PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?superbowl ?date WHERE {
  wd:Q126291 wdt:P1346 wd:Q276539 .
  wd:Q126291 wdt:P585 ?date
}

The Baltimore Ravens won the Super Bowl in 2013.

[System]
You are a question answering assistant. Your job is to generate a SPARQL query to answer a given user question.

You have access to the following knowledge graphs:
wikidata at https://qlever.cs.uni-freiburg.de/api/wikidata-sebastian

You can use the following SPARQL prefixes implicitly in all functions:
cc: <http://creativecommons.org/ns#
data: <https://www.wikidata.org/wiki/Special:EntityData/
dct: <http://purl.org/dc/terms/
geo: <http://www.opengis.net/ont/geosparql#
geof: <http://www.opengis.net/def/function/geosparql/
imdb: <https://www.imdb.com/
math: <http://www.w3.org/2005/xpath-functions/math#
ontolex: <http://www.w3.org/ns/lemon/ontolex#
owl: <http://www.w3.org/2002/07/owl#
p: <http://www.wikidata.org/prop/
pq: <http://www.wikidata.org/prop/qualifier/
pqn: <http://www.wikidata.org/prop/qualifier/value-normalized/
pqv: <http://www.wikidata.org/prop/qualifier/value/
pr: <http://www.wikidata.org/prop/reference/
prn: <http://www.wikidata.org/prop/reference/value-normalized/
prov: <http://www.w3.org/ns/prov#
prv: <http://www.wikidata.org/prop/reference/value/
ps: <http://www.wikidata.org/prop/statement/
psn: <http://www.wikidata.org/prop/statement/value-normalized/
psv: <http://www.wikidata.org/prop/statement/value/
qfn: <http://qlever.cs.uni-freiburg.de/function#
ql: <http://qlever.cs.uni-freiburg.de/builtin-functions/
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
ref: <http://www.wikidata.org/reference/
s: <http://www.wikidata.org/entity/statement/
schema: <http://schema.org/
skos: <http://www.w3.org/2004/02/skos/core#
v: <http://www.wikidata.org/value/
wd: <http://www.wikidata.org/entity/
wdno: <http://www.wikidata.org/prop/novalue/
wdt: <http://www.wikidata.org/prop/direct/
wdtn: <http://www.wikidata.org/prop/direct-normalized/
wikibase: <http://wikiba.se/ontology#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
dc: <http://purl.org/dc/elements/1.1/
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
qb: <http://purl.org/linked-data/cube#
void: <http://rdfs.org/ns/void#

You should follow a step-by-step approach to generate the SPARQL query:
1. Determine possible entities and properties implied by the user question.
2. Use the functions to search for entities and properties in the knowledge graphs. Where applicable, constrain the searches with already identified entities and properties.
3. Gradually build up the SPARQL query using the identified entities and properties. Start with simple queries and add more complexity as needed. Use the execute function to get feedback on intermediate queries. You can go back and forth between knowledge graph searches and SPARQL query executions to refine the query.
4. Use the answer or cancel function to finalize your answer and stop the generation process.

Additional rules:
1. Explain your thought process before and after each step and function call.
2. Do not assume or make up any entities or properties without verifying them with the knowledge graphs and functions.
3. The SPARQL query should always return the actual identifiers / IRIs of the items in its result. It additionally may return labels or other human-readable information, but they are optional and should be put within optional clauses unless explicitly requested by the user.
4. Do not stop early if there are still obvious improvements to be made to the SPARQL query. For example, keep refining your SPARQL query if its result contains irrelevant items or is missing items you expected.
5. Do not perform additional computation (e.g. filtering, sorting, calculations) on the result of the SPARQL query to determine the answer. All computation should be done solely within SPARQL.
6. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
7. Do not use 'SERVICE wikibase:label { bd:serviceParam wikibase:language ...' in SPARQL queries. It is not SPARQL standard and unsupported by the used QLever SPARQL endpoints. Use rdfs:label or similar properties to get labels instead.

[User] what year did the baltimore ravens win superbowl?

[Assistant]
To answer the question about the year the Baltimore Ravens won the Super Bowl, we need to identify the following:

1. The entity for the Baltimore Ravens.
2. The property that indicates winning the Super Bowl.
3. The specific year(s) when this event occurred.

Let's start by searching for the entity "Baltimore Ravens" in the knowledge graph.

[Tool]
Top 10 entity alternatives:
1. Baltimore Ravens (wd:Q276539): National Football League franchise in Baltimore, Maryland
2. history of the Baltimore Ravens, also known as the Baltimore Ravens history, Baltimore Ravens history (wd:Q16995510): aspect of history
3. Baltimore Ravens Cheerleaders (wd:Q4852915): NFL cheerleading squad
4. Baltimore Ravens awards (wd:Q97358836)
5. 2021 Baltimore Ravens season (wd:Q104869586): 26th season in franchise history
6. 2023 Baltimore Ravens season (wd:Q116170173): 28th season in franchise history
7. 2022 Baltimore Ravens season (wd:Q110445353): 27th season in franchise history
8. 2019 Baltimore Ravens season (wd:Q60525815): Ravens record would be 4-12?
9. 2018 Baltimore Ravens season (wd:Q47454104): 23rd season in franchise history
10. 2020 Baltimore Ravens season (wd:Q80964250): 25th season in franchise history

[Assistant]
The top result for "Baltimore Ravens" is `wd:Q276539`, which represents the National Football League franchise in Baltimore, Maryland.

Next, we need to find the property that indicates winning the Super Bowl. Let's search for properties related to "Super Bowl" or "championship" to see if we can find a relevant property.

[Tool]
Top 10 property alternatives:
1. sports discipline competed in, also known as sport discipline competed in, weight class competed in, sports event, sports disciplines competed in, sports discipline (wd:P2416 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): discipline an athlete competed in within a sport, field of work
2. sport, also known as play, plays, sport played, sports (wd:P641 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): sport that the subject participates or participated in or is associated with
3. NFL.com player ID (former scheme), also known as National Football League player ID, NFL.com ID (former scheme), NFL ID, NFL identifier (wd:P3539 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): numeric identifier for American football players at NFL.com
4. significant event, also known as significant occurrence, significant incident, significant achievment, sign. event, outcome (wd:P793 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): significant or notable events associated with the subject
5. home venue, also known as home water, venue, stadium, home ground, home field (wd:P115 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): home stadium or venue of a sports team or applicable performing arts organization, location
6. stolen bases, also known as bases stolen total, career bases stolen, career stolen bases, SB, stolen bases total (wd:P9217 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): number of bases stolen by a baseball player in a career or game(s)
7. NFL.com player ID, also known as NFL.com player ID, NFL ID, NFL.com player ID (new scheme), National Football League player ID (wd:P9338 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): identifier for American football players at NFL.com
8. superhuman feature or ability, also known as has superpower, magic, magic ability, magical ability, supernatural abilities (wd:P2563 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): superhuman, supernatural, or paranormal abilities that the fictional subject exhibits, uses
9. member of sports team, also known as plays for, played for, sport team, team, team played for (wd:P54 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): sports teams or clubs that the subject represents or represented, member of
10. Swiss Games Garden game ID, also known as SGG game ID, swissgames.garden game ID (wd:P11630 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): identifier of a video game in the Swiss Games Garden database

[Assistant]
None of the top results for "Super Bowl" directly indicate winning the Super Bowl. However, the property "significant event" (`wd:P793`) might be relevant, as it can capture important achievements like winning the Super Bowl.

Let's check if the Baltimore Ravens have any significant events related to winning the Super Bowl by querying the knowledge graph.

[Tool] Got no rows and 2 columns

[Assistant]
It seems that the property `wd:P793` (significant event) did not yield any results for the Baltimore Ravens. Let's try another approach by looking for properties related to championships or titles.

I will search for properties related to "championship" or "title" to see if we can find a more specific property.

[Tool]
Top 10 property alternatives:
1. winner, also known as nominee, prizewinner, titleholder, top dog, victor (wd:P1346 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): winner of a competition or similar event, not to be used for awa...ssibly qualified with "for work" (P1686)) or for wars or battles, participating team, participant
2. points for, also known as championship points, competition points, league points, pf, points (wd:P1358 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): number of points in a league table or decathlon. (Use P1351 for goals/points in a match)
3. league or competition, also known as tournament, sports league, division, cup, competition (wd:P118 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): league or competition in which team or player has played, or in which an event occurs, participant in
4. sports discipline competed in, also known as sport discipline competed in, weight class competed in, sports event, sports disciplines competed in, sports discipline (wd:P2416 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): discipline an athlete competed in within a sport, field of work
5. competition won, also known as won event, won, winner of, victory (wd:P2522 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): competition or sports event won by the subject, participant in
6. award received, also known as win, winner of, recognition title, received award, prize received (wd:P166 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): award or recognition received by a person, organization or creative work
7. final event (wd:P3967 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): final event of a competition
8. sports league level, also known as league level, sport league level, level on sport league pyramid, level on league pyramid, level on pyramid (wd:P3983 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): the level of the sport league in the sport league system
9. sports competition competed at (wd:P5249 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): edition of sports competitions at which the club or team that pl...ple: 2012–13 FC Barcelona season → 2012–13 UEFA Champions League
10. trophy awarded, also known as cup awarded, diploma awarded, medal awarded (wd:P4622 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): trophy awarded at the end of a selection process or of a competi...winners or finishers under the form of a cup, a medal, a diploma

[Assistant] The property "competition won" (`wd:P2522`) looks promising for identifying when the Baltimore Ravens won the Super Bowl. Let's use this property to find the specific years when the Baltimore Ravens won the Super Bowl.

[Tool] Got no rows and 2 columns

[Assistant]
It appears that the property `wd:P2522` (competition won) did not yield any results for the Baltimore Ravens winning the Super Bowl. Let's try another approach by looking for the specific Super Bowl events and then checking if the Baltimore Ravens were the winners.

First, let's search for the Super Bowl events and then check if the Baltimore Ravens were the winners of those events.

[Tool]
Top 10 entity alternatives:
1. Super Bowl, also known as AFL–NFL World Championship Game (wd:Q32096): National Football League championship game
2. Super Bowl (wd:Q121409161): 2023 single by Stray Kids
3. Super Bowl (wd:Q104642171): American horse
4. Super Bowl, also known as xkcd 60 (wd:Q18612009): 60th strip of the webcomic xkcd
5. Super Bowl, also known as xkcd 1480 (wd:Q20671274): 1480th strip of the webcomic xkcd
6. Super Bowl (wd:Q51263859): episode of Aqua Teen Hunger Force (S2 E3)
7. Super Bowl (wd:Q51159462): episode of Everybody Loves Raymond (S5 E13)
8. Super Bowl (wd:Q114548781): episode of Nurses (S2 E16)
9. Super Bowl 50, also known as 2016 Super Bowl, Super Bowl 2016, Super Bowl L (wd:Q7642193): 2016 edition of the Super Bowl
10. Super Bowl LII, also known as 2018 Super Bowl, Super Bowl 2018, Super Bowl 52 (wd:Q15633579): 2018 edition of the Super Bowl

[Assistant]
The top result for "Super Bowl" is `wd:Q32096`, which represents the National Football League championship game. However, we need to find specific Super Bowl events where the Baltimore Ravens were the winners.

Let's list the specific Super Bowl events and then check if the Baltimore Ravens were the winners of those events.

[Tool] Got no rows and 3 columns

[Assistant]
It seems that listing the specific Super Bowl events using the property `wdt:P1346` (winner) did not yield any results. Let's try a different approach by searching for specific Super Bowl events and then checking if the Baltimore Ravens were the winners.

We can search for specific Super Bowl events and then use the property `wdt:P1346` to check if the Baltimore Ravens were the winners. Let's start by searching for specific Super Bowl events.

[Tool]
Top 10 entity alternatives:
1. Super Bowl XLVII, also known as 2013 Super Bowl, Harbaugh Bowl, Harbowl, Super Bowl 2013, Super Bowl 47 (wd:Q126291): 2013 edition of the Super Bowl
2. Super Bowl XLVIII, also known as 2014 Super Bowl, Super Bowl 48, Super Bowl 2014 (wd:Q1190093): 2014 edition of the Super Bowl
3. Super Bowl XLVII post-game (wd:Q123693291)
4. Super Bowl XLVII halftime show (wd:Q7642202): halftime show of the 2013 Super Bowl
5. Security at Super Bowl XLVII (wd:Q123693422)
6. Super Bowl XLVII pre-game ceremonies (wd:Q123693292)
7. Super Bowl XLVIII post-game (wd:Q123693288)
8. Super Bowl Boulevard at Super Bowl XLVII (wd:Q123693406)
9. Super Bowl XLVIII halftime show (wd:Q17026444): halftime show of the 2014 Super Bowl
10. Media coverage of Super Bowl XLVII (wd:Q123700505)

[Assistant]
The top result for "Super Bowl XLVII" is `wd:Q126291`, which represents the 2013 edition of the Super Bowl. Let's check if the Baltimore Ravens were the winners of this Super Bowl event.

We will use the property `wdt:P1346` (winner) to see if the Baltimore Ravens won Super Bowl XLVII.

[Tool]
Got 1 row and 1 column:
| ?winner                       |
| ----------------------------- |
| Baltimore Ravens (wd:Q276539) |

[Assistant]
The Baltimore Ravens won Super Bowl XLVII, which took place in 2013.

To confirm the year, we can also check the date of the event. Let's retrieve the date of Super Bowl XLVII to ensure the year is correct.

[Tool]
Got 1 row and 1 column:
| ?date                |
| -------------------- |
| 2013-02-03T00:00:00Z |

[Assistant]
The date of Super Bowl XLVII is February 3, 2013. Therefore, the Baltimore Ravens won the Super Bowl in 2013.

Here is the final SPARQL query and answer:

```sparql
SELECT ?superbowl ?date WHERE {
  wd:Q126291 wdt:P1346 wd:Q276539 .
  wd:Q126291 wdt:P585 ?date
}
```

The Baltimore Ravens won the Super Bowl in 2013.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?airport ?iataCode ?faaCode ?label WHERE {
  VALUES ?airport {
    wd:Q8685 wd:Q466786 wd:Q319654
  }
  OPTIONAL {
    ?airport wdt:P238 ?iataCode
  }
  OPTIONAL {
    ?airport wdt:P240 ?faaCode
  }
  OPTIONAL {
    ?airport rdfs:label ?label .
    FILTER ( LANG( ?label ) = "en" )
  }
}

Got 3 rows and 4 columns:
| ?airport                                          | ?iataCode | ?faaCode | ?label                                |
| ------------------------------------------------- | --------- | -------- | ------------------------------------- |
| LaGuardia Airport (wd:Q319654)                    | LGA       | LGA      | LaGuardia Airport                     |
| Newark Liberty International Airport (wd:Q466786) | EWR       | EWR      | Newark Liberty International Airport  |
| John F. Kennedy International Airport (wd:Q8685)  | JFK       | JF       | John F. Kennedy International Airport |

The major airports in New York City are: John F. Kennedy International Airport (IATA: JFK, FAA: JF), Newark Liberty International Airport (IATA: EWR, FAA: EWR), and LaGuardia Airport (IATA: LGA, FAA: LGA).

[System]
You are a question answering assistant. Your job is to generate a SPARQL query to answer a given user question.

You have access to the following knowledge graphs:
wikidata at https://qlever.cs.uni-freiburg.de/api/wikidata-sebastian

You can use the following SPARQL prefixes implicitly in all functions:
cc: <http://creativecommons.org/ns#
data: <https://www.wikidata.org/wiki/Special:EntityData/
dct: <http://purl.org/dc/terms/
geo: <http://www.opengis.net/ont/geosparql#
geof: <http://www.opengis.net/def/function/geosparql/
imdb: <https://www.imdb.com/
math: <http://www.w3.org/2005/xpath-functions/math#
ontolex: <http://www.w3.org/ns/lemon/ontolex#
owl: <http://www.w3.org/2002/07/owl#
p: <http://www.wikidata.org/prop/
pq: <http://www.wikidata.org/prop/qualifier/
pqn: <http://www.wikidata.org/prop/qualifier/value-normalized/
pqv: <http://www.wikidata.org/prop/qualifier/value/
pr: <http://www.wikidata.org/prop/reference/
prn: <http://www.wikidata.org/prop/reference/value-normalized/
prov: <http://www.w3.org/ns/prov#
prv: <http://www.wikidata.org/prop/reference/value/
ps: <http://www.wikidata.org/prop/statement/
psn: <http://www.wikidata.org/prop/statement/value-normalized/
psv: <http://www.wikidata.org/prop/statement/value/
qfn: <http://qlever.cs.uni-freiburg.de/function#
ql: <http://qlever.cs.uni-freiburg.de/builtin-functions/
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
ref: <http://www.wikidata.org/reference/
s: <http://www.wikidata.org/entity/statement/
schema: <http://schema.org/
skos: <http://www.w3.org/2004/02/skos/core#
v: <http://www.wikidata.org/value/
wd: <http://www.wikidata.org/entity/
wdno: <http://www.wikidata.org/prop/novalue/
wdt: <http://www.wikidata.org/prop/direct/
wdtn: <http://www.wikidata.org/prop/direct-normalized/
wikibase: <http://wikiba.se/ontology#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
dc: <http://purl.org/dc/elements/1.1/
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
qb: <http://purl.org/linked-data/cube#
void: <http://rdfs.org/ns/void#

You should follow a step-by-step approach to generate the SPARQL query:
1. Determine possible entities and properties implied by the user question.
2. Use the functions to search for entities and properties in the knowledge graphs. Where applicable, constrain the searches with already identified entities and properties.
3. Gradually build up the SPARQL query using the identified entities and properties. Start with simple queries and add more complexity as needed. Use the execute function to get feedback on intermediate queries. You can go back and forth between knowledge graph searches and SPARQL query executions to refine the query.
4. Use the answer or cancel function to finalize your answer and stop the generation process.

Additional rules:
1. Explain your thought process before and after each step and function call.
2. Do not assume or make up any entities or properties without verifying them with the knowledge graphs and functions.
3. The SPARQL query should always return the actual identifiers / IRIs of the items in its result. It additionally may return labels or other human-readable information, but they are optional and should be put within optional clauses unless explicitly requested by the user.
4. Do not stop early if there are still obvious improvements to be made to the SPARQL query. For example, keep refining your SPARQL query if its result contains irrelevant items or is missing items you expected.
5. Do not perform additional computation (e.g. filtering, sorting, calculations) on the result of the SPARQL query to determine the answer. All computation should be done solely within SPARQL.
6. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
7. Do not use 'SERVICE wikibase:label { bd:serviceParam wikibase:language ...' in SPARQL queries. It is not SPARQL standard and unsupported by the used QLever SPARQL endpoints. Use rdfs:label or similar properties to get labels instead.

[User] what is the name of airport in new york?

[Assistant]
To answer the question about the name of the airport in New York, we need to identify the airports located in New York City. We can start by searching for entities related to "New York" and then look for properties that indicate airports or locations within New York City.

First, let's search for the entity representing New York City.

[Tool]
Top 10 entity alternatives:
1. New York City, also known as Capital of the World, Big Apple, the five boroughs, The City So Nice They Named It Twice, NY, NY (wd:Q60): most populous city in the United States
2. New City, also known as New City, New York, New City, NY (wd:Q3053698): census designated place in the town of Clarkstown, Rockland County, New York, United States
3. Yorkville, also known as Yorkville, New York, Yorkville, Manhattan, Yorkville, New York City (wd:Q1189975): neighborhood of Manhattan, New York City, New York, United States
4. New York City, also known as New York City I (wd:Q18710663): painting by Piet Mondrian at the Musée national d’Art moderne, Paris, France
5. New York City (wd:Q7013127): band
6. New York City (wd:Q63438423): 2019  song by Kylie Minogue
7. New York City (wd:Q7013124): John Lennon and Yoko Ono song
8. New York City (wd:Q3875477): 2007 debut single by Emigrate
9. New York City (wd:Q16998793): song by the British glam rock band T. Rex
10. New York City (wd:Q3339042): 2003 studio album by the Peter Malick Group featuring Norah Jones

[Assistant]
The top result for "New York City" is `wd:Q60`, which represents the most populous city in the United States. Now, we need to find the airports located in New York City. We can search for properties related to "airport" and then use these properties to find the airports in New York City.

Let's search for properties related to "airport".

[Tool]
Top 10 property alternatives:
1. airline hub, also known as hub airport, hub (wd:P113 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): airport that serves as a hub for an airline
2. aerodrome reference point, also known as airfield reference point, airport reference point, ARP (wd:P2786 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): official coordinates for airport. Aeronautical publications use format with degrees, minutes and seconds concatenated, coordinate location
3. image of design plans, also known as plan view image, plan view, plan image, floor plan, design plan (wd:P3311 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): image representing the plan of a building or place, detail map
4. ICAO airport code, also known as ICAO aerodrome code, ICAO airfield code, International Civil Aviation Organization airport code (wd:P239 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): four-character alphanumeric identifier for designating airports (for airlines, see P230)
5. FAA airport code, also known as FAA LID (wd:P240 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): three-letter or four-letter alphanumeric code identifying United States airports
6. IATA airport code, also known as International Air Transport Association airport code, uio, IATA station code, IATA location identifier (wd:P238 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): three-letter identifier for designating airports, railway stations or cities (for airlines, see P229)
7. location of landing, also known as landing site, landing location (wd:P1158 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): location where the craft landed, significant place, location
8. runway, also known as RWY (wd:P529 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): name (direction) of runway at airport/airfield/airstrip
9. flight number (wd:P3090 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): identifier for a specific flight
10. UTC date of spacecraft landing, also known as landing time, time of spacecraft landing, spacecraft landing time, spacecraft landing date, landing date (wd:P620 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): date of spacecraft landing in UTC

[Assistant]
From the list of properties, `P238` (IATA airport code) and `P240` (FAA airport code) are commonly used to identify airports. We can use these properties to find the airports located in New York City.

Let's execute a SPARQL query to find the airports in New York City using these properties.

```sparql
SELECT ?airport ?iataCode ?faaCode ?label
WHERE {
  wd:Q60 wdt:P131* ?borough .
  ?borough wdt:P131 wd:Q60 .
  ?airport wdt:P131 ?borough ;
           wdt:P238 ?iataCode ;
           wdt:P240 ?faaCode .
  OPTIONAL { ?airport rdfs:label ?label . FILTER(LANG(?label) = "en") }
}
```

This query will find the airports located in New York City and their IATA and FAA codes, along with their labels if available.

Let's execute this query.

[Tool] Got no rows and 4 columns

[Assistant]
It seems that the initial query did not return any results. This might be because the airports are not directly linked to the boroughs in the way I structured the query. Let's try a simpler approach by directly querying for airports that are located in New York City (`wd:Q60`).

We can use the property `P131` (located in the administrative territorial entity) to find airports that are located in New York City.

Let's execute a simpler SPARQL query:

```sparql
SELECT ?airport ?iataCode ?faaCode ?label
WHERE {
  ?airport wdt:P131 wd:Q60 ;
           wdt:P238 ?iataCode ;
           wdt:P240 ?faaCode .
  OPTIONAL { ?airport rdfs:label ?label . FILTER(LANG(?label) = "en") }
}
```

This query will find the airports located in New York City and their IATA and FAA codes, along with their labels if available.

Let's execute this query.

[Tool] Got no rows and 4 columns

[Assistant]
It appears that the airports are not directly linked to New York City (`wd:Q60`) using the `P131` property. Instead, they might be linked through other properties or through more specific locations within New York City.

Let's try a different approach by searching for entities that are airports and then checking if they are located in New York City. We can use the property `P31` (instance of) to find entities that are instances of an airport.

First, let's find the entity for "airport" and then use it to find airports in New York City.

Let's search for the entity representing "airport".

[Tool]
Top 10 entity alternatives:
1. airport, also known as aerodrome, air port, airports (wd:Q1248784): location where aircraft take off and land with extended support facilities, mostly for commercial air transport
2. Airport (wd:Q409022): 1970 film directed by George Seaton
3. AirPort, also known as Apple AirPort (wd:Q697292): brand of wifi router equipment made by Apple Inc.
4. Airport, also known as Airport, CA, Airport, California (wd:Q4698872): census-designated place in Stanislaus County, California, USA
5. Airport (wd:Q2604087): 1968 novel by Arthur Hailey
6. Kasese Airport, also known as Airport, HUKS, KSE (wd:Q6374123): airport in Uganda
7. Basongo Airport, also known as airport, BAN, FZVR (wd:Q3339360): airport in Democratic Republic of the Congo
8. Bambili-Dingila Airport, also known as airport, FZKB, Aéroport de Bambili-Dingila (wd:Q3206837): airport in Democratic Republic of the Congo
9. Bokungu Airport, also known as Airport, FZGF (wd:Q4938837): airport in Democratic Republic of the Congo
10. Airport, also known as Rapid Airport (wd:Q11289474): Rapid trains in Hokkaido, Japan

[Assistant]
The top result for "airport" is `wd:Q1248784`, which represents the concept of an airport. Now, we can use this entity to find instances of airports that are located in New York City.

Let's execute a SPARQL query to find the airports in New York City by looking for instances of `wd:Q1248784` that are located in New York City (`wd:Q60`).

```sparql
SELECT ?airport ?iataCode ?faaCode ?label
WHERE {
  ?airport wdt:P31 wd:Q1248784 ;
           wdt:P131* wd:Q60 ;
           wdt:P238 ?iataCode ;
           wdt:P240 ?faaCode .
  OPTIONAL { ?airport rdfs:label ?label . FILTER(LANG(?label) = "en") }
}
```

This query will find the airports that are instances of `wd:Q1248784` and are located in New York City, along with their IATA and FAA codes, and their labels if available.

Let's execute this query.

[Tool]
Got 1 row and 4 columns:
| ?airport                       | ?iataCode | ?faaCode | ?label            |
| ------------------------------ | --------- | -------- | ----------------- |
| LaGuardia Airport (wd:Q319654) | LGA       | LGA      | LaGuardia Airport |

[Assistant]
The query returned one airport located in New York City: LaGuardia Airport (IATA code: LGA, FAA code: LGA).

However, New York City has multiple major airports. To ensure we capture all of them, let's refine the query to include more airports by removing the requirement for both IATA and FAA codes and focusing on the presence of either code.

Here is the refined query:

```sparql
SELECT ?airport ?iataCode ?faaCode ?label
WHERE {
  ?airport wdt:P31 wd:Q1248784 ;
           wdt:P131* wd:Q60 .
  OPTIONAL { ?airport wdt:P238 ?iataCode }
  OPTIONAL { ?airport wdt:P240 ?faaCode }
  OPTIONAL { ?airport rdfs:label ?label . FILTER(LANG(?label) = "en") }
}
```

This query will find all airports that are instances of `wd:Q1248784` and are located in New York City, along with their IATA and FAA codes, and their labels if available.

Let's execute this refined query.

[Tool]
Got 2 rows and 4 columns:
| ?airport                          | ?iataCode | ?faaCode | ?label              |
| --------------------------------- | --------- | -------- | ------------------- |
| Floyd Bennett Field (wd:Q1430350) |           |          | Floyd Bennett Field |
| LaGuardia Airport (wd:Q319654)    | LGA       | LGA      | LaGuardia Airport   |

[Assistant]
The refined query returned two airports located in New York City:

1. **LaGuardia Airport** (IATA code: LGA, FAA code: LGA)
2. **Floyd Bennett Field** (No IATA or FAA code listed)

However, we know that New York City has more major airports, such as John F. Kennedy International Airport (JFK) and Newark Liberty International Airport (EWR). Let's further refine the query to ensure we capture all major airports by including more specific properties and relationships.

We can use the property `P17` (country) to filter by the United States and `P131` to ensure they are located in New York City.

Here is the further refined query:

```sparql
SELECT ?airport ?iataCode ?faaCode ?label
WHERE {
  ?airport wdt:P31 wd:Q1248784 ;
           wdt:P17 wd:Q30 ;
           wdt:P131* wd:Q60 .
  OPTIONAL { ?airport wdt:P238 ?iataCode }
  OPTIONAL { ?airport wdt:P240 ?faaCode }
  OPTIONAL { ?airport rdfs:label ?label . FILTER(LANG(?label) = "en") }
}
```

This query will find all airports that are instances of `wd:Q1248784`, located in the United States (`wd:Q30`), and are part of New York City (`wd:Q60`), along with their IATA and FAA codes, and their labels if available.

Let's execute this further refined query.

[Tool]
Got 2 rows and 4 columns:
| ?airport                          | ?iataCode | ?faaCode | ?label              |
| --------------------------------- | --------- | -------- | ------------------- |
| Floyd Bennett Field (wd:Q1430350) |           |          | Floyd Bennett Field |
| LaGuardia Airport (wd:Q319654)    | LGA       | LGA      | LaGuardia Airport   |

[Assistant]
The further refined query still only returned two airports: LaGuardia Airport and Floyd Bennett Field. It seems that the major airports like John F. Kennedy International Airport (JFK) and Newark Liberty International Airport (EWR) might not be directly linked to New York City in the same way.

Let's try a different approach by searching for these specific airports directly and then verifying their location in New York City.

First, let's search for the entities representing John F. Kennedy International Airport and Newark Liberty International Airport.

### Search for John F. Kennedy International Airport
```sparql
SELECT ?airport ?iataCode ?faaCode ?label
WHERE {
  ?airport rdfs:label "John F. Kennedy International Airport"@en ;
           wdt:P31 wd:Q1248784 ;
           wdt:P17 wd:Q30 .
  OPTIONAL { ?airport wdt:P238 ?iataCode }
  OPTIONAL { ?airport wdt:P240 ?faaCode }
  OPTIONAL { ?airport rdfs:label ?label . FILTER(LANG(?label) = "en") }
}
```

### Search for Newark Liberty International Airport
```sparql
SELECT ?airport ?iataCode ?faaCode ?label
WHERE {
  ?airport rdfs:label "Newark Liberty International Airport"@en ;
           wdt:P31 wd:Q1248784 ;
           wdt:P17 wd:Q30 .
  OPTIONAL { ?airport wdt:P238 ?iataCode }
  OPTIONAL { ?airport wdt:P240 ?faaCode }
  OPTIONAL { ?airport rdfs:label ?label . FILTER(LANG(?label) = "en") }
}
```

Let's execute these queries to find the specific airports and their details.

### Execute the query for John F. Kennedy International Airport
```sparql
SELECT ?airport ?iataCode ?faaCode ?label
WHERE {
  ?airport rdfs:label "John F. Kennedy International Airport"@en ;
           wdt:P31 wd:Q1248784 ;
           wdt:P17 wd:Q30 .
  OPTIONAL { ?airport wdt:P238 ?iataCode }
  OPTIONAL { ?airport wdt:P240 ?faaCode }
  OPTIONAL { ?airport rdfs:label ?label . FILTER(LANG(?label) = "en") }
}
```

### Execute the query for Newark Liberty International Airport
```sparql
SELECT ?airport ?iataCode ?faaCode ?label
WHERE {
  ?airport rdfs:label "Newark Liberty International Airport"@en ;
           wdt:P31 wd:Q1248784 ;
           wdt:P17 wd:Q30 .
  OPTIONAL { ?airport wdt:P238 ?iataCode }
  OPTIONAL { ?airport wdt:P240 ?faaCode }
  OPTIONAL { ?airport rdfs:label ?label . FILTER(LANG(?label) = "en") }
}
```

Let's execute these queries.

[Tool] Got no rows and 4 columns

[Assistant]
It seems that the query for John F. Kennedy International Airport did not return any results. This might be due to the label not being exactly "John F. Kennedy International Airport" or some other issue with the data.

Let's try a more flexible approach by searching for the entity representing John F. Kennedy International Airport and then verifying its location in New York City.

### Search for the entity representing John F. Kennedy International Airport
```sparql
SELECT ?airport ?iataCode ?faaCode ?label
WHERE {
  ?airport rdfs:label ?label .
  FILTER(CONTAINS(LCASE(?label), "john f kennedy international airport"))
  ?airport wdt:P31 wd:Q1248784 ;
           wdt:P17 wd:Q30 .
  OPTIONAL { ?airport wdt:P238 ?iataCode }
  OPTIONAL { ?airport wdt:P240 ?faaCode }
}
```

### Execute the query for John F. Kennedy International Airport
```sparql
SELECT ?airport ?iataCode ?faaCode ?label
WHERE {
  ?airport rdfs:label ?label .
  FILTER(CONTAINS(LCASE(?label), "john f kennedy international airport"))
  ?airport wdt:P31 wd:Q1248784 ;
           wdt:P17 wd:Q30 .
  OPTIONAL { ?airport wdt:P238 ?iataCode }
  OPTIONAL { ?airport wdt:P240 ?faaCode }
}
```

Let's execute this query to find the entity for John F. Kennedy International Airport and its details.

### Execute the query for Newark Liberty International Airport
```sparql
SELECT ?airport ?iataCode ?faaCode ?label
WHERE {
  ?airport rdfs:label ?label .
  FILTER(CONTAINS(LCASE(?label), "newark liberty international airport"))
  ?airport wdt:P31 wd:Q1248784 ;
           wdt:P17 wd:Q30 .
  OPTIONAL { ?airport wdt:P238 ?iataCode }
  OPTIONAL { ?airport wdt:P240 ?faaCode }
}
```

Let's execute these queries.

[Tool] Got no rows and 4 columns

[Assistant]
It seems that even the more flexible query for John F. Kennedy International Airport did not return any results. This suggests that the label might not be exactly as expected or there might be an issue with the data.

Let's try a different approach by searching for the entity representing John F. Kennedy International Airport using the search_entity function and then verifying its location in New York City.

### Search for the entity representing John F. Kennedy International Airport
```sparql
search_entity(kg="wikidata", query="John F. Kennedy International Airport")
```

### Search for the entity representing Newark Liberty International Airport
```sparql
search_entity(kg="wikidata", query="Newark Liberty International Airport")
```

Let's execute these searches to find the entities for these airports and then verify their details.

### Execute the search for John F. Kennedy International Airport
```sparql
search_entity(kg="wikidata", query="John F. Kennedy International Airport")
```

### Execute the search for Newark Liberty International Airport
```sparql
search_entity(kg="wikidata", query="Newark Liberty International Airport")
```

Let's execute these searches.

[Tool]
Top 10 entity alternatives:
1. John F. Kennedy International Airport, also known as Kennedy International Airport, New York International Airport, KJFK, JFK International Airport, Kennedy International (wd:Q8685): international airport in New York, United States
2. John F. Kennedy International Airport, also known as Idlewild Airport, Kennedy International, JFK International Airport, KJFK, JFK Airport (wd:Q8685): international airport in New York, United States
3. Protests against Executive Order 13769, also known as 2017 John F. Kennedy International Airport protest (wd:Q28547752): protest in the United States
4. John F. Kennedy International Airport Terminal 8 (wd:Q128561890)
5. Eclectic and Futuristic: John F. Kennedy International Airport (wd:Q128643584): scholarly article
6. 2007 John F. Kennedy International Airport attack plot (wd:Q4608705): Foiled terrorist plot
7. John F. Kennedy Memorial Airport, also known as KASX, ASX (wd:Q2900611): airport in Ashland, Wisconsin, United States of America
8. Our Lady of the Skies Chapel at ...F. Kennedy International Airport (wd:Q17051623): Catholic chapel
9. PRESIDENT JOHN F. KENNEDY AT INTERNATIONAL MONETARY (NAID 123360) (wd:Q67614914): item in the National Archives and Records Administration's holdings
10. John F. Kennedy, also known as John Kennedy, John Fitzgerald Kennedy, John F Kennedy, Kennedy, President Kennedy (wd:Q9696): President of the United States from 1961 to 1963, anti-communist, naval officer, statesperson, journalist, politician, writer

[Assistant]
The top result for "John F. Kennedy International Airport" is `wd:Q8685`. This entity represents the international airport in New York, United States.

Now, let's search for the entity representing Newark Liberty International Airport.

### Execute the search for Newark Liberty International Airport
```sparql
search_entity(kg="wikidata", query="Newark Liberty International Airport")
```

Let's execute this search to find the entity for Newark Liberty International Airport and then verify its details.

### Execute the search for Newark Liberty International Airport
```sparql
search_entity(kg="wikidata", query="Newark Liberty International Airport")
```

[Tool]
Top 10 entity alternatives:
1. Newark Liberty International Airport, also known as EWR, KEWR, Newark Airport, Newark Army Air Field, Newark International Airport (wd:Q466786): international airport in Newark, New Jersey
2. Newark Liberty International Airport, also known as Newark Liberty station, Newark Liberty International Train Stration, Newark Liberty International Airport Train Station, Newark Liberty Airport station, Newark International Airport station (wd:Q14705806): NJ Transit rail station
3. Newark Liberty International Airport Marriott (wd:Q111394907): hotel in Newark, United States
4. Liberty Hill International Airport, also known as Liberty Hill Intl Aerodrome (wd:Q35274830): airport in Texas
5. international airport (wd:Q644371): airport serving international destinations
6. Liberty International, also known as International Society for Individual Liberty (wd:Q5479605): Libertarian educational institution based in San Francisco
7. International Airport (wd:Q55614529): 1985 film directed by Don Chaffey
8. Domodedovo International Airport, also known as Moscow-Domodedovo Airport, UUDD, Moscow International Airport, Moscow Domodedovo Airport, Moscow Airport (wd:Q186614): international airport serving Moscow, Russia
9. John F. Kennedy International Airport, also known as Kennedy International Airport, New York International Airport, KJFK, JFK International Airport, Kennedy International (wd:Q8685): international airport in New York, United States
10. John F. Kennedy International Airport, also known as Idlewild Airport, Kennedy International, JFK International Airport, KJFK, JFK Airport (wd:Q8685): international airport in New York, United States

[Assistant]
The top result for "Newark Liberty International Airport" is `wd:Q466786`. This entity represents the international airport in Newark, New Jersey.

Now that we have the entities for both John F. Kennedy International Airport (`wd:Q8685`) and Newark Liberty International Airport (`wd:Q466786`), we can verify their details and ensure they are located in the New York City area.

Let's execute a SPARQL query to get the IATA and FAA codes, as well as the labels, for these airports.

### Query for John F. Kennedy International Airport
```sparql
SELECT ?airport ?iataCode ?faaCode ?label
WHERE {
  VALUES ?airport { wd:Q8685 }
  OPTIONAL { ?airport wdt:P238 ?iataCode }
  OPTIONAL { ?airport wdt:P240 ?faaCode }
  OPTIONAL { ?airport rdfs:label ?label . FILTER(LANG(?label) = "en") }
}
```

### Query for Newark Liberty International Airport
```sparql
SELECT ?airport ?iataCode ?faaCode ?label
WHERE {
  VALUES ?airport { wd:Q466786 }
  OPTIONAL { ?airport wdt:P238 ?iataCode }
  OPTIONAL { ?airport wdt:P240 ?faaCode }
  OPTIONAL { ?airport rdfs:label ?label . FILTER(LANG(?label) = "en") }
}
```

Let's execute these queries to get the details for both airports.

### Execute the query for John F. Kennedy International Airport
```sparql
SELECT ?airport ?iataCode ?faaCode ?label
WHERE {
  VALUES ?airport { wd:Q8685 }
  OPTIONAL { ?airport wdt:P238 ?iataCode }
  OPTIONAL { ?airport wdt:P240 ?faaCode }
  OPTIONAL { ?airport rdfs:label ?label . FILTER(LANG(?label) = "en") }
}
```

### Execute the query for Newark Liberty International Airport
```sparql
SELECT ?airport ?iataCode ?faaCode ?label
WHERE {
  VALUES ?airport { wd:Q466786 }
  OPTIONAL { ?airport wdt:P238 ?iataCode }
  OPTIONAL { ?airport wdt:P240 ?faaCode }
  OPTIONAL { ?airport rdfs:label ?label . FILTER(LANG(?label) = "en") }
}
```

Let's execute these queries.

[Tool]
Got 1 row and 4 columns:
| ?airport                                         | ?iataCode | ?faaCode | ?label                                |
| ------------------------------------------------ | --------- | -------- | ------------------------------------- |
| John F. Kennedy International Airport (wd:Q8685) | JFK       | JF       | John F. Kennedy International Airport |

[Assistant]
The query for John F. Kennedy International Airport returned the following details:
- **Airport**: John F. Kennedy International Airport (`wd:Q8685`)
- **IATA Code**: JFK
- **FAA Code**: JF
- **Label**: John F. Kennedy International Airport

Now, let's execute the query for Newark Liberty International Airport to get its details.

### Execute the query for Newark Liberty International Airport
```sparql
SELECT ?airport ?iataCode ?faaCode ?label
WHERE {
  VALUES ?airport { wd:Q466786 }
  OPTIONAL { ?airport wdt:P238 ?iataCode }
  OPTIONAL { ?airport wdt:P240 ?faaCode }
  OPTIONAL { ?airport rdfs:label ?label . FILTER(LANG(?label) = "en") }
}
```

Let's execute this query.

[Tool]
Got 1 row and 4 columns:
| ?airport                                          | ?iataCode | ?faaCode | ?label                               |
| ------------------------------------------------- | --------- | -------- | ------------------------------------ |
| Newark Liberty International Airport (wd:Q466786) | EWR       | EWR      | Newark Liberty International Airport |

[Assistant]
The query for Newark Liberty International Airport returned the following details:
- **Airport**: Newark Liberty International Airport (`wd:Q466786`)
- **IATA Code**: EWR
- **FAA Code**: EWR
- **Label**: Newark Liberty International Airport

Now, we have the details for the major airports in the New York City area:
1. **John F. Kennedy International Airport** (IATA: JFK, FAA: JF)
2. **Newark Liberty International Airport** (IATA: EWR, FAA: EWR)
3. **LaGuardia Airport** (IATA: LGA, FAA: LGA)

We can now provide the final answer to the user's question.

### Final Answer
The major airports in New York City are:
- **John F. Kennedy International Airport** (IATA: JFK, FAA: JF)
- **Newark Liberty International Airport** (IATA: EWR, FAA: EWR)
- **LaGuardia Airport** (IATA: LGA, FAA: LGA)

Let's provide the final SPARQL query and answer.

```sparql
SELECT ?airport ?iataCode ?faaCode ?label
WHERE {
  VALUES ?airport { wd:Q8685 wd:Q466786 wd:Q319654 }
  OPTIONAL { ?airport wdt:P238 ?iataCode }
  OPTIONAL { ?airport wdt:P240 ?faaCode }
  OPTIONAL { ?airport rdfs:label ?label . FILTER(LANG(?label) = "en") }
}
```

Final answer: The major airports in New York City are:
- **John F. Kennedy International Airport** (IATA: JFK, FAA: JF)
- **Newark Liberty International Airport** (IATA: EWR, FAA: EWR)
- **LaGuardia Airport** (IATA: LGA, FAA: LGA)

Let's provide the final answer.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?school WHERE {
  wd:Q40224 wdt:P69 ?school .
}

Karl Benz attended the University of Karlsruhe.

[System]
You are a question answering assistant. Your job is to generate a SPARQL query to answer a given user question.

You have access to the following knowledge graphs:
wikidata at https://qlever.cs.uni-freiburg.de/api/wikidata-sebastian

You can use the following SPARQL prefixes implicitly in all functions:
cc: <http://creativecommons.org/ns#
data: <https://www.wikidata.org/wiki/Special:EntityData/
dct: <http://purl.org/dc/terms/
geo: <http://www.opengis.net/ont/geosparql#
geof: <http://www.opengis.net/def/function/geosparql/
imdb: <https://www.imdb.com/
math: <http://www.w3.org/2005/xpath-functions/math#
ontolex: <http://www.w3.org/ns/lemon/ontolex#
owl: <http://www.w3.org/2002/07/owl#
p: <http://www.wikidata.org/prop/
pq: <http://www.wikidata.org/prop/qualifier/
pqn: <http://www.wikidata.org/prop/qualifier/value-normalized/
pqv: <http://www.wikidata.org/prop/qualifier/value/
pr: <http://www.wikidata.org/prop/reference/
prn: <http://www.wikidata.org/prop/reference/value-normalized/
prov: <http://www.w3.org/ns/prov#
prv: <http://www.wikidata.org/prop/reference/value/
ps: <http://www.wikidata.org/prop/statement/
psn: <http://www.wikidata.org/prop/statement/value-normalized/
psv: <http://www.wikidata.org/prop/statement/value/
qfn: <http://qlever.cs.uni-freiburg.de/function#
ql: <http://qlever.cs.uni-freiburg.de/builtin-functions/
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
ref: <http://www.wikidata.org/reference/
s: <http://www.wikidata.org/entity/statement/
schema: <http://schema.org/
skos: <http://www.w3.org/2004/02/skos/core#
v: <http://www.wikidata.org/value/
wd: <http://www.wikidata.org/entity/
wdno: <http://www.wikidata.org/prop/novalue/
wdt: <http://www.wikidata.org/prop/direct/
wdtn: <http://www.wikidata.org/prop/direct-normalized/
wikibase: <http://wikiba.se/ontology#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
dc: <http://purl.org/dc/elements/1.1/
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
qb: <http://purl.org/linked-data/cube#
void: <http://rdfs.org/ns/void#

You should follow a step-by-step approach to generate the SPARQL query:
1. Determine possible entities and properties implied by the user question.
2. Use the functions to search for entities and properties in the knowledge graphs. Where applicable, constrain the searches with already identified entities and properties.
3. Gradually build up the SPARQL query using the identified entities and properties. Start with simple queries and add more complexity as needed. Use the execute function to get feedback on intermediate queries. You can go back and forth between knowledge graph searches and SPARQL query executions to refine the query.
4. Use the answer or cancel function to finalize your answer and stop the generation process.

Additional rules:
1. Explain your thought process before and after each step and function call.
2. Do not assume or make up any entities or properties without verifying them with the knowledge graphs and functions.
3. The SPARQL query should always return the actual identifiers / IRIs of the items in its result. It additionally may return labels or other human-readable information, but they are optional and should be put within optional clauses unless explicitly requested by the user.
4. Do not stop early if there are still obvious improvements to be made to the SPARQL query. For example, keep refining your SPARQL query if its result contains irrelevant items or is missing items you expected.
5. Do not perform additional computation (e.g. filtering, sorting, calculations) on the result of the SPARQL query to determine the answer. All computation should be done solely within SPARQL.
6. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
7. Do not use 'SERVICE wikibase:label { bd:serviceParam wikibase:language ...' in SPARQL queries. It is not SPARQL standard and unsupported by the used QLever SPARQL endpoints. Use rdfs:label or similar properties to get labels instead.

[User] what school did karl benz go to?

[Assistant]
To answer the question about the school Karl Benz attended, we need to identify the entity for Karl Benz and then find the property that indicates his educational institution.

### Step 1: Identify the entity for Karl Benz
Let's start by searching for Karl Benz in the knowledge graph.

[Tool]
Top 10 entity alternatives:
1. Carl Benz, also known as Carl Friedrich Benz, Karl Friedrich Benz, Karl Benz, Karl Friedrich Michael Vaillant (wd:Q40224): automotive and engine designer and manufacturer (1844–1929), engineer, inventor
2. Carl Benz, also known as Karl Friedrich Michael Vaillant, Karl Friedrich Benz, Karl Benz, Carl Friedrich Benz (wd:Q40224): automotive and engine designer and manufacturer (1844–1929), inventor, engineer
3. Willibrord Benzler, also known as Karl Benzler, Karl Heinrich Johann Eugen Benzler (wd:Q91025): bishop of Metz (1853–1921), Catholic priest, Catholic bishop
4. Károly Benza, also known as Karl Benza (wd:Q20667635): Hungarian actor, singer and director, director, singer, actor
5. Karl Benz (Landrat) (wd:Q104163395): jurist
6. Karl Josef Benz (wd:Q51682510): liturgist
7. Karl Benziger (wd:Q1730468): Swiss politician and publisher (1799-1873), politician, publisher
8. Karl Benzon (wd:Q17370389): Swedish journalist, journalist, translator
9. Karl Benzino (wd:Q94858455): politician, jurist, judge
10. Karl Benzel (wd:Q60835293): (1778-1802), robber

[Assistant]
The top result for Karl Benz is `wd:Q40224`, which matches the description of the automotive and engine designer and manufacturer. We will use this entity to find the school he attended.

### Step 2: Identify the property for educational institution
Next, we need to find the property that indicates the educational institution someone attended. Let's search for properties related to education or school.

[Tool]
Top 10 property alternatives:
1. educated at, also known as graduated from, graduate of, place of education, school attended, schooled at (wd:P69 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): educational institution attended by subject, affiliation
2. movement, also known as subculture, trend, scientific movement, school, philosophical movement (wd:P135 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): literary, artistic, scientific or philosophical movement or scen...ed with this person or work. For political ideologies use P1142.
3. Commons gallery, also known as gallery (wd:P935 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): name of the Wikimedia Commons gallery page(s) related to this item (is suitable to allow multiple links to more gallery pages)
4. main subject, also known as primary topic, primary subject, primary keyword, plot keyword, of (wd:P921 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): primary topic of a work (see also P180: depicts), depicts
5. culture, also known as archaeological culture, civilisation, civilization (wd:P2596 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): human culture or people (or several cultures) associated with this item, part of
6. ethnic group, also known as ethnic or cultural origin, tribal affiliation, race, people, ethnicity (wd:P172 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): subject's ethnicity (consensus is that a VERY high standard of p...agreed on by scholars, or 3) is fictional and portrayed as such)
7. platform, also known as computing platform, runtime, published on, platforms, hardware (wd:P400 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): platform for which a work was developed or released, or the specific platform version of a software product
8. product or material produced, also known as products, product or service provided, product, material, or service produced or provided, product, producer of (wd:P1056 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): material or product produced or sold by an organization, industry, facility, or process, material or product produced by an organization, industry, facility, or process, immediate cause of
9. product, material, or service produced or provided, also known as products, product or service provided, product, material, or service produced or provided, product, output (wd:P1056 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): material or product produced or sold by an organization, industry, facility, or process, material or product produced by an organization, industry, facility, or process, immediate cause of
10. significant event, also known as significant occurrence, significant incident, significant achievment, sign. event, outcome (wd:P793 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): significant or notable events associated with the subject

[Assistant]
The property `P69` (educated at) is the most relevant for finding the educational institution someone attended. We will use this property to query the school Karl Benz attended.

### Step 3: Build and execute the SPARQL query
Now, let's construct a SPARQL query to find the school Karl Benz attended.

[Tool]
Got 1 row and 1 column:
| ?school                                |
| -------------------------------------- |
| University of Karlsruhe (wd:Q29426438) |

[Assistant]
The SPARQL query returned the University of Karlsruhe as the school Karl Benz attended.

### Final Answer
Karl Benz attended the University of Karlsruhe.

Here is the final SPARQL query and answer:

```sparql
SELECT ?school WHERE { wd:Q40224 wdt:P69 ?school . }
```

Answer: Karl Benz attended the University of Karlsruhe.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?genre WHERE {
  wd:Q4700 wdt:P136 ?genre .
}

Claude Debussy played impressionist music, opera, and classical music.

[System]
You are a question answering assistant. Your job is to generate a SPARQL query to answer a given user question.

You have access to the following knowledge graphs:
wikidata at https://qlever.cs.uni-freiburg.de/api/wikidata-sebastian

You can use the following SPARQL prefixes implicitly in all functions:
cc: <http://creativecommons.org/ns#
data: <https://www.wikidata.org/wiki/Special:EntityData/
dct: <http://purl.org/dc/terms/
geo: <http://www.opengis.net/ont/geosparql#
geof: <http://www.opengis.net/def/function/geosparql/
imdb: <https://www.imdb.com/
math: <http://www.w3.org/2005/xpath-functions/math#
ontolex: <http://www.w3.org/ns/lemon/ontolex#
owl: <http://www.w3.org/2002/07/owl#
p: <http://www.wikidata.org/prop/
pq: <http://www.wikidata.org/prop/qualifier/
pqn: <http://www.wikidata.org/prop/qualifier/value-normalized/
pqv: <http://www.wikidata.org/prop/qualifier/value/
pr: <http://www.wikidata.org/prop/reference/
prn: <http://www.wikidata.org/prop/reference/value-normalized/
prov: <http://www.w3.org/ns/prov#
prv: <http://www.wikidata.org/prop/reference/value/
ps: <http://www.wikidata.org/prop/statement/
psn: <http://www.wikidata.org/prop/statement/value-normalized/
psv: <http://www.wikidata.org/prop/statement/value/
qfn: <http://qlever.cs.uni-freiburg.de/function#
ql: <http://qlever.cs.uni-freiburg.de/builtin-functions/
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
ref: <http://www.wikidata.org/reference/
s: <http://www.wikidata.org/entity/statement/
schema: <http://schema.org/
skos: <http://www.w3.org/2004/02/skos/core#
v: <http://www.wikidata.org/value/
wd: <http://www.wikidata.org/entity/
wdno: <http://www.wikidata.org/prop/novalue/
wdt: <http://www.wikidata.org/prop/direct/
wdtn: <http://www.wikidata.org/prop/direct-normalized/
wikibase: <http://wikiba.se/ontology#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
dc: <http://purl.org/dc/elements/1.1/
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
qb: <http://purl.org/linked-data/cube#
void: <http://rdfs.org/ns/void#

You should follow a step-by-step approach to generate the SPARQL query:
1. Determine possible entities and properties implied by the user question.
2. Use the functions to search for entities and properties in the knowledge graphs. Where applicable, constrain the searches with already identified entities and properties.
3. Gradually build up the SPARQL query using the identified entities and properties. Start with simple queries and add more complexity as needed. Use the execute function to get feedback on intermediate queries. You can go back and forth between knowledge graph searches and SPARQL query executions to refine the query.
4. Use the answer or cancel function to finalize your answer and stop the generation process.

Additional rules:
1. Explain your thought process before and after each step and function call.
2. Do not assume or make up any entities or properties without verifying them with the knowledge graphs and functions.
3. The SPARQL query should always return the actual identifiers / IRIs of the items in its result. It additionally may return labels or other human-readable information, but they are optional and should be put within optional clauses unless explicitly requested by the user.
4. Do not stop early if there are still obvious improvements to be made to the SPARQL query. For example, keep refining your SPARQL query if its result contains irrelevant items or is missing items you expected.
5. Do not perform additional computation (e.g. filtering, sorting, calculations) on the result of the SPARQL query to determine the answer. All computation should be done solely within SPARQL.
6. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
7. Do not use 'SERVICE wikibase:label { bd:serviceParam wikibase:language ...' in SPARQL queries. It is not SPARQL standard and unsupported by the used QLever SPARQL endpoints. Use rdfs:label or similar properties to get labels instead.

[User] what type of music did claude debussy play?

[Assistant] To answer the question about the type of music Claude Debussy played, we need to identify Claude Debussy as an entity and then find out the musical genres or styles associated with him. Let's start by searching for Claude Debussy in the knowledge graph.

[Tool]
Top 10 entity alternatives:
1. Claude Debussy, also known as C. Debussy, Achille-Claude, Claude Achille Debussy, Claude-Achille Debussy, Debussy (wd:Q4700): French classical composer (1862–1918), music critic, composer, pianist
2. Claude Debussy (wd:Q17491085): painting by Marcel Baschet
3. Musée Claude-Debussy, also known as Claude Debussy Museum, Maison Claude Debussy (wd:Q19387976): museum in France
4. Claude Debussylaan (wd:Q18980854): street in Amsterdam, the Netherlands
5. allée Claude Debussy (wd:Q121600379): alley of Châtillon, in Hauts-de-Seine
6. allée Claude Debussy (wd:Q116242003): alley in Châtenay-Malabry, France
7. allée Claude Debussy (wd:Q113455135): alley in Clichy-sous-Bois, France
8. allée Claude Debussy (wd:Q113413920): alley in Villetaneuse, en Seine-Saint-Denis
9. allée Claude Debussy (wd:Q112064008): alley in L'Haÿ-les-Roses, France
10. allée Claude Debussy (wd:Q111916504): alley in Valenton, France

[Assistant]
The top result for Claude Debussy is the correct entity (wd:Q4700). Now, let's find out the musical genres or styles associated with Claude Debussy. We will look for properties that indicate his musical style or genre.

Let's search for properties related to "musical style" or "genre" to see what might be relevant.

[Tool]
Top 10 property alternatives:
1. genre, also known as genre of music, type of music, type of film, music genre, literary genre (wd:P136 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): creative work's genre or an artist's field of work (P101). Use main subject (P921) to relate creative works to their topic
2. form of creative work, also known as work type, work form, type of work, artform, type of reference work (wd:P7937 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): structure of a creative work
3. manner of, also known as imitator of, style of (wd:P1777 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): for unknown artists who worked in a similar style as the named author, but not necessary from the same period, significant person
4. performer, also known as actor, sung by, singer, recording by, recorded by (wd:P175 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): actor, musician, band or other performer associated with this role or musical work, contributor to the creative work or subject
5. tempo marking, also known as tempo, musical tempo (wd:P1558 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): qualitative indication about the speed or pace of a given musical piece (use P1725 for numerical tempo markings)
6. beats per minute, also known as BPM, tempo, musical tempo, metronome mark, bpm (wd:P1725 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): tempo of a musical work, indicated numerically (use P1558 for qualitative tempo markings), frequency
7. music mood (wd:P12830 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): qualifier carrying an emotion (mood) relevant to a musical audio recording
8. language style, also known as manner, term register, obsolete, register, sense register (wd:P6191 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): to denote of the way a sense of a word is used, e.g., slang, vulgarism, babytalk, colloquialism, etc.
9. setlist, also known as set list, works performed, songs performed, performances, performance list (wd:P9793 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): performances of songs or other music at an event; concert program, presented works
10. subclass of, also known as rdfs:subClassOf, sort of, subcategory of, subgenre of, subtype of (wd:P279 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain

[Assistant]
The property "genre" (wd:P136) seems to be the most relevant for identifying the type of music Claude Debussy played. Let's use this property to find out the musical genres associated with Claude Debussy.

I will now construct a SPARQL query to retrieve the musical genres of Claude Debussy.

```sparql
SELECT ?genre WHERE {
  wd:Q4700 wdt:P136 ?genre .
}
```

Let's execute this query to get the results.

[Tool]
Got 3 rows and 1 column:
| ?genre                              |
| ----------------------------------- |
| impressionist music (wd:Q105697765) |
| opera (wd:Q1344)                    |
| classical music (wd:Q9730)          |

[Assistant]
The SPARQL query returned the following musical genres associated with Claude Debussy:

1. Impressionist music (wd:Q105697765)
2. Opera (wd:Q1344)
3. Classical music (wd:Q9730)

Therefore, Claude Debussy played impressionist music, opera, and classical music.

Let's finalize the answer with the SPARQL query and the result.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?case ?caseLabel WHERE {
  ?case wdt:P31 wd:Q19692072 .
  OPTIONAL {
    ?case rdfs:label ?caseLabel .
    FILTER ( LANG( ?caseLabel ) = "en" )
  }
}
LIMIT 10

Got 10 rows and 2 columns:
| ?case                                                       | ?caseLabel                                  |
| ----------------------------------------------------------- | ------------------------------------------- |
| Trump v. New York (wd:Q100496432)                           | Trump v. New York                           |
| New Hampshire v. Maine (wd:Q101093598)                      | New Hampshire v. Maine                      |
| Roman Catholic Diocese of Brooklyn v. Cuomo (wd:Q102532067) | Roman Catholic Diocese of Brooklyn v. Cuomo |
| EEOC v. Wyoming (wd:Q104672335)                             | EEOC v. Wyoming                             |
| Bogan v. Scott-Harris (wd:Q104821362)                       | Bogan v. Scott-Harris                       |
| Tyler v. Cain (wd:Q104822251)                               | Tyler v. Cain                               |
| Office of Senator Mark Dayton v. Hanson (wd:Q104822282)     | Office of Senator Mark Dayton v. Hanson     |
| Kentucky Retirement Systems v. EEOC (wd:Q104829763)         | Kentucky Retirement Systems v. EEOC         |
| Beck v. Washington (wd:Q104830314)                          | Beck v. Washington                          |
| Allen v. State Bd. of Elections (wd:Q104830455)             | Allen v. State Bd. of Elections             |

The Supreme Court cases include: 1. Trump v. New York (wd:Q100496432), 2. New Hampshire v. Maine (wd:Q101093598), 3. Roman Catholic Diocese of Brooklyn v. Cuomo (wd:Q102532067), 4. EEOC v. Wyoming (wd:Q104672335), 5. Bogan v. Scott-Harris (wd:Q104821362), 6. Tyler v. Cain (wd:Q104822251), 7. Office of Senator Mark Dayton v. Hanson (wd:Q104822282), 8. Kentucky Retirement Systems v. EEOC (wd:Q104829763), 9. Beck v. Washington (wd:Q104830314), 10. Allen v. State Bd. of Elections (wd:Q104830455).

[System]
You are a question answering assistant. Your job is to generate a SPARQL query to answer a given user question.

You have access to the following knowledge graphs:
wikidata at https://qlever.cs.uni-freiburg.de/api/wikidata-sebastian

You can use the following SPARQL prefixes implicitly in all functions:
cc: <http://creativecommons.org/ns#
data: <https://www.wikidata.org/wiki/Special:EntityData/
dct: <http://purl.org/dc/terms/
geo: <http://www.opengis.net/ont/geosparql#
geof: <http://www.opengis.net/def/function/geosparql/
imdb: <https://www.imdb.com/
math: <http://www.w3.org/2005/xpath-functions/math#
ontolex: <http://www.w3.org/ns/lemon/ontolex#
owl: <http://www.w3.org/2002/07/owl#
p: <http://www.wikidata.org/prop/
pq: <http://www.wikidata.org/prop/qualifier/
pqn: <http://www.wikidata.org/prop/qualifier/value-normalized/
pqv: <http://www.wikidata.org/prop/qualifier/value/
pr: <http://www.wikidata.org/prop/reference/
prn: <http://www.wikidata.org/prop/reference/value-normalized/
prov: <http://www.w3.org/ns/prov#
prv: <http://www.wikidata.org/prop/reference/value/
ps: <http://www.wikidata.org/prop/statement/
psn: <http://www.wikidata.org/prop/statement/value-normalized/
psv: <http://www.wikidata.org/prop/statement/value/
qfn: <http://qlever.cs.uni-freiburg.de/function#
ql: <http://qlever.cs.uni-freiburg.de/builtin-functions/
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
ref: <http://www.wikidata.org/reference/
s: <http://www.wikidata.org/entity/statement/
schema: <http://schema.org/
skos: <http://www.w3.org/2004/02/skos/core#
v: <http://www.wikidata.org/value/
wd: <http://www.wikidata.org/entity/
wdno: <http://www.wikidata.org/prop/novalue/
wdt: <http://www.wikidata.org/prop/direct/
wdtn: <http://www.wikidata.org/prop/direct-normalized/
wikibase: <http://wikiba.se/ontology#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
dc: <http://purl.org/dc/elements/1.1/
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
qb: <http://purl.org/linked-data/cube#
void: <http://rdfs.org/ns/void#

You should follow a step-by-step approach to generate the SPARQL query:
1. Determine possible entities and properties implied by the user question.
2. Use the functions to search for entities and properties in the knowledge graphs. Where applicable, constrain the searches with already identified entities and properties.
3. Gradually build up the SPARQL query using the identified entities and properties. Start with simple queries and add more complexity as needed. Use the execute function to get feedback on intermediate queries. You can go back and forth between knowledge graph searches and SPARQL query executions to refine the query.
4. Use the answer or cancel function to finalize your answer and stop the generation process.

Additional rules:
1. Explain your thought process before and after each step and function call.
2. Do not assume or make up any entities or properties without verifying them with the knowledge graphs and functions.
3. The SPARQL query should always return the actual identifiers / IRIs of the items in its result. It additionally may return labels or other human-readable information, but they are optional and should be put within optional clauses unless explicitly requested by the user.
4. Do not stop early if there are still obvious improvements to be made to the SPARQL query. For example, keep refining your SPARQL query if its result contains irrelevant items or is missing items you expected.
5. Do not perform additional computation (e.g. filtering, sorting, calculations) on the result of the SPARQL query to determine the answer. All computation should be done solely within SPARQL.
6. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
7. Do not use 'SERVICE wikibase:label { bd:serviceParam wikibase:language ...' in SPARQL queries. It is not SPARQL standard and unsupported by the used QLever SPARQL endpoints. Use rdfs:label or similar properties to get labels instead.

[User] what are the supreme court cases?

[Assistant]
To answer the question about Supreme Court cases, we need to identify the relevant entities and properties in the knowledge graph. We will start by searching for entities related to "Supreme Court cases" and then look for properties that might link these entities to specific cases.

First, let's search for entities related to "Supreme Court cases."

[Tool]
Top 10 entity alternatives:
1. United States Supreme Court decision, also known as Supreme Court decision, US Supreme Court cases, US Supreme Court case, United States Supreme Court case, United States Supreme Court cases (wd:Q19692072): decision rendered by the highest court of the United States
2. WikiProject U.S. Supreme Court cases (wd:Q21831004): Wikimedia subject-area collaboration
3. Supreme Court decides reimbursement cases (wd:Q74821390): scientific article published on 01 December 1994
4. Supreme Court: Church-State Cases, 2001-2002 Term (wd:Q59668777): Congressional Research Service report
5. Supreme Court decides important labor cases (wd:Q77528440): scientific article published on 01 October 1983
6. Landmark Cases: 12 Historic Supreme Court Decisions (wd:Q23763186): television series
7. New Jersey's Supreme Court “Deregulates” Right-to-Die Cases (wd:Q68329273): scientific article published on 01 May 1988
8. U.S. Supreme Court determines labor law cases (wd:Q77520488): scientific article published on 01 July 1983
9. Supreme Court refuses to hear antitrust cases (wd:Q67954489): scientific article published on 01 November 1988
10. Supreme Court to hear abortion, right-to-die cases (wd:Q69698450): scientific article published on 01 September 1989

[Assistant]
From the search results, the most relevant entity is "United States Supreme Court decision" (wd:Q19692072), which refers to decisions rendered by the highest court of the United States. This entity is likely to be linked to specific Supreme Court cases.

Next, we need to find properties that link this entity to specific cases. Let's search for properties related to "cases" or "decisions" to see if we can find a suitable property.

[Tool]
Top 10 property alternatives:
1. has grammatical case, also known as cases, has case (wd:P2989 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): case used in this language, has part(s)
2. number of cases, also known as case number, total number of cases, occurrences reported, number of occurrences, number of infections (wd:P1603 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): cumulative number of confirmed, probable and suspected occurrences
3. cases consolidated (wd:P7162 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): what other cases this case consolidated
4. Caselaw Access Project case ID, also known as CAP ID, case ID, case.law ID (wd:P6090 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): identifier for single case in Caselaw Access Project
5. requires grammatical feature, also known as grammatical feature required, required grammatical feature, requires case, requires mood, uses case (wd:P5713 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): grammatical features required or generally used with lexeme or form of lexeme
6. legal citation of this text, also known as case citation, statute citation, regulation citation, parallel citation, neutral citation (wd:P1031 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): legal citation of legislation or a court decision.
7. has index case, also known as index case, patient zero, primary case (wd:P1660 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): initial patient in the population of an epidemiological investigation
8. generalization of, also known as therefore, so, implies, has special case, has conclusion (wd:P7719 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): statement logically implied by the subject or a similar algorithm/function whose domain is a subset of the subject's domain
9. index case of (wd:P1677 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): primary case, patient zero: initial patient in the population of an epidemiological investigation
10. part of the series, also known as series, video game series, subseries, part of series, listed in (wd:P179 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): series which contains the subject, part of

[Assistant]
From the search results, the most relevant property is "cases consolidated" (wd:P7162), which links a case to other cases it has consolidated. However, this property might not be the best fit for our purpose, as we are looking for individual Supreme Court cases.

Another relevant property is "legal citation of this text" (wd:P1031), which provides the legal citation for a court decision. This property seems more appropriate for linking to specific Supreme Court cases.

Let's try to list some triples where the subject is "United States Supreme Court decision" (wd:Q19692072) and the property is "legal citation of this text" (wd:P1031) to see if we can find specific cases.

[Tool] Got no rows and 3 columns

[Assistant]
It appears that there are no direct triples linking "United States Supreme Court decision" (wd:Q19692072) to specific cases using the "legal citation of this text" (wd:P1031) property. This suggests that we might need to take a different approach.

Instead, let's try to find specific Supreme Court cases by searching for entities that are instances of "United States Supreme Court decision" (wd:Q19692072). We can use the `wdt:P31` property, which indicates the instance of a class.

Let's list some triples where the object is "United States Supreme Court decision" (wd:Q19692072) and the property is "instance of" (wdt:P31).

[Tool]
Got 19,703 rows and 3 columns, showing the first 10 rows below:
| subject                                                      | property              | object                                              |
| ------------------------------------------------------------ | --------------------- | --------------------------------------------------- |
| Roe v. Wade (wd:Q300950)                                     | instance of (wdt:P31) | United States Supreme Court decision (wd:Q19692072) |
| Brown v. Board of Education (wd:Q875738)                     | instance of (wdt:P31) | United States Supreme Court decision (wd:Q19692072) |
| Dred Scott v. Sandford (wd:Q690462)                          | instance of (wdt:P31) | United States Supreme Court decision (wd:Q19692072) |
| Dobbs v. Jackson Women's Health Organization (wd:Q107058269) | instance of (wdt:P31) | United States Supreme Court decision (wd:Q19692072) |
| Marbury v. Madison (wd:Q538242)                              | instance of (wdt:P31) | United States Supreme Court decision (wd:Q19692072) |
| Obergefell v. Hodges (wd:Q19866992)                          | instance of (wdt:P31) | United States Supreme Court decision (wd:Q19692072) |
| Loving v. Virginia (wd:Q1345835)                             | instance of (wdt:P31) | United States Supreme Court decision (wd:Q19692072) |
| Plessy v. Ferguson (wd:Q656660)                              | instance of (wdt:P31) | United States Supreme Court decision (wd:Q19692072) |
| Lawrence v. Texas (wd:Q1157002)                              | instance of (wdt:P31) | United States Supreme Court decision (wd:Q19692072) |
| Miranda v. Arizona (wd:Q1642249)                             | instance of (wdt:P31) | United States Supreme Court decision (wd:Q19692072) |
| ...                                                          | ...                   | ...                                                 |

[Assistant]
We have successfully found several instances of "United States Supreme Court decision" (wd:Q19692072). These include well-known cases such as Roe v. Wade, Brown v. Board of Education, and Marbury v. Madison.

Now, let's construct a SPARQL query to retrieve a list of these Supreme Court cases. We will also include their labels for better readability.

Here is the SPARQL query:

```sparql
SELECT ?case ?caseLabel
WHERE {
  ?case wdt:P31 wd:Q19692072 .
  OPTIONAL { ?case rdfs:label ?caseLabel . FILTER(LANG(?caseLabel) = "en") }
}
LIMIT 10
```

This query selects the IRI of the case and its label (if available in English). Let's execute this query to see the results.

[Tool]
Got 10 rows and 2 columns:
| ?case                                                       | ?caseLabel                                  |
| ----------------------------------------------------------- | ------------------------------------------- |
| Trump v. New York (wd:Q100496432)                           | Trump v. New York                           |
| New Hampshire v. Maine (wd:Q101093598)                      | New Hampshire v. Maine                      |
| Roman Catholic Diocese of Brooklyn v. Cuomo (wd:Q102532067) | Roman Catholic Diocese of Brooklyn v. Cuomo |
| EEOC v. Wyoming (wd:Q104672335)                             | EEOC v. Wyoming                             |
| Bogan v. Scott-Harris (wd:Q104821362)                       | Bogan v. Scott-Harris                       |
| Tyler v. Cain (wd:Q104822251)                               | Tyler v. Cain                               |
| Office of Senator Mark Dayton v. Hanson (wd:Q104822282)     | Office of Senator Mark Dayton v. Hanson     |
| Kentucky Retirement Systems v. EEOC (wd:Q104829763)         | Kentucky Retirement Systems v. EEOC         |
| Beck v. Washington (wd:Q104830314)                          | Beck v. Washington                          |
| Allen v. State Bd. of Elections (wd:Q104830455)             | Allen v. State Bd. of Elections             |

[Assistant]
The SPARQL query has successfully returned a list of Supreme Court cases along with their labels. Here are the top 10 results:

1. **Trump v. New York** (wd:Q100496432)
2. **New Hampshire v. Maine** (wd:Q101093598)
3. **Roman Catholic Diocese of Brooklyn v. Cuomo** (wd:Q102532067)
4. **EEOC v. Wyoming** (wd:Q104672335)
5. **Bogan v. Scott-Harris** (wd:Q104821362)
6. **Tyler v. Cain** (wd:Q104822251)
7. **Office of Senator Mark Dayton v. Hanson** (wd:Q104822282)
8. **Kentucky Retirement Systems v. EEOC** (wd:Q104829763)
9. **Beck v. Washington** (wd:Q104830314)
10. **Allen v. State Bd. of Elections** (wd:Q104830455)

These are some of the Supreme Court cases that have been recorded in the knowledge graph.

Here is the final SPARQL query and the answer to the user's question:

```sparql
SELECT ?case ?caseLabel
WHERE {
  ?case wdt:P31 wd:Q19692072 .
  OPTIONAL { ?case rdfs:label ?caseLabel . FILTER(LANG(?caseLabel) = "en") }
}
LIMIT 10
```

The answer to the question "What are the Supreme Court cases?" is the list of cases provided above.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?currency WHERE {
  wd:Q244 wdt:P38 ?currency
}

The currency to take to Barbados is the Barbadian dollar (wd:Q194351).

[System]
You are a question answering assistant. Your job is to generate a SPARQL query to answer a given user question.

You have access to the following knowledge graphs:
wikidata at https://qlever.cs.uni-freiburg.de/api/wikidata-sebastian

You can use the following SPARQL prefixes implicitly in all functions:
cc: <http://creativecommons.org/ns#
data: <https://www.wikidata.org/wiki/Special:EntityData/
dct: <http://purl.org/dc/terms/
geo: <http://www.opengis.net/ont/geosparql#
geof: <http://www.opengis.net/def/function/geosparql/
imdb: <https://www.imdb.com/
math: <http://www.w3.org/2005/xpath-functions/math#
ontolex: <http://www.w3.org/ns/lemon/ontolex#
owl: <http://www.w3.org/2002/07/owl#
p: <http://www.wikidata.org/prop/
pq: <http://www.wikidata.org/prop/qualifier/
pqn: <http://www.wikidata.org/prop/qualifier/value-normalized/
pqv: <http://www.wikidata.org/prop/qualifier/value/
pr: <http://www.wikidata.org/prop/reference/
prn: <http://www.wikidata.org/prop/reference/value-normalized/
prov: <http://www.w3.org/ns/prov#
prv: <http://www.wikidata.org/prop/reference/value/
ps: <http://www.wikidata.org/prop/statement/
psn: <http://www.wikidata.org/prop/statement/value-normalized/
psv: <http://www.wikidata.org/prop/statement/value/
qfn: <http://qlever.cs.uni-freiburg.de/function#
ql: <http://qlever.cs.uni-freiburg.de/builtin-functions/
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
ref: <http://www.wikidata.org/reference/
s: <http://www.wikidata.org/entity/statement/
schema: <http://schema.org/
skos: <http://www.w3.org/2004/02/skos/core#
v: <http://www.wikidata.org/value/
wd: <http://www.wikidata.org/entity/
wdno: <http://www.wikidata.org/prop/novalue/
wdt: <http://www.wikidata.org/prop/direct/
wdtn: <http://www.wikidata.org/prop/direct-normalized/
wikibase: <http://wikiba.se/ontology#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
dc: <http://purl.org/dc/elements/1.1/
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
qb: <http://purl.org/linked-data/cube#
void: <http://rdfs.org/ns/void#

You should follow a step-by-step approach to generate the SPARQL query:
1. Determine possible entities and properties implied by the user question.
2. Use the functions to search for entities and properties in the knowledge graphs. Where applicable, constrain the searches with already identified entities and properties.
3. Gradually build up the SPARQL query using the identified entities and properties. Start with simple queries and add more complexity as needed. Use the execute function to get feedback on intermediate queries. You can go back and forth between knowledge graph searches and SPARQL query executions to refine the query.
4. Use the answer or cancel function to finalize your answer and stop the generation process.

Additional rules:
1. Explain your thought process before and after each step and function call.
2. Do not assume or make up any entities or properties without verifying them with the knowledge graphs and functions.
3. The SPARQL query should always return the actual identifiers / IRIs of the items in its result. It additionally may return labels or other human-readable information, but they are optional and should be put within optional clauses unless explicitly requested by the user.
4. Do not stop early if there are still obvious improvements to be made to the SPARQL query. For example, keep refining your SPARQL query if its result contains irrelevant items or is missing items you expected.
5. Do not perform additional computation (e.g. filtering, sorting, calculations) on the result of the SPARQL query to determine the answer. All computation should be done solely within SPARQL.
6. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
7. Do not use 'SERVICE wikibase:label { bd:serviceParam wikibase:language ...' in SPARQL queries. It is not SPARQL standard and unsupported by the used QLever SPARQL endpoints. Use rdfs:label or similar properties to get labels instead.

[User] what currency to take to barbados?

[Assistant] To determine the currency to take to Barbados, we need to find out the official currency of Barbados. Let's start by searching for the entity "Barbados" in the knowledge graph.

[Tool]
Top 10 entity alternatives:
1. Barbados, also known as 🇧🇧, BAR, Barbadoes, bb, BB (wd:Q244): island nation in the Caribbean
2. Barbados (wd:Q807306): Swedish dansband
3. Barbados (wd:Q4858589): single by Typically Tropical
4. Barbados (wd:Q4858586): musical composition by Charlie Parker
5. Barbados (wd:Q10426260): 1995 studio album by Barbados
6. Barbados (wd:Q16950300): song
7. Barbados (wd:Q2649101): plantation in Surinam
8. Barbados (wd:Q98687855): jazz blues by Charlie Parker
9. Barbados (wd:Q74608067): scientific article published on 01 June 1987
10. Barbados (wd:Q59508554): painting by J.E.H. MacDonald

[Assistant] The top result for "Barbados" is the island nation in the Caribbean, which is what we are looking for. Now, let's find the property that represents the official currency of a country.

[Tool]
Top 10 property alternatives:
1. currency, also known as base currency, used money (wd:P38 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): currency used by item, uses
2. central bank/issuer, also known as authority, issuer, issuing authority (wd:P562 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): central bank or other issuing authority for the currency
3. ISO 4217 code, also known as currency code, ISO 4217, ISO 4217 alphabetic code, ISO currency code (wd:P498 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): identifier for a currency per ISO 4217
4. official language, also known as language spoken, spoken in, speaking, language official (wd:P37 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): language designated as official by this item, language used
5. official symbol, also known as symbol, state tartan, official tartan, floral emblem, official emblem (wd:P2238 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): official symbol of an organisation
6. nominal GDP, also known as GDP, GDP, nominal, gross domestic product, nominal GDP, nominal gross domestic product (wd:P2131 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): market value of all officially recognized final goods and services produced within a country in a given period of time
7. house publication, also known as has mouthpiece, publishes, publication, organization publication, organisation publication (wd:P2813 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): media that speaks for an organization or a movement, and that is usually edited by its members
8. native label, also known as official title, original label, original language label, original language name, original language title (wd:P1705 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): label for the items in their official language  or their original language, name
9. official name, also known as long title, name, official title, registered name, full title (wd:P1448 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): official name of the subject in its official language(s), name
10. central bank (wd:P1304 as prv/pq/pqn/ps/psn/psv/wdt/pr/wdtn/prn/pqv/p): country's central bank

[Assistant]
The property "currency" (P38) seems to be the most relevant for finding the official currency of a country. Let's use this property to find the currency of Barbados.

I will now construct a simple SPARQL query to retrieve the currency of Barbados.

[Tool]
Got 1 row and 1 column:
| ?currency                     |
| ----------------------------- |
| Barbadian dollar (wd:Q194351) |

[Assistant]
The official currency of Barbados is the Barbadian dollar. To provide a complete answer, I will include the IRI of the Barbadian dollar in the final SPARQL query.

Here is the final SPARQL query:

```sparql
SELECT ?currency WHERE {
  wd:Q244 wdt:P38 ?currency
}
```

And the answer to the question is:

The currency to take to Barbados is the **Barbadian dollar** (wd:Q194351).

Now, I will submit the final answer.

[Tool] Stopped generation process