PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
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 similar examples.

[Tool]
Example 1:
Question:
Give me all films produced by Steven Spielberg with a budget of at least $80 million.

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P162 wd:Q8877 ; wdt:P31 / wdt:P279 * wd:Q11424 ; wdt:P2130 ?cost .
  FILTER ( ?cost > 80000000 )
}

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
Steven Spielberg, also known as Steven Allan Spielberg, Spielberg (wd:Q8877): American filmmaker (born 1946), science fiction writer, television producer, executive producer, film screenwriter, businessperson, art collector, film director, film producer, entrepreneur, screenwriter, film editor, film actor, director, producer, writer, actor

Using properties:
producer, also known as music producer, record producer, produced by, film producer, creative producer (wd:P162 as wdt): person(s) who produced the film, musical work, theatrical produc...etc.) [for production company, use P272, video games - use P178]
capital cost, also known as budget, cost, unit cost (wd:P2130 as wdt): amount of money spent on building or producing an object
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 2:
Question:
In which films did Julia Roberts as well as Richard Gere play?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P31 wd:Q11424 .
  ?uri wdt:P161 wd:Q48410 .
  ?uri wdt:P161 wd:Q40523 .
}

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
Julia Roberts, also known as Julia Fiona Roberts (wd:Q40523): American actress (born 1967), television actor, film producer, voice actor, stage actor, film actor, actor, model
Richard Gere, also known as Richard Tiffany Gere (wd:Q48410): American actor (born 1949), human rights activist, television actor, film director, film producer, photographer, voice actor, stage actor, film actor, actor

Using properties:
cast member, also known as feat., starring (film/play), film starring, starring, actor (wd:P161 as wdt): actor in the subject production [use "character role" (P453) and...use "recorded participant" (P11108) for non-fiction productions], significant person, performer
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)

[Assistant] Based on the examples, we need to identify the entity for "Anne with an E" and the property for "directed by". Let's start by searching for the entity "Anne with an E".

[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 correct entity for "Anne with an E" is the first option, which is a Canadian drama television series. Now let's find the property for "directed by".

[Tool]
Top 10 property alternatives:
1. editor-in-chief, also known as lead editor, managing editor, executive editor, editor in chief, directed by (wd:P5769 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): a publication's editorial leader who has final responsibility fo...publishing articles or books in collections or periodical papers
2. director, also known as film director, movie director, game director, directed by (wd:P57 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): director(s) of film, TV-series, stageplay, video game or similar, contributor to the creative work or subject, creator
3. position held by head of the organization, also known as position held by head of the organisation, secretary's position, president's position, position held by head of the organization, office held by head of the organisation (wd:P2388 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): position of the head of this item
4. director / manager, also known as leader, manager, manager/director, managing director, Officer-in-Charge (wd:P1037 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): person who manages any kind of group, significant person
5. director of photography, also known as cinematographer, DOP (wd:P344 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): person responsible for the framing, lighting, and filtration of the subject work, film crew member
6. screenwriter, also known as story by, teleplay by, video game writer, writer (of screenplays), writing credits (wd:P58 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): person(s) who wrote the script for subject item, film crew member, author
7. produced by, also known as secreted by (wd:P2849 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): links a class of biological features (such as a biochemical compound) with another class of biological entities known to produce it, has immediate cause
8. manufacturer, also known as manufactured by, provider, provided by, product of, producer (wd:P176 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): (main or final) manufacturer or producer of this product
9. fabrication method, also known as process used, method of cooking, preparation method, obtained by, method used (wd:P2079 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): method, process or technique used to grow, cook, weave, build, assemble, manufacture the item, uses
10. producer, also known as music producer, record producer, produced by, film producer, creative producer (wd:P162 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): person(s) who produced the film, musical work, theatrical produc...etc.) [for production company, use P272, video games - use P178]

[Assistant]
The correct property for "directed by" is the second option, which is `director` (P57). Now we can construct a simple SPARQL query to find out who directed "Anne with an E".

Let's execute the query:

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

This query will return the directors of the TV series "Anne with an E".

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

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

Here is the final SPARQL query and answer:

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

Answer: 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 matches the expected answer, and no further refinements are necessary.


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?person ?personLabel WHERE {
  ?person wdt:P20 wd:Q934 .
  OPTIONAL {
    ?person rdfs:label ?personLabel .
    FILTER ( LANG( ?personLabel ) = "en" )
  }
}

Sigizmund Levanevsky (wd:Q1333099)

[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 died at the North Pole?

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

[Tool]
Example 1:
Question:
Where did Abraham Lincoln die?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q91 wdt:P20 ?uri
}

Using entities:
Abraham Lincoln, also known as President Lincoln, Uncle Abe, Honest Abe, Abe Lincoln (wd:Q91): president of the United States from 1861 to 1865, military officer, statesperson, postmaster, politician, farmer, writer, lawyer

Using properties:
place of death, also known as POD, location of death, killed in, deathplace, died in (wd:P20 as wdt): most specific known (e.g. city instead of country, or hospital i... city) death location of a person, animal or fictional character, location

Example 2:
Question:
Where did Abraham Lincoln die?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q91 wdt:P20 ?uri .
}

Using entities:
Abraham Lincoln, also known as President Lincoln, Uncle Abe, Honest Abe, Abe Lincoln (wd:Q91): president of the United States from 1861 to 1865, military officer, statesperson, postmaster, politician, farmer, writer, lawyer

Using properties:
place of death, also known as POD, location of death, killed in, deathplace, died in (wd:P20 as wdt): most specific known (e.g. city instead of country, or hospital i... city) death location of a person, animal or fictional character, location

Example 3:
Question:
Who discovered Pluto?

SPARQL:
SELECT ?o1 WHERE {
  wd:Q339 wdt:P61 ?o1 .
  ?o1 wdt:P31 wd:Q5 .
}

Using entities:
Pluto, also known as (134340) Pluto, Sol j (wd:Q339): dwarf planet in the Solar System
human, also known as human being, person, people, nonfictional human, non-fictional human (wd:Q5): any member of Homo sapiens, unique extant species of the genus Homo

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

[Assistant] Based on the examples, we can see that the property `wdt:P20` is used to denote the place of death. We need to find individuals who died at the North Pole. Let's search for the entity representing the North Pole.

[Tool]
Top 10 entity alternatives:
1. North Pole, also known as terrestrial North Pole, geographic North Pole (wd:Q934): one of the two points where the Earth's axis of rotation intersects its surface
2. North Pole, also known as North Pole, Alaska (wd:Q79494): city in Alaska, USA
3. North Pole, also known as North Pole, ID, North Pole, Idaho (wd:Q7056392): unincorporated community in Idaho
4. North Pole, also known as North Pole, New York, North Pole, NY (wd:Q4327488): human settlement in United States of America
5. North Pole (wd:Q130803558): pub in Wateringbury, Tonbridge and Malling, Kent, England
6. North Pole (wd:Q21958071): mine in Western Australia
7. North Pole (wd:Q21983876): mountain in Australia
8. North Pole (wd:Q31972103): mine in Masvingo Province, Zimbabwe
9. North Pole (wd:Q34886291): mountain in United States of America
10. North Pole (wd:Q49057483): mountain in Idaho, United States of America

[Assistant]
The correct entity for the North Pole is `wd:Q934`. Now, let's construct a SPARQL query to find individuals who died at the North Pole using the `wdt:P20` property.

Here is the initial SPARQL query:

```sparql
SELECT ?person WHERE {
  ?person wdt:P20 wd:Q934 .
}
```

Let's execute this query to see if it returns any results.

[Tool]
Got 1 row and 1 column:
| person                             |
| ---------------------------------- |
| Sigizmund Levanevsky (wd:Q1333099) |

[Assistant]
The SPARQL query returned one result: Sigizmund Levanevsky (wd:Q1333099).

To provide a more complete answer, let's include the label of the person in the result. We can do this by adding an optional clause to fetch the label.

Here is the refined SPARQL query:

```sparql
SELECT ?person ?personLabel WHERE {
  ?person wdt:P20 wd:Q934 .
  OPTIONAL { ?person rdfs:label ?personLabel . FILTER(LANG(?personLabel) = "en") }
}
```

Let's execute this query to get the label of the person.

[Tool]
Got 1 row and 2 columns:
| person                             | personLabel               |
| ---------------------------------- | ------------------------- |
| Sigizmund Levanevsky (wd:Q1333099) | Sigizmund Levanevsky (en) |

[Assistant]
The SPARQL query has successfully returned the person who died at the North Pole along with their label. The person is Sigizmund Levanevsky.

Here is the final SPARQL query and answer:

```sparql
SELECT ?person ?personLabel WHERE {
  ?person wdt:P20 wd:Q934 .
  OPTIONAL { ?person rdfs:label ?personLabel . FILTER(LANG(?personLabel) = "en") }
}
```

Answer: Sigizmund Levanevsky (wd:Q1333099)

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

[Tool] Stopped generation process

[User]
Feedback (status=done):
The provided answer is correct and complete. The SPARQL query correctly identifies the person who died at the North Pole using the place of death property and returns the IRI along with the label.


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
ASK WHERE {
  wd:Q696071 wdt:P674 ?character .
  ?character wdt:P21 wd:Q6581072
}

No

[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] is there female main character in hunter x hunter?

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

[Tool] No examples found

[Assistant] Since we couldn't find any similar examples, let's proceed by searching for the entity "Hunter x Hunter" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Hunter × Hunter, also known as HUNTER × HUNTER, Hunter x Hunter, HXH (wd:Q696071): Japanese manga series
2. Hunter × Hunter, also known as H×H, Hunter x Hunter, HxH (wd:Q70209136): 2011 Japanese anime television series
3. Hunter × Hunter, also known as Hunter x Hunter, HxH (wd:Q21232458): 1999 Japanese anime television series
4. Monster Hunter Generations, also known as Monster Hunter X (wd:Q20856979): 2015 Japanese video game
5. Hunter Xcite (wd:Q60767092): sailboat class
6. Xavier Hunter (wd:Q100806788): basketball player
7. Hunter × Hunter: Phantom Rouge, also known as Hunter X Hunter: Phantom Rouge (wd:Q11398237): 2013 film by Yūzō Satō
8. City Hunter XYZ (wd:Q97170832): fan film by Filip Wong
9. Monster Hunter Generations Ultimate, also known as Monster Hunter XX (wd:Q30302563): 2017 video game
10. XX: Beautiful Hunter (wd:Q8042573): 1994 film by Masaru Konuma

[Assistant]
We have multiple options for "Hunter x Hunter". Since the question is about the main characters, it would be better to choose the manga series (wd:Q696071) as it is the source material and likely to contain information about the main characters.

Now, let's search for the property "main character" in relation to the chosen entity.

[Tool]
Top 10 property alternatives:
1. characters, also known as appears in this work (wd:P674 as wdt/p): characters which appear in this item (like plays, operas, operettas, books, comics, films, TV series, video games)
2. 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)
3. author, also known as writer, written by, poet, playwright, maker (wd:P50 as wdt/p): main creator(s) of a written work (use on works, not humans); us...hor name string) when Wikidata item is unknown or does not exist, creator
4. 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
5. 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
6. 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
7. 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))
8. MyAnimeList manga ID, also known as Manga ID MAL, MAL manga ID (wd:P4087 as wdt/p): identifier for a manga or novel on MyAnimeList
9. start time, also known as start date, since, valid, join time, joined (wd:P580 as wdt/p): time an entity begins to exist or a statement starts being valid, point in time
10. topic's main template, also known as topic has template, topic's main module, topic's main infobox, topic infobox, main template (wd:P1424 as wdt/p): the main template relating to a topic

No other items found

[Assistant]
The property "characters" (wdt:P674) seems to be the closest match for finding the main characters of the manga series "Hunter x Hunter".

Next, we need to check if these characters have a gender property and if any of them are female. Let's first list the characters of "Hunter x Hunter" (wd:Q696071).

[Tool]
Got 6 rows and 3 columns:
| subject                      | property              | object                            |
| ---------------------------- | --------------------- | --------------------------------- |
| Hunter × Hunter (wd:Q696071) | characters (wdt:P674) | Gon Freecss (wd:Q2717608)         |
| Hunter × Hunter (wd:Q696071) | characters (wdt:P674) | Killua Zoldyck (wd:Q2717625)      |
| Hunter × Hunter (wd:Q696071) | characters (wdt:P674) | Kurapika (wd:Q2537895)            |
| Hunter × Hunter (wd:Q696071) | characters (wdt:P674) | Leorio Paladiknight (wd:Q2611418) |
| Hunter × Hunter (wd:Q696071) | characters (wdt:P674) | Hisoka (wd:Q2280812)              |
| Hunter × Hunter (wd:Q696071) | characters (wdt:P674) | Ging Freecss (wd:Q20381047)       |

[Assistant]
We have a list of characters from "Hunter x Hunter". Now, let's check if any of these characters are female by querying for their gender. We will use the property "sex or gender" (wdt:P21) to find out.

Let's execute a SPARQL query to find the gender of these characters.

[Tool]
Got 6 rows and 2 columns:
| character                         | gender             |
| --------------------------------- | ------------------ |
| Ging Freecss (wd:Q20381047)       | male (wd:Q6581097) |
| Hisoka (wd:Q2280812)              | male (wd:Q6581097) |
| Kurapika (wd:Q2537895)            | male (wd:Q6581097) |
| Leorio Paladiknight (wd:Q2611418) | male (wd:Q6581097) |
| Gon Freecss (wd:Q2717608)         | male (wd:Q6581097) |
| Killua Zoldyck (wd:Q2717625)      | male (wd:Q6581097) |

[Assistant]
Based on the results, all the main characters listed for "Hunter x Hunter" are male. Therefore, there are no female main characters among the ones listed.

To confirm this, we can use an ASK query to check if there are any female main characters in "Hunter x Hunter".

Let's execute the ASK query.

[Tool] False

[Assistant]
The ASK query returned `False`, confirming that there are no female main characters listed for "Hunter x Hunter".

Therefore, the answer to the question "Is there a female main character in Hunter x Hunter?" is:

**No**

Here is the final SPARQL query and answer:

```sparql
ASK WHERE { wd:Q696071 wdt:P674 ?character . ?character wdt:P21 wd:Q6581070 }
```

Answer: No

[Tool] Stopped generation process

[User]
Feedback (status=retry):
The SPARQL query is using the wrong entity for 'female'. The entity used (wd:Q6581070) refers to a village in Turkey, not the female gender. The correct entity for 'female' is wd:Q6581072. Please update the query to use the correct entity.

[Assistant]
Thank you for the feedback. I will update the SPARQL query to use the correct entity for 'female', which is `wd:Q6581072`.

Let's execute the corrected ASK query:

```sparql
ASK WHERE { wd:Q696071 wdt:P674 ?character . ?character wdt:P21 wd:Q6581072 }
```

This query will check if there are any female main characters in "Hunter x Hunter".

Let's execute the updated query.

[Tool] False

[Assistant]
The updated ASK query still returned `False`, confirming that there are no female main characters listed for "Hunter x Hunter".

Therefore, the answer to the question "Is there a female main character in Hunter x Hunter?" is:

**No**

Here is the final SPARQL query and answer:

```sparql
ASK WHERE { wd:Q696071 wdt:P674 ?character . ?character wdt:P21 wd:Q6581072 }
```

Answer: No

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query is correctly formulated as an ASK query and it checks whether there is a female main character in Hunter × Hunter by looking at the characters appearing in the work and their gender. The execution result is False, which means there is no female main character, and the answer 'No' is correct.


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?numberOfSeasons WHERE {
  wd:Q30603704 wdt:P2437 ?numberOfSeasons .
}

Got 1 row and 1 column:
| numberOfSeasons |
| --------------- |
| 3 (xsd:decimal) |

The TV show "Pose" has 3 seasons.

[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 seasons does Pose have?

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

[Tool] No examples found

[Assistant] Since no examples were found, we need to manually construct the query. First, let's identify the entity for the TV show "Pose".

[Tool]
Top 10 entity alternatives:
1. 2D Human Pose Estimation in TV Shows (wd:Q57588979)
2. television program, also known as TV show, television broadcast, T.V. show, T. V. show, TV programme (wd:Q15416): segment of audiovisual content intended for broadcast on television
3. television series, also known as TV show, TV-series, television show, TV series (wd:Q5398426): connected set of television program episodes under the same title
4. Show TV (wd:Q2006738): Turkish television station
5. TV Show (wd:Q3281229): 2007 album by Sergey Lazarev
6. reality television, also known as reality TV program, TV reality show, TV reality program, television reality show, television reality program (wd:Q182415): genre of television programming that documents unscripted situations and actual occurrences
7. Showtime, also known as Showtime (TV network), Showtime TV, Showtime Networks, Showtime Entertainment, SHO (wd:Q23589): American premium cable TV channel
8. television presenter, also known as television anchor, TV show host, TV presenter, TV host, TV anchor (wd:Q947873): person who introduces or hosts television programs
9. variety show, also known as television variety program, television variety show, variety, variety entertainment, variety program (wd:Q336181): television genre characterized by a series of specialty acts such as music, dance, comedy, magic, acrobatics and ventriloquism
10. reality show, also known as reality television program, TV reality show, TV reality program, television reality show, television reality program (wd:Q3421644): television genre

[Assistant] From the search results, the correct entity for the TV show "Pose" seems to be missing. Let's try searching again with a more specific query.

[Tool]
Top 10 entity alternatives:
1. television series, also known as TV show, TV-series, television show, TV series (wd:Q5398426): connected set of television program episodes under the same title
2. TV Séries (wd:Q10377585): Brazilian magazine
3. TV Series (wd:Q50816416): academic journal
4. season, also known as series of television program, TV series, TV season, television series, television season (wd:P4908): season of a series (televison, web, podcast)
5. miniseries, also known as event series, TV miniseries, TV mini series, television miniseries, mini-series (wd:Q1259759): TV shows or series that have a predetermined number of episodes
6. Halo, also known as Halo: The Television Series, Halo (TV Series) (wd:Q55279915): television series
7. children's television series, also known as children's TV series, kids tv, kids TV series (wd:Q1273568): television programs designed for, and marketed to children
8. Chinese television drama, also known as cdrama, C-drama, Chinese drama, Chinese drama serial, Chinese drama series (wd:Q28195059): Chinese television drama series originating from Mainland China or the Greater China region
9. television series season, also known as TV show season, TV series season, TV season, season of a TV show, television show season (wd:Q3464665): set of episodes produced for a television series
10. drama television series, also known as drama TV series, dramatic television series, television drama series, TV drama series, drama series (wd:Q1366112): television series genre

[Assistant]
It looks like the entity for the TV show "Pose" is still not among the top results. Let's try a different approach and look for the property that represents the number of seasons for a TV show.

I will search for the property "number of seasons" in Wikidata.

[Tool]
Top 10 property alternatives:
1. number of seasons, also known as no. of seasons, no. of series, number of series, seasons (wd:P2437 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): number of seasons a television or radio series has had, number of parts of this work
2. sports season of league or competition, also known as sport season, seasons of, edition of annual athletics meeting, season of sports tournament, season of sports league (wd:P3450 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): property that shows the competition of which the item is a season. Use P5138 for "season of club or team"., part of
3. season, also known as TV series, television season, TV season, television series, series of television program (wd:P4908 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): season of a series (televison, web, podcast), part of
4. refine date, also known as date refinement, exact time, hour, local time, quarter (wd:P4241 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): qualifier to narrow down the precision of a date not fully specified by the Wikidata time datatype precision field
5. volume, also known as volume of serial, volume of a book, volume number, vol., tome (wd:P478 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): volume of a book or music release in a collection/series or a published collection of journal issues in a serial publication
6. season of club or team, also known as season of athletics program, season of club, season of team, sports season of club or team (wd:P5138 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): club or team that played the season
7. season ends (wd:P6118 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): to indicate what month of the year a sports season ends
8. number of episodes, also known as series length, no. of episodes, episodes, episode count (wd:P1113 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): number of episodes in a film/TV/radio series, number of parts of this work
9. season starts, also known as season begins (wd:P4794 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): month or season when sports competition begins
10. valid in period, also known as valid in season, valid for season, valid for period, valid for, time period (wd:P1264 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): time period when a statement is valid

[Assistant]
The property "number of seasons" (P2437) is the one we're looking for. Now, let's find the entity for the TV show "Pose" again, and then we can construct the SPARQL query to get the number of seasons.

Let's try searching for the entity "Pose" with a broader query to see if we can find it.

[Tool]
Top 10 entity alternatives:
1. Pose (wd:Q30603704): 2018 American drama television series
2. posture, also known as attitude, human position, pose (wd:Q8514257): physical configuration that a human or animal body can take
3. Pose (wd:Q29966944): 2017 song by Rihanna
4. pose (wd:Q1671968): position and orientation of an object in an image
5. Pose (wd:Q432543): single
6. Pose (wd:Q108250580): 2021 song by Red Velvet
7. Pose (wd:Q85794108): 2021 single by Yo Gotti featuring Lil Uzi Vert
8. pose, also known as posing (wd:Q2105738): sustained posture or positioning of the torso and limbs
9. Pose (wd:Q6083341): Album by Second
10. Pose (wd:Q36886624): family name

[Assistant]
The correct entity for the TV show "Pose" is `wd:Q30603704`. Now that we have the entity and the property, we can construct the SPARQL query to find the number of seasons for the TV show "Pose".

Let's write and execute the SPARQL query:

```sparql
SELECT ?numberOfSeasons
WHERE {
  wd:Q30603704 wdt:P2437 ?numberOfSeasons .
}
```

This query will return the number of seasons for the TV show "Pose".

[Tool]
Got 1 row and 1 column:
| numberOfSeasons |
| --------------- |
| 3 (xsd:decimal) |

[Assistant]
The TV show "Pose" has 3 seasons.

Here is the final SPARQL query and answer:

```sparql
SELECT ?numberOfSeasons
WHERE {
  wd:Q30603704 wdt:P2437 ?numberOfSeasons .
}
```

Answer: The TV show "Pose" has 3 seasons.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query correctly retrieves the number of seasons for the TV show 'Pose' from Wikidata.


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ( COUNT( ?award ) AS ?count ) WHERE {
  wd:Q312674 wdt:P166 ?award .
  ?award wdt:P31 wd:Q1011547 .
}

Got 1 row and 1 column:
| count       |
| ----------- |
| 2 (xsd:int) |

Giorgio Moroder has won the Golden Globe Award 2 times.

[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 times has Giorgio Moroder won golden globe award?

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

[Tool]
Example 1:
Question:
How many films did Leonardo DiCaprio star in?

SPARQL:
SELECT ( COUNT( DISTINCT ?uri ) AS ?c ) WHERE {
  ?uri wdt:P31 wd:Q11424 ; wdt:P161 wd:Q38111 .
}

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
Leonardo DiCaprio (wd:Q38111): American actor and film producer (born 1974), television actor, environmentalist, film producer, screenwriter, stage actor, film actor, actor

Using properties:
cast member, also known as feat., starring (film/play), film starring, starring, actor (wd:P161 as wdt): actor in the subject production [use "character role" (P453) and...use "recorded participant" (P11108) for non-fiction productions], significant person, performer
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:
How many James Bond movies do exist?

SPARQL:
SELECT ( COUNT( ?s1 ) as ?c ) WHERE {
  ?s1 wdt:P31 wd:Q11424 .
  ?s1 ?p wd:Q2009573 .
  FILTER ( ?p = wdt:P144 || ?p = wdt:P674 )
}

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
James Bond, also known as Agent 007, double o seven, 007 (wd:Q2009573): secret agent, invented by novellist Ian Fleming, 00 Agent, spy

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)

[Assistant] Based on these examples, we need to identify the entity for Giorgio Moroder and the property for winning an award. Let's start by searching for Giorgio Moroder.

[Tool]
Top 10 entity alternatives:
1. Giorgio Moroder, also known as Hansjörg Moroder, Giovanni Giorgio Moroder (wd:Q312674): Italian record producer and composer, film score composer, record producer, film producer, disc jockey, songwriter, composer
2. Giorgio by Moroder (wd:Q15720519): 2013 song by Daft Punk
3. Metropolis, also known as Giorgio Moroder Presents Metropolis (wd:Q76367460): 1984 version by Giorgio Moroder
4. Philip Oakey & Giorgio Moroder (wd:Q7184164): 1989 self-titled studio album by Philip Oakey & Giorgio Moroder
5. Giorgione, also known as Giorgon, Giorgioni di Castelfranco, Giorgioni da Castel Franco, Giorgioni, Giorgione of Castel Franco (wd:Q8459): Venetian Renaissance painter (1478–1510), visual artist, painter
6. Rubén Omar Romano, also known as Rubén Romano, Rubén Omar Romano Cachía, Ruben Omar Romano, Giorgio (wd:Q322982): Argentine footballer and manager, association football manager, association football player
7. Giorgio (wd:Q5563455): 1958 single by Lys Assia
8. Giorgio, also known as Giorgio (first name), Giorgio (given name) (wd:Q12900074): male given name
9. Moroder (wd:Q18288660): family name
10. Giorgio (wd:Q37251262): family name

[Assistant] The correct entity for Giorgio Moroder is `wd:Q312674`. Next, let's find the property for winning an award. We can search for properties related to awards.

[Tool]
Top 10 property alternatives:
1. award received, also known as win, winner of, recognition title, received award, prize received (wd:P166 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): award or recognition received by a person, organization or creative work
2. conferred by, also known as presented by, officiated by, issued by, granted by, given by (wd:P1027 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): person or organization who grants an award, certification, grant, or role
3. winner, also known as nominee, prizewinner, titleholder, top dog, victor (wd:P1346 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): winner of a competition or similar event, not to be used for awa...ssibly qualified with "for work" (P1686)) or for wars or battles, participating team, participant
4. grants, also known as awards, bestows, confers, gives out (wd:P5460 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): confers degree, honor, award, prize, title, certificate or medal denoting achievement to a person or organization
5. nominated for, also known as nominee for, shortlisted for, selected for award, selected for, nominee for award (wd:P1411 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): award nomination received by a person, organisation or creative work (inspired from "award received" (Property:P166))
6. for work, also known as awarded for work, citation for award, for their work on, nominated for work (wd:P1686 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): qualifier of award received (P166) to specify the work that an award was given to the creator for
7. award rationale, also known as award citation, rationale, award reason (wd:P6208 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): qualifier to add short citation explaining the official reason f... or add comments about a person's award. No quote marked needed.
8. nominee, also known as award nominee (wd:P2453 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): qualifier used with “nominated for” to specify which person or organization was nominated
9. eligible recipient, also known as eligible recipient for award, type of recipient who is eligible for this award (wd:P8115 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): party that is eligible to receive something, like an award, grant or prize
10. awarded for period (wd:P4566 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): period during which the achievement must have been made to be eligible for an award

[Assistant] The property for receiving an award is `wdt:P166`. Now, let's find the entity for the Golden Globe Award.

[Tool]
Top 10 entity alternatives:
1. Golden Globe Awards, also known as Golden Globes (wd:Q1011547): award of the Hollywood Foreign Press Association
2. Golden Globe Awards, also known as Golden Globes (wd:Q1011547): award of the Hollywood Foreign Press Association
3. Golden Globe Award ceremony (wd:Q19311591)
4. Golden Globe (film/television award) (wd:L365855-S1)
5. 72nd Golden Globe Awards, also known as Golden Globe Awards 2015 (wd:Q17278465): award ceremony
6. 73rd Golden Globe Awards (wd:Q21094897): Golden Globe Award ceremony
7. 70th Golden Globe Awards (wd:Q490861): award ceremony
8. 74th Golden Globe Awards, also known as Golden Globe 2017 (wd:Q26212578): Golden Globe Award ceremony
9. 69th Golden Globe Awards (wd:Q8671): award for best in film and television of 2011
10. 71st Golden Globe Awards (wd:Q15139147): award ceremony

[Assistant]
The correct entity for the Golden Globe Award is `wd:Q1011547`. Now, we can construct a SPARQL query to count the number of times Giorgio Moroder has won the Golden Globe Award.

Let's execute the following SPARQL query:

```sparql
SELECT (COUNT(?award) AS ?count)
WHERE {
  wd:Q312674 wdt:P166 ?award .
  ?award wdt:P31 wd:Q1011547 .
}
```

This query will count the number of times Giorgio Moroder has won the Golden Globe Award.

[Tool]
Got 1 row and 1 column:
| count       |
| ----------- |
| 2 (xsd:int) |

[Assistant]
The SPARQL query has returned that Giorgio Moroder has won the Golden Globe Award 2 times.

Here is the final SPARQL query and answer:

```sparql
SELECT (COUNT(?award) AS ?count)
WHERE {
  wd:Q312674 wdt:P166 ?award .
  ?award wdt:P31 wd:Q1011547 .
}
```

Answer: Giorgio Moroder has won the Golden Globe Award 2 times.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query is correct and returns the accurate count of Giorgio Moroder's Golden Globe Awards. The answer provided is correct and complete.


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT DISTINCT ?creator WHERE {
  wd:Q2394280 wdt:P170 ?creator .
}

William Hanna

[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 was the creator of Boo-Boo Bear ?

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

[Tool]
Example 1:
Question:
Who created Goofy?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q111135 wdt:P170 ?uri .
}

Using entities:
Goofy, also known as Dippy Dawg, George Geef, Goofy Goof (wd:Q111135): Disney cartoon character

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:
Who created Family Guy?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q5930 wdt:P170 ?uri
}

Using entities:
Family Guy (wd:Q5930): American adult animated sitcom

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 3:
Question:
Who created Batman?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q2695156 wdt:P170 ?uri .
}

Using entities:
Batman, also known as the Bat-Man, the Batman, the Caped Crusader, the Bat, Sir Hemingford Grey (wd:Q2695156): fictional character in DC Comics, chief executive officer, businessperson, martial artist, philanthropist, vigilante, detective, superhero

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)

[Assistant]
From the examples, we can see that the property `creator` (P170) is used to link a creative work to its creator. We need to find the entity for Boo-Boo Bear and then use the `creator` property to find the creator.
Let's search for the entity "Boo-Boo Bear".

[Tool]
Top 10 entity alternatives:
1. Boo-Boo Bear, also known as Boo Boo (wd:Q2394280): fictional character in Yogi Bear
2. Yogi Bear and Boo-Boo Bear, also known as Boo Boo and Yogi, Boo-Boo Bear and Yogi Bear, Yogi and Boo Boo (wd:Q132184386): fictional characters from Yogi Bear
3. Ursidae, also known as bear, bear family, ursid, ursids (wd:Q11788): family of mammals
4. Ursus, also known as 🐻 (wd:Q243359): genus of mammals
5. Tu-95, also known as Bear, Tu-95 Bear (wd:Q181815): bomber aircraft family by Tupolev
6. Bégard, also known as Bear (wd:Q217002): commune in Côtes-d'Armor, France
7. short, also known as bear, going short, selling short, shorting, short position (wd:Q106526): practice of selling securities or other financial instruments that are not currently owned
8. Jack Nicklaus, also known as Golden Bear, William Nicklaus, Karnak, Jack William Nicklaus, Bear (wd:Q9424): golf course designer, American golfer, golfer
9. Bear, also known as Bear, DE (wd:Q755632): census-designated place in Delaware, United States
10. bear, also known as bear community, gay bears, gay bear community, bear culture, gay bear (wd:Q618804): term in gay culture for heavily hairy, and usually muscular and bearded men

[Assistant]
The top result for "Boo-Boo Bear" is `wd:Q2394280`. Let's use this entity and the `creator` property to find the creator of Boo-Boo Bear.
I'll run a SPARQL query to find the creator of Boo-Boo Bear.

[Tool]
Got 1 row and 1 column:
| creator                    |
| -------------------------- |
| William Hanna (wd:Q263324) |

[Assistant]
The creator of Boo-Boo Bear is William Hanna.

Let's confirm this with the final SPARQL query and provide the answer.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query correctly identifies William Hanna as the creator of Boo-Boo Bear using the appropriate entity and property from Wikidata.


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?player ( COUNT( ?award ) AS ?awardCount ) WHERE {
  VALUES ?player {
    wd:Q41421 wd:Q25369
  }
  .
  ?player wdt:P166 ?award .
}
GROUP BY ?player

Michael Jordan has won more NBA awards than Kobe Bean Bryant.

[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 has won more NBA awards, Michael Jordan or Kobe Bean Bryant？

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

[Tool]
Example 1:
Question:
How tall is Michael Jordan?

SPARQL:
SELECT DISTINCT ?val WHERE {
  wd:Q41421 wdt:P2048 ?val .
}

Using entities:
Michael Jordan, also known as His Airness, Air Jordan (wd:Q41421): American basketball player and businessman (born 1963), basketball player, baseball player, autobiographer, spokesperson, entrepreneur

Using properties:
height, also known as heighth, size, depth over terrain (wd:P2048 as wdt): vertical length of an entity, length

Example 2:
Question:
Who are the four youngest MVP basketball players?

SPARQL:
SELECT ?uri WHERE {
  ?uri wdt:P106 wd:Q3665646 ; wdt:P569 ?birthDate ; wdt:P166 ?award .
  ?award rdfs:label ?awardLabel
  FILTER ( CONTAINS( STR( ?awardLabel ) , "MVP" ) || CONTAINS( STR( ?awardLabel ) , "Most Valuable Player" ) ) .
  FILTER ( LANGMATCHES( LANG( ?awardLabel ) , "EN" ) )
}
ORDER BY DESC ( ?birthDate )
LIMIT 4

Using entities:
basketball player, also known as basketballer, basketeer, cager, hoopster, professional basketball player (wd:Q3665646): person who plays in basketball

Using properties:
occupation, also known as job, work, vocation, profession, employment (wd:P106 as wdt): occupation of a person; see also "field of work" (Property:P101), "position held" (Property:P39)
award received, also known as win, winner of, recognition title, received award, prize received (wd:P166 as wdt): award or recognition received by a person, organization or creative work
date of birth, also known as year of birth, birthdate, DOB, born on, born (wd:P569 as wdt): date on which the subject was born, inception

Example 3:
Question:
Who is the tallest basketball player?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P106 wd:Q3665646 ; wdt:P2048 ?height .
}
ORDER BY DESC ( ?height )
LIMIT 1

Using entities:
basketball player, also known as basketballer, basketeer, cager, hoopster, professional basketball player (wd:Q3665646): person who plays in basketball

Using properties:
occupation, also known as job, work, vocation, profession, employment (wd:P106 as wdt): occupation of a person; see also "field of work" (Property:P101), "position held" (Property:P39)
height, also known as heighth, size, depth over terrain (wd:P2048 as wdt): vertical length of an entity, length

[Assistant] From the examples, we can see that the property `P166` is used to represent the awards received. We will need to find the entities for Michael Jordan and Kobe Bean Bryant, and then count their respective awards.

[Tool]
Top 10 entity alternatives:
1. Michael Jordan, also known as His Airness, Air Jordan (wd:Q41421): American basketball player and businessman (born 1963), basketball player, baseball player, autobiographer, spokesperson, entrepreneur
2. Michael I. Jordan, also known as Michael Irwin Jordan, Michael Jordan (wd:Q3308285): American computer scientist, University of California, Berkeley, artificial intelligence researcher, computer scientist, university teacher, statistician
3. Michael Jordan, also known as Michael William Jordan (wd:Q6831716): English footballer (born 1984), association football player
4. Michael Jordan (wd:Q65029442): American football offensive lineman, American football player
5. Mike Jordan, also known as Michael Jordan, Michael H. Jordan, Michael Henry Jordan, M. H. Jordan (wd:Q6847437): American baseball player and politician (1863-1940), baseball player, politician
6. Michael Jordan, also known as Mike Jordan (wd:Q27069141): American football cornerback, American football player
7. Michael Jordan (wd:Q6831715): Irish politician, politician, farmer
8. Michael Jordan (wd:Q6831719): British mycologist, writer, and television presenter, television presenter, mushroom gatherer, mycologist, announcer, writer
9. Michael Jordan (wd:Q1928047): German draughtsperson, artist and comics artist, university teacher, comics artist, draftsperson, artist
10. Michael Jordan (wd:Q100983906): college basketball player (2001–2001) New Mexico, basketball player

[Assistant]
The correct entity for Michael Jordan, the basketball player, is `wd:Q41421`.

Now let's find the entity for Kobe Bean 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 Bean Bryant is `wd:Q25369`.

Next, we will write a SPARQL query to count the number of NBA awards each player has received. We will use the `P166` property to find the awards received by each player.

Let's start by writing a simple query to list the awards received by both players.

[Tool]
Got 33 rows and 2 columns, showing the first 5 and last 5 rows below:
| player                     | award                                                                |
| -------------------------- | -------------------------------------------------------------------- |
| Kobe Bryant (wd:Q25369)    | NBA All-Defensive Team (wd:Q1465181)                                 |
| Kobe Bryant (wd:Q25369)    | NBA Most Valuable Player Award (wd:Q222047)                          |
| Kobe Bryant (wd:Q25369)    | NBA All-Star Game Kobe Bryant Most Valuable Player Award (wd:Q31391) |
| Kobe Bryant (wd:Q25369)    | BET Award for Athlete of the Century (wd:Q4835465)                   |
| Kobe Bryant (wd:Q25369)    | All-NBA Team (wd:Q674359)                                            |
| ...                        | ...                                                                  |
| Michael Jordan (wd:Q41421) | All-NBA Team (wd:Q674359)                                            |
| Michael Jordan (wd:Q41421) | Bill Russell NBA Finals Most Valuable Player Award (wd:Q739499)      |
| Michael Jordan (wd:Q41421) | Associated Press Athlete of the Year (wd:Q743122)                    |
| Michael Jordan (wd:Q41421) | wd:Q845884                                                           |
| Michael Jordan (wd:Q41421) | Best NBA Player ESPY Award (wd:Q943106)                              |

[Assistant]
We have successfully retrieved the awards received by both Michael Jordan and Kobe Bean Bryant. Now, let's modify the query to count the number of awards each player has received.

We will group the results by player and count the number of awards.

[Tool]
Got 2 rows and 2 columns:
| player                     | awardCount   |
| -------------------------- | ------------ |
| Kobe Bryant (wd:Q25369)    | 9 (xsd:int)  |
| Michael Jordan (wd:Q41421) | 24 (xsd:int) |

[Assistant]
The SPARQL query has successfully counted the number of awards received by both Michael Jordan and Kobe Bean Bryant. Michael Jordan has won 24 awards, while Kobe Bean Bryant has won 9 awards.

Therefore, Michael Jordan has won more NBA awards than Kobe Bean Bryant.

Here is the final SPARQL query and the answer:

```sparql
SELECT ?player (COUNT(?award) AS ?awardCount) WHERE {
  VALUES ?player { wd:Q41421 wd:Q25369 } .
  ?player wdt:P166 ?award .
} GROUP BY ?player
```

Answer: Michael Jordan has won more NBA awards than Kobe Bean Bryant.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query is well-constructed and returns the correct results. It correctly identifies the number of NBA awards won by both Michael Jordan and Kobe Bryant, and the answer provided is accurate based on the query results.


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?work ?workLabel WHERE {
  wd:Q550311 wdt:P50 ?work .
  FILTER ( ?work != wd:Q696071 ) .
  ?work wdt:P921 wd:Q16003532 .
  OPTIONAL {
    ?work rdfs:label ?workLabel .
    FILTER ( LANG( ?workLabel ) = 'en' )
  }
}

There are no other works by Yoshihiro Togashi, the author of Hunter x Hunter, that talk about middle school students.

[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] among the other representative work of the author of hunter x hunter, which one is talking about middle school student?

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

[Tool]
Example 1:
Question:
What other books have been written by the author of The Fault in Our Stars?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q658375 wdt:P50 ?author .
  ?uri wdt:P50 ?author .
  FILTER ( ?uri != wd:Q658375 )
}

Using entities:
The Fault in Our Stars, also known as TFiOS (wd:Q658375): novel by John Green

Using properties:
author, also known as writer, written by, poet, playwright, maker (wd:P50 as wdt): main creator(s) of a written work (use on works, not humans); us...hor name string) when Wikidata item is unknown or does not exist, creator

Example 2:
Question:
Who wrote The Hunger Games?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q11679 wdt:P50 ?uri .
}

Using entities:
The Hunger Games, also known as Hunger Games Saga, The Hunger Games Saga (wd:Q11679): three books by Suzanne Collins (2008–2010)

Using properties:
author, also known as writer, written by, poet, playwright, maker (wd:P50 as wdt): main creator(s) of a written work (use on works, not humans); us...hor name string) when Wikidata item is unknown or does not exist, creator

Example 3:
Question:
Which country does the creator of Miffy come from?

SPARQL:
SELECT ?uri WHERE {
  wd:Q1501505 wdt:P170 ?creator .
  ?creator wdt:P27 ?uri .
}

Using entities:
Miffy (wd:Q1501505): female rabbit character in a series of picture books drawn and written by Dick Bruna

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)
country of citizenship, also known as subject of (country), nation of citizenship, national of, nationality, nation (wd:P27 as wdt): the object is a country that recognizes the subject as its citizen, country

[Assistant] Based on the examples, we need to identify the author of Hunter x Hunter and then find their other works. After that, we need to check which of those works talk about middle school students. Let's start by finding the author of Hunter x Hunter.

[Tool]
Top 10 entity alternatives:
1. Hunter × Hunter, also known as HUNTER × HUNTER, Hunter x Hunter, HXH (wd:Q696071): Japanese manga series
2. Hunter × Hunter, also known as H×H, Hunter x Hunter, HxH (wd:Q70209136): 2011 Japanese anime television series
3. Hunter × Hunter, also known as Hunter x Hunter, HxH (wd:Q21232458): 1999 Japanese anime television series
4. Monster Hunter Generations, also known as Monster Hunter X (wd:Q20856979): 2015 Japanese video game
5. Hunter Xcite (wd:Q60767092): sailboat class
6. Xavier Hunter (wd:Q100806788): basketball player
7. Hunter × Hunter: Phantom Rouge, also known as Hunter X Hunter: Phantom Rouge (wd:Q11398237): 2013 film by Yūzō Satō
8. City Hunter XYZ (wd:Q97170832): fan film by Filip Wong
9. Monster Hunter Generations Ultimate, also known as Monster Hunter XX (wd:Q30302563): 2017 video game
10. XX: Beautiful Hunter (wd:Q8042573): 1994 film by Masaru Konuma

[Assistant] The correct entity for Hunter x Hunter is `wd:Q696071`. Now let's find the author of this manga series.

[Tool]
Got 1 row and 1 column:
| author                         |
| ------------------------------ |
| Yoshihiro Togashi (wd:Q550311) |

[Assistant]
The author of Hunter x Hunter is Yoshihiro Togashi (`wd:Q550311`). Next, we need to find the other works by Yoshihiro Togashi and then check which of these works involve middle school students.

First, let's list the other works by Yoshihiro Togashi.

[Tool] Got no rows and 1 columns

[Assistant]
It seems that there are no other works listed for Yoshihiro Togashi in the knowledge graph. However, let's try a different approach to ensure we're not missing anything. We can search for works by Yoshihiro Togashi and then filter out Hunter x Hunter.

Let's try searching for works by Yoshihiro Togashi again, but this time we'll include the label to see if there are any other works that might not be directly linked.

[Tool] Got no rows and 2 columns

[Assistant]
It appears that there are no other works by Yoshihiro Togashi listed in the knowledge graph besides Hunter x Hunter. Given this information, it seems that there are no other representative works by Yoshihiro Togashi that talk about middle school students.

Since we have exhausted our options and found no other works by Yoshihiro Togashi, we can conclude that there are no other works by the author of Hunter x Hunter that talk about middle school students.

I will now provide the final answer.

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query is on the right track, but it only checks for other works by Yoshihiro Togashi without verifying if those works are about middle school students. To improve the query, we need to add a condition to check if the works involve middle school students. We can use the property `wdt:P921` (main subject) or `wdt:P31` (instance of) to filter works that are about middle school students.

[Assistant]
Thank you for the feedback. Let's refine the query to include a condition that checks if the works involve middle school students. We can use the property `wdt:P921` (main subject) or `wdt:P31` (instance of) to filter works that are about middle school students.

First, let's find the entity for "middle school student" in the knowledge graph.

[Tool]
Top 10 entity alternatives:
1. middle school student, also known as middle school pupil (wd:Q16003532): student in late primary or early secondary education
2. Additional Incentive for Middle School Students (wd:Q126270572): scholarly article
3. Electronic bullying among middle school students (wd:Q39823864): scientific article
4. Doremi's Boyfriend is a Middle School Student!, also known as Ojamajo Doremi S01EP39, Ojamajo Doremi Season 1 Episode 39, Ojamajo Doremi Season 1 ep. 39, Ojamajo Doremi Season 1 ep 39, Ojamajo Doremi S1EP39 (wd:Q117319259): Episode 39 of Ojamajo Doremi Season 1
5. Reading Preferences of Middle School Students (wd:Q129963064): scholarly article published on 17 April 2018
6. Recruiting middle school students into nursing (wd:Q37192510): scientific article published on June 2008
7. Middle school students' knowledge of autism. (wd:Q46354310): scientific article published in June 2011
8. Middle school students’self-concept and mental health (wd:Q120364046): scholarly article in Journal of Anhui Normal University (Hum.& Soc.Sci.), no. 1, 2006
9. Measuring Middle School Students’ Algebra Readiness (wd:Q99717858)
10. Sally Ride EarthKAM, also known as Earth Knowledge Acquired by Middle school students, A, ISS EarthKAM (wd:Q5974510): NASA outreach program

[Assistant]
The correct entity for "middle school student" is `wd:Q16003532`. Now, let's refine the SPARQL query to include a condition that checks if the works involve middle school students using the property `wdt:P921` (main subject).

Here is the refined SPARQL query:

```sparql
SELECT ?work ?workLabel WHERE {
  wd:Q550311 wdt:P50 ?work .
  FILTER(?work != wd:Q696071) .
  ?work wdt:P921 wd:Q16003532 .
  OPTIONAL { ?work rdfs:label ?workLabel . FILTER(LANG(?workLabel) = 'en') }
}
```

This query will find other works by Yoshihiro Togashi that have "middle school student" as a main subject.

Let's execute this query.

[Tool] Got no rows and 2 columns

[Assistant]
The refined query did not return any results, indicating that there are no other works by Yoshihiro Togashi that have "middle school student" as a main subject.

Given this information, we can conclude that there are no other works by Yoshihiro Togashi, the author of Hunter x Hunter, that talk about middle school students.

I will now provide the final answer.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query is correctly formulated and the answer provided is accurate based on the available data. There are no other works by Yoshihiro Togashi that talk about middle school students.