PREFIX ps: <http://www.wikidata.org/prop/statement/>
PREFIX p: <http://www.wikidata.org/prop/>
PREFIX pq: <http://www.wikidata.org/prop/qualifier/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT DISTINCT ?team ?startDate ?endDate WHERE {
  wd:Q119562 p:P54 ?statement .
  ?statement ps:P54 ?team .
  OPTIONAL {
    ?statement pq:P580 ?startDate .
  }
  OPTIONAL {
    ?statement pq:P582 ?endDate .
  }
}
ORDER BY DESC ( ?endDate )

The most recent team Aguero played for is FC Barcelona (wd:Q7156).

[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 team did aguero play for?

[Assistant] Let's start by looking at some similar examples.

[Tool]
Example 1:
Question:
what team does sergio aguero play for?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q119562 wdt:P54 ?x .
}

Using entities:
Sergio Agüero, also known as Sergio Leonel Agüero del Castillo (wd:Q119562): Argentine association football player, association football player, online streamer, businessperson

Using properties:
member of sports team, also known as plays for, played for, sport team, team, team played for (wd:P54 as wdt): sports teams or clubs that the subject represents or represented, member of

Example 2:
Question:
what club did aguero play for before man city?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q119562 p:P54 ?p .
  ?p ps:P54 ?x ; pq:P582 ?y .
  ?x wdt:P31 wd:Q476028 .
  wd:Q1920 p:P54 ?q .
  ?q ps:P54 wd:Q50602 ; pq:P580 ?z .
  FILTER ( ?y <= ?z )
}

Using entities:
Sergio Agüero, also known as Sergio Leonel Agüero del Castillo (wd:Q119562): Argentine association football player, association football player, online streamer, businessperson
Samir Nasri (wd:Q1920): French footballer (born 1987), association football player
association football club, also known as association soccer club, F.C., FC, football club, soccer club (wd:Q476028): sports club devoted to association football (soccer)
Manchester City F.C., also known as The Sky Blues, City, The Citizens, The Blues, Sky Blues (wd:Q50602): association football club in Manchester, England

Using properties:
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
member of sports team, also known as plays for, played for, sport team, team, team played for (wd:P54 as ps/p): sports teams or clubs that the subject represents or represented, member of
start time, also known as start date, since, valid, join time, joined (wd:P580 as pq): time an entity begins to exist or a statement starts being valid, point in time
end time, also known as ends, stop time, endtime, fall date, ineffective (wd:P582 as pq): moment when an entity ceases to exist and a statement stops being entirely valid or no longer be true, point in time

Example 3:
Question:
what team did kaka play for in 2009?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q531814 p:P54 ?p .
  ?p ps:P54 ?x ; pq:P580 ?y ; pq:P582 ?z .
  FILTER ( ( ?y < "2010-01-01T00:00:00Z" ^^ xsd:dateTime ) && ( ?z >= "2009-01-01T00:00:00Z" ^^ xsd:dateTime ) )
}

Using entities:
Kaká, also known as Ricardo Kaká (wd:Q531814): Brazilian association football player, born 1982, association football player, diplomat, writer

Using properties:
member of sports team, also known as plays for, played for, sport team, team, team played for (wd:P54 as ps/p): sports teams or clubs that the subject represents or represented, member of
start time, also known as start date, since, valid, join time, joined (wd:P580 as pq): time an entity begins to exist or a statement starts being valid, point in time
end time, also known as ends, stop time, endtime, fall date, ineffective (wd:P582 as pq): moment when an entity ceases to exist and a statement stops being entirely valid or no longer be true, point in time

[Assistant] From the examples, we can see that the property `P54` is used to represent the teams that a player has been a member of. Let's first identify the entity for Aguero.

[Tool]
Top 10 entity alternatives:
1. Agüero, also known as Agüero, Huesca (wd:Q399683): municipality in the province of Huesca, Spain
2. Agüero (wd:Q3296607): metro station in Buenos Aires
3. Agüero (wd:Q24013451): in Huesca province, Spain
4. Agüero (wd:Q61754610): family name
5. Aguero (wd:Q21491256): family name
6. Blanka Aguero, also known as Aguero, B.Shaw, Blanka Shaw (wd:Q21608757): bryologist, botanist
7. Sergio Agüero, also known as Sergio Leonel Agüero del Castillo (wd:Q119562): Argentine association football player, association football player, online streamer, businessperson
8. Taismary Agüero (wd:Q2590488): beach volleyball player, volleyball player
9. Fernando Agüero (wd:Q888684): politician (1920-2011), politician
10. Pablo Agüero (wd:Q3359955): Argentine film director, film director, screenwriter, director

[Assistant]
The correct entity for Aguero is `wd:Q119562`. Now let's use the property `P54` to find out which teams he has played for.

I'll start with a simple query to list all the teams Aguero has played for.

[Tool]
Got 8 rows and 1 column:
| team                                                           |
| -------------------------------------------------------------- |
| Argentina national under-20 football team (wd:Q1187790)        |
| Club Atlético Independiente (wd:Q214978)                       |
| Argentina national under-23 football team (wd:Q2462207)        |
| Argentina national under-17 football team (wd:Q2902690)        |
| Manchester City F.C. (wd:Q50602)                               |
| FC Barcelona (wd:Q7156)                                        |
| Argentina men's national association football team (wd:Q79800) |
| Atlético de Madrid (wd:Q8701)                                  |

[Assistant]
The query returned all the teams that Aguero has played for throughout his career. However, the user might be interested in knowing the specific team he played for during a certain period or the last team he played for. To provide a more precise answer, I will modify the query to include the start and end dates of his tenure with each team.

Let's update the query to include the start and end dates of his tenures with the teams.

[Tool]
Got 8 rows and 3 columns:
| team                                                           | startDate                           | endDate                             |
| -------------------------------------------------------------- | ----------------------------------- | ----------------------------------- |
| FC Barcelona (wd:Q7156)                                        | 2021-07-01T00:00:00Z (xsd:dateTime) | 2021-12-01T00:00:00Z (xsd:dateTime) |
| Manchester City F.C. (wd:Q50602)                               | 2011-01-01T00:00:00Z (xsd:dateTime) | 2021-07-01T00:00:00Z (xsd:dateTime) |
| Argentina men's national association football team (wd:Q79800) | 2006-01-01T00:00:00Z (xsd:dateTime) | 2021-01-01T00:00:00Z (xsd:dateTime) |
| Atlético de Madrid (wd:Q8701)                                  | 2006-01-01T00:00:00Z (xsd:dateTime) | 2011-01-01T00:00:00Z (xsd:dateTime) |
| Argentina national under-23 football team (wd:Q2462207)        | 2008-01-01T00:00:00Z (xsd:dateTime) | 2008-01-01T00:00:00Z (xsd:dateTime) |
| Argentina national under-20 football team (wd:Q1187790)        | 2005-01-01T00:00:00Z (xsd:dateTime) | 2007-01-01T00:00:00Z (xsd:dateTime) |
| Club Atlético Independiente (wd:Q214978)                       | 2003-01-01T00:00:00Z (xsd:dateTime) | 2006-01-01T00:00:00Z (xsd:dateTime) |
| Argentina national under-17 football team (wd:Q2902690)        | 2004-01-01T00:00:00Z (xsd:dateTime) | 2004-01-01T00:00:00Z (xsd:dateTime) |

[Assistant]
The updated query provides a more detailed view of the teams Aguero has played for, along with the start and end dates of his tenures. Here are the teams and the periods he played for them:

1. **FC Barcelona** (wd:Q7156) - July 1, 2021 to December 1, 2021
2. **Manchester City F.C.** (wd:Q50602) - January 1, 2011 to July 1, 2021
3. **Argentina men's national association football team** (wd:Q79800) - January 1, 2006 to January 1, 2021
4. **Atlético de Madrid** (wd:Q8701) - January 1, 2006 to January 1, 2011
5. **Argentina national under-23 football team** (wd:Q2462207) - January 1, 2008 to January 1, 2008
6. **Argentina national under-20 football team** (wd:Q1187790) - January 1, 2005 to January 1, 2007
7. **Club Atlético Independiente** (wd:Q214978) - January 1, 2003 to January 1, 2006
8. **Argentina national under-17 football team** (wd:Q2902690) - January 1, 2004 to January 1, 2004

Since the user's question is general and does not specify a particular time period, the most recent team Aguero played for is **FC Barcelona**.

Here is the final SPARQL query and answer:

```sparql
SELECT DISTINCT ?team ?startDate ?endDate WHERE {
  wd:Q119562 p:P54 ?statement .
  ?statement ps:P54 ?team .
  OPTIONAL { ?statement pq:P580 ?startDate . }
  OPTIONAL { ?statement pq:P582 ?endDate . }
} ORDER BY DESC(?endDate)
```

Answer: The most recent team Aguero played for is **FC Barcelona** (wd:Q7156).

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT DISTINCT ?language WHERE {
  wd:Q155 wdt:P37 ?language .
}

Portuguese (wd:Q5146)

[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 official language of brazil now?

[Assistant] Let's start by looking at some similar examples.

[Tool]
Example 1:
Question:
what is the language spoken in brazil?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q155 wdt:P37 ?x .
}

Using entities:
Brazil, also known as br, Brasil, Federative Republic of Brazil (wd:Q155): country in South America

Using properties:
official language, also known as language spoken, spoken in, speaking, language official (wd:P37 as wdt): language designated as official by this item, language used

Example 2:
Question:
what language brazil use?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q155 wdt:P37 ?x .
}

Using entities:
Brazil, also known as br, Brasil, Federative Republic of Brazil (wd:Q155): country in South America

Using properties:
official language, also known as language spoken, spoken in, speaking, language official (wd:P37 as wdt): language designated as official by this item, language used

Example 3:
Question:
what main language do they speak in brazil?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q155 wdt:P37 ?x .
}

Using entities:
Brazil, also known as br, Brasil, Federative Republic of Brazil (wd:Q155): country in South America

Using properties:
official language, also known as language spoken, spoken in, speaking, language official (wd:P37 as wdt): language designated as official by this item, language used

[Assistant] Based on the examples, it seems like we need to find the official language of Brazil. Let's confirm the entity for Brazil and the property for official language.

[Tool]
Top 10 entity alternatives:
1. Brazil, also known as Federative Republic of Brazil, Brasil, br (wd:Q155): country in South America
2. Brazil, also known as br, Brasil, Federative Republic of Brazil (wd:Q155): country in South America
3. Brazil (wd:Q25057): 1985 dystopian film by Terry Gilliam
4. Brazil, also known as Brazil, Indiana, Brazil, IN (wd:Q1998364): city in Clay County, Indiana, United States
5. State of Brazil, also known as Brazil, Colonial Brazil, Portuguese Brazil (wd:Q11876909): state of the Portuguese Empire
6. Brazil, also known as Stars and Guitars (wd:Q4958616): 1944 film by Joseph Santley
7. Brazil, also known as Brazil, IA (wd:Q17511873): human settlement in Appanoose County, Iowa, United States
8. Viceroyalty of Brazil, also known as Brasil, Brazil (wd:Q2920081): Portuguese viceroyalty in Brazil 1763-1815
9. Brazil (wd:Q13581616): 1991 song by Bebi Dol
10. Brazil (wd:Q4095706): 1994 novel by John Updike

[Assistant] The first result is the correct entity for Brazil. Now let's confirm the property for official language.

[Tool]
Top 10 property alternatives:
1. official language, also known as language spoken, spoken in, speaking, language official (wd:P37 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): language designated as official by this item, language used
2. native label, also known as official title, original label, original language label, original language name, original language title (wd:P1705 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): label for the items in their official language  or their original language, name
3. Ethnologue language status, also known as EGIDS, language status (wd:P3823 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): language status identifier by Ethnologue.com using EGIDS scale
4. language regulatory body, also known as language authority, regulatory body (wd:P1018 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): regulatory body of a language
5. languages spoken, written or signed, also known as language of expression, language, wrote language, writes language, uses language (wd:P1412 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): language(s) that a person or a people speaks, writes or signs, including the native language(s)
6. native language, also known as first language, L1 speaker of, language native, mother tongue (wd:P103 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): language or languages a person has learned from early childhood, languages spoken, written or signed
7. language used, also known as languages used, medium of instruction, used language, uses language, working language (wd:P2936 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): language widely used (spoken or written) in this place or at this event or organisation
8. language of work or name, also known as language of the reference, language of URL, language of website, language of work, named in language (wd:P407 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): language associated with this creative work (such as books, show...guage" (P103) and "languages spoken, written or signed" (P1412))
9. programmed in, also known as language, programming language, written in (wd:P277 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): the programming language(s) in which the software is developed
10. writing language, also known as languages of writings, languages written, language written, writing languages, language of works (wd:P6886 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): language in which the writer has written their work, languages spoken, written or signed

[Assistant]
The first result is the correct property for official language. We can now construct our SPARQL query to find the official language of Brazil.

Let's run the query.

[Tool]
Got 1 row and 1 column:
| language              |
| --------------------- |
| Portuguese (wd:Q5146) |

[Assistant]
The official language of Brazil is Portuguese.

I will now provide the final answer.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT DISTINCT ?zipCode WHERE {
  wd:Q653636 wdt:P281 ?zipCode .
}

The zip codes for Morgantown, WV are 26501, 26504, and 26506.

[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 zip code for morgantown wv?

[Assistant] Let's start by looking at some similar examples.

[Tool]
Example 1:
Question:
what is the zip code for clarksville tn?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q328941 wdt:P281 ?x .
}

Using entities:
Clarksville, also known as Clarksville, TN, Clarksville, Tennessee (wd:Q328941): city in Tennessee, United States

Using properties:
postal code, also known as pincode, PIN Code, postal index number, post code, postcode (wd:P281 as wdt): identifier assigned by postal authorities for the subject area or building

Example 2:
Question:
what is zip code for chicago illinois?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q1297 wdt:P281 ?x .
}

Using entities:
Chicago, also known as The Second City, The Queen of the West, The Windy City, City of Broad Shoulders, City by the Lake (wd:Q1297): city and county seat of Cook County, Illinois, United States of America

Using properties:
postal code, also known as pincode, PIN Code, postal index number, post code, postcode (wd:P281 as wdt): identifier assigned by postal authorities for the subject area or building

Example 3:
Question:
what is the zip code for cordova tn?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q5170040 wdt:P281 ?x .
}

Using entities:
Cordova, also known as Cordova, Tennessee (wd:Q5170040): unincorporated community in Shelby County, Tennessee

Using properties:
postal code, also known as pincode, PIN Code, postal index number, post code, postcode (wd:P281 as wdt): identifier assigned by postal authorities for the subject area or building

[Assistant] Based on these examples, we need to identify the entity for Morgantown, WV, and then use the property `postal code` (P281) to find the zip code.

[Tool]
Top 10 entity alternatives:
1. Morgantown, also known as Morgantown, West Virginia, Morgantown, WV (wd:Q653636): city in and county seat of Monongalia County, West Virginia, United States
2. Morgantown urban area, also known as Morgantown, WV, Morgantown, WV Urban Area (wd:Q125805682): urban area in West Virginia
3. Morgantown metropolitan area, also known as Morgantown, WV Metropolitan Statistical Area, Morgantown, WV MSA, Morgantown, WV metropolitan area, Morgantown MSA, Morgantown metropolitan statistical area (wd:Q17081833): metropolitan area in West Virginia, United States
4. Morgantown, also known as Morgantown, Kentucky, Morgantown, KY (wd:Q2555567): county seat in Kentucky
5. Morgantown, also known as Morgantown, Indiana (wd:Q644950): human settlement in Morgan County, Indiana, United States of America
6. Morgantown, also known as Morgantown, PA, Morgantown, Pennsylvania (wd:Q3960025): census-designated place in Berks and Lancaster counties in Pennsylvania, United States
7. Morgantown, also known as Morgantown, Adams County, Mississippi, Morgantown, MS (wd:Q13580455): census-designated place in Adams County, Mississippi
8. Morgantown, also known as Morgantown, MS (wd:Q3612521): unincorporated community in Marion County, Mississippi
9. Morgantown, also known as Morgantown, Maryland, Morgantown, MD (wd:Q6911981): unincorporated community in Charles County, Maryland
10. Morgantown, also known as Morgantown, MS (wd:Q20658077): human settlement in Oktibbeha County, Mississippi, United States

[Assistant]
The correct entity for Morgantown, WV is `Morgantown, also known as Morgantown, West Virginia, Morgantown, WV (wd:Q653636)`.

Now, let's construct a SPARQL query to find the zip code for Morgantown, WV using the property `postal code` (P281).

[Tool]
Got 4 rows and 1 column:
| zipCode     |
| ----------- |
| 26501       |
| 26501–26508 |
| 26504       |
| 26506       |

[Assistant]
The zip codes for Morgantown, WV are 26501, 26504, and 26506. Note that 26501–26508 is a range that includes multiple zip codes.

Here is the final SPARQL query and answer:

```sparql
SELECT DISTINCT ?zipCode WHERE {
  wd:Q653636 wdt:P281 ?zipCode .
}
```

Answer: The zip codes for Morgantown, WV are 26501, 26504, and 26506.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT DISTINCT ?series ?date WHERE {
  ?series wdt:P1346 wd:Q203008 ; wdt:P585 ?date .
}
ORDER BY DESC ( ?date )
LIMIT 1

The LA Kings last won a playoff series in 2014 during the 2014 Stanley Cup Finals.

[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] when was the last time the la kings won a playoff series?

[Assistant] Let's start by looking at some similar examples.

[Tool]
Example 1:
Question:
when did kings last win stanley cup?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?y wdt:P31 / wdt:P279 * wd:Q211872 ; wdt:P1346 wd:Q203008 ; wdt:P585 ?x .
}
ORDER BY DESC ( ?x )
LIMIT 1

Using entities:
Los Angeles Kings, also known as LA Kings (wd:Q203008): hockey team of the National Hockey League
Stanley Cup, also known as Dominion Hockey Challenge Cup, Lord Stanley's Cup, Lord Stanley's Mug (wd:Q211872): championship trophy awarded annually in the National Hockey League

Using properties:
winner, also known as nominee, prizewinner, titleholder, top dog, victor (wd:P1346 as wdt): 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
subclass of, also known as rdfs:subClassOf, sort of, subcategory of, subgenre of, subtype of (wd:P279 as wdt): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
point in time, also known as at time, as of, year, when, time of event (wd:P585 as wdt): date something took place, existed or a statement was true; for providing time use the "refine date" property (P4241)

Example 2:
Question:
when was the last time the san francisco giants won a world series?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?y wdt:P3450 wd:Q265538 ; wdt:P1346 wd:Q308966 ; wdt:P585 ?x .
}
ORDER BY DESC ( ?x )
LIMIT 1

Using entities:
World Series, also known as Fall Classic (wd:Q265538): Championship of Major League Baseball
San Francisco Giants, also known as Giants, Gothams, New York Giants, New York Gothams, SFG (wd:Q308966): baseball team and Major League Baseball franchise in San Francisco, California, United States

Using properties:
winner, also known as nominee, prizewinner, titleholder, top dog, victor (wd:P1346 as wdt): 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
sports season of league or competition, also known as sport season, seasons of, edition of annual athletics meeting, season of sports tournament, season of sports league (wd:P3450 as wdt): property that shows the competition of which the item is a season. Use P5138 for "season of club or team"., part of
point in time, also known as at time, as of, year, when, time of event (wd:P585 as wdt): date something took place, existed or a statement was true; for providing time use the "refine date" property (P4241)

Example 3:
Question:
when did the lakers win the championship?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?y wdt:P31 / wdt:P279 * wd:Q842375 ; wdt:P1346 wd:Q121783 ; wdt:P585 ?x .
}

Using entities:
Los Angeles Lakers, also known as Lakers, L.A. Lakers, LA Lakers, Minneapolis Lakers, Purple and Gold (wd:Q121783): American professional basketball team
NBA Finals, also known as NBA World Championship Series [until 1987], NBA title, National Basketball Association ...Championship Series [until 1987], BAA World Championship Series [1947-49], Basketball Association of Americ...ld Championship Series [1947-49] (wd:Q842375): championship series of the National Basketball Association, annu...al Basketball Association World Championship Series through 1987

Using properties:
winner, also known as nominee, prizewinner, titleholder, top dog, victor (wd:P1346 as wdt): 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
subclass of, also known as rdfs:subClassOf, sort of, subcategory of, subgenre of, subtype of (wd:P279 as wdt): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
point in time, also known as at time, as of, year, when, time of event (wd:P585 as wdt): date something took place, existed or a statement was true; for providing time use the "refine date" property (P4241)

[Assistant] Based on the examples, we need to identify the entity for the LA Kings and the property for winning a playoff series. Let's start by searching for the LA Kings entity.

[Tool]
Top 10 entity alternatives:
1. Los Angeles Kings, also known as LA Kings (wd:Q203008): hockey team of the National Hockey League
2. LA Kings (wd:Q114547435): episode of Cupcake Wars (S2 E3)
3. Kings Langley, also known as King's Langley, Kings Langley, Hertfordshire, Langley Regina, Langley Regis (wd:Q19807): village and civil parish in Hertfordshire, England
4. Latin Kings, also known as 18st, INCA, Reyes Latinos (wd:Q534297): largest Latino street gang
5. Kingston, also known as Kingston, Louisiana, Kingston, LA (wd:Q6413420): unincorporated community in DeSoto Parish, Louisiana
6. Kings Langley, also known as Kings Langley, New South Wales, Kings Langley, New South Wales, Australia (wd:Q6272013): suburb of Sydney, New South Wales, Australia
7. Tontos, also known as latin kings, Nortenos, Norteñas, Norputos, Norcacas (wd:Q2112443): gente
8. Kings Lake (wd:Q22073712): lake in McLeod County, Minnesota, United States
9. Kings Lake (wd:Q22420428): lake in Algoma District, Ontario, Canada
10. Kings Lake (wd:Q25204379): lake in Codington County, South Dakota, USA

[Assistant]
The correct entity for the LA Kings is `wd:Q203008`. Now, let's find the property for winning a playoff series. Since there isn't a direct property for "winning a playoff series," we can use the property for "winner" (`wdt:P1346`) and look for the specific playoff series events.

Let's list the triples where the LA Kings are the subject and the property is "winner" to see if we can find relevant playoff series wins.

[Tool] Got no rows and 3 columns

[Assistant]
It seems there are no direct triples where the LA Kings are the subject and the property is "winner." This might be because the specific playoff series events are not directly linked to the team in this way. Instead, we can try to find the playoff series events and then check if the LA Kings were the winners.

Let's search for the entity "playoff series" and see if we can find relevant events.

[Tool]
Top 10 entity alternatives:
1. series, also known as infinite sum, mathematical series (wd:Q170198): infinite sum
2. series, also known as epoch (wd:Q312468): sequence of rock depositions defining a chronostratigraphic unit corresponding to a specific epoch on the geologic time scale
3. series circuit, also known as connected in series, connection in series, in series, serial circuit, series (wd:Q13928914): basic arrangement of an electrical circuit that ties the current through the components
4. series (wd:Q3025161): taxonomic rank in botany
5. Playoff (wd:Q7203502): 2011 film by Eran Riklis
6. media mix, also known as media franchise, series, multimedia franchise, media series, franchise (wd:Q18591554): Japanese neologism for a media franchise
7. Playoff (wd:Q7203505): means of breaking tie in golf tournament
8. Séries Plus, also known as Séries+ (wd:Q3511327): Canadian French television channel
9. series (wd:Q12562111): informal lithodemic rank
10. series, also known as record series (wd:Q3511132): term in archival and library science; collection of records grou...eir original function, use, or other relationship to one another

[Assistant] The entity "Playoff" (wd:Q7203502) is not the correct one for our context. We need to find a more specific entity for playoff series in the context of the NHL. Let's try searching for "NHL playoff series" instead.

[Tool]
Top 10 entity alternatives:
1. NHL Stadium Series (wd:Q17487840): Series of regular season outdoor games played in the National Hockey League
2. NHL Global Series (wd:Q59310957): ice hockey event
3. NHL playoff ties (wd:Q128303856): hockey games with no winner
4. NHL Premiere Series (wd:Q74845684)
5. 2015 NHL Stadium Series (wd:Q17021931): National Hockey League season
6. 2014 NHL Stadium Series (wd:Q14542190): outdoor National Hockey League game
7. 2019 NHL Stadium Series (wd:Q48841416): outdoor National Hockey League game
8. 2020 NHL Stadium Series (wd:Q60768080): outdoor National Hockey League game
9. 2017 NHL Stadium Series (wd:Q28119272): Outdoor National Hockey League game
10. 2016 NHL Stadium Series (wd:Q19414416): Outdoor ice hockey games

[Assistant]
None of these entities seem to be the general concept of an NHL playoff series. However, we can try to find specific playoff series events and then check if the LA Kings were the winners.

Let's search for specific playoff series events involving the LA Kings. We can use the property "part of" (`wdt:P361`) to find events that the LA Kings participated in, and then check if they were the winners.

First, let's find the events where the LA Kings participated.

[Tool] Got no rows and 3 columns

[Assistant]
It seems there are no direct triples where the LA Kings are the subject and the property is "part of." This suggests that the participation in specific playoff series might not be directly linked in this way.

Instead, let's try to find the specific playoff series events and then check if the LA Kings were the winners. We can use the property "winner" (`wdt:P1346`) and look for events where the LA Kings were the winners.

Let's search for specific playoff series events and then filter by the LA Kings being the winners.

[Tool]
Got 1 row and 2 columns:
| series                                 | date                                |
| -------------------------------------- | ----------------------------------- |
| 2014 Stanley Cup Finals (wd:Q16958416) | 2014-01-01T00:00:00Z (xsd:dateTime) |

[Assistant]
The last time the LA Kings won a playoff series was during the 2014 Stanley Cup Finals, which occurred in 2014.

Here is the final SPARQL query and answer:

```sparql
SELECT DISTINCT ?series ?date WHERE {
  ?series wdt:P1346 wd:Q203008 ;
          wdt:P585 ?date .
}
ORDER BY DESC (?date)
LIMIT 1
```

The answer is: The LA Kings last won a playoff series in 2014 during the 2014 Stanley Cup Finals.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT DISTINCT ?occupation WHERE {
  wd:Q164526 wdt:P106 ?occupation .
}

George Wilson (Ajita Wilson) is an actor and a pornographic actor.

[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 does george wilson do for a living?

[Assistant] Let's start by looking at some similar examples.

[Tool]
Example 1:
Question:
what job did george w bush have?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q207 wdt:P106 ?x .
}

Using entities:
George W. Bush, also known as GWB, POTUS 43, George Bush, President Bush, Bush Jr. (wd:Q207): President of the United States from 2001 to 2009, motivational speaker, rugby union player, military officer, autobiographer, businessperson, statesperson, politician, financier, painter

Using properties:
occupation, also known as job, work, vocation, profession, employment (wd:P106 as wdt): occupation of a person; see also "field of work" (Property:P101), "position held" (Property:P39)

Example 2:
Question:
what did george eastman make?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?x wdt:P61 wd:Q189147 .
}

Using entities:
George Eastman, also known as geog. eastman, g. eastman, Geo. Eastman (wd:Q189147): American entrepreneur, inventor and photographer (1854–1932), patron of the arts, philanthropist, photographer, entrepreneur, inventor

Using properties:
discoverer or inventor, also known as inventor or discoverer, inventor, invented by, invented, introduced by (wd:P61 as wdt): subject who discovered, first described, invented, or developed this discovery or invention or scientific hypothesis or theory

Example 3:
Question:
what does tim gunn do for a living?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q978646 wdt:P106 ?x .
}

Using entities:
Tim Gunn, also known as Timothy MacKenzie Gunn (wd:Q978646): American actor and fashion consultant, reality television participant, university teacher, creative director, fashion designer, costume designer, television maker, photographer, voice actor, writer, actor

Using properties:
occupation, also known as job, work, vocation, profession, employment (wd:P106 as wdt): occupation of a person; see also "field of work" (Property:P101), "position held" (Property:P39)

[Assistant]
Based on the examples, we need to find the entity for George Wilson and then use the property P106 (occupation) to find out what he does for a living.

First, let's search for the entity "George Wilson".

[Tool]
Top 10 entity alternatives:
1. Ajita Wilson, also known as George Wilson (wd:Q164526): American actress (1950–1987), pornographic actor, actor
2. George Wilson (wd:Q651203): basketball player from the United States born in 1942, basketball player
3. George A. Wilson, also known as George Allison Wilson, George Wilson (wd:Q382688): American politician (1884-1953), politician, lawyer, judge
4. George Washington Wilson, also known as George Wilson, G. W. Wilson, George W. Wilson (wd:Q2179710): Scottish painter and photographer (1823-1893), businessperson, photographer, painter
5. George Wilson (wd:Q5546405): English footballer (1892-1961), association football player
6. George Wilson (wd:Q5546401): American football coach (1914-1978), American football player, American football coach, basketball player
7. George Wilson, also known as George Washington Wilson, Teddy Wilson (wd:Q5546410): baseball outfielder from the United States (1925-1974), baseball player
8. George Wilson, also known as George Patterson Wilson (wd:Q18392304): Australian footballer (1920-2014), Australian rules football player
9. George Wilson, also known as George Charles Lee Wilson (wd:Q18129280): New Zealand cricketer (1887-1917), cricketer
10. George Wilson, also known as Wilson, George, 1818-1859 (wd:Q14949042): Scottish chemist, Regius Professor of Technology at the University of Edinburgh (1818-1859), chemist

[Assistant]
There are multiple entities named "George Wilson". We need more context to identify the correct one. However, since the user didn't provide any additional details, I'll assume they are referring to the most prominent or well-known George Wilson.

Let's start with the first result, Ajita Wilson (George Wilson), and check if she has an occupation listed. If not, we'll move on to the next one.

I'll run a query to check the occupation of Ajita Wilson (George Wilson).

```sparql
SELECT DISTINCT ?occupation WHERE {
  wd:Q164526 wdt:P106 ?occupation .
}
```

Let's execute this query.

[Tool]
Got 2 rows and 1 column:
| occupation                      |
| ------------------------------- |
| actor (wd:Q33999)               |
| pornographic actor (wd:Q488111) |

[Assistant]
The entity "Ajita Wilson (George Wilson)" has occupations listed as "actor" and "pornographic actor".

Since this seems to be a plausible answer, I'll provide it as the final answer. If the user meant a different George Wilson, they can provide more context.

Here is the final SPARQL query and answer:

```sparql
SELECT DISTINCT ?occupation WHERE {
  wd:Q164526 wdt:P106 ?occupation .
}
```

Answer: George Wilson (Ajita Wilson) is an actor and a pornographic actor.

Let's finalize this with the `answer` function.

[Tool] Stopped generation process


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?location ?locationLabel WHERE {
  wd:Q1319988 wdt:P276 ?location .
  OPTIONAL {
    ?location rdfs:label ?locationLabel .
    FILTER ( LANG( ?locationLabel ) = 'en' )
  }
}

Got 3 rows and 2 columns:
| location                             | locationLabel               |
| ------------------------------------ | --------------------------- |
| Kent Island (wd:Q6391790)            | Kent Island (en)            |
| Sandy Point State Park (wd:Q7417421) | Sandy Point State Park (en) |
| Stevensville (wd:Q755246)            | Stevensville (en)           |

The Chesapeake Bay Bridge is located at Kent Island, Sandy Point State Park, and Stevensville.

[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] where is chesapeake bay bridge?

[Assistant] Let's start by looking at some similar examples.

[Tool]
Example 1:
Question:
where does the potomac river flow into?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q179444 wdt:P403 ?x .
}

Using entities:
Potomac River, also known as Co-hon-go-roo-ta River, Patawmeck River, Patawomeck River, Potowmac River (wd:Q179444): river in the mid-Atlantic United States

Using properties:
mouth of the watercourse, also known as flows into, watercourse outflow, watercourse ends in, watercourse drain, tributary of (wd:P403 as wdt): the body of water to which the watercourse drains

Example 2:
Question:
where was the battle of antietam creek?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q719252 wdt:P276 ?x .
}

Using entities:
Battle of Antietam, also known as Battle of Sharpsburg (wd:Q719252): 1862 major battle of the American Civil War

Using properties:
location, also known as neighbourhood, whereabouts, vilage, venue, town (wd:P276 as wdt): location of the object, structure or event. In the case of an ad...ity use P706. Use P7153 for locations associated with the object

Example 3:
Question:
where was the battle of appomattox located?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q1355391 wdt:P131 ?x .
}

Using entities:
Battle of Appomattox Court House (wd:Q1355391): 1865 battle of the American Civil War

Using properties:
located in the administrative territorial entity, also known as is in the settlement of, is in the voivodeship of, is in the shire of, is in the state of, is in the territory of (wd:P131 as wdt): the item is located on the territory of the following administra...if the item falls only partially into the administrative entity., location, part of

[Assistant] Based on these examples, it seems like we need to find the location of Chesapeake Bay Bridge. We can use the `P276` property for location. Let's first search for the entity "Chesapeake Bay Bridge".

[Tool]
Top 10 entity alternatives:
1. Chesapeake Bay Bridge, also known as Bay Bridge, Governor William Preston Lane Jr. Memorial Bridge, Gov. William Preston Lane Jr. Memorial Bridge, William Preston Lane Junior Memorial Bridge (wd:Q1319988): major dual-span bridge in the U.S. state of Maryland, spanning the Chesapeake Bay
2. Chesapeake Bay Bridge-Tunnel, also known as Lucius J. Kellam, Jr. Bridge-Tunnel, CBBT (wd:Q1070563): bridge-tunnel beginning in Chesapeake Bay, Virginia
3. Chesapeake Bay Bridge–Tunnel (wd:Q60094303): scholarly article
4. Chesapeake Bay Bridge and Tunnel (wd:Q114520613): episode of Modern Marvels (S8 E5)
5. Chesapeake Bay Bridge and Tunnel District (wd:Q5092828): political subdivision of Virginia
6. Chesapeake Bay (wd:Q216868): estuary in the U.S. states of Maryland, Delaware, District of Columbia, and Virginia
7. San Francisco–Oakland Bay Bridge, also known as Emperor Norton Bridge, Bay Bridge, San Francisco-Oakland Bay Bridge (wd:Q771404): suspension bridge connecting San Francisco and Oakland, California, USA
8. Yokohama Bay Bridge, also known as Bay Bridge (wd:Q1138655): cable-stayed bridge
9. Bridge Bay (wd:Q49374128): bay in Teton County, Wyoming, United States of America
10. Bay Bridge (wd:Q112811780): British racehorse

[Assistant]
The correct entity for "Chesapeake Bay Bridge" is `wd:Q1319988`. Now, let's use the `P276` property to find its location.

I'll construct a simple SPARQL query to retrieve the location of the Chesapeake Bay Bridge.

[Tool]
Got 3 rows and 1 column:
| location                             |
| ------------------------------------ |
| Kent Island (wd:Q6391790)            |
| Sandy Point State Park (wd:Q7417421) |
| Stevensville (wd:Q755246)            |

[Assistant]
The query returned multiple locations associated with the Chesapeake Bay Bridge. To provide a more precise answer, we can include the labels of these locations in the query. However, since the labels are optional, we will use `OPTIONAL` clauses to fetch them.

Let's refine the query to include the labels of the locations.

[Tool]
Got 3 rows and 2 columns:
| location                             | locationLabel               |
| ------------------------------------ | --------------------------- |
| Kent Island (wd:Q6391790)            | Kent Island (en)            |
| Sandy Point State Park (wd:Q7417421) | Sandy Point State Park (en) |
| Stevensville (wd:Q755246)            | Stevensville (en)           |

[Assistant]
The locations associated with the Chesapeake Bay Bridge are:

- Kent Island (wd:Q6391790)
- Sandy Point State Park (wd:Q7417421)
- Stevensville (wd:Q755246)

These locations are where the bridge connects across the Chesapeake Bay.

Here is the final SPARQL query and answer:

```sparql
SELECT ?location ?locationLabel WHERE {
  wd:Q1319988 wdt:P276 ?location .
  OPTIONAL { ?location rdfs:label ?locationLabel . FILTER(LANG(?locationLabel) = 'en') }
}
```

The Chesapeake Bay Bridge is located at Kent Island, Sandy Point State Park, and Stevensville.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT DISTINCT ?state WHERE {
  wd:Q734774 wdt:P131 ?city .
  ?city wdt:P131 ?state .
}

Saint Louis University is in the state of Missouri.

[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 state is saint louis university in?

[Assistant] Let's start by looking at some similar examples.

[Tool]
Example 1:
Question:
where is st louis cardinals stadium?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q504309 wdt:P115 ?x .
}

Using entities:
St. Louis Cardinals, also known as St. Louis Brown Stockings, St. Louis Perfectos, St. Louis Browns, Saint Louis Cardinals, Red Birds (wd:Q504309): Major League Baseball franchise in St. Louis, Missouri

Using properties:
home venue, also known as home water, venue, stadium, home ground, home field (wd:P115 as wdt): home stadium or venue of a sports team or applicable performing arts organization, location

Example 2:
Question:
where is the university of oregon state?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q861888 wdt:P131 ?x .
}

Using entities:
Oregon State University, also known as Corvallis College, Corvallis State Agricultural College, Oregon Agricultural College, Oregon State Agricultural College, Oregon State College (wd:Q861888): public research university in Corvallis, Oregon, United States

Using properties:
located in the administrative territorial entity, also known as is in the settlement of, is in the voivodeship of, is in the shire of, is in the state of, is in the territory of (wd:P131 as wdt): the item is located on the territory of the following administra...if the item falls only partially into the administrative entity., location, part of

Example 3:
Question:
where is santa clara university?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q992830 wdt:P131 ?x .
}

Using entities:
Santa Clara University, also known as University of Santa Clara, SCU (wd:Q992830): private non-profit Jesuit university located in Santa Clara, California, United States

Using properties:
located in the administrative territorial entity, also known as is in the settlement of, is in the voivodeship of, is in the shire of, is in the state of, is in the territory of (wd:P131 as wdt): the item is located on the territory of the following administra...if the item falls only partially into the administrative entity., location, part of

[Assistant]
Based on the examples, it seems like we need to find the entity for Saint Louis University and then use the property `located in the administrative territorial entity` (P131) to find the state where it is located.

First, let's search for the entity "Saint Louis University".

[Tool]
Top 10 entity alternatives:
1. Saint Louis University, also known as SLU, St. Louis University (wd:Q734774): private research university with campuses in St. Louis, Missouri, and Madrid, Spain
2. Saint Louis University, also known as SLU, St. Louis University (wd:Q7401676): university in Baguio, Philippines
3. Washington University in St. Louis, also known as WUSTL, WashU, Wash. U., Washington University in Saint Louis, Washington University (Saint Louis, Mo.) (wd:Q777403): private research university in St. Louis, Missouri, United States
4. Saint Louis Billikens, also known as Saint Louis University athletics (wd:Q7401655): athletic program of Saint Louis University
5. Saint Louis University Hospital, also known as Firmin Desloge Hospital, SLU Hospital (wd:Q7401677): hospital in Missouri, United States
6. Saint Louis University Bookstore (wd:Q99475974): bookshop in Saint Louis, MO, United States of America
7. Saint Louis University Madrid Campus (wd:Q4116998)
8. Maryville University, also known as Maryville College of the Sacred Heart, Maryville University of St. Louis, Maryville University of Saint Louis (wd:Q6781811): private university located in Missouri
9. University of Saint Louis Tuguegarao (wd:Q7896224): private university in the Philippines
10. Saint Louis University Law Journal (wd:Q15767443): journal

[Assistant]
The correct entity for Saint Louis University is `wd:Q734774`. Now, let's use the property `located in the administrative territorial entity` (P131) to find the state where Saint Louis University is located.

I will execute the following SPARQL query:

```sparql
SELECT DISTINCT ?state WHERE {
  wd:Q734774 wdt:P131 ?state .
}
```

This query will return the state where Saint Louis University is located.

[Tool]
Got 1 row and 1 column:
| state                 |
| --------------------- |
| St. Louis (wd:Q38022) |

[Assistant]
The result shows that Saint Louis University is located in St. Louis. However, this is the city, not the state. To get the state, we need to find the state where St. Louis is located. We can do this by querying the `located in the administrative territorial entity` (P131) property again for St. Louis.

Let's execute the following SPARQL query:

```sparql
SELECT DISTINCT ?state WHERE {
  wd:Q38022 wdt:P131 ?state .
}
```

This query will return the state where St. Louis is located.

[Tool]
Got 1 row and 1 column:
| state               |
| ------------------- |
| Missouri (wd:Q1581) |

[Assistant]
The result shows that St. Louis is located in Missouri. Therefore, Saint Louis University is in the state of Missouri.

Here is the final SPARQL query and answer:

```sparql
SELECT DISTINCT ?state WHERE {
  wd:Q734774 wdt:P131 ?city .
  ?city wdt:P131 ?state .
}
```

Answer: Saint Louis University is in the state of Missouri.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT DISTINCT ?team WHERE {
  wd:Q3887743 wdt:P54 ?team .
}

Got 3 rows and 1 column:
| team                             |
| -------------------------------- |
| Philadelphia Eagles (wd:Q219714) |
| Seattle Seahawks (wd:Q221878)    |
| Las Vegas Raiders (wd:Q324523)   |

Owen Schmitt has played for the Philadelphia Eagles, Seattle Seahawks, and Las Vegas Raiders.

[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] who does owen schmitt play for?

[Assistant] Let's start by looking at some similar examples.

[Tool]
Example 1:
Question:
who does john beck play for?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q6221292 wdt:P54 ?x .
}

Using entities:
John Beck, also known as John Dalton Beck (wd:Q6221292): American gridiron football player and coach (born 1981), American football player, Canadian football player

Using properties:
member of sports team, also known as plays for, played for, sport team, team, team played for (wd:P54 as wdt): sports teams or clubs that the subject represents or represented, member of

Example 2:
Question:
who does michael vick play for?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q505431 wdt:P54 ?x .
}

Using entities:
Michael Vick, also known as Michael Dwayne Vick (wd:Q505431): American football player (born 1980), American football player, offensive coordinator

Using properties:
member of sports team, also known as plays for, played for, sport team, team, team played for (wd:P54 as wdt): sports teams or clubs that the subject represents or represented, member of

Example 3:
Question:
what team does chris humphrey play for?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q797835 wdt:P54 ?x .
}

Using entities:
Chris Humphrey (wd:Q797835): Professional footballer (born 1987), association football player

Using properties:
member of sports team, also known as plays for, played for, sport team, team, team played for (wd:P54 as wdt): sports teams or clubs that the subject represents or represented, member of

[Assistant] Based on the examples, we need to identify Owen Schmitt and then use the `P54` property to find out which team he plays for. Let's first search for Owen Schmitt.

[Tool]
Top 10 entity alternatives:
1. Owen Schmitt (wd:Q3887743): American football fullback, American football player
2. Richard Owen, also known as R. Owen, Owen, Richard, Prof OWEN F.R.S., Sir Richard Owen, Sir R. Owen (wd:Q151556): English biologist and paleontologist (1804–1892), paleoanthropologist, university teacher, paleontologist, zoologist, anatomist, biologist, curator, writer
3. Wilfred Owen, also known as Owen, Wilfred Edward Salter Owen (wd:Q212719): English poet and soldier (1893-1918), writer, poet
4. Owen (wd:Q81006): municipality in Germany
5. Owen, also known as City of Owen, Owen, WI (wd:Q1569456): city in Clark County, Wisconsin
6. Schmitt (wd:Q654052): municipality of Germany
7. Owain Owain, also known as Owen Owen (wd:Q3398027): Welsh novelist, short story writer and poet (1929–1993), nuclear physicist, educator, writer, poet
8. Schmitt (wd:Q2246512): family name
9. Owen, also known as Owen surname, Owen family name (wd:Q1158545): family name
10. Mount Owen, also known as Mount Owen (Antarctica), Owen (wd:Q6922630): mountain in Antarctica

[Assistant] The top result is Owen Schmitt (wd:Q3887743), who is an American football fullback. Now, let's use the `P54` property to find out which team he plays for.

[Tool]
Got 3 rows and 1 column:
| team                             |
| -------------------------------- |
| Philadelphia Eagles (wd:Q219714) |
| Seattle Seahawks (wd:Q221878)    |
| Las Vegas Raiders (wd:Q324523)   |

[Assistant]
Owen Schmitt has played for multiple teams, including the Philadelphia Eagles, Seattle Seahawks, and Las Vegas Raiders. To provide a complete answer, we can list all the teams he has played for.

Here is the final SPARQL query:

```sparql
SELECT DISTINCT ?team WHERE {
  wd:Q3887743 wdt:P54 ?team .
}
```

And the answer is:
- Philadelphia Eagles
- Seattle Seahawks
- Las Vegas Raiders

Now, let's finalize the answer.

[Tool] Stopped generation process