niryuu/sni-each-converted huggingface dataset:
select the row whose juries record of all rows is maximum . the artist record of this row is danny saucedo .
Output: eq { hop { argmax { all_rows ; juries } ; artist } ; danny saucedo }
select the rows whose league record fuzzily matches to aa . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; league ; aa } } ; 3 }
the average of the us viewers ( millions ) record of all rows is 11.34 .
Output: round_eq { avg { all_rows ; us viewers ( millions ) } ; 11.34 }
the average of the points record of all rows is 9.4 .
Output: round_eq { avg { all_rows ; points } ; 9.4 }
select the rows whose tournament record fuzzily matches to bordeaux , france . take the date record of this row . select the rows whose tournament record fuzzily matches to san marino . take the date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; tournament ; bordeaux , france } ; date } ; hop { filter_eq { all_rows ; tournament ; san marino } ; date } }
select the rows whose position record fuzzily matches to fb . there is only one such row in the table . the name record of this unqiue row is bob briggs .
Output: and { only { filter_eq { all_rows ; position ; fb } } ; eq { hop { filter_eq { all_rows ; position ; fb } ; name } ; bob briggs } }
the average of the crowd record of all rows is 23360 .
Output: round_eq { avg { all_rows ; crowd } ; 23360 }
select the rows whose format record fuzzily matches to lp . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; format ; lp } } ; 2 }
select the rows whose silver record is equal to 3 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; silver ; 3 } } ; 2 }
for the party leader records of all rows , all of them fuzzily match to habib bourguiba .
Output: all_eq { all_rows ; party leader ; habib bourguiba }
select the rows whose to par record fuzzily matches to + 6 . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; to par ; + 6 } } ; 3 }
select the row whose assumed office record of all rows is minimum . the district record of this row is american samoa .
Output: eq { hop { argmin { all_rows ; assumed office } ; district } ; american samoa }
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 .
Output: and { only { filter_less { all_rows ; attendance ; 1500 } } ; eq { hop { filter_less { all_rows ; attendance ; 1500 } ; date } ; april 18 } }
the sum of the points record of all rows is 167 .
Output: round_eq { sum { all_rows ; points } ; 167 }
select the rows whose points record is less than 40 . there is only one such row in the table . the song record of this unqiue row is cold shoulder .
Output: and { only { filter_less { all_rows ; points ; 40 } } ; eq { hop { filter_less { all_rows ; points ; 40 } ; song } ; cold shoulder } }
select the rows whose songwriter ( s ) record fuzzily matches to wallis willis . there is only one such row in the table . the title record of this unqiue row is swing low , sweet chariot .
Output: and { only { filter_eq { all_rows ; songwriter ( s ) ; wallis willis } } ; eq { hop { filter_eq { all_rows ; songwriter ( s ) ; wallis willis } ; title } ; swing low , sweet chariot } }
select the rows whose tournament record fuzzily matches to italy . among these rows , select the rows whose opponent record fuzzily matches to marcel granollers . there is only one such row in the table . the date record of this unqiue row is 30 may 2006 .
Output: and { only { filter_eq { filter_eq { all_rows ; tournament ; italy } ; opponent ; marcel granollers } } ; eq { hop { filter_eq { filter_eq { all_rows ; tournament ; italy } ; opponent ; marcel granollers } ; date } ; 30 may 2006 } }
select the rows whose street address record fuzzily matches to north franklin street . among these rows , select the rows whose height ft ( m ) record is greater than 200 . the number of such rows is 2 .
Output: eq { count { filter_greater { filter_eq { all_rows ; street address ; north franklin street } ; height ft ( m ) ; 200 } } ; 2 }
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 .
Output: 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 }
for the preliminary average records of all rows , most of them are less than 9.0 .
Output: most_less { all_rows ; preliminary average ; 9.0 }
select the row whose average record of all rows is maximum . the player record of this row is alec bedser .
Output: eq { hop { argmax { all_rows ; average } ; player } ; alec bedser }
select the rows whose goal difference record is equal to -2 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; goal difference ; -2 } } ; 2 }
select the rows whose driver record fuzzily matches to graham hill . take the time / retired record of this row . select the rows whose driver record fuzzily matches to jim clark . take the time / retired record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; driver ; graham hill } ; time / retired } ; hop { filter_eq { all_rows ; driver ; jim clark } ; time / retired } }
select the rows whose platelet count record fuzzily matches to decreased or unaffected . there is only one such row in the table . the condition record of this unqiue row is bernard - soulier syndrome .
Output: and { only { filter_eq { all_rows ; platelet count ; decreased or unaffected } } ; eq { hop { filter_eq { all_rows ; platelet count ; decreased or unaffected } ; condition } ; bernard - soulier syndrome } }
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 .
Output: less { hop { filter_eq { all_rows ; country ; united kingdom } ; date } ; hop { filter_eq { all_rows ; country ; united states } ; date } }
select the rows whose weight ( lb ) record is less than 200 . the number of such rows is 4 .
Output: eq { count { filter_less { all_rows ; weight ( lb ) ; 200 } } ; 4 }
select the rows whose towns / villages record is greater than 350 . there is only one such row in the table . the name of county record of this unqiue row is borsod - abaúj - zemplén .
Output: and { only { filter_greater { all_rows ; towns / villages ; 350 } } ; eq { hop { filter_greater { all_rows ; towns / villages ; 350 } ; name of county } ; borsod - abaúj - zemplén } }
select the rows whose venue record fuzzily matches to rome . take the date record of this row . select the rows whose venue record fuzzily matches to beijing . take the date record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; venue ; rome } ; date } ; hop { filter_eq { all_rows ; venue ; beijing } ; date } }
select the row whose innings record of all rows is maximum . the player record of this row is ricky ponting .
Output: eq { hop { argmax { all_rows ; innings } ; player } ; ricky ponting }
select the rows whose game site record fuzzily matches to kingdome . the number of such rows is 8 .
Output: eq { count { filter_eq { all_rows ; game site ; kingdome } } ; 8 }
select the rows whose episodes record is equal to 18 . there is only one such row in the table . the season record of this unqiue row is 8 .
Output: and { only { filter_eq { all_rows ; episodes ; 18 } } ; eq { hop { filter_eq { all_rows ; episodes ; 18 } ; season } ; 8 } }
for the crowd records of all rows , most of them are greater than 15000 .
Output: most_greater { all_rows ; crowd ; 15000 }
select the row whose number of episodes record of all rows is maximum . the english title ( chinese title ) record of this row is secret of the heart 天地豪情 .
Output: eq { hop { argmax { all_rows ; number of episodes } ; english title ( chinese title ) } ; secret of the heart 天地豪情 }
the average of the crowd record of all rows is 18504 .
Output: round_eq { avg { all_rows ; crowd } ; 18504 }
select the rows whose date record fuzzily matches to 2004 . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; date ; 2004 } } ; 3 }
select the rows whose location in park record fuzzily matches to baja ridge . among these rows , select the rows whose thrill / intensity rating record fuzzily matches to maximum . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; location in park ; baja ridge } ; thrill / intensity rating ; maximum } } ; 2 }
the sum of the attendance record of all rows is 894532 .
Output: round_eq { sum { all_rows ; attendance } ; 894532 }
select the rows whose school / country record fuzzily matches to southern methodist . there is only one such row in the table . the player record of this unqiue row is jim krebs .
Output: and { only { filter_eq { all_rows ; school / country ; southern methodist } } ; eq { hop { filter_eq { all_rows ; school / country ; southern methodist } ; player } ; jim krebs } }
for the closed records of all rows , all of them are greater than 2005 .
Output: all_greater { all_rows ; closed ; 2005 }
select the rows whose score record fuzzily matches to ot . there is only one such row in the table . the date record of this unqiue row is december 26 .
Output: and { only { filter_eq { all_rows ; score ; ot } } ; eq { hop { filter_eq { all_rows ; score ; ot } ; date } ; december 26 } }
select the rows whose rank record fuzzily matches to 3 . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; rank ; 3 } } ; 3 }
the average of the s risk - weighted asset , million record of all rows is 60551 .
Output: round_eq { avg { all_rows ; s risk - weighted asset , million } ; 60551 }
select the rows whose result record does not match to retired . the number of such rows is 4 .
Output: eq { count { filter_not_eq { all_rows ; result ; retired } } ; 4 }
select the rows whose title record fuzzily matches to bamboo blade . take the last issue record of this row . select the rows whose title record fuzzily matches to black butler . take the last issue record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; title ; bamboo blade } ; last issue } ; hop { filter_eq { all_rows ; title ; black butler } ; last issue } }
the average of the erp w record of all rows is 66 .
Output: round_eq { avg { all_rows ; erp w } ; 66 }
select the rows whose away team record fuzzily matches to melbourne . among these rows , select the rows whose venue record fuzzily matches to windy hill . there is only one such row in the table . the home team record of this unqiue row is essendon .
Output: and { only { filter_eq { filter_eq { all_rows ; away team ; melbourne } ; venue ; windy hill } } ; eq { hop { filter_eq { filter_eq { all_rows ; away team ; melbourne } ; venue ; windy hill } ; home team } ; essendon } }
select the rows whose country record does not match to united states . select the row whose place record of these rows is 1st minimum . the player record of this row is seve ballesteros .
Output: eq { hop { nth_argmin { filter_not_eq { all_rows ; country ; united states } ; place ; 1 } ; player } ; seve ballesteros }
the sum of the crowd record of all rows is 105,000 .
Output: round_eq { sum { all_rows ; crowd } ; 105,000 }
select the rows whose top - 10 record is equal to 0 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; top - 10 ; 0 } } ; 2 }
select the rows whose 1st member record fuzzily matches to william hall . take the elected record of this row . select the rows whose 1st member record fuzzily matches to george gascoigne . take the elected record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; 1st member ; william hall } ; elected } ; hop { filter_eq { all_rows ; 1st member ; george gascoigne } ; elected } }
select the row whose finished record of all rows is minimum . the horse record of this row is big brown .
Output: eq { hop { argmin { all_rows ; finished } ; horse } ; big brown }
select the rows whose hometown record fuzzily matches to ca . there is only one such row in the table . the player record of this unqiue row is james kaprelian .
Output: and { only { filter_eq { all_rows ; hometown ; ca } } ; eq { hop { filter_eq { all_rows ; hometown ; ca } ; player } ; james kaprelian } }
select the rows whose date record fuzzily matches to april 2 . take the location attendance record of this row . select the rows whose date record fuzzily matches to april 13 . take the location attendance record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; date ; april 2 } ; location attendance } ; hop { filter_eq { all_rows ; date ; april 13 } ; location attendance } }
select the rows whose developer ( s ) record fuzzily matches to ea games . take the release date record of this row . select the rows whose developer ( s ) record fuzzily matches to masthead studios . take the release date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; developer ( s ) ; ea games } ; release date } ; hop { filter_eq { all_rows ; developer ( s ) ; masthead studios } ; release date } }
select the row whose age record of all rows is minimum . the candidate record of this row is samuel sam hooper .
Output: eq { hop { argmin { all_rows ; age } ; candidate } ; samuel sam hooper }
for the result records of all rows , all of them fuzzily match to re-elected .
Output: all_eq { all_rows ; result ; re-elected }
select the row whose first elected record of all rows is 1st minimum . the incumbent record of this row is george h pendleton .
Output: eq { hop { nth_argmin { all_rows ; first elected ; 1 } ; incumbent } ; george h pendleton }
the sum of the round record of all rows is 26 .
Output: round_eq { sum { all_rows ; round } ; 26 }
select the rows whose segment b record fuzzily matches to outboard motors . take the episode record of this row . select the rows whose segment b record fuzzily matches to s snowblower . take the episode record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; segment b ; outboard motors } ; episode } ; hop { filter_eq { all_rows ; segment b ; s snowblower } ; episode } }
for the directed by records of all rows , most of them fuzzily match to ciaran donnelly .
Output: most_eq { all_rows ; directed by ; ciaran donnelly }
select the rows whose club record fuzzily matches to ca osasuna . take the wins record of this row . select the rows whose club record fuzzily matches to real oviedo . take the wins record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; club ; ca osasuna } ; wins } ; hop { filter_eq { all_rows ; club ; real oviedo } ; wins } }
for the points records of all rows , most of them are greater than 50 .
Output: most_greater { all_rows ; points ; 50 }
select the rows whose reason for change record fuzzily matches to resigned . among these rows , select the rows whose date successor seated record fuzzily matches to november . the number of such rows is 4 .
Output: eq { count { filter_eq { filter_eq { all_rows ; reason for change ; resigned } ; date successor seated ; november } } ; 4 }
select the rows whose staffel a record fuzzily matches to tsg wismar . there is only one such row in the table .
Output: only { filter_eq { all_rows ; staffel a ; tsg wismar } }
select the rows whose first elected record fuzzily matches to 196 . there is only one such row in the table . the incumbent record of this unqiue row is clarence long .
Output: and { only { filter_eq { all_rows ; first elected ; 196 } } ; eq { hop { filter_eq { all_rows ; first elected ; 196 } ; incumbent } ; clarence long } }
select the rows whose manner of departure record fuzzily matches to gardening leave 1 . there is only one such row in the table . the club record of this unqiue row is kedah fa .
Output: and { only { filter_eq { all_rows ; manner of departure ; gardening leave 1 } } ; eq { hop { filter_eq { all_rows ; manner of departure ; gardening leave 1 } ; club } ; kedah fa } }
select the row whose top 10 record of all rows is maximum . the year record of this row is 2005 .
Output: eq { hop { argmax { all_rows ; top 10 } ; year } ; 2005 }
select the row whose viewership record of all rows is maximum . the episode record of this row is 1 .
Output: eq { hop { argmax { all_rows ; viewership } ; episode } ; 1 }
select the rows whose race title record is arbitrary . the number of such rows is 5 .
Output: eq { count { filter_all { all_rows ; race title } } ; 5 }
select the rows whose name record fuzzily matches to alfred pfaff . take the goals record of this row . select the rows whose name record fuzzily matches to lothar schämer . take the goals record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; name ; alfred pfaff } ; goals } ; hop { filter_eq { all_rows ; name ; lothar schämer } ; goals } }
select the rows whose year record is equal to 2005 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; year ; 2005 } } ; 2 }
select the rows whose category record is equal to 1 . there is only one such row in the table . the leader at the summit record of this unqiue row is richard virenque .
Output: and { only { filter_eq { all_rows ; category ; 1 } } ; eq { hop { filter_eq { all_rows ; category ; 1 } ; leader at the summit } ; richard virenque } }
select the rows whose wins record is greater than 0 . there is only one such row in the table . the tournament record of this unqiue row is the open championship .
Output: and { only { filter_greater { all_rows ; wins ; 0 } } ; eq { hop { filter_greater { all_rows ; wins ; 0 } ; tournament } ; the open championship } }
the average of the first quarter record of all rows is 305221 .
Output: round_eq { avg { all_rows ; first quarter } ; 305221 }
the 1st minimum year record of all rows is 1996 . the year record of the row with 1st minimum year record is 1996 . the category record of the row with 1st minimum year record is 60 minute category . the result record of the row with 1st minimum year record is nominated .
Output: and { eq { nth_min { all_rows ; year ; 1 } ; 1996 } ; and { eq { hop { nth_argmin { all_rows ; year ; 1 } ; year } ; 1996 } ; and { eq { hop { nth_argmin { all_rows ; year ; 1 } ; category } ; 60 minute category } ; eq { hop { nth_argmin { all_rows ; year ; 1 } ; result } ; nominated } } } }
select the rows whose season record fuzzily matches to 2 . take the potential prize money record of this row . select the rows whose season record fuzzily matches to 1 . take the potential prize money record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; season ; 2 } ; potential prize money } ; hop { filter_eq { all_rows ; season ; 1 } ; potential prize money } }
select the rows whose home record fuzzily matches to minnesota . among these rows , select the rows whose attendance record is greater than or equal to 19360 . the number of such rows is 2 .
Output: eq { count { filter_greater_eq { filter_eq { all_rows ; home ; minnesota } ; attendance ; 19360 } } ; 2 }
the sum of the gold record of all rows is 70 .
Output: round_eq { sum { all_rows ; gold } ; 70 }
select the rows whose position record fuzzily matches to guard . select the row whose years in orlando record of these rows is minimum . the player record of this row is todd lichti .
Output: eq { hop { argmin { filter_eq { all_rows ; position ; guard } ; years in orlando } ; player } ; todd lichti }
select the rows whose weeks on top record is equal to 11 . there is only one such row in the table . the song record of this unqiue row is mambo no 5 .
Output: and { only { filter_eq { all_rows ; weeks on top ; 11 } } ; eq { hop { filter_eq { all_rows ; weeks on top ; 11 } ; song } ; mambo no 5 } }
select the row whose number of electorates ( 2003 ) record of all rows is 2nd maximum . the constituency number record of this row is 49 .
Output: eq { hop { nth_argmax { all_rows ; number of electorates ( 2003 ) ; 2 } ; constituency number } ; 49 }
select the rows whose years owned record fuzzily matches to 1995 - 2008 . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; years owned ; 1995 - 2008 } } ; 4 }
select the rows whose average record is greater than 500 . the number of such rows is 2 .
Output: eq { count { filter_greater { all_rows ; average ; 500 } } ; 2 }
select the rows whose tournament record fuzzily matches to world cross country championships . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; tournament ; world cross country championships } } ; 3 }
select the rows whose tournament record fuzzily matches to hamburg . take the week record of this row . select the rows whose tournament record fuzzily matches to rome . take the week record of this row . the first record is 7 days larger than the second record . the week record of the first row is may 14 . the week record of the second row is may 7 .
Output: and { eq { diff { hop { filter_eq { all_rows ; tournament ; hamburg } ; week } ; hop { filter_eq { all_rows ; tournament ; rome } ; week } } ; 7 days } ; and { eq { hop { filter_eq { all_rows ; tournament ; hamburg } ; week } ; may 14 } ; eq { hop { filter_eq { all_rows ; tournament ; rome } ; week } ; may 7 } } }
select the rows whose tournament record fuzzily matches to united kingdom . select the row whose date record of these rows is 2nd minimum . the surface record of this row is hard .
Output: eq { hop { nth_argmin { filter_eq { all_rows ; tournament ; united kingdom } ; date ; 2 } ; surface } ; hard }
select the row whose result record of all rows is minimum . the driver record of this row is emerson fittipaldi .
Output: eq { hop { argmin { all_rows ; result } ; driver } ; emerson fittipaldi }
select the row whose candidates record of all rows is maximum . the incumbent record of this row is bill clay .
Output: eq { hop { argmax { all_rows ; candidates } ; incumbent } ; bill clay }
select the rows whose province record fuzzily matches to alberta . take the south asians 2011 record of this row . select the rows whose province record fuzzily matches to quebec . take the south asians 2011 record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; province ; alberta } ; south asians 2011 } ; hop { filter_eq { all_rows ; province ; quebec } ; south asians 2011 } }
select the rows whose number of borough councilors record is equal to 4 . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; number of borough councilors ; 4 } } ; 3 }
select the rows whose notes record fuzzily matches to co - directed . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; notes ; co - directed } } ; 2 }
select the row whose inhabitants record of all rows is maximum . the municipality record of this row is trento .
Output: eq { hop { argmax { all_rows ; inhabitants } ; municipality } ; trento }
the average of the raiders points record of all rows is 26.79 .
Output: round_eq { avg { all_rows ; raiders points } ; 26.79 }
select the row whose built record of all rows is minimum . the name record of this row is humpback .
Output: eq { hop { argmin { all_rows ; built } ; name } ; humpback }
select the rows whose venue record fuzzily matches to junction oval . take the crowd record of this row . select the rows whose venue record fuzzily matches to western oval . take the crowd record of this row . the first record is 8099 larger than the second record .
Output: eq { diff { hop { filter_eq { all_rows ; venue ; junction oval } ; crowd } ; hop { filter_eq { all_rows ; venue ; western oval } ; crowd } } ; 8099 }
select the rows whose entrant record fuzzily matches to marlboro brm . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; entrant ; marlboro brm } } ; 4 }
select the rows whose steals record fuzzily matches to 3 tied . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; steals ; 3 tied } } ; 4 }
the maximum frequency mhz record of all rows is 107.3 . the city of license record of the row with superlative frequency mhz record is hill city , kansas .
Output: and { eq { max { all_rows ; frequency mhz } ; 107.3 } ; eq { hop { argmax { all_rows ; frequency mhz } ; city of license } ; hill city , kansas } }
for the high points records of all rows , most of them fuzzily match to kevin martin .
Output: most_eq { all_rows ; high points ; kevin martin }
the sum of the gold record of all rows is 94 .
Output: round_eq { sum { all_rows ; gold } ; 94 }
for the fat16 records of all rows , all of them fuzzily match to yes .
Output: all_eq { all_rows ; fat16 ; yes }
select the rows whose score record fuzzily matches to 0 - . there is only one such row in the table . the game record of this unqiue row is 30 .
Output: and { only { filter_eq { all_rows ; score ; 0 - } } ; eq { hop { filter_eq { all_rows ; score ; 0 - } ; game } ; 30 } }
for the date records of all rows , all of them fuzzily match to 30 august 1975 .
Output: all_eq { all_rows ; date ; 30 august 1975 }
select the rows whose player record fuzzily matches to payne stewart . take the score record of this row . select the rows whose player record fuzzily matches to fred couples . take the score record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; player ; payne stewart } ; score } ; hop { filter_eq { all_rows ; player ; fred couples } ; score } }
select the rows whose rank record fuzzily matches to 2 . take the silver record of this row . select the rows whose rank record fuzzily matches to 1 . take the silver record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; rank ; 2 } ; silver } ; hop { filter_eq { all_rows ; rank ; 1 } ; silver } }
the average of the score record of all rows is 206 .
Output: round_eq { avg { all_rows ; score } ; 206 }
select the rows whose crowd record is less than 15000 . among these rows , select the rows whose venue record fuzzily matches to lake oval . there is only one such row in the table . the home team record of this unqiue row is south melbourne .
Output: and { only { filter_eq { filter_less { all_rows ; crowd ; 15000 } ; venue ; lake oval } } ; eq { hop { filter_eq { filter_less { all_rows ; crowd ; 15000 } ; venue ; lake oval } ; home team } ; south melbourne } }
select the row whose pick record of all rows is 2nd maximum . the player record of this row is tom neville .
Output: eq { hop { nth_argmax { all_rows ; pick ; 2 } ; player } ; tom neville }
the average of the high points record of all rows is 30 .
Output: round_eq { avg { all_rows ; high points } ; 30 }
select the rows whose 2002 population record is greater than 20000 . there is only one such row in the table . the commune record of this unqiue row is province .
Output: and { only { filter_greater { all_rows ; 2002 population ; 20000 } } ; eq { hop { filter_greater { all_rows ; 2002 population ; 20000 } ; commune } ; province } }
the 2nd minimum date record of all rows is 31 may .
Output: eq { nth_min { all_rows ; date ; 2 } ; 31 may }
the sum of the result record of all rows is 456 .
Output: round_eq { sum { all_rows ; result } ; 456 }
select the rows whose college record fuzzily matches to ohio state . there is only one such row in the table . the player record of this unqiue row is kirk barton .
Output: and { only { filter_eq { all_rows ; college ; ohio state } } ; eq { hop { filter_eq { all_rows ; college ; ohio state } ; player } ; kirk barton } }
select the rows whose party record fuzzily matches to republican . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; party ; republican } } ; 2 }
select the row whose weight record of all rows is maximum . the diameter record of this row is 28 mm .
Output: eq { hop { argmax { all_rows ; weight } ; diameter } ; 28 mm }
select the rows whose runner ( s ) - up record fuzzily matches to phil mickelson . take the margin of victory record of this row . select the rows whose runner ( s ) - up record fuzzily matches to dudley hart . take the margin of victory record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; runner ( s ) - up ; phil mickelson } ; margin of victory } ; hop { filter_eq { all_rows ; runner ( s ) - up ; dudley hart } ; margin of victory } }
select the rows whose winner record fuzzily matches to new york jets . among these rows , select the rows whose location record fuzzily matches to shea stadium . the number of such rows is 7 .
Output: eq { count { filter_eq { filter_eq { all_rows ; winner ; new york jets } ; location ; shea stadium } } ; 7 }
select the rows whose episode title record fuzzily matches to new york . take the original airdate record of this row . select the rows whose episode title record fuzzily matches to beneath vesuvius . take the original airdate record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; episode title ; new york } ; original airdate } ; hop { filter_eq { all_rows ; episode title ; beneath vesuvius } ; original airdate } }
the maximum margin record of all rows is 9 strokes . the year record of the row with superlative margin record is 4 . 1965 . the championship record of the row with superlative margin record is masters tournament ( 2 ) .
Output: and { eq { max { all_rows ; margin } ; 9 strokes } ; and { eq { hop { argmax { all_rows ; margin } ; year } ; 4 . 1965 } ; eq { hop { argmax { all_rows ; margin } ; championship } ; masters tournament ( 2 ) } } }
select the rows whose first place record is equal to 0 . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; first place ; 0 } } ; 3 }
select the row whose omim record of all rows is maximum . the enzyme record of this row is uroporphyrinogen iii synthase .
Output: eq { hop { argmax { all_rows ; omim } ; enzyme } ; uroporphyrinogen iii synthase }
select the rows whose crowd record is greater than 10000 . the number of such rows is 4 .
Output: eq { count { filter_greater { all_rows ; crowd ; 10000 } } ; 4 }
for the radial velocity ( m / s ) records of all rows , most of them are less than 10 .
Output: most_less { all_rows ; radial velocity ( m / s ) ; 10 }
for the date records of all rows , most of them are greater than 2000 .
Output: most_greater { all_rows ; date ; 2000 }
select the rows whose date record fuzzily matches to 10 september . take the score record of this row . select the rows whose date record fuzzily matches to 20 august . take the score record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; date ; 10 september } ; score } ; hop { filter_eq { all_rows ; date ; 20 august } ; score } }
the maximum away record of all rows is 2 - 0 .
Output: eq { max { all_rows ; away } ; 2 - 0 }
select the rows whose ( m ) leading goalkicker record fuzzily matches to darren roffey . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; ( m ) leading goalkicker ; darren roffey } } ; 3 }
select the rows whose matches record is equal to 4 . there is only one such row in the table . the player record of this unqiue row is ernie toshack .
Output: and { only { filter_eq { all_rows ; matches ; 4 } } ; eq { hop { filter_eq { all_rows ; matches ; 4 } ; player } ; ernie toshack } }
select the rows whose time / retired record fuzzily matches to clutch . for the laps records of these rows , most of them are greater than 30 .
Output: most_greater { filter_eq { all_rows ; time / retired ; clutch } ; laps ; 30 }
select the rows whose goals record is greater than 120 . there is only one such row in the table . the club record of this unqiue row is real madrid .
Output: and { only { filter_greater { all_rows ; goals ; 120 } } ; eq { hop { filter_greater { all_rows ; goals ; 120 } ; club } ; real madrid } }
select the row whose year record of all rows is 2nd maximum . the game record of this row is the elder scrolls v : skyrim .
Output: eq { hop { nth_argmax { all_rows ; year ; 2 } ; game } ; the elder scrolls v : skyrim }
the average of the assets 2013 ( bil ) record of all rows is 18.9 .
Output: round_eq { avg { all_rows ; assets 2013 ( bil ) } ; 18.9 }
select the rows whose venue record fuzzily matches to antwerp . among these rows , select the rows whose winner record fuzzily matches to steve davis . the number of such rows is 1 .
Output: eq { count { filter_eq { filter_eq { all_rows ; venue ; antwerp } ; winner ; steve davis } } ; 1 }
select the rows whose urban settlement record fuzzily matches to sremska kamenica . take the population ( 2002 ) record of this row . select the rows whose urban settlement record fuzzily matches to sremski karlovci . take the population ( 2002 ) record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; urban settlement ; sremska kamenica } ; population ( 2002 ) } ; hop { filter_eq { all_rows ; urban settlement ; sremski karlovci } ; population ( 2002 ) } }
select the row whose attendance record of all rows is maximum . the week record of this row is 14 .
Output: eq { hop { argmax { all_rows ; attendance } ; week } ; 14 }
select the rows whose city of license record fuzzily matches to stuart , florida . take the frequency mhz record of this row . select the rows whose city of license record fuzzily matches to tradition , florida . take the frequency mhz record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; city of license ; stuart , florida } ; frequency mhz } ; hop { filter_eq { all_rows ; city of license ; tradition , florida } ; frequency mhz } }
select the rows whose official name record fuzzily matches to hillsborough . take the population record of this row . select the rows whose official name record fuzzily matches to hopewell . take the population record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; official name ; hillsborough } ; population } ; hop { filter_eq { all_rows ; official name ; hopewell } ; population } }
select the rows whose year record is equal to 1967 . the sum of the points record of these rows is 2 .
Output: round_eq { sum { filter_eq { all_rows ; year ; 1967 } ; points } ; 2 }
select the rows whose team record fuzzily matches to golden state . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; team ; golden state } } ; 2 }
select the row whose attendance record of all rows is maximum . the week record of this row is 9 .
Output: eq { hop { argmax { all_rows ; attendance } ; week } ; 9 }
select the row whose crowd record of all rows is 2nd maximum . the home team record of this row is richmond .
Output: eq { hop { nth_argmax { all_rows ; crowd ; 2 } ; home team } ; richmond }
select the row whose winning score record of all rows is maximum . the tournament record of this row is us senior open .
Output: eq { hop { argmax { all_rows ; winning score } ; tournament } ; us senior open }
select the rows whose candidates record fuzzily matches to unopposed . there is only one such row in the table . the incumbent record of this unqiue row is bob brady .
Output: and { only { filter_eq { all_rows ; candidates ; unopposed } } ; eq { hop { filter_eq { all_rows ; candidates ; unopposed } ; incumbent } ; bob brady } }
the sum of the round record of all rows is 37 .
Output: round_eq { sum { all_rows ; round } ; 37 }
select the rows whose date record fuzzily matches to 14 march 1984 . there is only one such row in the table . the home team record of this unqiue row is derby county .
Output: and { only { filter_eq { all_rows ; date ; 14 march 1984 } } ; eq { hop { filter_eq { all_rows ; date ; 14 march 1984 } ; home team } ; derby county } }
select the row whose casualties record of all rows is maximum . the date record of this row is 2009 - 09 - 16 .
Output: eq { hop { argmax { all_rows ; casualties } ; date } ; 2009 - 09 - 16 }
select the rows whose title record fuzzily matches to the tale of the time trap . take the us air date record of this row . select the rows whose title record fuzzily matches to the tale of the last dance . take the us air date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; title ; the tale of the time trap } ; us air date } ; hop { filter_eq { all_rows ; title ; the tale of the last dance } ; us air date } }
select the rows whose candidates record does not match to unopposed . the average of the candidates record of these rows is 72 .
Output: round_eq { avg { filter_not_eq { all_rows ; candidates ; unopposed } ; candidates } ; 72 }
select the rows whose name record fuzzily matches to phil karn . take the goals record of this row . select the rows whose name record fuzzily matches to michael apple . take the goals record of this row . the first record is 2 larger than the second record .
Output: eq { diff { hop { filter_eq { all_rows ; name ; phil karn } ; goals } ; hop { filter_eq { all_rows ; name ; michael apple } ; goals } } ; 2 }
select the rows whose title record fuzzily matches to episode 4 . take the original air date record of this row . select the rows whose title record fuzzily matches to episode 5 . take the original air date record of this row . the second record is 7 larger than the first record .
Output: eq { diff { hop { filter_eq { all_rows ; title ; episode 4 } ; original air date } ; hop { filter_eq { all_rows ; title ; episode 5 } ; original air date } } ; -7 }
select the row whose commissioned record of all rows is 1st minimum . the name record of this row is cygnet .
Output: eq { hop { nth_argmin { all_rows ; commissioned ; 1 } ; name } ; cygnet }
select the rows whose incumbent record fuzzily matches to william h perry . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to william elliott . take the first elected record of this row . the first record is equal to the second record . the first elected record of the first row is 1884 . the first elected record of the second row is 1884 .
Output: and { eq { hop { filter_eq { all_rows ; incumbent ; william h perry } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; william elliott } ; first elected } } ; and { eq { hop { filter_eq { all_rows ; incumbent ; william h perry } ; first elected } ; 1884 } ; eq { hop { filter_eq { all_rows ; incumbent ; william elliott } ; first elected } ; 1884 } } }
select the rows whose opponent in the final record fuzzily matches to petra kvitová . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; opponent in the final ; petra kvitová } } ; 3 }
select the row whose year ( s ) won record of all rows is maximum . the player record of this row is tiger woods .
Output: eq { hop { argmax { all_rows ; year ( s ) won } ; player } ; tiger woods }
select the row whose average record of all rows is maximum . the player record of this row is andrew caddick .
Output: eq { hop { argmax { all_rows ; average } ; player } ; andrew caddick }
select the row whose attendance record of all rows is maximum . the date record of this row is march 20 .
Output: eq { hop { argmax { all_rows ; attendance } ; date } ; march 20 }
select the rows whose name record fuzzily matches to ishara amerasinghe . take the wickets record of this row . select the rows whose name record fuzzily matches to muttiah muralitharan . take the wickets record of this row . the first record is equal to the second record . the wickets record of the first row is 9 . the wickets record of the second row is 9 .
Output: and { eq { hop { filter_eq { all_rows ; name ; ishara amerasinghe } ; wickets } ; hop { filter_eq { all_rows ; name ; muttiah muralitharan } ; wickets } } ; and { eq { hop { filter_eq { all_rows ; name ; ishara amerasinghe } ; wickets } ; 9 } ; eq { hop { filter_eq { all_rows ; name ; muttiah muralitharan } ; wickets } ; 9 } } }
select the rows whose open cup record fuzzily matches to did not qualify . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; open cup ; did not qualify } } ; 3 }
select the row whose result record of all rows is maximum . the opponent record of this row is buffalo bills .
Output: eq { hop { argmax { all_rows ; result } ; opponent } ; buffalo bills }
select the row whose date record of all rows is 2nd minimum . the surface record of this row is clay .
Output: eq { hop { nth_argmin { all_rows ; date ; 2 } ; surface } ; clay }
for the time records of all rows , most of them fuzzily match to 21 . .
Output: most_eq { all_rows ; time ; 21 . }
select the row whose races record of all rows is maximum . the season record of this row is 2010 .
Output: eq { hop { argmax { all_rows ; races } ; season } ; 2010 }
the sum of the runs record of all rows is 3324 .
Output: round_eq { sum { all_rows ; runs } ; 3324 }
for the team records of all rows , most of them fuzzily match to iowa .
Output: most_eq { all_rows ; team ; iowa }
select the rows whose style record fuzzily matches to tango . the sum of the total record of these rows is 54 .
Output: round_eq { sum { filter_eq { all_rows ; style ; tango } ; total } ; 54 }
for the results records of all rows , most of them fuzzily match to safe .
Output: most_eq { all_rows ; results ; safe }
the average of the won record of all rows is 26 .
Output: round_eq { avg { all_rows ; won } ; 26 }
select the row whose joined record of all rows is 3rd maximum . the school record of this row is south bend clay .
Output: eq { hop { nth_argmax { all_rows ; joined ; 3 } ; school } ; south bend clay }
select the rows whose toxic materials record fuzzily matches to yes . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; toxic materials ; yes } } ; 4 }
for the power ( kw ) records of all rows , most of them fuzzily match to 10 kw .
Output: most_eq { all_rows ; power ( kw ) ; 10 kw }
the average of the duration ( days ) record of all rows is 56.37 .
Output: round_eq { avg { all_rows ; duration ( days ) } ; 56.37 }
select the rows whose country record fuzzily matches to ghana . there is only one such row in the table . the airport record of this unqiue row is kotoka international airport .
Output: and { only { filter_eq { all_rows ; country ; ghana } } ; eq { hop { filter_eq { all_rows ; country ; ghana } ; airport } ; kotoka international airport } }
for the reason for change records of all rows , most of them fuzzily match to died .
Output: most_eq { all_rows ; reason for change ; died }
select the row whose start record of all rows is minimum . the year record of this row is 1952 .
Output: eq { hop { argmin { all_rows ; start } ; year } ; 1952 }
select the rows whose visitor record fuzzily matches to cavaliers . take the date record of this row . select the rows whose visitor record fuzzily matches to hornets . take the date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; visitor ; cavaliers } ; date } ; hop { filter_eq { all_rows ; visitor ; hornets } ; date } }
the average of the height record of all rows is 6 - 5 .
Output: round_eq { avg { all_rows ; height } ; 6 - 5 }
select the rows whose location attendance record fuzzily matches to liacouras center . for the location attendance records of these rows , most of them are greater than 5000 .
Output: most_greater { filter_eq { all_rows ; location attendance ; liacouras center } ; location attendance ; 5000 }
select the row whose round ( overall ) record of all rows is 2nd minimum . the player record of this row is quentin coryatt .
Output: eq { hop { nth_argmin { all_rows ; round ( overall ) ; 2 } ; player } ; quentin coryatt }
the average of the roll record of all rows is 99.61 .
Output: round_eq { avg { all_rows ; roll } ; 99.61 }
select the rows whose decade record fuzzily matches to 2000s . there is only one such row in the table . the song title record of this unqiue row is dirty little secret .
Output: and { only { filter_eq { all_rows ; decade ; 2000s } } ; eq { hop { filter_eq { all_rows ; decade ; 2000s } ; song title } ; dirty little secret } }
select the rows whose incumbent record fuzzily matches to james m ashley . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to samuel shellabarger . take the first elected record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; incumbent ; james m ashley } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; samuel shellabarger } ; first elected } }
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 .
Output: and { only { filter_eq { all_rows ; owner ; university } } ; eq { hop { filter_eq { all_rows ; owner ; university } ; frequency } ; fm 101.7 } }
select the row whose population record of all rows is maximum . the island record of this row is bute .
Output: eq { hop { argmax { all_rows ; population } ; island } ; bute }
select the rows whose yards record is equal to 39 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; yards ; 39 } } ; 2 }
select the row whose position record of all rows is minimum . the club record of this row is sporting de gijón .
Output: eq { hop { argmin { all_rows ; position } ; club } ; sporting de gijón }
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 ) .
Output: eq { hop { nth_argmax { all_rows ; number of episodes ; 3 } ; english title ( chinese title ) } ; journey to the west ii 西遊記 ( 貳 ) }
select the row whose silver record of all rows is 2nd maximum . the nation record of this row is cyprus .
Output: eq { hop { nth_argmax { all_rows ; silver ; 2 } ; nation } ; cyprus }
select the rows whose location record fuzzily matches to tifton . there is only one such row in the table . the institution record of this unqiue row is abraham baldwin agricultural college .
Output: and { only { filter_eq { all_rows ; location ; tifton } } ; eq { hop { filter_eq { all_rows ; location ; tifton } ; institution } ; abraham baldwin agricultural college } }
for the parish ( prestegjeld ) records of all rows , all of them fuzzily match to kinn parish .
Output: all_eq { all_rows ; parish ( prestegjeld ) ; kinn parish }
select the row whose south asians 2011 record of all rows is maximum . the province record of this row is ontario .
Output: eq { hop { argmax { all_rows ; south asians 2011 } ; province } ; ontario }
select the rows whose county record fuzzily matches to wexford . there is only one such row in the table . the player record of this unqiue row is christy kehoe .
Output: and { only { filter_eq { all_rows ; county ; wexford } } ; eq { hop { filter_eq { all_rows ; county ; wexford } ; player } ; christy kehoe } }
select the row whose enrollment record of all rows is maximum . the location record of this row is cleveland , tennessee .
Output: eq { hop { argmax { all_rows ; enrollment } ; location } ; cleveland , tennessee }
the sum of the charlton goals record of all rows is 178 .
Output: round_eq { sum { all_rows ; charlton goals } ; 178 }
for the population records of all rows , most of them are less than 1000 .
Output: most_less { all_rows ; population ; 1000 }
select the rows whose manager record does not match to warren buffett . among these rows , select the rows whose fund record fuzzily matches to ltd . the number of such rows is 3 .
Output: eq { count { filter_eq { filter_not_eq { all_rows ; manager ; warren buffett } ; fund ; ltd } } ; 3 }
the sum of the fatalities record of all rows is 899 .
Output: round_eq { sum { all_rows ; fatalities } ; 899 }