niryuu/sni-each-converted huggingface dataset:
and { only { filter_eq { all_rows ; power rpm ; 6800 } } ; eq { hop { filter_eq { all_rows ; power rpm ; 6800 } ; model } ; sl 63 amg } } = true
Output: select the rows whose power rpm record is equal to 6800 . there is only one such row in the table . the model record of this unqiue row is sl 63 amg .
eq { count { filter_eq { filter_greater { all_rows ; wins ; 4 } ; loses ; 2 } } ; 2 } = true
Output: select the rows whose wins record is greater than 4 . among these rows , select the rows whose loses record is equal to 2 . the number of such rows is 2 .
most_eq { all_rows ; pole position ; michael andretti } = true
Output: for the pole position records of all rows , most of them fuzzily match to michael andretti .
most_eq { all_rows ; high assists ; baron davis } = true
Output: for the high assists records of all rows , most of them fuzzily match to baron davis .
eq { count { filter_eq { filter_eq { all_rows ; director ; pamela fryman } ; original air date ; april } } ; 3 } = true
Output: select the rows whose director record fuzzily matches to pamela fryman . among these rows , select the rows whose original air date record fuzzily matches to april . the number of such rows is 3 .
and { only { filter_eq { all_rows ; district ; new york } } ; eq { hop { filter_eq { all_rows ; district ; new york } ; vacator } ; jonathan havens ( dr ) } } = true
Output: select the rows whose district record fuzzily matches to new york . there is only one such row in the table . the vacator record of this unqiue row is jonathan havens  dr  .
and { only { filter_eq { all_rows ; region ; united states } } ; eq { hop { filter_eq { all_rows ; region ; united states } ; date } ; 17 march 2009 } } = true
Output: select the rows whose region record fuzzily matches to united states . there is only one such row in the table . the date record of this unqiue row is 17 march 2009 .
eq { hop { argmin { all_rows ; game } ; location attendance } ; chicago stadium } = true
Output: select the row whose game record of all rows is minimum . the location attendance record of this row is chicago stadium .
and { only { filter_eq { all_rows ; style ; west coast swing } } ; eq { hop { filter_eq { all_rows ; style ; west coast swing } ; week } ; 4 } } = true
Output: select the rows whose style record fuzzily matches to west coast swing . there is only one such row in the table . the week record of this unqiue row is 4 .
greater { hop { filter_eq { all_rows ; venue ; mcg } ; crowd } ; hop { filter_eq { all_rows ; venue ; lake oval } ; crowd } } = true
Output: select the rows whose venue record fuzzily matches to mcg . take the crowd record of this row . select the rows whose venue record fuzzily matches to lake oval . take the crowd record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; directed by ; brent woods } } ; 2 } = true
Output: select the rows whose directed by record fuzzily matches to brent woods . the number of such rows is 2 .
round_eq { avg { all_rows ; viewers ( in millions ) } ; 12.56 } = true
Output: the average of the viewers  in millions  record of all rows is 12.56 .
eq { count { filter_eq { all_rows ; pos ; cf } } ; 2 } = true
Output: select the rows whose pos record fuzzily matches to cf . the number of such rows is 2 .
greater { hop { filter_eq { all_rows ; rank ; 3 } ; silver } ; hop { filter_eq { all_rows ; rank ; 2 } ; silver } } = true
Output: select the rows whose rank record fuzzily matches to 3 . take the silver record of this row . select the rows whose rank record fuzzily matches to 2 . take the silver record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; state ; new york } } ; 3 } = true
Output: select the rows whose state record fuzzily matches to new york . the number of such rows is 3 .
eq { count { filter_eq { all_rows ; affiliation ; private } } ; 2 } = true
Output: select the rows whose affiliation record fuzzily matches to private . the number of such rows is 2 .
round_eq { avg { all_rows ; weight ( kg ) } ; 55.625 } = true
Output: the average of the weight  kg  record of all rows is 55.625 .
eq { count { filter_eq { all_rows ; note ; reportedly still active as of 2009 } } ; 4 } = true
Output: select the rows whose note record fuzzily matches to reportedly still active as of 2009 . the number of such rows is 4 .
and { only { filter_eq { all_rows ; location ; california , united states } } ; eq { hop { filter_eq { all_rows ; location ; california , united states } ; opponent } ; muhsin corbbrey } } = true
Output: select the rows whose location record fuzzily matches to california , united states . there is only one such row in the table . the opponent record of this unqiue row is muhsin corbbrey .
and { only { filter_eq { all_rows ; college ; nu } } ; eq { hop { filter_eq { all_rows ; college ; nu } ; player } ; rey mendoza } } = true
Output: select the rows whose college record fuzzily matches to nu . there is only one such row in the table . the player record of this unqiue row is rey mendoza .
all_eq { all_rows ; on dinosaur trail ; yes } = true
Output: for the on dinosaur trail records of all rows , all of them fuzzily match to yes .
eq { count { filter_eq { all_rows ; site / stadium ; rosenblatt stadium } } ; 7 } = true
Output: select the rows whose site  stadium record fuzzily matches to rosenblatt stadium . the number of such rows is 7 .
and { only { filter_eq { all_rows ; reactor type ; abwr } } ; eq { hop { filter_eq { all_rows ; reactor type ; abwr } ; unit } ; hamaoka - 5 } } = true
Output: select the rows whose reactor type record fuzzily matches to abwr . there is only one such row in the table . the unit record of this unqiue row is hamaoka  5 .
round_eq { avg { all_rows ; tied } ; 4.16 } = true
Output: the average of the tied record of all rows is 4.16 .
eq { diff { hop { filter_eq { all_rows ; airline ; tam } ; rank } ; hop { filter_eq { all_rows ; airline ; gol } ; rank } } ; -1 better } = true
Output: select the rows whose airline record fuzzily matches to tam . take the rank record of this row . select the rows whose airline record fuzzily matches to gol . take the rank record of this row . the second record is 1 better larger than the first record .
round_eq { avg { all_rows ; points against } ; 292 } = true
Output: the average of the points against record of all rows is 292 .
greater { hop { filter_eq { all_rows ; year ; 2013 } ; points } ; hop { filter_eq { all_rows ; year ; 2006 } ; points } } = true
Output: select the rows whose year record fuzzily matches to 2013 . take the points record of this row . select the rows whose year record fuzzily matches to 2006 . take the points record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; result ; win } } ; 6 } = true
Output: select the rows whose result record fuzzily matches to win . the number of such rows is 6 .
round_eq { avg { all_rows ; home team score } ; 10 } = true
Output: the average of the home team score record of all rows is 10 .
most_greater_eq { filter_eq { all_rows ; date ; 13 december } ; crowd ; 3000 } = true
Output: select the rows whose date record fuzzily matches to 13 december . for the crowd records of these rows , most of them are greater than or equal to 3000 .
eq { count { filter_eq { all_rows ; education programs / guided tours ; yes } } ; 7 } = true
Output: select the rows whose education programs  guided tours record fuzzily matches to yes . the number of such rows is 7 .
greater { hop { filter_eq { all_rows ; aircraft ; bell 212 } ; in service } ; hop { filter_eq { all_rows ; aircraft ; bell 412 } ; in service } } = true
Output: select the rows whose aircraft record fuzzily matches to bell 212 . take the in service record of this row . select the rows whose aircraft record fuzzily matches to bell 412 . take the in service record of this row . the first record is greater than the second record .
all_eq { filter_eq { all_rows ; result ; re - elected } ; party ; democratic } = true
Output: select the rows whose result record fuzzily matches to re  elected . for the party records of these rows , all of them fuzzily match to democratic .
and { eq { nth_min { all_rows ; no in season ; 3 } ; 3 } ; eq { hop { nth_argmin { all_rows ; no in season ; 3 } ; family / families } ; the orm family } } = true
Output: the 3rd minimum no in season record of all rows is 3 . the family  families record of the row with 3rd minimum no in season record is the orm family .
eq { hop { nth_argmin { all_rows ; pick ; 1 } ; player } ; sterling sharpe } = true
Output: select the row whose pick record of all rows is 1st minimum . the player record of this row is sterling sharpe .
eq { count { filter_eq { all_rows ; gold ; satu pusila } } ; 3 } = true
Output: select the rows whose gold record fuzzily matches to satu pusila . the number of such rows is 3 .
eq { hop { argmax { all_rows ; % of votes khuzestan } ; candidates } ; mehdi karroubi } = true
Output: select the row whose  of votes khuzestan record of all rows is maximum . the candidates record of this row is mehdi karroubi .
less { hop { filter_eq { all_rows ; successor ; hopkins holsey ( j ) } ; date successor seated } ; hop { filter_eq { all_rows ; successor ; john young ( aj ) } ; date successor seated } } = true
Output: select the rows whose successor record fuzzily matches to hopkins holsey  j  . take the date successor seated record of this row . select the rows whose successor record fuzzily matches to john young  aj  . take the date successor seated record of this row . the first record is less than the second record .
and { only { filter_eq { all_rows ; position ; offensive tackle } } ; eq { hop { filter_eq { all_rows ; position ; offensive tackle } ; name } ; john williams } } = true
Output: select the rows whose position record fuzzily matches to offensive tackle . there is only one such row in the table . the name record of this unqiue row is john williams .
eq { count { filter_eq { filter_eq { all_rows ; directed by ; win phelps } ; written by ; david e kelley and william m finkelstein } } ; 2 } = true
Output: select the rows whose directed by record fuzzily matches to win phelps . among these rows , select the rows whose written by record fuzzily matches to david e kelley and william m finkelstein . the number of such rows is 2 .
round_eq { sum { all_rows ; swedish championship titles } ; 81 } = true
Output: the sum of the swedish championship titles record of all rows is 81 .
eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; away team } ; st kilda } = true
Output: select the row whose crowd record of all rows is 1st maximum . the away team record of this row is st kilda .
round_eq { avg { all_rows ; lost } ; 13.3 } = true
Output: the average of the lost record of all rows is 13.3 .
eq { hop { argmin { all_rows ; year } ; lifetime india distributor share } ; 99 , 02 , 00000 } = true
Output: select the row whose year record of all rows is minimum . the lifetime india distributor share record of this row is 99 , 02 , 00000 .
eq { diff { hop { filter_eq { all_rows ; winner ; sergio rivero } ; year } ; hop { filter_eq { all_rows ; winner ; vicente seguí porres } ; year } } ; 2 years } = true
Output: select the rows whose winner record fuzzily matches to sergio rivero . take the year record of this row . select the rows whose winner record fuzzily matches to vicente seguí porres . take the year record of this row . the first record is 2 years larger than the second record .
eq { hop { argmax { all_rows ; uci protour points } ; country } ; spain } = true
Output: select the row whose uci protour points record of all rows is maximum . the country record of this row is spain .
and { only { filter_eq { all_rows ; position ; running back } } ; eq { hop { filter_eq { all_rows ; position ; running back } ; player } ; tom moore } } = true
Output: select the rows whose position record fuzzily matches to running back . there is only one such row in the table . the player record of this unqiue row is tom moore .
eq { hop { nth_argmax { all_rows ; location / attendance ; 1 } ; opponent } ; los angeles } = true
Output: select the row whose location  attendance record of all rows is 1st maximum . the opponent record of this row is los angeles .
eq { count { filter_eq { all_rows ; outcome ; winner } } ; 7 } = true
Output: select the rows whose outcome record fuzzily matches to winner . the number of such rows is 7 .
and { only { filter_eq { filter_eq { all_rows ; 1st leg ; 1 - 0 } ; 2nd leg ; 0-0 } } ; eq { hop { filter_eq { filter_eq { all_rows ; 1st leg ; 1 - 0 } ; 2nd leg ; 0-0 } ; team 2 } ; jeunesse esch } } = true
Output: select the rows whose 1st leg record fuzzily matches to 1  0 . among these rows , select the rows whose 2nd leg record fuzzily matches to 00 . there is only one such row in the table . the team 2 record of this unqiue row is jeunesse esch .
eq { diff { hop { filter_eq { all_rows ; player ; jack snow } ; pick } ; hop { filter_eq { all_rows ; player ; lou bobich } ; pick } } ; 1 } = true
Output: select the rows whose player record fuzzily matches to jack snow . take the pick record of this row . select the rows whose player record fuzzily matches to lou bobich . take the pick record of this row . the first record is 1 larger than the second record .
round_eq { sum { filter_eq { all_rows ; year ; 1967 } ; points } ; 2 } = true
Output: select the rows whose year record is equal to 1967 . the sum of the points record of these rows is 2 .
most_eq { all_rows ; friendly ; world cup 2006 qualifier } = true
Output: for the friendly records of all rows , most of them fuzzily match to world cup 2006 qualifier .
greater { hop { filter_eq { all_rows ; company ; royal dutch shell } ; market value ( billion ) } ; hop { filter_eq { all_rows ; company ; american international group } ; market value ( billion ) } } = true
Output: select the rows whose company record fuzzily matches to royal dutch shell . take the market value  billion  record of this row . select the rows whose company record fuzzily matches to american international group . take the market value  billion  record of this row . the first record is greater than the second record .
eq { hop { nth_argmax { filter_eq { all_rows ; date ; 2 april } ; crowd ; 1 } ; home team } ; collingwood } = true
Output: select the rows whose date record fuzzily matches to 2 april . select the row whose crowd record of these rows is 1st maximum . the home team record of this row is collingwood .
round_eq { avg { all_rows ; runs } ; 143 } = true
Output: the average of the runs record of all rows is 143 .
round_eq { avg { all_rows ; points } ; 158 } = true
Output: the average of the points record of all rows is 158 .
only { filter_eq { all_rows ; date ; may } } = true
Output: select the rows whose date record fuzzily matches to may . there is only one such row in the table .
greater { hop { filter_eq { all_rows ; version ; maxi remix } ; length } ; hop { filter_eq { all_rows ; version ; single version } ; length } } = true
Output: select the rows whose version record fuzzily matches to maxi remix . take the length record of this row . select the rows whose version record fuzzily matches to single version . take the length record of this row . the first record is greater than the second record .
round_eq { avg { all_rows ; score } ; 96.5 } = true
Output: the average of the score record of all rows is 96.5 .
and { only { filter_greater { all_rows ; votes ; 80 } } ; eq { hop { filter_greater { all_rows ; votes ; 80 } ; artist } ; sahlene } } = true
Output: select the rows whose votes record is greater than 80 . there is only one such row in the table . the artist record of this unqiue row is sahlene .
and { eq { max { all_rows ; home team score } ; 15.17 ( 107 ) } ; and { eq { hop { argmax { all_rows ; home team score } ; home team } ; south melbourne } ; eq { hop { argmax { all_rows ; home team score } ; date } ; 7 august 1926 } } } = true
Output: the maximum home team score record of all rows is 15.17  107  . the home team record of the row with superlative home team score record is south melbourne . the date record of the row with superlative home team score record is 7 august 1926 .
greater { hop { filter_eq { all_rows ; station ; kami - mio } ; distance ( km ) } ; hop { filter_eq { all_rows ; station ; funao } ; distance ( km ) } } = true
Output: select the rows whose station record fuzzily matches to kami  mio . take the distance  km  record of this row . select the rows whose station record fuzzily matches to funao . take the distance  km  record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; transfer window ; winter } } ; 3 } = true
Output: select the rows whose transfer window record fuzzily matches to winter . the number of such rows is 3 .
round_eq { avg { all_rows ; matches } ; 8 } = true
Output: the average of the matches record of all rows is 8 .
eq { count { filter_eq { all_rows ; counties represented ; baltimore county , howard } } ; 2 } = true
Output: select the rows whose counties represented record fuzzily matches to baltimore county , howard . the number of such rows is 2 .
round_eq { sum { filter_eq { all_rows ; opponent ; chargers } ; result } ; 40 } = true
Output: select the rows whose opponent record fuzzily matches to chargers . the sum of the result record of these rows is 40 .
most_greater { filter_eq { all_rows ; constituency ; south west devon } ; electorate ; 9000 } = true
Output: select the rows whose constituency record fuzzily matches to south west devon . for the electorate records of these rows , most of them are greater than 9000 .
most_eq { all_rows ; name ; - } = true
Output: for the name records of all rows , most of them fuzzily match to  .
round_eq { avg { all_rows ; crowd } ; 17724 } = true
Output: the average of the crowd record of all rows is 17724 .
round_eq { avg { filter_not_eq { all_rows ; start ; dnq } ; start } ; 19 } = true
Output: select the rows whose start record does not match to dnq . the average of the start record of these rows is 19 .
eq { hop { nth_argmax { all_rows ; total ; 2 } ; nation } ; denmark ( den ) } = true
Output: select the row whose total record of all rows is 2nd maximum . the nation record of this row is denmark  den  .
less { hop { filter_eq { all_rows ; visitor ; colorado } ; date } ; hop { filter_eq { all_rows ; visitor ; florida } ; date } } = true
Output: select the rows whose visitor record fuzzily matches to colorado . take the date record of this row . select the rows whose visitor record fuzzily matches to florida . take the date record of this row . the first record is less than the second record .
round_eq { sum { filter_eq { all_rows ; opposing teams ; new zealand } ; against } ; 18 } = true
Output: select the rows whose opposing teams record fuzzily matches to new zealand . the sum of the against record of these rows is 18 .
most_greater { all_rows ; capacity ; 40000 } = true
Output: for the capacity records of all rows , most of them are greater than 40000 .
most_eq { all_rows ; champions ; sparta prague } = true
Output: for the champions records of all rows , most of them fuzzily match to sparta prague .
eq { count { filter_eq { all_rows ; first elected ; special } } ; 4 } = true
Output: select the rows whose first elected record fuzzily matches to special . the number of such rows is 4 .
round_eq { avg { filter_greater_eq { all_rows ; season ; 2000 } ; goals } ; 1 } = true
Output: select the rows whose season record is greater than or equal to 2000 . the average of the goals record of these rows is 1 .
most_less { filter_eq { all_rows ; county ; new haven } ; proposed ; 2000 } = true
Output: select the rows whose county record fuzzily matches to new haven . for the proposed records of these rows , most of them are less than 2000 .
round_eq { sum { filter_eq { all_rows ; industry ; oil and gas } ; profits ( billion ) } ; 151.9 } = true
Output: select the rows whose industry record fuzzily matches to oil and gas . the sum of the profits  billion  record of these rows is 151.9 .
most_greater_eq { all_rows ; result ; 5 } = true
Output: for the result records of all rows , most of them are greater than or equal to 5 .
most_eq { all_rows ; formats ; vinyl } = true
Output: for the formats records of all rows , most of them fuzzily match to vinyl .
eq { count { filter_eq { all_rows ; top scorer ( points ) ; giorgio chinaglia } } ; 5 } = true
Output: select the rows whose top scorer  points  record fuzzily matches to giorgio chinaglia . the number of such rows is 5 .
eq { hop { argmax { all_rows ; goodman } ; dance / song } ; tango / hung up } = true
Output: select the row whose goodman record of all rows is maximum . the dance  song record of this row is tango  hung up .
eq { count { filter_greater_eq { all_rows ; from ; 1930 } } ; 3 } = true
Output: select the rows whose from record is greater than or equal to 1930 . the number of such rows is 3 .
round_eq { sum { filter_eq { all_rows ; away team ; melbourne } ; crowd } ; 25000 } = true
Output: select the rows whose away team record fuzzily matches to melbourne . the sum of the crowd record of these rows is 25000 .
eq { count { filter_eq { all_rows ; opponent ; buffalo braves } } ; 3 } = true
Output: select the rows whose opponent record fuzzily matches to buffalo braves . the number of such rows is 3 .
less { hop { filter_eq { all_rows ; family / families ; the swift family } ; original air date } ; hop { filter_eq { all_rows ; family / families ; the potter family } ; original air date } } = true
Output: select the rows whose family  families record fuzzily matches to the swift family . take the original air date record of this row . select the rows whose family  families record fuzzily matches to the potter family . take the original air date record of this row . the first record is less than the second record .
most_eq { all_rows ; country ; england } = true
Output: for the country records of all rows , most of them fuzzily match to england .
and { only { filter_eq { all_rows ; engine type ; diesel - electric } } ; eq { hop { filter_eq { all_rows ; engine type ; diesel - electric } ; make & model } ; orion vii 3rd generation } } = true
Output: select the rows whose engine type record fuzzily matches to diesel  electric . there is only one such row in the table . the make  model record of this unqiue row is orion vii 3rd generation .
and { only { filter_eq { all_rows ; partial thromboplastin time ; shortened } } ; eq { hop { filter_eq { all_rows ; partial thromboplastin time ; shortened } ; condition } ; c1inh deficiency } } = true
Output: select the rows whose partial thromboplastin time record fuzzily matches to shortened . there is only one such row in the table . the condition record of this unqiue row is c1inh deficiency .
eq { hop { nth_argmax { all_rows ; price ( usd ) ; 1 } ; model number } ; itanium 2 9040 } = true
Output: select the row whose price  usd  record of all rows is 1st maximum . the model number record of this row is itanium 2 9040 .
and { only { filter_eq { all_rows ; wsu year ( s ) ; 1959 - 62 } } ; eq { hop { filter_eq { all_rows ; wsu year ( s ) ; 1959 - 62 } ; name } ; gene wiley } } = true
Output: select the rows whose wsu year  s  record fuzzily matches to 1959  62 . there is only one such row in the table . the name record of this unqiue row is gene wiley .
eq { hop { nth_argmin { all_rows ; race ; 2 } ; season } ; 2008 } = true
Output: select the row whose race record of all rows is 2nd minimum . the season record of this row is 2008 .
greater { hop { filter_eq { all_rows ; album ; come on over } ; weeks at number one } ; hop { filter_eq { all_rows ; album ; let 's talk about love } ; weeks at number one } } = true
Output: select the rows whose album record fuzzily matches to come on over . take the weeks at number one record of this row . select the rows whose album record fuzzily matches to let s talk about love . take the weeks at number one record of this row . the first record is greater than the second record .
most_eq { all_rows ; date of successors formal installation ; elected } = true
Output: for the date of successors formal installation records of all rows , most of them fuzzily match to elected .
and { eq { hop { filter_eq { all_rows ; city ; barisal } ; country } ; hop { filter_eq { all_rows ; city ; chittagong } ; country } } ; and { eq { hop { filter_eq { all_rows ; city ; barisal } ; country } ; bangladesh } ; eq { hop { filter_eq { all_rows ; city ; chittagong } ; country } ; bangladesh } } } = true
Output: select the rows whose city record fuzzily matches to barisal . take the country record of this row . select the rows whose city record fuzzily matches to chittagong . take the country record of this row . the first record fuzzily matches to the second record . the country record of the first row is bangladesh . the country record of the second row is bangladesh .
eq { count { filter_eq { filter_eq { all_rows ; type ; transfer } ; moving to ; milan } } ; 2 } = true
Output: select the rows whose type record fuzzily matches to transfer . among these rows , select the rows whose moving to record fuzzily matches to milan . the number of such rows is 2 .
most_eq { all_rows ; origin of programming ; india } = true
Output: for the origin of programming records of all rows , most of them fuzzily match to india .
eq { hop { argmin { all_rows ; total } ; player } ; seve ballesteros } = true
Output: select the row whose total record of all rows is minimum . the player record of this row is seve ballesteros .
eq { count { filter_eq { filter_greater { all_rows ; wins ; 90 } ; first win ; french grand prix } } ; 2 } = true
Output: select the rows whose wins record is greater than 90 . among these rows , select the rows whose first win record fuzzily matches to french grand prix . the number of such rows is 2 .
eq { hop { argmax { all_rows ; winnings } ; year } ; 2004 } = true
Output: select the row whose winnings record of all rows is maximum . the year record of this row is 2004 .
eq { hop { argmax { all_rows ; change ( 2010 to 2011 ) } ; country } ; uruguay } = true
Output: select the row whose change  2010 to 2011  record of all rows is maximum . the country record of this row is uruguay .
eq { hop { argmax { all_rows ; award } ; film } ; hotel infinity } = true
Output: select the row whose award record of all rows is maximum . the film record of this row is hotel infinity .
eq { hop { nth_argmax { all_rows ; sites ; 2 } ; circuit } ; amc entertainment inc } = true
Output: select the row whose sites record of all rows is 2nd maximum . the circuit record of this row is amc entertainment inc .
most_eq { all_rows ; team classification ; predictor-lotto } = true
Output: for the team classification records of all rows , most of them fuzzily match to predictorlotto .
eq { count { filter_eq { all_rows ; result ; bottom 3 } } ; 2 } = true
Output: select the rows whose result record fuzzily matches to bottom 3 . the number of such rows is 2 .
round_eq { avg { all_rows ; frequency } ; 2200 mhz } = true
Output: the average of the frequency record of all rows is 2200 mhz .
round_eq { avg { all_rows ; saturated fat } ; 22 } = true
Output: the average of the saturated fat record of all rows is 22 .
and { only { filter_eq { filter_eq { all_rows ; high points ; damon stoudamire } ; team ; miami } } ; eq { hop { filter_eq { filter_eq { all_rows ; high points ; damon stoudamire } ; team ; miami } ; date } ; april 5 } } = true
Output: select the rows whose high points record fuzzily matches to damon stoudamire . among these rows , select the rows whose team record fuzzily matches to miami . there is only one such row in the table . the date record of this unqiue row is april 5 .
eq { count { filter_eq { all_rows ; country ; portugal } } ; 5 } = true
Output: select the rows whose country record fuzzily matches to portugal . the number of such rows is 5 .
all_eq { all_rows ; ends ; 2008 } = true
Output: for the ends records of all rows , all of them are equal to 2008 .
eq { count { filter_less { filter_less { all_rows ; population ; 60000000 } ; gdp ( billion us ) ; 100 } } ; 2 } = true
Output: select the rows whose population record is less than 60000000 . among these rows , select the rows whose gdp  billion us  record is less than 100 . the number of such rows is 2 .
eq { hop { argmax { all_rows ; result } ; date } ; 8 october 2010 } = true
Output: select the row whose result record of all rows is maximum . the date record of this row is 8 october 2010 .
eq { count { filter_eq { all_rows ; surface ; hard } } ; 7 } = true
Output: select the rows whose surface record fuzzily matches to hard . the number of such rows is 7 .
eq { count { filter_eq { all_rows ; class ; b } } ; 4 } = true
Output: select the rows whose class record fuzzily matches to b . the number of such rows is 4 .
most_less_eq { filter_eq { all_rows ; school / club team ; minnesota } ; round ; 11 } = true
Output: select the rows whose school  club team record fuzzily matches to minnesota . for the round records of these rows , most of them are less than or equal to 11 .
round_eq { avg { all_rows ; time } ; 13.4 } = true
Output: the average of the time record of all rows is 13.4 .
round_eq { avg { all_rows ; altitude ( km ) } ; 244.35 } = true
Output: the average of the altitude  km  record of all rows is 244.35 .
eq { count { filter_eq { all_rows ; socialist labor ticket ; ( none ) } } ; 2 } = true
Output: select the rows whose socialist labor ticket record fuzzily matches to  none  . the number of such rows is 2 .
only { filter_greater { filter_eq { all_rows ; date ; september } ; attendance ; 70000 } } = true
Output: select the rows whose date record fuzzily matches to september . among these rows , select the rows whose attendance record is greater than 70000 . there is only one such row in the table .
greater { hop { filter_eq { all_rows ; english name ; capital region of denmark } ; population ( january 1 , 2008 ) } ; hop { filter_eq { all_rows ; english name ; region of southern denmark } ; population ( january 1 , 2008 ) } } = true
Output: select the rows whose english name record fuzzily matches to capital region of denmark . take the population  january 1 , 2008  record of this row . select the rows whose english name record fuzzily matches to region of southern denmark . take the population  january 1 , 2008  record of this row . the first record is greater than the second record .
less { hop { filter_eq { all_rows ; title ; rumble } ; original air date } ; hop { filter_eq { all_rows ; title ; sweetie } ; original air date } } = true
Output: select the rows whose title record fuzzily matches to rumble . take the original air date record of this row . select the rows whose title record fuzzily matches to sweetie . take the original air date record of this row . the first record is less than the second record .
and { only { filter_eq { all_rows ; first elected ; 1941 } } ; eq { hop { filter_eq { all_rows ; first elected ; 1941 } ; incumbent } ; thomas j lane } } = true
Output: select the rows whose first elected record is equal to 1941 . there is only one such row in the table . the incumbent record of this unqiue row is thomas j lane .
all_eq { all_rows ; category ; short film 2005 prix uip } = true
Output: for the category records of all rows , all of them fuzzily match to short film 2005 prix uip .
round_eq { avg { all_rows ; floors } ; 70 } = true
Output: the average of the floors record of all rows is 70 .
eq { hop { nth_argmin { all_rows ; votes ; 1 } ; party } ; country party } = true
Output: select the row whose votes record of all rows is 1st minimum . the party record of this row is country party .
and { only { filter_eq { all_rows ; written by ; diana sproveri } } ; eq { hop { filter_eq { all_rows ; written by ; diana sproveri } ; title } ; my boss ate my homework } } = true
Output: select the rows whose written by record fuzzily matches to diana sproveri . there is only one such row in the table . the title record of this unqiue row is my boss ate my homework .
greater { hop { filter_eq { all_rows ; sspec number ; slj6b ( b1 ) } ; release price ( usd ) } ; hop { filter_eq { all_rows ; sspec number ; slj6c ( b1 ) } ; release price ( usd ) } } = true
Output: select the rows whose sspec number record fuzzily matches to slj6b  b1  . take the release price  usd  record of this row . select the rows whose sspec number record fuzzily matches to slj6c  b1  . take the release price  usd  record of this row . the first record is greater than the second record .
less { hop { filter_eq { all_rows ; dual archimedean solid ; truncated icosahedron } ; edges } ; hop { filter_eq { all_rows ; dual archimedean solid ; truncated icosidodecahedron } ; edges } } = true
Output: select the rows whose dual archimedean solid record fuzzily matches to truncated icosahedron . take the edges record of this row . select the rows whose dual archimedean solid record fuzzily matches to truncated icosidodecahedron . take the edges record of this row . the first record is less than the second record .
most_eq { all_rows ; time ; n / a } = true
Output: for the time records of all rows , most of them fuzzily match to n  a .
eq { count { filter_eq { all_rows ; opponent ; new york knicks } } ; 2 } = true
Output: select the rows whose opponent record fuzzily matches to new york knicks . the number of such rows is 2 .
eq { count { filter_greater { all_rows ; wins ; 10 } } ; 2 } = true
Output: select the rows whose wins record is greater than 10 . the number of such rows is 2 .
eq { hop { nth_argmax { all_rows ; attendance ; 3 } ; opponent } ; denver broncos } = true
Output: select the row whose attendance record of all rows is 3rd maximum . the opponent record of this row is denver broncos .
eq { hop { argmax { all_rows ; production in 2011 ( 1000 ton ) } ; country } ; china } = true
Output: select the row whose production in 2011  1000 ton  record of all rows is maximum . the country record of this row is china .
eq { count { filter_eq { all_rows ; score ; 1-0 } } ; 3 } = true
Output: select the rows whose score record fuzzily matches to 10 . the number of such rows is 3 .
eq { hop { argmax { all_rows ; qual } ; start } ; 5 } = true
Output: select the row whose qual record of all rows is maximum . the start record of this row is 5 .
most_eq { all_rows ; score ; 70 } = true
Output: for the score records of all rows , most of them are equal to 70 .
eq { hop { nth_argmax { all_rows ; matches ; 2 } ; goalkeeper } ; joel rodríguez } = true
Output: select the row whose matches record of all rows is 2nd maximum . the goalkeeper record of this row is joel rodríguez .
most_eq { all_rows ; sprints classification ; rené weissinger } = true
Output: for the sprints classification records of all rows , most of them fuzzily match to rené weissinger .
less { hop { filter_eq { all_rows ; home captain ; joe darling } ; date } ; hop { filter_eq { all_rows ; home captain ; hugh trumble } ; date } } = true
Output: select the rows whose home captain record fuzzily matches to joe darling . take the date record of this row . select the rows whose home captain record fuzzily matches to hugh trumble . take the date record of this row . the first record is less than the second record .
most_greater { all_rows ; serial number ; 934000 } = true
Output: for the serial number records of all rows , most of them are greater than 934000 .
most_eq { all_rows ; ends ; 2010 } = true
Output: for the ends records of all rows , most of them are equal to 2010 .
eq { count { filter_eq { all_rows ; position ; defensive back } } ; 2 } = true
Output: select the rows whose position record fuzzily matches to defensive back . the number of such rows is 2 .
and { only { filter_eq { filter_eq { all_rows ; country ; france } ; weeks at 1 ; 8 } } ; eq { hop { filter_eq { filter_eq { all_rows ; country ; france } ; weeks at 1 ; 8 } ; number - one single ( s ) } ; n'importe quoi } } = true
Output: select the rows whose country record fuzzily matches to france . among these rows , select the rows whose weeks at 1 record is equal to 8 . there is only one such row in the table . the number  one single  s  record of this unqiue row is nimporte quoi .
most_eq { all_rows ; date ; 10 } = true
Output: for the date records of all rows , most of them fuzzily match to 10 .
round_eq { avg { all_rows ; speed } ; 92.14 } = true
Output: the average of the speed record of all rows is 92.14 .
eq { count { filter_eq { all_rows ; location ; florida } } ; 3 } = true
Output: select the rows whose location record fuzzily matches to florida . the number of such rows is 3 .
eq { count { filter_eq { all_rows ; position ; wr } } ; 2 } = true
Output: select the rows whose position record fuzzily matches to wr . the number of such rows is 2 .
and { eq { diff { hop { filter_eq { all_rows ; programming ; main wybe programming } ; channel } ; hop { filter_eq { all_rows ; programming ; russia today } ; channel } } ; -0.3 } ; and { eq { hop { filter_eq { all_rows ; programming ; main wybe programming } ; channel } ; 35.1 } ; eq { hop { filter_eq { all_rows ; programming ; russia today } ; channel } ; 35.4 } } } = true
Output: select the rows whose programming record fuzzily matches to main wybe programming . take the channel record of this row . select the rows whose programming record fuzzily matches to russia today . take the channel record of this row . the second record is 0.3 larger than the first record . the channel record of the first row is 35.1 . the channel record of the second row is 35.4 .
most_eq { all_rows ; team ; holden racing team } = true
Output: for the team records of all rows , most of them fuzzily match to holden racing team .
and { only { filter_eq { all_rows ; school / club team ; wisconsin } } ; eq { hop { filter_eq { all_rows ; school / club team ; wisconsin } ; player } ; todd nelson } } = true
Output: select the rows whose school  club team record fuzzily matches to wisconsin . there is only one such row in the table . the player record of this unqiue row is todd nelson .
greater { hop { filter_eq { all_rows ; venue ; vicente calderón } ; capacity } ; hop { filter_eq { all_rows ; venue ; estadio de vallecas } ; capacity } } = true
Output: select the rows whose venue record fuzzily matches to vicente calderón . take the capacity record of this row . select the rows whose venue record fuzzily matches to estadio de vallecas . take the capacity record of this row . the first record is greater than the second record .
most_eq { all_rows ; location ; busch stadium } = true
Output: for the location records of all rows , most of them fuzzily match to busch stadium .
eq { count { filter_eq { all_rows ; shooter ; usa } } ; 2 } = true
Output: select the rows whose shooter record fuzzily matches to usa . the number of such rows is 2 .
eq { hop { argmax { all_rows ; hk viewers } ; english title } ; la femme desperado } = true
Output: select the row whose hk viewers record of all rows is maximum . the english title record of this row is la femme desperado .
eq { count { filter_eq { all_rows ; score ; 0 - 1 } } ; 2 } = true
Output: select the rows whose score record fuzzily matches to 0  1 . the number of such rows is 2 .
eq { count { filter_eq { all_rows ; class ; 125cc } } ; 4 } = true
Output: select the rows whose class record fuzzily matches to 125cc . the number of such rows is 4 .
greater { hop { filter_eq { all_rows ; year ; 1989 } ; points } ; hop { filter_eq { all_rows ; year ; 1992 } ; points } } = true
Output: select the rows whose year record fuzzily matches to 1989 . take the points record of this row . select the rows whose year record fuzzily matches to 1992 . take the points record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; location attendance ; pepsi center } } ; 7 } = true
Output: select the rows whose location attendance record fuzzily matches to pepsi center . the number of such rows is 7 .
and { only { filter_eq { all_rows ; moving parts ; yes } } ; eq { hop { filter_eq { all_rows ; moving parts ; yes } ; technology } ; flow } } = true
Output: select the rows whose moving parts record fuzzily matches to yes . there is only one such row in the table . the technology record of this unqiue row is flow .
eq { hop { nth_argmax { all_rows ; attendance ; 3 } ; game } ; 1 } = true
Output: select the row whose attendance record of all rows is 3rd maximum . the game record of this row is 1 .
round_eq { avg { all_rows ; swimsuit } ; 8.255 } = true
Output: the average of the swimsuit record of all rows is 8.255 .
only { filter_eq { all_rows ; home ; oslo } } = true
Output: select the rows whose home record fuzzily matches to oslo . there is only one such row in the table .
and { eq { max { all_rows ; games } ; 38 } ; eq { hop { argmax { all_rows ; games } ; name } ; andrew panko } } = true
Output: the maximum games record of all rows is 38 . the name record of the row with superlative games record is andrew panko .
most_not_eq { all_rows ; event ; team competitions } = true
Output: for the event records of all rows , most of them do not match to team competitions .
round_eq { sum { all_rows ; points } ; 5 } = true
Output: the sum of the points record of all rows is 5 .
eq { hop { argmax { all_rows ; attendance } ; week } ; 16 } = true
Output: select the row whose attendance record of all rows is maximum . the week record of this row is 16 .
less { hop { filter_eq { all_rows ; first member ; john ivatt briscoe } ; election } ; hop { filter_eq { all_rows ; first member ; peter john locke king } ; election } } = true
Output: select the rows whose first member record fuzzily matches to john ivatt briscoe . take the election record of this row . select the rows whose first member record fuzzily matches to peter john locke king . take the election record of this row . the first record is less than the second record .
greater { hop { filter_eq { all_rows ; opponent ; manny morales } ; round } ; hop { filter_eq { all_rows ; opponent ; alberto rodriguez } ; round } } = true
Output: select the rows whose opponent record fuzzily matches to manny morales . take the round record of this row . select the rows whose opponent record fuzzily matches to alberto rodriguez . take the round record of this row . the first record is greater than the second record .
and { only { filter_eq { all_rows ; date ; march } } ; eq { hop { filter_eq { all_rows ; date ; march } ; goal } ; 3 } } = true
Output: select the rows whose date record fuzzily matches to march . there is only one such row in the table . the goal record of this unqiue row is 3 .
eq { hop { nth_argmin { all_rows ; react ; 2 } ; athlete } ; jaysuma saidy ndure } = true
Output: select the row whose react record of all rows is 2nd minimum . the athlete record of this row is jaysuma saidy ndure .
and { only { filter_eq { all_rows ; surface ; carpet ( i ) } } ; eq { hop { filter_eq { all_rows ; surface ; carpet ( i ) } ; tournament } ; opole } } = true
Output: select the rows whose surface record fuzzily matches to carpet  i  . there is only one such row in the table . the tournament record of this unqiue row is opole .
eq { hop { argmax { all_rows ; win % } ; matches } ; 6 } = true
Output: select the row whose win  record of all rows is maximum . the matches record of this row is 6 .
eq { count { filter_less { filter_eq { all_rows ; ground ; football park } ; crowd ; 16000 } } ; 1 } = true
Output: select the rows whose ground record fuzzily matches to football park . among these rows , select the rows whose crowd record is less than 16000 . the number of such rows is 1 .
eq { count { filter_eq { filter_eq { all_rows ; opponent ; minnesota vikings } ; result ; l } } ; 1 } = true
Output: select the rows whose opponent record fuzzily matches to minnesota vikings . among these rows , select the rows whose result record fuzzily matches to l . the number of such rows is 1 .
eq { count { filter_eq { all_rows ; results ; re - elected } } ; 7 } = true
Output: select the rows whose results record fuzzily matches to re  elected . the number of such rows is 7 .
eq { min { filter_eq { all_rows ; runner - up ; asc jeanne d'arc } ; season } ; 1947 } = true
Output: select the rows whose runner  up record fuzzily matches to asc jeanne darc . the minimum season record of these rows is 1947 .
round_eq { avg { all_rows ; wins } ; 10.9 } = true
Output: the average of the wins record of all rows is 10.9 .
eq { count { filter_eq { all_rows ; directed by ; andy cadiff } } ; 3 } = true
Output: select the rows whose directed by record fuzzily matches to andy cadiff . the number of such rows is 3 .
most_eq { all_rows ; county ; perry } = true
Output: for the county records of all rows , most of them fuzzily match to perry .
eq { count { filter_all { all_rows ; nationality } } ; 9 } = true
Output: select the rows whose nationality record is arbitrary . the number of such rows is 9 .
eq { hop { nth_argmax { all_rows ; goals against ; 1 } ; team } ; ashton athletic } = true
Output: select the row whose goals against record of all rows is 1st maximum . the team record of this row is ashton athletic .
eq { hop { argmin { all_rows ; attendance } ; venue } ; atlanta stadium } = true
Output: select the row whose attendance record of all rows is minimum . the venue record of this row is atlanta stadium .
most_eq { all_rows ; margin of victory ; 1 stroke } = true
Output: for the margin of victory records of all rows , most of them fuzzily match to 1 stroke .
round_eq { sum { all_rows ; attendance } ; 658,531 } = true
Output: the sum of the attendance record of all rows is 658,531 .
round_eq { sum { all_rows ; us viewers ( in millions ) } ; 62.28 } = true
Output: the sum of the us viewers  in millions  record of all rows is 62.28 .
round_eq { sum { all_rows ; attendance } ; 120528 } = true
Output: the sum of the attendance record of all rows is 120528 .
eq { hop { nth_argmin { all_rows ; year erected ; 3 } ; statue } ; benito juarez } = true
Output: select the row whose year erected record of all rows is 3rd minimum . the statue record of this row is benito juarez .
eq { hop { nth_argmax { all_rows ; party list votes ; 1 } ; party } ; social democratic party ( spd ) } = true
Output: select the row whose party list votes record of all rows is 1st maximum . the party record of this row is social democratic party  spd  .
eq { hop { argmax { all_rows ; away team score } ; away team } ; south melbourne } = true
Output: select the row whose away team score record of all rows is maximum . the away team record of this row is south melbourne .
eq { hop { argmin { all_rows ; laid down } ; name } ; cambrian ( ex - spitfire ) } = true
Output: select the row whose laid down record of all rows is minimum . the name record of this row is cambrian  ex  spitfire  .
eq { max { filter_eq { all_rows ; date ; november } ; final score } ; 24 - 38 } = true
Output: select the rows whose date record fuzzily matches to november . the maximum final score record of these rows is 24  38 .
greater { hop { filter_eq { all_rows ; venue ; princes park } ; crowd } ; hop { filter_eq { all_rows ; venue ; brunswick street oval } ; crowd } } = true
Output: select the rows whose venue record fuzzily matches to princes park . take the crowd record of this row . select the rows whose venue record fuzzily matches to brunswick street oval . take the crowd record of this row . the first record is greater than the second record .
eq { hop { argmin { all_rows ; result } ; driver } ; emerson fittipaldi } = true
Output: select the row whose result record of all rows is minimum . the driver record of this row is emerson fittipaldi .
greater { hop { filter_eq { all_rows ; championship ; australian open } ; score in the final } ; hop { filter_eq { all_rows ; championship ; wimbledon } ; score in the final } } = true
Output: select the rows whose championship record fuzzily matches to australian open . take the score in the final record of this row . select the rows whose championship record fuzzily matches to wimbledon . take the score in the final record of this row . the first record is greater than the second record .
and { only { filter_eq { all_rows ; mean ; 3.0 } } ; eq { hop { filter_eq { all_rows ; mean ; 3.0 } ; drug } ; heroin } } = true
Output: select the rows whose mean record is equal to 3.0 . there is only one such row in the table . the drug record of this unqiue row is heroin .