niryuu/sni-each-converted huggingface dataset:
greater { hop { filter_eq { all_rows ; tournament ; masters tournament } ; top - 5 } ; hop { filter_eq { all_rows ; tournament ; us open } ; top - 5 } } = true
Output: select the rows whose tournament record fuzzily matches to masters tournament . take the top  5 record of this row . select the rows whose tournament record fuzzily matches to us open . take the top  5 record of this row . the first record is greater than the second record .
eq { hop { argmax { filter_eq { all_rows ; crime ; murder } ; crime } ; character } ; henry darius } = true
Output: select the rows whose crime record fuzzily matches to murder . select the row whose crime record of these rows is maximum . the character record of this row is henry darius .
round_eq { avg { all_rows ; capacity } ; 37730 } = true
Output: the average of the capacity record of all rows is 37730 .
eq { hop { argmax { all_rows ; 2nd run } ; name } ; artūrs matisons ( lat ) } = true
Output: select the row whose 2nd run record of all rows is maximum . the name record of this row is artūrs matisons  lat  .
round_eq { avg { filter_eq { all_rows ; high rebounds ; tim duncan } ; high rebounds } ; 11.5 } = true
Output: select the rows whose high rebounds record fuzzily matches to tim duncan . the average of the high rebounds record of these rows is 11.5 .
eq { diff { hop { filter_eq { all_rows ; ship name ; ashantian } ; tonnage ( grt ) } ; hop { filter_eq { all_rows ; ship name ; manchester brigade } ; tonnage ( grt ) } } ; -1125 } = true
Output: select the rows whose ship name record fuzzily matches to ashantian . take the tonnage  grt  record of this row . select the rows whose ship name record fuzzily matches to manchester brigade . take the tonnage  grt  record of this row . the second record is 1125 larger than the first record .
and { only { filter_greater { all_rows ; us viewers ( million ) ; 1.7 } } ; eq { hop { filter_greater { all_rows ; us viewers ( million ) ; 1.7 } ; no } ; 7 } } = true
Output: select the rows whose us viewers  million  record is greater than 1.7 . there is only one such row in the table . the no record of this unqiue row is 7 .
all_eq { all_rows ; played ; 22 } = true
Output: for the played records of all rows , all of them are equal to 22 .
eq { hop { argmax { all_rows ; margin } ; club } ; fitzroy } = true
Output: select the row whose margin record of all rows is maximum . the club record of this row is fitzroy .
less { hop { filter_eq { all_rows ; chassis ; hesketh 308e } ; year } ; hop { filter_eq { all_rows ; chassis ; surtees ts19 } ; year } } = true
Output: select the rows whose chassis record fuzzily matches to hesketh 308e . take the year record of this row . select the rows whose chassis record fuzzily matches to surtees ts19 . take the year record of this row . the first record is less than the second record .
most_eq { all_rows ; winner ; blue } = true
Output: for the winner records of all rows , most of them fuzzily match to blue .
eq { hop { argmax { all_rows ; gdp per capita ( us ) } ; member countries } ; luxembourg } = true
Output: select the row whose gdp per capita  us  record of all rows is maximum . the member countries record of this row is luxembourg .
eq { hop { argmax { all_rows ; gdp per capita ( us ) } ; member countries } ; austria } = true
Output: select the row whose gdp per capita  us  record of all rows is maximum . the member countries record of this row is austria .
most_eq { filter_less { all_rows ; event ; 65 kg } ; quarterfinal ; did not advance } = true
Output: select the rows whose event record is less than 65 kg . for the quarterfinal records of these rows , most of them fuzzily match to did not advance .
most_eq { all_rows ; new entries this round ; none } = true
Output: for the new entries this round records of all rows , most of them fuzzily match to none .
eq { count { filter_eq { all_rows ; built ; 2011 } } ; 3 } = true
Output: select the rows whose built record fuzzily matches to 2011 . the number of such rows is 3 .
and { only { filter_less { all_rows ; attendance ; 1500 } } ; eq { hop { filter_less { all_rows ; attendance ; 1500 } ; date } ; april 18 } } = true
Output: select the rows whose attendance record is less than 1500 . there is only one such row in the table . the date record of this unqiue row is april 18 .
eq { count { filter_eq { all_rows ; length of stay permitted ; unlimited } } ; 3 } = true
Output: select the rows whose length of stay permitted record fuzzily matches to unlimited . the number of such rows is 3 .
all_eq { filter_eq { all_rows ; date ; december 1976 } ; result ; w } = true
Output: select the rows whose date record fuzzily matches to december 1976 . for the result records of these rows , all of them fuzzily match to w .
eq { hop { nth_argmin { all_rows ; date of vacancy ; 1 } ; outgoing manager } ; jürgen röber } = true
Output: select the row whose date of vacancy record of all rows is 1st minimum . the outgoing manager record of this row is jürgen röber .
eq { diff { hop { filter_eq { all_rows ; area ; azad kashmir } ; % muslim } ; hop { filter_eq { all_rows ; area ; gilgit - baltistan } ; % muslim } } ; 1 % } = true
Output: select the rows whose area record fuzzily matches to azad kashmir . take the  muslim record of this row . select the rows whose area record fuzzily matches to gilgit  baltistan . take the  muslim record of this row . the first record is 1  larger than the second record .
eq { hop { nth_argmax { all_rows ; year joined ; 1 } ; school } ; elkhart christian } = true
Output: select the row whose year joined record of all rows is 1st maximum . the school record of this row is elkhart christian .
eq { count { filter_eq { all_rows ; engine ; 2.7 } } ; 3 } = true
Output: select the rows whose engine record fuzzily matches to 2.7 . the number of such rows is 3 .
eq { count { filter_eq { all_rows ; country ; scotland } } ; 2 } = true
Output: select the rows whose country record fuzzily matches to scotland . the number of such rows is 2 .
and { only { filter_eq { all_rows ; tournament at which lead began ; australian championships } } ; eq { hop { filter_eq { all_rows ; tournament at which lead began ; australian championships } ; player } ; roy emerson } } = true
Output: select the rows whose tournament at which lead began record fuzzily matches to australian championships . there is only one such row in the table . the player record of this unqiue row is roy emerson .
most_eq { all_rows ; result ; 1st } = true
Output: for the result records of all rows , most of them fuzzily match to 1st .
and { only { filter_greater { all_rows ; snatch ; 170.0 } } ; eq { hop { filter_greater { all_rows ; snatch ; 170.0 } ; name } ; joël mackenzie ( cub ) } } = true
Output: select the rows whose snatch record is greater than 170.0 . there is only one such row in the table . the name record of this unqiue row is joël mackenzie  cub  .
eq { hop { argmax { all_rows ; points for } ; team } ; swansea } = true
Output: select the row whose points for record of all rows is maximum . the team record of this row is swansea .
round_eq { avg { filter_less_eq { all_rows ; season ; 1958 } ; attendance } ; 10158.5 } = true
Output: select the rows whose season record is less than or equal to 1958 . the average of the attendance record of these rows is 10158.5 .
eq { hop { argmax { all_rows ; total } ; rank } ; 1 } = true
Output: select the row whose total record of all rows is maximum . the rank record of this row is 1 .
all_eq { all_rows ; date ; 2 august 1980 } = true
Output: for the date records of all rows , all of them fuzzily match to 2 august 1980 .
round_eq { avg { filter_eq { all_rows ; year ; 2006 } ; margin } ; 3.5 } = true
Output: select the rows whose year record is equal to 2006 . the average of the margin record of these rows is 3.5 .
and { only { filter_eq { all_rows ; team ( s ) ; dingman brothers racing } } ; eq { hop { filter_eq { all_rows ; team ( s ) ; dingman brothers racing } ; year } ; 1987 } } = true
Output: select the rows whose team  s  record fuzzily matches to dingman brothers racing . there is only one such row in the table . the year record of this unqiue row is 1987 .
eq { count { filter_eq { filter_eq { all_rows ; nationality ; united states } ; position ; center } } ; 2 } = true
Output: select the rows whose nationality record fuzzily matches to united states . among these rows , select the rows whose position record fuzzily matches to center . the number of such rows is 2 .
eq { diff { hop { filter_eq { all_rows ; race ; indianapolis 500 } ; date } ; hop { filter_eq { all_rows ; race ; monaco grand prix } ; date } } ; 1 day } = true
Output: select the rows whose race record fuzzily matches to indianapolis 500 . take the date record of this row . select the rows whose race record fuzzily matches to monaco grand prix . take the date record of this row . the first record is 1 day larger than the second record .
and { eq { hop { argmax { all_rows ; time ( et ) } ; week } ; 9 } ; eq { hop { argmax { all_rows ; time ( et ) } ; opponent } ; pittsburgh steelers } } = true
Output: select the row whose time  et  record of all rows is maximum . the week record of this row is 9 . the opponent record of this row is pittsburgh steelers .
most_greater { all_rows ; percent of total imports ; 10 } = true
Output: for the percent of total imports records of all rows , most of them are greater than 10 .
greater { hop { filter_eq { all_rows ; incumbent ; robert taylor } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; mark alexander } ; first elected } } = true
Output: select the rows whose incumbent record fuzzily matches to robert taylor . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to mark alexander . take the first elected record of this row . the first record is greater than the second record .
eq { hop { nth_argmin { all_rows ; year ; 1 } ; english title } ; my old sweetheart } = true
Output: select the row whose year record of all rows is 1st minimum . the english title record of this row is my old sweetheart .
eq { diff { hop { filter_eq { all_rows ; title ; trust and consequence } ; original air date } ; hop { filter_eq { all_rows ; title ; father , can you hair me } ; original air date } } ; -7 days } = true
Output: select the rows whose title record fuzzily matches to trust and consequence . take the original air date record of this row . select the rows whose title record fuzzily matches to father , can you hair me . take the original air date record of this row . the second record is 7 days larger than the first record .
most_greater_eq { filter_eq { all_rows ; country ; united states } ; money ; 120000 } = true
Output: select the rows whose country record fuzzily matches to united states . for the money records of these rows , most of them are greater than or equal to 120000 .
eq { count { filter_all { all_rows ; nation } } ; 5 } = true
Output: select the rows whose nation record is arbitrary . the number of such rows is 5 .
greater { hop { filter_eq { all_rows ; country ; italy } ; negotiable debt at mid - 2005 ( us dollar bn equivalent ) } ; hop { filter_eq { all_rows ; country ; germany } ; negotiable debt at mid - 2005 ( us dollar bn equivalent ) } } = true
Output: select the rows whose country record fuzzily matches to italy . take the negotiable debt at mid  2005  us dollar bn equivalent  record of this row . select the rows whose country record fuzzily matches to germany . take the negotiable debt at mid  2005  us dollar bn equivalent  record of this row . the first record is greater than the second record .
greater { hop { filter_eq { all_rows ; engine ; 112 degree v4 ' big bang ' ( 2 - stroke ) } ; crankshaft } ; hop { filter_eq { all_rows ; engine ; 90 degree v4 ' twin pulse ' } ; crankshaft } } = true
Output: select the rows whose engine record fuzzily matches to 112 degree v4  big bang   2  stroke  . take the crankshaft record of this row . select the rows whose engine record fuzzily matches to 90 degree v4  twin pulse  . take the crankshaft record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; matches ; 40 } } ; 2 } = true
Output: select the rows whose matches record is equal to 40 . the number of such rows is 2 .
eq { hop { argmax { filter_eq { all_rows ; week ; may } ; week } ; surface } ; clay } = true
Output: select the rows whose week record fuzzily matches to may . select the row whose week record of these rows is maximum . the surface record of this row is clay .
eq { count { filter_eq { all_rows ; winning party 2003 ; liberal democrats } } ; 3 } = true
Output: select the rows whose winning party 2003 record fuzzily matches to liberal democrats . the number of such rows is 3 .
eq { count { filter_eq { all_rows ; location ; three rivers stadium } } ; 8 } = true
Output: select the rows whose location record fuzzily matches to three rivers stadium . the number of such rows is 8 .
eq { hop { argmax { all_rows ; attendance } ; opponent } ; detroit lions } = true
Output: select the row whose attendance record of all rows is maximum . the opponent record of this row is detroit lions .
round_eq { avg { all_rows ; away team score } ; 13.81 } = true
Output: the average of the away team score record of all rows is 13.81 .
greater { hop { filter_eq { all_rows ; metropolitan area ; jakarta } ; population } ; hop { filter_eq { all_rows ; metropolitan area ; beijing } ; population } } = true
Output: select the rows whose metropolitan area record fuzzily matches to jakarta . take the population record of this row . select the rows whose metropolitan area record fuzzily matches to beijing . take the population record of this row . the first record is greater than the second record .
and { only { filter_eq { all_rows ; team ( league ) ; lake superior state university ( ncaa ) } } ; eq { hop { filter_eq { all_rows ; team ( league ) ; lake superior state university ( ncaa ) } ; player } ; paul constantin } } = true
Output: select the rows whose team  league  record fuzzily matches to lake superior state university  ncaa  . there is only one such row in the table . the player record of this unqiue row is paul constantin .
eq { count { filter_eq { all_rows ; position ; forward } } ; 3 } = true
Output: select the rows whose position record fuzzily matches to forward . the number of such rows is 3 .
and { eq { max { all_rows ; score } ; l 83 - 74 } ; eq { hop { argmax { all_rows ; score } ; date } ; february 5 } } = true
Output: the maximum score record of all rows is l 83  74 . the date record of the row with superlative score record is february 5 .
eq { count { filter_eq { all_rows ; livery ; br rail blue } } ; 4 } = true
Output: select the rows whose livery record fuzzily matches to br rail blue . the number of such rows is 4 .
eq { hop { nth_argmax { all_rows ; weeks on top ; 2 } ; song } ; livin ' la vida loca } = true
Output: select the row whose weeks on top record of all rows is 2nd maximum . the song record of this row is livin  la vida loca .
only { filter_eq { all_rows ; date ; july 2010 } } = true
Output: select the rows whose date record fuzzily matches to july 2010 . there is only one such row in the table .
less { hop { filter_eq { all_rows ; nominee ; raymond massey } ; year } ; hop { filter_eq { all_rows ; nominee ; anthony hopkins } ; year } } = true
Output: select the rows whose nominee record fuzzily matches to raymond massey . take the year record of this row . select the rows whose nominee record fuzzily matches to anthony hopkins . take the year record of this row . the first record is less than the second record .
and { only { filter_eq { all_rows ; 2nd leg ; 4 } } ; and { eq { hop { filter_eq { all_rows ; 2nd leg ; 4 } ; team 1 } ; rabotnički } ; eq { hop { filter_eq { all_rows ; 2nd leg ; 4 } ; team 2 } ; ilinden } } } = true
Output: select the rows whose 2nd leg record fuzzily matches to 4 . there is only one such row in the table . the team 1 record of this unqiue row is rabotnički . the team 2 record of this unqiue row is ilinden .
eq { hop { argmax { all_rows ; frequency mhz } ; call sign } ; w283au } = true
Output: select the row whose frequency mhz record of all rows is maximum . the call sign record of this row is w283au .
eq { hop { nth_argmax { all_rows ; population ( 2013 ) ; 2 } ; province } ; kwazulu - natal } = true
Output: select the row whose population  2013  record of all rows is 2nd maximum . the province record of this row is kwazulu  natal .
and { only { filter_eq { filter_eq { all_rows ; nationality ; united states } ; position ; goalie } } ; eq { hop { filter_eq { filter_eq { all_rows ; nationality ; united states } ; position ; goalie } ; player } ; sal locascio } } = true
Output: select the rows whose nationality record fuzzily matches to united states . among these rows , select the rows whose position record fuzzily matches to goalie . there is only one such row in the table . the player record of this unqiue row is sal locascio .
eq { count { filter_eq { all_rows ; high rebounds ; allen } } ; 5 } = true
Output: select the rows whose high rebounds record fuzzily matches to allen . the number of such rows is 5 .
less { hop { filter_eq { all_rows ; incumbent ; bill harsha } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; ralph regula } ; first elected } } = true
Output: select the rows whose incumbent record fuzzily matches to bill harsha . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to ralph regula . take the first elected record of this row . the first record is less than the second record .
eq { count { filter_eq { all_rows ; time / retired ; engine } } ; 3 } = true
Output: select the rows whose time  retired record fuzzily matches to engine . the number of such rows is 3 .
eq { count { filter_eq { all_rows ; method ; decision } } ; 5 } = true
Output: select the rows whose method record fuzzily matches to decision . the number of such rows is 5 .
most_eq { all_rows ; manufacturer ; beyer , peacock & co } = true
Output: for the manufacturer records of all rows , most of them fuzzily match to beyer , peacock  co .
only { filter_eq { all_rows ; event ; medley } } = true
Output: select the rows whose event record fuzzily matches to medley . there is only one such row in the table .
eq { count { filter_eq { filter_eq { all_rows ; date ; december } ; game site ; hoosier dome } } ; 2 } = true
Output: select the rows whose date record fuzzily matches to december . among these rows , select the rows whose game site record fuzzily matches to hoosier dome . the number of such rows is 2 .
eq { count { filter_not_eq { all_rows ; top 5 ; 0 } } ; 10 } = true
Output: select the rows whose top 5 record is not equal to 0 . the number of such rows is 10 .
eq { count { filter_eq { all_rows ; sydney ; yes } } ; 13 } = true
Output: select the rows whose sydney record fuzzily matches to yes . the number of such rows is 13 .
eq { hop { argmax { all_rows ; total } ; nation } ; south korea } = true
Output: select the row whose total record of all rows is maximum . the nation record of this row is south korea .
eq { hop { nth_argmax { all_rows ; distance ; 1 } ; location } ; aplin } = true
Output: select the row whose distance record of all rows is 1st maximum . the location record of this row is aplin .
eq { hop { nth_argmax { all_rows ; number of episodes ; 3 } ; english title ( chinese title ) } ; journey to the west ii 西遊記 ( 貳 ) } = true
Output: select the row whose number of episodes record of all rows is 3rd maximum . the english title  chinese title  record of this row is journey to the west ii 西遊記  貳  .
round_eq { avg { all_rows ; score } ; 4.67 } = true
Output: the average of the score record of all rows is 4.67 .
eq { hop { nth_argmax { all_rows ; location attendance ; 1 } ; team } ; detroit } = true
Output: select the row whose location attendance record of all rows is 1st maximum . the team record of this row is detroit .
round_eq { avg { all_rows ; attendance } ; 20240 } = true
Output: the average of the attendance record of all rows is 20240 .
eq { count { filter_eq { all_rows ; nationality ; canada } } ; 5 } = true
Output: select the rows whose nationality record fuzzily matches to canada . the number of such rows is 5 .
eq { hop { nth_argmax { filter_eq { all_rows ; original air date ; august } ; us viewers ( million ) ; 2 } ; title } ; a little death } = true
Output: select the rows whose original air date record fuzzily matches to august . select the row whose us viewers  million  record of these rows is 2nd maximum . the title record of this row is a little death .
eq { count { filter_all { all_rows ; event } } ; 14 } = true
Output: select the rows whose event record is arbitrary . the number of such rows is 14 .
eq { hop { nth_argmax { all_rows ; attendance ; 2 } ; date } ; november 17 , 1968 } = true
Output: select the row whose attendance record of all rows is 2nd maximum . the date record of this row is november 17 , 1968 .
and { only { filter_eq { all_rows ; venue ; estadio jalisco , guadalajara , mexico } } ; eq { hop { filter_eq { all_rows ; venue ; estadio jalisco , guadalajara , mexico } ; date } ; 10 june 1970 } } = true
Output: select the rows whose venue record fuzzily matches to estadio jalisco , guadalajara , mexico . there is only one such row in the table . the date record of this unqiue row is 10 june 1970 .
eq { hop { nth_argmax { all_rows ; enrollment 08 - 09 ; 2 } ; school } ; heritage } = true
Output: select the row whose enrollment 08  09 record of all rows is 2nd maximum . the school record of this row is heritage .
most_greater { all_rows ; first elected ; 1920 } = true
Output: for the first elected records of all rows , most of them are greater than 1920 .
eq { hop { nth_argmax { filter_eq { all_rows ; capital ; warsaw } ; area in 1000 km square ( 1930 ) ; 1 } ; voivodeship separate city } ; warszawskie } = true
Output: select the rows whose capital record fuzzily matches to warsaw . select the row whose area in 1000 km square  1930  record of these rows is 1st maximum . the voivodeship separate city record of this row is warszawskie .
eq { count { filter_eq { all_rows ; producer ; charles salmon } } ; 3 } = true
Output: select the rows whose producer record fuzzily matches to charles salmon . the number of such rows is 3 .
eq { hop { argmax { all_rows ; total } ; player } ; tom kite } = true
Output: select the row whose total record of all rows is maximum . the player record of this row is tom kite .
eq { count { filter_all { all_rows ; institution } } ; 9 } = true
Output: select the rows whose institution record is arbitrary . the number of such rows is 9 .
most_eq { all_rows ; affiliation ; public } = true
Output: for the affiliation records of all rows , most of them fuzzily match to public .
and { eq { nth_min { all_rows ; date ; 4 } ; december 20 } ; eq { hop { nth_argmin { all_rows ; date ; 4 } ; team } ; kansas } } = true
Output: the 4th minimum date record of all rows is december 20 . the team record of the row with 4th minimum date record is kansas .
eq { count { filter_eq { all_rows ; directed by ; linda day } } ; 9 } = true
Output: select the rows whose directed by record fuzzily matches to linda day . the number of such rows is 9 .
round_eq { avg { all_rows ; speed } ; 121.208 } = true
Output: the average of the speed record of all rows is 121.208 .
and { eq { diff { hop { filter_eq { all_rows ; athlete ; emily freeman } ; rank } ; hop { filter_eq { all_rows ; athlete ; aleksandra fedoriva } ; rank } } ; -1 } ; and { eq { hop { filter_eq { all_rows ; athlete ; emily freeman } ; rank } ; 7 } ; eq { hop { filter_eq { all_rows ; athlete ; aleksandra fedoriva } ; rank } ; 8 } } } = true
Output: select the rows whose athlete record fuzzily matches to emily freeman . take the rank record of this row . select the rows whose athlete record fuzzily matches to aleksandra fedoriva . take the rank record of this row . the second record is 1 larger than the first record . the rank record of the first row is 7 . the rank record of the second row is 8 .
round_eq { avg { filter_eq { all_rows ; date ; 16 june 1956 } ; crowd } ; 24,570 } = true
Output: select the rows whose date record fuzzily matches to 16 june 1956 . the average of the crowd record of these rows is 24,570 .
and { eq { nth_max { all_rows ; points ; 2 } ; 79 } ; and { eq { hop { nth_argmax { all_rows ; points ; 2 } ; artist } ; staffan broms } ; eq { hop { nth_argmax { all_rows ; points ; 2 } ; song } ; dags igen att vara kära } } } = true
Output: the 2nd maximum points record of all rows is 79 . the artist record of the row with 2nd maximum points record is staffan broms . the song record of the row with 2nd maximum points record is dags igen att vara kära .
most_eq { all_rows ; pole position ; rick mears } = true
Output: for the pole position records of all rows , most of them fuzzily match to rick mears .
and { only { filter_eq { all_rows ; focal plane in ft ( m ) ; n / a } } ; eq { hop { filter_eq { all_rows ; focal plane in ft ( m ) ; n / a } ; lighthouse } ; basco } } = true
Output: select the rows whose focal plane in ft  m  record fuzzily matches to n  a . there is only one such row in the table . the lighthouse record of this unqiue row is basco .
round_eq { avg { all_rows ; laps } ; 200 } = true
Output: the average of the laps record of all rows is 200 .
eq { count { filter_eq { all_rows ; role ; guy holden } } ; 3 } = true
Output: select the rows whose role record fuzzily matches to guy holden . the number of such rows is 3 .
and { greater { hop { filter_eq { all_rows ; date ; january 22 } ; attendance } ; hop { filter_eq { all_rows ; date ; january 23 } ; attendance } } ; and { eq { hop { filter_eq { all_rows ; date ; january 22 } ; attendance } ; 17061 } ; eq { hop { filter_eq { all_rows ; date ; january 23 } ; attendance } ; 13916 } } } = true
Output: select the rows whose date record fuzzily matches to january 22 . take the attendance record of this row . select the rows whose date record fuzzily matches to january 23 . take the attendance record of this row . the first record is greater than the second record . the attendance record of the first row is 17061 . the attendance record of the second row is 13916 .
most_less { all_rows ; year ; 2009 } = true
Output: for the year records of all rows , most of them are less than 2009 .
and { only { filter_eq { all_rows ; college ; eckerd } } ; eq { hop { filter_eq { all_rows ; college ; eckerd } ; player } ; jay washington } } = true
Output: select the rows whose college record fuzzily matches to eckerd . there is only one such row in the table . the player record of this unqiue row is jay washington .
only { filter_eq { all_rows ; m60a3 patton ; 105 mm m68 rifled tank - gun } } = true
Output: select the rows whose m60a3 patton record fuzzily matches to 105 mm m68 rifled tank  gun . there is only one such row in the table .
and { only { filter_eq { all_rows ; attendance ; canceled } } ; eq { hop { filter_eq { all_rows ; attendance ; canceled } ; venue } ; blackcomb mountain } } = true
Output: select the rows whose attendance record fuzzily matches to canceled . there is only one such row in the table . the venue record of this unqiue row is blackcomb mountain .
round_eq { avg { all_rows ; attendance } ; 23036 } = true
Output: the average of the attendance record of all rows is 23036 .
round_eq { avg { all_rows ; apparent magnitude } ; 10.95 } = true
Output: the average of the apparent magnitude record of all rows is 10.95 .
eq { count { filter_eq { all_rows ; time ; +40 } } ; 2 } = true
Output: select the rows whose time record fuzzily matches to +40 . the number of such rows is 2 .
eq { min { filter_eq { all_rows ; born / died ; present } ; born / died } ; 1928 - present } = true
Output: select the rows whose born  died record fuzzily matches to present . the minimum born  died record of these rows is 1928  present .
eq { count { filter_greater { filter_eq { all_rows ; date ; november } ; attendance ; 60000 } } ; 1 } = true
Output: select the rows whose date record fuzzily matches to november . among these rows , select the rows whose attendance record is greater than 60000 . the number of such rows is 1 .
eq { count { filter_eq { all_rows ; competition ; world cup qualification } } ; 2 } = true
Output: select the rows whose competition record fuzzily matches to world cup qualification . the number of such rows is 2 .
round_eq { avg { all_rows ; points } ; 68.75 } = true
Output: the average of the points record of all rows is 68.75 .
greater { hop { filter_eq { all_rows ; building ; titanium la portada } ; number of floors } ; hop { filter_eq { all_rows ; building ; torre mall center } ; number of floors } } = true
Output: select the rows whose building record fuzzily matches to titanium la portada . take the number of floors record of this row . select the rows whose building record fuzzily matches to torre mall center . take the number of floors record of this row . the first record is greater than the second record .
eq { diff { hop { filter_eq { all_rows ; player ; lee janzen } ; score } ; hop { filter_eq { all_rows ; player ; chris perry } ; score } } ; -1 } = true
Output: select the rows whose player record fuzzily matches to lee janzen . take the score record of this row . select the rows whose player record fuzzily matches to chris perry . take the score record of this row . the second record is 1 larger than the first record .
eq { hop { nth_argmin { all_rows ; date ; 7 } ; venue } ; olympic stadium , tokyo , japan } = true
Output: select the row whose date record of all rows is 7th minimum . the venue record of this row is olympic stadium , tokyo , japan .
all_eq { filter_eq { all_rows ; college ; north carolina } ; nba draft ; undrafted } = true
Output: select the rows whose college record fuzzily matches to north carolina . for the nba draft records of these rows , all of them fuzzily match to undrafted .
and { eq { nth_min { all_rows ; date of inauguration ; 1 } ; 26 january 1950 } ; eq { hop { nth_argmin { all_rows ; date of inauguration ; 1 } ; president } ; prasad , rajendra rajendra prasad } } = true
Output: the 1st minimum date of inauguration record of all rows is 26 january 1950 . the president record of the row with 1st minimum date of inauguration record is prasad , rajendra rajendra prasad .
eq { count { filter_eq { all_rows ; party ; democratic } } ; 7 } = true
Output: select the rows whose party record fuzzily matches to democratic . the number of such rows is 7 .
greater { hop { filter_eq { all_rows ; team ; ateneo de manila university } ; sets won } ; hop { filter_eq { all_rows ; team ; college of saint benilde } ; sets won } } = true
Output: select the rows whose team record fuzzily matches to ateneo de manila university . take the sets won record of this row . select the rows whose team record fuzzily matches to college of saint benilde . take the sets won record of this row . the first record is greater than the second record .
round_eq { avg { filter_eq { all_rows ; high assists ; hinrich } ; high assists } ; 7.8 } = true
Output: select the rows whose high assists record fuzzily matches to hinrich . the average of the high assists record of these rows is 7.8 .
eq { hop { nth_argmax { all_rows ; transfer fee ; 2 } ; name } ; mccartney } = true
Output: select the row whose transfer fee record of all rows is 2nd maximum . the name record of this row is mccartney .
greater { hop { filter_eq { all_rows ; incumbent ; rené louis derouen } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; bolivar e kemp } ; first elected } } = true
Output: select the rows whose incumbent record fuzzily matches to rené louis derouen . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to bolivar e kemp . take the first elected record of this row . the first record is greater than the second record .
less { hop { filter_eq { all_rows ; notes ; enid } ; built } ; hop { filter_eq { all_rows ; notes ; snowdon } ; built } } = true
Output: select the rows whose notes record fuzzily matches to enid . take the built record of this row . select the rows whose notes record fuzzily matches to snowdon . take the built record of this row . the first record is less than the second record .
most_eq { all_rows ; label ; columbia } = true
Output: for the label records of all rows , most of them fuzzily match to columbia .
less { hop { filter_eq { all_rows ; region ; japan } ; date } ; hop { filter_eq { all_rows ; region ; united kingdom } ; date } } = true
Output: select the rows whose region record fuzzily matches to japan . take the date record of this row . select the rows whose region record fuzzily matches to united kingdom . take the date record of this row . the first record is less than the second record .
eq { count { filter_eq { all_rows ; high points ; richard hamilton } } ; 4 } = true
Output: select the rows whose high points record fuzzily matches to richard hamilton . the number of such rows is 4 .
eq { count { filter_eq { all_rows ; partner ; fernando verdasco } } ; 7 } = true
Output: select the rows whose partner record fuzzily matches to fernando verdasco . the number of such rows is 7 .
and { only { filter_eq { all_rows ; mascot ; tigers } } ; eq { hop { filter_eq { all_rows ; mascot ; tigers } ; school } ; yorktown } } = true
Output: select the rows whose mascot record fuzzily matches to tigers . there is only one such row in the table . the school record of this unqiue row is yorktown .
eq { count { filter_eq { filter_eq { all_rows ; operator ; london overground } ; platform ; 1 } } ; 2 } = true
Output: select the rows whose operator record fuzzily matches to london overground . among these rows , select the rows whose platform record is equal to 1 . the number of such rows is 2 .
less { hop { filter_eq { all_rows ; incumbent ; joe hendricks } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; robert l f sikes } ; first elected } } = true
Output: select the rows whose incumbent record fuzzily matches to joe hendricks . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to robert l f sikes . take the first elected record of this row . the first record is less than the second record .
round_eq { avg { all_rows ; points } ; 16.88 } = true
Output: the average of the points record of all rows is 16.88 .
and { only { filter_eq { all_rows ; primary conference ; big east conference } } ; eq { hop { filter_eq { all_rows ; primary conference ; big east conference } ; institution } ; georgetown university } } = true
Output: select the rows whose primary conference record fuzzily matches to big east conference . there is only one such row in the table . the institution record of this unqiue row is georgetown university .
only { filter_eq { all_rows ; 1965 ; dnf } } = true
Output: select the rows whose 1965 record fuzzily matches to dnf . there is only one such row in the table .
eq { count { filter_eq { all_rows ; years owned ; 1995 - 2008 } } ; 4 } = true
Output: select the rows whose years owned record fuzzily matches to 1995  2008 . the number of such rows is 4 .
eq { hop { nth_argmax { all_rows ; jump 1 ; 2 } ; athlete name } ; dmitriy bondarenko ( urs ) } = true
Output: select the row whose jump 1 record of all rows is 2nd maximum . the athlete name record of this row is dmitriy bondarenko  urs  .
and { eq { nth_min { all_rows ; rank ; 6 } ; 6 } ; and { eq { hop { nth_argmin { all_rows ; rank ; 6 } ; team } ; college of saint benilde } ; eq { hop { nth_argmin { all_rows ; rank ; 6 } ; loss } ; 4 } } } = true
Output: the 6th minimum rank record of all rows is 6 . the team record of the row with 6th minimum rank record is college of saint benilde . the loss record of the row with 6th minimum rank record is 4 .
and { only { filter_eq { all_rows ; written by ; brad kern } } ; eq { hop { filter_eq { all_rows ; written by ; brad kern } ; title } ; the honeymoon 's over } } = true
Output: select the rows whose written by record fuzzily matches to brad kern . there is only one such row in the table . the title record of this unqiue row is the honeymoon s over .
most_eq { all_rows ; video ; 480i } = true
Output: for the video records of all rows , most of them fuzzily match to 480i .
round_eq { avg { all_rows ; episode } ; 240 } = true
Output: the average of the episode record of all rows is 240 .
eq { hop { nth_argmin { all_rows ; original air date ; 1 } ; title } ; mystic } = true
Output: select the row whose original air date record of all rows is 1st minimum . the title record of this row is mystic .
eq { count { filter_greater_eq { filter_less { all_rows ; away team score ; 10 } ; crowd ; 23000 } } ; 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 greater than or equal to 23000 . the number of such rows is 3 .
eq { hop { argmax { all_rows ; gold } ; rank } ; 1 } = true
Output: select the row whose gold record of all rows is maximum . the rank record of this row is 1 .
eq { count { filter_eq { all_rows ; nationality ; austria } } ; 3 } = true
Output: select the rows whose nationality record fuzzily matches to austria . the number of such rows is 3 .
most_eq { all_rows ; date ; february } = true
Output: for the date records of all rows , most of them fuzzily match to february .
most_eq { all_rows ; year joined ; 1998 } = true
Output: for the year joined records of all rows , most of them are equal to 1998 .
all_eq { filter_eq { all_rows ; location attendance ; staples center } ; high assists ; baron davis } = true
Output: select the rows whose location attendance record fuzzily matches to staples center . for the high assists records of these rows , all of them fuzzily match to baron davis .
round_eq { avg { all_rows ; attendance } ; 50799 } = true
Output: the average of the attendance record of all rows is 50799 .
most_eq { all_rows ; engine ; v8 } = true
Output: for the engine records of all rows , most of them fuzzily match to v8 .
and { only { filter_eq { all_rows ; venue ; anyang stadium , anyang } } ; eq { hop { filter_eq { all_rows ; venue ; anyang stadium , anyang } ; date } ; 15 august 2012 } } = true
Output: select the rows whose venue record fuzzily matches to anyang stadium , anyang . there is only one such row in the table . the date record of this unqiue row is 15 august 2012 .
and { eq { nth_max { all_rows ; silver ; 3 } ; 42 } ; eq { hop { nth_argmax { all_rows ; silver ; 3 } ; rank } ; 2 } } = true
Output: the 3rd maximum silver record of all rows is 42 . the rank record of the row with 3rd maximum silver record is 2 .
all_eq { all_rows ; nationality ; canada } = true
Output: for the nationality records of all rows , all of them fuzzily match to canada .
eq { count { filter_eq { filter_eq { all_rows ; location / attendance ; the omni } ; score ; l } } ; 2 } = true
Output: select the rows whose location  attendance record fuzzily matches to the omni . among these rows , select the rows whose score record fuzzily matches to l . the number of such rows is 2 .
and { not_eq { hop { filter_eq { all_rows ; name ; telmatosaurus } ; location } ; hop { filter_eq { all_rows ; name ; brachiosaurus } ; location } } ; and { eq { hop { filter_eq { all_rows ; name ; telmatosaurus } ; location } ; romania } ; eq { hop { filter_eq { all_rows ; name ; brachiosaurus } ; location } ; usa } } } = true
Output: select the rows whose name record fuzzily matches to telmatosaurus . take the location record of this row . select the rows whose name record fuzzily matches to brachiosaurus . take the location record of this row . the first record does not match to the second record . the location record of the first row is romania . the location record of the second row is usa .
eq { hop { argmax { all_rows ; points } ; artist } ; andré claveau } = true
Output: select the row whose points record of all rows is maximum . the artist record of this row is andré claveau .
and { only { filter_eq { all_rows ; difference ; 6 } } ; eq { hop { filter_eq { all_rows ; difference ; 6 } ; team } ; santos } } = true
Output: select the rows whose difference record is equal to 6 . there is only one such row in the table . the team record of this unqiue row is santos .
eq { hop { argmax { all_rows ; goals } ; player } ; mile sterjovski } = true
Output: select the row whose goals record of all rows is maximum . the player record of this row is mile sterjovski .
most_eq { all_rows ; transmission ; analog & digital } = true
Output: for the transmission records of all rows , most of them fuzzily match to analog  digital .
round_eq { sum { all_rows ; runs } ; 3423 } = true
Output: the sum of the runs record of all rows is 3423 .
round_eq { avg { all_rows ; population ( 2010 ) } ; 17935 } = true
Output: the average of the population  2010  record of all rows is 17935 .
eq { diff { hop { filter_eq { all_rows ; player ; shane watson ( queensland ) } ; score } ; hop { filter_eq { all_rows ; player ; david hussey ( victoria ) } ; score } } ; -1 } = true
Output: select the rows whose player record fuzzily matches to shane watson  queensland  . take the score record of this row . select the rows whose player record fuzzily matches to david hussey  victoria  . take the score record of this row . the second record is 1 larger than the first record .
most_greater_eq { filter_eq { all_rows ; original air date ; april } ; us viewers ( million ) ; 9 } = true
Output: select the rows whose original air date record fuzzily matches to april . for the us viewers  million  records of these rows , most of them are greater than or equal to 9 .
most_eq { all_rows ; extra points ; 0 } = true
Output: for the extra points records of all rows , most of them are equal to 0 .
eq { count { filter_eq { all_rows ; played ; 30 } } ; 16 } = true
Output: select the rows whose played record fuzzily matches to 30 . the number of such rows is 16 .
round_eq { avg { all_rows ; percentage } ; 50 % } = true
Output: the average of the percentage record of all rows is 50  .
eq { hop { argmax { all_rows ; height } ; player } ; anthony randolph } = true
Output: select the row whose height record of all rows is maximum . the player record of this row is anthony randolph .
eq { hop { argmin { all_rows ; start } ; year } ; 1936 } = true
Output: select the row whose start record of all rows is minimum . the year record of this row is 1936 .
less { hop { filter_eq { all_rows ; player ; ido kozikaro } ; height } ; hop { filter_eq { all_rows ; player ; lior eliyahu } ; height } } = true
Output: select the rows whose player record fuzzily matches to ido kozikaro . take the height record of this row . select the rows whose player record fuzzily matches to lior eliyahu . take the height record of this row . the first record is less than the second record .
most_eq { all_rows ; position ; tackle } = true
Output: for the position records of all rows , most of them fuzzily match to tackle .
eq { count { filter_eq { filter_eq { all_rows ; gold ; 0 } ; silver ; 1 } } ; 2 } = true
Output: select the rows whose gold record is equal to 0 . among these rows , select the rows whose silver record is equal to 1 . the number of such rows is 2 .
most_less { all_rows ; matches ; 100 } = true
Output: for the matches records of all rows , most of them are less than 100 .
greater { hop { filter_eq { all_rows ; year ; 1967 } ; laps } ; hop { filter_eq { all_rows ; year ; 1971 } ; laps } } = true
Output: select the rows whose year record fuzzily matches to 1967 . take the laps record of this row . select the rows whose year record fuzzily matches to 1971 . take the laps record of this row . the first record is greater than the second record .
eq { diff { hop { filter_eq { all_rows ; year ; 1976 } ; points } ; hop { filter_eq { all_rows ; year ; 1975 } ; points } } ; 6 points } = true
Output: select the rows whose year record fuzzily matches to 1976 . take the points record of this row . select the rows whose year record fuzzily matches to 1975 . take the points record of this row . the first record is 6 points larger than the second record .
eq { count { filter_eq { all_rows ; leading scorer ; ricky davis } } ; 3 } = true
Output: select the rows whose leading scorer record fuzzily matches to ricky davis . the number of such rows is 3 .
less { hop { filter_eq { all_rows ; line ; sofia - dragoman } ; length } ; hop { filter_eq { all_rows ; line ; vidin - sofia } ; length } } = true
Output: select the rows whose line record fuzzily matches to sofia  dragoman . take the length record of this row . select the rows whose line record fuzzily matches to vidin  sofia . take the length record of this row . the first record is less than the second record .
eq { count { filter_eq { all_rows ; original air date ; april 2005 } } ; 5 } = true
Output: select the rows whose original air date record fuzzily matches to april 2005 . the number of such rows is 5 .
eq { count { filter_eq { all_rows ; champion ; karrie webb } } ; 2 } = true
Output: select the rows whose champion record fuzzily matches to karrie webb . the number of such rows is 2 .
less { hop { filter_eq { all_rows ; partnering ; simon stadler } ; date } ; hop { filter_eq { all_rows ; partnering ; paolo lorenzi } ; date } } = true
Output: select the rows whose partnering record fuzzily matches to simon stadler . take the date record of this row . select the rows whose partnering record fuzzily matches to paolo lorenzi . take the date record of this row . the first record is less than the second record .
eq { hop { argmax { all_rows ; redshift ( km / s ) } ; name } ; ngc 1596 } = true
Output: select the row whose redshift  km  s  record of all rows is maximum . the name record of this row is ngc 1596 .
and { only { filter_eq { all_rows ; tied ; 1 } } ; eq { hop { filter_eq { all_rows ; tied ; 1 } ; player } ; kyle mccallan } } = true
Output: select the rows whose tied record is equal to 1 . there is only one such row in the table . the player record of this unqiue row is kyle mccallan .
eq { diff { hop { filter_eq { all_rows ; date ; october 3 } ; attendance } ; hop { filter_eq { all_rows ; date ; september 26 } ; attendance } } ; 8306 } = true
Output: select the rows whose date record fuzzily matches to october 3 . take the attendance record of this row . select the rows whose date record fuzzily matches to september 26 . take the attendance record of this row . the first record is 8306 larger than the second record .
eq { count { filter_eq { all_rows ; continent ; asia } } ; 2 } = true
Output: select the rows whose continent record fuzzily matches to asia . the number of such rows is 2 .
less { hop { filter_eq { all_rows ; title ; smart girl } ; original air date } ; hop { filter_eq { all_rows ; title ; number 1 fan } ; original air date } } = true
Output: select the rows whose title record fuzzily matches to smart girl . take the original air date record of this row . select the rows whose title record fuzzily matches to number 1 fan . take the original air date record of this row . the first record is less than the second record .
and { only { filter_eq { all_rows ; venue ; edinburgh } } ; eq { hop { filter_eq { all_rows ; venue ; edinburgh } ; opposing teams } ; scotland } } = true
Output: select the rows whose venue record fuzzily matches to edinburgh . there is only one such row in the table . the opposing teams record of this unqiue row is scotland .
and { only { filter_eq { all_rows ; motogp winner ; andrea dovizioso } } ; eq { hop { filter_eq { all_rows ; motogp winner ; andrea dovizioso } ; round } ; 10 } } = true
Output: select the rows whose motogp winner record fuzzily matches to andrea dovizioso . there is only one such row in the table . the round record of this unqiue row is 10 .
eq { hop { argmax { filter_eq { all_rows ; location ; philips arena } ; attendance } ; date } ; december 30 } = true
Output: select the rows whose location record fuzzily matches to philips arena . select the row whose attendance record of these rows is maximum . the date record of this row is december 30 .
and { only { filter_eq { all_rows ; goal difference ; 0 } } ; eq { hop { filter_eq { all_rows ; goal difference ; 0 } ; club } ; atlético madrileño } } = true
Output: select the rows whose goal difference record is equal to 0 . there is only one such row in the table . the club record of this unqiue row is atlético madrileño .
all_eq { all_rows ; directed by ; ted wass } = true
Output: for the directed by records of all rows , all of them fuzzily match to ted wass .
most_eq { all_rows ; venue ; lansdowne road , dublin , ireland } = true
Output: for the venue records of all rows , most of them fuzzily match to lansdowne road , dublin , ireland .