PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?stadium WHERE {
  wd:Q599903 wdt:P115 ?stadium .
}

The Ireland rugby union team plays at the Aviva Stadium.

[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 do ireland play rugby union?

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

[Tool]
Example 1:
Question:
where is ellis park rugby stadium?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q182081 wdt:P276 ?x .
}

Using entities:
Ellis Park Stadium, also known as Emirates Airline Park, Ellis Park, Coca-Cola Park (wd:Q182081): stadium in Johannesburg, South Africa

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 2:
Question:
where does the shannon river flow?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q7488940 wdt:P131 ?x .
}

Using entities:
Shannon River (wd:Q7488940): river in Western Australia, Australia

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:
who does northern ireland belong to?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q26 wdt:P131 ?x .
}

Using entities:
Northern Ireland, also known as UKN, Tuaisceart Éireann, The Six Counties, The North, N. Ireland (wd:Q26): part of the United Kingdom situated on the island of Ireland

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, we need to find the home stadium of the Irish rugby team. Let's first look for the entity representing the Irish rugby team.

[Tool]
Top 10 entity alternatives:
1. Ireland rugby union team, also known as Ireland national rugby team, Ireland national rugby union team (wd:Q599903): Men’s rugby union team representing the island of Ireland
2. Ireland women's national rugby union team (wd:Q3589823): women’s rugby union team representing the island of Ireland
3. Ireland national rugby union sevens team, also known as Ireland national rugby sevens team, Ireland sevens rugby team (wd:Q7902223): national sevens rugby team
4. Ireland national under-20 rugby union team (wd:Q6068684): rugby team
5. Ireland national schoolboy rugby union team (wd:Q6068681): sports team
6. Ireland women's national rugby union sevens team, also known as Ireland women's national sevens team (wd:Q6068719): sevens national rugby team
7. History of the Ireland national rugby union team (wd:Q23808242)
8. Irish Rugby Football Union, also known as Ireland Rugby Union, IRFU (wd:Q1672926): governing body for rugby union on the island of Ireland
9. rugby union in Ireland, also known as Ireland rugby union (wd:Q654217): third most popular form of football in Ireland, organised on an all-island basis
10. rugby union team, also known as rugby team (wd:Q14645593): group of people that play rugby union

[Assistant] We'll use the top result, the Ireland rugby union team (wd:Q599903). Now let's find the property that represents the home stadium of a sports team.

[Tool]
Top 10 property alternatives:
1. home venue, also known as home water, venue, stadium, home ground, home field (wd:P115 as wdt/p): home stadium or venue of a sports team or applicable performing arts organization, location
2. location, also known as neighbourhood, whereabouts, vilage, venue, town (wd:P276 as wdt/p): location of the object, structure or event. In the case of an ad...ity use P706. Use P7153 for locations associated with the object
3. operating area, also known as province served, serves jurisdiction, serves geographic region, serves geographic area, serves area (wd:P2541 as wdt/p): geographic area or jurisdiction an organisation or industry operates in, serves, or has responsibility for, location
4. sport, also known as play, plays, sport played, sports (wd:P641 as wdt/p): sport that the subject participates or participated in or is associated with
5. country, also known as sovereign state, state, land, host country (wd:P17 as wdt/p): sovereign state that this item is in (not to be used for human beings), located in the administrative territorial entity
6. ranking, also known as peak position, place, placing, position, rank (wd:P1352 as wdt/p): subject's ordinal position as qualitatively evaluated relative to other members of a group
7. head coach, also known as team manager, trainer, senior coach, manager, coached by (wd:P286 as wdt/p): on-field manager or head coach of a sports club (not to be confu...eneral manager P505, which is not a coaching position) or person
8. official website, also known as site (official), home page, site official, URL, URL (official) (wd:P856 as wdt/p): URL of the official page of an item (current or former). Usage: ...but see the "Hijacked or dead websites" section of the Talk page, URL
9. nickname, also known as informal name, moniker, other name, othername, other_names (wd:P1449 as wdt/p): informal name (for a pseudonym use P742), name
10. inception, also known as initial release date, founded in, founded on, founded on date, founding date (wd:P571 as wdt/p): time when an entity begins to exist; for date of official opening use P1619, start time

No other items found

[Assistant]
The property `home venue` (wdt:P115) seems to be the correct choice. Let's construct a SPARQL query to find the home stadium of the Ireland rugby union team.

```sparql
SELECT ?stadium WHERE {
  wd:Q599903 wdt:P115 ?stadium .
}
```

Now, let's execute this query.

[Tool]
Got 1 row and 1 column:
| stadium                    |
| -------------------------- |
| Aviva Stadium (wd:Q244402) |

[Assistant]
The home stadium of the Ireland rugby union team is the Aviva Stadium (wd:Q244402).

Here is the final SPARQL query and answer:

```sparql
SELECT ?stadium WHERE {
  wd:Q599903 wdt:P115 ?stadium .
}
```

Answer: The Ireland rugby union team plays at the Aviva Stadium.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query correctly identifies the home venue of the Ireland rugby union team, which is the Aviva Stadium. The query follows all the rules and returns the IRI of the stadium as required.


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT DISTINCT ?team WHERE {
  wd:Q119562 wdt:P54 ?team .
  ?team wdt:P31 wd:Q476028 .
}

Aguero played for Manchester City F.C. and Atlético de Madrid.

[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 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 the teams he 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 multiple teams that Aguero has played for. To provide a more specific answer, we need to filter the results to show only the professional clubs he played for, excluding the national youth teams. We can achieve this by checking if the team is an instance of an association football club (`wd:Q476028`).

Let's refine the query:

[Tool]
Got 2 rows and 1 column:
| team                             |
| -------------------------------- |
| Manchester City F.C. (wd:Q50602) |
| Atlético de Madrid (wd:Q8701)    |

[Assistant]
The refined query shows that Aguero played for Manchester City F.C. and Atlético de Madrid. These are the professional clubs he has been a part of.

Now, let's finalize the answer:

```sparql
SELECT DISTINCT ?team WHERE {
  wd:Q119562 wdt:P54 ?team .
  ?team wdt:P31 wd:Q476028 .
}
```

The teams Aguero played for are:
- Manchester City F.C. (wd:Q50602)
- Atlético de Madrid (wd:Q8701)

Let's provide the final answer:

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query correctly identifies the teams that Sergio Agüero played for, and the execution result matches the expected answer.


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

The official language of Brazil is Portuguese.

[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 similar examples, we can see that the property `wdt:P37` is used to represent the official language of a country. Let's confirm this by searching for the property "official language".

[Tool]
Top 10 property alternatives:
1. official language, also known as language spoken, spoken in, speaking, language official (wd:P37 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/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 pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/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 pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/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 pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/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 pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/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 pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/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 pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/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 pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/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 pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/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 pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): language in which the writer has written their work, languages spoken, written or signed

[Assistant] The top result confirms that `wdt:P37` is indeed the property for "official language". Now let's search for the entity representing Brazil.

[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 top result confirms that `wd:Q155` is the entity representing Brazil. We can now construct a SPARQL query to find the official language of Brazil using the property `wdt:P37`.

Let's execute the SPARQL query:

```sparql
SELECT ?language WHERE {
  wd:Q155 wdt:P37 ?language .
}
```

This query will return the official language of Brazil.

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

[Assistant]
The SPARQL query has returned that the official language of Brazil is Portuguese (`wd:Q5146`).

Now, let's provide the final answer.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query correctly identifies the official language of Brazil as Portuguese, which is the expected answer.


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
SELECT DISTINCT ?zipCode WHERE {
  wd:Q653636 wdt:P281 ?zipCode .
  FILTER ( isLiteral( ?zipCode ) && DATATYPE( ?zipCode ) = xsd:string ) FILTER ( ! REGEX( ?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 the examples, we need to find the entity for Morgantown, WV and then use the property `P281` to get the zip code. Let's first search for the entity representing Morgantown, WV.

[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 `wd:Q653636`. Now, let's construct a SPARQL query to get the zip code for this entity using the property `P281`.

Here is the SPARQL query:

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

Let's execute this query.

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

[Assistant]
The query returned multiple zip codes for Morgantown, WV. These are 26501, 26501–26508, 26504, and 26506.

To provide a clear and concise answer, we can list these zip codes. Here is the final SPARQL query and the answer:

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

Answer: The zip codes for Morgantown, WV are 26501, 26501–26508, 26504, and 26506.

Now, let's finalize the answer.

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query is mostly correct, but the result includes a range (26501–26508) which might not be what the user expects. It would be better to list all individual zip codes separately. Consider refining the query to ensure that only distinct individual zip codes are returned.

[Assistant]
Thank you for the feedback. To ensure that only distinct individual zip codes are returned, we can modify the query to filter out ranges and return only individual zip codes. We can achieve this by checking if the value is a numeric string.

Here is the refined SPARQL query:

```sparql
SELECT DISTINCT ?zipCode WHERE {
  wd:Q653636 wdt:P281 ?zipCode .
  FILTER(isLiteral(?zipCode) && DATATYPE(?zipCode) = xsd:string)
  FILTER NOT EXISTS {
    ?zipCode bif:contains "–"
  }
}
```

Let's execute this refined query.

[Tool]
SPARQL execution failed:
Invalid SPARQL query: Prefix bif was not registered using a PREFIX declaration

[Assistant]
It looks like the `bif:contains` function is not supported by the QLever SPARQL endpoint. Instead, we can use the `REGEX` function to filter out the range values.

Here is the revised SPARQL query:

```sparql
SELECT DISTINCT ?zipCode WHERE {
  wd:Q653636 wdt:P281 ?zipCode .
  FILTER(isLiteral(?zipCode) && DATATYPE(?zipCode) = xsd:string)
  FILTER(!REGEX(?zipCode, "–"))
}
```

Let's execute this revised query.

[Tool]
Got 3 rows and 1 column:
| zipCode |
| ------- |
| 26501   |
| 26504   |
| 26506   |

[Assistant]
The revised query has successfully filtered out the range and returned the individual zip codes for Morgantown, WV. The zip codes are 26501, 26504, and 26506.

Here is the final SPARQL query and the answer:

```sparql
SELECT DISTINCT ?zipCode WHERE {
  wd:Q653636 wdt:P281 ?zipCode .
  FILTER(isLiteral(?zipCode) && DATATYPE(?zipCode) = xsd:string)
  FILTER(!REGEX(?zipCode, "–"))
}
```

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

Now, let's finalize the answer.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query successfully retrieves the zip codes for Morgantown, WV from Wikidata, and the results match the expected zip codes. No further refinement is needed.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT DISTINCT ?endTime WHERE {
  ?event wdt:P1346 wd:Q203008 ; wdt:P582 ?endTime .
}
ORDER BY DESC ( ?endTime )
LIMIT 1

The last time the LA Kings won a playoff series was on June 13, 2014.

[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 entity "LA Kings".

[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`. Next, let's find the property for winning a playoff series. We can use the property `P1346` (winner) and `P585` (point in time) as seen in the examples. However, we need to ensure that the context is correct, i.e., the LA Kings winning a playoff series.

Let's start with a simple query to see if we can find the relevant information.

[Tool] Got no rows and 2 columns

[Assistant]
It seems that the query did not return any results. This might be because the specific context of a playoff series is not directly linked in the way we expected. Let's try a different approach by looking for the entity representing a playoff series and then linking it to the LA Kings.

First, let's search for the entity "playoff series" or "playoffs" to see if we can find a relevant entity.

[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:Q7203505) seems to be the most relevant for our context. However, it is specifically about breaking ties in golf tournaments. Let's try another search for "hockey playoffs" to see if we can find a more specific entity.

[Tool]
Top 10 entity alternatives:
1. Hockeyallsvenskan playoffs (wd:Q111969569): ice hockey competition in Sweden
2. 2023 Hockeyallsvenskan playoffs (wd:Q117038027): 2023 edition of the Hockeyallsvenskan playoffs
3. 2024 Hockeyallsvenskan playoffs (wd:Q118114224): 2024 edition of the Hockeyallsvenskan playoffs
4. 2021 Hockeyallsvenskan playoffs (wd:Q111969530): 2021 edition of the Hockeyallsvenskan playoffs
5. 2022 Hockeyallsvenskan playoffs (wd:Q111969546): 2022 edition of the Hockeyallsvenskan playoffs
6. 2025 Hockeyallsvenskan playoffs (wd:Q125697037): 2025 edition of the Hockeyallsvenskan playoffs
7. 2026 Hockeyallsvenskan playoffs (wd:Q125697045): 2026 edition of the Hockeyallsvenskan playoffs
8. 2014–15 Champions Hockey League playoffs, also known as 2014-15 Champions Hockey League playoffs, playoffs (wd:Q18199909)
9. Can hockey playoffs harm your hearing? (wd:Q43178607): scientific article published in December 2006
10. ice hockey, also known as hockey (wd:Q41466): team sport played on ice using sticks, skates, and a puck

[Assistant]
None of the entities listed seem to be a general concept of hockey playoffs. Let's try a different approach by searching for the property that links a team to a playoff series. We can use the property `P1346` (winner) and look for objects that are related to playoff series.

Let's search for objects of the property `P1346` with the query "playoff series".

[Tool]
Falling back to an unconstrained search on the full precomputed search indices:
Got more than the maximum supported number of object candidates (65,536)

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

No other items found

No literal items found

[Assistant]
The search for objects of the property `P1346` with the query "playoff series" did not yield any useful results. This suggests that the specific context of a playoff series might not be directly linked in the way we expected.

Given the lack of direct links, let's try a different approach by looking for the most recent playoff series where the LA Kings were involved. We can use the property `P1346` (winner) and `P585` (point in time) to find the most recent instance where the LA Kings won a playoff series.

Let's execute a query to find the most recent playoff series where the LA Kings were the winner.

[Tool]
Got 1 row and 2 columns:
| series | time                                |
| ------ | ----------------------------------- |
|        | 2014-01-01T00:00:00Z (xsd:dateTime) |

[Assistant]
The query returned a result indicating that the LA Kings won a playoff series in 2014. However, the `series` variable is empty, which suggests that the specific series is not directly linked in the data.

To provide a more complete answer, let's try to find the specific series where the LA Kings won in 2014. We can use the `P361` (part of) property to link the event to a larger context, such as the Stanley Cup Playoffs.

Let's execute a query to find the specific series where the LA Kings won in 2014.

[Tool] Got no rows and 2 columns

[Assistant]
It seems that the specific series where the LA Kings won in 2014 is not directly linked in the data. However, we know from the previous query that the LA Kings won a playoff series in 2014.

Since the specific series is not available, we can conclude that the last time the LA Kings won a playoff series was in 2014.

Let's finalize the answer with the information we have.

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query is on the right track, but it could be more precise. The property `wdt:P585` (point in time) is too general and might not specifically refer to the end of a playoff series. Consider using a more specific property or adding a filter to ensure the time refers to the conclusion of a playoff series. Additionally, the label for the time should be included in the result for better readability.

[Assistant]
Thank you for the feedback. To make the query more precise, we can try to use a more specific property or add a filter to ensure the time refers to the conclusion of a playoff series. We can also include the label for the time for better readability.

Let's try to find a more specific property that might indicate the end of a playoff series. One possibility is to use the property `P580` (start time) and `P582` (end time) to ensure we are looking at the conclusion of the series.

We will also include the label for the time to make the result more readable.

Let's execute a refined query.

[Tool]
Got 1 row and 3 columns:
| series | endTime                             | endTimeLabel |
| ------ | ----------------------------------- | ------------ |
|        | 2014-06-13T00:00:00Z (xsd:dateTime) |              |

[Assistant]
The refined query returned the end time of the most recent playoff series where the LA Kings won, which is June 13, 2014. However, the label for the end time is not available, so we will just use the date.

To finalize the answer, we can state that the last time the LA Kings won a playoff series was on June 13, 2014.

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

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query effectively retrieves the most recent end time of a playoff series where the LA Kings were the winners, which corresponds to the answer provided.


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?occupation WHERE {
  wd:Q382688 wdt:P106 ?occupation .
}

George Wilson (George A. Wilson) is a judge, lawyer, and politician.

[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, it seems like we need to find the entity for George Wilson and then look for his occupation using the `wdt:P106` property.

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 to identify which one is the correct one based on context. Since the user didn't provide any additional context, I'll assume the most prominent or recent one, which is likely the first one listed, Ajita Wilson (George Wilson).

Let's check the occupations of the top few entities to see if any match what we might expect for a living.

First, let's check the occupation of Ajita Wilson (George Wilson).

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

[Assistant] Ajita Wilson (George Wilson) is an American actress and pornographic actor. However, this might not be the George Wilson the user is referring to. Let's check the next most prominent entity, George Wilson (wd:Q651203), who is a basketball player.

[Tool]
Got 1 row and 1 column:
| occupation                      |
| ------------------------------- |
| basketball player (wd:Q3665646) |

[Assistant] George Wilson (wd:Q651203) is a basketball player. This might also not be the George Wilson the user is referring to. Let's check the next entity, George A. Wilson (wd:Q382688), who is an American politician and lawyer.

[Tool]
Got 3 rows and 1 column:
| occupation             |
| ---------------------- |
| judge (wd:Q16533)      |
| lawyer (wd:Q40348)     |
| politician (wd:Q82955) |

[Assistant]
George A. Wilson (wd:Q382688) has multiple occupations: judge, lawyer, and politician. This seems like a plausible match for the George Wilson the user is asking about.

To confirm, let's check if there are any other notable George Wilsons in the list. If not, we can conclude that George A. Wilson is the correct entity.

Let's check the next entity, George Washington Wilson (wd:Q2179710), who is a Scottish painter and photographer.

[Tool]
Got 3 rows and 1 column:
| occupation                 |
| -------------------------- |
| painter (wd:Q1028181)      |
| photographer (wd:Q33231)   |
| businessperson (wd:Q43845) |

[Assistant]
George Washington Wilson (wd:Q2179710) is a painter, photographer, and businessperson. This is another plausible match, but it seems less likely given the context of the question.

Given the occupations of the entities we've checked, the most likely match is George A. Wilson (wd:Q382688), who is a judge, lawyer, and politician.

Let's finalize the answer with the occupations of George A. Wilson.

Here is the final SPARQL query and answer:

```sparql
SELECT ?occupation WHERE {
  wd:Q382688 wdt:P106 ?occupation .
}
```

Answer: George Wilson (George A. Wilson) is a judge, lawyer, and politician.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query correctly identifies the occupations of George Wilson, and the answer provided is accurate and comprehensive.


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
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 the examples, it seems like we need to find the location of the Chesapeake Bay Bridge. We can use the `P276` property, which represents the location of an object, structure, or event. Let's first find the entity for the 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 the Chesapeake Bay Bridge is `wd:Q1319988`. Now let's use the `P276` property to find its location.

I will now construct a SPARQL query to find 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, which suggests that the bridge spans across different points. 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 modify 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 Chesapeake Bay Bridge is located at the following points:

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

These locations indicate the areas where the bridge spans across the Chesapeake Bay.

Now, I will provide the final SPARQL query and the answer to the user's question.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The provided SPARQL query is correct and returns the relevant locations for the Chesapeake Bay Bridge. The answer is accurate and complete.


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

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, 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 it is in.

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'll construct a SPARQL query to get this information.

[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, we need to confirm if St. Louis is a city or a state. To clarify, I will check the type of the entity `wd:Q38022`.

Let's run another query to get the label and type of `wd:Q38022`.

[Tool]
Got 402 rows and 2 columns, showing the first 5 and last 5 rows below:
| label             | type                                                |
| ----------------- | --------------------------------------------------- |
| Béésh Názhah (nv) | city in the United States (wd:Q1093829)             |
| Béésh Názhah (nv) | independent city in the United States (wd:Q1266818) |
| Béésh Názhah (nv) | big city (wd:Q1549591)                              |
| Hālgaluis (ang)   | city in the United States (wd:Q1093829)             |
| Hālgaluis (ang)   | independent city in the United States (wd:Q1266818) |
| ...               | ...                                                 |
| 聖路易斯 (zh)         | independent city in the United States (wd:Q1266818) |
| 聖路易斯 (zh)         | big city (wd:Q1549591)                              |
| 聖路易斯 (zh-hant)    | city in the United States (wd:Q1093829)             |
| 聖路易斯 (zh-hant)    | independent city in the United States (wd:Q1266818) |
| 聖路易斯 (zh-hant)    | big city (wd:Q1549591)                              |

[Assistant]
The results show that `wd:Q38022` (St. Louis) is primarily classified as a city in the United States and an independent city in the United States. This indicates that St. Louis is a city, not a state.

To find the state where St. Louis is located, we need to look at the higher-level administrative division. We can use the `located in the administrative territorial entity` (P131) property again to find the state.

Let's run a query to find 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 the state of Missouri.

Therefore, the state where Saint Louis University is located is Missouri.

Here is the final SPARQL query and answer:

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query effectively retrieves the state where Saint Louis University is located.