niryuu/sni-each-converted huggingface dataset:
eq { count { filter_eq { all_rows ; team ( s ) ; stegell motorsports } } ; 4 } = true
Output: select the rows whose team  s  record fuzzily matches to stegell motorsports . the number of such rows is 4 .
eq { count { filter_eq { all_rows ; result ; win } } ; 4 } = true
Output: select the rows whose result record fuzzily matches to win . the number of such rows is 4 .
greater { hop { filter_eq { all_rows ; country ; nigeria } ; penalty } ; hop { filter_eq { all_rows ; country ; uzbekistan } ; penalty } } = true
Output: select the rows whose country record fuzzily matches to nigeria . take the penalty record of this row . select the rows whose country record fuzzily matches to uzbekistan . take the penalty record of this row . the first record is greater than the second record .
eq { hop { nth_argmax { all_rows ; speed ; 2 } ; pilot } ; uli schwenk } = true
Output: select the row whose speed record of all rows is 2nd maximum . the pilot record of this row is uli schwenk .
and { eq { hop { filter_eq { all_rows ; trim ; xe ( 2009 ) } ; transmission } ; hop { filter_eq { all_rows ; trim ; xr ( 2009 ) } ; transmission } } ; and { eq { hop { filter_eq { all_rows ; trim ; xe ( 2009 ) } ; transmission } ; 6 - speed 6t40 } ; eq { hop { filter_eq { all_rows ; trim ; xr ( 2009 ) } ; transmission } ; 6 - speed 6t40 } } } = true
Output: select the rows whose trim record fuzzily matches to xe  2009  . take the transmission record of this row . select the rows whose trim record fuzzily matches to xr  2009  . take the transmission record of this row . the first record fuzzily matches to the second record . the transmission record of the first row is 6  speed 6t40 . the transmission record of the second row is 6  speed 6t40 .
eq { hop { argmin { all_rows ; date } ; name } ; targa florio } = true
Output: select the row whose date record of all rows is minimum . the name record of this row is targa florio .
most_eq { all_rows ; uccfs ; 2006 } = true
Output: for the uccfs records of all rows , most of them are equal to 2006 .
most_eq { all_rows ; result ; icl india by 4 wickets } = true
Output: for the result records of all rows , most of them fuzzily match to icl india by 4 wickets .
eq { hop { argmax { all_rows ; goals } ; club } ; real madrid } = true
Output: select the row whose goals record of all rows is maximum . the club record of this row is real madrid .
and { only { filter_greater { all_rows ; result ; 7th } } ; eq { hop { filter_greater { all_rows ; result ; 7th } ; year } ; 2002 } } = true
Output: select the rows whose result record is greater than 7th . there is only one such row in the table . the year record of this unqiue row is 2002 .
eq { hop { nth_argmax { filter_less { all_rows ; population ; 10,000,000 } ; gdp ( nominal ) ; 2 } ; country } ; tajikistan } = true
Output: select the rows whose population record is less than 10,000,000 . select the row whose gdp  nominal  record of these rows is 2nd maximum . the country record of this row is tajikistan .
eq { diff { hop { filter_eq { all_rows ; city of license ; plainview , texas } ; frequency mhz } ; hop { filter_eq { all_rows ; city of license ; pampa , texas } ; frequency mhz } } ; -0.8 } = true
Output: select the rows whose city of license record fuzzily matches to plainview , texas . take the frequency mhz record of this row . select the rows whose city of license record fuzzily matches to pampa , texas . take the frequency mhz record of this row . the second record is 0.8 larger than the first record .
most_eq { all_rows ; site / stadium ; jackie robinson stadium } = true
Output: for the site  stadium records of all rows , most of them fuzzily match to jackie robinson stadium .
most_less { all_rows ; prone ; 200 } = true
Output: for the prone records of all rows , most of them are less than 200 .
round_eq { avg { all_rows ; erp w } ; 33.2 } = true
Output: the average of the erp w record of all rows is 33.2 .
round_eq { avg { all_rows ; quantity made } ; 3.83 } = true
Output: the average of the quantity made record of all rows is 3.83 .
most_eq { all_rows ; high assists ; joe johnson } = true
Output: for the high assists records of all rows , most of them fuzzily match to joe johnson .
and { only { filter_eq { all_rows ; writer ; ed roe } } ; eq { hop { filter_eq { all_rows ; writer ; ed roe } ; production code } ; 402 } } = true
Output: select the rows whose writer record fuzzily matches to ed roe . there is only one such row in the table . the production code record of this unqiue row is 402 .
less { hop { filter_eq { all_rows ; name ; amy van dyken } ; time } ; hop { filter_eq { all_rows ; name ; vivienne rignall } ; time } } = true
Output: select the rows whose name record fuzzily matches to amy van dyken . take the time record of this row . select the rows whose name record fuzzily matches to vivienne rignall . take the time record of this row . the first record is less than the second record .
eq { hop { nth_argmax { all_rows ; population ; 3 } ; english name } ; suibin county } = true
Output: select the row whose population record of all rows is 3rd maximum . the english name record of this row is suibin county .
eq { hop { nth_argmin { all_rows ; date ; 2 } ; tournament } ; cuenca , ecuador } = true
Output: select the row whose date record of all rows is 2nd minimum . the tournament record of this row is cuenca , ecuador .
eq { hop { nth_argmax { filter_eq { all_rows ; definition ; civil parish } ; population ; 1 } ; town } ; ripon } = true
Output: select the rows whose definition record fuzzily matches to civil parish . select the row whose population record of these rows is 1st maximum . the town record of this row is ripon .
round_eq { sum { all_rows ; overall } ; 503 } = true
Output: the sum of the overall record of all rows is 503 .
round_eq { avg { all_rows ; pick } ; 27.8 } = true
Output: the average of the pick record of all rows is 27.8 .
eq { count { filter_eq { filter_eq { all_rows ; nationality ; canada } ; position ; lw } } ; 6 } = true
Output: select the rows whose nationality record fuzzily matches to canada . among these rows , select the rows whose position record fuzzily matches to lw . the number of such rows is 6 .
and { only { filter_eq { filter_eq { all_rows ; points ; 6 } ; difference ; -10 } } ; eq { hop { filter_eq { filter_eq { all_rows ; points ; 6 } ; difference ; -10 } ; team } ; amãrica } } = true
Output: select the rows whose points record is equal to 6 . among these rows , select the rows whose difference record is equal to 10 . there is only one such row in the table . the team record of this unqiue row is amãrica .
and { only { filter_eq { all_rows ; venue ; chile } } ; eq { hop { filter_eq { all_rows ; venue ; chile } ; date } ; september 4 , 2001 } } = true
Output: select the rows whose venue record fuzzily matches to chile . there is only one such row in the table . the date record of this unqiue row is september 4 , 2001 .
and { less { hop { filter_eq { all_rows ; player ; laura diaz } ; score } ; hop { filter_eq { all_rows ; player ; kristy mcpherson } ; score } } ; and { eq { hop { filter_eq { all_rows ; player ; laura diaz } ; score } ; 66 } ; eq { hop { filter_eq { all_rows ; player ; kristy mcpherson } ; score } ; 67 } } } = true
Output: select the rows whose player record fuzzily matches to laura diaz . take the score record of this row . select the rows whose player record fuzzily matches to kristy mcpherson . take the score record of this row . the first record is less than the second record . the score record of the first row is 66 . the score record of the second row is 67 .
greater { hop { filter_eq { all_rows ; opponent ; cincinnati bengals } ; attendance } ; hop { filter_eq { all_rows ; opponent ; houston oilers } ; attendance } } = true
Output: select the rows whose opponent record fuzzily matches to cincinnati bengals . take the attendance record of this row . select the rows whose opponent record fuzzily matches to houston oilers . take the attendance record of this row . the first record is greater than the second record .
eq { hop { argmax { all_rows ; bronze } ; nation } ; france ( fra ) } = true
Output: select the row whose bronze record of all rows is maximum . the nation record of this row is france  fra  .
round_eq { sum { all_rows ; losses } ; 212 } = true
Output: the sum of the losses record of all rows is 212 .
eq { hop { nth_argmax { all_rows ; date ; 2 } ; location attendance } ; verizon center 14485 } = true
Output: select the row whose date record of all rows is 2nd maximum . the location attendance record of this row is verizon center 14485 .
eq { diff { hop { filter_eq { all_rows ; player ; mathew goggin } ; money } ; hop { filter_eq { all_rows ; player ; justin leonard } ; money } } ; 66600 } = true
Output: select the rows whose player record fuzzily matches to mathew goggin . take the money record of this row . select the rows whose player record fuzzily matches to justin leonard . take the money record of this row . the first record is 66600 larger than the second record .
eq { hop { argmax { all_rows ; points } ; singer } ; olta boka } = true
Output: select the row whose points record of all rows is maximum . the singer record of this row is olta boka .
eq { count { filter_eq { all_rows ; races ; 16 } } ; 4 } = true
Output: select the rows whose races record is equal to 16 . the number of such rows is 4 .
less { hop { filter_eq { all_rows ; opponent ; dallas cowboys } ; date } ; hop { filter_eq { all_rows ; opponent ; green bay packers } ; date } } = true
Output: select the rows whose opponent record fuzzily matches to dallas cowboys . take the date record of this row . select the rows whose opponent record fuzzily matches to green bay packers . take the date record of this row . the first record is less than the second record .
eq { hop { argmax { filter_greater_eq { all_rows ; period ; 2000 } ; win % } ; manager } ; voit talgaev } = true
Output: select the rows whose period record is greater than or equal to 2000 . select the row whose win  record of these rows is maximum . the manager record of this row is voit talgaev .
greater { hop { filter_eq { all_rows ; opponent ; christian wellisch } ; round } ; hop { filter_eq { all_rows ; opponent ; pat harmon } ; round } } = true
Output: select the rows whose opponent record fuzzily matches to christian wellisch . take the round record of this row . select the rows whose opponent record fuzzily matches to pat harmon . take the round record of this row . the first record is greater than the second record .
only { filter_greater { filter_eq { all_rows ; period active ; present } ; claimed sales ; 75 million } } = true
Output: select the rows whose period active record fuzzily matches to present . among these rows , select the rows whose claimed sales record is greater than 75 million . there is only one such row in the table .
round_eq { sum { all_rows ; total dismissals } ; 463 } = true
Output: the sum of the total dismissals record of all rows is 463 .
all_greater { all_rows ; earnings ; 300,000 } = true
Output: for the earnings records of all rows , all of them are greater than 300,000 .
round_eq { avg { filter_greater { all_rows ; size ( steps ) ; 4 } ; size ( cents ) } ; 510 } = true
Output: select the rows whose size  steps  record is greater than 4 . the average of the size  cents  record of these rows is 510 .
round_eq { avg { all_rows ; goals } ; 26.2 } = true
Output: the average of the goals record of all rows is 26.2 .
and { eq { max { filter_eq { all_rows ; region ; middle east } ; joined opec } ; 1967 } ; eq { hop { argmax { filter_eq { all_rows ; region ; middle east } ; joined opec } ; country } ; united arab emirates } } = true
Output: select the rows whose region record fuzzily matches to middle east . the maximum joined opec record of these rows is 1967 . the country record of the row with superlative joined opec record is united arab emirates .
and { eq { max { all_rows ; points } ; 33 } ; eq { hop { argmax { all_rows ; points } ; year } ; 1971 } } = true
Output: the maximum points record of all rows is 33 . the year record of the row with superlative points record is 1971 .
and { only { filter_greater { all_rows ; score ; 280 } } ; eq { hop { filter_greater { all_rows ; score ; 280 } ; year } ; 1998 } } = true
Output: select the rows whose score record is greater than 280 . there is only one such row in the table . the year record of this unqiue row is 1998 .
and { eq { nth_min { all_rows ; us release date ; 1 } ; 27 june 2006 } ; eq { hop { nth_argmin { all_rows ; us release date ; 1 } ; title } ; galaxy angel } } = true
Output: the 1st minimum us release date record of all rows is 27 june 2006 . the title record of the row with 1st minimum us release date record is galaxy angel .
round_eq { sum { filter_eq { all_rows ; date ; september } ; attendance } ; 70000 } = true
Output: select the rows whose date record fuzzily matches to september . the sum of the attendance record of these rows is 70000 .
less { hop { filter_eq { all_rows ; player ; eric king } ; round } ; hop { filter_eq { all_rows ; player ; lionel gates } ; round } } = true
Output: select the rows whose player record fuzzily matches to eric king . take the round record of this row . select the rows whose player record fuzzily matches to lionel gates . take the round record of this row . the first record is less than the second record .
eq { count { filter_eq { all_rows ; original air date ; 2012 } } ; 9 } = true
Output: select the rows whose original air date record fuzzily matches to 2012 . the number of such rows is 9 .
eq { count { filter_eq { all_rows ; republican ticket ; frank } } ; 2 } = true
Output: select the rows whose republican ticket record fuzzily matches to frank . the number of such rows is 2 .
eq { hop { nth_argmax { all_rows ; location attendance ; 1 } ; team } ; knicks } = true
Output: select the row whose location attendance record of all rows is 1st maximum . the team record of this row is knicks .
most_greater { all_rows ; attendance ; 60000 } = true
Output: for the attendance records of all rows , most of them are greater than 60000 .
round_eq { avg { all_rows ; gold } ; .8 } = true
Output: the average of the gold record of all rows is .8 .
eq { count { filter_eq { filter_greater { all_rows ; december ; 15 } ; score ; 4-2 } } ; 2 } = true
Output: select the rows whose december record is greater than 15 . among these rows , select the rows whose score record fuzzily matches to 42 . the number of such rows is 2 .
only { filter_eq { filter_eq { all_rows ; opponent ; arizona } ; result ; ot } } = true
Output: select the rows whose opponent record fuzzily matches to arizona . among these rows , select the rows whose result record fuzzily matches to ot . there is only one such row in the table .
round_eq { avg { filter_not_eq { all_rows ; margin of victory ; playoff } ; margin of victory } ; 1.6 } = true
Output: select the rows whose margin of victory record does not match to playoff . the average of the margin of victory record of these rows is 1.6 .
eq { count { filter_eq { all_rows ; apps ; 37 } } ; 2 } = true
Output: select the rows whose apps record is equal to 37 . the number of such rows is 2 .
most_greater { all_rows ; top - 10 ; 0 } = true
Output: for the top  10 records of all rows , most of them are greater than 0 .
eq { count { filter_eq { all_rows ; loser ; iceland } } ; 1 } = true
Output: select the rows whose loser record fuzzily matches to iceland . the number of such rows is 1 .
and { only { filter_less { all_rows ; points ; 15 } } ; eq { hop { filter_less { all_rows ; points ; 15 } ; club ( city / town ) } ; santel 's ( santa elena ) } } = true
Output: select the rows whose points record is less than 15 . there is only one such row in the table . the club  city  town  record of this unqiue row is santel s  santa elena  .
round_eq { avg { all_rows ; attendance } ; 45,972 } = true
Output: the average of the attendance record of all rows is 45,972 .
round_eq { sum { all_rows ; quantity made } ; 22 } = true
Output: the sum of the quantity made record of all rows is 22 .
round_eq { sum { filter_eq { all_rows ; date ; 2003 } ; score } ; 8 } = true
Output: select the rows whose date record fuzzily matches to 2003 . the sum of the score record of these rows is 8 .
most_eq { all_rows ; writer ; bill gallagher } = true
Output: for the writer records of all rows , most of them fuzzily match to bill gallagher .
round_eq { avg { all_rows ; home team score } ; 17.67 } = true
Output: the average of the home team score record of all rows is 17.67 .
round_eq { avg { all_rows ; notes } ; 1.86 } = true
Output: the average of the notes record of all rows is 1.86 .
and { eq { max { all_rows ; points } ; 11 } ; eq { hop { argmax { all_rows ; points } ; entrant } ; team lotus } } = true
Output: the maximum points record of all rows is 11 . the entrant record of the row with superlative points record is team lotus .
round_eq { avg { all_rows ; score } ; 206 } = true
Output: the average of the score record of all rows is 206 .
eq { hop { nth_argmax { filter_greater_eq { all_rows ; won ; 10 } ; lost ; 3 } ; club } ; pontyclun rfc } = true
Output: select the rows whose won record is greater than or equal to 10 . select the row whose lost record of these rows is 3rd maximum . the club record of this row is pontyclun rfc .
eq { count { filter_eq { all_rows ; nationality ; united states } } ; 3 } = true
Output: select the rows whose nationality record fuzzily matches to united states . the number of such rows is 3 .
and { only { filter_eq { all_rows ; audition city ; são paulo } } ; eq { hop { filter_eq { all_rows ; audition city ; são paulo } ; guest fourth judge } ; peninha } } = true
Output: select the rows whose audition city record fuzzily matches to são paulo . there is only one such row in the table . the guest fourth judge record of this unqiue row is peninha .
round_eq { avg { all_rows ; viewing figure } ; 7.2 } = true
Output: the average of the viewing figure record of all rows is 7.2 .
most_less { all_rows ; year completed ; 1970 } = true
Output: for the year completed records of all rows , most of them are less than 1970 .
round_eq { avg { all_rows ; total } ; 5 } = true
Output: the average of the total record of all rows is 5 .
eq { max { all_rows ; enrollment } ; 580 } = true
Output: the maximum enrollment record of all rows is 580 .
eq { count { filter_eq { all_rows ; entrant ; marlboro brm } } ; 4 } = true
Output: select the rows whose entrant record fuzzily matches to marlboro brm . the number of such rows is 4 .
eq { hop { nth_argmin { all_rows ; reason for change ; 1 } ; vacator } ; george h christopher ( d ) } = true
Output: select the row whose reason for change record of all rows is 1st minimum . the vacator record of this row is george h christopher  d  .
greater { hop { filter_eq { all_rows ; nation ; united kingdom } ; silver } ; hop { filter_eq { all_rows ; nation ; finland } ; silver } } = true
Output: select the rows whose nation record fuzzily matches to united kingdom . take the silver record of this row . select the rows whose nation record fuzzily matches to finland . take the silver record of this row . the first record is greater than the second record .
and { only { filter_eq { all_rows ; population ( 1 july 2005 est ) ; 0 } } ; eq { hop { filter_eq { all_rows ; population ( 1 july 2005 est ) ; 0 } ; subdivisions } ; redonda } } = true
Output: select the rows whose population  1 july 2005 est  record is equal to 0 . there is only one such row in the table . the subdivisions record of this unqiue row is redonda .
eq { hop { argmax { all_rows ; losses } ; team } ; liverpool city } = true
Output: select the row whose losses record of all rows is maximum . the team record of this row is liverpool city .
round_eq { avg { all_rows ; enrollment 08 - 09 } ; 923.5 } = true
Output: the average of the enrollment 08  09 record of all rows is 923.5 .
eq { hop { nth_argmin { all_rows ; years for jazz ; 2 } ; player } ; j j anderson } = true
Output: select the row whose years for jazz record of all rows is 2nd minimum . the player record of this row is j j anderson .
eq { count { filter_eq { all_rows ; country ; madagascar } } ; 3 } = true
Output: select the rows whose country record fuzzily matches to madagascar . the number of such rows is 3 .
less { hop { filter_eq { all_rows ; incumbent ; frank park } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; carl vinson } ; first elected } } = true
Output: select the rows whose incumbent record fuzzily matches to frank park . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to carl vinson . take the first elected record of this row . the first record is less than the second record .
eq { diff { hop { filter_eq { all_rows ; date ; november 8 , 1992 } ; date } ; hop { filter_eq { all_rows ; date ; november 22 , 1992 } ; date } } ; -14 days } = true
Output: select the rows whose date record fuzzily matches to november 8 , 1992 . take the date record of this row . select the rows whose date record fuzzily matches to november 22 , 1992 . take the date record of this row . the second record is 14 days larger than the first record .
greater { hop { filter_eq { all_rows ; nation ; kingfisher east bengal fc } ; winners } ; hop { filter_eq { all_rows ; nation ; pahang fa } ; winners } } = true
Output: select the rows whose nation record fuzzily matches to kingfisher east bengal fc . take the winners record of this row . select the rows whose nation record fuzzily matches to pahang fa . take the winners record of this row . the first record is greater than the second record .
round_eq { sum { all_rows ; laps } ; 974 } = true
Output: the sum of the laps record of all rows is 974 .
round_eq { avg { all_rows ; attendance } ; 36092.5 } = true
Output: the average of the attendance record of all rows is 36092.5 .
eq { hop { argmax { all_rows ; total } ; 1953 } ; robert f wagner , jr } = true
Output: select the row whose total record of all rows is maximum . the 1953 record of this row is robert f wagner , jr .
most_not_eq { all_rows ; qual 2 ; - } = true
Output: for the qual 2 records of all rows , most of them are not equal to  .
eq { count { filter_eq { all_rows ; silver ; 6 } } ; 2 } = true
Output: select the rows whose silver record is equal to 6 . the number of such rows is 2 .
greater { hop { filter_eq { all_rows ; game ; 4 } ; attendance } ; hop { filter_eq { all_rows ; game ; 1 } ; attendance } } = true
Output: select the rows whose game record fuzzily matches to 4 . take the attendance record of this row . select the rows whose game record fuzzily matches to 1 . take the attendance record of this row . the first record is greater than the second record .
eq { hop { argmax { all_rows ; matches } ; player } ; adam gilchrist } = true
Output: select the row whose matches record of all rows is maximum . the player record of this row is adam gilchrist .
eq { hop { nth_argmin { all_rows ; rnd ; 3 } ; date } ; 1 march } = true
Output: select the row whose rnd record of all rows is 3rd minimum . the date record of this row is 1 march .
and { only { filter_less { all_rows ; population ( in 2008 ) ; 1000 } } ; eq { hop { filter_less { all_rows ; population ( in 2008 ) ; 1000 } ; suburb } ; o'malley } } = true
Output: select the rows whose population  in 2008  record is less than 1000 . there is only one such row in the table . the suburb record of this unqiue row is omalley .
and { only { filter_eq { all_rows ; team ; mopar team oreca } } ; eq { hop { filter_eq { all_rows ; team ; mopar team oreca } ; laps } ; 1 } } = true
Output: select the rows whose team record fuzzily matches to mopar team oreca . there is only one such row in the table . the laps record of this unqiue row is 1 .
and { only { filter_eq { all_rows ; top - 5 ; 0 } } ; eq { hop { filter_eq { all_rows ; top - 5 ; 0 } ; tournament } ; pga championship } } = true
Output: select the rows whose top  5 record is equal to 0 . there is only one such row in the table . the tournament record of this unqiue row is pga championship .
most_eq { all_rows ; 1st party ; unionist } = true
Output: for the 1st party records of all rows , most of them fuzzily match to unionist .
eq { count { filter_eq { filter_less { all_rows ; date ; jun 27 } ; set 5 ; na } } ; 2 } = true
Output: select the rows whose date record is less than jun 27 . among these rows , select the rows whose set 5 record fuzzily matches to na . the number of such rows is 2 .
less { hop { filter_eq { all_rows ; university ; mcgill university } ; founded } ; hop { filter_eq { all_rows ; university ; université de sherbrooke } ; founded } } = true
Output: select the rows whose university record fuzzily matches to mcgill university . take the founded record of this row . select the rows whose university record fuzzily matches to université de sherbrooke . take the founded record of this row . the first record is less than the second record .
eq { count { filter_eq { all_rows ; nominating festival ; prix uip valladolid } } ; 2 } = true
Output: select the rows whose nominating festival record fuzzily matches to prix uip valladolid . the number of such rows is 2 .
eq { hop { argmax { filter_eq { all_rows ; location attendance ; pepsi center } ; location attendance } ; date } ; november 1 } = true
Output: select the rows whose location attendance record fuzzily matches to pepsi center . select the row whose location attendance record of these rows is maximum . the date record of this row is november 1 .
round_eq { avg { filter_eq { all_rows ; venue ; melbourne cricket ground , melbourne } ; runs } ; 397 } = true
Output: select the rows whose venue record fuzzily matches to melbourne cricket ground , melbourne . the average of the runs record of these rows is 397 .
most_less { filter_eq { all_rows ; broadcast ; espn } ; attendance ; 100000 } = true
Output: select the rows whose broadcast record fuzzily matches to espn . for the attendance records of these rows , most of them are less than 100000 .
round_eq { avg { all_rows ; total } ; 15 } = true
Output: the average of the total record of all rows is 15 .
greater { hop { filter_eq { all_rows ; title of movie ; black tide 《 黑潮 》 } ; year } ; hop { filter_eq { all_rows ; title of movie ; burn ! motorbike 《 燃燒吧 ！ 機車 》 } ; year } } = true
Output: select the rows whose title of movie record fuzzily matches to black tide 《 黑潮 》 . take the year record of this row . select the rows whose title of movie record fuzzily matches to burn ! motorbike 《 燃燒吧 ！ 機車 》 . take the year record of this row . the first record is greater than the second record .
eq { hop { nth_argmax { filter_less { all_rows ; equatorial bulge ; 100 } ; equatorial diameter ; 1 } ; body } ; earth } = true
Output: select the rows whose equatorial bulge record is less than 100 . select the row whose equatorial diameter record of these rows is 1st maximum . the body record of this row is earth .
round_eq { avg { all_rows ; height ft ( m ) } ; 325.4 } = true
Output: the average of the height ft  m  record of all rows is 325.4 .
greater { hop { filter_eq { all_rows ; nation ; japan ( jpn ) } ; silver } ; hop { filter_eq { all_rows ; nation ; indonesia ( ina ) } ; silver } } = true
Output: select the rows whose nation record fuzzily matches to japan  jpn  . take the silver record of this row . select the rows whose nation record fuzzily matches to indonesia  ina  . take the silver record of this row . the first record is greater than the second record .
greater { hop { filter_eq { all_rows ; date ; september 22 } ; result } ; hop { filter_eq { all_rows ; date ; november 17 } ; result } } = true
Output: select the rows whose date record fuzzily matches to september 22 . take the result record of this row . select the rows whose date record fuzzily matches to november 17 . take the result record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; area ; gore } } ; 7 } = true
Output: select the rows whose area record fuzzily matches to gore . the number of such rows is 7 .
most_eq { all_rows ; title playoff ; 0 } = true
Output: for the title playoff records of all rows , most of them are equal to 0 .
and { only { filter_eq { all_rows ; school colors ; scarlet } } ; eq { hop { filter_eq { all_rows ; school colors ; scarlet } ; institution } ; allen community college } } = true
Output: select the rows whose school colors record fuzzily matches to scarlet . there is only one such row in the table . the institution record of this unqiue row is allen community college .
most_eq { all_rows ; language ; hindi } = true
Output: for the language records of all rows , most of them fuzzily match to hindi .
eq { count { filter_eq { all_rows ; year of publication ; 2007 } } ; 4 } = true
Output: select the rows whose year of publication record fuzzily matches to 2007 . the number of such rows is 4 .
most_eq { all_rows ; city of license ; colorado } = true
Output: for the city of license records of all rows , most of them fuzzily match to colorado .
eq { diff { hop { filter_eq { all_rows ; bsu head coach ; bus conner } ; year } ; hop { filter_eq { all_rows ; bsu head coach ; greg graham } ; year } } ; -32 years } = true
Output: select the rows whose bsu head coach record fuzzily matches to bus conner . take the year record of this row . select the rows whose bsu head coach record fuzzily matches to greg graham . take the year record of this row . the second record is 32 years larger than the first record .
eq { count { filter_eq { all_rows ; opponent in final ; sho sasaki } } ; 2 } = true
Output: select the rows whose opponent in final record fuzzily matches to sho sasaki . the number of such rows is 2 .
eq { count { filter_eq { all_rows ; royal house ; twelfth } } ; 3 } = true
Output: select the rows whose royal house record fuzzily matches to twelfth . the number of such rows is 3 .
eq { count { filter_greater { filter_eq { all_rows ; date ; october } ; attendance ; 50000 } } ; 2 } = true
Output: select the rows whose date record fuzzily matches to october . among these rows , select the rows whose attendance record is greater than 50000 . the number of such rows is 2 .
most_greater_eq { all_rows ; base ; 11.5 } = true
Output: for the base records of all rows , most of them are greater than or equal to 11.5 .
round_eq { sum { filter_eq { all_rows ; date ; january } ; score } ; 2 } = true
Output: select the rows whose date record fuzzily matches to january . the sum of the score record of these rows is 2 .
eq { hop { nth_argmax { all_rows ; attendance ; 2 } ; date } ; 23 } = true
Output: select the row whose attendance record of all rows is 2nd maximum . the date record of this row is 23 .
eq { hop { nth_argmax { all_rows ; points ; 2 } ; club } ; ud salamanca } = true
Output: select the row whose points record of all rows is 2nd maximum . the club record of this row is ud salamanca .
eq { hop { argmax { all_rows ; total } ; nbr class } ; 396 } = true
Output: select the row whose total record of all rows is maximum . the nbr class record of this row is 396 .
round_eq { avg { all_rows ; bodyweight } ; 100.55 } = true
Output: the average of the bodyweight record of all rows is 100.55 .
eq { hop { argmax { all_rows ; viewers } ; episode } ; episode 1 } = true
Output: select the row whose viewers record of all rows is maximum . the episode record of this row is episode 1 .
most_eq { filter_eq { all_rows ; artist ; various artists ( compilation ) } ; date ; 1992 } = true
Output: select the rows whose artist record fuzzily matches to various artists  compilation  . for the date records of these rows , most of them fuzzily match to 1992 .
and { only { filter_eq { all_rows ; manufacturer ; oldsmobile } } ; eq { hop { filter_eq { all_rows ; manufacturer ; oldsmobile } ; driver } ; steve grissom } } = true
Output: select the rows whose manufacturer record fuzzily matches to oldsmobile . there is only one such row in the table . the driver record of this unqiue row is steve grissom .
most_greater { all_rows ; starts ; 33 } = true
Output: for the starts records of all rows , most of them are greater than 33 .
round_eq { sum { all_rows ; number of reviews } ; 83 } = true
Output: the sum of the number of reviews record of all rows is 83 .
round_eq { avg { all_rows ; attendance } ; 33151 } = true
Output: the average of the attendance record of all rows is 33151 .
round_eq { avg { all_rows ; total flights } ; 16.63 } = true
Output: the average of the total flights record of all rows is 16.63 .
greater { hop { filter_eq { all_rows ; team ; switzerland } ; points } ; hop { filter_eq { all_rows ; team ; germany } ; points } } = true
Output: select the rows whose team record fuzzily matches to switzerland . take the points record of this row . select the rows whose team record fuzzily matches to germany . take the points record of this row . the first record is greater than the second record .
eq { hop { argmax { all_rows ; reg gp } ; player } ; alexander edler } = true
Output: select the row whose reg gp record of all rows is maximum . the player record of this row is alexander edler .
round_eq { avg { all_rows ; points } ; 265 } = true
Output: the average of the points record of all rows is 265 .
eq { hop { nth_argmin { all_rows ; date ; 1 } ; opponent } ; miami dolphins } = true
Output: select the row whose date record of all rows is 1st minimum . the opponent record of this row is miami dolphins .
most_greater { all_rows ; attendance ; 1500 } = true
Output: for the attendance records of all rows , most of them are greater than 1500 .
eq { count { filter_eq { filter_eq { all_rows ; nationality ; canada } ; college / junior / club team ( league ) ; london knights ( ohl ) } } ; 2 } = true
Output: select the rows whose nationality record fuzzily matches to canada . among these rows , select the rows whose college  junior  club team  league  record fuzzily matches to london knights  ohl  . the number of such rows is 2 .
and { only { filter_eq { all_rows ; segment c ; part } } ; eq { hop { filter_eq { all_rows ; segment c ; part } ; episode } ; 259 } } = true
Output: select the rows whose segment c record fuzzily matches to part . there is only one such row in the table . the episode record of this unqiue row is 259 .
eq { hop { argmin { all_rows ; ascent time } ; rider } ; roberto heras ( esp ) } = true
Output: select the row whose ascent time record of all rows is minimum . the rider record of this row is roberto heras  esp  .
most_eq { all_rows ; engine ; ford } = true
Output: for the engine records of all rows , most of them fuzzily match to ford .
eq { hop { nth_argmax { all_rows ; dcsf number ; 2 } ; name } ; st albert the great } = true
Output: select the row whose dcsf number record of all rows is 2nd maximum . the name record of this row is st albert the great .
eq { count { filter_eq { all_rows ; erp w ; 10 } } ; 4 } = true
Output: select the rows whose erp w record is equal to 10 . the number of such rows is 4 .
most_eq { all_rows ; outcome ; runner-up } = true
Output: for the outcome records of all rows , most of them fuzzily match to runnerup .
eq { hop { nth_argmin { all_rows ; time ; 1 } ; opponent } ; mark sinclair } = true
Output: select the row whose time record of all rows is 1st minimum . the opponent record of this row is mark sinclair .
eq { hop { argmax { all_rows ; age at appointment } ; foreign nationality } ; canadian } = true
Output: select the row whose age at appointment record of all rows is maximum . the foreign nationality record of this row is canadian .
round_eq { sum { filter_eq { all_rows ; nation ; united states } ; fastest time ( s ) } ; 66.98 s } = true
Output: select the rows whose nation record fuzzily matches to united states . the sum of the fastest time  s  record of these rows is 66.98 s .
eq { hop { argmax { all_rows ; attendance } ; date } ; march 25 } = true
Output: select the row whose attendance record of all rows is maximum . the date record of this row is march 25 .
eq { count { filter_eq { all_rows ; round ; 2 } } ; 2 } = true
Output: select the rows whose round record is equal to 2 . the number of such rows is 2 .
round_eq { avg { all_rows ; t c ( k ) } ; 99.8 degrees } = true
Output: the average of the t c  k  record of all rows is 99.8 degrees .
most_eq { all_rows ; music ; sukshinder shinda } = true
Output: for the music records of all rows , most of them fuzzily match to sukshinder shinda .
only { filter_eq { all_rows ; country ; japan } } = true
Output: select the rows whose country record fuzzily matches to japan . there is only one such row in the table .
eq { count { filter_greater_eq { all_rows ; first elected ; 2000 } } ; 5 } = true
Output: select the rows whose first elected record is greater than or equal to 2000 . the number of such rows is 5 .
less { hop { filter_eq { all_rows ; player ; leron mitchell } ; pick } ; hop { filter_eq { all_rows ; player ; aaron wagner } ; pick } } = true
Output: select the rows whose player record fuzzily matches to leron mitchell . take the pick record of this row . select the rows whose player record fuzzily matches to aaron wagner . take the pick record of this row . the first record is less than the second record .
eq { hop { nth_argmax { all_rows ; year joined ; 1 } ; school } ; fishers } = true
Output: select the row whose year joined record of all rows is 1st maximum . the school record of this row is fishers .
eq { count { filter_eq { filter_eq { all_rows ; team ; det } ; goal ; jiri hudler } } ; 2 } = true
Output: select the rows whose team record fuzzily matches to det . among these rows , select the rows whose goal record fuzzily matches to jiri hudler . the number of such rows is 2 .
eq { hop { nth_argmax { all_rows ; wins ; 1 } ; club } ; real murcia } = true
Output: select the row whose wins record of all rows is 1st maximum . the club record of this row is real murcia .
and { only { filter_eq { all_rows ; round ; 6 } } ; eq { hop { filter_eq { all_rows ; round ; 6 } ; player } ; frank blum } } = true
Output: select the rows whose round record is equal to 6 . there is only one such row in the table . the player record of this unqiue row is frank blum .
eq { hop { argmax { all_rows ; opening week nett gross } ; movie } ; chennai express } = true
Output: select the row whose opening week nett gross record of all rows is maximum . the movie record of this row is chennai express .
eq { count { filter_eq { all_rows ; writer ( s ) ; ira ungerleider } } ; 2 } = true
Output: select the rows whose writer  s  record fuzzily matches to ira ungerleider . the number of such rows is 2 .
eq { hop { nth_argmin { all_rows ; against ; 2 } ; opposing team } ; north and midland of scotland } = true
Output: select the row whose against record of all rows is 2nd minimum . the opposing team record of this row is north and midland of scotland .
and { only { filter_eq { all_rows ; 100 + ; 207 } } ; eq { hop { filter_eq { all_rows ; 100 + ; 207 } ; name } ; terry jenkins } } = true
Output: select the rows whose 100 + record is equal to 207 . there is only one such row in the table . the name record of this unqiue row is terry jenkins .
eq { hop { nth_argmax { all_rows ; enrollment ; 2 } ; institution } ; minot state university } = true
Output: select the row whose enrollment record of all rows is 2nd maximum . the institution record of this row is minot state university .
eq { count { filter_eq { filter_eq { all_rows ; result ; w } ; opponent ; oakland raiders } } ; 2 } = true
Output: select the rows whose result record fuzzily matches to w . among these rows , select the rows whose opponent record fuzzily matches to oakland raiders . the number of such rows is 2 .
eq { count { filter_eq { all_rows ; 2011 ; 2r } } ; 2 } = true
Output: select the rows whose 2011 record fuzzily matches to 2r . the number of such rows is 2 .
round_eq { avg { all_rows ; wickets } ; 33 } = true
Output: the average of the wickets record of all rows is 33 .
eq { count { filter_eq { all_rows ; partner ; igor sijsling } } ; 2 } = true
Output: select the rows whose partner record fuzzily matches to igor sijsling . the number of such rows is 2 .
most_greater_eq { all_rows ; attendance ; 10,00 } = true
Output: for the attendance records of all rows , most of them are greater than or equal to 10,00 .
eq { count { filter_greater { filter_less { all_rows ; away team score ; 10 } ; crowd ; 20000 } } ; 2 } = 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 20000 . the number of such rows is 2 .
eq { hop { argmax { all_rows ; party list votes } ; party } ; christian democratic union ( cdu ) } = true
Output: select the row whose party list votes record of all rows is maximum . the party record of this row is christian democratic union  cdu  .
greater { hop { filter_eq { all_rows ; surface ; hard } ; score } ; hop { filter_eq { all_rows ; surface ; clay } ; score } } = true
Output: select the rows whose surface record fuzzily matches to hard . take the score record of this row . select the rows whose surface record fuzzily matches to clay . take the score record of this row . the first record is greater than the second record .
most_eq { all_rows ; status ; tour match } = true
Output: for the status records of all rows , most of them fuzzily match to tour match .
round_eq { avg { all_rows ; margin of victory } ; 2 } = true
Output: the average of the margin of victory record of all rows is 2 .
and { only { filter_less { all_rows ; avg attend ; 4000 } } ; eq { hop { filter_less { all_rows ; avg attend ; 4000 } ; indoor year } ; 1983 - 1984 } } = true
Output: select the rows whose avg attend record is less than 4000 . there is only one such row in the table . the indoor year record of this unqiue row is 1983  1984 .
eq { hop { argmax { all_rows ; winning score } ; year } ; 2001 } = true
Output: select the row whose winning score record of all rows is maximum . the year record of this row is 2001 .
round_eq { sum { all_rows ; money } ; 4769400 } = true
Output: the sum of the money record of all rows is 4769400 .
greater { hop { filter_eq { all_rows ; diemaco model ; lsw } ; barrel length } ; hop { filter_eq { all_rows ; diemaco model ; c8 } ; barrel length } } = true
Output: select the rows whose diemaco model record fuzzily matches to lsw . take the barrel length record of this row . select the rows whose diemaco model record fuzzily matches to c8 . take the barrel length record of this row . the first record is greater than the second record .
most_greater_eq { all_rows ; height ; 6 ' 0 } = true
Output: for the height records of all rows , most of them are greater than or equal to 6  0 .
eq { hop { nth_argmax { all_rows ; district ; 2 } ; incumbent } ; christopher shays } = true
Output: select the row whose district record of all rows is 2nd maximum . the incumbent record of this row is christopher shays .
most_less { all_rows ; wins ; 20 } = true
Output: for the wins records of all rows , most of them are less than 20 .
eq { count { filter_eq { all_rows ; yards ; 39 } } ; 2 } = true
Output: select the rows whose yards record is equal to 39 . the number of such rows is 2 .
round_eq { sum { all_rows ; worldwide gross } ; 2632 } = true
Output: the sum of the worldwide gross record of all rows is 2632 .
eq { hop { argmax { all_rows ; erp w } ; frequency mhz } ; 105.1 fm } = true
Output: select the row whose erp w record of all rows is maximum . the frequency mhz record of this row is 105.1 fm .
eq { hop { argmax { all_rows ; first season } ; club } ; godoy cruz } = true
Output: select the row whose first season record of all rows is maximum . the club record of this row is godoy cruz .
eq { hop { argmax { all_rows ; quantity } ; manufacturer } ; beyer , peacock & co } = true
Output: select the row whose quantity record of all rows is maximum . the manufacturer record of this row is beyer , peacock  co .
greater { hop { filter_eq { all_rows ; texas vs ; missouri } ; current streak } ; hop { filter_eq { all_rows ; texas vs ; oklahoma } ; current streak } } = true
Output: select the rows whose texas vs record fuzzily matches to missouri . take the current streak record of this row . select the rows whose texas vs record fuzzily matches to oklahoma . take the current streak record of this row . the first record is greater than the second record .
and { only { filter_eq { all_rows ; ties ; 3 } } ; eq { hop { filter_eq { all_rows ; ties ; 3 } ; season } ; 2008 } } = true
Output: select the rows whose ties record is equal to 3 . there is only one such row in the table . the season record of this unqiue row is 2008 .
most_eq { all_rows ; high points ; kevin durant } = true
Output: for the high points records of all rows , most of them fuzzily match to kevin durant .
and { only { filter_eq { all_rows ; owner ; university } } ; eq { hop { filter_eq { all_rows ; owner ; university } ; frequency } ; fm 101.7 } } = true
Output: select the rows whose owner record fuzzily matches to university . there is only one such row in the table . the frequency record of this unqiue row is fm 101.7 .
round_eq { avg { all_rows ; prize } ; 3137419 } = true
Output: the average of the prize record of all rows is 3137419 .
greater { hop { filter_eq { all_rows ; game ; 49 } ; score } ; hop { filter_eq { all_rows ; game ; 55 } ; score } } = true
Output: select the rows whose game record fuzzily matches to 49 . take the score record of this row . select the rows whose game record fuzzily matches to 55 . take the score record of this row . the first record is greater than the second record .
less { hop { filter_eq { all_rows ; country ; united kingdom } ; date } ; hop { filter_eq { all_rows ; country ; united states } ; date } } = true
Output: select the rows whose country record fuzzily matches to united kingdom . take the date record of this row . select the rows whose country record fuzzily matches to united states . take the date record of this row . the first record is less than the second record .
eq { count { filter_not_eq { all_rows ; us hot 100 ; - } } ; 2 } = true
Output: select the rows whose us hot 100 record is not equal to  . the number of such rows is 2 .
eq { hop { nth_argmin { all_rows ; date ; 3 } ; tournament } ; chennai , india } = true
Output: select the row whose date record of all rows is 3rd minimum . the tournament record of this row is chennai , india .
and { eq { max { all_rows ; population ( 2010 census ) } ; 241528 } ; eq { hop { argmax { all_rows ; population ( 2010 census ) } ; district } ; sampaloc } } = true
Output: the maximum population  2010 census  record of all rows is 241528 . the district record of the row with superlative population  2010 census  record is sampaloc .
greater { hop { filter_eq { all_rows ; year ; 1995 } ; points } ; hop { filter_eq { all_rows ; year ; 1996 } ; points } } = true
Output: select the rows whose year record fuzzily matches to 1995 . take the points record of this row . select the rows whose year record fuzzily matches to 1996 . take the points record of this row . the first record is greater than the second record .
eq { count { filter_all { all_rows ; episode air date } } ; 7 } = true
Output: select the rows whose episode air date record is arbitrary . the number of such rows is 7 .
eq { count { filter_eq { all_rows ; record ; postponed ( rain ) } } ; 2 } = true
Output: select the rows whose record record fuzzily matches to postponed  rain  . the number of such rows is 2 .
eq { count { filter_eq { all_rows ; place ; t4 } } ; 3 } = true
Output: select the rows whose place record fuzzily matches to t4 . the number of such rows is 3 .
and { less { hop { filter_eq { all_rows ; film title used in nomination ; vukovar poste restante } ; year ( ceremony ) } ; hop { filter_eq { all_rows ; film title used in nomination ; underground } ; year ( ceremony ) } } ; and { eq { hop { filter_eq { all_rows ; film title used in nomination ; vukovar poste restante } ; year ( ceremony ) } ; 1994 ( 67th ) } ; eq { hop { filter_eq { all_rows ; film title used in nomination ; underground } ; year ( ceremony ) } ; 1995 ( 68th ) } } } = true
Output: select the rows whose film title used in nomination record fuzzily matches to vukovar poste restante . take the year  ceremony  record of this row . select the rows whose film title used in nomination record fuzzily matches to underground . take the year  ceremony  record of this row . the first record is less than the second record . the year  ceremony  record of the first row is 1994  67th  . the year  ceremony  record of the second row is 1995  68th  .
round_eq { sum { all_rows ; gold } ; 94 } = true
Output: the sum of the gold record of all rows is 94 .