PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?prize ?prizeLabel WHERE {
  wd:Q7197 wdt:P1411 ?prize .
  OPTIONAL {
    ?prize rdfs:label ?prizeLabel .
    FILTER ( LANG( ?prizeLabel ) = "en" )
  }
}

Simone de Beauvoir was nominated for the Nobel Prize in Literature.

[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] For what literature prize was Simone de Beauvoir nominated?

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

[Tool]
Example 1:
Question:
How many languages are spoken in Colombia?

SPARQL:
SELECT ( COUNT( ?s1 ) as ?c ) WHERE {
  ?s1 ?p1 wd:Q739 .
  ?s1 wdt:P31 wd:Q34770 .
}

Using entities:
language, also known as human language, particular language, languages, idioms, lang (wd:Q34770): particular system of communication, often named for the region or peoples that use it
Colombia, also known as The Republic of Colombia, Republic of Colombia, ISO 3166-1:CO, Colombiá, COL (wd:Q739): sovereign state in South America

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)

Example 2:
Question:
In which state Penn State University is located?

SPARQL:
SELECT ?res WHERE {
  wd:Q739627 wdt:P131 ?res .
  ?res wdt:P31 / wdt:P279 * wd:Q106458883 .
}

Using entities:
state (wd:Q106458883): title used for first-level administrative division in several countries
Pennsylvania State University, also known as Penn State, PennState, Penn State University, Pennsylvania agricultural college, Pennsylvania State University, State College (wd:Q739627): public university in Pennsylvania, 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
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)

Example 3:
Question:
What is the highest mountain in Saxony Germany?

SPARQL:
SELECT ?res WHERE {
  ?res wdt:P31 wd:Q8502 .
  ?res wdt:P131 + wd:Q1202 .
  ?res wdt:P2044 ?height .
}
ORDER BY DESC ( ?height )
LIMIT 1

Using entities:
Saxony, also known as DE-SN, Free State of Saxony, Swobodny stat Sakska, Freistaat Sachsen, Sachsen (wd:Q1202): state in Germany
mountain, also known as mt., mtn., mountains, mount (wd:Q8502): large natural elevation of the Earth's surface

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
elevation above sea level, also known as MAMSL, MASL, height, elevation above mean sea level, elevation (wd:P2044 as wdt): height of the item (geographical object) as measured relative to sea level
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)

[Assistant] Based on the examples, we need to identify the entity for Simone de Beauvoir and the property for nominations or prizes. Let's start by searching for Simone de Beauvoir.

[Tool]
Top 10 entity alternatives:
1. Simone de Beauvoir (wd:Q7197): French philosopher, social theorist and activist (1908–1986), political philosopher, women letter writer, philosophy teacher, political activist, literary critic, autobiographer, philosopher, journalist, novelist, feminist, essayist, diarist, author, writer
2. Simone de Beauvoir (wd:Q66049034): 1979 documentary film directed by Josée Dayan
3. Simone de Beauvoir (wd:Q87280632): episode of In Our Time
4. Simone de Beauvoir Prize (wd:Q2316171)
5. Simone de Beauvoir Park, also known as Fontainas Park (wd:Q23796565): park in Brussels, Belgium
6. allée Simone de Beauvoir (wd:Q111916916): alley in Arcueil, France
7. mail Simone de Beauvoir (wd:Q113391499): street in Bobigny, France
8. place Simone de Beauvoir (wd:Q113414051): square in Stains, en Seine-Saint-Denis
9. rue Simone de Beauvoir (wd:Q111302129): street in Bondy, France
10. rue Simone de Beauvoir (wd:Q111847967): street in Limeil-Brévannes, France

[Assistant] The correct entity for Simone de Beauvoir is wd:Q7197. Next, let's find out the property for nominations or prizes.

[Tool]
Top 10 property alternatives:
1. nominated for, also known as nominee for, shortlisted for, selected for award, selected for, nominee for award (wd:P1411 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): award nomination received by a person, organisation or creative work (inspired from "award received" (Property:P166))
2. nominated by (wd:P4353 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): who nominated a person for an office or award; can be used as a qualifier
3. for work, also known as awarded for work, citation for award, for their work on, nominated for work (wd:P1686 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): qualifier of award received (P166) to specify the work that an award was given to the creator for
4. nominee, also known as award nominee (wd:P2453 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): qualifier used with “nominated for” to specify which person or organization was nominated
5. award received, also known as win, winner of, recognition title, received award, prize received (wd:P166 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): award or recognition received by a person, organization or creative work
6. conferred by, also known as presented by, officiated by, issued by, granted by, given by (wd:P1027 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): person or organization who grants an award, certification, grant, or role
7. winner, also known as nominee, prizewinner, titleholder, top dog, victor (wd:P1346 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): 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
8. eligible recipient, also known as eligible recipient for award, type of recipient who is eligible for this award (wd:P8115 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): party that is eligible to receive something, like an award, grant or prize
9. Academy Awards Database nominee ID, also known as Oscar nominee ID (wd:P6150 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): identifier for nominees in the official Academy Awards database
10. award rationale, also known as award citation, rationale, award reason (wd:P6208 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): qualifier to add short citation explaining the official reason f... or add comments about a person's award. No quote marked needed.

[Assistant]
The property for nominations is wd:P1411. Now, let's construct a simple SPARQL query to find the literature prize for which Simone de Beauvoir was nominated.

```sparql
SELECT ?prize WHERE {
  wd:Q7197 wdt:P1411 ?prize .
}
```

I will execute this query to see the results.

[Tool]
Got 1 row and 1 column:
| prize                                 |
| ------------------------------------- |
| Nobel Prize in Literature (wd:Q37922) |

[Assistant]
The SPARQL query returned the Nobel Prize in Literature as the prize for which Simone de Beauvoir was nominated.

To ensure the answer is clear, I will include the label of the prize in the final query.

```sparql
SELECT ?prize ?prizeLabel WHERE {
  wd:Q7197 wdt:P1411 ?prize .
  OPTIONAL { ?prize rdfs:label ?prizeLabel . FILTER(LANG(?prizeLabel) = "en") }
}
```

I will execute this refined query to confirm the result.

[Tool]
Got 1 row and 2 columns:
| prize                                 | prizeLabel                     |
| ------------------------------------- | ------------------------------ |
| Nobel Prize in Literature (wd:Q37922) | Nobel Prize in Literature (en) |

[Assistant]
The refined SPARQL query confirms that Simone de Beauvoir was nominated for the Nobel Prize in Literature.

Here is the final SPARQL query and answer:

```sparql
SELECT ?prize ?prizeLabel WHERE {
  wd:Q7197 wdt:P1411 ?prize .
  OPTIONAL { ?prize rdfs:label ?prizeLabel . FILTER(LANG(?prizeLabel) = "en") }
}
```

Answer: Simone de Beauvoir was nominated for the Nobel Prize in Literature.

I will now provide the final answer using the `answer` function.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?movie WHERE {
  wd:Q169604 wdt:P527 ?movie .
}

The movies that belong to the Cornetto trilogy are 'The World's End', 'Hot Fuzz', and 'Shaun of the Dead'.

[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] Which movies belong to the Cornetto trilogy ?

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

[Tool]
Example 1:
Question:
What is the highest mountain in Germany?

SPARQL:
SELECT ?uri WHERE {
  ?uri wdt:P31 wd:Q8502 ; wdt:P2044 ?elevation ; wdt:P17 wd:Q183 .
}
ORDER BY DESC ( ?elevation )
LIMIT 1

Using entities:
Germany, also known as BR Deutschland, Bundesrepublik Deutschland, Deutschland, Federal Republic of Germany (wd:Q183): country in Central Europe
mountain, also known as mt., mtn., mountains, mount (wd:Q8502): large natural elevation of the Earth's surface

Using properties:
country, also known as sovereign state, state, land, host country (wd:P17 as wdt): sovereign state that this item is in (not to be used for human beings), located in the administrative territorial entity
elevation above sea level, also known as MAMSL, MASL, height, elevation above mean sea level, elevation (wd:P2044 as wdt): height of the item (geographical object) as measured relative to sea level
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)

Example 2:
Question:
Who created Wikipedia?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q52 wdt:P112 ?uri .
}

Using entities:
Wikipedia, also known as Wikipedia, the free encyclopedia, The Free Encyclopedia, Wikipedia, the free encyclopedia that anyone can edit, WP, Wikipaedia (wd:Q52): free multilingual online encyclopedia

Using properties:
founded by, also known as starter, co-founded by, started by, institutor, instituted by (wd:P112 as wdt): founder or co-founder of this organization, religion, place or entity, creator

Example 3:
Question:
How many people live in Eurasia?

SPARQL:
SELECT DISTINCT ?val WHERE {
  wd:Q5401 wdt:P1082 ?val .
}

Using entities:
Eurasia, also known as Euroasia (wd:Q5401): continental landmass comprising Europe and Asia

Using properties:
population, also known as inhabitants, human population (wd:P1082 as wdt): number of people inhabiting the place; number of people of subject

[Assistant] None of these examples directly relate to the Cornetto trilogy, but we can see how entities and properties are used in the queries. Let's start by searching for the entity "Cornetto trilogy".

[Tool]
Top 10 entity alternatives:
1. Three Flavours Cornetto trilogy, also known as Blood and Ice Cream trilogy, Cornetto trilogy, The Cornetto Trilogy (wd:Q169604): 2004–2013 three films directed by Edgar Wright
2. trilogy (wd:Q149010): set of three works of art that are connected
3. cornett, also known as zink, cornetto (wd:Q204638): early wind instrument with a cup mouthpiece
4. Trilogy (wd:Q930723): 1972 studio album by Emerson, Lake & Palmer
5. Cornetto, also known as Drumstick (ice cream) (wd:Q1899311): type of ice cream
6. Trilogy (wd:Q1753107): Yngwie Malmsteen studio album
7. Trilogy (wd:Q7842079): 2012 compilation album by the Weeknd
8. cornetto (wd:Q15629893): pastry similar to a croissant
9. Trilogy (wd:Q1041486): ATB album
10. Cornetto (wd:Q5171700): human settlement in Città di Castello, Province of Perugia, Umbria, Italy

[Assistant] The first result, "Three Flavours Cornetto trilogy" (wd:Q169604), seems to be the correct entity for the Cornetto trilogy. Now, let's look for the movies that belong to this trilogy. We need to find a property that links the trilogy to its constituent movies. A common property for this relationship might be "part of" or "has part". Let's search for such properties.

[Tool]
Top 10 property alternatives:
1. has part(s), also known as has component, incorporates, has ingredient, has ingredients, has member (wd:P527 as p/wdt): part of this subject; inverse property of "part of" (P361). See also "has parts of the class" (P2670).
2. instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as p/wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
3. country of origin, also known as origin country, place of origin, originates from, country, CoO (wd:P495 as p/wdt): country of origin of this item (creative work, food, phrase, product, etc.), significant place, country
4. original language of film or TV show, also known as media language, original language, language of the original work, created on language (wd:P364 as p/wdt): language in which a film or a performance work was originally cr... works and songs; use P407 ("language of work or name") instead., language of work or name
5. native label, also known as official title, original label, original language label, original language name, original language title (wd:P1705 as p/wdt): label for the items in their official language  or their original language, name
6. publication date, also known as launched, published, publication time, publication, pubdate (wd:P577 as p/wdt): date or point in time when a work was first published or released, point in time
7. cast member, also known as feat., starring (film/play), film starring, starring, actor (wd:P161 as p/wdt): actor in the subject production [use "character role" (P453) and...use "recorded participant" (P11108) for non-fiction productions], significant person, performer
8. Freebase ID, also known as Freebase, MID, Freebase identifier, FID (wd:P646 as p/wdt): identifier for a page in the Freebase database. Format: "/m/0" f...r IDs starting with "/g/", use Google Knowledge Graph ID (P2671)
9. genre, also known as genre of music, type of music, type of film, music genre, literary genre (wd:P136 as p/wdt): creative work's genre or an artist's field of work (P101). Use main subject (P921) to relate creative works to their topic
10. topic's main template, also known as topic has template, topic's main module, topic's main infobox, topic infobox, main template (wd:P1424 as p/wdt): the main template relating to a topic

No other items found

[Assistant]
The property "has part(s)" (wd:P527) seems to be the most relevant for linking the trilogy to its constituent movies. Let's construct a SPARQL query to find the movies that are part of the Cornetto trilogy.

Here is the initial SPARQL query:

```sparql
SELECT ?movie WHERE {
  wd:Q169604 wdt:P527 ?movie .
}
```

Let's execute this query to see the results.

[Tool]
Got 3 rows and 1 column:
| movie                          |
| ------------------------------ |
| The World's End (wd:Q722379)   |
| Hot Fuzz (wd:Q729788)          |
| Shaun of the Dead (wd:Q858724) |

[Assistant]
The SPARQL query successfully returned the movies that belong to the Cornetto trilogy. The movies are:

- The World's End (wd:Q722379)
- Hot Fuzz (wd:Q729788)
- Shaun of the Dead (wd:Q858724)

Here is the final SPARQL query and the answer:

```sparql
SELECT ?movie WHERE {
  wd:Q169604 wdt:P527 ?movie .
}
```

Answer: The movies that belong to the Cornetto trilogy are "The World's End", "Hot Fuzz", and "Shaun of the Dead".

[Tool] Stopped generation process


PREFIX ps: <http://www.wikidata.org/prop/statement/>
PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
PREFIX pq: <http://www.wikidata.org/prop/qualifier/>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX p: <http://www.wikidata.org/prop/>
ASK WHERE {
  wd:Q169452 p:P54 ?shaqStatement .
  ?shaqStatement ps:P54 wd:Q121783 .
  ?shaqStatement pq:P580 ?shaqStartYear .
  ?shaqStatement pq:P582 ?shaqEndYear .
  wd:Q25369 p:P54 ?kobeStatement .
  ?kobeStatement ps:P54 wd:Q121783 .
  ?kobeStatement pq:P580 ?kobeStartYear .
  ?kobeStatement pq:P582 ?kobeEndYear .
  FILTER ( ?shaqStartYear <= "2000-01-01T00:00:00Z" ^^ xsd:dateTime && ?shaqEndYear >= "2000-01-01T00:00:00Z" ^^ xsd:dateTime ) FILTER ( ?kobeStartYear <= "2000-01-01T00:00:00Z" ^^ xsd:dateTime && ?kobeEndYear >= "2000-01-01T00:00:00Z" ^^ xsd:dateTime )
}

True

[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] Was Shaquille O'Neal the teammate of Kobe Bryant in the year of 2000?

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

[Tool]
Example 1:
Question:
What is the revenue of IBM?

SPARQL:
SELECT ?o1 WHERE {
  wd:Q37156 wdt:P2139 ?o1 .
}

Using entities:
IBM, also known as 18M, International Business Machines Corporation (Armonk, NY), International Business Machines Corporation, IBM Corporation, IBM Corp. (wd:Q37156): American multinational technology corporation

Using properties:
total revenue, also known as turnover, sales, revenue, income, gross revenue (wd:P2139 as wdt): income gained by an organization during a given time frame. Not to be confused with fiscal revenue

Example 2:
Question:
Which holidays are celebrated around the world?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?type wdt:P279 * wd:Q1445650 .
  ?uri wdt:P31 ?type .
}

Using entities:
holiday, also known as observances, Praznik, popular festival, observance, holidays (wd:Q1445650): festive day set aside by custom or by law

Using properties:
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)

Example 3:
Question:
Give me all Australian nonprofit organizations.

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P31 wd:Q163740 .
  {
    ?uri wdt:P159 | wdt:P17 wd:Q408 .
  } UNION {
    ?uri wdt:P159 | wdt:P17 ?city .
    ?city wdt:P159 wd:Q408
  }
}

Using entities:
nonprofit organization, also known as Organizations, Nonprofit, np organization, NPO, NPI, not-for-profit organization (wd:Q163740): organization operated for a collective benefit
Australia, also known as AU, The Commonwealth of Australia, Stria, Straya, Stralia (wd:Q408): country in Oceania

Using properties:
headquarters location, also known as headquarters locality, seat, headquarters town, HQ, HQ location (wd:P159 as wdt): city or town where an organization's headquarters is or has been situated. Use P276 qualifier for specific building, significant place
country, also known as sovereign state, state, land, host country (wd:P17 as wdt): sovereign state that this item is in (not to be used for human beings), located in the administrative territorial entity
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)

[Assistant] Based on the examples, we need to identify the entities and properties relevant to our question. We are looking for information about Shaquille O'Neal and Kobe Bryant being teammates in the year 2000. Let's start by searching for the entities Shaquille O'Neal and Kobe Bryant.

[Tool]
Top 10 entity alternatives:
1. Shaquille O'Neal, also known as Shaquille O’Neal, Shaquille Rashaun O'Neal, Shaq O'Neal, Shaquille Rashaun O’Neal, Shaq (wd:Q169452): American basketball player, real estate entrepreneur, television producer, sports commentator, basketball player, businessperson, film producer, screenwriter, film actor, Thai boxer, podcaster, singer, rapper, actor
2. Shaquille O'Neal (wd:Q125446160): episode of All That (S5 E3)
3. Shaquille Cleare, also known as Shaquille O'Neal Cleare (wd:Q61953486): college basketball player (2013–2017) Maryland; Texas, basketball player
4. Shaquille O'Neal Motors (wd:Q114535400): episode of Fresh Off the Boat (S2 E3)
5. Shaq Belson, also known as Shaquille Belson, Shaquille O'Neal Belson (wd:Q106725566): American American football player, American football player
6. Shaquille Allen, also known as Shaquille O'Neal Allen (wd:Q106725550): American football player
7. Upload with Shaquille O'Neal (wd:Q7898386): American comedy television series
8. Statue of Shaquille O'Neal (wd:Q114363921): sculpture in Los Angeles, California, U.S
9. The Best of Shaquille O'Neal (wd:Q7717219): album by Shaquille O'Neal
10. A Message from Shaquille O'Neal (wd:Q110058550): sketch from Saturday Night Live

[Tool]
Top 10 entity alternatives:
1. Kobe Bryant, also known as Kobe Bean Bryant, Kobe B. Bryant, Kobe, KB24, Black Mamba (wd:Q25369): American basketball player (1978–2020), professional athlete, basketball player, screenwriter
2. Kobe Bryant Day (wd:Q98969272): proposed Federal Holiday
3. death of Kobe Bryant, also known as Kobe Bryant death, Kobe Bryant's death (wd:Q83737550): 26 January 2020 death of Kobe Bryant in helicopter crash
4. Kobe Bryant Boulevard (wd:Q107299206): proposed street renaming in Los Angeles
5. NBA All-Star Game Kobe Bryant Most Valuable Player Award, also known as Kobe Bryant Most Valuable Player Award, Kobe Bryant MVP Award, NBA All-Star Game Kobe Bryant MVP Award (wd:Q31391): basketball award of the National Basketball Association (NBA)
6. Kobe Bryant China Fund (wd:Q6424297): for education, sports, and culture programs for children from China and the United States
7. Statue of Kobe Bryant (wd:Q125101336): sculpture in Los Angeles, California, U.S
8. Kobe Bryant/Ice Cube (wd:Q125446155): episode of All That (S4 E19)
9. Kobe Bryant sexual assault case (wd:Q6424298): legal case
10. Kobe Bryant in NBA Courtside (wd:Q3198120): 1998 video game

[Assistant] We have found the correct entities for Shaquille O'Neal and Kobe Bryant. Now, let's search for the property that represents being a teammate or playing for the same team.

[Tool]
Top 10 property alternatives:
1. partner in business or sport, also known as professional partner, sport partner, sports partner, team mate, team-mate (wd:P1327 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): professional collaborator, significant person
2. member of sports team, also known as plays for, played for, sport team, team, team played for (wd:P54 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): sports teams or clubs that the subject represents or represented, member of
3. position played on team / speciality, also known as position (on team), speciality, specialism, position, player position (wd:P413 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): position or specialism of a player on a team
4. team captain, also known as captain, sports team captain (wd:P634 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): captain of this sports team
5. unmarried partner, also known as romantic partner, partner of, partners, posslq, relationship (wd:P451 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): someone with whom the person is in a relationship without being married. Use "spouse" (P26) for married couples, significant person
6. spouse, also known as married partner, married to, marry, partner, spouses (wd:P26 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): the subject has the object as their spouse (husband, wife, partn...etc.). Use "unmarried partner" (P451) for non-married companions, significant person, relative
7. supported sports team, also known as Fan of sports team, team supported, Supports sports team, Sports team supported, is a fan of team (wd:P6758 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): sports team for which a person or a group is a supporter
8. participating team, also known as teams (wd:P1923 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): like 'Participant' (P710) but for teams. For an event like a cyc...member of sports team' (P54) as a qualifier for the individuals), participant
9. instrument, also known as uses this instrument, teaches instrument, instruments, plays this instrument, plays instrument (wd:P1303 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): musical instrument that a person plays or teaches or used in a music occupation, uses
10. participant, also known as suspect, player, party, participants, has participant (wd:P710 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): person, group of people or organization (object) that actively t...ct has role" (P3831). Use P1923 for participants that are teams., significant person

[Assistant]
The property `member of sports team` (P54) seems to be the most relevant for representing being a teammate. Next, we need to check if both Shaquille O'Neal and Kobe Bryant were members of the same team in the year 2000. We will look for the teams they played for and then check if they were on the same team in 2000.

First, let's find out the teams Shaquille O'Neal and Kobe Bryant played for.

### Query for Shaquille O'Neal's Teams
```sparql
SELECT ?team WHERE {
  wd:Q169452 wdt:P54 ?team .
}
```

### Query for Kobe Bryant's Teams
```sparql
SELECT ?team WHERE {
  wd:Q25369 wdt:P54 ?team .
}
```

Let's execute these queries.

[Tool]
Got 7 rows and 1 column:
| team                                      |
| ----------------------------------------- |
| Los Angeles Lakers (wd:Q121783)           |
| Boston Celtics (wd:Q131371)               |
| Orlando Magic (wd:Q161337)                |
| Cleveland Cavaliers (wd:Q162990)          |
| Phoenix Suns (wd:Q164177)                 |
| Miami Heat (wd:Q169138)                   |
| LSU Tigers men's basketball (wd:Q6460027) |

[Tool]
Got 1 row and 1 column:
| team                            |
| ------------------------------- |
| Los Angeles Lakers (wd:Q121783) |

[Assistant]
From the results, we can see that Shaquille O'Neal played for several teams, including the Los Angeles Lakers. Kobe Bryant played for the Los Angeles Lakers as well.

Next, we need to verify if they were both on the Los Angeles Lakers in the year 2000. We will check the years they played for the Los Angeles Lakers.

### Query for Shaquille O'Neal's Years with the Los Angeles Lakers
```sparql
SELECT ?year WHERE {
  wd:Q169452 p:P54 ?statement .
  ?statement ps:P54 wd:Q121783 .
  ?statement pq:P580 ?startYear .
  ?statement pq:P582 ?endYear .
  BIND(?startYear AS ?year)
  FILTER(?year <= 2000)
}
```

### Query for Kobe Bryant's Years with the Los Angeles Lakers
```sparql
SELECT ?year WHERE {
  wd:Q25369 p:P54 ?statement .
  ?statement ps:P54 wd:Q121783 .
  ?statement pq:P580 ?startYear .
  ?statement pq:P582 ?endYear .
  BIND(?startYear AS ?year)
  FILTER(?year <= 2000)
}
```

Let's execute these queries to find the years they played for the Los Angeles Lakers.

[Tool] Got no rows and 1 columns

[Tool] Got no rows and 1 columns

[Assistant]
It seems that the queries did not return any results, which suggests that the start and end years for their tenures with the Los Angeles Lakers might not be directly available in the way we queried. Let's try a different approach by checking the specific years they played for the Los Angeles Lakers.

### Query for Shaquille O'Neal's Years with the Los Angeles Lakers
```sparql
SELECT ?startYear ?endYear WHERE {
  wd:Q169452 p:P54 ?statement .
  ?statement ps:P54 wd:Q121783 .
  ?statement pq:P580 ?startYear .
  ?statement pq:P582 ?endYear .
}
```

### Query for Kobe Bryant's Years with the Los Angeles Lakers
```sparql
SELECT ?startYear ?endYear WHERE {
  wd:Q25369 p:P54 ?statement .
  ?statement ps:P54 wd:Q121783 .
  ?statement pq:P580 ?startYear .
  ?statement pq:P582 ?endYear .
}
```

Let's execute these queries to get the start and end years for their tenures with the Los Angeles Lakers.

[Tool]
Got 1 row and 2 columns:
| startYear                           | endYear                             |
| ----------------------------------- | ----------------------------------- |
| 1996-01-01T00:00:00Z (xsd:dateTime) | 2004-07-14T00:00:00Z (xsd:dateTime) |

[Tool]
Got 1 row and 2 columns:
| startYear                           | endYear                             |
| ----------------------------------- | ----------------------------------- |
| 1996-01-01T00:00:00Z (xsd:dateTime) | 2016-01-01T00:00:00Z (xsd:dateTime) |

[Assistant]
From the results, we can see that:

- Shaquille O'Neal played for the Los Angeles Lakers from 1996 to 2004.
- Kobe Bryant played for the Los Angeles Lakers from 1996 to 2016.

Since both players were on the Los Angeles Lakers from 1996 to 2004, they were indeed teammates in the year 2000.

To confirm this with a single ASK query, we can check if there is an overlap in their tenures during the year 2000.

### ASK Query
```sparql
ASK WHERE {
  wd:Q169452 p:P54 ?shaqStatement .
  ?shaqStatement ps:P54 wd:Q121783 .
  ?shaqStatement pq:P580 ?shaqStartYear .
  ?shaqStatement pq:P582 ?shaqEndYear .
  wd:Q25369 p:P54 ?kobeStatement .
  ?kobeStatement ps:P54 wd:Q121783 .
  ?kobeStatement pq:P580 ?kobeStartYear .
  ?kobeStatement pq:P582 ?kobeEndYear .
  FILTER(?shaqStartYear <= "2000-01-01T00:00:00Z"^^xsd:dateTime && ?shaqEndYear >= "2000-01-01T00:00:00Z"^^xsd:dateTime)
  FILTER(?kobeStartYear <= "2000-01-01T00:00:00Z"^^xsd:dateTime && ?kobeEndYear >= "2000-01-01T00:00:00Z"^^xsd:dateTime)
}
```

Let's execute this ASK query to confirm if they were teammates in the year 2000.

[Tool] True

[Assistant]
The ASK query confirms that Shaquille O'Neal and Kobe Bryant were indeed teammates on the Los Angeles Lakers in the year 2000.

Therefore, the answer to the question is **True**.

Here is the final SPARQL query and answer:

```sparql
ASK WHERE {
  wd:Q169452 p:P54 ?shaqStatement .
  ?shaqStatement ps:P54 wd:Q121783 .
  ?shaqStatement pq:P580 ?shaqStartYear .
  ?shaqStatement pq:P582 ?shaqEndYear .
  wd:Q25369 p:P54 ?kobeStatement .
  ?kobeStatement ps:P54 wd:Q121783 .
  ?kobeStatement pq:P580 ?kobeStartYear .
  ?kobeStatement pq:P582 ?kobeEndYear .
  FILTER(?shaqStartYear <= "2000-01-01T00:00:00Z"^^xsd:dateTime && ?shaqEndYear >= "2000-01-01T00:00:00Z"^^xsd:dateTime)
  FILTER(?kobeStartYear <= "2000-01-01T00:00:00Z"^^xsd:dateTime && ?kobeEndYear >= "2000-01-01T00:00:00Z"^^xsd:dateTime)
}
```

Answer: True

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ( COUNT( DISTINCT ?city ) AS ?cityCount ) WHERE {
  VALUES ?region {
    wd:Q15175 wd:Q8653 wd:Q15219
  }
  ?city wdt:P131 * ?region ; wdt:P31 ?cityType .
  VALUES ?cityType {
    wd:Q515 wd:Q1549591 wd:Q134266
  }
}

Got 1 row and 1 column:
| cityCount    |
| ------------ |
| 23 (xsd:int) |

[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] How many cities are part of the Pearl River Delta?

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

[Tool]
Example 1:
Question:
Who painted The Storm on the Sea of Galilee?

SPARQL:
SELECT ?uri WHERE {
  wd:Q2246489 wdt:P170 ?uri
}

Using entities:
The Storm on the Sea of Galilee, also known as Christ in the Storm on the Sea of Galilee, St. Pieters scheepje, Storm on the Sea of Galilee (wd:Q2246489): stolen painting by Rembrandt

Using properties:
creator, also known as created by, visual artist, sculptor, sculpted by, realized by (wd:P170 as wdt): maker of this creative work or other object (where no more specific property exists)

Example 2:
Question:
Where is the birthplace of Goethe?

SPARQL:
SELECT ?res WHERE {
  wd:Q5879 wdt:P19 ?res .
}

Using entities:
Johann Wolfgang von Goethe, also known as Johann Wolfgang Von Goethe, Goethe (wd:Q5879): German writer, artist, natural scientist and politician (1749–1832), theatre manager, autobiographer, art historian, travel writer, visual artist, statesperson, mineralogist, art theorist, music critic, philosopher, playwright, theologian, art critic, politician, librettist, librarian, physicist, zoologist, diplomat, aphorist, polymath, botanist, composer, novelist, literary, diarist, painter, writer, jurist, lawyer, poet

Using properties:
place of birth, also known as POB, location of birth, location born, born in, birthplace (wd:P19 as wdt): most specific known birth location of a person, animal or fictional character, location

Example 3:
Question:
Show me all Czech movies.

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P31 wd:Q11424 .
  {
    ?uri wdt:P495 wd:Q213 .
  } UNION {
    ?uri wdt:P495 wd:Q33946 .
  }
}

Using entities:
film, also known as cinematic work, moving picture, movie, motion pictures, motion picture (wd:Q11424): sequence of images that give the impression of movement, stored on film stock
Czech Republic, also known as CZ, CZE, CZR, Czechia (wd:Q213): country in Central Europe
Czechoslovakia, also known as TCH, People's Republic of Czechoslovakia, CSFR, Federation of Czechoslovakia, ČSFR (wd:Q33946): country in Central Europe, 1918–1992

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)
country of origin, also known as origin country, place of origin, originates from, country, CoO (wd:P495 as wdt): country of origin of this item (creative work, food, phrase, product, etc.), significant place, country

[Assistant] Based on the examples, we need to identify the entity for the Pearl River Delta and then find the cities that are part of it. Let's first search for the entity representing the Pearl River Delta.

[Tool]
Top 10 entity alternatives:
1. Pearl River Delta, also known as Canton River, Zhujiang Kou (wd:Q1144286): low-lying area surrounding the Pearl River estuary
2. Hong Kong–Zhuhai–Macau Bridge, also known as Hong Kong-Zhuhai-Macau Bridge, Pearl River Delta bridge, Pearl River Bridge Project, Macau-Hong Kong bridge, HZMB (wd:Q1191515): fixed link consisting of tunnel and multiple bridges
3. Pearl River Delta Economic Zone (wd:Q7158154): Economic region in southeastern China
4. Foshan Gaoming International Airport, also known as Pearl River Delta International Airport (wd:Q59569145): proposed airport in China
5. Tastefully in Pearl River Delta (wd:Q10921831)
6. G94 Pearl River Delta Ring Expressway (wd:Q594813): beltway connecting Hong Kong and Macau SARs, and cities near Zhujiangkou in Guangdong
7. Geology of the Pearl River Delta (wd:Q123455489)
8. Pearl River Delta Metropolitan Region Intercity Railway, also known as PRDIR (wd:Q4121252): Regional rail network in South China
9. Guangdong Pearl River Delta Intercity Railway Co., Ltd., also known as PRDIR (wd:Q116828690): company in Guangzhou, China
10. Pearl River Delta’s Regional Differentiation and Synergy (wd:Q119500806): scholarly article in Jinan Journal (Philosophy & Social Science Edition), no. 6, 2010

[Assistant] The most relevant entity for the Pearl River Delta is `wd:Q1144286`. Now, let's find out what properties are associated with this entity to understand how cities might be linked to it.

[Tool]
Got 228 rows and 3 columns, showing the first 10 rows below:
| subject                         | property                                                    | object                                                                              |
| ------------------------------- | ----------------------------------------------------------- | ----------------------------------------------------------------------------------- |
| Pearl River Delta (wd:Q1144286) | instance of (wdt:P31)                                       | river delta (wd:Q43197)                                                             |
| Pearl River Delta (wd:Q1144286) | country (wdt:P17)                                           | People's Republic of China (wd:Q148)                                                |
| Pearl River Delta (wd:Q1144286) | located in the administrative territorial entity (wdt:P131) | Guangdong (wd:Q15175)                                                               |
| Pearl River Delta (wd:Q1144286) | coordinate location (wdt:P625)                              | POINT(113.733333 22.533333) (geo:wktLiteral)                                        |
| Pearl River Delta (wd:Q1144286) | Commons category (wdt:P373)                                 | Pearl River Delta                                                                   |
| Pearl River Delta (wd:Q1144286) | image (wdt:P18)                                             | <http://commons.wikimedia.org/wiki/Special:FilePath/Geography%20of%20Guangdong.gif> |
| Pearl River Delta (wd:Q1144286) | has part(s) (wdt:P527)                                      | Guangzhou (wd:Q16572)                                                               |
| Pearl River Delta (wd:Q1144286) | different from (wdt:P1889)                                  | wd:Q3332782                                                                         |
| Pearl River Delta (wd:Q1144286) | topic's main category (wdt:P910)                            | wd:Q8719165                                                                         |
| Pearl River Delta (wd:Q1144286) | page banner (wdt:P948)                                      | <http://commons.wikimedia.org/wiki/Special:FilePath/Shenzhen%20banner.jpg>          |
| ...                             | ...                                                         | ...                                                                                |

[Assistant]
From the list of properties, we see that the Pearl River Delta has parts, which include cities. The property `has part(s)` (`wdt:P527`) links the Pearl River Delta to its constituent cities. We can use this property to find the cities that are part of the Pearl River Delta.

Let's construct a SPARQL query to list these cities and count them.

```sparql
SELECT (COUNT(?city) AS ?cityCount)
WHERE {
  wd:Q1144286 wdt:P527 ?city .
}
```

Now, let's execute this query to get the number of cities.

[Tool]
Got 1 row and 1 column:
| cityCount   |
| ----------- |
| 3 (xsd:int) |

[Assistant]
The query returned that there are 3 cities that are part of the Pearl River Delta. However, this seems too low, so let's verify the cities listed to ensure we are capturing all relevant cities.

We can modify the query to list the cities and their labels to see if we are missing any.

```sparql
SELECT ?city ?cityLabel
WHERE {
  wd:Q1144286 wdt:P527 ?city .
  OPTIONAL { ?city rdfs:label ?cityLabel . FILTER(LANG(?cityLabel) = "en") }
}
```

Let's execute this query to see the cities.

[Tool]
Got 3 rows and 2 columns:
| city                  | cityLabel      |
| --------------------- | -------------- |
| Shenzhen (wd:Q15174)  | Shenzhen (en)  |
| Guangzhou (wd:Q16572) | Guangzhou (en) |
| Zhuhai (wd:Q59095)    | Zhuhai (en)    |

[Assistant]
The query returned three cities: Shenzhen, Guangzhou, and Zhuhai. These are indeed major cities in the Pearl River Delta, but it seems like there might be more cities that are part of the region.

To ensure we capture all relevant cities, let's check if there are other properties or relationships that might link cities to the Pearl River Delta. One possibility is to look for cities that are located within the administrative region of the Pearl River Delta.

Let's try another approach by searching for cities that are located in the administrative territorial entity of the Pearl River Delta.

```sparql
SELECT ?city ?cityLabel
WHERE {
  ?city wdt:P131* wd:Q1144286 .
  OPTIONAL { ?city rdfs:label ?cityLabel . FILTER(LANG(?cityLabel) = "en") }
}
```

This query will find cities that are directly or indirectly located within the Pearl River Delta. Let's execute this query.

[Tool]
Got 1 row and 2 columns:
| city                            | cityLabel              |
| ------------------------------- | ---------------------- |
| Pearl River Delta (wd:Q1144286) | Pearl River Delta (en) |

[Assistant]
It seems that the query did not return any additional cities, only the Pearl River Delta itself. This suggests that the relationship `located in the administrative territorial entity` (`wdt:P131`) might not be the right approach for finding all cities in the region.

Given that the initial query using `has part(s)` (`wdt:P527`) returned only three cities, and considering the Pearl River Delta is a well-known economic and urban region, it's likely that there are more cities that are part of it but not directly linked through this property.

Let's try another approach by searching for cities that are part of the administrative regions within the Pearl River Delta, such as Guangdong province and the Special Administrative Regions of Hong Kong and Macau.

We can use the property `located in the administrative territorial entity` (`wdt:P131`) to find cities within these regions.

```sparql
SELECT ?city ?cityLabel
WHERE {
  VALUES ?region { wd:Q15175 wd:Q8653 wd:Q15219 }
  ?city wdt:P131* ?region .
  OPTIONAL { ?city rdfs:label ?cityLabel . FILTER(LANG(?cityLabel) = "en") }
}
```

This query will find cities located in Guangdong province (wd:Q15175), Hong Kong (wd:Q8653), and Macau (wd:Q15219). Let's execute this query.

[Tool]
Got 32,981 rows and 2 columns, showing the first 5 and last 5 rows below:
| city                                                                  | cityLabel                                                   |
| --------------------------------------------------------------------- | ----------------------------------------------------------- |
| Fengxi District (wd:Q1001024)                                         | Fengxi District (en)                                        |
| Luohu District (wd:Q1001376)                                          | Luohu District (en)                                         |
| Yantian District (wd:Q1001381)                                        | Yantian District (en)                                       |
| Longgang District (wd:Q1001406)                                       | Longgang District (en)                                      |
| Foshan railway station (wd:Q1002783)                                  | Foshan railway station (en)                                 |
| ...                                                                   | ...                                                         |
| Site of Palace and Garden, Nanyue King Museum (wd:Q99575674)          | Site of Palace and Garden, Nanyue King Museum (en)          |
| Dapeng Subdistrict (wd:Q996489)                                       | Dapeng Subdistrict (en)                                     |
| Wutong Mountain (wd:Q996504)                                          | Wutong Mountain (en)                                        |
| Qianhaiwan Station (wd:Q996539)                                       | Qianhaiwan Station (en)                                     |
| 2020 detainment of Hong Kong residents at sea by China (wd:Q99770698) | 2020 detainment of Hong Kong residents at sea by China (en) |

[Assistant]
The query returned a large number of administrative units, including districts and subdistricts, which are not necessarily cities. To refine our query, we need to filter for entities that are specifically classified as cities.

We can use the property `instance of` (`wdt:P31`) to filter for entities that are instances of a city (`wd:Q515`).

Let's modify the query to include this filter:

```sparql
SELECT ?city ?cityLabel
WHERE {
  VALUES ?region { wd:Q15175 wd:Q8653 wd:Q15219 }
  ?city wdt:P131* ?region ;
        wdt:P31 wd:Q515 .
  OPTIONAL { ?city rdfs:label ?cityLabel . FILTER(LANG(?cityLabel) = "en") }
}
```

This query will find cities that are located in Guangdong province, Hong Kong, and Macau. Let's execute this query to get the list of cities.

[Tool]
Got 1 row and 2 columns:
| city                 | cityLabel     |
| -------------------- | ------------- |
| Shenzhen (wd:Q15174) | Shenzhen (en) |

[Assistant]
The query returned only one city, Shenzhen, which is not comprehensive enough. It seems that the filtering for cities using `instance of` (`wdt:P31`) and `city` (`wd:Q515`) might be too restrictive.

Let's try a more inclusive approach by looking for entities that are classified as urban areas or municipalities, which might include more cities in the Pearl River Delta.

We can use the property `instance of` (`wdt:P31`) to filter for entities that are instances of urban areas (`wd:Q1549591`) or municipalities (`wd:Q134266`).

Let's modify the query to include these additional filters:

```sparql
SELECT ?city ?cityLabel
WHERE {
  VALUES ?region { wd:Q15175 wd:Q8653 wd:Q15219 }
  ?city wdt:P131* ?region ;
        wdt:P31 ?cityType .
  VALUES ?cityType { wd:Q515 wd:Q1549591 wd:Q134266 }
  OPTIONAL { ?city rdfs:label ?cityLabel . FILTER(LANG(?cityLabel) = "en") }
}
```

This query will find cities, urban areas, and municipalities that are located in Guangdong province, Hong Kong, and Macau. Let's execute this query to get the list of cities.

[Tool]
Got 24 rows and 2 columns, showing the first 5 and last 5 rows below:
| city                   | cityLabel      |
| ---------------------- | -------------- |
| Lianzhou (wd:Q1023910) | Lianzhou (en)  |
| Xingning (wd:Q1023920) | Xingning (en)  |
| Shenzhen (wd:Q15174)   | Shenzhen (en)  |
| Shenzhen (wd:Q15174)   | Shenzhen (en)  |
| Guangzhou (wd:Q16572)  | Guangzhou (en) |
| ...                    | ...            |
| Qingyuan (wd:Q59213)   | Qingyuan (en)  |
| Dongguan (wd:Q59218)   | Dongguan (en)  |
| Zhongshan (wd:Q59223)  | Zhongshan (en) |
| Chaozhou (wd:Q59227)   | Chaozhou (en)  |
| Yunfu (wd:Q59233)      | Yunfu (en)     |

[Assistant]
The query returned 24 cities, which is a more comprehensive list of cities in the Pearl River Delta region. This includes major cities like Shenzhen, Guangzhou, and Dongguan, as well as other significant urban areas.

To provide a final answer, we can count the number of distinct cities returned by the query.

Let's modify the query to count the distinct cities:

```sparql
SELECT (COUNT(DISTINCT ?city) AS ?cityCount)
WHERE {
  VALUES ?region { wd:Q15175 wd:Q8653 wd:Q15219 }
  ?city wdt:P131* ?region ;
        wdt:P31 ?cityType .
  VALUES ?cityType { wd:Q515 wd:Q1549591 wd:Q134266 }
}
```

Let's execute this query to get the final count of cities.

[Tool]
Got 1 row and 1 column:
| cityCount    |
| ------------ |
| 23 (xsd:int) |

[Assistant]
The final query returned that there are 23 distinct cities that are part of the Pearl River Delta region.

Therefore, the answer to the question "How many cities are part of the Pearl River Delta?" is:

**23**

Here is the final SPARQL query:

```sparql
SELECT (COUNT(DISTINCT ?city) AS ?cityCount)
WHERE {
  VALUES ?region { wd:Q15175 wd:Q8653 wd:Q15219 }
  ?city wdt:P131* ?region ;
        wdt:P31 ?cityType .
  VALUES ?cityType { wd:Q515 wd:Q1549591 wd:Q134266 }
}
```

I will now provide the final answer using the `answer` function.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?publisher WHERE {
  wd:Q2357719 wdt:P123 ?publisher .
}

Hi-Rez Studios

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

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

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

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

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

[User] Who is the publisher of Smite ?

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

[Tool]
Example 1:
Question:
Where is Fort Knox located?

SPARQL:
SELECT ?uri {
  wd:Q8962067 wdt:P131 + ?uri .
}

Using entities:
Fort Knox, also known as Fort Knox CDP, Fort Knox, Kentucky, Fort Knox, KY (wd:Q8962067): census-designated place in Kentucky, 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 2:
Question:
What is the highest mountain in Italy?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P31 wd:Q8502 ; wdt:P17 wd:Q38 ; wdt:P2044 ?elevation .
}
ORDER BY DESC ( ?elevation )
LIMIT 1

Using entities:
Italy, also known as Italia, Republic of Italy, Repubblica Italiana, Italian Republic, ITA (wd:Q38): country in Southern Europe
mountain, also known as mt., mtn., mountains, mount (wd:Q8502): large natural elevation of the Earth's surface

Using properties:
country, also known as sovereign state, state, land, host country (wd:P17 as wdt): sovereign state that this item is in (not to be used for human beings), located in the administrative territorial entity
elevation above sea level, also known as MAMSL, MASL, height, elevation above mean sea level, elevation (wd:P2044 as wdt): height of the item (geographical object) as measured relative to sea level
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)

Example 3:
Question:
Show me all books in Asimov's Foundation series.

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q1564644 wdt:P527 ?uri
}

Using entities:
Foundation series, also known as The Foundation Series (wd:Q1564644): series of science fiction books by Isaac Asimov

Using properties:
has part(s), also known as has component, incorporates, has ingredient, has ingredients, has member (wd:P527 as wdt): part of this subject; inverse property of "part of" (P361). See also "has parts of the class" (P2670).

[Assistant] Based on these examples, we need to identify the entity for the game "Smite" and then find the property that links it to its publisher. Let's first search for the entity "Smite".

[Tool]
Top 10 entity alternatives:
1. game, also known as games, plays and games, recreational game, recreational games (wd:Q11410): structured form of play
2. game, also known as game animal, game animals, quarry (wd:Q223930): wild animals under pursuit or taken in hunting
3. The Game, also known as Game, Jayceon Terrell Taylor, Jayceon Taylor (wd:Q189936): American rapper, record producer and actor, record producer, music executive, songwriter, musician, rapper, actor
4. The Game, also known as Game (wd:Q723187): 1997 film directed by David Fincher
5. Smite (wd:Q2357719): 2014 video game
6. Game (wd:Q1007948): album by J-pop group Perfume
7. dueling scar, also known as Mensur scar, bragging scar, smite (wd:Q340053): facial scars left by the sport of academic fencing
8. Game Digital plc, also known as Game, GAME, Game Digital, The Game Group plc (wd:Q5519813): British video games retail company
9. Game (wd:Q2402016): 2011 Hindi film directed by Abhinay Deo
10. Game (wd:Q16248471): 2002 film

[Assistant] The correct entity for the game "Smite" is `wd:Q2357719`. Now, let's find the property that links this game to its publisher. We can search for properties related to "publisher" for this specific entity.

[Tool]
Top 10 property alternatives:
1. publisher, also known as video game publisher, software publisher, comic book publisher, sheet music publisher, publishing house (wd:P123 as p/wdt): organization or person responsible for publishing books, periodicals, printed music, podcasts, games or software
2. distribution format, also known as book format, type of media, publication format, method of distribution, medium (wd:P437 as p/wdt): method (or type) of distribution for the subject
3. logo image, also known as masthead, wordmark, trademark, trade mark, sign (wd:P154 as p/wdt): graphic mark or emblem commonly used by commercial enterprises, organizations and products, icon
4. publication date, also known as launched, published, publication time, publication, pubdate (wd:P577 as p/wdt): date or point in time when a work was first published or released, point in time
5. platform, also known as computing platform, runtime, published on, platforms, hardware (wd:P400 as p/wdt): platform for which a work was developed or released, or the specific platform version of a software product
6. title, also known as name, titled, page name, original title, known as (wd:P1476 as p/wdt): published name of a work, such as a newspaper article, a literary work, piece of music, a website, or a performance work, name
7. distributed by, also known as distributor (wd:P750 as p/wdt): distributor of a creative work; distributor for a record label; news agency; film distributor
8. genre, also known as genre of music, type of music, type of film, music genre, literary genre (wd:P136 as p/wdt): creative work's genre or an artist's field of work (P101). Use main subject (P921) to relate creative works to their topic
9. developer, also known as written by, video game developer, video game dev, game developer, game dev (wd:P178 as p/wdt): organization or person that developed the item, creator
10. 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 p/wdt): language associated with this creative work (such as books, show...guage" (P103) and "languages spoken, written or signed" (P1412))

No other items found

[Assistant]
The property that links the game "Smite" to its publisher is `wdt:P123`. Now, let's construct a SPARQL query to find the publisher of the game "Smite".

```sparql
SELECT ?publisher WHERE {
  wd:Q2357719 wdt:P123 ?publisher .
}
```

Let's execute this query to get the result.

[Tool]
Got 1 row and 1 column:
| publisher                    |
| ---------------------------- |
| Hi-Rez Studios (wd:Q5750244) |

[Assistant]
The publisher of the game "Smite" is Hi-Rez Studios.

Here is the final SPARQL query and answer:

```sparql
SELECT ?publisher WHERE {
  wd:Q2357719 wdt:P123 ?publisher .
}
```

Answer: Hi-Rez Studios

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ( COUNT( ?resort ) AS ?count ) WHERE {
  ?resort wdt:P31 wd:Q130003 ; wdt:P17 wd:Q142 .
}

[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] How many ski resorts were there ever in France?

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

[Tool]
Example 1:
Question:
Give me the homepage of Forbes.

SPARQL:
SELECT ?uri WHERE {
  wd:Q25161472 wdt:P856 ?uri
}

Using entities:
Forbes, also known as forbes.com, Forbes magazine (wd:Q25161472): American business magazine

Using properties:
official website, also known as site (official), home page, site official, URL, URL (official) (wd:P856 as wdt): 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

Example 2:
Question:
Which instruments does Cat Stevens play?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q154216 wdt:P1303 ?uri .
}

Using entities:
Cat Stevens, also known as Yusuf Islam, Yusuf Islam (Cat Stevens), Yusuf/Cat Stevens, Yusuf (wd:Q154216): British musician (born 1948), singer-songwriter, recording artist, guitarist, musician, composer, singer

Using properties:
instrument, also known as uses this instrument, teaches instrument, instruments, plays this instrument, plays instrument (wd:P1303 as wdt): musical instrument that a person plays or teaches or used in a music occupation, uses

Example 3:
Question:
Which programming languages were influenced by Perl?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P31 wd:Q9143 ; wdt:P737 wd:Q42478 .
}

Using entities:
Perl, also known as Perl/CGI, Practical Extraction and Reporting Language, perl, Pearl (wd:Q42478): interpreted programming language first released in 1987
programming language, also known as coding language, computer language, computer programming, computer programming language (wd:Q9143): language for communicating instructions to a machine

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)
influenced by, also known as favorite player, has influence, informed by, role model (wd:P737 as wdt): this person, idea, etc. is informed by that other person, idea, etc., e.g. “Heidegger was influenced by Aristotle”

[Assistant] Based on the examples, we need to identify the entity for "France" and the property for "ski resort". Let's start by searching for the entity "France".

[Tool]
Top 10 entity alternatives:
1. France, also known as FR, FRA, French Republic (wd:Q142): country in Western Europe
2. France men's national association football team, also known as France, France men's national football team, France national association football team, France national football team, France national soccer team (wd:Q47774): national association football team representing France
3. French Fifth Republic, also known as 5th French Republic, French Republic, French 5th Republic, France, Fifth French Republic (wd:Q200686): current system of government of France (1958–present)
4. Bourbon Restoration in France, also known as France, Kingdom of France (wd:Q207162): period of French history, 1814–1830
5. French Community, also known as France (wd:Q1120363): International political association
6. France at the 2008 Summer Olympics, also known as France (wd:Q146845): sporting event delegation
7. France, also known as French battleship France (wd:Q49644): 1912 Courbet-class battleship
8. France, also known as On a Half Clear Morning (wd:Q76488728): 2021 film directed by Bruno Dumont
9. La France, also known as France (wd:Q3209212): 2007 film by Serge Bozon
10. France (wd:Q5478259): constituency of the European Parliament

[Assistant] The correct entity for "France" is `wd:Q142`. Next, let's search for the property related to "ski resort".

[Tool]
Top 10 property alternatives:
1. Skiresort.info ID, also known as Ski resort ID, Skiresort ID (wd:P6389 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): Record on skiresort.info for any specific ski resort
2. SkiMo Stats ID, also known as skimostats.com ID, skimostats.com identifier, SkiMo Stats identifier (wd:P8146 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): alphabetical identifier for ski mountaineers at skimostats.com
3. mountain range, also known as system of mountains, hill range, range, parent range, mountain system (wd:P4552 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): range or subrange to which the geographical item belongs, geomorphological unit
4. winter view, also known as snowy picture, winter image, snowy image, snowscape, image in winter (wd:P5252 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): image of the subject in winter, preferably with snow, image
5. FIS alpine skier ID, also known as FIS alpine skier identifier (wd:P2772 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): identifier for an alpine skier, in the International Ski Federation database
6. FIS grass skier ID, also known as FIS grass skier identifier (wd:P8845 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): identifier for a grass skier in the International Ski Federation database
7. Skimap area ID (wd:P6400 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): Record page on www.Skimap.org for ski resort, with current and historical official published maps of lifts and ski-runs
8. Ski-DB skier ID, also known as Alpine Ski Database skier ID, Ski-db.com ID, Ski-db.com skier ID, Ski-DB ID, SKI-DB skier ID (wd:P3619 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): ID for a ski racer at Ski-DB Alpine Ski Database
9. FIS ski jumper ID, also known as FIS ski jumper identifier (wd:P2775 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): identifier for a ski jumper, in the International Ski Federation database
10. Swedish Portrait Archive ID, also known as SPA (wd:P4819 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): identifier for a person in the Swedish Portrait Archive

[Assistant] None of the properties listed directly correspond to "ski resort". However, we can try to find entities that are ski resorts and then count them. We can use the property `P31` (instance of) to look for instances of ski resorts. First, let's find the entity for "ski resort".

[Tool]
Top 10 entity alternatives:
1. ski resort, also known as ski field, skifield, skiing centre, ski station, winter sports resort (wd:Q130003): mountainside developed for skiing, snowboarding, and other winter sports
2. Dizin Ski Resort, also known as Dizin (wd:Q259613): ski resort in Karaj County, Iran
3. Masikryong Ski Resort (wd:Q16602030): Ski resort in Kangwon-do, North Korea
4. Sundance Resort, also known as Sundance Ski Resort, Sundance Ski Area, Sundance Mountain Resort (wd:Q2027196): resort in the Wasatch Range in Utah County, Utah, United States
5. Yabuli Ski Resort (wd:Q1005993): ski resort in China
6. Candanchú ski resort, also known as Candanchu, Candanchú (wd:Q588136): ski resort in Spain
7. Perisher Ski Resort (wd:Q1629394): Australian ski resort
8. Brezovica Ski Resort, also known as Brezovica Ski Center (wd:Q16953614): Ski resort in Kosovo
9. Darbandsar Ski Resort (wd:Q5875140): ski resort in Iran
10. Furano Ski Resort (wd:Q5509476): Ski Resort in Furano, Hokkaido

[Assistant] The correct entity for "ski resort" is `wd:Q130003`. Now, we can construct a SPARQL query to find all ski resorts in France by using the `P17` (country) property to filter by France (`wd:Q142`). Let's execute this query.

[Tool]
Got 261 rows and 1 column, showing the first 5 and last 5 rows below:
| resort                                     |
| ------------------------------------------ |
| Val Thorens (wd:Q1026835)                  |
| Domaine nordique du Meygal (wd:Q104666663) |
| La Rosière (wd:Q1058626)                   |
| Tignes Val Claret (wd:Q107451789)          |
| Saint-Michel-de-Chaillol (wd:Q1085709)     |
| ...                                        |
| Le Corbier (wd:Q944476)                    |
| Crévoux (wd:Q950651)                       |
| La Plagne (wd:Q969723)                     |
| Les Deux Alpes (wd:Q974478)                |
| La Stèle (wd:Q97774986)                    |

[Assistant]
The query returned 261 ski resorts in France. To get the exact count, we can modify the query to count the number of results.

Let's execute the modified query to get the count.

[Tool]
Got 1 row and 1 column:
| count         |
| ------------- |
| 261 (xsd:int) |

[Assistant]
The query returned the count of 261 ski resorts in France.

Therefore, the answer to the question "How many ski resorts were there ever in France?" is 261.

Here is the final SPARQL query:

```sparql
SELECT (COUNT(?resort) AS ?count) WHERE {
  ?resort wdt:P31 wd:Q130003 ;
          wdt:P17 wd:Q142 .
}
```

And the answer is 261.

[Tool] Stopped generation process