niryuu/sni-each-converted huggingface dataset:
greater { hop { filter_eq { all_rows ; manager ; bob paisley } ; years won } ; hop { filter_eq { all_rows ; manager ; brian clough } ; years won } } = true
Output: select the rows whose manager record fuzzily matches to bob paisley . take the years won record of this row . select the rows whose manager record fuzzily matches to brian clough . take the years won record of this row . the first record is greater than the second record .
less { hop { filter_eq { all_rows ; incumbent ; john w taylor } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; john b yates } ; first elected } } = true
Output: select the rows whose incumbent record fuzzily matches to john w taylor . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to john b yates . take the first elected record of this row . the first record is less than the second record .
eq { hop { argmin { all_rows ; length } ; vessel } ; marianarray } = true
Output: select the row whose length record of all rows is minimum . the vessel record of this row is marianarray .
and { only { filter_eq { all_rows ; nba draft ; pick } } ; eq { hop { filter_eq { all_rows ; nba draft ; pick } ; player } ; marquis teague } } = true
Output: select the rows whose nba draft record fuzzily matches to pick . there is only one such row in the table . the player record of this unqiue row is marquis teague .
eq { count { filter_eq { all_rows ; high points ; vince carter } } ; 9 } = true
Output: select the rows whose high points record fuzzily matches to vince carter . the number of such rows is 9 .
most_eq { all_rows ; country ; soviet union } = true
Output: for the country records of all rows , most of them fuzzily match to soviet union .
and { only { filter_eq { all_rows ; margin ; 8 strokes } } ; eq { hop { filter_eq { all_rows ; margin ; 8 strokes } ; championship } ; us women 's open } } = true
Output: select the rows whose margin record fuzzily matches to 8 strokes . there is only one such row in the table . the championship record of this unqiue row is us women s open .
and { eq { hop { argmax { all_rows ; crowd } ; home team } ; richmond } ; eq { hop { argmax { all_rows ; crowd } ; away team } ; essendon } } = true
Output: select the row whose crowd record of all rows is maximum . the home team record of this row is richmond . the away team record of this row is essendon .
and { only { filter_eq { filter_eq { all_rows ; winner ; rangers } ; runner - up ; morton } } ; eq { hop { filter_eq { filter_eq { all_rows ; winner ; rangers } ; runner - up ; morton } ; season } ; 1941 - 42 } } = true
Output: select the rows whose winner record fuzzily matches to rangers . among these rows , select the rows whose runner  up record fuzzily matches to morton . there is only one such row in the table . the season record of this unqiue row is 1941  42 .
greater { hop { filter_eq { all_rows ; city of license ; kingston } ; frequency } ; hop { filter_eq { all_rows ; city of license ; pembroke } ; frequency } } = true
Output: select the rows whose city of license record fuzzily matches to kingston . take the frequency record of this row . select the rows whose city of license record fuzzily matches to pembroke . take the frequency record of this row . the first record is greater than the second record .
round_eq { avg { filter_eq { all_rows ; date ; 1987 } ; winning score } ; 305.5 } = true
Output: select the rows whose date record fuzzily matches to 1987 . the average of the winning score record of these rows is 305.5 .
eq { count { filter_all { all_rows ; club } } ; 11 } = true
Output: select the rows whose club record is arbitrary . the number of such rows is 11 .
eq { hop { argmin { filter_eq { all_rows ; manner of departure ; retired } ; date of vacancy } ; outgoing manager } ; mustafa denizli } = true
Output: select the rows whose manner of departure record fuzzily matches to retired . select the row whose date of vacancy record of these rows is minimum . the outgoing manager record of this row is mustafa denizli .
round_eq { avg { all_rows ; score } ; 3.7-2 .7 } = true
Output: the average of the score record of all rows is 3.72 .7 .
round_eq { avg { all_rows ; effective exhaust velocity ( m / s ) } ; 33869 } = true
Output: the average of the effective exhaust velocity  m  s  record of all rows is 33869 .
round_eq { sum { all_rows ; crowd } ; 139,789 } = true
Output: the sum of the crowd record of all rows is 139,789 .
and { eq { nth_min { all_rows ; date of vacancy ; 1 } ; 7 sep 2010 } ; eq { hop { nth_argmin { all_rows ; date of vacancy ; 1 } ; outgoing head coach } ; rasoul korbekandi } } = true
Output: the 1st minimum date of vacancy record of all rows is 7 sep 2010 . the outgoing head coach record of the row with 1st minimum date of vacancy record is rasoul korbekandi .
and { only { filter_eq { all_rows ; party ; republican } } ; eq { hop { filter_eq { all_rows ; party ; republican } ; incumbent } ; jimmy quillen } } = true
Output: select the rows whose party record fuzzily matches to republican . there is only one such row in the table . the incumbent record of this unqiue row is jimmy quillen .
and { only { filter_eq { all_rows ; format ; public broadcasting } } ; eq { hop { filter_eq { all_rows ; format ; public broadcasting } ; branding } ; cbc radio 2 } } = true
Output: select the rows whose format record fuzzily matches to public broadcasting . there is only one such row in the table . the branding record of this unqiue row is cbc radio 2 .
eq { hop { argmax { all_rows ; against } ; opposing team } ; australia } = true
Output: select the row whose against record of all rows is maximum . the opposing team record of this row is australia .
and { only { filter_eq { all_rows ; authority ; state integrated } } ; eq { hop { filter_eq { all_rows ; authority ; state integrated } ; name } ; st mary 's catholic school } } = true
Output: select the rows whose authority record fuzzily matches to state integrated . there is only one such row in the table . the name record of this unqiue row is st mary s catholic school .
all_greater_eq { all_rows ; crowd ; 8000 } = true
Output: for the crowd records of all rows , all of them are greater than or equal to 8000 .
most_eq { all_rows ; site ; ames } = true
Output: for the site records of all rows , most of them fuzzily match to ames .
eq { hop { argmin { filter_eq { all_rows ; partner ; tami whitlinger - jones } ; date } ; surface } ; carpet } = true
Output: select the rows whose partner record fuzzily matches to tami whitlinger  jones . select the row whose date record of these rows is minimum . the surface record of this row is carpet .
eq { count { filter_greater { filter_eq { all_rows ; power ( kw ) ; 10 kw } ; frequency ; 100.0 } } ; 2 } = true
Output: select the rows whose power  kw  record fuzzily matches to 10 kw . among these rows , select the rows whose frequency record is greater than 100.0 . the number of such rows is 2 .
eq { count { filter_eq { filter_eq { all_rows ; location in park ; baja ridge } ; thrill / intensity rating ; maximum } } ; 2 } = true
Output: select the rows whose location in park record fuzzily matches to baja ridge . among these rows , select the rows whose thrill  intensity rating record fuzzily matches to maximum . the number of such rows is 2 .
eq { hop { nth_argmax { all_rows ; start ; 1 } ; player } ; nicky little } = true
Output: select the row whose start record of all rows is 1st maximum . the player record of this row is nicky little .
eq { count { filter_eq { all_rows ; points ; 0 } } ; 4 } = true
Output: select the rows whose points record is equal to 0 . the number of such rows is 4 .
round_eq { avg { all_rows ; home } ; 3 } = true
Output: the average of the home record of all rows is 3 .
eq { hop { argmax { all_rows ; attendance } ; date } ; december 14 , 1958 } = true
Output: select the row whose attendance record of all rows is maximum . the date record of this row is december 14 , 1958 .
eq { diff { hop { filter_eq { all_rows ; year ; 1982 } ; prize money } ; hop { filter_eq { all_rows ; year ; 1983 } ; prize money } } ; 60000 } = true
Output: select the rows whose year record fuzzily matches to 1982 . take the prize money record of this row . select the rows whose year record fuzzily matches to 1983 . take the prize money record of this row . the first record is 60000 larger than the second record .
eq { hop { nth_argmax { all_rows ; air date ; 2 } ; guest host } ; ross kemp } = true
Output: select the row whose air date record of all rows is 2nd maximum . the guest host record of this row is ross kemp .
eq { min { all_rows ; year } ; 1968 } = true
Output: the minimum year record of all rows is 1968 .
and { only { filter_eq { all_rows ; years for jazz ; present } } ; eq { hop { filter_eq { all_rows ; years for jazz ; present } ; player } ; derrick favors } } = true
Output: select the rows whose years for jazz record fuzzily matches to present . there is only one such row in the table . the player record of this unqiue row is derrick favors .
eq { max { all_rows ; score ( l ) = score in legs , ( s ) = score in sets } ; 11 - 5 ( l ) } = true
Output: the maximum score  l  = score in legs ,  s  = score in sets record of all rows is 11  5  l  .
eq { hop { nth_argmax { all_rows ; points ; 2 } ; name } ; tom hilde } = true
Output: select the row whose points record of all rows is 2nd maximum . the name record of this row is tom hilde .
round_eq { avg { all_rows ; average ratings } ; 14.57 } = true
Output: the average of the average ratings record of all rows is 14.57 .
and { only { filter_eq { all_rows ; tournament ; world cup } } ; eq { hop { filter_eq { all_rows ; tournament ; world cup } ; year } ; 1998 } } = true
Output: select the rows whose tournament record fuzzily matches to world cup . there is only one such row in the table . the year record of this unqiue row is 1998 .
eq { hop { nth_argmax { all_rows ; bronze ; 3 } ; nation } ; soviet union } = true
Output: select the row whose bronze record of all rows is 3rd maximum . the nation record of this row is soviet union .
greater { hop { filter_eq { all_rows ; unit ; novovoronezh - 2 } ; shutdown } ; hop { filter_eq { all_rows ; unit ; novovoronezh - 1 } ; shutdown } } = true
Output: select the rows whose unit record fuzzily matches to novovoronezh  2 . take the shutdown record of this row . select the rows whose unit record fuzzily matches to novovoronezh  1 . take the shutdown record of this row . the first record is greater than the second record .
eq { hop { argmax { all_rows ; events } ; player } ; allen doyle } = true
Output: select the row whose events record of all rows is maximum . the player record of this row is allen doyle .
eq { count { filter_eq { all_rows ; withdrawn ; 1951 } } ; 5 } = true
Output: select the rows whose withdrawn record is equal to 1951 . the number of such rows is 5 .
eq { hop { nth_argmin { all_rows ; date ; 1 } ; visitor } ; new jersey } = true
Output: select the row whose date record of all rows is 1st minimum . the visitor record of this row is new jersey .
all_eq { all_rows ; songwriter ( s ) ; ( adapted by malcolm dodds ) } = true
Output: for the songwriter  s  records of all rows , all of them fuzzily match to  adapted by malcolm dodds  .
round_eq { sum { all_rows ; gold } ; 16 } = true
Output: the sum of the gold record of all rows is 16 .
round_eq { sum { filter_less_eq { all_rows ; year ; 2009 } ; tournaments played } ; 52 } = true
Output: select the rows whose year record is less than or equal to 2009 . the sum of the tournaments played record of these rows is 52 .
eq { count { filter_all { all_rows ; driver } } ; 10 } = true
Output: select the rows whose driver record is arbitrary . the number of such rows is 10 .
only { filter_eq { all_rows ; directed by ; peter deluise } } = true
Output: select the rows whose directed by record fuzzily matches to peter deluise . there is only one such row in the table .
round_eq { sum { all_rows ; matches } ; 84 } = true
Output: the sum of the matches record of all rows is 84 .
eq { min { all_rows ; year } ; 2001 - 2002 } = true
Output: the minimum year record of all rows is 2001  2002 .
eq { hop { nth_argmin { all_rows ; date ; 1 } ; winning driver } ; eugenio silvani } = true
Output: select the row whose date record of all rows is 1st minimum . the winning driver record of this row is eugenio silvani .
eq { count { filter_greater { all_rows ; us viewers ( in millions ) ; 10 } } ; 2 } = true
Output: select the rows whose us viewers  in millions  record is greater than 10 . the number of such rows is 2 .
eq { count { filter_eq { all_rows ; result ; drew } } ; 2 } = true
Output: select the rows whose result record fuzzily matches to drew . the number of such rows is 2 .
eq { hop { nth_argmax { all_rows ; joined ; 3 } ; school } ; south bend clay } = true
Output: select the row whose joined record of all rows is 3rd maximum . the school record of this row is south bend clay .
eq { hop { argmax { all_rows ; catches } ; player } ; m kaif } = true
Output: select the row whose catches record of all rows is maximum . the player record of this row is m kaif .
eq { hop { argmax { all_rows ; opened } ; name } ; ben 10 - ultimate mission } = true
Output: select the row whose opened record of all rows is maximum . the name record of this row is ben 10  ultimate mission .
most_greater { all_rows ; us viewers ( millions ) ; 5 } = true
Output: for the us viewers  millions  records of all rows , most of them are greater than 5 .
and { eq { hop { argmax { all_rows ; crowd } ; home team } ; st kilda } ; eq { hop { argmax { all_rows ; crowd } ; away team } ; richmond } } = 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 richmond .
eq { count { filter_eq { all_rows ; location ; metlife stadium } } ; 3 } = true
Output: select the rows whose location record fuzzily matches to metlife stadium . the number of such rows is 3 .
and { only { filter_eq { all_rows ; goals ; ( 1 ) } } ; eq { hop { filter_eq { all_rows ; goals ; ( 1 ) } ; player } ; dario vidošić } } = true
Output: select the rows whose goals record fuzzily matches to  1  . there is only one such row in the table . the player record of this unqiue row is dario vidošić .
most_eq { all_rows ; label ; alfa records } = true
Output: for the label records of all rows , most of them fuzzily match to alfa records .
round_eq { avg { filter_not_eq { all_rows ; webcast ; - } ; frequency } ; 958 } = true
Output: select the rows whose webcast record is not equal to  . the average of the frequency record of these rows is 958 .
all_greater { filter_eq { all_rows ; party ; republican } ; candidates ; 60 } = true
Output: select the rows whose party record fuzzily matches to republican . for the candidates records of these rows , all of them are greater than 60 .
round_eq { avg { all_rows ; goals } ; 15 } = true
Output: the average of the goals record of all rows is 15 .
most_eq { all_rows ; crankshaft ; 180 degree } = true
Output: for the crankshaft records of all rows , most of them fuzzily match to 180 degree .
eq { count { filter_all { all_rows ; the mole } } ; 14 } = true
Output: select the rows whose the mole record is arbitrary . the number of such rows is 14 .
eq { count { filter_eq { all_rows ; sequence length ; 2805nt / 934aa } } ; 2 } = true
Output: select the rows whose sequence length record fuzzily matches to 2805nt  934aa . the number of such rows is 2 .
and { only { filter_eq { all_rows ; podiums ; 4 } } ; eq { hop { filter_eq { all_rows ; podiums ; 4 } ; season } ; 2008 } } = true
Output: select the rows whose podiums record is equal to 4 . there is only one such row in the table . the season record of this unqiue row is 2008 .
eq { hop { argmin { all_rows ; uk broadcast date } ; episode title } ; the gold rush line } = true
Output: select the row whose uk broadcast date record of all rows is minimum . the episode title record of this row is the gold rush line .
most_less { all_rows ; score ; 71 } = true
Output: for the score records of all rows , most of them are less than 71 .
eq { hop { nth_argmax { all_rows ; year ; 2 } ; game } ; the elder scrolls v : skyrim } = true
Output: select the row whose year record of all rows is 2nd maximum . the game record of this row is the elder scrolls v : skyrim .
round_eq { sum { all_rows ; round } ; 19 } = true
Output: the sum of the round record of all rows is 19 .
round_eq { avg { all_rows ; episodes } ; 10.73 } = true
Output: the average of the episodes record of all rows is 10.73 .
eq { hop { nth_argmin { all_rows ; commissioned ; 2 } ; name } ; ledbury } = true
Output: select the row whose commissioned record of all rows is 2nd minimum . the name record of this row is ledbury .
greater { hop { filter_eq { all_rows ; player ; joe denly } ; runs } ; hop { filter_eq { all_rows ; player ; phil mustard } ; runs } } = true
Output: select the rows whose player record fuzzily matches to joe denly . take the runs record of this row . select the rows whose player record fuzzily matches to phil mustard . take the runs record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; club ; real madrid } } ; 9 } = true
Output: select the rows whose club record fuzzily matches to real madrid . the number of such rows is 9 .
eq { hop { nth_argmin { all_rows ; score ; 4 } ; player } ; geoff ogilvy } = true
Output: select the row whose score record of all rows is 4th minimum . the player record of this row is geoff ogilvy .
eq { hop { nth_argmax { all_rows ; mean free path ; 2 } ; vacuum range } ; ultra high vacuum } = true
Output: select the row whose mean free path record of all rows is 2nd maximum . the vacuum range record of this row is ultra high vacuum .
eq { count { filter_eq { all_rows ; record label ; wild world } } ; 3 } = true
Output: select the rows whose record label record fuzzily matches to wild world . the number of such rows is 3 .
most_less { all_rows ; wins ; 7 } = true
Output: for the wins records of all rows , most of them are less than 7 .
round_eq { avg { all_rows ; games played } ; 65.2 } = true
Output: the average of the games played record of all rows is 65.2 .
less { hop { filter_eq { all_rows ; player ; curtis hunt } ; rd } ; hop { filter_eq { all_rows ; player ; carl valimont } ; rd } } = true
Output: select the rows whose player record fuzzily matches to curtis hunt . take the rd record of this row . select the rows whose player record fuzzily matches to carl valimont . take the rd record of this row . the first record is less than the second record .
and { only { filter_eq { all_rows ; opponent in the final ; marianne witmeyer } } ; eq { hop { filter_eq { all_rows ; opponent in the final ; marianne witmeyer } ; tournament } ; hong kong } } = true
Output: select the rows whose opponent in the final record fuzzily matches to marianne witmeyer . there is only one such row in the table . the tournament record of this unqiue row is hong kong .
eq { count { filter_eq { filter_eq { all_rows ; gender ; f } ; residence ; halifax } } ; 2 } = true
Output: select the rows whose gender record fuzzily matches to f . among these rows , select the rows whose residence record fuzzily matches to halifax . the number of such rows is 2 .
and { only { filter_eq { all_rows ; nat ; ury } } ; eq { hop { filter_eq { all_rows ; nat ; ury } ; name } ; garcía } } = true
Output: select the rows whose nat record fuzzily matches to ury . there is only one such row in the table . the name record of this unqiue row is garcía .
round_eq { sum { all_rows ; apps } ; 106 } = true
Output: the sum of the apps record of all rows is 106 .
eq { hop { nth_argmin { all_rows ; date of birth ; 2 } ; president } ; jules rimet } = true
Output: select the row whose date of birth record of all rows is 2nd minimum . the president record of this row is jules rimet .
eq { count { filter_eq { all_rows ; height ; 6 - 5 } } ; 4 } = true
Output: select the rows whose height record fuzzily matches to 6  5 . the number of such rows is 4 .
eq { min { filter_eq { all_rows ; location ; fleetcenter } ; date } ; fri mar 1 } = true
Output: select the rows whose location record fuzzily matches to fleetcenter . the minimum date record of these rows is fri mar 1 .
eq { hop { argmax { all_rows ; number of seasons in superettan } ; club } ; assyriska ff } = true
Output: select the row whose number of seasons in superettan record of all rows is maximum . the club record of this row is assyriska ff .
round_eq { sum { all_rows ; capacity } ; 185080 } = true
Output: the sum of the capacity record of all rows is 185080 .
eq { count { filter_eq { all_rows ; dance styles ; rumba } } ; 5 } = true
Output: select the rows whose dance styles record fuzzily matches to rumba . the number of such rows is 5 .
less { hop { filter_eq { all_rows ; dance ; lyrical jazz } ; week } ; hop { filter_eq { all_rows ; dance ; jive } ; week } } = true
Output: select the rows whose dance record fuzzily matches to lyrical jazz . take the week record of this row . select the rows whose dance record fuzzily matches to jive . take the week record of this row . the first record is less than the second record .
eq { hop { argmax { all_rows ; points against } ; club } ; st albans rfc } = true
Output: select the row whose points against record of all rows is maximum . the club record of this row is st albans rfc .
eq { hop { nth_argmax { all_rows ; attendance ; 1 } ; date } ; january 19 } = true
Output: select the row whose attendance record of all rows is 1st maximum . the date record of this row is january 19 .
most_eq { all_rows ; high rebounds ; david lee } = true
Output: for the high rebounds records of all rows , most of them fuzzily match to david lee .
eq { count { filter_less { all_rows ; result ; 20.3 } } ; 7 } = true
Output: select the rows whose result record is less than 20.3 . the number of such rows is 7 .
and { only { filter_eq { all_rows ; score ; l } } ; eq { hop { filter_eq { all_rows ; score ; l } ; date } ; april 5 } } = true
Output: select the rows whose score record fuzzily matches to l . there is only one such row in the table . the date record of this unqiue row is april 5 .
and { only { filter_less { filter_eq { all_rows ; date ; 21 - 11 - 1973 } ; attendance ; 10000 } } ; eq { hop { filter_less { filter_eq { all_rows ; date ; 21 - 11 - 1973 } ; attendance ; 10000 } ; tie no } ; 6 } } = true
Output: select the rows whose date record fuzzily matches to 21  11  1973 . among these rows , select the rows whose attendance record is less than 10000 . there is only one such row in the table . the tie no record of this unqiue row is 6 .
round_eq { avg { all_rows ; votes } ; 46.4 } = true
Output: the average of the votes record of all rows is 46.4 .
and { only { filter_eq { all_rows ; location ; tifton } } ; eq { hop { filter_eq { all_rows ; location ; tifton } ; institution } ; abraham baldwin agricultural college } } = true
Output: select the rows whose location record fuzzily matches to tifton . there is only one such row in the table . the institution record of this unqiue row is abraham baldwin agricultural college .
eq { hop { argmin { all_rows ; time } ; nationality } ; australia } = true
Output: select the row whose time record of all rows is minimum . the nationality record of this row is australia .
round_eq { sum { all_rows ; laps } ; 810 } = true
Output: the sum of the laps record of all rows is 810 .
and { only { filter_eq { all_rows ; college ; central florida } } ; eq { hop { filter_eq { all_rows ; college ; central florida } ; name } ; paul miranda } } = true
Output: select the rows whose college record fuzzily matches to central florida . there is only one such row in the table . the name record of this unqiue row is paul miranda .
and { only { filter_eq { all_rows ; career win - loss ; 2 - } } ; eq { hop { filter_eq { all_rows ; career win - loss ; 2 - } ; tournament } ; us open } } = true
Output: select the rows whose career win  loss record fuzzily matches to 2  . there is only one such row in the table . the tournament record of this unqiue row is us open .
round_eq { avg { all_rows ; points } ; 325.4 } = true
Output: the average of the points record of all rows is 325.4 .
less { hop { filter_eq { all_rows ; sport ; soccer } ; 1st tournament } ; hop { filter_eq { all_rows ; sport ; bowling } ; 1st tournament } } = true
Output: select the rows whose sport record fuzzily matches to soccer . take the 1st tournament record of this row . select the rows whose sport record fuzzily matches to bowling . take the 1st tournament record of this row . the first record is less than the second record .
round_eq { sum { filter_eq { all_rows ; date ; august } ; attendance } ; 46879 } = true
Output: select the rows whose date record fuzzily matches to august . the sum of the attendance record of these rows is 46879 .
eq { count { filter_eq { all_rows ; money ; 122100 } } ; 6 } = true
Output: select the rows whose money record is equal to 122100 . the number of such rows is 6 .
eq { hop { argmax { all_rows ; ratings } ; guest ( s ) } ; matt lucas } = true
Output: select the row whose ratings record of all rows is maximum . the guest  s  record of this row is matt lucas .
eq { diff { hop { filter_eq { all_rows ; player ; chris garner } ; years in toronto } ; hop { filter_eq { all_rows ; player ; dion glover } ; years in toronto } } ; -7 years } = true
Output: select the rows whose player record fuzzily matches to chris garner . take the years in toronto record of this row . select the rows whose player record fuzzily matches to dion glover . take the years in toronto record of this row . the second record is 7 years larger than the first record .
and { only { filter_eq { all_rows ; round ; 3 } } ; eq { hop { filter_eq { all_rows ; round ; 3 } ; opponent } ; ian freeman } } = true
Output: select the rows whose round record is equal to 3 . there is only one such row in the table . the opponent record of this unqiue row is ian freeman .
eq { hop { argmax { filter_eq { all_rows ; opened ; 1978 } ; acres } ; location } ; sai kung district } = true
Output: select the rows whose opened record is equal to 1978 . select the row whose acres record of these rows is maximum . the location record of this row is sai kung district .
less { hop { filter_eq { all_rows ; visitor ; edmonton } ; date } ; hop { filter_eq { all_rows ; visitor ; colorado } ; date } } = true
Output: select the rows whose visitor record fuzzily matches to edmonton . take the date record of this row . select the rows whose visitor record fuzzily matches to colorado . take the date record of this row . the first record is less than the second record .
greater { hop { filter_eq { all_rows ; driver ; sarah fisher } ; laps } ; hop { filter_eq { all_rows ; driver ; dan wheldon } ; laps } } = true
Output: select the rows whose driver record fuzzily matches to sarah fisher . take the laps record of this row . select the rows whose driver record fuzzily matches to dan wheldon . take the laps record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; manner of departure ; mutual consent } } ; 3 } = true
Output: select the rows whose manner of departure record fuzzily matches to mutual consent . the number of such rows is 3 .
eq { hop { filter_eq { all_rows ; event ; masters } ; 2012 - 13 } ; hop { filter_eq { all_rows ; event ; autumn gold } ; 2012 - 13 } } = true
Output: select the rows whose event record fuzzily matches to masters . take the 2012  13 record of this row . select the rows whose event record fuzzily matches to autumn gold . take the 2012  13 record of this row . the first record fuzzily matches to the second record .
all_less_eq { all_rows ; time ; 2:31.71 } = true
Output: for the time records of all rows , all of them are less than or equal to 2:31.71 .
round_eq { avg { all_rows ; win % } ; 505 } = true
Output: the average of the win  record of all rows is 505 .
eq { count { filter_eq { all_rows ; manner of departure ; end of contract } } ; 2 } = true
Output: select the rows whose manner of departure record fuzzily matches to end of contract . the number of such rows is 2 .
and { only { filter_less { all_rows ; result ; 10 } } ; eq { hop { filter_less { all_rows ; result ; 10 } ; date } ; sunday , september 27 } } = true
Output: select the rows whose result record is less than 10 . there is only one such row in the table . the date record of this unqiue row is sunday , september 27 .
and { only { filter_eq { all_rows ; county ; wexford } } ; eq { hop { filter_eq { all_rows ; county ; wexford } ; player } ; christy kehoe } } = true
Output: select the rows whose county record fuzzily matches to wexford . there is only one such row in the table . the player record of this unqiue row is christy kehoe .
eq { hop { nth_argmax { all_rows ; quota ; 2 } ; constituency } ; connachtulster } = true
Output: select the row whose quota record of all rows is 2nd maximum . the constituency record of this row is connachtulster .
eq { hop { nth_argmax { all_rows ; time ; 2 } ; wrestler } ; chris masters } = true
Output: select the row whose time record of all rows is 2nd maximum . the wrestler record of this row is chris masters .
and { only { filter_eq { all_rows ; opponent ; chicago blackhawks } } ; eq { hop { filter_eq { all_rows ; opponent ; chicago blackhawks } ; game } ; 36 } } = true
Output: select the rows whose opponent record fuzzily matches to chicago blackhawks . there is only one such row in the table . the game record of this unqiue row is 36 .
and { only { filter_eq { all_rows ; ceased to be duchess ; husband 's execution } } ; eq { hop { filter_eq { all_rows ; ceased to be duchess ; husband 's execution } ; name } ; louise marie adélaïde de bourbon } } = true
Output: select the rows whose ceased to be duchess record fuzzily matches to husband s execution . there is only one such row in the table . the name record of this unqiue row is louise marie adélaïde de bourbon .
eq { count { filter_all { all_rows ; no in series } } ; 5 } = true
Output: select the rows whose no in series record is arbitrary . the number of such rows is 5 .
greater { hop { filter_eq { all_rows ; player ; eddie keher } ; county team } ; hop { filter_eq { all_rows ; player ; jimmy doyle } ; county team } } = true
Output: select the rows whose player record fuzzily matches to eddie keher . take the county team record of this row . select the rows whose player record fuzzily matches to jimmy doyle . take the county team record of this row . the first record is greater than the second record .
greater { hop { filter_eq { all_rows ; driver ; richard philippe } ; nor 1 } ; hop { filter_eq { all_rows ; driver ; stefano coletti } ; nor 1 } } = true
Output: select the rows whose driver record fuzzily matches to richard philippe . take the nor 1 record of this row . select the rows whose driver record fuzzily matches to stefano coletti . take the nor 1 record of this row . the first record is greater than the second record .
most_eq { all_rows ; owner ( s ) ; private owner } = true
Output: for the owner  s  records of all rows , most of them fuzzily match to private owner .
most_greater_eq { all_rows ; us viewers ( millions ) ; 15 } = true
Output: for the us viewers  millions  records of all rows , most of them are greater than or equal to 15 .
most_less { all_rows ; gdp ( billion us ) ; 100 billion } = true
Output: for the gdp  billion us  records of all rows , most of them are less than 100 billion .
and { only { filter_greater { all_rows ; us viewers ( million ) ; 11.0 } } ; and { eq { hop { filter_greater { all_rows ; us viewers ( million ) ; 11.0 } ; no } ; 1 } ; eq { hop { filter_greater { all_rows ; us viewers ( million ) ; 11.0 } ; title } ; two of a kind } } } = true
Output: select the rows whose us viewers  million  record is greater than 11.0 . there is only one such row in the table . the no record of this unqiue row is 1 . the title record of this unqiue row is two of a kind .
most_eq { all_rows ; nationality ; french } = true
Output: for the nationality records of all rows , most of them fuzzily match to french .
less { hop { filter_eq { all_rows ; name ; darwin robinson } ; round } ; hop { filter_eq { all_rows ; name ; joe miller } ; round } } = true
Output: select the rows whose name record fuzzily matches to darwin robinson . take the round record of this row . select the rows whose name record fuzzily matches to joe miller . take the round record of this row . the first record is less than the second record .
eq { hop { argmin { all_rows ; first elected } ; party } ; democratic } = true
Output: select the row whose first elected record of all rows is minimum . the party record of this row is democratic .
and { eq { max { all_rows ; date } ; 31 may 2011 } ; eq { hop { argmax { all_rows ; date } ; album } ; l'un n'empãªche pas l'autre } } = true
Output: the maximum date record of all rows is 31 may 2011 . the album record of the row with superlative date record is lun nempãªche pas lautre .
round_eq { sum { all_rows ; crowd } ; 129,800 } = true
Output: the sum of the crowd record of all rows is 129,800 .
and { eq { nth_max { all_rows ; crowd ; 1 } ; 40000 } ; eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; venue } ; victoria park } } = true
Output: the 1st maximum crowd record of all rows is 40000 . the venue record of the row with 1st maximum crowd record is victoria park .
eq { hop { nth_argmax { all_rows ; release price ( usd ) ; 2 } ; model number } ; core i7 - 870 } = true
Output: select the row whose release price  usd  record of all rows is 2nd maximum . the model number record of this row is core i7  870 .
eq { hop { nth_argmax { all_rows ; date ; 2 } ; opponent in the final } ; katerina maleeva } = true
Output: select the row whose date record of all rows is 2nd maximum . the opponent in the final record of this row is katerina maleeva .
most_greater { filter_greater { all_rows ; home team score ; 5 } ; crowd ; 20000 } = true
Output: select the rows whose home team score record is greater than 5 . for the crowd records of these rows , most of them are greater than 20000 .
eq { hop { nth_argmin { all_rows ; races ; 2 } ; season } ; 2010 } = true
Output: select the row whose races record of all rows is 2nd minimum . the season record of this row is 2010 .
and { only { filter_eq { all_rows ; content ; salute } } ; eq { hop { filter_eq { all_rows ; content ; salute } ; television service } ; erde und mensch } } = true
Output: select the rows whose content record fuzzily matches to salute . there is only one such row in the table . the television service record of this unqiue row is erde und mensch .
most_eq { filter_eq { all_rows ; home ; los angeles } ; attendance ; 18118 } = true
Output: select the rows whose home record fuzzily matches to los angeles . for the attendance records of these rows , most of them are equal to 18118 .
eq { hop { nth_argmax { all_rows ; losses ; 2 } ; club } ; club sestao } = true
Output: select the row whose losses record of all rows is 2nd maximum . the club record of this row is club sestao .
less { hop { filter_eq { all_rows ; name ; richard schryer } ; round } ; hop { filter_eq { all_rows ; name ; james carter } ; round } } = true
Output: select the rows whose name record fuzzily matches to richard schryer . take the round record of this row . select the rows whose name record fuzzily matches to james carter . take the round record of this row . the first record is less than the second record .
and { only { filter_eq { all_rows ; of stars ; 15 } } ; eq { hop { filter_eq { all_rows ; of stars ; 15 } ; season } ; 4 - autumn 2008 } } = true
Output: select the rows whose of stars record is equal to 15 . there is only one such row in the table . the season record of this unqiue row is 4  autumn 2008 .
eq { hop { nth_argmax { all_rows ; face value ; 4 } ; ecosystem } ; alpine tundra } = true
Output: select the row whose face value record of all rows is 4th maximum . the ecosystem record of this row is alpine tundra .
eq { count { filter_eq { filter_eq { all_rows ; party ; democratic } ; first elected ; 1974 } } ; 2 } = true
Output: select the rows whose party record fuzzily matches to democratic . among these rows , select the rows whose first elected record is equal to 1974 . the number of such rows is 2 .
round_eq { avg { all_rows ; overall rank } ; 72 } = true
Output: the average of the overall rank record of all rows is 72 .
most_eq { all_rows ; place ; t5 } = true
Output: for the place records of all rows , most of them fuzzily match to t5 .
round_eq { avg { all_rows ; wins } ; 1.43 } = true
Output: the average of the wins record of all rows is 1.43 .
less { hop { filter_eq { all_rows ; song title ; nee maatalo } ; year } ; hop { filter_eq { all_rows ; song title ; hey po } ; year } } = true
Output: select the rows whose song title record fuzzily matches to nee maatalo . take the year record of this row . select the rows whose song title record fuzzily matches to hey po . take the year record of this row . the first record is less than the second record .
eq { count { filter_eq { all_rows ; league cup ; 1 } } ; 4 } = true
Output: select the rows whose league cup record is equal to 1 . the number of such rows is 4 .
eq { count { filter_eq { filter_greater_eq { all_rows ; gold ; 1 } ; silver ; 2 } } ; 3 } = true
Output: select the rows whose gold record is greater than or equal to 1 . among these rows , select the rows whose silver record is equal to 2 . the number of such rows is 3 .
and { only { filter_eq { all_rows ; score ; w } } ; eq { hop { filter_eq { all_rows ; score ; w } ; date } ; april 20 } } = true
Output: select the rows whose score record fuzzily matches to w . there is only one such row in the table . the date record of this unqiue row is april 20 .
most_eq { all_rows ; team ; motopark academy } = true
Output: for the team records of all rows , most of them fuzzily match to motopark academy .
eq { nth_min { all_rows ; number ; 2 } ; 18 } = true
Output: the 2nd minimum number record of all rows is 18 .
less { hop { filter_eq { all_rows ; member countries ; france } ; gdp per capita ( us ) } ; hop { filter_eq { all_rows ; member countries ; west germany } ; gdp per capita ( us ) } } = true
Output: select the rows whose member countries record fuzzily matches to france . take the gdp per capita  us  record of this row . select the rows whose member countries record fuzzily matches to west germany . take the gdp per capita  us  record of this row . the first record is less than the second record .
eq { count { filter_eq { all_rows ; laps ; 58 } } ; 5 } = true
Output: select the rows whose laps record is equal to 58 . the number of such rows is 5 .
and { only { filter_eq { all_rows ; country ; france } } ; eq { hop { filter_eq { all_rows ; country ; france } ; player } ; thomas levet } } = true
Output: select the rows whose country record fuzzily matches to france . there is only one such row in the table . the player record of this unqiue row is thomas levet .
less { hop { filter_eq { all_rows ; year ; 1986 } ; position } ; hop { filter_eq { all_rows ; year ; 1987 } ; position } } = true
Output: select the rows whose year record fuzzily matches to 1986 . take the position record of this row . select the rows whose year record fuzzily matches to 1987 . take the position record of this row . the first record is less than the second record .
most_greater { all_rows ; duration ; 4:00 } = true
Output: for the duration records of all rows , most of them are greater than 4:00 .
eq { hop { argmax { all_rows ; completion % } ; year } ; 2010 } = true
Output: select the row whose completion  record of all rows is maximum . the year record of this row is 2010 .
and { only { filter_eq { all_rows ; status ; static display } } ; eq { hop { filter_eq { all_rows ; status ; static display } ; name / number } ; no 45 } } = true
Output: select the rows whose status record fuzzily matches to static display . there is only one such row in the table . the name  number record of this unqiue row is no 45 .
eq { hop { argmin { all_rows ; events won ( uk series ) } ; name } ; corey rennell } = true
Output: select the row whose events won  uk series  record of all rows is minimum . the name record of this row is corey rennell .
eq { diff { hop { filter_eq { all_rows ; ballpark ; seals stadium } ; opened } ; hop { filter_eq { all_rows ; ballpark ; memorial stadium } ; opened } } ; -19 } = true
Output: select the rows whose ballpark record fuzzily matches to seals stadium . take the opened record of this row . select the rows whose ballpark record fuzzily matches to memorial stadium . take the opened record of this row . the second record is 19 larger than the first record .
eq { nth_min { all_rows ; year ; 1 } ; 1977 } = true
Output: the 1st minimum year record of all rows is 1977 .
and { only { filter_eq { all_rows ; country ; italy } } ; eq { hop { filter_eq { all_rows ; country ; italy } ; athlete } ; beatrice adelizzi & giulia lapi } } = true
Output: select the rows whose country record fuzzily matches to italy . there is only one such row in the table . the athlete record of this unqiue row is beatrice adelizzi  giulia lapi .
most_eq { all_rows ; date ; 03 } = true
Output: for the date records of all rows , most of them fuzzily match to 03 .
eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; venue } ; western oval } = true
Output: select the row whose crowd record of all rows is 1st maximum . the venue record of this row is western oval .
round_eq { avg { all_rows ; wins } ; 0.6 } = true
Output: the average of the wins record of all rows is 0.6 .
and { only { filter_greater { all_rows ; position ; 15 } } ; and { eq { hop { filter_greater { all_rows ; position ; 15 } ; year } ; 2005 } ; eq { hop { filter_greater { all_rows ; position ; 15 } ; competition } ; world youth championships } } } = true
Output: select the rows whose position record is greater than 15 . there is only one such row in the table . the year record of this unqiue row is 2005 . the competition record of this unqiue row is world youth championships .
round_eq { avg { filter_eq { all_rows ; high assists ; mario chalmers } ; high assists } ; 8.33 } = true
Output: select the rows whose high assists record fuzzily matches to mario chalmers . the average of the high assists record of these rows is 8.33 .
eq { hop { argmax { all_rows ; deaths } ; ship name } ; manchester brigade } = true
Output: select the row whose deaths record of all rows is maximum . the ship name record of this row is manchester brigade .
round_eq { avg { all_rows ; score } ; 69 } = true
Output: the average of the score record of all rows is 69 .
eq { hop { argmax { all_rows ; total dismissals } ; no } ; 13 } = true
Output: select the row whose total dismissals record of all rows is maximum . the no record of this row is 13 .
and { only { filter_eq { all_rows ; total ; 5 } } ; eq { hop { filter_eq { all_rows ; total ; 5 } ; country } ; russia } } = true
Output: select the rows whose total record is equal to 5 . there is only one such row in the table . the country record of this unqiue row is russia .
and { only { filter_eq { all_rows ; college ; tulane } } ; eq { hop { filter_eq { all_rows ; college ; tulane } ; name } ; patrick ramsey } } = true
Output: select the rows whose college record fuzzily matches to tulane . there is only one such row in the table . the name record of this unqiue row is patrick ramsey .
and { only { filter_eq { all_rows ; qual 1 ; - } } ; eq { hop { filter_eq { all_rows ; qual 1 ; - } ; name } ; a j allmendinger } } = true
Output: select the rows whose qual 1 record is equal to  . there is only one such row in the table . the name record of this unqiue row is a j allmendinger .
and { only { filter_less { all_rows ; rating ; 3 } } ; eq { hop { filter_less { all_rows ; rating ; 3 } ; ride } ; taxi jam } } = true
Output: select the rows whose rating record is less than 3 . there is only one such row in the table . the ride record of this unqiue row is taxi jam .
eq { max { filter_eq { all_rows ; season ; 199 } ; goals } ; 37 } = true
Output: select the rows whose season record fuzzily matches to 199 . the maximum goals record of these rows is 37 .
and { only { filter_eq { all_rows ; venue ; tahiti } } ; eq { hop { filter_eq { all_rows ; venue ; tahiti } ; date } ; 11 may 1996 } } = true
Output: select the rows whose venue record fuzzily matches to tahiti . there is only one such row in the table . the date record of this unqiue row is 11 may 1996 .
greater { hop { filter_eq { all_rows ; incumbent ; william h bates } ; candidates } ; hop { filter_eq { all_rows ; incumbent ; harold donohue } ; candidates } } = true
Output: select the rows whose incumbent record fuzzily matches to william h bates . take the candidates record of this row . select the rows whose incumbent record fuzzily matches to harold donohue . take the candidates record of this row . the first record is greater than the second record .
eq { hop { nth_argmax { all_rows ; goals ; 3 } ; name } ; dean windass } = true
Output: select the row whose goals record of all rows is 3rd maximum . the name record of this row is dean windass .
and { only { filter_eq { all_rows ; weeks on top ; 11 } } ; eq { hop { filter_eq { all_rows ; weeks on top ; 11 } ; song } ; mambo no 5 } } = true
Output: select the rows whose weeks on top record is equal to 11 . there is only one such row in the table . the song record of this unqiue row is mambo no 5 .
all_greater { all_rows ; attendance ; 25000 } = true
Output: for the attendance records of all rows , all of them are greater than 25000 .
eq { count { filter_eq { all_rows ; margin of victory ; playoff } } ; 2 } = true
Output: select the rows whose margin of victory record fuzzily matches to playoff . the number of such rows is 2 .
eq { count { filter_all { all_rows ; nation } } ; 17 } = true
Output: select the rows whose nation record is arbitrary . the number of such rows is 17 .
eq { count { filter_greater { all_rows ; viewership ; 6.00 million } } ; 4 } = true
Output: select the rows whose viewership record is greater than 6.00 million . the number of such rows is 4 .
eq { count { filter_eq { all_rows ; candidates ; unopposed } } ; 5 } = true
Output: select the rows whose candidates record fuzzily matches to unopposed . the number of such rows is 5 .
round_eq { avg { all_rows ; total passengers } ; 4773780 } = true
Output: the average of the total passengers record of all rows is 4773780 .
all_eq { all_rows ; s host ; bob costas and tom hammond } = true
Output: for the s host records of all rows , all of them fuzzily match to bob costas and tom hammond .
only { filter_eq { all_rows ; player ; vic / sa } } = true
Output: select the rows whose player record fuzzily matches to vic  sa . there is only one such row in the table .
eq { hop { nth_argmin { all_rows ; year ; 2 } ; nomination } ; jam hsiao } = true
Output: select the row whose year record of all rows is 2nd minimum . the nomination record of this row is jam hsiao .
and { only { filter_eq { all_rows ; date ; - } } ; eq { hop { filter_eq { all_rows ; date ; - } ; week } ; 6 } } = true
Output: select the rows whose date record is equal to  . there is only one such row in the table . the week record of this unqiue row is 6 .
and { only { filter_eq { all_rows ; college ; pittsburgh } } ; eq { hop { filter_eq { all_rows ; college ; pittsburgh } ; player } ; marty schottenheimer } } = true
Output: select the rows whose college record fuzzily matches to pittsburgh . there is only one such row in the table . the player record of this unqiue row is marty schottenheimer .
eq { diff { hop { filter_eq { all_rows ; artist ; ronnie carroll } ; points } ; hop { filter_eq { all_rows ; artist ; carmela corren } ; points } } ; 12 } = true
Output: select the rows whose artist record fuzzily matches to ronnie carroll . take the points record of this row . select the rows whose artist record fuzzily matches to carmela corren . take the points record of this row . the first record is 12 larger than the second record .
eq { count { filter_eq { all_rows ; earpads ; comfort pads } } ; 4 } = true
Output: select the rows whose earpads record fuzzily matches to comfort pads . the number of such rows is 4 .
eq { hop { nth_argmax { all_rows ; enrollment ; 2 } ; school } ; portage } = true
Output: select the row whose enrollment record of all rows is 2nd maximum . the school record of this row is portage .