niryuu/sni-each-converted huggingface dataset:
and { eq { max { all_rows ; goals } ; 15 } ; eq { hop { argmax { all_rows ; goals } ; scorer } ; choi sang - kuk } } = true
Output: the maximum goals record of all rows is 15 . the scorer record of the row with superlative goals record is choi sang  kuk .
and { only { filter_eq { all_rows ; country ; spain } } ; eq { hop { filter_eq { all_rows ; country ; spain } ; english title } ; bad education } } = true
Output: select the rows whose country record fuzzily matches to spain . there is only one such row in the table . the english title record of this unqiue row is bad education .
round_eq { avg { all_rows ; score } ; 1.67 } = true
Output: the average of the score record of all rows is 1.67 .
less { hop { filter_eq { all_rows ; tournament ; nice } ; date } ; hop { filter_eq { all_rows ; tournament ; estoril } ; date } } = true
Output: select the rows whose tournament record fuzzily matches to nice . take the date record of this row . select the rows whose tournament record fuzzily matches to estoril . take the date record of this row . the first record is less than the second record .
most_eq { all_rows ; hand guards ; m4 } = true
Output: for the hand guards records of all rows , most of them fuzzily match to m4 .
greater { hop { filter_eq { all_rows ; tournament ; turespana masters - open andalucia } ; margin of victory } ; hop { filter_eq { all_rows ; tournament ; bmw international open } ; margin of victory } } = true
Output: select the rows whose tournament record fuzzily matches to turespana masters  open andalucia . take the margin of victory record of this row . select the rows whose tournament record fuzzily matches to bmw international open . take the margin of victory record of this row . the first record is greater than the second record .
greater { hop { filter_eq { all_rows ; name ; dante taylor } ; weight ( lb ) } ; hop { filter_eq { all_rows ; name ; nasir robinson } ; weight ( lb ) } } = true
Output: select the rows whose name record fuzzily matches to dante taylor . take the weight  lb  record of this row . select the rows whose name record fuzzily matches to nasir robinson . take the weight  lb  record of this row . the first record is greater than the second record .
and { only { filter_eq { filter_eq { all_rows ; original air date ; august } ; written by ; dana calvo } } ; eq { hop { filter_eq { filter_eq { all_rows ; original air date ; august } ; written by ; dana calvo } ; title } ; houses of the holy } } = true
Output: select the rows whose original air date record fuzzily matches to august . among these rows , select the rows whose written by record fuzzily matches to dana calvo . there is only one such row in the table . the title record of this unqiue row is houses of the holy .
most_eq { all_rows ; position ; defence } = true
Output: for the position records of all rows , most of them fuzzily match to defence .
round_eq { avg { filter_eq { all_rows ; game site ; gillette stadium } ; result } ; 26 } = true
Output: select the rows whose game site record fuzzily matches to gillette stadium . the average of the result record of these rows is 26 .
round_eq { sum { filter_eq { all_rows ; date ; september } ; attendance } ; 226814 } = true
Output: select the rows whose date record fuzzily matches to september . the sum of the attendance record of these rows is 226814 .
eq { count { filter_eq { all_rows ; pole position ; alain prost } } ; 2 } = true
Output: select the rows whose pole position record fuzzily matches to alain prost . the number of such rows is 2 .
eq { diff { hop { filter_eq { all_rows ; player ; wayne ryan } ; games played } ; hop { filter_eq { all_rows ; player ; tim regan } ; games played } } ; -2 } = true
Output: select the rows whose player record fuzzily matches to wayne ryan . take the games played record of this row . select the rows whose player record fuzzily matches to tim regan . take the games played record of this row . the second record is 2 larger than the first record .
eq { hop { argmax { all_rows ; torque ( nm ) / rpm } ; model / engine } ; 2.0 duratec he } = true
Output: select the row whose torque  nm   rpm record of all rows is maximum . the model  engine record of this row is 2.0 duratec he .
eq { hop { argmax { filter_eq { all_rows ; college ; north carolina } ; height } ; player } ; jeff lebo } = true
Output: select the rows whose college record fuzzily matches to north carolina . select the row whose height record of these rows is maximum . the player record of this row is jeff lebo .
less { hop { filter_eq { all_rows ; incumbent ; david trimble } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; thomas p moore } ; first elected } } = true
Output: select the rows whose incumbent record fuzzily matches to david trimble . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to thomas p moore . take the first elected record of this row . the first record is less than the second record .
all_eq { all_rows ; region ; colorado } = true
Output: for the region records of all rows , all of them fuzzily match to colorado .
eq { count { filter_eq { all_rows ; peak ; 1 } } ; 4 } = true
Output: select the rows whose peak record is equal to 1 . the number of such rows is 4 .
round_eq { sum { filter_less_eq { all_rows ; won ; 5 } ; points } ; 75 } = true
Output: select the rows whose won record is less than or equal to 5 . the sum of the points record of these rows is 75 .
eq { hop { nth_argmin { all_rows ; week ; 2 } ; date } ; february 3 } = true
Output: select the row whose week record of all rows is 2nd minimum . the date record of this row is february 3 .
less { hop { filter_eq { all_rows ; reserve ; victoria peak } ; est } ; hop { filter_eq { all_rows ; reserve ; thousand foot falls } ; est } } = true
Output: select the rows whose reserve record fuzzily matches to victoria peak . take the est record of this row . select the rows whose reserve record fuzzily matches to thousand foot falls . take the est record of this row . the first record is less than the second record .
round_eq { sum { all_rows ; against } ; 49 } = true
Output: the sum of the against record of all rows is 49 .
and { only { filter_eq { all_rows ; position ; running back } } ; eq { hop { filter_eq { all_rows ; position ; running back } ; player } ; doug goodwin } } = true
Output: select the rows whose position record fuzzily matches to running back . there is only one such row in the table . the player record of this unqiue row is doug goodwin .
most_eq { all_rows ; rhode island ; lincoln ll lincoln } = true
Output: for the rhode island records of all rows , most of them fuzzily match to lincoln ll lincoln .
eq { hop { argmax { all_rows ; points } ; club } ; caernarfon rfc } = true
Output: select the row whose points record of all rows is maximum . the club record of this row is caernarfon rfc .
eq { count { filter_greater { filter_eq { all_rows ; nationality ; united states } ; result ; 12.6 } } ; 2 } = true
Output: select the rows whose nationality record fuzzily matches to united states . among these rows , select the rows whose result record is greater than 12.6 . the number of such rows is 2 .
eq { count { filter_eq { all_rows ; date ; 10 march 1984 } } ; 3 } = true
Output: select the rows whose date record fuzzily matches to 10 march 1984 . the number of such rows is 3 .
eq { count { filter_less { all_rows ; attendance ; 40000 } } ; 1 } = true
Output: select the rows whose attendance record is less than 40000 . the number of such rows is 1 .
eq { count { filter_eq { filter_eq { all_rows ; spectators ; 50000 } ; res ; 3 } } ; 2 } = true
Output: select the rows whose spectators record is equal to 50000 . among these rows , select the rows whose res record fuzzily matches to 3 . the number of such rows is 2 .
eq { hop { argmax { all_rows ; power } ; name } ; 9 adr } = true
Output: select the row whose power record of all rows is maximum . the name record of this row is 9 adr .
most_eq { all_rows ; gender ; coed } = true
Output: for the gender records of all rows , most of them fuzzily match to coed .
eq { hop { argmin { all_rows ; start } ; year } ; 1952 } = true
Output: select the row whose start record of all rows is minimum . the year record of this row is 1952 .
only { filter_eq { all_rows ; hometown ; brampton , on } } = true
Output: select the rows whose hometown record fuzzily matches to brampton , on . there is only one such row in the table .
greater { hop { filter_eq { all_rows ; date ; october 16 , 1977 } ; attendance } ; hop { filter_eq { all_rows ; date ; november 13 , 1977 } ; attendance } } = true
Output: select the rows whose date record fuzzily matches to october 16 , 1977 . take the attendance record of this row . select the rows whose date record fuzzily matches to november 13 , 1977 . take the attendance record of this row . the first record is greater than the second record .
less { hop { filter_eq { all_rows ; winning team ; hwa 1 } ; date } ; hop { filter_eq { all_rows ; winning team ; opel team phoenix } ; date } } = true
Output: select the rows whose winning team record fuzzily matches to hwa 1 . take the date record of this row . select the rows whose winning team record fuzzily matches to opel team phoenix . take the date record of this row . the first record is less than the second record .
eq { max { filter_eq { all_rows ; location / attendance ; the omni } ; score } ; w 124 - 91 } = true
Output: select the rows whose location  attendance record fuzzily matches to the omni . the maximum score record of these rows is w 124  91 .
greater { hop { filter_eq { all_rows ; date ; november 27 , 1989 ( mon ) } ; result } ; hop { filter_eq { all_rows ; date ; november 19 , 1989 } ; result } } = true
Output: select the rows whose date record fuzzily matches to november 27 , 1989  mon  . take the result record of this row . select the rows whose date record fuzzily matches to november 19 , 1989 . take the result record of this row . the first record is greater than the second record .
most_eq { all_rows ; top - 10 ; 0 } = true
Output: for the top  10 records of all rows , most of them are equal to 0 .
eq { hop { nth_argmin { all_rows ; attendance ; 2 } ; date } ; november 4 , 2002 } = true
Output: select the row whose attendance record of all rows is 2nd minimum . the date record of this row is november 4 , 2002 .
eq { hop { nth_argmax { all_rows ; attendance ; 3 } ; date } ; november 27 , 1988 } = true
Output: select the row whose attendance record of all rows is 3rd maximum . the date record of this row is november 27 , 1988 .
eq { count { filter_eq { all_rows ; position ; left wing } } ; 4 } = true
Output: select the rows whose position record fuzzily matches to left wing . the number of such rows is 4 .
eq { hop { nth_argmax { all_rows ; size ; 2 } ; campus } ; kendall campus } = true
Output: select the row whose size record of all rows is 2nd maximum . the campus record of this row is kendall campus .
eq { hop { argmax { all_rows ; profits ( billion ) } ; company } ; exxonmobil } = true
Output: select the row whose profits  billion  record of all rows is maximum . the company record of this row is exxonmobil .
eq { hop { nth_argmin { all_rows ; first elected ; 2 } ; district } ; louisiana 2 } = true
Output: select the row whose first elected record of all rows is 2nd minimum . the district record of this row is louisiana 2 .
greater { hop { filter_eq { all_rows ; team ; east germany } ; lost } ; hop { filter_eq { all_rows ; team ; poland } ; lost } } = true
Output: select the rows whose team record fuzzily matches to east germany . take the lost record of this row . select the rows whose team record fuzzily matches to poland . take the lost record of this row . the first record is greater than the second record .
and { only { filter_eq { all_rows ; format ( s ) ; download } } ; eq { hop { filter_eq { all_rows ; format ( s ) ; download } ; year } ; 2008 } } = true
Output: select the rows whose format  s  record fuzzily matches to download . there is only one such row in the table . the year record of this unqiue row is 2008 .
eq { hop { argmax { all_rows ; opponents } ; opponent } ; new york jets } = true
Output: select the row whose opponents record of all rows is maximum . the opponent record of this row is new york jets .
eq { count { filter_eq { all_rows ; games ; 14 } } ; 3 } = true
Output: select the rows whose games record is equal to 14 . the number of such rows is 3 .
eq { hop { argmax { all_rows ; oilers first downs } ; opponent } ; new england patriots } = true
Output: select the row whose oilers first downs record of all rows is maximum . the opponent record of this row is new england patriots .
eq { hop { nth_argmax { all_rows ; listed ; 3 } ; county } ; lake } = true
Output: select the row whose listed record of all rows is 3rd maximum . the county record of this row is lake .
eq { hop { argmax { all_rows ; points } ; team } ; alianza fc } = true
Output: select the row whose points record of all rows is maximum . the team record of this row is alianza fc .
round_eq { avg { filter_eq { all_rows ; location attendance ; energy solutions arena } ; location attendance } ; 19911 } = true
Output: select the rows whose location attendance record fuzzily matches to energy solutions arena . the average of the location attendance record of these rows is 19911 .
eq { hop { argmax { all_rows ; enrollment } ; school } ; fort wayne homestead } = true
Output: select the row whose enrollment record of all rows is maximum . the school record of this row is fort wayne homestead .
eq { count { filter_eq { all_rows ; pos ; d } } ; 4 } = true
Output: select the rows whose pos record fuzzily matches to d . the number of such rows is 4 .
most_greater { all_rows ; points for ; 400 } = true
Output: for the points for records of all rows , most of them are greater than 400 .
and { only { filter_eq { all_rows ; total ; 35994 } } ; eq { hop { filter_eq { all_rows ; total ; 35994 } ; department } ; la paz } } = true
Output: select the rows whose total record is equal to 35994 . there is only one such row in the table . the department record of this unqiue row is la paz .
eq { count { filter_all { all_rows ; nation } } ; 16 } = true
Output: select the rows whose nation record is arbitrary . the number of such rows is 16 .
eq { count { filter_eq { all_rows ; length ; 60 mins } } ; 12 } = true
Output: select the rows whose length record fuzzily matches to 60 mins . the number of such rows is 12 .
eq { hop { argmax { all_rows ; area ( km square ) } ; name } ; suðurland } = true
Output: select the row whose area  km square  record of all rows is maximum . the name record of this row is suðurland .
eq { hop { argmax { all_rows ; area km 2 } ; official name } ; kent } = true
Output: select the row whose area km 2 record of all rows is maximum . the official name record of this row is kent .
eq { hop { nth_argmax { all_rows ; crowd ; 2 } ; venue } ; vfl park } = true
Output: select the row whose crowd record of all rows is 2nd maximum . the venue record of this row is vfl park .
eq { count { filter_eq { all_rows ; language ; english } } ; 3 } = true
Output: select the rows whose language record fuzzily matches to english . the number of such rows is 3 .
eq { count { filter_eq { all_rows ; high assists ; mo williams } } ; 2 } = true
Output: select the rows whose high assists record fuzzily matches to mo williams . the number of such rows is 2 .
eq { count { filter_eq { all_rows ; final position / round ; runner - up } } ; 4 } = true
Output: select the rows whose final position  round record fuzzily matches to runner  up . the number of such rows is 4 .
eq { count { filter_eq { all_rows ; result ; re - elected } } ; 8 } = true
Output: select the rows whose result record fuzzily matches to re  elected . the number of such rows is 8 .
less { hop { filter_eq { all_rows ; visitor ; suns } ; date } ; hop { filter_eq { all_rows ; visitor ; timberwolves } ; date } } = true
Output: select the rows whose visitor record fuzzily matches to suns . take the date record of this row . select the rows whose visitor record fuzzily matches to timberwolves . take the date record of this row . the first record is less than the second record .
and { only { filter_greater { all_rows ; of candidates nominated ; 50 } } ; eq { hop { filter_greater { all_rows ; of candidates nominated ; 50 } ; election } ; 2008 } } = true
Output: select the rows whose of candidates nominated record is greater than 50 . there is only one such row in the table . the election record of this unqiue row is 2008 .
round_eq { avg { all_rows ; attendance } ; 15000 } = true
Output: the average of the attendance record of all rows is 15000 .
eq { hop { nth_argmax { all_rows ; population ( 2007 ) ; 2 } ; municipality } ; labo } = true
Output: select the row whose population  2007  record of all rows is 2nd maximum . the municipality record of this row is labo .
eq { hop { nth_argmin { all_rows ; date ; 17 } ; competition } ; 2012 philippine peace cup } = true
Output: select the row whose date record of all rows is 17th minimum . the competition record of this row is 2012 philippine peace cup .
and { only { filter_eq { all_rows ; high points ; gerald wallace } } ; eq { hop { filter_eq { all_rows ; high points ; gerald wallace } ; date } ; february 8 } } = true
Output: select the rows whose high points record fuzzily matches to gerald wallace . there is only one such row in the table . the date record of this unqiue row is february 8 .
round_eq { avg { all_rows ; attendance } ; 57797 } = true
Output: the average of the attendance record of all rows is 57797 .
most_eq { all_rows ; series name ; international formula master } = true
Output: for the series name records of all rows , most of them fuzzily match to international formula master .
round_eq { avg { all_rows ; floors } ; 24.6 } = true
Output: the average of the floors record of all rows is 24.6 .
all_eq { all_rows ; home captain ; alec stewart } = true
Output: for the home captain records of all rows , all of them fuzzily match to alec stewart .
eq { count { filter_eq { all_rows ; timeslot ; live 3p-6p } } ; 5 } = true
Output: select the rows whose timeslot record fuzzily matches to live 3p6p . the number of such rows is 5 .
most_eq { all_rows ; winning party 2003 ; labour } = true
Output: for the winning party 2003 records of all rows , most of them fuzzily match to labour .
round_eq { sum { all_rows ; tries for } ; 475 } = true
Output: the sum of the tries for record of all rows is 475 .
and { only { filter_less { filter_eq { all_rows ; dominant religion ( 2002 ) ; orthodox christianity } ; population ( 2011 ) ; 2100 } } ; eq { hop { filter_less { filter_eq { all_rows ; dominant religion ( 2002 ) ; orthodox christianity } ; population ( 2011 ) ; 2100 } ; settlement } ; stepanovićevo } } = true
Output: select the rows whose dominant religion  2002  record fuzzily matches to orthodox christianity . among these rows , select the rows whose population  2011  record is less than 2100 . there is only one such row in the table . the settlement record of this unqiue row is stepanovićevo .
greater { hop { filter_eq { all_rows ; player ; dave viti } ; round } ; hop { filter_eq { all_rows ; player ; billy neighbors } ; round } } = true
Output: select the rows whose player record fuzzily matches to dave viti . take the round record of this row . select the rows whose player record fuzzily matches to billy neighbors . take the round record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; goals ; 64 } } ; 3 } = true
Output: select the rows whose goals record is equal to 64 . the number of such rows is 3 .
all_eq { all_rows ; status ; running } = true
Output: for the status records of all rows , all of them fuzzily match to running .
and { only { filter_eq { all_rows ; shirt sponsor ; caja granada } } ; eq { hop { filter_eq { all_rows ; shirt sponsor ; caja granada } ; team } ; granada } } = true
Output: select the rows whose shirt sponsor record fuzzily matches to caja granada . there is only one such row in the table . the team record of this unqiue row is granada .
eq { hop { argmax { all_rows ; opponents } ; opponent } ; penn state } = true
Output: select the row whose opponents record of all rows is maximum . the opponent record of this row is penn state .
most_eq { all_rows ; high assists ; whalen } = true
Output: for the high assists records of all rows , most of them fuzzily match to whalen .
eq { count { filter_eq { all_rows ; years ; 192 } } ; 2 } = true
Output: select the rows whose years record fuzzily matches to 192 . the number of such rows is 2 .
most_eq { all_rows ; high points ; parker } = true
Output: for the high points records of all rows , most of them fuzzily match to parker .
round_eq { avg { all_rows ; score } ; 102 } = true
Output: the average of the score record of all rows is 102 .
greater { hop { filter_eq { all_rows ; club ; ca osasuna } ; wins } ; hop { filter_eq { all_rows ; club ; real oviedo } ; wins } } = true
Output: select the rows whose club record fuzzily matches to ca osasuna . take the wins record of this row . select the rows whose club record fuzzily matches to real oviedo . take the wins record of this row . the first record is greater than the second record .
eq { count { filter_all { all_rows ; name } } ; 17 } = true
Output: select the rows whose name record is arbitrary . the number of such rows is 17 .
and { only { filter_greater { all_rows ; goalsagainst ; 300 } } ; eq { hop { filter_greater { all_rows ; goalsagainst ; 300 } ; season } ; 1997 - 98 } } = true
Output: select the rows whose goalsagainst record is greater than 300 . there is only one such row in the table . the season record of this unqiue row is 1997  98 .
and { only { filter_eq { all_rows ; country ; canada } } ; eq { hop { filter_eq { all_rows ; country ; canada } ; champion } ; jessica shepley } } = true
Output: select the rows whose country record fuzzily matches to canada . there is only one such row in the table . the champion record of this unqiue row is jessica shepley .
eq { hop { nth_argmin { all_rows ; year joined ; 6 } ; school } ; sheridan } = true
Output: select the row whose year joined record of all rows is 6th minimum . the school record of this row is sheridan .
round_eq { avg { all_rows ; viewers ( in millions ) } ; 7.76 } = true
Output: the average of the viewers  in millions  record of all rows is 7.76 .
eq { count { filter_eq { all_rows ; shot % ; 78 % } } ; 2 } = true
Output: select the rows whose shot  record fuzzily matches to 78  . the number of such rows is 2 .
round_eq { avg { all_rows ; population census 2009 } ; 554218 } = true
Output: the average of the population census 2009 record of all rows is 554218 .
eq { hop { argmax { all_rows ; attendance } ; date } ; nov 23 } = true
Output: select the row whose attendance record of all rows is maximum . the date record of this row is nov 23 .
eq { hop { argmax { filter_eq { all_rows ; nationality ; japan } ; time } ; name } ; noriko inada } = true
Output: select the rows whose nationality record fuzzily matches to japan . select the row whose time record of these rows is maximum . the name record of this row is noriko inada .
round_eq { avg { all_rows ; round } ; 1.7 } = true
Output: the average of the round record of all rows is 1.7 .
greater { hop { filter_eq { all_rows ; location ; bridgewater , massachusetts } ; enrollment } ; hop { filter_eq { all_rows ; location ; fitchburg , massachusetts } ; enrollment } } = true
Output: select the rows whose location record fuzzily matches to bridgewater , massachusetts . take the enrollment record of this row . select the rows whose location record fuzzily matches to fitchburg , massachusetts . take the enrollment record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; pos ; gk } } ; 2 } = true
Output: select the rows whose pos record fuzzily matches to gk . the number of such rows is 2 .
and { only { filter_eq { all_rows ; cover model ; no model pictured } } ; eq { hop { filter_eq { all_rows ; cover model ; no model pictured } ; date } ; 1 - 84 } } = true
Output: select the rows whose cover model record fuzzily matches to no model pictured . there is only one such row in the table . the date record of this unqiue row is 1  84 .
and { only { filter_eq { all_rows ; owner ; eternacom } } ; eq { hop { filter_eq { all_rows ; owner ; eternacom } ; frequency } ; 103.5 fm } } = true
Output: select the rows whose owner record fuzzily matches to eternacom . there is only one such row in the table . the frequency record of this unqiue row is 103.5 fm .
greater { hop { filter_eq { all_rows ; venue ; rome } ; date } ; hop { filter_eq { all_rows ; venue ; beijing } ; date } } = true
Output: select the rows whose venue record fuzzily matches to rome . take the date record of this row . select the rows whose venue record fuzzily matches to beijing . take the date record of this row . the first record is greater than the second record .
eq { hop { nth_argmin { all_rows ; year ; 8 } ; silver } ; dana brozkova } = true
Output: select the row whose year record of all rows is 8th minimum . the silver record of this row is dana brozkova .
greater { hop { filter_eq { all_rows ; title ; merchants of brooklyn } ; year } ; hop { filter_eq { all_rows ; title ; crysis warhead } ; year } } = true
Output: select the rows whose title record fuzzily matches to merchants of brooklyn . take the year record of this row . select the rows whose title record fuzzily matches to crysis warhead . take the year record of this row . the first record is greater than the second record .
and { eq { max { all_rows ; goal difference } ; + 33 } ; eq { hop { argmax { all_rows ; goal difference } ; club } ; real sociedad } } = true
Output: the maximum goal difference record of all rows is + 33 . the club record of the row with superlative goal difference record is real sociedad .
eq { hop { nth_argmax { filter_greater_eq { all_rows ; laps ; 200 } ; rank ; 2 } ; year } ; 1953 } = true
Output: select the rows whose laps record is greater than or equal to 200 . select the row whose rank record of these rows is 2nd maximum . the year record of this row is 1953 .
eq { hop { nth_argmin { all_rows ; date of departure ; 2 } ; outgoing manager } ; josé trejo } = true
Output: select the row whose date of departure record of all rows is 2nd minimum . the outgoing manager record of this row is josé trejo .
eq { hop { argmax { all_rows ; to par } ; player } ; tiger woods } = true
Output: select the row whose to par record of all rows is maximum . the player record of this row is tiger woods .
eq { count { filter_eq { all_rows ; location ; roskilde ring } } ; 8 } = true
Output: select the rows whose location record fuzzily matches to roskilde ring . the number of such rows is 8 .
and { eq { max { all_rows ; points } ; 15 } ; and { eq { hop { argmax { all_rows ; points } ; place } ; 1 } ; eq { hop { argmax { all_rows ; points } ; country } ; australia } } } = true
Output: the maximum points record of all rows is 15 . the place record of the row with superlative points record is 1 . the country record of the row with superlative points record is australia .
and { only { filter_eq { all_rows ; start ; 10 } } ; eq { hop { filter_eq { all_rows ; start ; 10 } ; year } ; 1958 } } = true
Output: select the rows whose start record is equal to 10 . there is only one such row in the table . the year record of this unqiue row is 1958 .
eq { count { filter_eq { all_rows ; receiving tds ; 0 } } ; 2 } = true
Output: select the rows whose receiving tds record is equal to 0 . the number of such rows is 2 .
eq { hop { argmax { all_rows ; worldwide gross } ; title } ; the lord of the rings : the two towers } = true
Output: select the row whose worldwide gross record of all rows is maximum . the title record of this row is the lord of the rings : the two towers .
eq { count { filter_eq { all_rows ; titles ; 1 } } ; 2 } = true
Output: select the rows whose titles record is equal to 1 . the number of such rows is 2 .
most_eq { filter_greater { all_rows ; date ; sun mar 24 } ; location ; fleetcenter } = true
Output: select the rows whose date record is greater than sun mar 24 . for the location records of these rows , most of them fuzzily match to fleetcenter .
greater { hop { filter_eq { all_rows ; official name ; saint george } ; population } ; hop { filter_eq { all_rows ; official name ; saint andrews } ; population } } = true
Output: select the rows whose official name record fuzzily matches to saint george . take the population record of this row . select the rows whose official name record fuzzily matches to saint andrews . take the population record of this row . the first record is greater than the second record .
and { only { filter_eq { all_rows ; result ; retired } } ; eq { hop { filter_eq { all_rows ; result ; retired } ; incumbent } ; harold r collier } } = true
Output: select the rows whose result record fuzzily matches to retired . there is only one such row in the table . the incumbent record of this unqiue row is harold r collier .
eq { count { filter_eq { all_rows ; tournament ; switzerland } } ; 3 } = true
Output: select the rows whose tournament record fuzzily matches to switzerland . the number of such rows is 3 .
eq { hop { nth_argmin { all_rows ; founded ; 3 } ; institution } ; george washington university } = true
Output: select the row whose founded record of all rows is 3rd minimum . the institution record of this row is george washington university .
most_eq { all_rows ; comment ; landed with 747 } = true
Output: for the comment records of all rows , most of them fuzzily match to landed with 747 .
all_eq { all_rows ; doctor ; 6th } = true
Output: for the doctor records of all rows , all of them fuzzily match to 6th .
and { eq { max { all_rows ; saturated fat } ; 52 g ( 55 % ) } ; eq { hop { argmax { all_rows ; saturated fat } ; } ; suet } } = true
Output: the maximum saturated fat record of all rows is 52 g  55   . the record of the row with superlative saturated fat record is suet .
greater { hop { filter_eq { all_rows ; team 1 ; energy invest rustavi } ; agg } ; hop { filter_eq { all_rows ; team 1 ; spartak pleven } ; agg } } = true
Output: select the rows whose team 1 record fuzzily matches to energy invest rustavi . take the agg record of this row . select the rows whose team 1 record fuzzily matches to spartak pleven . take the agg record of this row . the first record is greater than the second record .
and { eq { hop { filter_eq { all_rows ; date ; 10 june 2009 } ; competition } ; hop { filter_eq { all_rows ; date ; 5 september 2009 } ; competition } } ; and { eq { hop { filter_eq { all_rows ; date ; 10 june 2009 } ; competition } ; 2010 fifa world cup qualification } ; eq { hop { filter_eq { all_rows ; date ; 5 september 2009 } ; competition } ; 2010 fifa world cup qualification } } } = true
Output: select the rows whose date record fuzzily matches to 10 june 2009 . take the competition record of this row . select the rows whose date record fuzzily matches to 5 september 2009 . take the competition record of this row . the first record fuzzily matches to the second record . the competition record of the first row is 2010 fifa world cup qualification . the competition record of the second row is 2010 fifa world cup qualification .
less { hop { filter_eq { all_rows ; chassis ; brm p201 } ; year } ; hop { filter_eq { all_rows ; chassis ; hill gh1 } ; year } } = true
Output: select the rows whose chassis record fuzzily matches to brm p201 . take the year record of this row . select the rows whose chassis record fuzzily matches to hill gh1 . take the year record of this row . the first record is less than the second record .
less { hop { filter_eq { all_rows ; runner - up ; dynamo moscow } ; season } ; hop { filter_eq { all_rows ; runner - up ; lokomotiv moscow } ; season } } = true
Output: select the rows whose runner  up record fuzzily matches to dynamo moscow . take the season record of this row . select the rows whose runner  up record fuzzily matches to lokomotiv moscow . take the season record of this row . the first record is less than the second record .
most_greater_eq { all_rows ; year born ; 1980 } = true
Output: for the year born records of all rows , most of them are greater than or equal to 1980 .
less { hop { filter_eq { all_rows ; driver ; kyle busch } ; car } ; hop { filter_eq { all_rows ; driver ; matt crafton } ; car } } = true
Output: select the rows whose driver record fuzzily matches to kyle busch . take the car record of this row . select the rows whose driver record fuzzily matches to matt crafton . take the car record of this row . the first record is less than the second record .
less { hop { filter_eq { all_rows ; country ; romania } ; time } ; hop { filter_eq { all_rows ; country ; ukraine } ; time } } = true
Output: select the rows whose country record fuzzily matches to romania . take the time record of this row . select the rows whose country record fuzzily matches to ukraine . take the time record of this row . the first record is less than the second record .
round_eq { avg { all_rows ; attendance } ; 23350 } = true
Output: the average of the attendance record of all rows is 23350 .
eq { count { filter_eq { all_rows ; opponent ; south sydney rabbitohs } } ; 2 } = true
Output: select the rows whose opponent record fuzzily matches to south sydney rabbitohs . the number of such rows is 2 .
and { only { filter_eq { all_rows ; founding university ; university at buffalo , suny } } ; eq { hop { filter_eq { all_rows ; founding university ; university at buffalo , suny } ; organization } ; delta epsilon sigma iota } } = true
Output: select the rows whose founding university record fuzzily matches to university at buffalo , suny . there is only one such row in the table . the organization record of this unqiue row is delta epsilon sigma iota .
eq { count { filter_eq { all_rows ; bronze ; 15 } } ; 2 } = true
Output: select the rows whose bronze record is equal to 15 . the number of such rows is 2 .
most_eq { all_rows ; date ; january } = true
Output: for the date records of all rows , most of them fuzzily match to january .
eq { diff { hop { filter_eq { all_rows ; host city ; buffalo , new york } ; year } ; hop { filter_eq { all_rows ; host city ; orlando , florida } ; year } } ; -1 } = true
Output: select the rows whose host city record fuzzily matches to buffalo , new york . take the year record of this row . select the rows whose host city record fuzzily matches to orlando , florida . take the year record of this row . the second record is 1 larger than the first record .
eq { hop { argmax { all_rows ; max altitude ( miles ) } ; pilot } ; joseph a walker } = true
Output: select the row whose max altitude  miles  record of all rows is maximum . the pilot record of this row is joseph a walker .
and { only { filter_greater { all_rows ; regular season ; 600 } } ; eq { hop { filter_greater { all_rows ; regular season ; 600 } ; name } ; bobby hull } } = true
Output: select the rows whose regular season record is greater than 600 . there is only one such row in the table . the name record of this unqiue row is bobby hull .
eq { count { filter_all { all_rows ; place } } ; 11 } = true
Output: select the rows whose place record is arbitrary . the number of such rows is 11 .
most_greater { all_rows ; speed ; 90 } = true
Output: for the speed records of all rows , most of them are greater than 90 .
eq { hop { nth_argmax { all_rows ; bronze ; 2 } ; nation } ; sweden } = true
Output: select the row whose bronze record of all rows is 2nd maximum . the nation record of this row is sweden .
eq { count { filter_eq { all_rows ; winner / nominee ( s ) ; dev patel } } ; 1 } = true
Output: select the rows whose winner  nominee  s  record fuzzily matches to dev patel . the number of such rows is 1 .
only { filter_not_eq { all_rows ; nation ; kenya } } = true
Output: select the rows whose nation record does not match to kenya . there is only one such row in the table .
most_eq { filter_eq { all_rows ; began play ; 19 } ; club ; rochester } = true
Output: select the rows whose began play record fuzzily matches to 19 . for the club records of these rows , most of them fuzzily match to rochester .
eq { count { filter_eq { all_rows ; high assists ; mo williams } } ; 5 } = true
Output: select the rows whose high assists record fuzzily matches to mo williams . the number of such rows is 5 .
eq { hop { nth_argmin { all_rows ; first elected ; 4 } ; incumbent } ; robert aderholt } = true
Output: select the row whose first elected record of all rows is 4th minimum . the incumbent record of this row is robert aderholt .
greater { hop { filter_eq { all_rows ; date ; 6 june 2010 ( round 13 ) } ; total } ; hop { filter_eq { all_rows ; date ; 5 april 2008 ( round 4 ) } ; total } } = true
Output: select the rows whose date record fuzzily matches to 6 june 2010  round 13  . take the total record of this row . select the rows whose date record fuzzily matches to 5 april 2008  round 4  . take the total record of this row . the first record is greater than the second record .
less { hop { filter_eq { all_rows ; round winner ; larry perkins } ; date } ; hop { filter_eq { all_rows ; round winner ; mark skaife } ; date } } = true
Output: select the rows whose round winner record fuzzily matches to larry perkins . take the date record of this row . select the rows whose round winner record fuzzily matches to mark skaife . take the date record of this row . the first record is less than the second record .
eq { max { filter_eq { all_rows ; venue ; johannesburg } ; date } ; 23 - 03 - 2003 } = true
Output: select the rows whose venue record fuzzily matches to johannesburg . the maximum date record of these rows is 23  03  2003 .
eq { hop { nth_argmin { all_rows ; score ; 2 } ; date } ; august 22 , 2007 } = true
Output: select the row whose score record of all rows is 2nd minimum . the date record of this row is august 22 , 2007 .
and { eq { nth_min { all_rows ; round ; 1 } ; 1 } ; eq { hop { nth_argmin { all_rows ; round ; 1 } ; player } ; john hummer } } = true
Output: the 1st minimum round record of all rows is 1 . the player record of the row with 1st minimum round record is john hummer .
greater { hop { filter_eq { all_rows ; team ; aa das palmeiras } ; drawn } ; hop { filter_eq { all_rows ; team ; minas gerais } ; drawn } } = true
Output: select the rows whose team record fuzzily matches to aa das palmeiras . take the drawn record of this row . select the rows whose team record fuzzily matches to minas gerais . take the drawn record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; open cup ; did not qualify } } ; 3 } = true
Output: select the rows whose open cup record fuzzily matches to did not qualify . the number of such rows is 3 .
most_eq { all_rows ; of weeks ; 13 } = true
Output: for the of weeks records of all rows , most of them are equal to 13 .
less { hop { filter_eq { all_rows ; cfl team ; hamilton tiger - cats } ; pick } ; hop { filter_eq { all_rows ; cfl team ; british columbia lions } ; pick } } = true
Output: select the rows whose cfl team record fuzzily matches to hamilton tiger  cats . take the pick record of this row . select the rows whose cfl team record fuzzily matches to british columbia lions . take the pick record of this row . the first record is less than the second record .
eq { hop { argmax { all_rows ; 1st leg } ; team 1 } ; flamengo } = true
Output: select the row whose 1st leg record of all rows is maximum . the team 1 record of this row is flamengo .
eq { count { filter_eq { all_rows ; w / l ; win } } ; 9 } = true
Output: select the rows whose w  l record fuzzily matches to win . the number of such rows is 9 .
and { only { filter_eq { all_rows ; model ; short wing } } ; eq { hop { filter_eq { all_rows ; model ; short wing } ; manufacturer } ; pipistrel } } = true
Output: select the rows whose model record fuzzily matches to short wing . there is only one such row in the table . the manufacturer record of this unqiue row is pipistrel .
and { only { filter_eq { all_rows ; college ; houston } } ; eq { hop { filter_eq { all_rows ; college ; houston } ; name } ; jimmy herndon } } = true
Output: select the rows whose college record fuzzily matches to houston . there is only one such row in the table . the name record of this unqiue row is jimmy herndon .
less { hop { filter_eq { all_rows ; team ; palmeiras } ; against } ; hop { filter_eq { all_rows ; team ; flamengo } ; against } } = true
Output: select the rows whose team record fuzzily matches to palmeiras . take the against record of this row . select the rows whose team record fuzzily matches to flamengo . take the against record of this row . the first record is less than the second record .
most_less { all_rows ; difference ; 0 } = true
Output: for the difference records of all rows , most of them are less than 0 .
round_eq { avg { all_rows ; individual winners } ; 2.08 } = true
Output: the average of the individual winners record of all rows is 2.08 .
eq { count { filter_less { filter_less { all_rows ; away team score ; 10 } ; crowd ; 40000 } } ; 3 } = true
Output: select the rows whose away team score record is less than 10 . among these rows , select the rows whose crowd record is less than 40000 . the number of such rows is 3 .
and { eq { nth_min { all_rows ; pick ; 3 } ; 3 } ; and { eq { hop { nth_argmin { all_rows ; pick ; 3 } ; player } ; candice wiggins } ; eq { hop { nth_argmin { all_rows ; pick ; 3 } ; school / club team } ; stanford } } } = true
Output: the 3rd minimum pick record of all rows is 3 . the player record of the row with 3rd minimum pick record is candice wiggins . the school  club team record of the row with 3rd minimum pick record is stanford .
eq { count { filter_eq { all_rows ; partner ; tatiana poutchek } } ; 2 } = true
Output: select the rows whose partner record fuzzily matches to tatiana poutchek . the number of such rows is 2 .
round_eq { avg { all_rows ; viewers ( millions ) } ; 7.52 } = true
Output: the average of the viewers  millions  record of all rows is 7.52 .
less { hop { filter_eq { all_rows ; name ; dick farman } ; overall } ; hop { filter_eq { all_rows ; name ; paul coop } ; overall } } = true
Output: select the rows whose name record fuzzily matches to dick farman . take the overall record of this row . select the rows whose name record fuzzily matches to paul coop . take the overall record of this row . the first record is less than the second record .
eq { hop { nth_argmax { all_rows ; total ; 2 } ; athlete } ; monica pinette } = true
Output: select the row whose total record of all rows is 2nd maximum . the athlete record of this row is monica pinette .
eq { hop { argmax { all_rows ; score } ; tournament } ; 1999 } = true
Output: select the row whose score record of all rows is maximum . the tournament record of this row is 1999 .
eq { hop { argmin { all_rows ; qual 1 } ; name } ; paul tracy } = true
Output: select the row whose qual 1 record of all rows is minimum . the name record of this row is paul tracy .
most_eq { all_rows ; language ( s ) ; macedonian } = true
Output: for the language  s  records of all rows , most of them fuzzily match to macedonian .
eq { hop { argmax { all_rows ; overall record } ; texas vs } ; texas a & m } = true
Output: select the row whose overall record record of all rows is maximum . the texas vs record of this row is texas a  m .
most_eq { all_rows ; result ; win } = true
Output: for the result records of all rows , most of them fuzzily match to win .
most_eq { all_rows ; type ; switcher } = true
Output: for the type records of all rows , most of them fuzzily match to switcher .
and { only { filter_eq { filter_eq { all_rows ; coach ; manzoor elahi } ; captain ; abdur rauf } } ; eq { hop { filter_eq { filter_eq { all_rows ; coach ; manzoor elahi } ; captain ; abdur rauf } ; team } ; multan tigers } } = true
Output: select the rows whose coach record fuzzily matches to manzoor elahi . among these rows , select the rows whose captain record fuzzily matches to abdur rauf . there is only one such row in the table . the team record of this unqiue row is multan tigers .
eq { hop { argmin { all_rows ; capacity } ; name } ; 1.2 petrol } = true
Output: select the row whose capacity record of all rows is minimum . the name record of this row is 1.2 petrol .
most_greater_eq { all_rows ; screen size ( inch ) ; 7 } = true
Output: for the screen size  inch  records of all rows , most of them are greater than or equal to 7 .
greater { hop { filter_eq { all_rows ; nation ; mexico } ; total } ; hop { filter_eq { all_rows ; nation ; canada } ; total } } = true
Output: select the rows whose nation record fuzzily matches to mexico . take the total record of this row . select the rows whose nation record fuzzily matches to canada . take the total record of this row . the first record is greater than the second record .
most_eq { all_rows ; reason for change ; died } = true
Output: for the reason for change records of all rows , most of them fuzzily match to died .
eq { hop { argmax { all_rows ; enrollment } ; institution } ; rochester institute of technology } = true
Output: select the row whose enrollment record of all rows is maximum . the institution record of this row is rochester institute of technology .
eq { count { filter_greater_eq { all_rows ; % ( 2040 ) ; 10 } } ; 3 } = true
Output: select the rows whose   2040  record is greater than or equal to 10 . the number of such rows is 3 .
round_eq { avg { all_rows ; money raised , 2q } ; 10,369,822.5 } = true
Output: the average of the money raised , 2q record of all rows is 10,369,822.5 .
most_greater_eq { all_rows ; money ; 400 } = true
Output: for the money records of all rows , most of them are greater than or equal to 400 .
eq { hop { argmax { all_rows ; home team score } ; away team score } ; 13.14 ( 92 ) } = true
Output: select the row whose home team score record of all rows is maximum . the away team score record of this row is 13.14  92  .
round_eq { avg { filter_eq { all_rows ; date ; december } ; score } ; 104.6 } = true
Output: select the rows whose date record fuzzily matches to december . the average of the score record of these rows is 104.6 .
all_eq { all_rows ; competition ; friendly match } = true
Output: for the competition records of all rows , all of them fuzzily match to friendly match .
eq { hop { nth_argmin { all_rows ; rank ; 7 } ; nation } ; bulgaria } = true
Output: select the row whose rank record of all rows is 7th minimum . the nation record of this row is bulgaria .
eq { hop { nth_argmax { all_rows ; pick ; 2 } ; player } ; pat tillman } = true
Output: select the row whose pick record of all rows is 2nd maximum . the player record of this row is pat tillman .
most_eq { all_rows ; horse ; ypäjä karuso } = true
Output: for the horse records of all rows , most of them fuzzily match to ypäjä karuso .
eq { count { filter_less { all_rows ; 2011 gdp ( ppp ) billions of usd ; 100 } } ; 5 } = true
Output: select the rows whose 2011 gdp  ppp  billions of usd record is less than 100 . the number of such rows is 5 .
eq { hop { argmin { all_rows ; original air date } ; nick prod } ; 1001 } = true
Output: select the row whose original air date record of all rows is minimum . the nick prod record of this row is 1001 .
and { greater { hop { filter_eq { all_rows ; year ; 1995 } ; pts } ; hop { filter_eq { all_rows ; year ; 1991 } ; pts } } ; and { eq { hop { filter_eq { all_rows ; year ; 1995 } ; pts } ; 13 } ; eq { hop { filter_eq { all_rows ; year ; 1991 } ; pts } ; 1 } } } = true
Output: select the rows whose year record fuzzily matches to 1995 . take the pts record of this row . select the rows whose year record fuzzily matches to 1991 . take the pts record of this row . the first record is greater than the second record . the pts record of the first row is 13 . the pts record of the second row is 1 .
greater { hop { filter_eq { all_rows ; year ; 2000 } ; goals scored } ; hop { filter_eq { all_rows ; year ; 1999 } ; goals scored } } = true
Output: select the rows whose year record fuzzily matches to 2000 . take the goals scored record of this row . select the rows whose year record fuzzily matches to 1999 . take the goals scored record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; venue ; twickenham , london } } ; 2 } = true
Output: select the rows whose venue record fuzzily matches to twickenham , london . the number of such rows is 2 .