niryuu/sni-each-converted huggingface dataset:
eq { hop { nth_argmin { all_rows ; date ; 2 } ; tournament } ; genoa , italy } = true
Output: select the row whose date record of all rows is 2nd minimum . the tournament record of this row is genoa , italy .
eq { count { filter_eq { all_rows ; original air date ; august } } ; 3 } = true
Output: select the rows whose original air date record fuzzily matches to august . the number of such rows is 3 .
most_less { all_rows ; total ; 300 } = true
Output: for the total records of all rows , most of them are less than 300 .
and { only { filter_eq { all_rows ; kit maker ; erreà } } ; eq { hop { filter_eq { all_rows ; kit maker ; erreà } ; team } ; middlesbrough } } = true
Output: select the rows whose kit maker record fuzzily matches to erreà . there is only one such row in the table . the team record of this unqiue row is middlesbrough .
all_eq { all_rows ; story by ; david simon & ed burns } = true
Output: for the story by records of all rows , all of them fuzzily match to david simon  ed burns .
eq { count { filter_eq { all_rows ; series result ; drawn } } ; 7 } = true
Output: select the rows whose series result record fuzzily matches to drawn . the number of such rows is 7 .
most_greater { all_rows ; goals ; 1000 } = true
Output: for the goals records of all rows , most of them are greater than 1000 .
and { only { filter_greater { all_rows ; goals ; 120 } } ; eq { hop { filter_greater { all_rows ; goals ; 120 } ; club } ; real madrid } } = true
Output: select the rows whose goals record is greater than 120 . there is only one such row in the table . the club record of this unqiue row is real madrid .
most_greater { filter_eq { all_rows ; team ; pescarolo sport } ; laps ; 300 } = true
Output: select the rows whose team record fuzzily matches to pescarolo sport . for the laps records of these rows , most of them are greater than 300 .
and { only { filter_eq { all_rows ; game site ; wrigley field } } ; eq { hop { filter_eq { all_rows ; game site ; wrigley field } ; date } ; october 21 , 1962 } } = true
Output: select the rows whose game site record fuzzily matches to wrigley field . there is only one such row in the table . the date record of this unqiue row is october 21 , 1962 .
all_eq { all_rows ; report ; bbc } = true
Output: for the report records of all rows , all of them fuzzily match to bbc .
and { only { filter_greater { filter_eq { all_rows ; runner - up ; asec abidjan } ; score ; 5 } } ; eq { hop { filter_greater { filter_eq { all_rows ; runner - up ; asec abidjan } ; score ; 5 } ; winner } ; us gorée } } = true
Output: select the rows whose runner  up record fuzzily matches to asec abidjan . among these rows , select the rows whose score record is greater than 5 . there is only one such row in the table . the winner record of this unqiue row is us gorée .
eq { count { filter_eq { all_rows ; owner ( s ) ; private owner } } ; 5 } = true
Output: select the rows whose owner  s  record fuzzily matches to private owner . the number of such rows is 5 .
eq { count { filter_eq { all_rows ; builder ; peterson shipbuilders } } ; 11 } = true
Output: select the rows whose builder record fuzzily matches to peterson shipbuilders . the number of such rows is 11 .
round_eq { sum { all_rows ; score } ; 16 } = true
Output: the sum of the score record of all rows is 16 .
less { hop { filter_eq { all_rows ; champions ; jimmy connors } ; year } ; hop { filter_eq { all_rows ; champions ; john mcenroe } ; year } } = true
Output: select the rows whose champions record fuzzily matches to jimmy connors . take the year record of this row . select the rows whose champions record fuzzily matches to john mcenroe . take the year record of this row . the first record is less than the second record .
eq { hop { nth_argmax { all_rows ; total ; 1 } ; nation } ; venezuela } = true
Output: select the row whose total record of all rows is 1st maximum . the nation record of this row is venezuela .
all_eq { filter_eq { all_rows ; manufacturer ; chevrolet } ; team ; morgan-mcclure } = true
Output: select the rows whose manufacturer record fuzzily matches to chevrolet . for the team records of these rows , all of them fuzzily match to morganmcclure .
and { only { filter_eq { all_rows ; country ; ger } } ; eq { hop { filter_eq { all_rows ; country ; ger } ; name } ; bopp } } = true
Output: select the rows whose country record fuzzily matches to ger . there is only one such row in the table . the name record of this unqiue row is bopp .
most_less { filter_less { all_rows ; points ; 20 } ; lost ; 10 } = true
Output: select the rows whose points record is less than 20 . for the lost records of these rows , most of them are less than 10 .
eq { count { filter_eq { all_rows ; round ; 2 } } ; 4 } = true
Output: select the rows whose round record is equal to 2 . the number of such rows is 4 .
most_greater_eq { all_rows ; gold ; 1 } = true
Output: for the gold records of all rows , most of them are greater than or equal to 1 .
greater { hop { filter_eq { all_rows ; mother tongue ; romanian } ; population ( 2006 ) } ; hop { filter_eq { all_rows ; mother tongue ; polish } ; population ( 2006 ) } } = true
Output: select the rows whose mother tongue record fuzzily matches to romanian . take the population  2006  record of this row . select the rows whose mother tongue record fuzzily matches to polish . take the population  2006  record of this row . the first record is greater than the second record .
greater { hop { filter_eq { all_rows ; opponent ; mark kerr } ; round } ; hop { filter_eq { all_rows ; opponent ; dieusel berto } ; round } } = true
Output: 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 .
greater { hop { filter_eq { all_rows ; team ; juventus } ; against } ; hop { filter_eq { all_rows ; team ; corinthians } ; against } } = true
Output: 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 .
and { only { filter_eq { all_rows ; year ; 1999 } } ; eq { hop { filter_eq { all_rows ; year ; 1999 } ; version } ; album version } } = true
Output: select the rows whose year record is equal to 1999 . there is only one such row in the table . the version record of this unqiue row is album version .
greater { hop { filter_eq { all_rows ; home team ; north melbourne } ; home team score } ; hop { filter_eq { all_rows ; home team ; essendon } ; home team score } } = true
Output: select the rows whose home team record fuzzily matches to north melbourne . take the home team score record of this row . select the rows whose home team record fuzzily matches to essendon . take the home team score record of this row . the first record is greater than the second record .
round_eq { avg { all_rows ; level } ; 8 } = true
Output: the average of the level record of all rows is 8 .
eq { min { filter_eq { all_rows ; team ( s ) ; haas cnc racing } ; avg finish } ; 29.2 } = true
Output: select the rows whose team  s  record fuzzily matches to haas cnc racing . the minimum avg finish record of these rows is 29.2 .
eq { count { filter_all { all_rows ; player } } ; 16 } = true
Output: select the rows whose player record is arbitrary . the number of such rows is 16 .
and { eq { max { all_rows ; year } ; 2006 } ; eq { hop { argmax { all_rows ; year } ; label } ; sony japan } } = true
Output: the maximum year record of all rows is 2006 . the label record of the row with superlative year record is sony japan .
most_eq { all_rows ; original air date ; 2009 } = true
Output: for the original air date records of all rows , most of them fuzzily match to 2009 .
eq { hop { argmax { all_rows ; population } ; english name } ; luobei county } = true
Output: select the row whose population record of all rows is maximum . the english name record of this row is luobei county .
and { only { filter_eq { all_rows ; surface ; clay } } ; eq { hop { filter_eq { all_rows ; surface ; clay } ; track } ; little valley speedway } } = true
Output: select the rows whose surface record fuzzily matches to clay . there is only one such row in the table . the track record of this unqiue row is little valley speedway .
and { only { filter_eq { all_rows ; locale ; prince edward island } } ; eq { hop { filter_eq { all_rows ; locale ; prince edward island } ; skip } ; rod macdonald } } = true
Output: select the rows whose locale record fuzzily matches to prince edward island . there is only one such row in the table . the skip record of this unqiue row is rod macdonald .
eq { hop { argmax { all_rows ; silver } ; nation } ; soviet union } = true
Output: select the row whose silver record of all rows is maximum . the nation record of this row is soviet union .
and { eq { diff { hop { filter_eq { all_rows ; player ; terry hardy } ; round } ; hop { filter_eq { all_rows ; player ; zack walz } ; round } } ; -1 } ; and { eq { hop { filter_eq { all_rows ; player ; terry hardy } ; round } ; 5 } ; eq { hop { filter_eq { all_rows ; player ; zack walz } ; round } ; 6 } } } = true
Output: select the rows whose player record fuzzily matches to terry hardy . take the round record of this row . select the rows whose player record fuzzily matches to zack walz . take the round record of this row . the second record is 1 larger than the first record . the round record of the first row is 5 . the round record of the second row is 6 .
most_greater_eq { filter_eq { all_rows ; date ; november } ; attendance ; 10000 } = true
Output: select the rows whose date record fuzzily matches to november . for the attendance records of these rows , most of them are greater than or equal to 10000 .
most_eq { filter_eq { all_rows ; ihsaa class ; aa } ; county ; 28 greene } = true
Output: select the rows whose ihsaa class record fuzzily matches to aa . for the county records of these rows , most of them fuzzily match to 28 greene .
greater { hop { filter_eq { all_rows ; games ; 1958 tokyo } ; total } ; hop { filter_eq { all_rows ; games ; 1966 bangkok } ; total } } = true
Output: select the rows whose games record fuzzily matches to 1958 tokyo . take the total record of this row . select the rows whose games record fuzzily matches to 1966 bangkok . take the total record of this row . the first record is greater than the second record .
greater { hop { filter_eq { all_rows ; round ; 3 } ; pick } ; hop { filter_eq { all_rows ; round ; 2 } ; pick } } = true
Output: select the rows whose round record fuzzily matches to 3 . take the pick record of this row . select the rows whose round record fuzzily matches to 2 . take the pick record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; ihsaa football class ; aaaa } } ; 6 } = true
Output: select the rows whose ihsaa football class record fuzzily matches to aaaa . the number of such rows is 6 .
eq { hop { argmax { all_rows ; year } ; chassis } ; ags jh25b ags jh27 } = true
Output: select the row whose year record of all rows is maximum . the chassis record of this row is ags jh25b ags jh27 .
most_less { all_rows ; founded ; 1980 } = true
Output: for the founded records of all rows , most of them are less than 1980 .
eq { count { filter_eq { all_rows ; type ; public } } ; 3 } = true
Output: select the rows whose type record fuzzily matches to public . the number of such rows is 3 .
round_eq { sum { filter_eq { all_rows ; high rebounds ; tyrus thomas } ; high rebounds } ; 58 } = true
Output: select the rows whose high rebounds record fuzzily matches to tyrus thomas . the sum of the high rebounds record of these rows is 58 .
eq { hop { argmax { all_rows ; time } ; opponent } ; bob schrijber } = true
Output: select the row whose time record of all rows is maximum . the opponent record of this row is bob schrijber .
and { only { filter_eq { all_rows ; location ; brazil } } ; eq { hop { filter_eq { all_rows ; location ; brazil } ; race name } ; tour de santa catarina } } = true
Output: 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 .
greater { hop { filter_eq { all_rows ; name ; simon ammann } ; 1st ( m ) } ; hop { filter_eq { all_rows ; name ; thomas morgenstern } ; 1st ( m ) } } = true
Output: select the rows whose name record fuzzily matches to simon ammann . take the 1st  m  record of this row . select the rows whose name record fuzzily matches to thomas morgenstern . take the 1st  m  record of this row . the first record is greater than the second record .
most_greater { all_rows ; population ( 2011 ) ; 4000000 } = true
Output: for the population  2011  records of all rows , most of them are greater than 4000000 .
eq { diff { hop { filter_eq { all_rows ; name ; centennial collegiate vocational institute } ; enrollment } ; hop { filter_eq { all_rows ; name ; john f ross collegiate vocational institute } ; enrollment } } ; -362 } = true
Output: select the rows whose name record fuzzily matches to centennial collegiate vocational institute . take the enrollment record of this row . select the rows whose name record fuzzily matches to john f ross collegiate vocational institute . take the enrollment record of this row . the second record is 362 larger than the first record .
all_eq { filter_eq { all_rows ; date ; may 2008 } ; original artist ; beatles } = true
Output: select the rows whose date record fuzzily matches to may 2008 . for the original artist records of these rows , all of them fuzzily match to beatles .
eq { count { filter_eq { filter_eq { all_rows ; results ; re - elected } ; first elected ; 1990 } } ; 2 } = true
Output: select the rows whose results record fuzzily matches to re  elected . among these rows , select the rows whose first elected record is equal to 1990 . the number of such rows is 2 .
round_eq { avg { all_rows ; top - 10 } ; 3.75 } = true
Output: the average of the top  10 record of all rows is 3.75 .
eq { hop { argmin { all_rows ; year } ; english title } ; demon skyscraper } = true
Output: select the row whose year record of all rows is minimum . the english title record of this row is demon skyscraper .
eq { hop { argmin { all_rows ; max power } ; vehicle } ; nissan y11 ad van } = true
Output: select the row whose max power record of all rows is minimum . the vehicle record of this row is nissan y11 ad van .
eq { hop { nth_argmin { all_rows ; time ; 3 } ; rider } ; ross johnson } = true
Output: select the row whose time record of all rows is 3rd minimum . the rider record of this row is ross johnson .
round_eq { avg { all_rows ; attendance } ; 49849.4 } = true
Output: the average of the attendance record of all rows is 49849.4 .
most_less { all_rows ; position ; 50 } = true
Output: for the position records of all rows , most of them are less than 50 .
all_greater { all_rows ; population density ( per km square ) ; 100 } = true
Output: for the population density  per km square  records of all rows , all of them are greater than 100 .
eq { hop { argmax { all_rows ; winner } ; episode } ; 2x10 } = true
Output: select the row whose winner record of all rows is maximum . the episode record of this row is 2x10 .
most_eq { all_rows ; latest stable release ; 2013 } = true
Output: for the latest stable release records of all rows , most of them fuzzily match to 2013 .
eq { hop { nth_argmax { all_rows ; location attendance ; 3 } ; date } ; 10 january 2008 } = true
Output: select the row whose location attendance record of all rows is 3rd maximum . the date record of this row is 10 january 2008 .
round_eq { sum { filter_greater_eq { all_rows ; year ; 1990 } ; points } ; 11 } = true
Output: select the rows whose year record is greater than or equal to 1990 . the sum of the points record of these rows is 11 .
and { only { filter_greater { all_rows ; gold ; 1 } } ; eq { hop { filter_greater { all_rows ; gold ; 1 } ; nation } ; germany } } = true
Output: select the rows whose gold record is greater than 1 . there is only one such row in the table . the nation record of this unqiue row is germany .
eq { hop { argmax { all_rows ; 1st leg } ; team 1 } ; elan chalon } = true
Output: select the row whose 1st leg record of all rows is maximum . the team 1 record of this row is elan chalon .
and { only { filter_eq { all_rows ; director ; ron satlof } } ; eq { hop { filter_eq { all_rows ; director ; ron satlof } ; title } ; the blue ribbon hostage } } = true
Output: select the rows whose director record fuzzily matches to ron satlof . there is only one such row in the table . the title record of this unqiue row is the blue ribbon hostage .
eq { count { filter_eq { all_rows ; result ; re - elected } } ; 2 } = true
Output: select the rows whose result record fuzzily matches to re  elected . the number of such rows is 2 .
round_eq { sum { all_rows ; crowd } ; 84000 } = true
Output: the sum of the crowd record of all rows is 84000 .
all_eq { all_rows ; date ; 16 august } = true
Output: for the date records of all rows , all of them fuzzily match to 16 august .
eq { hop { argmax { all_rows ; pts } ; team } ; castres olympique } = true
Output: select the row whose pts record of all rows is maximum . the team record of this row is castres olympique .
eq { diff { hop { filter_eq { all_rows ; date ; december 17 , 2000 } ; attendance } ; hop { filter_eq { all_rows ; date ; december 24 , 2000 } ; attendance } } ; 23883 } = true
Output: select the rows whose date record fuzzily matches to december 17 , 2000 . take the attendance record of this row . select the rows whose date record fuzzily matches to december 24 , 2000 . take the attendance record of this row . the first record is 23883 larger than the second record .
and { only { filter_eq { all_rows ; directed by ; kathy bates } } ; eq { hop { filter_eq { all_rows ; directed by ; kathy bates } ; title } ; out , out brief candle } } = true
Output: select the rows whose directed by record fuzzily matches to kathy bates . there is only one such row in the table . the title record of this unqiue row is out , out brief candle .
eq { hop { argmax { all_rows ; design flow ( lpm ) } ; location } ; marcala , hon } = true
Output: select the row whose design flow  lpm  record of all rows is maximum . the location record of this row is marcala , hon .
eq { count { filter_eq { all_rows ; championship ; world snooker championship } } ; 2 } = true
Output: select the rows whose championship record fuzzily matches to world snooker championship . the number of such rows is 2 .
and { only { filter_eq { all_rows ; country ; kazakhstan } } ; eq { hop { filter_eq { all_rows ; country ; kazakhstan } ; athlete } ; inga dudchenko } } = true
Output: select the rows whose country record fuzzily matches to kazakhstan . there is only one such row in the table . the athlete record of this unqiue row is inga dudchenko .
eq { hop { argmax { all_rows ; points } ; club } ; gilfach goch rfc } = true
Output: select the row whose points record of all rows is maximum . the club record of this row is gilfach goch rfc .
less { hop { filter_eq { all_rows ; winner ; bernard gallacher } ; score } ; hop { filter_eq { all_rows ; winner ; peter butler } ; score } } = true
Output: select the rows whose winner record fuzzily matches to bernard gallacher . take the score record of this row . select the rows whose winner record fuzzily matches to peter butler . take the score record of this row . the first record is less than the second record .
greater { hop { filter_eq { all_rows ; year ; 1956 } ; laps } ; hop { filter_eq { all_rows ; year ; 1959 } ; laps } } = true
Output: select the rows whose year record fuzzily matches to 1956 . take the laps record of this row . select the rows whose year record fuzzily matches to 1959 . take the laps record of this row . the first record is greater than the second record .
and { only { filter_eq { all_rows ; villages ; 25 } } ; eq { hop { filter_eq { all_rows ; villages ; 25 } ; name } ; wuji town } } = true
Output: select the rows whose villages record is equal to 25 . there is only one such row in the table . the name record of this unqiue row is wuji town .
only { filter_eq { all_rows ; sunday ; rylan clark } } = true
Output: select the rows whose sunday record fuzzily matches to rylan clark . there is only one such row in the table .
eq { hop { nth_argmax { all_rows ; number of viewers ; 2 } ; show } ; warehouse 13 } = true
Output: select the row whose number of viewers record of all rows is 2nd maximum . the show record of this row is warehouse 13 .
most_eq { all_rows ; owgr points ; 6 } = true
Output: for the owgr points records of all rows , most of them are equal to 6 .
eq { count { filter_eq { all_rows ; nickname ; saints } } ; 1 } = true
Output: select the rows whose nickname record fuzzily matches to saints . the number of such rows is 1 .
eq { hop { nth_argmax { all_rows ; points ; 3 } ; rider } ; mick grant } = true
Output: select the row whose points record of all rows is 3rd maximum . the rider record of this row is mick grant .
less { hop { filter_eq { all_rows ; event ; 10000 m } ; position } ; hop { filter_eq { all_rows ; event ; 5000 m } ; position } } = true
Output: select the rows whose event record fuzzily matches to 10000 m . take the position record of this row . select the rows whose event record fuzzily matches to 5000 m . take the position record of this row . the first record is less than the second record .
eq { hop { nth_argmin { all_rows ; release date ; 1 } ; developer ( s ) } ; valve corporation } = true
Output: select the row whose release date record of all rows is 1st minimum . the developer  s  record of this row is valve corporation .
all_eq { all_rows ; engine ; motori moderni tipo 615 - 90 1.5 v6 t } = true
Output: for the engine records of all rows , all of them fuzzily match to motori moderni tipo 615  90 1.5 v6 t .
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 } } = true
Output: 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 .
greater { hop { filter_eq { all_rows ; artist ; the playtones } ; total } ; hop { filter_eq { all_rows ; artist ; brolle } ; total } } = true
Output: select the rows whose artist record fuzzily matches to the playtones . take the total record of this row . select the rows whose artist record fuzzily matches to brolle . take the total record of this row . the first record is greater than the second record .
eq { hop { nth_argmax { all_rows ; points ; 1 } ; driver } ; dan wheldon } = true
Output: select the row whose points record of all rows is 1st maximum . the driver record of this row is dan wheldon .
and { only { filter_eq { all_rows ; image support ; no } } ; eq { hop { filter_eq { all_rows ; image support ; no } ; format } ; plain text } } = true
Output: select the rows whose image support record fuzzily matches to no . there is only one such row in the table . the format record of this unqiue row is plain text .
and { eq { nth_min { all_rows ; year ; 2 } ; 1979 } ; eq { hop { nth_argmin { all_rows ; year ; 2 } ; issued title } ; 1958 miles } } = true
Output: the 2nd minimum year record of all rows is 1979 . the issued title record of the row with 2nd minimum year record is 1958 miles .
and { eq { hop { argmax { filter_greater_eq { all_rows ; lost ; 10 } ; goals against } ; team } ; rossendale united } ; eq { hop { argmax { filter_greater_eq { all_rows ; lost ; 10 } ; goals against } ; won } ; 10 } } = true
Output: select the rows whose lost record is greater than or equal to 10 . select the row whose goals against record of these rows is maximum . the team record of this row is rossendale united . the won record of this row is 10 .
eq { hop { nth_argmin { filter_eq { all_rows ; opponent ; jacksonville jaguars } ; date ; 2 } ; attendance } ; 68809 } = true
Output: select the rows whose opponent record fuzzily matches to jacksonville jaguars . select the row whose date record of these rows is 2nd minimum . the attendance record of this row is 68809 .
eq { hop { nth_argmin { all_rows ; no in season ; 1 } ; title } ; grampires ( part 1 ) } = true
Output: select the row whose no in season record of all rows is 1st minimum . the title record of this row is grampires  part 1  .
all_eq { all_rows ; director ; robert alvarez } = true
Output: for the director records of all rows , all of them fuzzily match to robert alvarez .
eq { hop { nth_argmax { all_rows ; score ; 3 } ; home team } ; sydney spirit } = true
Output: select the row whose score record of all rows is 3rd maximum . the home team record of this row is sydney spirit .
most_eq { filter_eq { all_rows ; co - driver ; leo kinnunen } ; pos ; ret } = true
Output: select the rows whose co  driver record fuzzily matches to leo kinnunen . for the pos records of these rows , most of them fuzzily match to ret .
eq { count { filter_all { all_rows ; name } } ; 8 } = true
Output: select the rows whose name record is arbitrary . the number of such rows is 8 .
and { only { filter_eq { all_rows ; 1st leg ; 1 - 1 } } ; and { eq { hop { filter_eq { all_rows ; 1st leg ; 1 - 1 } ; team 1 } ; bolton wanderers } ; eq { hop { filter_eq { all_rows ; 1st leg ; 1 - 1 } ; team 2 } ; sporting cp } } } = true
Output: select the rows whose 1st leg record fuzzily matches to 1  1 . there is only one such row in the table . the team 1 record of this unqiue row is bolton wanderers . the team 2 record of this unqiue row is sporting cp .
eq { hop { nth_argmax { all_rows ; date ; 2 } ; venue } ; vasil levski national stadium , sofia } = true
Output: select the row whose date record of all rows is 2nd maximum . the venue record of this row is vasil levski national stadium , sofia .
greater { hop { filter_eq { all_rows ; batsmen ; iain anderson alan hill } ; runs } ; hop { filter_eq { all_rows ; batsmen ; chris taylor ant botha } ; runs } } = true
Output: select the rows whose batsmen record fuzzily matches to iain anderson alan hill . take the runs record of this row . select the rows whose batsmen record fuzzily matches to chris taylor ant botha . take the runs record of this row . the first record is greater than the second record .
most_eq { all_rows ; owner ; greentrains } = true
Output: for the owner records of all rows , most of them fuzzily match to greentrains .
round_eq { avg { all_rows ; crowd } ; 16000 } = true
Output: the average of the crowd record of all rows is 16000 .
eq { count { filter_eq { all_rows ; worst dancer ( s ) ; john barnes } } ; 2 } = true
Output: select the rows whose worst dancer  s  record fuzzily matches to john barnes . the number of such rows is 2 .
most_eq { all_rows ; high points ; r pierce } = true
Output: for the high points records of all rows , most of them fuzzily match to r pierce .
eq { hop { nth_argmin { all_rows ; february ; 1 } ; opponent } ; vancouver canucks } = true
Output: select the row whose february record of all rows is 1st minimum . the opponent record of this row is vancouver canucks .
only { filter_eq { all_rows ; shooter ; ( cub ) } } = true
Output: select the rows whose shooter record fuzzily matches to  cub  . there is only one such row in the table .
eq { hop { nth_argmax { all_rows ; attendance ; 2 } ; date } ; october 21 } = true
Output: select the row whose attendance record of all rows is 2nd maximum . the date record of this row is october 21 .
eq { count { filter_eq { all_rows ; outcome of election ; ndc opposition } } ; 2 } = true
Output: select the rows whose outcome of election record fuzzily matches to ndc opposition . the number of such rows is 2 .
and { only { filter_eq { filter_eq { all_rows ; score ; w } ; team ; memphis } } ; eq { hop { filter_eq { filter_eq { all_rows ; score ; w } ; team ; memphis } ; date } ; january 2 } } = true
Output: select the rows whose score record fuzzily matches to w . among these rows , select the rows whose team record fuzzily matches to memphis . there is only one such row in the table . the date record of this unqiue row is january 2 .
and { eq { nth_min { filter_eq { all_rows ; result ; l } ; date ; 3 } ; november 17 , 1985 } ; eq { hop { nth_argmin { filter_eq { all_rows ; result ; l } ; date ; 3 } ; attendance } ; 29960 } } = true
Output: select the rows whose result record fuzzily matches to l . the 3rd minimum date record of these rows is november 17 , 1985 . the attendance record of the row with 3rd minimum date record is 29960 .
round_eq { avg { all_rows ; number of episodes } ; 29.87 } = true
Output: the average of the number of episodes record of all rows is 29.87 .
eq { diff { hop { filter_eq { all_rows ; artist ; hind } ; points } ; hop { filter_eq { all_rows ; artist ; gisela } ; points } } ; 5 } = true
Output: select the rows whose artist record fuzzily matches to hind . take the points record of this row . select the rows whose artist record fuzzily matches to gisela . take the points record of this row . the first record is 5 larger than the second record .
eq { hop { argmax { all_rows ; viewers } ; no } ; 1 } = true
Output: select the row whose viewers record of all rows is maximum . the no record of this row is 1 .
most_eq { all_rows ; method ; punches } = true
Output: for the method records of all rows , most of them fuzzily match to punches .
and { only { filter_eq { all_rows ; winner / nominee ( s ) ; n/a } } ; eq { hop { filter_eq { all_rows ; winner / nominee ( s ) ; n/a } ; film } ; the rugrats movie } } = true
Output: select the rows whose winner  nominee  s  record fuzzily matches to na . there is only one such row in the table . the film record of this unqiue row is the rugrats movie .
greater { hop { filter_eq { all_rows ; year ; 1964 } ; points } ; hop { filter_eq { all_rows ; year ; 1963 } ; points } } = true
Output: 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 .
eq { count { filter_eq { filter_eq { all_rows ; date ; july } ; fastest lap ; nigel mansell } } ; 2 } = true
Output: select the rows whose date record fuzzily matches to july . among these rows , select the rows whose fastest lap record fuzzily matches to nigel mansell . the number of such rows is 2 .
all_eq { all_rows ; winner ; ( ita ) } = true
Output: for the winner records of all rows , all of them fuzzily match to  ita  .
and { only { filter_eq { all_rows ; total ; 5 } } ; eq { hop { filter_eq { all_rows ; total ; 5 } ; nation } ; germany } } = true
Output: 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 .
eq { diff { hop { filter_eq { all_rows ; name / designation ; saturn machine pistol } ; year of intro } ; hop { filter_eq { all_rows ; name / designation ; serlea } ; year of intro } } ; -5 years } = true
Output: select the rows whose name  designation record fuzzily matches to saturn machine pistol . take the year of intro record of this row . select the rows whose name  designation record fuzzily matches to serlea . take the year of intro record of this row . the second record is 5 years larger than the first record .
eq { hop { argmax { all_rows ; attendance } ; date } ; march 20 } = true
Output: select the row whose attendance record of all rows is maximum . the date record of this row is march 20 .
and { eq { hop { filter_eq { all_rows ; edition ; 2008 davis cup europe / africa group ii } ; surface } ; hop { filter_eq { all_rows ; edition ; 2013 davis cup europe / africa group ii } ; surface } } ; and { eq { hop { filter_eq { all_rows ; edition ; 2008 davis cup europe / africa group ii } ; surface } ; clay } ; eq { hop { filter_eq { all_rows ; edition ; 2013 davis cup europe / africa group ii } ; surface } ; clay } } } = true
Output: select the rows whose edition record fuzzily matches to 2008 davis cup europe  africa group ii . take the surface record of this row . select the rows whose edition record fuzzily matches to 2013 davis cup europe  africa group ii . take the surface record of this row . the first record fuzzily matches to the second record . the surface record of the first row is clay . the surface record of the second row is clay .
eq { count { filter_eq { all_rows ; guest fourth judge ; luiza possi } } ; 2 } = true
Output: select the rows whose guest fourth judge record fuzzily matches to luiza possi . the number of such rows is 2 .
and { less { hop { filter_eq { all_rows ; tournament ; commonwealth games } ; result } ; hop { filter_eq { all_rows ; tournament ; african championships } ; result } } ; and { eq { hop { filter_eq { all_rows ; tournament ; commonwealth games } ; year } ; 2006 } ; eq { hop { filter_eq { all_rows ; tournament ; african championships } ; year } ; 2006 } } } = true
Output: 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 african championships . take the result record of this row . the first record is less than the second record . the year record of the first row is 2006 . the year record of the second row is 2006 .
eq { hop { argmax { all_rows ; runs } ; player } ; adam gilchrist } = true
Output: select the row whose runs record of all rows is maximum . the player record of this row is adam gilchrist .
eq { count { filter_eq { all_rows ; men 's singles ; kenneth jonassen } } ; 2 } = true
Output: select the rows whose men s singles record fuzzily matches to kenneth jonassen . the number of such rows is 2 .
eq { hop { argmax { all_rows ; enrollment } ; institution } ; massachusetts institute of technology } = true
Output: select the row whose enrollment record of all rows is maximum . the institution record of this row is massachusetts institute of technology .
and { eq { nth_min { all_rows ; place ; 5 } ; 5 } ; eq { hop { nth_argmin { all_rows ; place ; 5 } ; player } ; fred couples } } = true
Output: the 5th minimum place record of all rows is 5 . the player record of the row with 5th minimum place record is fred couples .
most_eq { all_rows ; venue ; millennium stadium } = true
Output: for the venue records of all rows , most of them fuzzily match to millennium stadium .
most_greater { filter_eq { all_rows ; time / retired ; clutch } ; laps ; 30 } = true
Output: select the rows whose time  retired record fuzzily matches to clutch . for the laps records of these rows , most of them are greater than 30 .
and { only { filter_not_eq { all_rows ; country ; united states } } ; eq { hop { filter_not_eq { all_rows ; country ; united states } ; player } ; david graham } } = true
Output: select the rows whose country record does not match to united states . there is only one such row in the table . the player record of this unqiue row is david graham .
most_eq { all_rows ; position ; rhp } = true
Output: for the position records of all rows , most of them fuzzily match to rhp .
eq { hop { nth_argmax { all_rows ; enrollment ; 2 } ; school } ; triton central } = true
Output: select the row whose enrollment record of all rows is 2nd maximum . the school record of this row is triton central .
eq { count { filter_greater { filter_greater { all_rows ; home team score ; 15.12 } ; crowd ; 17500 } } ; 3 } = true
Output: select the rows whose home team score record is greater than 15.12 . among these rows , select the rows whose crowd record is greater than 17500 . the number of such rows is 3 .
eq { diff { hop { filter_eq { all_rows ; season ; 2010 } ; races } ; hop { filter_eq { all_rows ; season ; 2012 } ; races } } ; 6 } = true
Output: select the rows whose season record fuzzily matches to 2010 . take the races record of this row . select the rows whose season record fuzzily matches to 2012 . take the races record of this row . the first record is 6 larger than the second record .
only { filter_eq { all_rows ; high assists ; delonte west } } = true
Output: select the rows whose high assists record fuzzily matches to delonte west . there is only one such row in the table .
eq { count { filter_greater { filter_eq { all_rows ; visitor ; minnesota } ; attendance ; 15000 } } ; 5 } = true
Output: select the rows whose visitor record fuzzily matches to minnesota . among these rows , select the rows whose attendance record is greater than 15000 . the number of such rows is 5 .
and { only { filter_eq { all_rows ; event ; 50 km } } ; and { eq { hop { filter_eq { all_rows ; event ; 50 km } ; year } ; 2011 } ; eq { hop { filter_eq { all_rows ; event ; 50 km } ; competition } ; world championships } } } = true
Output: select the rows whose event record fuzzily matches to 50 km . there is only one such row in the table . the year record of this unqiue row is 2011 . the competition record of this unqiue row is world championships .
round_eq { avg { all_rows ; population served } ; 4038 } = true
Output: the average of the population served record of all rows is 4038 .
and { only { filter_eq { all_rows ; nationality ; united states } } ; eq { hop { filter_eq { all_rows ; nationality ; united states } ; player } ; ed hospodar } } = true
Output: select the rows whose nationality record fuzzily matches to united states . there is only one such row in the table . the player record of this unqiue row is ed hospodar .
less { hop { filter_eq { all_rows ; opponent ; atlanta falcons } ; attendance } ; hop { filter_eq { all_rows ; opponent ; san francisco 49ers } ; attendance } } = true
Output: select the rows whose opponent record fuzzily matches to atlanta falcons . take the attendance record of this row . select the rows whose opponent record fuzzily matches to san francisco 49ers . take the attendance record of this row . the first record is less than the second record .
and { only { filter_eq { all_rows ; internl tourism receipts 2011 ( million usd ) ; n/d } } ; and { eq { hop { filter_eq { all_rows ; internl tourism receipts 2011 ( million usd ) ; n/d } ; selected caribbean and n latin america countries } ; cuba } ; eq { hop { filter_eq { all_rows ; internl tourism receipts 2011 ( million usd ) ; n/d } ; revenues as % of exports goods and services 2011 } ; n / d } } } = true
Output: select the rows whose internl tourism receipts 2011  million usd  record fuzzily matches to nd . there is only one such row in the table . the selected caribbean and n latin america countries record of this unqiue row is cuba . the revenues as  of exports goods and services 2011 record of this unqiue row is n  d .
eq { hop { argmax { all_rows ; children together } ; name } ; martin van buren } = true
Output: select the row whose children together record of all rows is maximum . the name record of this row is martin van buren .
most_eq { all_rows ; type ; mountain } = true
Output: for the type records of all rows , most of them fuzzily match to mountain .
greater { hop { filter_eq { all_rows ; home team ; geelong } ; home team score } ; hop { filter_eq { all_rows ; home team ; richmond } ; home team score } } = true
Output: select the rows whose home team record fuzzily matches to geelong . take the home team score record of this row . select the rows whose home team record fuzzily matches to richmond . take the home team score record of this row . the first record is greater than the second record .
eq { hop { argmax { all_rows ; points } ; november } ; 29 } = true
Output: select the row whose points record of all rows is maximum . the november record of this row is 29 .
round_eq { avg { all_rows ; enrollment } ; 4067 } = true
Output: the average of the enrollment record of all rows is 4067 .
eq { hop { argmax { all_rows ; top - 10 } ; tournament } ; masters tournament } = true
Output: select the row whose top  10 record of all rows is maximum . the tournament record of this row is masters tournament .
round_eq { avg { all_rows ; gold } ; .82 } = true
Output: the average of the gold record of all rows is .82 .
eq { count { filter_eq { all_rows ; position ; 1st } } ; 3 } = true
Output: select the rows whose position record fuzzily matches to 1st . the number of such rows is 3 .
eq { count { filter_eq { all_rows ; took office ; 195 } } ; 2 } = true
Output: select the rows whose took office record fuzzily matches to 195 . the number of such rows is 2 .
eq { hop { nth_argmax { all_rows ; gold ; 2 } ; nation } ; romania } = true
Output: select the row whose gold record of all rows is 2nd maximum . the nation record of this row is romania .
round_eq { avg { all_rows ; matches } ; 4.1 } = true
Output: the average of the matches record of all rows is 4.1 .
eq { hop { nth_argmax { all_rows ; density square ; 1 } ; municipality } ; malmö } = true
Output: select the row whose density square record of all rows is 1st maximum . the municipality record of this row is malmö .
and { eq { hop { argmax { all_rows ; score } ; tie no } ; replay } ; and { eq { hop { argmax { all_rows ; score } ; home team } ; southampton } ; eq { hop { argmax { all_rows ; score } ; away team } ; sheffield wednesday } } } = true
Output: select the row whose score record of all rows is maximum . the tie no record of this row is replay . the home team record of this row is southampton . the away team record of this row is sheffield wednesday .
eq { count { filter_all { all_rows ; accolade } } ; 6 } = true
Output: select the rows whose accolade record is arbitrary . the number of such rows is 6 .
most_eq { all_rows ; original air date ; 2007 } = true
Output: for the original air date records of all rows , most of them fuzzily match to 2007 .
eq { hop { nth_argmax { filter_eq { all_rows ; winning driver ; petri suvanto } ; date ; 1 } ; circuit } ; road america } = true
Output: select the rows whose winning driver record fuzzily matches to petri suvanto . select the row whose date record of these rows is 1st maximum . the circuit record of this row is road america .
round_eq { avg { all_rows ; total } ; 145.5 } = true
Output: the average of the total record of all rows is 145.5 .
eq { hop { nth_argmax { all_rows ; gold ; 2 } ; nation } ; argentina ( arg ) } = true
Output: select the row whose gold record of all rows is 2nd maximum . the nation record of this row is argentina  arg  .
and { eq { hop { argmax { all_rows ; 1st leg } ; team 1 } ; flamengo } ; eq { hop { argmax { all_rows ; 1st leg } ; team 2 } ; américa } } = true
Output: select the row whose 1st leg record of all rows is maximum . the team 1 record of this row is flamengo . the team 2 record of this row is américa .
and { eq { max { all_rows ; us viewers ( million ) } ; 6.14 } ; eq { hop { argmax { all_rows ; us viewers ( million ) } ; no in season } ; 1 } } = true
Output: the maximum us viewers  million  record of all rows is 6.14 . the no in season record of the row with superlative us viewers  million  record is 1 .
most_eq { all_rows ; top - 5 ; 0 } = true
Output: for the top  5 records of all rows , most of them are equal to 0 .
eq { hop { argmax { all_rows ; omim } ; enzyme } ; uroporphyrinogen iii synthase } = true
Output: select the row whose omim record of all rows is maximum . the enzyme record of this row is uroporphyrinogen iii synthase .
greater { hop { filter_eq { all_rows ; year ; 1983 } ; points } ; hop { filter_eq { all_rows ; year ; 1984 } ; points } } = true
Output: select the rows whose year record fuzzily matches to 1983 . take the points record of this row . select the rows whose year record fuzzily matches to 1984 . take the points record of this row . the first record is greater than the second record .
and { only { filter_not_eq { all_rows ; percentage of votes ; 100 % } } ; eq { hop { filter_not_eq { all_rows ; percentage of votes ; 100 % } ; election date } ; 1981 } } = true
Output: select the rows whose percentage of votes record does not match to 100  . there is only one such row in the table . the election date record of this unqiue row is 1981 .
eq { hop { nth_argmin { all_rows ; date ; 2 } ; tournament } ; kish island } = true
Output: select the row whose date record of all rows is 2nd minimum . the tournament record of this row is kish island .
less { hop { filter_eq { all_rows ; season ; 2001 - 02 } ; regular season 1 } ; hop { filter_eq { all_rows ; season ; 2002 - 03 } ; regular season 1 } } = true
Output: select the rows whose season record fuzzily matches to 2001  02 . take the regular season 1 record of this row . select the rows whose season record fuzzily matches to 2002  03 . take the regular season 1 record of this row . the first record is less than the second record .
eq { count { filter_eq { all_rows ; alma mater ; university of guelph } } ; 3 } = true
Output: select the rows whose alma mater record fuzzily matches to university of guelph . the number of such rows is 3 .
eq { count { filter_eq { all_rows ; first air date ; july 4 , 1987 } } ; 2 } = true
Output: select the rows whose first air date record fuzzily matches to july 4 , 1987 . the number of such rows is 2 .
greater { hop { filter_eq { all_rows ; year ; 2011 } ; purse } ; hop { filter_eq { all_rows ; year ; 2005 } ; purse } } = true
Output: select the rows whose year record fuzzily matches to 2011 . take the purse record of this row . select the rows whose year record fuzzily matches to 2005 . take the purse record of this row . the first record is greater than the second record .
greater { hop { filter_eq { all_rows ; season ; 1978 - 79 } ; goals for } ; hop { filter_eq { all_rows ; season ; 1975 - 76 } ; goals for } } = true
Output: select the rows whose season record fuzzily matches to 1978  79 . take the goals for record of this row . select the rows whose season record fuzzily matches to 1975  76 . take the goals for record of this row . the first record is greater than the second record .
eq { hop { nth_argmax { all_rows ; games ; 2 } ; player } ; david gonzalvez } = true
Output: select the row whose games record of all rows is 2nd maximum . the player record of this row is david gonzalvez .
eq { hop { nth_argmin { all_rows ; first elected ; 2 } ; incumbent } ; john e raker } = true
Output: select the row whose first elected record of all rows is 2nd minimum . the incumbent record of this row is john e raker .
and { only { filter_eq { all_rows ; results ; retired } } ; eq { hop { filter_eq { all_rows ; results ; retired } ; incumbent } ; ernest istook } } = true
Output: select the rows whose results record fuzzily matches to retired . there is only one such row in the table . the incumbent record of this unqiue row is ernest istook .
less { hop { filter_eq { all_rows ; name ; coby fleener } ; round } ; hop { filter_eq { all_rows ; name ; josh chapman } ; round } } = true
Output: select the rows whose name record fuzzily matches to coby fleener . take the round record of this row . select the rows whose name record fuzzily matches to josh chapman . take the round record of this row . the first record is less than the second record .
and { only { filter_eq { all_rows ; home ; ny rangers } } ; eq { hop { filter_eq { all_rows ; home ; ny rangers } ; date } ; december 1 } } = true
Output: select the rows whose home record fuzzily matches to ny rangers . there is only one such row in the table . the date record of this unqiue row is december 1 .
most_eq { all_rows ; became consort ; husband 's ascession } = true
Output: for the became consort records of all rows , most of them fuzzily match to husband s ascession .
eq { hop { argmax { all_rows ; average } ; player } ; andrew caddick } = true
Output: select the row whose average record of all rows is maximum . the player record of this row is andrew caddick .
most_eq { all_rows ; co - drivers ; mark blundell } = true
Output: for the co  drivers records of all rows , most of them fuzzily match to mark blundell .
and { only { filter_eq { all_rows ; matches ; 4 } } ; eq { hop { filter_eq { all_rows ; matches ; 4 } ; player } ; ernie toshack } } = true
Output: select the rows whose matches record is equal to 4 . there is only one such row in the table . the player record of this unqiue row is ernie toshack .