabine peters .
Output: and { only { filter_eq { filter_eq { all_rows ; event ; championship test } ; horse ; donna dm } } ; eq { hop { filter_eq { filter_eq { all_rows ; event ; championship test } ; horse ; donna dm } ; athlete } ; sabine peters } }
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 .
Output: less { hop { filter_eq { all_rows ; tournament ; commonwealth games } ; result } ; hop { filter_eq { all_rows ; tournament ; summer olympics } ; result } }
select the rows whose top 5 record is equal to 1 . the number of such rows is 1 .
Output: eq { count { filter_eq { all_rows ; top 5 ; 1 } } ; 1 }
select the rows whose country record fuzzily matches to france . there is only one such row in the table . the player record of this unqiue row is thomas levet .
Output: and { only { filter_eq { all_rows ; country ; france } } ; eq { hop { filter_eq { all_rows ; country ; france } ; player } ; thomas levet } }
select the row whose seats in hamburgische bürgerschaft record of all rows is maximum . the ideology record of this row is social democracy .
Output: eq { hop { argmax { all_rows ; seats in hamburgische bürgerschaft } ; ideology } ; social democracy }
select the rows whose team record fuzzily matches to queens park rangers . take the date of vacancy record of this row . select the rows whose team record fuzzily matches to norwich city . take the date of vacancy record of this row . the second record is 7 days larger than the first record .
Output: eq { diff { hop { filter_eq { all_rows ; team ; queens park rangers } ; date of vacancy } ; hop { filter_eq { all_rows ; team ; norwich city } ; date of vacancy } } ; -7 days }
select the rows whose date of birth record is greater than or equal to 1 january 1971 . for the batting style records of these rows , most of them fuzzily match to right hand bat .
Output: most_eq { filter_greater_eq { all_rows ; date of birth ; 1 january 1971 } ; batting style ; right hand bat }
for the competition records of all rows , most of them do not match to friendly .
Output: most_not_eq { all_rows ; competition ; friendly }
select the rows whose position record fuzzily matches to c . there is only one such row in the table . the player record of this unqiue row is jeff winchester .
Output: and { only { filter_eq { all_rows ; position ; c } } ; eq { hop { filter_eq { all_rows ; position ; c } ; player } ; jeff winchester } }
the average of the enrollment record of all rows is 2030 .
Output: round_eq { avg { all_rows ; enrollment } ; 2030 }
select the rows whose school record fuzzily matches to leo . take the year joined record of this row . select the rows whose school record fuzzily matches to south adams . take the year joined record of this row . the second record is 20 years larger than the first record .
Output: eq { diff { hop { filter_eq { all_rows ; school ; leo } ; year joined } ; hop { filter_eq { all_rows ; school ; south adams } ; year joined } } ; -20 years }
select the rows whose team record fuzzily matches to juventus . take the against record of this row . select the rows whose team record fuzzily matches to corinthians . take the against record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; team ; juventus } ; against } ; hop { filter_eq { all_rows ; team ; corinthians } ; against } }
select the row whose date ( from ) record of all rows is minimum . the name of system record of this row is edmonton radial railway .
Output: eq { hop { argmin { all_rows ; date ( from ) } ; name of system } ; edmonton radial railway }
select the row whose ties played record of all rows is 2nd maximum . the player record of this row is dovydas šakinis .
Output: eq { hop { nth_argmax { all_rows ; ties played ; 2 } ; player } ; dovydas šakinis }
select the rows whose location record fuzzily matches to brazil . there is only one such row in the table . the race name record of this unqiue row is tour de santa catarina .
Output: and { only { filter_eq { all_rows ; location ; brazil } } ; eq { hop { filter_eq { all_rows ; location ; brazil } ; race name } ; tour de santa catarina } }
select the row whose home team score record of all rows is maximum . the home team record of this row is essendon .
Output: eq { hop { argmax { all_rows ; home team score } ; home team } ; essendon }
select the rows whose bowling style record fuzzily matches to left arm slow chinaman . there is only one such row in the table . the player record of this unqiue row is michael bevan .
Output: and { only { filter_eq { all_rows ; bowling style ; left arm slow chinaman } } ; eq { hop { filter_eq { all_rows ; bowling style ; left arm slow chinaman } ; player } ; michael bevan } }
select the rows whose country record fuzzily matches to united states . there is only one such row in the table . the label record of this unqiue row is astralwerks .
Output: and { only { filter_eq { all_rows ; country ; united states } } ; eq { hop { filter_eq { all_rows ; country ; united states } ; label } ; astralwerks } }
for the date records of all rows , all of them fuzzily match to 16 august .
Output: all_eq { all_rows ; date ; 16 august }
select the row whose original air date record of all rows is 1st maximum . the title record of this row is i 'll take you .
Output: eq { hop { nth_argmax { all_rows ; original air date ; 1 } ; title } ; i 'll take you }
select the rows whose decision record fuzzily matches to labarbera . among these rows , select the rows whose attendance record is greater than 18000 . there is only one such row in the table . the date record of this unqiue row is november 3 .
Output: and { only { filter_greater { filter_eq { all_rows ; decision ; labarbera } ; attendance ; 18000 } } ; eq { hop { filter_greater { filter_eq { all_rows ; decision ; labarbera } ; attendance ; 18000 } ; date } ; november 3 } }
select the row whose goals record of all rows is maximum . the player record of this row is mile sterjovski .
Output: eq { hop { argmax { all_rows ; goals } ; player } ; mile sterjovski }
select the rows whose industry record fuzzily matches to banking . the average of the profits ( billion ) record of these rows is 13.9 billion .
Output: round_eq { avg { filter_eq { all_rows ; industry ; banking } ; profits ( billion ) } ; 13.9 billion }
select the rows whose outcome record fuzzily matches to winners . for the surface records of these rows , most of them fuzzily match to clay .
Output: most_eq { filter_eq { all_rows ; outcome ; winners } ; surface ; clay }
select the rows whose first air date record fuzzily matches to july 4 , 1987 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; first air date ; july 4 , 1987 } } ; 2 }
select the rows whose host record fuzzily matches to united arab emirates . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; host ; united arab emirates } } ; 2 }
select the rows whose score record is equal to 70 . for the country records of these rows , most of them fuzzily match to united states .
Output: most_eq { filter_eq { all_rows ; score ; 70 } ; country ; united states }
select the rows whose record record fuzzily matches to postponed ( rain ) . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; record ; postponed ( rain ) } } ; 2 }
the average of the attendance record of all rows is 2900 .
Output: round_eq { avg { all_rows ; attendance } ; 2900 }
select the row whose location attendance record of all rows is 1st maximum . the team record of this row is cleveland .
Output: eq { hop { nth_argmax { all_rows ; location attendance ; 1 } ; team } ; cleveland }
for the percentage of possible points records of all rows , most of them are less than 70 .
Output: most_less { all_rows ; percentage of possible points ; 70 }
select the row whose matches record of all rows is maximum . the name record of this row is glenn ferguson .
Output: eq { hop { argmax { all_rows ; matches } ; name } ; glenn ferguson }
select the row whose attendance record of all rows is 2nd maximum . the date record of this row is saturday , april 10 .
Output: eq { hop { nth_argmax { all_rows ; attendance ; 2 } ; date } ; saturday , april 10 }
select the rows whose county record fuzzily matches to eureka . take the mccain % record of this row . select the rows whose county record fuzzily matches to mineral . take the mccain % record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; county ; eureka } ; mccain % } ; hop { filter_eq { all_rows ; county ; mineral } ; mccain % } }
select the rows whose time / retired record fuzzily matches to engine . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; time / retired ; engine } } ; 3 }
select the rows whose director record fuzzily matches to neema barnette . there is only one such row in the table . the original air date record of this unqiue row is october 21 , 2003 .
Output: and { only { filter_eq { all_rows ; director ; neema barnette } } ; eq { hop { filter_eq { all_rows ; director ; neema barnette } ; original air date } ; october 21 , 2003 } }
for the location records of all rows , most of them fuzzily match to busch stadium .
Output: most_eq { all_rows ; location ; busch stadium }
the average of the crowd record of all rows is 21486 .
Output: round_eq { avg { all_rows ; crowd } ; 21486 }
for the attendance records of all rows , most of them are greater than 50,000 .
Output: most_greater { all_rows ; attendance ; 50,000 }
select the row whose date record of all rows is minimum . the race record of this row is manikato stakes .
Output: eq { hop { argmin { all_rows ; date } ; race } ; manikato stakes }
the average of the to par record of all rows is 2.125 .
Output: round_eq { avg { all_rows ; to par } ; 2.125 }
select the rows whose institution record is arbitrary . the number of such rows is 12 .
Output: eq { count { filter_all { all_rows ; institution } } ; 12 }
select the rows whose result record fuzzily matches to retired . there is only one such row in the table . the incumbent record of this unqiue row is edward boland .
Output: and { only { filter_eq { all_rows ; result ; retired } } ; eq { hop { filter_eq { all_rows ; result ; retired } ; incumbent } ; edward boland } }
select the row whose pick record of all rows is 2nd maximum . the player record of this row is obed cetoute .
Output: eq { hop { nth_argmax { all_rows ; pick ; 2 } ; player } ; obed cetoute }
select the rows whose metropolitan area record fuzzily matches to new york , new york . take the since record of this row . select the rows whose metropolitan area record fuzzily matches to chicago , illinois . take the since record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; metropolitan area ; new york , new york } ; since } ; hop { filter_eq { all_rows ; metropolitan area ; chicago , illinois } ; since } }
select the rows whose position record fuzzily matches to fl . there is only one such row in the table .
Output: only { filter_eq { all_rows ; position ; fl } }
select the row whose production num record of all rows is 3rd maximum . the title record of this row is red - headed baby .
Output: eq { hop { nth_argmax { all_rows ; production num ; 3 } ; title } ; red - headed baby }
the average of the attendance record of all rows is 19893 .
Output: round_eq { avg { all_rows ; attendance } ; 19893 }
select the rows whose college / junior / club team record fuzzily matches to wchl . among these rows , select the rows whose round record is greater than 2 . the number of such rows is 2 .
Output: eq { count { filter_greater { filter_eq { all_rows ; college / junior / club team ; wchl } ; round ; 2 } } ; 2 }
select the rows whose venue record fuzzily matches to punt road oval . take the crowd record of this row . select the rows whose venue record fuzzily matches to glenferrie oval . take the crowd record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; venue ; punt road oval } ; crowd } ; hop { filter_eq { all_rows ; venue ; glenferrie oval } ; crowd } }
select the rows whose film title used in nomination record fuzzily matches to back in trouble . take the year ( ceremony ) record of this row . select the rows whose film title used in nomination record fuzzily matches to renart the fox . take the year ( ceremony ) record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; film title used in nomination ; back in trouble } ; year ( ceremony ) } ; hop { filter_eq { all_rows ; film title used in nomination ; renart the fox } ; year ( ceremony ) } }
the maximum money record of all rows is 2400 . the player record of the row with superlative money record is jimmy demaret .
Output: and { eq { max { all_rows ; money } ; 2400 } ; eq { hop { argmax { all_rows ; money } ; player } ; jimmy demaret } }
select the rows whose event record fuzzily matches to billabong pro . for the winner records of these rows , most of them fuzzily match to usa .
Output: most_eq { filter_eq { all_rows ; event ; billabong pro } ; winner ; usa }
select the rows whose opponent record fuzzily matches to mark kerr . take the round record of this row . select the rows whose opponent record fuzzily matches to dieusel berto . take the round record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; opponent ; mark kerr } ; round } ; hop { filter_eq { all_rows ; opponent ; dieusel berto } ; round } }
select the rows whose home record fuzzily matches to spurs . among these rows , select the rows whose leading scorer record fuzzily matches to tim duncan . the number of such rows is 3 .
Output: eq { count { filter_eq { filter_eq { all_rows ; home ; spurs } ; leading scorer ; tim duncan } } ; 3 }
select the row whose average record of all rows is 1st maximum . the player record of this row is max waller .
Output: eq { hop { nth_argmax { all_rows ; average ; 1 } ; player } ; max waller }
select the row whose gold record of all rows is maximum . the nation record of this row is algeria .
Output: eq { hop { argmax { all_rows ; gold } ; nation } ; algeria }
select the rows whose position record fuzzily matches to 1st . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; position ; 1st } } ; 3 }
select the rows whose builder record fuzzily matches to north british . take the date record of this row . select the rows whose builder record fuzzily matches to g & swr kilmarnock . take the date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; builder ; north british } ; date } ; hop { filter_eq { all_rows ; builder ; g & swr kilmarnock } ; date } }
the average of the attendance record of all rows is 17392 .
Output: round_eq { avg { all_rows ; attendance } ; 17392 }
select the rows whose nation record is arbitrary . the number of such rows is 12 .
Output: eq { count { filter_all { all_rows ; nation } } ; 12 }
select the rows whose erp w record fuzzily matches to 10 . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; erp w ; 10 } } ; 5 }
select the row whose total record of all rows is 2nd maximum . the athlete record of this row is andrea fuentes & gemma mengual .
Output: eq { hop { nth_argmax { all_rows ; total ; 2 } ; athlete } ; andrea fuentes & gemma mengual }
select the rows whose sport record fuzzily matches to m cross country . take the date record of this row . select the rows whose sport record fuzzily matches to w gymnastics . take the date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; sport ; m cross country } ; date } ; hop { filter_eq { all_rows ; sport ; w gymnastics } ; date } }
select the rows whose event record fuzzily matches to bellator 14 . take the round record of this row . select the rows whose event record fuzzily matches to unconquered 1 : november reign . take the round record of this row . the first record is 1 larger than the second record .
Output: eq { diff { hop { filter_eq { all_rows ; event ; bellator 14 } ; round } ; hop { filter_eq { all_rows ; event ; unconquered 1 : november reign } ; round } } ; 1 }
the average of the blackberry record of all rows is 3.19 % .
Output: round_eq { avg { all_rows ; blackberry } ; 3.19 % }
select the rows whose road record fuzzily matches to i - 80 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; road ; i - 80 } } ; 2 }
select the rows whose womens singles record fuzzily matches to aya umemura . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; womens singles ; aya umemura } } ; 2 }
select the rows whose location record fuzzily matches to rca dome . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; location ; rca dome } } ; 5 }
select the rows whose matches record is equal to 40 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; matches ; 40 } } ; 2 }
select the rows whose frequency mhz record is less than 100 . the number of such rows is 3 .
Output: eq { count { filter_less { all_rows ; frequency mhz ; 100 } } ; 3 }
select the rows whose podiums record is greater than 9 . there is only one such row in the table . the season record of this unqiue row is 2008 .
Output: and { only { filter_greater { all_rows ; podiums ; 9 } } ; eq { hop { filter_greater { all_rows ; podiums ; 9 } ; season } ; 2008 } }
select the rows whose home team score record is greater than or equal to 11 . for the crowd records of these rows , most of them are greater than 10000 .
Output: most_greater { filter_greater_eq { all_rows ; home team score ; 11 } ; crowd ; 10000 }
select the row whose score record of all rows is 2nd minimum . the player record of this row is billy casper .
Output: eq { hop { nth_argmin { all_rows ; score ; 2 } ; player } ; billy casper }
select the row whose opened record of all rows is maximum . the opened record of this row is 1969 .
Output: eq { hop { argmax { all_rows ; opened } ; opened } ; 1969 }
the 1st maximum total record of all rows is 85100 . the raion ( district ) or city record of the row with 1st maximum total record is city of izmayil .
Output: and { eq { nth_max { all_rows ; total ; 1 } ; 85100 } ; eq { hop { nth_argmax { all_rows ; total ; 1 } ; raion ( district ) or city } ; city of izmayil } }
select the rows whose round record is arbitrary . the number of such rows is 7 .
Output: eq { count { filter_all { all_rows ; round } } ; 7 }
select the rows whose written by record fuzzily matches to man of action . among these rows , select the rows whose directed by record fuzzily matches to sam montes . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; written by ; man of action } ; directed by ; sam montes } } ; 2 }
select the rows whose series result record fuzzily matches to drawn . the number of such rows is 7 .
Output: eq { count { filter_eq { all_rows ; series result ; drawn } } ; 7 }
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 .
Output: greater { hop { filter_eq { all_rows ; nation ; puerto rico } ; total } ; hop { filter_eq { all_rows ; nation ; dominican republic } ; total } }
the average of the draw record of all rows is 4.5 .
Output: round_eq { avg { all_rows ; draw } ; 4.5 }
select the rows whose winner record fuzzily matches to none . there is only one such row in the table . the season record of this unqiue row is 3 .
Output: and { only { filter_eq { all_rows ; winner ; none } } ; eq { hop { filter_eq { all_rows ; winner ; none } ; season } ; 3 } }
the average of the innings record of all rows is 22 .
Output: round_eq { avg { all_rows ; innings } ; 22 }
the average of the home team score record of all rows is 15.18 .
Output: round_eq { avg { all_rows ; home team score } ; 15.18 }
select the row whose week record of all rows is 6th minimum . the date record of this row is october 12 , 2008 .
Output: eq { hop { nth_argmin { all_rows ; week ; 6 } ; date } ; october 12 , 2008 }
select the rows whose venue record fuzzily matches to western oval . take the crowd record of this row . select the rows whose venue record fuzzily matches to victoria park . take the crowd record of this row . the first record is greater than the second record . the crowd record of the first row is 23000 . the crowd record of the second row is 17500 .
Output: and { greater { hop { filter_eq { all_rows ; venue ; western oval } ; crowd } ; hop { filter_eq { all_rows ; venue ; victoria park } ; crowd } } ; and { eq { hop { filter_eq { all_rows ; venue ; western oval } ; crowd } ; 23000 } ; eq { hop { filter_eq { all_rows ; venue ; victoria park } ; crowd } ; 17500 } } }
select the rows whose race winner record fuzzily matches to andrew pitt . among these rows , select the rows whose fastest lap record fuzzily matches to gianluca vizziello . there is only one such row in the table . the date record of this unqiue row is 27 april .
Output: and { only { filter_eq { filter_eq { all_rows ; race winner ; andrew pitt } ; fastest lap ; gianluca vizziello } } ; eq { hop { filter_eq { filter_eq { all_rows ; race winner ; andrew pitt } ; fastest lap ; gianluca vizziello } ; date } ; 27 april } }
select the rows whose executive record fuzzily matches to second . among these rows , select the rows whose first minister record fuzzily matches to ian paisley . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; executive ; second } ; first minister ; ian paisley } } ; 2 }
for the gold records of all rows , most of them are greater than or equal to 1 .
Output: most_greater_eq { all_rows ; gold ; 1 }
select the rows whose country record fuzzily matches to united states . for the score records of these rows , most of them are greater than 142 .
Output: most_greater { filter_eq { all_rows ; country ; united states } ; score ; 142 }
select the row whose gross capacity record of all rows is 3rd maximum . the unit record of this row is hamaoka - 3 .
Output: eq { hop { nth_argmax { all_rows ; gross capacity ; 3 } ; unit } ; hamaoka - 3 }
select the row whose hard discounters record of all rows is maximum . the country record of this row is spain .
Output: eq { hop { argmax { all_rows ; hard discounters } ; country } ; spain }
select the rows whose location record fuzzily matches to borisov . there is only one such row in the table . the team record of this unqiue row is bate .
Output: and { only { filter_eq { all_rows ; location ; borisov } } ; eq { hop { filter_eq { all_rows ; location ; borisov } ; team } ; bate } }
select the rows whose date record fuzzily matches to 2004 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; date ; 2004 } } ; 2 }
select the rows whose organization record fuzzily matches to delta zeta . take the founding date record of this row . select the rows whose organization record fuzzily matches to alpha gamma delta . take the founding date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; organization ; delta zeta } ; founding date } ; hop { filter_eq { all_rows ; organization ; alpha gamma delta } ; founding date } }
select the row whose time / retired record of all rows is maximum . the rider record of this row is dani pedrosa .
Output: eq { hop { argmax { all_rows ; time / retired } ; rider } ; dani pedrosa }
select the row whose attendance record of all rows is maximum . the opponent record of this row is college all - stars at chicago .
Output: eq { hop { argmax { all_rows ; attendance } ; opponent } ; college all - stars at chicago }
the average of the capacity record of all rows is 9037 .
Output: round_eq { avg { all_rows ; capacity } ; 9037 }
select the rows whose year record fuzzily matches to 1998 . take the result record of this row . select the rows whose year record fuzzily matches to 2010 . take the result record of this row . the first record fuzzily matches to the second record . the result record of the first row is champions . the result record of the second row is champions .
Output: and { eq { hop { filter_eq { all_rows ; year ; 1998 } ; result } ; hop { filter_eq { all_rows ; year ; 2010 } ; result } } ; and { eq { hop { filter_eq { all_rows ; year ; 1998 } ; result } ; champions } ; eq { hop { filter_eq { all_rows ; year ; 2010 } ; result } ; champions } } }
select the rows whose to par record is equal to -4 . there is only one such row in the table . the player record of this unqiue row is jim colbert .
Output: and { only { filter_eq { all_rows ; to par ; -4 } } ; eq { hop { filter_eq { all_rows ; to par ; -4 } ; player } ; jim colbert } }
select the rows whose against record is less than 10 . among these rows , select the rows whose status record fuzzily matches to tour match . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_less { all_rows ; against ; 10 } ; status ; tour match } } ; 2 }
for the writer records of all rows , all of them fuzzily match to mike bullen .
Output: all_eq { all_rows ; writer ; mike bullen }
select the row whose points record of all rows is maximum . the singer record of this row is olta boka .
Output: eq { hop { argmax { all_rows ; points } ; singer } ; olta boka }
select the row whose date record of all rows is 1st minimum . the championship record of this row is merion , us .
Output: eq { hop { nth_argmin { all_rows ; date ; 1 } ; championship } ; merion , us }
select the rows whose traction type record fuzzily matches to steam . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; traction type ; steam } } ; 2 }
for the cores records of all rows , all of them are equal to 4 .
Output: all_eq { all_rows ; cores ; 4 }
select the rows whose label record fuzzily matches to rock records . among these rows , select the rows whose release date record fuzzily matches to 2000 . there is only one such row in the table . the english title record of this unqiue row is courage .
Output: and { only { filter_eq { filter_eq { all_rows ; label ; rock records } ; release date ; 2000 } } ; eq { hop { filter_eq { filter_eq { all_rows ; label ; rock records } ; release date ; 2000 } ; english title } ; courage } }
the average of the points record of all rows is 19.91 .
Output: round_eq { avg { all_rows ; points } ; 19.91 }
the minimum date record of all rows is 27 january .
Output: eq { min { all_rows ; date } ; 27 january }
select the row whose no in season record of all rows is 2nd minimum . the written by record of this row is jeremy carver .
Output: eq { hop { nth_argmin { all_rows ; no in season ; 2 } ; written by } ; jeremy carver }
select the row whose points record of all rows is maximum . the team record of this row is corinthians .
Output: eq { hop { argmax { all_rows ; points } ; team } ; corinthians }
select the rows whose vacator record fuzzily matches to clifford p case ( r ) . take the reason for change record of this row . select the rows whose vacator record fuzzily matches to paul w shafer ( r ) . take the reason for change record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; vacator ; clifford p case ( r ) } ; reason for change } ; hop { filter_eq { all_rows ; vacator ; paul w shafer ( r ) } ; reason for change } }
for the treaty of cession records of all rows , most of them fuzzily match to 28 may 1956 .
Output: most_eq { all_rows ; treaty of cession ; 28 may 1956 }
the average of the number of clubs record of all rows is 13 .
Output: round_eq { avg { all_rows ; number of clubs } ; 13 }
select the rows whose total record is equal to 35994 . there is only one such row in the table . the department record of this unqiue row is la paz .
Output: and { only { filter_eq { all_rows ; total ; 35994 } } ; eq { hop { filter_eq { all_rows ; total ; 35994 } ; department } ; la paz } }
select the rows whose elevation ( m ) record is less than 3000 . among these rows , select the rows whose col ( m ) record is equal to 0 . there is only one such row in the table . the peak record of this unqiue row is mount ruapehu .
Output: and { only { filter_eq { filter_less { all_rows ; elevation ( m ) ; 3000 } ; col ( m ) ; 0 } } ; eq { hop { filter_eq { filter_less { all_rows ; elevation ( m ) ; 3000 } ; col ( m ) ; 0 } ; peak } ; mount ruapehu } }
select the row whose result record of all rows is maximum . the opponent record of this row is st louis rams .
Output: eq { hop { argmax { all_rows ; result } ; opponent } ; st louis rams }
select the rows whose telenovela record fuzzily matches to mentir para vivir . among these rows , select the rows whose series premiere record fuzzily matches to september 2 . there is only one such row in the table .
Output: only { filter_eq { filter_eq { all_rows ; telenovela ; mentir para vivir } ; series premiere ; september 2 } }
select the rows whose home record fuzzily matches to philadelphia . among these rows , select the rows whose attendance record is less than 19500 . the number of such rows is 4 .
Output: eq { count { filter_less { filter_eq { all_rows ; home ; philadelphia } ; attendance ; 19500 } } ; 4 }
select the rows whose position record fuzzily matches to left wing . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; position ; left wing } } ; 3 }
for the goals for records of all rows , most of them are greater than 50 .
Output: most_greater { all_rows ; goals for ; 50 }
select the rows whose authority record fuzzily matches to state integrated . there is only one such row in the table . the name record of this unqiue row is st joseph 's school .
Output: and { only { filter_eq { all_rows ; authority ; state integrated } } ; eq { hop { filter_eq { all_rows ; authority ; state integrated } ; name } ; st joseph 's school } }
select the row whose district record of all rows is 2nd maximum . the incumbent record of this row is christopher shays .
Output: eq { hop { nth_argmax { all_rows ; district ; 2 } ; incumbent } ; christopher shays }
select the rows whose downhill record is equal to - . there is only one such row in the table . the season record of this unqiue row is 1990 .
Output: and { only { filter_eq { all_rows ; downhill ; - } } ; eq { hop { filter_eq { all_rows ; downhill ; - } ; season } ; 1990 } }
select the row whose incorporated record of all rows is 2nd minimum . the company record of this row is airline allied services ltd .
Output: eq { hop { nth_argmin { all_rows ; incorporated ; 2 } ; company } ; airline allied services ltd }
select the rows whose player record fuzzily matches to tony lema . take the to par record of this row . select the rows whose player record fuzzily matches to johnny miller ( a ) . take the to par record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; player ; tony lema } ; to par } ; hop { filter_eq { all_rows ; player ; johnny miller ( a ) } ; to par } }
select the rows whose years record is equal to 1947 . there is only one such row in the table . the name record of this unqiue row is benito lorenzi .
Output: and { only { filter_eq { all_rows ; years ; 1947 } } ; eq { hop { filter_eq { all_rows ; years ; 1947 } ; name } ; benito lorenzi } }
select the row whose bronze record of all rows is maximum . the rank record of this row is 1 .
Output: eq { hop { argmax { all_rows ; bronze } ; rank } ; 1 }
the average of the attendance record of all rows is 28,610 .
Output: round_eq { avg { all_rows ; attendance } ; 28,610 }
select the rows whose w record is greater than 2 . the number of such rows is 2 .
Output: eq { count { filter_greater { all_rows ; w ; 2 } } ; 2 }
select the rows whose surface record fuzzily matches to clay . among these rows , select the rows whose partner record fuzzily matches to anna linkova . there is only one such row in the table . the date record of this unqiue row is august 7 , 1995 .
Output: and { only { filter_eq { filter_eq { all_rows ; surface ; clay } ; partner ; anna linkova } } ; eq { hop { filter_eq { filter_eq { all_rows ; surface ; clay } ; partner ; anna linkova } ; date } ; august 7 , 1995 } }
select the rows whose year founded record is greater than 2000 . the number of such rows is 3 .
Output: eq { count { filter_greater { all_rows ; year founded ; 2000 } } ; 3 }
select the rows whose nationality record fuzzily matches to united states . there is only one such row in the table . the athlete record of this unqiue row is shawn crawford .
Output: and { only { filter_eq { all_rows ; nationality ; united states } } ; eq { hop { filter_eq { all_rows ; nationality ; united states } ; athlete } ; shawn crawford } }
select the rows whose high points record fuzzily matches to gerald wallace . there is only one such row in the table . the date record of this unqiue row is february 8 .
Output: and { only { filter_eq { all_rows ; high points ; gerald wallace } } ; eq { hop { filter_eq { all_rows ; high points ; gerald wallace } ; date } ; february 8 } }
select the row whose date record of all rows is 2nd maximum . the tournament record of this row is makarska , croatia itf 75000 .
Output: eq { hop { nth_argmax { all_rows ; date ; 2 } ; tournament } ; makarska , croatia itf 75000 }
the average of the enrollment record of all rows is 772 .
Output: round_eq { avg { all_rows ; enrollment } ; 772 }
select the rows whose technology record fuzzily matches to wifi . there is only one such row in the table .
Output: only { filter_eq { all_rows ; technology ; wifi } }
select the row whose attendance record of all rows is maximum . the date record of this row is november 9 . the opponent record of this row is new york jets .
Output: and { eq { hop { argmax { all_rows ; attendance } ; date } ; november 9 } ; eq { hop { argmax { all_rows ; attendance } ; opponent } ; new york jets } }
select the rows whose game record is arbitrary . the number of such rows is 15 .
Output: eq { count { filter_all { all_rows ; game } } ; 15 }
select the rows whose school colors record fuzzily matches to white . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; school colors ; white } } ; 4 }
select the rows whose attendance record is greater than 50000 . among these rows , select the rows whose date record fuzzily matches to december . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_greater { all_rows ; attendance ; 50000 } ; date ; december } } ; 2 }
select the rows whose first member record fuzzily matches to john ivatt briscoe . take the election record of this row . select the rows whose first member record fuzzily matches to peter john locke king . take the election record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; first member ; john ivatt briscoe } ; election } ; hop { filter_eq { all_rows ; first member ; peter john locke king } ; election } }
select the rows whose nation record fuzzily matches to ecuador . take the 2011 ( imf ) record of this row . select the rows whose nation record fuzzily matches to paraguay . take the 2011 ( imf ) record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; nation ; ecuador } ; 2011 ( imf ) } ; hop { filter_eq { all_rows ; nation ; paraguay } ; 2011 ( imf ) } }
select the rows whose year record fuzzily matches to 1964 . take the points record of this row . select the rows whose year record fuzzily matches to 1963 . take the points record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; year ; 1964 } ; points } ; hop { filter_eq { all_rows ; year ; 1963 } ; points } }
select the rows whose method record fuzzily matches to draw . there is only one such row in the table . the opponent record of this unqiue row is yoko takahashi .
Output: and { only { filter_eq { all_rows ; method ; draw } } ; eq { hop { filter_eq { all_rows ; method ; draw } ; opponent } ; yoko takahashi } }
select the row whose years for grizzlies record of all rows is minimum . the player record of this row is bryant reeves .
Output: eq { hop { argmin { all_rows ; years for grizzlies } ; player } ; bryant reeves }
the sum of the earnings record of all rows is 7546842 .
Output: round_eq { sum { all_rows ; earnings } ; 7546842 }
for the result records of all rows , all of them fuzzily match to w .
Output: all_eq { all_rows ; result ; w }
the maximum date record of all rows is 31 may 2011 . the album record of the row with superlative date record is l'un n'empãªche pas l'autre .
Output: and { eq { max { all_rows ; date } ; 31 may 2011 } ; eq { hop { argmax { all_rows ; date } ; album } ; l'un n'empãªche pas l'autre } }
select the rows whose country record fuzzily matches to canada . there is only one such row in the table . the champion record of this unqiue row is jessica shepley .
Output: and { only { filter_eq { all_rows ; country ; canada } } ; eq { hop { filter_eq { all_rows ; country ; canada } ; champion } ; jessica shepley } }
select the rows whose winner record fuzzily matches to kiveton park . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; winner ; kiveton park } } ; 2 }
select the rows whose high assists record fuzzily matches to rondo . the number of such rows is 6 .
Output: eq { count { filter_eq { all_rows ; high assists ; rondo } } ; 6 }
select the rows whose title record fuzzily matches to wreck - it ralph . take the worldwide gross record of this row . select the rows whose title record fuzzily matches to the twilight saga : breaking dawn - part 2 . take the worldwide gross record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; title ; wreck - it ralph } ; worldwide gross } ; hop { filter_eq { all_rows ; title ; the twilight saga : breaking dawn - part 2 } ; worldwide gross } }
select the rows whose round of 16 record fuzzily matches to 15-8 . there is only one such row in the table . the athlete record of this unqiue row is alexander achten .
Output: and { only { filter_eq { all_rows ; round of 16 ; 15-8 } } ; eq { hop { filter_eq { all_rows ; round of 16 ; 15-8 } ; athlete } ; alexander achten } }
select the rows whose defensive record fuzzily matches to matt disher . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; defensive ; matt disher } } ; 3 }
select the row whose elevation ( m ) record of all rows is maximum . the peak record of this row is galdhøpiggen .
Output: eq { hop { argmax { all_rows ; elevation ( m ) } ; peak } ; galdhøpiggen }
select the row whose laps record of all rows is maximum . the year record of this row is 1948 .
Output: eq { hop { argmax { all_rows ; laps } ; year } ; 1948 }
select the row whose date won record of all rows is 4th maximum . the champion ( s ) record of this row is atomo & sonico .
Output: eq { hop { nth_argmax { all_rows ; date won ; 4 } ; champion ( s ) } ; atomo & sonico }
the average of the population served record of all rows is 4038 .
Output: round_eq { avg { all_rows ; population served } ; 4038 }
select the rows whose average record is less than 1.0 . select the row whose points record of these rows is maximum . the team record of this row is estudiantes de la plata .
Output: eq { hop { argmax { filter_less { all_rows ; average ; 1.0 } ; points } ; team } ; estudiantes de la plata }
select the row whose team number record of all rows is 3rd maximum . the team name record of this row is sap g33k .
Output: eq { hop { nth_argmax { all_rows ; team number ; 3 } ; team name } ; sap g33k }
select the rows whose first elected record is equal to 1882 . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; first elected ; 1882 } } ; 3 }
for the release date records of all rows , all of them fuzzily match to q1 , 2007 .
Output: all_eq { all_rows ; release date ; q1 , 2007 }
for the laid down records of all rows , most of them fuzzily match to february 1935 .
Output: most_eq { all_rows ; laid down ; february 1935 }
select the rows whose nationality record fuzzily matches to united states . among these rows , select the rows whose college / junior / club team ( league ) record fuzzily matches to university of notre dame . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; nationality ; united states } ; college / junior / club team ( league ) ; university of notre dame } } ; 2 }
select the rows whose competition record fuzzily matches to 2002 african cup of nations . take the date record of this row . select the rows whose competition record fuzzily matches to 2006 fifa world cup qualification . take the date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; competition ; 2002 african cup of nations } ; date } ; hop { filter_eq { all_rows ; competition ; 2006 fifa world cup qualification } ; date } }
select the rows whose content record fuzzily matches to programmi per adulti 24h / 24 . among these rows , select the rows whose television service record fuzzily matches to boy & boy . there is only one such row in the table . the n degree record of this unqiue row is 992 .
Output: and { only { filter_eq { filter_eq { all_rows ; content ; programmi per adulti 24h / 24 } ; television service ; boy & boy } } ; eq { hop { filter_eq { filter_eq { all_rows ; content ; programmi per adulti 24h / 24 } ; television service ; boy & boy } ; n degree } ; 992 } }
select the rows whose date record fuzzily matches to october . among these rows , select the rows whose result record fuzzily matches to w . the number of such rows is 3 .
Output: eq { count { filter_eq { filter_eq { all_rows ; date ; october } ; result ; w } } ; 3 }
for the title playoff records of all rows , most of them are equal to 0 .
Output: most_eq { all_rows ; title playoff ; 0 }
for the discipline records of all rows , most of them fuzzily match to stock car .
Output: most_eq { all_rows ; discipline ; stock car }
select the rows whose livery record fuzzily matches to dark blue . there is only one such row in the table . the number & name record of this unqiue row is no d4279 arthur wright .
Output: and { only { filter_eq { all_rows ; livery ; dark blue } } ; eq { hop { filter_eq { all_rows ; livery ; dark blue } ; number & name } ; no d4279 arthur wright } }
select the row whose original airdate record of all rows is maximum . the episode record of this row is 13 .
Output: eq { hop { argmax { all_rows ; original airdate } ; episode } ; 13 }
select the rows whose metres record is less than 200 . the number of such rows is 3 .
Output: eq { count { filter_less { all_rows ; metres ; 200 } } ; 3 }
select the row whose vessels record of all rows is 2nd maximum . the ship name record of this row is theofilos .
Output: eq { hop { nth_argmax { all_rows ; vessels ; 2 } ; ship name } ; theofilos }
select the rows whose performer 2 record is arbitrary . the number of such rows is 6 .
Output: eq { count { filter_all { all_rows ; performer 2 } } ; 6 }
select the rows whose driver record fuzzily matches to clint bowyer . take the average speed ( mph ) record of this row . select the rows whose driver record fuzzily matches to greg biffle . take the average speed ( mph ) record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; driver ; clint bowyer } ; average speed ( mph ) } ; hop { filter_eq { all_rows ; driver ; greg biffle } ; average speed ( mph ) } }
select the rows whose location record fuzzily matches to nijmegen . among these rows , select the rows whose traction type record fuzzily matches to electric . there is only one such row in the table . the date ( from ) record of this unqiue row is 4 june 1911 .
Output: and { only { filter_eq { filter_eq { all_rows ; location ; nijmegen } ; traction type ; electric } } ; eq { hop { filter_eq { filter_eq { all_rows ; location ; nijmegen } ; traction type ; electric } ; date ( from ) } ; 4 june 1911 } }
select the rows whose draw record is greater than 0 . the number of such rows is 3 .
Output: eq { count { filter_greater { all_rows ; draw ; 0 } } ; 3 }
select the rows whose player record fuzzily matches to mathew goggin . take the money record of this row . select the rows whose player record fuzzily matches to justin leonard . take the money record of this row . the first record is 66600 larger than the second record .
Output: eq { diff { hop { filter_eq { all_rows ; player ; mathew goggin } ; money } ; hop { filter_eq { all_rows ; player ; justin leonard } ; money } } ; 66600 }
select the rows whose weeks record is greater than 16 . there is only one such row in the table .
Output: only { filter_greater { all_rows ; weeks ; 16 } }
select the rows whose venue record fuzzily matches to away . the average of the attendance record of these rows is 1,692 .
Output: round_eq { avg { filter_eq { all_rows ; venue ; away } ; attendance } ; 1,692 }
select the row whose completion % record of all rows is maximum . the year record of this row is 2010 .
Output: eq { hop { argmax { all_rows ; completion % } ; year } ; 2010 }
select the row whose candidates record of all rows is 2nd maximum . the incumbent record of this row is elijah cummings .
Output: eq { hop { nth_argmax { all_rows ; candidates ; 2 } ; incumbent } ; elijah cummings }
select the row whose year record of all rows is 4th minimum . the song title record of this row is nee maatalo .
Output: eq { hop { nth_argmin { all_rows ; year ; 4 } ; song title } ; nee maatalo }
for the socialist records of all rows , most of them are greater than or equal to 42 % .
Output: most_greater_eq { all_rows ; socialist ; 42 % }
select the rows whose winning team record fuzzily matches to iowa state . the number of such rows is 8 .
Output: eq { count { filter_eq { all_rows ; winning team ; iowa state } } ; 8 }
select the rows whose 1st party record fuzzily matches to liberal . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; 1st party ; liberal } } ; 4 }