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 use them as a reference for structuring our query. Let's first search for the entity "Cornetto trilogy" in the knowledge graph.

[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 top result is the "Three Flavours Cornetto trilogy" (wd:Q169604), which is exactly what we're looking for. Now let's find out which movies belong to this trilogy. We need to look for properties that connect the trilogy to its constituent movies.

I'll search for properties related to "movies" or "films" for the entity "Three Flavours Cornetto trilogy".

[Tool]
Top 10 property alternatives:
1. genre, also known as genre of music, type of music, type of film, music genre, literary genre (wd:P136 as wdt/p): creative work's genre or an artist's field of work (P101). Use main subject (P921) to relate creative works to their topic
2. cast member, also known as feat., starring (film/play), film starring, starring, actor (wd:P161 as wdt/p): actor in the subject production [use "character role" (P453) and...use "recorded participant" (P11108) for non-fiction productions], significant person, performer
3. screenwriter, also known as story by, teleplay by, video game writer, writer (of screenplays), writing credits (wd:P58 as wdt/p): person(s) who wrote the script for subject item, film crew member, author
4. native label, also known as official title, original label, original language label, original language name, original language title (wd:P1705 as wdt/p): label for the items in their official language  or their original language, name
5. director, also known as film director, movie director, game director, directed by (wd:P57 as wdt/p): director(s) of film, TV-series, stageplay, video game or similar, contributor to the creative work or subject, creator
6. Eiga.com movie ID (wd:P7222 as wdt/p): identifier of movie on Eiga.com database
7. has part(s), also known as has component, incorporates, has ingredient, has ingredients, has member (wd:P527 as wdt/p): part of this subject; inverse property of "part of" (P361). See also "has parts of the class" (P2670).
8. publication date, also known as launched, published, publication time, publication, pubdate (wd:P577 as wdt/p): date or point in time when a work was first published or released, point in time
9. 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 wdt/p): 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
10. country of origin, also known as origin country, place of origin, originates from, country, CoO (wd:P495 as wdt/p): country of origin of this item (creative work, food, phrase, product, etc.), significant place, country

No other items found

[Assistant]
The property "has part(s)" (wd:P527) seems to be the most relevant for connecting the trilogy to its constituent movies. Let's use this property to find the movies that are part of the "Three Flavours Cornetto trilogy".

I'll construct a SPARQL query to retrieve the movies that are part of the trilogy.

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

Let's execute this query.

[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 retrieved the movies that are part of the "Three Flavours Cornetto trilogy". The movies are:

1. **The World's End** (wd:Q722379)
2. **Hot Fuzz** (wd:Q729788)
3. **Shaun of the Dead** (wd:Q858724)

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

```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

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query successfully retrieves the movies that belong to the Cornetto trilogy, and the human-readable answer lists all three movies correctly.


PREFIX pq: <http://www.wikidata.org/prop/qualifier/>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX p: <http://www.wikidata.org/prop/>
PREFIX ps: <http://www.wikidata.org/prop/statement/>
ASK {
  wd:Q169452 p:P54 ?statement1 .
  ?statement1 ps:P54 wd:Q121783 .
  ?statement1 pq:P580 ?startDate1 .
  ?statement1 pq:P582 ?endDate1 .
  FILTER ( YEAR( ?startDate1 ) <= 2000 && YEAR( ?endDate1 ) >= 2000 ) .
  wd:Q25369 p:P54 ?statement2 .
  ?statement2 ps:P54 wd:Q121783 .
  ?statement2 pq:P580 ?startDate2 .
  ?statement2 pq:P582 ?endDate2 .
  FILTER ( YEAR( ?startDate2 ) <= 2000 && YEAR( ?endDate2 ) >= 2000 ) .
}

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

[Assistant] The correct entity for Shaquille O'Neal is `wd:Q169452`. Now let's search for Kobe Bryant.

[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]
The correct entity for Kobe Bryant is `wd:Q25369`. Next, we need to find out if they were teammates in the year 2000. To do this, we need to look for a property that indicates their team membership and the year.

Let's search for the property that represents team membership.

[Tool]
Top 10 property alternatives:
1. member of sports team, also known as plays for, played for, sport team, team, team played for (wd:P54 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): sports teams or clubs that the subject represents or represented, member of
2. represents, also known as represents organisation, stands for, symbolises, symbolizes, team for (wd:P1268 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): organization, individual, or concept that an entity represents
3. participating team, also known as teams (wd:P1923 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): like 'Participant' (P710) but for teams. For an event like a cyc...member of sports team' (P54) as a qualifier for the individuals), participant
4. category for members of a team, also known as category for players, category for team members (wd:P6112 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): Wikimedia category for players playing for a sports team, topic's main category
5. size of team at start (wd:P2103 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): number of players/sled dogs/etc. in a team at the start of a match or race
6. partner in business or sport, also known as professional partner, sport partner, sports partner, team mate, team-mate (wd:P1327 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): professional collaborator, significant person
7. backup or reserve team or crew, also known as backup crew, backup team, reserve crew, reserve team (wd:P3015 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): team or crew that is kept ready to act as reserve
8. 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 pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): sports team for which a person or a group is a supporter
9. position played on team / speciality, also known as position (on team), speciality, specialism, position, player position (wd:P413 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): position or specialism of a player on a team
10. size of team at finish, also known as size of team at the end (wd:P2105 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): number of players/sled dogs/etc. in a team at the end of the race or match

[Assistant]
The property `member of sports team` (wd:P54) seems to be the most relevant for indicating team membership. We need to check if both Shaquille O'Neal and Kobe Bryant were members of the same team in the year 2000.

Let's start by checking Shaquille O'Neal's team membership in the year 2000.

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

[Assistant] Shaquille O'Neal was a member of the Los Angeles Lakers in the year 2000. Now, let's check if Kobe Bryant was also a member of the Los Angeles Lakers in the same year.

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

[Assistant]
Kobe Bryant was also a member of the Los Angeles Lakers in the year 2000. Since both Shaquille O'Neal and Kobe Bryant were members of the Los Angeles Lakers in the year 2000, they were indeed teammates.

We can now formulate an ASK query to confirm this.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query is correctly formulated to check if Shaquille O'Neal and Kobe Bryant were teammates on the Los Angeles Lakers in the year 2000. The query returns 'True', which is the correct answer based on historical facts.


Failed to generate SPARQL query

[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 representing the Pearl River Delta and then find the number of cities associated with it. Let's start by searching for the entity "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`. Next, we need to find out what properties are associated with this entity to understand how cities might be linked to it. Let's list the triples where `wd:Q1144286` is the subject.

[Tool]
Got 231 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 triples, we see that the Pearl River Delta has parts, specifically cities like Guangzhou (`wd:Q16572`). We can use the `has part(s)` property (`wdt:P527`) to find all the cities that are part of the Pearl River Delta. Let's construct a SPARQL query to count these cities.

Here is the initial SPARQL query:

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

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

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

[Assistant]
The initial query returned 3 cities. However, this seems too low for the Pearl River Delta, which is known to include more cities. Let's verify if there are other properties that might link cities to the Pearl River Delta. We can also check if there are other entities that represent the region more comprehensively.

Let's search for properties related to "city" or "metropolitan area" for the Pearl River Delta entity.

[Tool]
Top 10 property alternatives:
1. 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/p): the item is located on the territory of the following administra...if the item falls only partially into the administrative entity., location, part of
2. 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
3. image, also known as still capture, still, screenshot, screen capture, portrait (wd:P18 as wdt/p): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)
4. coordinate location, also known as point on a map, gps coordinates, gps location, latitude, location (wd:P625 as wdt/p): geocoordinates of the subject. For Earth, please note that only WGS84 coordinating system is supported at the moment
5. instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt/p): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
6. Freebase ID, also known as Freebase, MID, Freebase identifier, FID (wd:P646 as wdt/p): identifier for a page in the Freebase database. Format: "/m/0" f...r IDs starting with "/g/", use Google Knowledge Graph ID (P2671)
7. topic's main category, also known as article's main category, category, category for this topic, catmain, has category (wd:P910 as wdt/p): main Wikimedia category
8. has part(s), also known as has component, incorporates, has ingredient, has ingredients, has member (wd:P527 as wdt/p): part of this subject; inverse property of "part of" (P361). See also "has parts of the class" (P2670).
9. different from, also known as not to be confused with, to be distinguished from, similar to, see also, rejected match (wd:P1889 as wdt/p): item that is different from another item, with which it may be confused

No other items found

[Assistant]
The search for properties related to "city" or "metropolitan area" did not yield any new properties beyond `has part(s)` (`wdt:P527`). Given that the initial query only returned 3 cities, which seems low, let's consider the possibility that the cities might be linked through other entities or properties.

One approach is to look at the broader region, such as the Pearl River Delta Economic Zone (`wd:Q7158154`), and see if it has more cities associated with it. Let's try the same query with this entity.

Here is the updated SPARQL query:

```sparql
SELECT (COUNT(?city) AS ?cityCount)
WHERE {
  wd:Q7158154 wdt:P527 ?city .
}
```

Let's execute this query to see if it returns more cities.

[Tool]
Got 1 row and 1 column:
| cityCount   |
| ----------- |
| 0 (xsd:int) |

[Assistant]
The query for the Pearl River Delta Economic Zone (`wd:Q7158154`) did not return any cities, which suggests that this entity might not be the correct one to use for this purpose.

Given that the initial query with `wd:Q1144286` only returned 3 cities, which seems low, let's consider another approach. We can look for cities that are located in the administrative territorial entity of Guangdong (`wd:Q15175`), which includes the Pearl River Delta region.

Here is the updated SPARQL query:

```sparql
SELECT (COUNT(DISTINCT ?city) AS ?cityCount)
WHERE {
  ?city wdt:P131* wd:Q15175 .
}
```

This query will count the distinct cities that are located in the administrative territorial entity of Guangdong, which should include the cities in the Pearl River Delta.

Let's execute this query.

[Tool]
Got 1 row and 1 column:
| cityCount       |
| --------------- |
| 33150 (xsd:int) |

[Assistant]
The query returned 33,150 cities located in the administrative territorial entity of Guangdong. This number seems excessively high and likely includes many small towns and villages, not just major cities.

To refine the query, we can filter for cities that are instances of a city or metropolitan area. We can use the `instance of` property (`wdt:P31`) to filter for cities.

Here is the refined SPARQL query:

```sparql
SELECT (COUNT(DISTINCT ?city) AS ?cityCount)
WHERE {
  ?city wdt:P131* wd:Q15175 ;
        wdt:P31 wd:Q515 .  # City (Q515)
}
```

This query will count the distinct cities that are located in the administrative territorial entity of Guangdong and are instances of a city.

Let's execute this query.

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

[Assistant]
The refined query returned only 1 city, which still seems too low. It's possible that the cities in the Pearl River Delta are not directly classified as instances of `wd:Q515` (City) but might be classified under other types of urban areas or metropolitan regions.

Let's try a more inclusive approach by including other types of urban areas, such as metropolitan areas (`wd:Q1549591`), urban agglomerations, and other relevant types.

Here is the updated SPARQL query:

```sparql
SELECT (COUNT(DISTINCT ?city) AS ?cityCount)
WHERE {
  ?city wdt:P131* wd:Q15175 ;
        wdt:P31/wdt:P279* wd:Q515 .  # City or its subclasses
}
```

This query will count the distinct cities and their subclasses that are located in the administrative territorial entity of Guangdong.

Let's execute this query.

[Tool]
Got 1 row and 1 column:
| cityCount    |
| ------------ |
| 24 (xsd:int) |

[Assistant]
The updated query returned 24 cities, which seems more reasonable for the Pearl River Delta region.

Therefore, the number of cities that are part of the Pearl River Delta is 24.

Here is the final SPARQL query and the answer:

```sparql
SELECT (COUNT(DISTINCT ?city) AS ?cityCount)
WHERE {
  ?city wdt:P131* wd:Q15175 ;
        wdt:P31/wdt:P279* wd:Q515 .  # City or its subclasses
}
```

Answer: The number of cities that are part of the Pearl River Delta is 24.

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

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The query is on the right track, but it might include too many settlements. The Pearl River Delta region typically refers to a specific urban agglomeration in Guangdong province, which includes major cities like Guangzhou, Shenzhen, and others. The current query counts all cities within the entire province of Guangdong, which is broader than the Pearl River Delta region. Consider refining the query to specifically target the Pearl River Delta region.

[Assistant]
Thank you for the feedback. To refine the query, we need to focus specifically on the major cities within the Pearl River Delta region. The Pearl River Delta region typically includes the following major cities: Guangzhou, Shenzhen, Zhuhai, Zhongshan, Dongguan, Foshan, Jiangmen, Huizhou, and Zhaoqing.

Let's create a list of these cities and ensure our query counts only these specific cities.

First, let's find the entities for these cities:

1. Guangzhou
2. Shenzhen
3. Zhuhai
4. Zhongshan
5. Dongguan
6. Foshan
7. Jiangmen
8. Huizhou
9. Zhaoqing

We can use the `search_entity` function to find the IRIs for these cities.

### Step 1: Find the IRIs for the cities

Let's start with Guangzhou.

[Tool]
Top 10 entity alternatives:
1. Guangzhou, also known as Can-ton, Kwongchow, Kwangtong, Kwang-tong, Kuang-chou (wd:Q16572): capital city of Guangdong Province, China
2. Chinese destroyer Guangzhou, also known as Guangzhou (wd:Q19376036): 2002 Type 052B destroyer
3. Guangzhou, also known as Guang Prefecture, Guang Zhou, Xingwang Prefecture (wd:Q3270603): administrative division of ancient China
4. Guangzhou (wd:Q8479601): administrative division in Henan province of ancient China
5. Guangzhou (wd:Q103929975): encyclopedia article
6. Guangzhou (wd:Q111398733): fictional location in Water Margin
7. Guangzhou (wd:Q112881751): 2009 master's thesis by James Muir at Auckland University of Technology
8. Guangzhou (wd:Q132803946): journal article from 'Cities' published in 2003
9. Guangzhou (wd:Q58926451): scientific article published in Nature
10. Guangzhou Baiyun International Airport, also known as Baiyun Airport, ZGGG, CAN, Guangzhou Airport (wd:Q44868): international airport in Guangzhou, China

[Assistant]
The most relevant entity for Guangzhou is `wd:Q16572`.

Next, let's find the entity for Shenzhen.

[Tool]
Top 10 entity alternatives:
1. Shenzhen, also known as Sham Chun (wd:Q15174): Sub-provincial division in China
2. Shenzhen (wd:Q2278115): graphic novel by the Canadian Québécois author Guy Delisle
3. Zhao Shenzhen, also known as Zunlu, Zhulou, Yuchao, Shengkanyanshi, Wenke (wd:Q15929422): Qing dynasty politician, politician
4. Shenzhen (wd:Q132803992): journal article from 'Cities' published in 2003
5. Shenzhen (wd:Q58926455): scientific article published in Nature
6. Shenzhen (wd:L453047-F1)
7. Shenzhen (wd:L453047)
8. Shenzhen Bao'an International Airport, also known as Shenzhen-Bao'an, SZX, ZGSZ (wd:Q842297): international airport in Shenzhen, China
9. Shenzhen Metro (wd:Q867680): rapid transit network in Shenzhen, China
10. Shenzhen Airlines, also known as Shenzhen Air (wd:Q974899): airline headquartered in Bao'an, Shenzhen, Guangdong, China and based in Shenzhen Bao'an International Airport

[Assistant]
The most relevant entity for Shenzhen is `wd:Q15174`.

Next, let's find the entity for Zhuhai.

[Tool]
Top 10 entity alternatives:
1. Zhuhai (wd:Q59095): prefecture-level city in Guangdong, China
2. Zhuhai (wd:Q14146858): town in Panzhou County, Guizhou, China
3. Zhuhai (wd:Q132400757): Chinese bus network
4. Zhuhai Championships (wd:Q65078216): tennis tournament
5. Zhuhai Open (wd:Q23038902): tennis tournament
6. Zhuhai Sports Center, also known as Zhuhai Stadium (wd:Q12379346): building in Zhuhai, China
7. Zhang Weiping, also known as Yunquanshanguan, Songxinzi, Tingsongyuan, Tingsonglu, Songxuan (wd:Q11067558): Qing dynasty person CBDB = 65898, ci lyric writer, calligrapher, physician, painter, poet
8. Nanshan Zhuhai (wd:Q30947204): Ecological area in Liyang, China
9. Zhuhai Subdistrict (wd:Q14721095)
10. Beijing Normal University at Zhuhai, also known as BNU Zhuhai (wd:Q65045396): Beijing Normal University's campus in Zhuhai, Guangdong Province

[Assistant]
The most relevant entity for Zhuhai is `wd:Q59095`.

Next, let's find the entity for Zhongshan.

[Tool]
Top 10 entity alternatives:
1. Sun Yat-sen, also known as Sun Teming, Sun Yi Xian, Sun Yat Sen, Sun Yi-hsien, Sun Yihsien (wd:Q8573): Chinese politician, physician, and revolutionary (1866–1925), philosopher, politician, physician
2. Zhongshan (wd:Q59223): prefecture-level city in Guangdong, China
3. Cao Kun, also known as Tsao-Kun, Zhongshan (wd:Q699591): Chinese general and warlord (1862–1938), politician, warlord
4. Zhongshan (wd:Q197913): former country
5. Zhongshan District, also known as Chungshan, Jhongshan, Jungshan, Zhongshan (wd:Q271167): A district in Taipei City, Taiwan
6. Zhongshan District, also known as Chungshan, Jhongshan, Jungshan, Zhongshan (wd:Q707552): A district in Keelung City, Taiwan
7. Yu Yue, also known as Ruimeixuan, Risunyizhai, Quyuanjushi, Renchunxuan, Quyuanxiansheng (wd:Q10887841): Chinese scholar and official (1821–1907), calligrapher, philosopher
8. Liao Shouheng, also known as Shouheng, Zhongshan, Yizhai, Yiyizhai (wd:Q11064297): Qing dynasty politician, politician
9. Lu Wenchao, also known as Lu Sizong, Shiyubaojing, Shisongshanren, Shaogong, Qingzhai (wd:Q10910066): Qing dynasty person CBDB = 33851, historian
10. Nakayama, also known as なかやま, 中山 (wd:Q6960378): Japanese family name (中山)

[Assistant]
The most relevant entity for Zhongshan is `wd:Q59223`.

Next, let's find the entity for Dongguan.

[Tool]
Top 10 entity alternatives:
1. Dongguan (wd:Q59218): prefecture-level city in Guangdong, China
2. Dongguan, also known as Dongguantun (wd:Q10869484): subdistrict in Kangping County, Liaoning, China
3. Qu Ruji, also known as Dongguan, Jiongqing, Yuanli (wd:Q15938420): Ming dynasty person CBDB = 30185, politician
4. Chen Shouqi, also known as Suichulou, Weiren, Weixing, Xiaolanghuan, Yinping (wd:Q16075705): Qing dynasty artist, politician, farmer, artist
5. Dongguan (wd:Q10869478): township in Dafang County, Guizhou, China
6. Dongguan County, also known as Dongguan (wd:Q11103954): former county in China
7. Dongguan (wd:Q113651649)
8. Dongguan, also known as Dongguan (Water Margin) (wd:Q112416235): fictional location in Water Margin
9. Dongguan (wd:Q14139860): community in Huocheng, Xinjiang
10. Dongguan (wd:Q13963571): village in Yongqing, Langfang, Hebei, China

[Assistant]
The most relevant entity for Dongguan is `wd:Q59218`.

Next, let's find the entity for Foshan.

[Tool]
Top 10 entity alternatives:
1. Foshan (wd:Q34412): prefecture-level city in Guangdong, China
2. Foshan (wd:Q103919387): encyclopedia article
3. Foshan Metro, also known as FMetro (wd:Q1660094): metro system of the city of Foshan in Guangdong Province of China
4. Foshan University (wd:Q676139): university
5. Guicheng Subdistrict, also known as Guicheng, Foshan (wd:Q5615379): subdistrict of Guangdong Province, China
6. Foshan Nanshi F.C., also known as Dongguan United, Dongguan United F.C., Foshan Nanshi (wd:Q108392296): Chinese association football club
7. Foshan Television, also known as FSTV (wd:Q2113670)
8. Foshan Tram (wd:Q10886742)
9. Foshan Township (wd:Q10886728)
10. Foshan Museum (wd:Q120313853)

[Assistant]
The most relevant entity for Foshan is `wd:Q34412`.

Next, let's find the entity for Jiangmen.

[Tool]
Top 10 entity alternatives:
1. Jiangmen (wd:Q59132): prefecture-level city in Guangdong, China
2. Liu Chengju, also known as Shuan, Jiangmen (wd:Q45691106): Qing dynasty person CBDB = 75175
3. Yao Yiguan, also known as Weizhi, Jiangmen (wd:Q45452549): Ming dynasty person CBDB = 129300, painter, poet
4. Jiangmen Stadium (wd:Q11869422): building in Jiangmen Stadium, China
5. Jiangmen Town (wd:Q11135550): township in Sichuan, China
6. Central Jiangmen (wd:Q126456111)
7. Jiangmen Polytechnic (wd:Q55453830): education organization in Jiangmen, China
8. Jiangmen Trader, also known as IMO 9610664 (wd:Q83631123): ship built in 2013
9. Jiangmen Prison, also known as Guangdong Jiangmen Prison (wd:Q2099288): prison in Jiangmen Prison, China
10. Jiangmenshikou (wd:Q111317571)

[Assistant]
The most relevant entity for Jiangmen is `wd:Q59132`.

Next, let's find the entity for Huizhou.

[Tool]
Top 10 entity alternatives:
1. Huizhou (wd:Q59173): prefecture-level city in Guangdong, China
2. Huizhou Chinese, also known as Hui dialect, Hui language, Huizhou, Huizhou dialect, Huizhou language (wd:Q56546): Sinitic language
3. Huizhou, also known as Hui Prefecture (wd:Q4358404): region in Anhui and Jiangxi, China
4. Tiantai Deshao, also known as Tiantaiguoshi, Shi Deshao, Shaoguoshi, Huizhou, Deshao (wd:Q9358642): Song dynasty person CBDB = 41843, bhikkhu, poet
5. Yong'en, also known as Yiyuan, Luyiyuan, Lanting zhuren, Huizhou, Gong (wd:Q11132088): prince Li (禮)
6. Huìzhōu, also known as 會洲, 会洲 (wd:Q124821936): Chinese given name (會洲 / 会洲)
7. Huizhou (wd:Q124821980): given name
8. Sun Daru, also known as Huizhou (wd:Q45709099): Qing dynasty person CBDB = 75976
9. Huizhou District (wd:Q1197985): district in Anhui, People's Republic of China
10. Huizhou Airport, also known as HUZ, ZGHZ (wd:Q3085214): airport in Pingtan, Guangdong, People's Republic of China

[Assistant]
The most relevant entity for Huizhou is `wd:Q59173`.

Finally, let's find the entity for Zhaoqing.


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]
From the examples, we can see that the SPARQL queries typically involve identifying the main entity (such as a place, person, or thing) and then querying specific properties or relationships associated with that entity. For the question "Who is the publisher of Smite?", we need to identify the game "Smite" and then find out who published it.

First, let's search for the entity "Smite" in Wikidata.

[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, we need to find the property that represents the publisher of a game. Let's search for properties related to "publisher" for the entity "Smite".

[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 wdt/p): 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 wdt/p): method (or type) of distribution for the subject
3. logo image, also known as masthead, wordmark, trademark, trade mark, sign (wd:P154 as wdt/p): 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 wdt/p): 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 wdt/p): 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 wdt/p): 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 wdt/p): 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 wdt/p): 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 wdt/p): 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 wdt/p): 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 for the publisher of a game is `wdt:P123`. Now, we can construct a SPARQL query to find the publisher of the game "Smite".

Let's execute the following SPARQL query:

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

This query will return the publisher of the game "Smite".

[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

Now, I will provide the final answer using the `answer` function.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query is correct and returns the expected result. The answer provided is accurate and complete.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ( COUNT( DISTINCT ?resort ) AS ?count ) WHERE {
  ?resort wdt:P31 / wdt:P279 * wd:Q130003 ; wdt:P131 * wd:Q142 .
}

There were 212 ski resorts in France.

[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 relevant entities and properties for our question. We are looking for ski resorts in France, so we need to find the entity for France and the property that indicates a location is a 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 that indicates a location is a ski resort. We can try searching for "ski resort" as a property.

[Tool]
Top 10 property alternatives:
1. Skiresort.info ID, also known as Ski resort ID, Skiresort ID (wd:P6389 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): 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 pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): 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 pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): 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 pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): 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 pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): 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 pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): identifier for a grass skier in the International Ski Federation database
7. Skimap area ID (wd:P6400 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): 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 pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): 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 pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): identifier for a ski jumper, in the International Ski Federation database
10. Swedish Portrait Archive ID, also known as SPA (wd:P4819 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): identifier for a person in the Swedish Portrait Archive

[Assistant] None of the top 10 properties directly indicate that a location is a ski resort. However, we can try searching for "ski resort" as an object of the property "instance of" (`P31`) to see if there is a specific entity for ski resorts.

[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. 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

No other items found

No literal items found

[Assistant]
The entity for "ski resort" is `wd:Q130003`. Now we can construct a SPARQL query to find all ski resorts in France. We will use the property "located in the administrative territorial entity" (`P131`) to filter for locations in France.

Let's start with a basic query to list all ski resorts in France:

```sparql
SELECT ?resort WHERE {
  ?resort wdt:P31 wd:Q130003 ;
          wdt:P131* wd:Q142 .
}
```

This query will return all ski resorts that are located in France, including those in sub-regions of France. Let's execute this query and see the results.

[Tool]
Got 212 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)     |
| ...                                        |
| Issarbe (wd:Q933474)                       |
| Crévoux (wd:Q950651)                       |
| La Plagne (wd:Q969723)                     |
| Les Deux Alpes (wd:Q974478)                |
| La Stèle (wd:Q97774986)                    |

[Assistant]
The query returned 212 ski resorts in France. To answer the question "How many ski resorts were there ever in France?", we need to count these results. We can modify the query to return the count of ski resorts:

```sparql
SELECT (COUNT(?resort) AS ?count) WHERE {
  ?resort wdt:P31 wd:Q130003 ;
          wdt:P131* wd:Q142 .
}
```

Let's execute this query to get the count.

[Tool]
Got 1 row and 1 column:
| count         |
| ------------- |
| 212 (xsd:int) |

[Assistant]
The query returned a count of 212 ski resorts in France. Therefore, the answer to the question "How many ski resorts were there ever in France?" is 212.

Here is the final SPARQL query and the answer:

```sparql
SELECT (COUNT(?resort) AS ?count) WHERE {
  ?resort wdt:P31 wd:Q130003 ;
          wdt:P131* wd:Q142 .
}
```

Answer: There were 212 ski resorts in France.

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query is mostly correct, but it could be refined to ensure that we are counting distinct ski resorts. This is important because the same ski resort might be listed multiple times under different administrative territorial entities. To fix this, you should add the DISTINCT keyword to the COUNT function: `SELECT (COUNT(DISTINCT ?resort) AS ?count) WHERE { ?resort wdt:P31 wd:Q130003 ; wdt:P131* wd:Q142 . }`

[Assistant]
Thank you for the feedback. Adding the `DISTINCT` keyword to the `COUNT` function will ensure that we are counting unique ski resorts. Let's refine the query and re-execute it:

```sparql
SELECT (COUNT(DISTINCT ?resort) AS ?count) WHERE {
  ?resort wdt:P31 wd:Q130003 ;
          wdt:P131* wd:Q142 .
}
```

Let's execute this refined query to get the count of distinct ski resorts in France.

[Tool]
Got 1 row and 1 column:
| count         |
| ------------- |
| 212 (xsd:int) |

[Assistant]
The refined query with the `DISTINCT` keyword still returns a count of 212 distinct ski resorts in France. Therefore, the answer to the question "How many ski resorts were there ever in France?" remains 212.

Here is the final SPARQL query and the answer:

```sparql
SELECT (COUNT(DISTINCT ?resort) AS ?count) WHERE {
  ?resort wdt:P31 wd:Q130003 ;
          wdt:P131* wd:Q142 .
}
```

Answer: There were 212 ski resorts in France.

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query is on the right track, but it might be missing some ski resorts that are not directly instances of 'ski resort' (wd:Q130003) but are subclasses or related types. Consider adding a filter to include subclasses of ski resorts or related concepts. Additionally, ensure that the query is correctly counting all distinct ski resorts, even if they are located in different administrative divisions.

[Assistant]
Thank you for the feedback. To ensure that we capture all ski resorts, including those that might be subclasses or related types, we can use the `wdt:P279*` property to include subclasses of ski resorts. This will help us capture a broader range of ski resorts.

Let's refine the query to include subclasses of ski resorts and ensure we are counting distinct ski resorts:

```sparql
SELECT (COUNT(DISTINCT ?resort) AS ?count) WHERE {
  ?resort wdt:P31/wdt:P279* wd:Q130003 ;
          wdt:P131* wd:Q142 .
}
```

This query will:
1. Find entities that are instances of ski resorts or subclasses of ski resorts.
2. Ensure that these entities are located in France or its administrative divisions.
3. Count the distinct ski resorts.

Let's execute this refined query to get the count of distinct ski resorts in France.

[Tool]
Got 1 row and 1 column:
| count         |
| ------------- |
| 212 (xsd:int) |

[Assistant]
The refined query, which includes subclasses of ski resorts, still returns a count of 212 distinct ski resorts in France. This suggests that the original query was already capturing all relevant ski resorts, and the inclusion of subclasses did not add any additional results.

Therefore, the answer to the question "How many ski resorts were there ever in France?" remains 212.

Here is the final SPARQL query and the answer:

```sparql
SELECT (COUNT(DISTINCT ?resort) AS ?count) WHERE {
  ?resort wdt:P31/wdt:P279* wd:Q130003 ;
          wdt:P131* wd:Q142 .
}
```

Answer: There were 212 ski resorts in France.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?director WHERE {
  wd:Q28091065 wdt:P57 ?director .
}

Niki Caro

[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 directed Anne with an E ?

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

[Tool]
Example 1:
Question:
Which companies work in the aerospace industry as well as in medicine?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?type wdt:P279 * wd:Q4830453 .
  ?aerospace wdt:P279 * wd:Q3477363 .
  ?healthcare wdt:P279 * wd:Q15067276 .
  ?pharma wdt:P279 * wd:Q507443 .
  ?uri wdt:P452 ?aerospace ; wdt:P452 ?healthcare .
}

Using entities:
health care industry, also known as healthcare industry, health industry, medical care industry, medical industry (wd:Q15067276): industry dedicated to providing health care services and products
aerospace industry, also known as aircraft industry (wd:Q3477363): industrial sector
business (wd:Q4830453): organization undertaking commercial, industrial, or professional activity
pharmaceutical industry, also known as drug industry, pharma, pharmaceutical wholesaler, pharma industry (wd:Q507443): develops, produces, and markets drugs or pharmaceuticals licensed for use as medications

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
industry, also known as branch, economic branch, field of action, field of exercise, sector (wd:P452 as wdt): specific industry of company or organization, part of

Example 2:
Question:
What is the birth name of Angela Merkel?

SPARQL:
SELECT DISTINCT ?name WHERE {
  wd:Q567 wdt:P1477 ?name .
}

Using entities:
Angela Merkel, also known as Angela Dorothea Merkel, leader of the free world, Angela Kasner (wd:Q567): chancellor of Germany from 2005 to 2021, politician, physicist

Using properties:
birth name, also known as née, name at birth, name before marriage, ne, né (wd:P1477 as wdt): full name of a person at birth, if different from their current, generally used name, name

Example 3:
Question:
What is the largest state in the United States?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P31 wd:Q35657 .
  ?uri wdt:P2046 ?area .
}
ORDER BY DESC ( ?area )
LIMIT 1

Using entities:
U.S. state, also known as U.S. states, US state, USA state, U.S.A. state, United States states (wd:Q35657): constituent political entity of the United States of America

Using properties:
area, also known as total area, surface area, surface, size, resolution (wd:P2046 as wdt): area occupied by an object
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, it seems like we need to find the director of the TV show "Anne with an E". Let's first search for the entity "Anne with an E" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Anne with an E (wd:Q28091065): Canadian drama television series
2. Anne Linda Chisa, also known as Anne with an E (wd:Q109236706): South African academic, podcaster
3. Anne with an E, season 1 (wd:Q30897460): Wikimedia list article
4. Anne with an E, season 2 (wd:Q56327877): season of Anne with an E
5. Anne with an E, season 3 (wd:Q97370516): season of Anne with an E
6. An Intimate Evening with Anne Murray (wd:Q4750087): live album
7. Annelid Phylogeny—Molecular Anal...th an Emphasis on Model Annelids (wd:Q57267390): article
8. Hat with Bird: Anne Estelle Rice (wd:Q119158898): painting by John Duncan Fergusson (1874–1961), Kelvingrove Art Gallery and Museum
9. Queery with Cameron Esposito: Anne McClain, also known as Anne McClain (wd:Q117712680): Queery with Cameron Esposito podcast episode
10. Live Body: An interview with Anne Emmanuelle Berger (wd:Q127594883): scholarly article

[Assistant] The top result is the TV series "Anne with an E" (wd:Q28091065). Now let's search for the property "director" in relation to this entity.

[Tool]
Top 10 property alternatives:
1. director, also known as film director, movie director, game director, directed by (wd:P57 as wdt/p): director(s) of film, TV-series, stageplay, video game or similar, contributor to the creative work or subject, creator
2. cast member, also known as feat., starring (film/play), film starring, starring, actor (wd:P161 as wdt/p): actor in the subject production [use "character role" (P453) and...use "recorded participant" (P11108) for non-fiction productions], significant person, performer
3. screenwriter, also known as story by, teleplay by, video game writer, writer (of screenplays), writing credits (wd:P58 as wdt/p): person(s) who wrote the script for subject item, film crew member, author
4. genre, also known as genre of music, type of music, type of film, music genre, literary genre (wd:P136 as wdt/p): creative work's genre or an artist's field of work (P101). Use main subject (P921) to relate creative works to their topic
5. title, also known as name, titled, page name, original title, known as (wd:P1476 as wdt/p): published name of a work, such as a newspaper article, a literary work, piece of music, a website, or a performance work, name
6. logo image, also known as masthead, wordmark, trademark, trade mark, sign (wd:P154 as wdt/p): graphic mark or emblem commonly used by commercial enterprises, organizations and products, icon
7. Kinobox film ID, also known as www.kinobox.cz/film/, kinobox.cz/film/, https://www.kinobox.cz/film/ (wd:P12096 as wdt/p): identifier for a film on the Kinobox.cz website
8. IMDb ID, also known as IMDb identifier, Internet Movie Database identifier (wd:P345 as wdt/p): identifier for the IMDb (with prefix 'tt', 'nm', 'co', 'ev', 'ch' or 'ni')
9. Rotten Tomatoes ID, also known as RottenTomatoes ID, Rotten Tomatoes identifier, RottenTomatoes identifier (wd:P1258 as wdt/p): identifier on Rotten Tomatoes
10. country of origin, also known as origin country, place of origin, originates from, country, CoO (wd:P495 as wdt/p): country of origin of this item (creative work, food, phrase, product, etc.), significant place, country

No other items found

[Assistant]
The property "director" (wd:P57) is the most relevant one. Let's construct a SPARQL query to find the director of "Anne with an E".

```sparql
SELECT ?director WHERE {
  wd:Q28091065 wdt:P57 ?director .
}
```

Now, let's execute this query.

[Tool]
Got 1 row and 1 column:
| director               |
| ---------------------- |
| Niki Caro (wd:Q449163) |

[Assistant]
The director of "Anne with an E" is Niki Caro (wd:Q449163).

Let's provide the final answer:

```sparql
SELECT ?director WHERE {
  wd:Q28091065 wdt:P57 ?director .
}
```

The director of "Anne with an E" is Niki Caro.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query correctly identifies the director of the TV series 'Anne with an E'. The result returned is accurate and matches the expected answer.