niryuu/sni-each-converted huggingface dataset:
most_eq { all_rows ; most spoken language ; sotho } = true
Output: for the most spoken language records of all rows , most of them fuzzily match to sotho .
eq { diff { hop { filter_eq { all_rows ; venue ; stuttgart , west germany } ; year } ; hop { filter_eq { all_rows ; venue ; seoul , south korea } ; year } } ; -2 years } = true
Output: select the rows whose venue record fuzzily matches to stuttgart , west germany . take the year record of this row . select the rows whose venue record fuzzily matches to seoul , south korea . take the year record of this row . the second record is 2 years larger than the first record .
most_eq { all_rows ; open cup ; did not qualify } = true
Output: for the open cup records of all rows , most of them fuzzily match to did not qualify .
eq { hop { nth_argmax { all_rows ; swing to gain ; 3 } ; constituency } ; ayr } = true
Output: select the row whose swing to gain record of all rows is 3rd maximum . the constituency record of this row is ayr .
and { eq { max { all_rows ; points } ; 78 } ; and { eq { hop { argmax { all_rows ; points } ; year } ; 1972 } ; eq { hop { argmax { all_rows ; points } ; class } ; 125cc } } } = true
Output: the maximum points record of all rows is 78 . the year record of the row with superlative points record is 1972 . the class record of the row with superlative points record is 125cc .
greater { hop { filter_eq { all_rows ; winner ; elio fox } ; prize money } ; hop { filter_eq { all_rows ; winner ; barry shulman } ; prize money } } = true
Output: select the rows whose winner record fuzzily matches to elio fox . take the prize money record of this row . select the rows whose winner record fuzzily matches to barry shulman . take the prize money record of this row . the first record is greater than the second record .
eq { count { filter_greater { all_rows ; economy rate ; 4.0 } } ; 4 } = true
Output: select the rows whose economy rate record is greater than 4.0 . the number of such rows is 4 .
eq { count { filter_eq { all_rows ; erp w ; 170 } } ; 4 } = true
Output: select the rows whose erp w record is equal to 170 . the number of such rows is 4 .
most_eq { all_rows ; planned release ; not yet } = true
Output: for the planned release records of all rows , most of them fuzzily match to not yet .
round_eq { sum { filter_eq { all_rows ; date ; october } ; attendance } ; 245,093 } = true
Output: select the rows whose date record fuzzily matches to october . the sum of the attendance record of these rows is 245,093 .
eq { hop { argmax { all_rows ; capacity } ; stadium } ; stadio san vito } = true
Output: select the row whose capacity record of all rows is maximum . the stadium record of this row is stadio san vito .
round_eq { avg { all_rows ; concacaf } ; 3.3 } = true
Output: the average of the concacaf record of all rows is 3.3 .
and { only { filter_eq { all_rows ; round ; 7th } } ; eq { hop { filter_eq { all_rows ; round ; 7th } ; player } ; jim norton } } = true
Output: select the rows whose round record fuzzily matches to 7th . there is only one such row in the table . the player record of this unqiue row is jim norton .
eq { count { filter_eq { all_rows ; position ; c } } ; 2 } = true
Output: select the rows whose position record fuzzily matches to c . the number of such rows is 2 .
eq { count { filter_eq { all_rows ; visitor ; colorado } } ; 3 } = true
Output: select the rows whose visitor record fuzzily matches to colorado . the number of such rows is 3 .
eq { count { filter_all { all_rows ; circuit } } ; 5 } = true
Output: select the rows whose circuit record is arbitrary . the number of such rows is 5 .
eq { diff { hop { filter_eq { all_rows ; name of rivalry ; auburn - tennessee football rivalry } ; games played } ; hop { filter_eq { all_rows ; name of rivalry ; auburn - clemson rivalry } ; games played } } ; 3 } = true
Output: select the rows whose name of rivalry record fuzzily matches to auburn  tennessee football rivalry . take the games played record of this row . select the rows whose name of rivalry record fuzzily matches to auburn  clemson rivalry . take the games played record of this row . the first record is 3 larger than the second record .
round_eq { sum { all_rows ; wins } ; 286 } = true
Output: the sum of the wins record of all rows is 286 .
eq { count { filter_eq { all_rows ; original air date ; february 2010 } } ; 3 } = true
Output: select the rows whose original air date record fuzzily matches to february 2010 . the number of such rows is 3 .
eq { count { filter_eq { filter_less { all_rows ; year completed ; 2000 } ; floors ( stories ) ; 12 } } ; 2 } = true
Output: select the rows whose year completed record is less than 2000 . among these rows , select the rows whose floors  stories  record is equal to 12 . the number of such rows is 2 .
eq { count { filter_eq { all_rows ; lost ; 5 } } ; 2 } = true
Output: select the rows whose lost record is equal to 5 . the number of such rows is 2 .
eq { hop { argmax { all_rows ; enrollment 08 - 09 } ; school } ; twin lakes } = true
Output: select the row whose enrollment 08  09 record of all rows is maximum . the school record of this row is twin lakes .
eq { count { filter_greater_eq { all_rows ; founded ; 1980 } } ; 3 } = true
Output: select the rows whose founded record is greater than or equal to 1980 . the number of such rows is 3 .
most_greater { filter_eq { all_rows ; directed by ; paul holahan } ; us viewers ( millions ) ; 4.5 } = true
Output: select the rows whose directed by record fuzzily matches to paul holahan . for the us viewers  millions  records of these rows , most of them are greater than 4.5 .
less { hop { filter_eq { all_rows ; name ; yogyakarta documentary film festival } ; est } ; hop { filter_eq { all_rows ; name ; vibgyor international film festival } ; est } } = true
Output: select the rows whose name record fuzzily matches to yogyakarta documentary film festival . take the est record of this row . select the rows whose name record fuzzily matches to vibgyor international film festival . take the est record of this row . the first record is less than the second record .
eq { count { filter_eq { all_rows ; director ; edward m abroms } } ; 3 } = true
Output: select the rows whose director record fuzzily matches to edward m abroms . the number of such rows is 3 .
eq { hop { nth_argmax { all_rows ; launch date ; 2 } ; name } ; corot } = true
Output: select the row whose launch date record of all rows is 2nd maximum . the name record of this row is corot .
most_less { filter_greater { all_rows ; gold ; 0 } ; total ; 10 } = true
Output: select the rows whose gold record is greater than 0 . for the total records of these rows , most of them are less than 10 .
and { only { filter_eq { filter_eq { all_rows ; rank points ; 8 } ; total ; 17 } } ; eq { hop { filter_eq { filter_eq { all_rows ; rank points ; 8 } ; total ; 17 } ; shooter } ; iulian raicea ( rou ) } } = true
Output: select the rows whose rank points record is equal to 8 . among these rows , select the rows whose total record is equal to 17 . there is only one such row in the table . the shooter record of this unqiue row is iulian raicea  rou  .
greater { hop { filter_eq { all_rows ; battling partners ; donald bradman and sid barnes } ; runs } ; hop { filter_eq { all_rows ; battling partners ; pat symcox and mark boucher } ; runs } } = true
Output: select the rows whose battling partners record fuzzily matches to donald bradman and sid barnes . take the runs record of this row . select the rows whose battling partners record fuzzily matches to pat symcox and mark boucher . take the runs record of this row . the first record is greater than the second record .
eq { hop { argmax { all_rows ; enrollment } ; location } ; cleveland , tennessee } = true
Output: select the row whose enrollment record of all rows is maximum . the location record of this row is cleveland , tennessee .
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 } = true
Output: 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 .
greater { hop { filter_eq { all_rows ; player ; ben handlogten } ; years for jazz } ; hop { filter_eq { all_rows ; player ; bobby hansen } ; years for jazz } } = true
Output: select the rows whose player record fuzzily matches to ben handlogten . take the years for jazz record of this row . select the rows whose player record fuzzily matches to bobby hansen . take the years for jazz record of this row . the first record is greater than the second record .
and { eq { hop { argmax { all_rows ; crowd } ; home team } ; st kilda } ; eq { hop { argmax { all_rows ; crowd } ; away team } ; melbourne } } = true
Output: select the row whose crowd record of all rows is maximum . the home team record of this row is st kilda . the away team record of this row is melbourne .
eq { count { filter_eq { all_rows ; score ; 145 } } ; 4 } = true
Output: select the rows whose score record fuzzily matches to 145 . the number of such rows is 4 .
eq { hop { nth_argmax { all_rows ; season premiere ; 2 } ; ranking } ; 67th } = true
Output: select the row whose season premiere record of all rows is 2nd maximum . the ranking record of this row is 67th .
round_eq { sum { filter_eq { all_rows ; location attendance ; united center } ; location attendance } ; 226050 } = true
Output: select the rows whose location attendance record fuzzily matches to united center . the sum of the location attendance record of these rows is 226050 .
only { filter_eq { all_rows ; ostrich ; egyptian goose } } = true
Output: select the rows whose ostrich record fuzzily matches to egyptian goose . there is only one such row in the table .
eq { hop { argmax { all_rows ; average ratings } ; romaji title } ; densha otoko } = true
Output: select the row whose average ratings record of all rows is maximum . the romaji title record of this row is densha otoko .
round_eq { sum { all_rows ; championships } ; 1 } = true
Output: the sum of the championships record of all rows is 1 .
less { hop { filter_eq { all_rows ; guest host ; pamela anderson } ; air date } ; hop { filter_eq { all_rows ; guest host ; mark ronson } ; air date } } = true
Output: select the rows whose guest host record fuzzily matches to pamela anderson . take the air date record of this row . select the rows whose guest host record fuzzily matches to mark ronson . take the air date record of this row . the first record is less than the second record .
only { filter_eq { all_rows ; origin ; european union } } = true
Output: select the rows whose origin record fuzzily matches to european union . there is only one such row in the table .
and { only { filter_eq { all_rows ; country ; brazil } } ; eq { hop { filter_eq { all_rows ; country ; brazil } ; player } ; angela park } } = true
Output: select the rows whose country record fuzzily matches to brazil . there is only one such row in the table . the player record of this unqiue row is angela park .
and { only { filter_eq { all_rows ; location ; borisov } } ; eq { hop { filter_eq { all_rows ; location ; borisov } ; team } ; bate } } = true
Output: 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 .
all_eq { all_rows ; played ; 34 } = true
Output: for the played records of all rows , all of them are equal to 34 .
round_eq { sum { all_rows ; gold } ; 26 } = true
Output: the sum of the gold record of all rows is 26 .
round_eq { avg { all_rows ; races } ; 14.4 } = true
Output: the average of the races record of all rows is 14.4 .
and { only { filter_greater { filter_eq { all_rows ; year ; 2008 } ; length ; 5:00 } } ; eq { hop { filter_greater { filter_eq { all_rows ; year ; 2008 } ; length ; 5:00 } ; music video } ; umbrella ( feat younha ) ( 우산 ) } } = true
Output: select the rows whose year record is equal to 2008 . among these rows , select the rows whose length record is greater than 5:00 . there is only one such row in the table . the music video record of this unqiue row is umbrella  feat younha   우산  .
eq { count { filter_eq { all_rows ; 1st party ; liberal } } ; 4 } = true
Output: select the rows whose 1st party record fuzzily matches to liberal . the number of such rows is 4 .
eq { hop { argmax { all_rows ; population } ; official name } ; quispamsis } = true
Output: select the row whose population record of all rows is maximum . the official name record of this row is quispamsis .
and { eq { nth_min { all_rows ; round ; 3 } ; 3 } ; eq { hop { nth_argmin { all_rows ; round ; 3 } ; circuit } ; wakefield park } } = true
Output: the 3rd minimum round record of all rows is 3 . the circuit record of the row with 3rd minimum round record is wakefield park .
eq { count { filter_less { filter_less { all_rows ; away team score ; 20 } ; crowd ; 20000 } } ; 3 } = true
Output: select the rows whose away team score record is less than 20 . among these rows , select the rows whose crowd record is less than 20000 . the number of such rows is 3 .
eq { count { filter_eq { all_rows ; result ; re-elected } } ; 2 } = true
Output: select the rows whose result record fuzzily matches to reelected . the number of such rows is 2 .
most_eq { all_rows ; clairon ; christa ludwig } = true
Output: for the clairon records of all rows , most of them fuzzily match to christa ludwig .
eq { count { filter_eq { all_rows ; high points ; al harrington } } ; 4 } = true
Output: select the rows whose high points record fuzzily matches to al harrington . the number of such rows is 4 .
eq { count { filter_all { all_rows ; player } } ; 11 } = true
Output: select the rows whose player record is arbitrary . the number of such rows is 11 .
eq { count { filter_eq { all_rows ; college / junior / club team ; calgary centennials } } ; 2 } = true
Output: select the rows whose college  junior  club team record fuzzily matches to calgary centennials . the number of such rows is 2 .
and { only { filter_eq { all_rows ; year ; 2005 } } ; eq { hop { filter_eq { all_rows ; year ; 2005 } ; group } ; method fest independent film festival } } = true
Output: select the rows whose year record is equal to 2005 . there is only one such row in the table . the group record of this unqiue row is method fest independent film festival .
eq { hop { nth_argmax { all_rows ; original air date ; 2 } ; production code } ; 407 } = true
Output: select the row whose original air date record of all rows is 2nd maximum . the production code record of this row is 407 .
greater { hop { filter_eq { all_rows ; opponent ; yukiya naito } ; round } ; hop { filter_eq { all_rows ; opponent ; maxim tarasov } ; round } } = true
Output: select the rows whose opponent record fuzzily matches to yukiya naito . take the round record of this row . select the rows whose opponent record fuzzily matches to maxim tarasov . take the round record of this row . the first record is greater than the second record .
eq { hop { nth_argmax { all_rows ; silver ; 2 } ; nation } ; kazakhstan } = true
Output: select the row whose silver record of all rows is 2nd maximum . the nation record of this row is kazakhstan .
all_eq { all_rows ; district ; srem } = true
Output: for the district records of all rows , all of them fuzzily match to srem .
eq { count { filter_not_eq { all_rows ; round of 16 ; did not advance } } ; 4 } = true
Output: select the rows whose round of 16 record does not match to did not advance . the number of such rows is 4 .
eq { count { filter_eq { all_rows ; innings ; 8 } } ; 3 } = true
Output: select the rows whose innings record is equal to 8 . the number of such rows is 3 .
round_eq { avg { all_rows ; rest mass mev / c 2 } ; 1352 } = true
Output: the average of the rest mass mev  c 2 record of all rows is 1352 .
greater { hop { filter_eq { all_rows ; english title ; sergey lazo } ; length } ; hop { filter_eq { all_rows ; english title ; sour grape } ; length } } = true
Output: select the rows whose english title record fuzzily matches to sergey lazo . take the length record of this row . select the rows whose english title record fuzzily matches to sour grape . take the length record of this row . the first record is greater than the second record .
round_eq { avg { all_rows ; against } ; 11.6 } = true
Output: the average of the against record of all rows is 11.6 .
eq { count { filter_eq { all_rows ; 2nd place team ; nitehawks } } ; 2 } = true
Output: select the rows whose 2nd place team record fuzzily matches to nitehawks . the number of such rows is 2 .
eq { hop { nth_argmax { all_rows ; slalom ; 3 } ; season } ; 1996 } = true
Output: select the row whose slalom record of all rows is 3rd maximum . the season record of this row is 1996 .
eq { hop { nth_argmax { all_rows ; record ; 2 } ; year } ; 1979 } = true
Output: select the row whose record record of all rows is 2nd maximum . the year record of this row is 1979 .
eq { hop { nth_argmin { filter_eq { all_rows ; engine ; 5.7 l hemi v8 } ; years ; 1 } ; notes } ; limited , overland } = true
Output: select the rows whose engine record fuzzily matches to 5.7 l hemi v8 . select the row whose years record of these rows is 1st minimum . the notes record of this row is limited , overland .
and { only { filter_eq { all_rows ; school / club team ; penn state } } ; eq { hop { filter_eq { all_rows ; school / club team ; penn state } ; player } ; john amaechi } } = true
Output: select the rows whose school  club team record fuzzily matches to penn state . there is only one such row in the table . the player record of this unqiue row is john amaechi .
and { eq { max { all_rows ; attendance } ; 19567 } ; eq { hop { argmax { all_rows ; attendance } ; opponent } ; philadelphia flyers } } = true
Output: the maximum attendance record of all rows is 19567 . the opponent record of the row with superlative attendance record is philadelphia flyers .
greater { hop { filter_eq { all_rows ; name of ground ; chester road north ground } ; worcs la matches } ; hop { filter_eq { all_rows ; name of ground ; racecourse ground } ; worcs la matches } } = true
Output: select the rows whose name of ground record fuzzily matches to chester road north ground . take the worcs la matches record of this row . select the rows whose name of ground record fuzzily matches to racecourse ground . take the worcs la matches record of this row . the first record is greater than the second record .
round_eq { avg { all_rows ; physical } ; 22 } = true
Output: the average of the physical record of all rows is 22 .
eq { count { filter_eq { all_rows ; tournament mvp ; sean elliott , arizona } } ; 2 } = true
Output: select the rows whose tournament mvp record fuzzily matches to sean elliott , arizona . the number of such rows is 2 .
eq { hop { argmax { all_rows ; population } ; island } ; bute } = true
Output: select the row whose population record of all rows is maximum . the island record of this row is bute .
round_eq { avg { filter_eq { all_rows ; constructor ; cooper-maserati } ; laps } ; 64 } = true
Output: select the rows whose constructor record fuzzily matches to coopermaserati . the average of the laps record of these rows is 64 .
and { only { filter_eq { all_rows ; nationality ; serbia } } ; eq { hop { filter_eq { all_rows ; nationality ; serbia } ; player } ; aleksandar radojeviä ‡ } } = true
Output: select the rows whose nationality record fuzzily matches to serbia . there is only one such row in the table . the player record of this unqiue row is aleksandar radojeviä ‡ .
and { only { filter_eq { filter_eq { all_rows ; region ; united states } ; format ; cd } } ; eq { hop { filter_eq { filter_eq { all_rows ; region ; united states } ; format ; cd } ; date } ; june 8 , 2004 } } = true
Output: select the rows whose region record fuzzily matches to united states . among these rows , select the rows whose format record fuzzily matches to cd . there is only one such row in the table . the date record of this unqiue row is june 8 , 2004 .
and { only { filter_eq { all_rows ; opponent ; tampa bay buccaneers } } ; eq { hop { filter_eq { all_rows ; opponent ; tampa bay buccaneers } ; date } ; september 26 , 1999 } } = true
Output: select the rows whose opponent record fuzzily matches to tampa bay buccaneers . there is only one such row in the table . the date record of this unqiue row is september 26 , 1999 .
and { only { filter_less { all_rows ; population ( 2010 census ) ; 5000 } } ; eq { hop { filter_less { all_rows ; population ( 2010 census ) ; 5000 } ; district } ; intramuros } } = true
Output: select the rows whose population  2010 census  record is less than 5000 . there is only one such row in the table . the district record of this unqiue row is intramuros .
most_eq { all_rows ; erp kw ; 60 } = true
Output: for the erp kw records of all rows , most of them are equal to 60 .
round_eq { sum { filter_greater_eq { all_rows ; week ; 13 } ; attendance } ; 95326 } = true
Output: select the rows whose week record is greater than or equal to 13 . the sum of the attendance record of these rows is 95326 .
and { only { filter_eq { all_rows ; object type ; irregular galaxy } } ; and { eq { hop { filter_eq { all_rows ; object type ; irregular galaxy } ; ngc number } ; 6240 } ; eq { hop { filter_eq { all_rows ; object type ; irregular galaxy } ; constellation } ; ophiuchus } } } = true
Output: select the rows whose object type record fuzzily matches to irregular galaxy . there is only one such row in the table . the ngc number record of this unqiue row is 6240 . the constellation record of this unqiue row is ophiuchus .
and { only { filter_eq { all_rows ; school / club team ; st john 's } } ; eq { hop { filter_eq { all_rows ; school / club team ; st john 's } ; player } ; mark jackson } } = true
Output: select the rows whose school  club team record fuzzily matches to st john s . there is only one such row in the table . the player record of this unqiue row is mark jackson .
most_eq { all_rows ; class ; d } = true
Output: for the class records of all rows , most of them fuzzily match to d .
greater { hop { filter_eq { all_rows ; urban settlement ; sremska kamenica } ; population ( 2002 ) } ; hop { filter_eq { all_rows ; urban settlement ; sremski karlovci } ; population ( 2002 ) } } = true
Output: select the rows whose urban settlement record fuzzily matches to sremska kamenica . take the population  2002  record of this row . select the rows whose urban settlement record fuzzily matches to sremski karlovci . take the population  2002  record of this row . the first record is greater than the second record .
round_eq { avg { all_rows ; attendance } ; 54939 } = true
Output: the average of the attendance record of all rows is 54939 .
eq { count { filter_greater { all_rows ; us rap ; 0 } } ; 3 } = true
Output: select the rows whose us rap record is greater than 0 . the number of such rows is 3 .
eq { hop { nth_argmax { all_rows ; year ; 2 } ; new jersey } ; paramus ll paramus } = true
Output: select the row whose year record of all rows is 2nd maximum . the new jersey record of this row is paramus ll paramus .
eq { hop { nth_argmax { all_rows ; year ; 2 } ; category } ; favorite tv actress - leading role in a drama } = true
Output: select the row whose year record of all rows is 2nd maximum . the category record of this row is favorite tv actress  leading role in a drama .
less { hop { filter_eq { all_rows ; host team ; san diego chargers } ; final score } ; hop { filter_eq { all_rows ; host team ; philadelphia eagles } ; final score } } = true
Output: select the rows whose host team record fuzzily matches to san diego chargers . take the final score record of this row . select the rows whose host team record fuzzily matches to philadelphia eagles . take the final score record of this row . the first record is less than the second record .
eq { hop { nth_argmin { all_rows ; enrollment ; 2 } ; institution } ; philander smith college } = true
Output: select the row whose enrollment record of all rows is 2nd minimum . the institution record of this row is philander smith college .
eq { hop { argmax { all_rows ; innings } ; player } ; ricky ponting } = true
Output: select the row whose innings record of all rows is maximum . the player record of this row is ricky ponting .
eq { hop { nth_argmax { all_rows ; win / loss ; 4 } ; season } ; 2006 } = true
Output: select the row whose win  loss record of all rows is 4th maximum . the season record of this row is 2006 .
only { filter_eq { all_rows ; high points ; charlie villanueva } } = true
Output: select the rows whose high points record fuzzily matches to charlie villanueva . there is only one such row in the table .
round_eq { sum { filter_eq { all_rows ; name ; jack riley } ; games } ; 375 } = true
Output: select the rows whose name record fuzzily matches to jack riley . the sum of the games record of these rows is 375 .
round_eq { avg { all_rows ; crowd } ; 14924 } = true
Output: the average of the crowd record of all rows is 14924 .
most_greater_eq { all_rows ; championships ; 1 } = true
Output: for the championships records of all rows , most of them are greater than or equal to 1 .
eq { count { filter_eq { all_rows ; result ; t } } ; 2 } = true
Output: select the rows whose result record fuzzily matches to t . the number of such rows is 2 .
eq { hop { argmax { filter_eq { all_rows ; gold ; 3 } ; silver } ; nation } ; china } = true
Output: select the rows whose gold record is equal to 3 . select the row whose silver record of these rows is maximum . the nation record of this row is china .
eq { count { filter_eq { all_rows ; team ; lietuvos rytas vilnius } } ; 2 } = true
Output: select the rows whose team record fuzzily matches to lietuvos rytas vilnius . the number of such rows is 2 .
and { only { filter_eq { all_rows ; location ; seoul , south korea } } ; eq { hop { filter_eq { all_rows ; location ; seoul , south korea } ; event } ; hero 's 2005 in seoul } } = true
Output: select the rows whose location record fuzzily matches to seoul , south korea . there is only one such row in the table . the event record of this unqiue row is hero s 2005 in seoul .
eq { count { filter_eq { all_rows ; wins ; 2 } } ; 2 } = true
Output: select the rows whose wins record is equal to 2 . the number of such rows is 2 .
less { hop { filter_eq { all_rows ; player ; jon gott } ; pick } ; hop { filter_eq { all_rows ; player ; paul woldu } ; pick } } = true
Output: select the rows whose player record fuzzily matches to jon gott . take the pick record of this row . select the rows whose player record fuzzily matches to paul woldu . take the pick record of this row . the first record is less than the second record .
eq { count { filter_eq { filter_greater { all_rows ; season ; 2005 } ; teams ; 16 } } ; 4 } = true
Output: select the rows whose season record is greater than 2005 . among these rows , select the rows whose teams record is equal to 16 . the number of such rows is 4 .
eq { hop { argmax { all_rows ; ios } ; source } ; wikimedia } = true
Output: select the row whose ios record of all rows is maximum . the source record of this row is wikimedia .
eq { hop { nth_argmin { all_rows ; overall ; 3 } ; name } ; james hamilton } = true
Output: select the row whose overall record of all rows is 3rd minimum . the name record of this row is james hamilton .
eq { hop { nth_argmin { all_rows ; year ; 2 } ; series title } ; dragon laws ii : kidnapped } = true
Output: select the row whose year record of all rows is 2nd minimum . the series title record of this row is dragon laws ii : kidnapped .
and { only { filter_eq { all_rows ; country ; spain } } ; eq { hop { filter_eq { all_rows ; country ; spain } ; player } ; seve ballesteros } } = true
Output: select the rows whose country record fuzzily matches to spain . there is only one such row in the table . the player record of this unqiue row is seve ballesteros .
less { hop { filter_eq { all_rows ; institution ; university of leicester } ; established } ; hop { filter_eq { all_rows ; institution ; university of east anglia } ; established } } = true
Output: select the rows whose institution record fuzzily matches to university of leicester . take the established record of this row . select the rows whose institution record fuzzily matches to university of east anglia . take the established record of this row . the first record is less than the second record .
and { only { filter_eq { filter_eq { all_rows ; displacement ; 1242 cc } ; torque ; at2500 rpm } } ; eq { hop { filter_eq { filter_eq { all_rows ; displacement ; 1242 cc } ; torque ; at2500 rpm } ; engine } ; 1.2 8v sohc } } = true
Output: select the rows whose displacement record fuzzily matches to 1242 cc . among these rows , select the rows whose torque record fuzzily matches to at2500 rpm . there is only one such row in the table . the engine record of this unqiue row is 1.2 8v sohc .
eq { diff { hop { filter_eq { all_rows ; game ; 1 } ; date } ; hop { filter_eq { all_rows ; game ; 2 } ; date } } ; -2 } = true
Output: select the rows whose game record fuzzily matches to 1 . take the date record of this row . select the rows whose game record fuzzily matches to 2 . take the date record of this row . the second record is 2 larger than the first record .
eq { hop { nth_argmin { all_rows ; year ; 14 } ; final position - vuelta } ; 66 } = true
Output: select the row whose year record of all rows is 14th minimum . the final position  vuelta record of this row is 66 .
eq { count { filter_eq { filter_eq { all_rows ; region ; east } ; state ; virginia } } ; 2 } = true
Output: select the rows whose region record fuzzily matches to east . among these rows , select the rows whose state record fuzzily matches to virginia . the number of such rows is 2 .
eq { count { filter_eq { filter_eq { all_rows ; surface ; hard } ; date ; 2007 } } ; 2 } = true
Output: select the rows whose surface record fuzzily matches to hard . among these rows , select the rows whose date record fuzzily matches to 2007 . the number of such rows is 2 .
round_eq { sum { all_rows ; yards } ; 178 } = true
Output: the sum of the yards record of all rows is 178 .
eq { hop { nth_argmin { all_rows ; pick ; 1 } ; nationality } ; canada } = true
Output: select the row whose pick record of all rows is 1st minimum . the nationality record of this row is canada .
eq { count { filter_eq { all_rows ; directed by ; adam bernstein } } ; 2 } = true
Output: select the rows whose directed by record fuzzily matches to adam bernstein . the number of such rows is 2 .
round_eq { sum { all_rows ; intake } ; 505 } = true
Output: the sum of the intake record of all rows is 505 .
eq { max { filter_eq { all_rows ; match points ; 2 - 2 } ; points margin } ; 20 } = true
Output: select the rows whose match points record fuzzily matches to 2  2 . the maximum points margin record of these rows is 20 .
all_eq { all_rows ; date ; 23 july } = true
Output: for the date records of all rows , all of them fuzzily match to 23 july .
only { filter_eq { all_rows ; class ; a } } = true
Output: select the rows whose class record fuzzily matches to a . there is only one such row in the table .
eq { count { filter_eq { all_rows ; no of barangays ; 11 } } ; 2 } = true
Output: select the rows whose no of barangays record is equal to 11 . the number of such rows is 2 .
eq { count { filter_eq { all_rows ; result ; lose } } ; 5 } = true
Output: select the rows whose result record fuzzily matches to lose . the number of such rows is 5 .
eq { hop { nth_argmax { all_rows ; 1 euro = ; 2 } ; currency } ; colombian peso ( cop ) } = true
Output: select the row whose 1 euro = record of all rows is 2nd maximum . the currency record of this row is colombian peso  cop  .
greater { hop { filter_eq { all_rows ; season ; 2 } ; potential prize money } ; hop { filter_eq { all_rows ; season ; 1 } ; potential prize money } } = true
Output: select the rows whose season record fuzzily matches to 2 . take the potential prize money record of this row . select the rows whose season record fuzzily matches to 1 . take the potential prize money record of this row . the first record is greater than the second record .
less { hop { filter_eq { all_rows ; school ; iit bombay } ; rank 06 - 10 } ; hop { filter_eq { all_rows ; school ; iit madras } ; rank 06 - 10 } } = true
Output: select the rows whose school record fuzzily matches to iit bombay . take the rank 06  10 record of this row . select the rows whose school record fuzzily matches to iit madras . take the rank 06  10 record of this row . the first record is less than the second record .
eq { count { filter_eq { all_rows ; original air date ; august } } ; 5 } = true
Output: select the rows whose original air date record fuzzily matches to august . the number of such rows is 5 .
and { only { filter_eq { all_rows ; time ; boat under weight } } ; eq { hop { filter_eq { all_rows ; time ; boat under weight } ; country } ; canada } } = true
Output: select the rows whose time record fuzzily matches to boat under weight . there is only one such row in the table . the country record of this unqiue row is canada .
eq { count { filter_eq { all_rows ; game site ; mile high stadium } } ; 7 } = true
Output: select the rows whose game site record fuzzily matches to mile high stadium . the number of such rows is 7 .
eq { min { filter_eq { all_rows ; game site ; forbes field } ; date } ; sunday september 20 } = true
Output: select the rows whose game site record fuzzily matches to forbes field . the minimum date record of these rows is sunday september 20 .
eq { hop { nth_argmax { all_rows ; location attendance ; 1 } ; team } ; washington } = true
Output: select the row whose location attendance record of all rows is 1st maximum . the team record of this row is washington .
most_eq { all_rows ; league ; segunda división b } = true
Output: for the league records of all rows , most of them fuzzily match to segunda división b .
eq { hop { argmax { all_rows ; wins } ; series } ; italian formula three } = true
Output: select the row whose wins record of all rows is maximum . the series record of this row is italian formula three .
eq { hop { argmax { all_rows ; runs } ; battling team } ; sri lanka } = true
Output: select the row whose runs record of all rows is maximum . the battling team record of this row is sri lanka .
and { eq { nth_min { all_rows ; pos ; 1 } ; 1 } ; eq { hop { nth_argmin { all_rows ; pos ; 1 } ; diff } ; + 176 } } = true
Output: the 1st minimum pos record of all rows is 1 . the diff record of the row with 1st minimum pos record is + 176 .
eq { hop { nth_argmax { all_rows ; erp / power w ; 2 } ; call sign } ; wrli - fm } = true
Output: select the row whose erp  power w record of all rows is 2nd maximum . the call sign record of this row is wrli  fm .
eq { hop { argmin { all_rows ; born } ; archbishop } ; jean baptiste lamy } = true
Output: select the row whose born record of all rows is minimum . the archbishop record of this row is jean baptiste lamy .
eq { count { filter_eq { all_rows ; winner 's share ; 8400 } } ; 3 } = true
Output: select the rows whose winner s share record is equal to 8400 . the number of such rows is 3 .
eq { count { filter_eq { all_rows ; games ; 74 } } ; 4 } = true
Output: select the rows whose games record is equal to 74 . the number of such rows is 4 .
eq { count { filter_eq { all_rows ; assists ; 40 } } ; 13 } = true
Output: select the rows whose assists record is equal to 40 . the number of such rows is 13 .
and { only { filter_eq { all_rows ; manner of departure ; resigned } } ; eq { hop { filter_eq { all_rows ; manner of departure ; resigned } ; outgoing manager } ; thomas doll } } = true
Output: select the rows whose manner of departure record fuzzily matches to resigned . there is only one such row in the table . the outgoing manager record of this unqiue row is thomas doll .
eq { count { filter_eq { all_rows ; time / retired ; +1 lap } } ; 3 } = true
Output: select the rows whose time  retired record fuzzily matches to +1 lap . the number of such rows is 3 .
eq { hop { nth_argmin { all_rows ; year built ; 2 } ; church name } ; dale kyrkje } = true
Output: select the row whose year built record of all rows is 2nd minimum . the church name record of this row is dale kyrkje .
all_eq { all_rows ; date ; 198 } = true
Output: for the date records of all rows , all of them fuzzily match to 198 .
most_greater { all_rows ; hk viewers ; 2 } = true
Output: for the hk viewers records of all rows , most of them are greater than 2 .
round_eq { avg { all_rows ; races } ; 8.73 } = true
Output: the average of the races record of all rows is 8.73 .
most_eq { all_rows ; pick ; 6 } = true
Output: for the pick records of all rows , most of them are equal to 6 .
greater { hop { filter_eq { all_rows ; location ; minsk } ; capacity } ; hop { filter_eq { all_rows ; location ; lida } ; capacity } } = true
Output: select the rows whose location record fuzzily matches to minsk . take the capacity record of this row . select the rows whose location record fuzzily matches to lida . take the capacity record of this row . the first record is greater than the second record .
and { only { filter_eq { all_rows ; manner of departure ; gardening leave 1 } } ; eq { hop { filter_eq { all_rows ; manner of departure ; gardening leave 1 } ; club } ; kedah fa } } = true
Output: select the rows whose manner of departure record fuzzily matches to gardening leave 1 . there is only one such row in the table . the club record of this unqiue row is kedah fa .
eq { count { filter_eq { all_rows ; top - 5 ; 0 } } ; 2 } = true
Output: select the rows whose top  5 record is equal to 0 . the number of such rows is 2 .
most_eq { all_rows ; final ; did not advance } = true
Output: for the final records of all rows , most of them fuzzily match to did not advance .
and { only { filter_eq { all_rows ; directed by ; tony goldwyn } } ; eq { hop { filter_eq { all_rows ; directed by ; tony goldwyn } ; title } ; in which sam receives an unexpected visitor } } = true
Output: select the rows whose directed by record fuzzily matches to tony goldwyn . there is only one such row in the table . the title record of this unqiue row is in which sam receives an unexpected visitor .
eq { hop { nth_argmin { all_rows ; no ; 2 } ; player } ; derrick favors } = true
Output: select the row whose no record of all rows is 2nd minimum . the player record of this row is derrick favors .
eq { count { filter_all { all_rows ; poison / klesha } } ; 5 } = true
Output: select the rows whose poison  klesha record is arbitrary . the number of such rows is 5 .
round_eq { avg { all_rows ; seats } ; 55 } = true
Output: the average of the seats record of all rows is 55 .
and { only { filter_eq { all_rows ; country ; australia } } ; eq { hop { filter_eq { all_rows ; country ; australia } ; player } ; mathew goggin } } = true
Output: select the rows whose country record fuzzily matches to australia . there is only one such row in the table . the player record of this unqiue row is mathew goggin .
most_eq { all_rows ; displacement ; 3.9 } = true
Output: for the displacement records of all rows , most of them are equal to 3.9 .
eq { count { filter_greater_eq { all_rows ; founded ; 1990 } } ; 2 } = true
Output: select the rows whose founded record is greater than or equal to 1990 . the number of such rows is 2 .
round_eq { avg { all_rows ; points won } ; 198 } = true
Output: the average of the points won record of all rows is 198 .
and { only { filter_eq { filter_greater { all_rows ; matches ; 200 } ; goals ; 134 } } ; eq { hop { filter_eq { filter_greater { all_rows ; matches ; 200 } ; goals ; 134 } ; name } ; jeff cunningham } } = true
Output: select the rows whose matches record is greater than 200 . among these rows , select the rows whose goals record is equal to 134 . there is only one such row in the table . the name record of this unqiue row is jeff cunningham .
greater { hop { filter_eq { all_rows ; date ; december 3 , 1967 } ; attendance } ; hop { filter_eq { all_rows ; date ; december 17 , 1967 } ; attendance } } = true
Output: select the rows whose date record fuzzily matches to december 3 , 1967 . take the attendance record of this row . select the rows whose date record fuzzily matches to december 17 , 1967 . take the attendance record of this row . the first record is greater than the second record .
greater { hop { filter_eq { all_rows ; name ; kseniya moskvina } ; time } ; hop { filter_eq { all_rows ; name ; sophie edington } ; time } } = true
Output: select the rows whose name record fuzzily matches to kseniya moskvina . take the time record of this row . select the rows whose name record fuzzily matches to sophie edington . take the time record of this row . the first record is greater than the second record .
all_eq { all_rows ; results ; re - elected } = true
Output: for the results records of all rows , all of them fuzzily match to re  elected .
eq { hop { argmax { filter_eq { all_rows ; year ; 2012 } ; worldwide gross } ; movie } ; ek tha tiger } = true
Output: select the rows whose year record is equal to 2012 . select the row whose worldwide gross record of these rows is maximum . the movie record of this row is ek tha tiger .
eq { hop { nth_argmin { all_rows ; rnd ; 1 } ; date } ; march 14 } = true
Output: select the row whose rnd record of all rows is 1st minimum . the date record of this row is march 14 .
eq { hop { nth_argmin { all_rows ; year ; 2 } ; event } ; monsters of rock } = true
Output: select the row whose year record of all rows is 2nd minimum . the event record of this row is monsters of rock .
round_eq { avg { all_rows ; points } ; 11 } = true
Output: the average of the points record of all rows is 11 .
and { only { filter_eq { filter_eq { all_rows ; protocol ; proprietary } ; license ; apache license } } ; eq { hop { filter_eq { filter_eq { all_rows ; protocol ; proprietary } ; license ; apache license } ; project } ; jxta remote desktop } } = true
Output: select the rows whose protocol record fuzzily matches to proprietary . among these rows , select the rows whose license record fuzzily matches to apache license . there is only one such row in the table . the project record of this unqiue row is jxta remote desktop .
most_eq { filter_eq { all_rows ; location attendance ; air canada centre } ; high assists ; rafer alston } = true
Output: select the rows whose location attendance record fuzzily matches to air canada centre . for the high assists records of these rows , most of them fuzzily match to rafer alston .
most_eq { all_rows ; decision ; backstrom } = true
Output: for the decision records of all rows , most of them fuzzily match to backstrom .
eq { hop { nth_argmin { all_rows ; goals scored ; 2 } ; team } ; once municipal } = true
Output: select the row whose goals scored record of all rows is 2nd minimum . the team record of this row is once municipal .
eq { hop { nth_argmax { all_rows ; enrollment ; 2 } ; school } ; fort wayne carroll } = true
Output: select the row whose enrollment record of all rows is 2nd maximum . the school record of this row is fort wayne carroll .
eq { hop { argmax { all_rows ; air date } ; guest host } ; cheryl cole , kimberley walsh and sarah harding } = true
Output: select the row whose air date record of all rows is maximum . the guest host record of this row is cheryl cole , kimberley walsh and sarah harding .
eq { count { filter_eq { all_rows ; score ; w } } ; 2 } = true
Output: select the rows whose score record fuzzily matches to w . the number of such rows is 2 .
eq { hop { argmin { all_rows ; date ( from ) } ; name of system } ; edmonton radial railway } = true
Output: select the row whose date  from  record of all rows is minimum . the name of system record of this row is edmonton radial railway .
and { only { filter_eq { all_rows ; college ; new mexico } } ; eq { hop { filter_eq { all_rows ; college ; new mexico } ; player } ; brian ramsay } } = true
Output: select the rows whose college record fuzzily matches to new mexico . there is only one such row in the table . the player record of this unqiue row is brian ramsay .
and { only { filter_greater { filter_eq { all_rows ; result ; w } ; attendance ; 50000 } } ; eq { hop { filter_greater { filter_eq { all_rows ; result ; w } ; attendance ; 50000 } ; date } ; 10 / 20 / 1928 } } = true
Output: select the rows whose result record fuzzily matches to w . among these rows , select the rows whose attendance record is greater than 50000 . there is only one such row in the table . the date record of this unqiue row is 10  20  1928 .
eq { hop { nth_argmax { all_rows ; slalom ; 2 } ; overall } ; 3 } = true
Output: select the row whose slalom record of all rows is 2nd maximum . the overall record of this row is 3 .
eq { count { filter_eq { all_rows ; location ; yankee stadium } } ; 3 } = true
Output: select the rows whose location record fuzzily matches to yankee stadium . the number of such rows is 3 .
most_greater { all_rows ; prize ; 500,000 } = true
Output: for the prize records of all rows , most of them are greater than 500,000 .
most_eq { all_rows ; game site ; mile high stadium } = true
Output: for the game site records of all rows , most of them fuzzily match to mile high stadium .
eq { count { filter_eq { filter_eq { all_rows ; team ; detroit } ; high rebounds ; shaquille o'neal } } ; 3 } = true
Output: select the rows whose team record fuzzily matches to detroit . among these rows , select the rows whose high rebounds record fuzzily matches to shaquille oneal . the number of such rows is 3 .
eq { hop { nth_argmin { all_rows ; county team ; 2 } ; player } ; mick mackey } = true
Output: select the row whose county team record of all rows is 2nd minimum . the player record of this row is mick mackey .
greater { hop { filter_eq { all_rows ; governorate ; piotrków governorate } ; population , in thousands , ( 1905 ) } ; hop { filter_eq { all_rows ; governorate ; płock governorate } ; population , in thousands , ( 1905 ) } } = true
Output: select the rows whose governorate record fuzzily matches to piotrków governorate . take the population , in thousands ,  1905  record of this row . select the rows whose governorate record fuzzily matches to płock governorate . take the population , in thousands ,  1905  record of this row . the first record is greater than the second record .
most_eq { all_rows ; sector ; services } = true
Output: for the sector records of all rows , most of them fuzzily match to services .
eq { hop { argmax { all_rows ; appearances } ; name } ; philippe jeannol } = true
Output: select the row whose appearances record of all rows is maximum . the name record of this row is philippe jeannol .
eq { hop { nth_argmin { all_rows ; departure pune ; 2 } ; train number } ; 99806 } = true
Output: select the row whose departure pune record of all rows is 2nd minimum . the train number record of this row is 99806 .
eq { hop { nth_argmax { all_rows ; length ; 3 } ; year } ; 1996 } = true
Output: select the row whose length record of all rows is 3rd maximum . the year record of this row is 1996 .
eq { count { filter_eq { all_rows ; platform ( s ) ; xbox 360 } } ; 3 } = true
Output: select the rows whose platform  s  record fuzzily matches to xbox 360 . the number of such rows is 3 .
round_eq { sum { all_rows ; apps } ; 137 } = true
Output: the sum of the apps record of all rows is 137 .
and { eq { max { all_rows ; goals } ; 1468 } ; eq { hop { argmax { all_rows ; goals } ; country } ; austria czech republic } } = true
Output: the maximum goals record of all rows is 1468 . the country record of the row with superlative goals record is austria czech republic .
eq { count { filter_eq { all_rows ; manufacturer ; jabiru } } ; 6 } = true
Output: select the rows whose manufacturer record fuzzily matches to jabiru . the number of such rows is 6 .
and { only { filter_eq { all_rows ; engine ; ford cosworth dfr v8 } } ; eq { hop { filter_eq { all_rows ; engine ; ford cosworth dfr v8 } ; year } ; 1989 } } = true
Output: select the rows whose engine record fuzzily matches to ford cosworth dfr v8 . there is only one such row in the table . the year record of this unqiue row is 1989 .
eq { hop { nth_argmax { all_rows ; height ; 3 } ; player } ; robert witka } = true
Output: select the row whose height record of all rows is 3rd maximum . the player record of this row is robert witka .