PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?placeOfBirth WHERE {
  wd:Q79904 wdt:P19 ?placeOfBirth .
}

Got 1 row and 1 column:
| placeOfBirth           |
| ---------------------- |
| New York City (wd:Q60) |

New York City

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

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

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

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

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

[User] Where was the author of The Catcher in the Rye born ?

[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 book The Pillars of the Earth?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q1163227 wdt:P50 ?uri .
}

Using entities:
The Pillars of the Earth, also known as The Pillars of the Earth (Kingsbridge, #1) (wd:Q1163227): 1989 novel by Ken Follett

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:
Who wrote the book The pillars of the Earth?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q1163227 wdt:P50 ?uri .
}

Using entities:
The Pillars of the Earth, also known as The Pillars of the Earth (Kingsbridge, #1) (wd:Q1163227): 1989 novel by Ken Follett

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

[Assistant] Based on the examples, we need to find the author of "The Catcher in the Rye" and then find where that person was born. Let's first search for the entity "The Catcher in the Rye".

[Tool]
Top 10 entity alternatives:
1. The Catcher in the Rye, also known as Catcher in the Rye (wd:Q183883): 1951 novel by J. D. Salinger
2. Catcher in the Rye (wd:Q5051738): Chinese punk rock band
3. Catcher In the Rye (wd:Q16184712): 2011 EP by Datarock
4. Catcher in the Rye (wd:Q110065569): 2021 single by Ross Jennings
5. Catcher in the Rye (wd:Q10443835): song by Guns N' Roses
6. The Catcher in the Rye (wd:Q16689546): 2012 debut studio album by Ann
7. The Catcher in the Rye (wd:Q125291968): 2001 Back Bay edition
8. Catchers in the rye (wd:Q68693473): scientific article published on 01 January 1990
9. The Catcher in the Rye in popular culture (wd:Q7721703): Influence of J.D. Salinger's 1951 novel
10. The Catcher in the Rye in popular culture (wd:Q7721703): Influence of J.D. Salinger's 1951 novel

[Assistant] The correct entity for "The Catcher in the Rye" is the first one, `wd:Q183883`. Now let's find the author of this book using the property `wdt:P50`.

[Tool]
Got 1 row and 1 column:
| author                     |
| -------------------------- |
| J. D. Salinger (wd:Q79904) |

[Assistant] The author of "The Catcher in the Rye" is J. D. Salinger (`wd:Q79904`). Next, we need to find where J. D. Salinger was born. We will use the property `wdt:P19` for place of birth.

[Tool]
Got 1 row and 1 column:
| placeOfBirth           |
| ---------------------- |
| New York City (wd:Q60) |

[Assistant]
J. D. Salinger, the author of "The Catcher in the Rye," was born in New York City (`wd:Q60`).

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

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query correctly identifies the place of birth of J. D. Salinger, the author of The Catcher in the Rye, as New York City.


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
ASK WHERE {
  wd:Q213849 wdt:P576 ?dissolvedDate
}

No, the ATI Company does not still exist.

[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] Does the ATI Company still exist?

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

[Tool]
Example 1:
Question:
Give me all companies in the advertising industry.

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P31 wd:Q4830453 ; wdt:P452 wd:Q39809 .
}

Using entities:
marketing, also known as communications, sales, public relations, publicity, promotion (wd:Q39809): study and process of promoting, distributing, and selling products or services to customers
business (wd:Q4830453): organization undertaking commercial, industrial, or professional activity

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

Example 2:
Question:
Who founded Intel?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q248 wdt:P112 ?uri .
}

Using entities:
Intel, also known as INTC, Integrated Electronics, Integrated Electronics Corporation, INTEL CORPORATION (Santa Clara, CA), N M Electronics (wd:Q248): American multinational technology corporation, American multinational technology company

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

[Assistant] Based on these examples, we need to find the entity for ATI Company and then check if it has a dissolution date or status indicating whether it still exists. Let's first search for the ATI Company entity.

[Tool]
Top 10 entity alternatives:
1. ATI Technologies, also known as Array Technologies Incorporated (wd:Q213849): Canadian technology corporation
2. Atipa Technologies (wd:Q67904906): American hardware manufacturer
3. technology, also known as technologies, technological sciences, technicology, tech, technological science (wd:Q11016): making, modification, usage, and knowledge of tools, machines, t... and methods of organization, or tools and techniques so created
4. Ati (wd:Q755791): city
5. Aashaadha, also known as Ashadha, Āṭi (wd:Q1105198): month in Hindu calender
6. Artigas Airport, also known as ATI, SUAG (wd:Q1430852): airport
7. Ati, also known as Ati language, Ati (Philippines) (wd:Q3217458): Malayo-Polynesian language spoken and native to the Ati people of Panay
8. The Alan Turing Institute, also known as Turing Institute, turing.ac.uk, ATI (wd:Q16826821): research institute in Britain
9. Boneh-ye Ati, also known as Boneh-ye ‘Āţī, ‘Āţī (wd:Q4941616): village in Iran
10. Allegheny Technologies, also known as Allegheny Technologies Incorporated, ATI, ATI Metals (wd:Q1361648): company

[Assistant]
The correct entity for ATI Technologies is `wd:Q213849`. Now, let's check if this entity has a dissolution date or a status indicating whether it still exists. We can look for properties such as "dissolved" or "status".

Let's search for properties related to "dissolved" or "status" for the ATI Technologies entity.

[Tool]
Top 10 property alternatives:
1. dissolved, abolished or demolished date, also known as ended, publication end date, final issue, date repealed, final year (wd:P576 as wdt/p): point in time at which the subject (organisation, building) ceas...g equipment,  "discontinued date" (P2669) for stopping a product, end time
2. replaced by, also known as heir, superseded by, successor, succeeded by, next job holder (wd:P1366 as wdt/p): other person or item which continues the item by replacing it in... nor identical, but adds to the series (e.g. books in a series)., followed by
3. total revenue, also known as turnover, sales, revenue, income, gross revenue (wd:P2139 as wdt/p): income gained by an organization during a given time frame. Not to be confused with fiscal revenue
4. inception, also known as initial release date, founded in, founded on, founded on date, founding date (wd:P571 as wdt/p): time when an entity begins to exist; for date of official opening use P1619, start time
5. different from, also known as not to be confused with, to be distinguished from, similar to, see also, rejected match (wd:P1889 as wdt/p): item that is different from another item, with which it may be confused
6. owned by, also known as belongs to, stockholders, stockholder, shareholders, shareholder (wd:P127 as wdt/p): owner of the subject

No other items found

[Assistant]
The property `dissolved, abolished or demolished date` (wd:P576) is relevant for checking if ATI Technologies has been dissolved. Let's run a SPARQL query to check if this property is present for the ATI Technologies entity.

If the property is present, it means the company has been dissolved. If it is not present, it suggests that the company might still exist.

Let's execute the SPARQL query.

[Tool] True

[Assistant]
The SPARQL query returned `True`, which means that the ATI Technologies entity has a dissolved date. Therefore, the ATI Company no longer exists.

Here is the final SPARQL query and answer:

```sparql
ASK WHERE { wd:Q213849 wdt:P576 ?dissolvedDate }
```

Answer: No, the ATI Company does not still exist.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query is correctly formulated as an ASK query and it checks whether the ATI Company has a dissolved date, which it does. The answer 'No, the ATI Company does not still exist.' is accurate based on the execution result of the query.


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT DISTINCT ?instrument WHERE {
  wd:Q1779 wdt:P1303 ?instrument .
}

Louis Armstrong played the voice, cornet, and trumpet.

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

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

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

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

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

[User] What instruments did Louis Armstrong play?

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

[Tool]
Example 1:
Question:
Give me a list of all trumpet players that were bandleaders.

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P106 wd:Q158852 ; wdt:P1303 wd:Q8338 .
}

Using entities:
conductor, also known as musical conductor, music directors (wd:Q158852): person who directs a musical group during a performance
trumpet, also known as Bb trumpet, B-flat trumpet, B♭ trumpet, natural trumpet, slide trumpet (wd:Q8338): musical instrument

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

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

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

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

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

Example 3:
Question:
Which instruments does Cat Stevens play?

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

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

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

[Assistant] From the examples, we can see that the property `wdt:P1303` is used to indicate the instruments played by a person. We need to find the entity for Louis Armstrong first.

[Tool]
Top 10 entity alternatives:
1. Louis Armstrong, also known as Satchelmouth, Satch, Satchmo, Louis Daniel Armstrong, Dipper (wd:Q1779): American jazz musician, trumpeter and singer (1901–1971), film score composer, radio personality, recording artist, jazz musician, street artist, film actor, songwriter, bandleader, trumpeter, conductor, writer, singer, actor
2. Louis Armstrong (wd:Q98823980): photograph by Lisette Model (MET, 2019.33)
3. Louis Armstrong (wd:Q132754304): online exhibition from Louis Armstrong House and Archives, Queens College, City University of New York
4. Louis Armstrong (wd:Q100721665): college basketball player (1991–1992) Northern Iowa, basketball player
5. Louis Armstrong Stadium (wd:Q1029132): tennis venue in New York City from 1978 to 2016
6. Louis Armstrong Park (wd:Q931042): park in New Orleans, Louisiana, U.S.
7. Louis Armstrong House, also known as Louis Armstrong House Museum (wd:Q6686652): NY location listed on National Register of Historic Places
8. Louise Armstrong (wd:Q6688616): American writer and feminist, human rights activist, children's writer, writer
9. Louis Armstrong Stadium (wd:Q47487977): tennis stadium opened in 2018, replacing a 1978 venue of the same name
10. Louis Armstrong Jazzfesztivál (wd:Q65218436)

[Assistant] The correct entity for Louis Armstrong is `wd:Q1779`. Now let's use this entity and the property `wdt:P1303` to find out what instruments he played.

[Tool]
Got 3 rows and 1 column:
| instrument           |
| -------------------- |
| voice (wd:Q17172850) |
| cornet (wd:Q202027)  |
| trumpet (wd:Q8338)   |

[Assistant]
The SPARQL query has successfully returned the instruments that Louis Armstrong played. Let's format the answer and provide it to the user.

Louis Armstrong played the following instruments:
- Voice
- Cornet
- Trumpet

Here is the final SPARQL query:

```sparql
SELECT DISTINCT ?instrument WHERE {
  wd:Q1779 wdt:P1303 ?instrument .
}
```

And here is the answer:

Louis Armstrong played the voice, cornet, and trumpet.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query returns the instruments that Louis Armstrong played, which are the voice, cornet, and trumpet. The query is well-formed and follows all the rules for SPARQL queries.


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ( COUNT( DISTINCT ?scale ) AS ?count ) WHERE {
  {
    ?scale wdt:P31 wd:Q179651 .
  } UNION {
    ?subclass wdt:P279 * wd:Q179651 .
    ?scale wdt:P31 ?subclass .
  } UNION {
    ?scale wdt:P31 / wdt:P279 * wd:Q179651 .
  } UNION {
    ?scale wdt:P31 / wdt:P279 * wd:Q1959825 .
  } UNION {
    ?scale wdt:P31 / wdt:P279 * wd:Q116748678 .
  }
}

There are 102 musical scales.

[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 musical scales are there?

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

[Tool]
Example 1:
Question:
How many programming languages are there?

SPARQL:
SELECT ( COUNT( DISTINCT ?sub ) AS ?count ) WHERE {
  ?sub wdt:P31 / wdt:P279 * wd:Q9143 .
}

Using entities:
programming language, also known as coding language, computer language, computer programming, computer programming language (wd:Q9143): language for communicating instructions to a machine

Using properties:
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:
How many airlines are there?

SPARQL:
SELECT ( COUNT( DISTINCT ?uri ) AS ?uriCount ) WHERE {
  ?uri wdt:P31 wd:Q46970
}

Using entities:
airline, also known as air company, air carrier (wd:Q46970): company that provides air transport services for traveling passengers and freight

Using properties:
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)

Example 3:
Question:
How many states are in Mexico?

SPARQL:
SELECT ( COUNT( ?uri ) AS ?count ) WHERE {
  ?uri wdt:P31 wd:Q15149663 .
}

Using entities:
state of Mexico, also known as Mexican state (wd:Q15149663): federative entity of Mexico

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 the examples, we need to find the entity representing "musical scale" and then count the number of instances of this entity. Let's first search for the entity "musical scale".

[Tool]
Top 10 entity alternatives:
1. scale, also known as gamut, musical scale (wd:Q179651): set of musical notes ordered by pitch
2. musical scale (wd:L490906-S1)
3. Japanese musical scale (wd:Q1683319): Scales used in Japanese traditional music
4. minor scale; musical scale (wd:L739761-S2)
5. A Perfect Musical Scale (wd:Q60136008): scientific article published in Nature
6. Synthetic Musical Scales (wd:Q58123356)
7. type of musical scale (wd:L640378-S8)
8. MUSICAL INTELLIGENCE AND SCALE PREFERENCE. (wd:Q30335517): scientific article
9. sixth note in musical scale (wd:L739149-S1)
10. sixth note of musical scale (wd:L1405299-S1)

[Assistant] The top result, "scale" (wd:Q179651), seems to be the correct entity for "musical scale". Now let's confirm that this entity is indeed the one we want by listing some of its properties and objects.

[Tool]
Got 400 rows and 3 columns, showing the first 10 rows below:
| subject            | property                                       | object                                                   |
| ------------------ | ---------------------------------------------- | -------------------------------------------------------- |
| scale (wd:Q179651) | instance of (wdt:P31)                          | musical-structural concept (wd:Q116748678)               |
| scale (wd:Q179651) | Commons category (wdt:P373)                    | Musical scales                                           |
| scale (wd:Q179651) | subclass of (wdt:P279)                         | scale (wd:Q1959825)                                      |
| scale (wd:Q179651) | described by source (wdt:P1343)                | Brockhaus and Efron Encyclopedic Dictionary (wd:Q602358) |
| scale (wd:Q179651) | on focus list of Wikimedia project (wdt:P5008) | wd:Q6173448                                              |
| scale (wd:Q179651) | topic's main category (wdt:P910)               | wd:Q7034249                                              |
| scale (wd:Q179651) | Commons gallery (wdt:P935)                     | Musical scale                                            |
| scale (wd:Q179651) | topic's main template (wdt:P1424)              | wd:Q26121140                                             |
| scale (wd:Q179651) | Freebase ID (wdt:P646)                         | /m/0b42b                                                 |
| scale (wd:Q179651) | GND ID (wdtn:P227)                             | <https://d-nb.info/gnd/4060402-0>                        |
| ...                | ...                                            | ...                                                      |

[Assistant]
The entity "scale" (wd:Q179651) is indeed a musical-structural concept and a subclass of "scale" (wd:Q1959825). This confirms that it is the correct entity for "musical scale".

Next, we need to count the number of instances of this entity. We will use the `instance of` property (wdt:P31) to find all instances of "musical scale" and then count them.

Let's construct and execute the SPARQL query to count the number of musical scales.

[Tool]
Got 1 row and 1 column:
| count        |
| ------------ |
| 22 (xsd:int) |

[Assistant]
The SPARQL query returned a count of 22 distinct musical scales.

Therefore, the answer to the question "How many musical scales are there?" is 22.

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query is a good start, but it only counts instances of the general concept of a musical scale (wd:Q179651). However, there are different types of scales, such as major, minor, chromatic, etc., which might not be directly classified as instances of wd:Q179651. We should consider querying for subclasses of musical scales as well to ensure we capture all relevant scales.

[Assistant]
Thank you for the feedback. To refine the query, we need to include both direct instances of "musical scale" (wd:Q179651) and instances of its subclasses. We can achieve this by using the `subclass of` property (wdt:P279) to include all subclasses of "musical scale" and then count the instances of these subclasses as well.

Let's construct and execute the refined SPARQL query.

[Tool]
Got 1 row and 1 column:
| count        |
| ------------ |
| 77 (xsd:int) |

[Assistant]
The refined SPARQL query returned a count of 77 distinct musical scales, including instances of subclasses of "musical scale".

Therefore, the answer to the question "How many musical scales are there?" is 77.

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query is on the right track, but it might be missing some musical scales that are not directly classified as instances or subclasses of 'musical scale'. Consider adding a broader search pattern to include scales that might be categorized differently or have alternative classifications. Additionally, ensure that the query does not include items that are not actually musical scales.

[Assistant]
Thank you for the feedback. To further refine the query, we need to ensure that we capture all relevant musical scales, even those that might be categorized differently or have alternative classifications. We can do this by including additional patterns that might capture these scales.

One approach is to look for entities that are related to musical scales through other properties, such as being a part of a musical system or being used in a specific musical context. We can also include a broader search pattern to ensure we don't miss any relevant scales.

Let's construct and execute the refined SPARQL query.

### Refined Query
1. Direct instances of "musical scale" (wd:Q179651).
2. Instances of subclasses of "musical scale".
3. Entities that are related to musical scales through other properties, such as being a part of a musical system or being used in a specific musical context.

Here is the updated SPARQL query:

```sparql
SELECT (COUNT(DISTINCT ?scale) AS ?count) WHERE {
  { ?scale wdt:P31 wd:Q179651 . }
  UNION
  { ?subclass wdt:P279* wd:Q179651 . ?scale wdt:P31 ?subclass . }
  UNION
  { ?scale wdt:P31/wdt:P279* wd:Q179651 . }
  UNION
  { ?scale wdt:P31/wdt:P279* wd:Q1959825 . }  # General scale
  UNION
  { ?scale wdt:P31/wdt:P279* wd:Q116748678 . }  # Musical-structural concept
}
```

This query includes:
- Direct instances of "musical scale" (wd:Q179651).
- Instances of subclasses of "musical scale".
- Entities that are instances of classes that are subclasses of "musical scale".
- Entities that are instances of classes that are subclasses of "scale" (wd:Q1959825).
- Entities that are instances of classes that are subclasses of "musical-structural concept" (wd:Q116748678).

Let's execute this query.

[Tool]
Got 1 row and 1 column:
| count         |
| ------------- |
| 102 (xsd:int) |

[Assistant]
The refined SPARQL query returned a count of 102 distinct musical scales, including instances of subclasses and related entities.

Therefore, the answer to the question "How many musical scales are there?" is 102.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?gender WHERE {
  wd:Q6711 wdt:P451 ?partner .
  ?partner wdt:P21 ?gender .
}

The binary gender of the partner of Allen Ginsberg is male.

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

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

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

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

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

[User] What is the binary gender of the partner of Allen Ginsberg ?

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

[Tool] No examples found

[Assistant]
Since no similar examples were found, we'll proceed by identifying the relevant entities and properties.

First, let's find the entity for Allen Ginsberg.

[Tool]
Top 10 entity alternatives:
1. Allen Ginsberg, also known as Irwin Allen Ginsberg (wd:Q6711): American poet and writer (1926–1997), autobiographer, photographer, screenwriter, playwright, musician, diarist, teacher, writer, poet
2. Allen Ginsberg (wd:Q102249265): Ph.D. Rutgers University, New Brunswick 1986
3. Allen Ginsberg Library (wd:Q117833470): this entity is a library in the state of colorado. it was identified and verified by the chr task force
4. Untitled (Allen Ginsberg) (wd:Q76558858): print in the National Gallery of Art (NGA 155538)
5. Allen Ginsberg no number (wd:Q64163104): photograph in the National Gallery of Art (NGA 88333)
6. Allen Ginsberg no number (wd:Q64163625): photograph in the National Gallery of Art (NGA 88331)
7. Allen Ginsberg no number (wd:Q64163102): photograph in the National Gallery of Art (NGA 88336)
8. Allen Ginsberg no number (wd:Q64163079): photograph in the National Gallery of Art (NGA 88334)
9. Allen Ginsberg no number (wd:Q64162725): photograph in the National Gallery of Art (NGA 88330)
10. Allen Ginsberg no number (wd:Q64162264): photograph in the National Gallery of Art (NGA 88332)

[Assistant]
The correct entity for Allen Ginsberg is `wd:Q6711`.

Next, we need to identify the property that represents a partner or significant other. Let's search for a property related to partners.

[Tool]
Top 10 property alternatives:
1. unmarried partner, also known as romantic partner, partner of, partners, posslq, relationship (wd:P451 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): someone with whom the person is in a relationship without being married. Use "spouse" (P26) for married couples, significant person
2. spouse, also known as married partner, married to, marry, partner, spouses (wd:P26 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): the subject has the object as their spouse (husband, wife, partn...etc.). Use "unmarried partner" (P451) for non-married companions, significant person, relative
3. partner in business or sport, also known as professional partner, sport partner, sports partner, team mate, team-mate (wd:P1327 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): professional collaborator, significant person
4. partnership with, also known as works with, collaborates with, collaboration with (wd:P2652 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): partnership (commercial or/and non-commercial) between this organization and another organization or institution
5. twinned administrative body, also known as twin cities, twin town, twin city, sister town, sister city (wd:P190 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): twin towns, sister cities, twinned municipalities and other loca..., either legally or informally acknowledged by their governments
6. contributor to the creative work or subject, also known as assistant, collaborator, contributor, contributor(s) to the subject, creative contributor (wd:P767 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): person or organization that contributed to a subject: co-creator of a creative work or subject, participant
7. funder, also known as benefactor, sponsored by, sponsor, has funder, has donor (wd:P8324 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): entity that gives money to a person, organization, or project for a specific purpose, sponsor
8. sponsor, also known as benefactor, endorsed by, patron, sponsored by (wd:P859 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): organization or individual that sponsors this item
9. commissioned by, also known as client, orderer, made for, commissioner, built for (wd:P88 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): person or organization that commissioned this work
10. companion of, also known as has astronomical companion, has companion, companion, astronomic companion of, astronomic companion (wd:P399 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): two or more astronomic bodies of the same type relating to each other

[Assistant]
The most relevant property for a partner or significant other is `wd:P451` (unmarried partner). We will use this property to find Allen Ginsberg's partner.

Now, let's write a simple SPARQL query to find the partner of Allen Ginsberg.

```sparql
SELECT ?partner
WHERE {
  wd:Q6711 wdt:P451 ?partner .
}
```

Let's execute this query to find the partner of Allen Ginsberg.

[Tool]
Got 1 row and 1 column:
| partner                      |
| ---------------------------- |
| Peter Orlovsky (wd:Q1983102) |

[Assistant]
The partner of Allen Ginsberg is Peter Orlovsky (`wd:Q1983102`).

Next, we need to find the binary gender of Peter Orlovsky. Let's search for the property that represents gender.

[Tool]
Top 10 property alternatives:
1. grammatical gender, also known as gender (wd:P5185 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): grammatical gender of the word
2. sex or gender, also known as gender or sex, sex, gender identity, gender expression, gender (wd:P21 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): sex or gender identity of human or animal. For human: male, fema...m, female organism. Groups of same gender use subclass of (P279)
3. requires grammatical feature, also known as grammatical feature required, required grammatical feature, requires case, requires mood, uses case (wd:P5713 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): grammatical features required or generally used with lexeme or form of lexeme
4. has grammatical gender, also known as grammatical genders, has genders (wd:P5109 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): specific form of noun class system of a language. Use qualifier ... subject of" (P805) to link to language specific item if present
5. personal pronoun, also known as gender pronoun, personal gender pronoun, personal pronouns, PGP, PGPs (wd:P6553 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): personal pronoun(s) this person goes by
6. semantic gender, also known as natural gender (wd:P10339 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): used when a word is only used to refer to people of this gender or to animals of this sex, independent of the grammatical gender
7. female form of label, also known as female form of label (string), female gender sense, feminine form of label (wd:P2521 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): female form of name or title (for male use P3321)
8. gender educated (wd:P7419 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): genders educated at this educational institution
9. male form of label, also known as male form of label (string), male gender sense, masculine form of label (wd:P3321 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): male form of name or title (for female use P2521)
10. given name version for other gender, also known as personal name version for other gender, other genders given name, male version of this female given name, gender inflection of given name, female version of this male given name (wd:P1560 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): equivalent name (with respect to the meaning of the name) in the...n of a female first name. Add primarily the closest matching one

[Assistant]
The most relevant property for gender is `wd:P21` (sex or gender). We will use this property to find the binary gender of Peter Orlovsky.

Now, let's write a SPARQL query to find the binary gender of Peter Orlovsky.

```sparql
SELECT ?gender
WHERE {
  wd:Q1983102 wdt:P21 ?gender .
}
```

Let's execute this query to find the binary gender of Peter Orlovsky.

[Tool]
Got 1 row and 1 column:
| gender             |
| ------------------ |
| male (wd:Q6581097) |

[Assistant]
The binary gender of Peter Orlovsky, the partner of Allen Ginsberg, is male (`wd:Q6581097`).

Here is the final SPARQL query and answer:

```sparql
SELECT ?gender
WHERE {
  wd:Q6711 wdt:P451 ?partner .
  ?partner wdt:P21 ?gender .
}
```

Answer: The binary gender of the partner of Allen Ginsberg is male.

Let's provide the final answer using the `answer` function.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query correctly identifies the binary gender of Allen Ginsberg's partner, which is male.


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?member ?dateOfBirth WHERE {
  wd:Q51885404 wdt:P527 ?member .
  ?member wdt:P569 ?dateOfBirth .
}
ORDER BY DESC ( ?dateOfBirth )
LIMIT 1

The youngest member of G-IDLE is Shuhua.

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

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

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

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

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

[User] who is the youngested member in g-idle?

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

[Tool]
Example 1:
Question:
Who is the youngest player in the Premier League?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?team wdt:P118 wd:Q9448 .
  ?uri wdt:P54 ?team ; wdt:P569 ?dateOfBirth .
}
ORDER BY DESC ( ?dateOfBirth )
LIMIT 1

Using entities:
Premier League, also known as England Premier League, English Premier League, English Premiership, EPL, PL (wd:Q9448): English men's association football top league

Using properties:
league or competition, also known as tournament, sports league, division, cup, competition (wd:P118 as wdt): league or competition in which team or player has played, or in which an event occurs, participant in
member of sports team, also known as plays for, played for, sport team, team, team played for (wd:P54 as wdt): sports teams or clubs that the subject represents or represented, member of
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 2:
Question:
Who is the youngest Darts player?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P106 wd:Q18574233 ; wdt:P569 ?dateOfBirth .
}
ORDER BY DESC ( ?dateOfBirth )
LIMIT 1

Using entities:
darts player, also known as dartsman, dartsperson, dartswoman (wd:Q18574233): person who plays darts

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)
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:
Give me all members of Prodigy.

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P463 wd:Q7256860 .
}

Using entities:
The Prodigy, also known as the prodigy, The Prodigy band (wd:Q7256860): English electronic dance music group

Using properties:
member of, also known as band member of, membership, member of musical group, in musical group, be member of (wd:P463 as wdt): organization, club or musical group to which the subject belongs...ical position, such as a member of parliament (use P39 for that), part of

[Assistant] Based on the examples, we need to identify the entity for the K-pop group G-IDLE and then find the youngest member by their date of birth. Let's first search for the entity representing G-IDLE.

[Tool]
Top 10 entity alternatives:
1. Neverland, also known as Neverland (fandom), Neverland (G-Idle) (wd:Q99515216): Neverland are the fanbase of (G)I-DLE.
2. Moon, also known as Moon ((G)I-dle song), Moon (G-Idle song) (wd:Q104969991): 2021 single by (G)I-dle
3. Lost, also known as Lost (G-Idle song) (wd:Q105132486): 2021 song by (G)I-DLE
4. Last Dance, also known as Last Dance ((G)I-dle song), Last Dance (G-Idle song) (wd:Q106584541): song written and composed by Kriz and Soyeon, adapted by GroovyRoom; recorded 2021 by (G)I-dle

[Assistant] None of these options seem to directly represent the K-pop group G-IDLE. Let's try searching again with a slightly different query to ensure we get the correct entity.

[Tool]
Top 10 entity alternatives:
1. (G)I-dle, also known as (G)I-DLE (wd:Q51885404): South Korean girl group
2. (G)I-DLE: I-TALK, also known as I-Talk (wd:Q98844809): original program made by Cube Entertainment, showing behind-the-scenes footage of MVs as series
3. Hwaa, also known as Hwaa ((G)I-dle song), Hwaa (song) (wd:Q104600483): 2021 single by (G)I-dle
4. Moon, also known as Moon ((G)I-dle song), Moon (G-Idle song) (wd:Q104969991): 2021 single by (G)I-dle
5. Last Dance, also known as Last Dance ((G)I-dle song), Last Dance (G-Idle song) (wd:Q106584541): song written and composed by Kriz and Soyeon, adapted by GroovyRoom; recorded 2021 by (G)I-dle
6. I-Land: Who Am I, also known as 2020 (G)I-dle Online Concert 'I-Land: Who Am I' (wd:Q96381600): 2020 (G)I-dle online concert
7. (G)I-dle Just Me ( )I-dle in Seoul, June 2022 (wd:Q124058812)
8. (G)I-dle Just Me ( )I-dle in Monterrey, 14 August 2022 (wd:Q124058808)
9. (G)I-dle at 2020 Tainan Christmas & New Year Eve Party (wd:Q124058809)
10. (G)I-dle Just Me ( )I-dle in San Francisco, 24 July 2022 (wd:Q124058817)

[Assistant]
The correct entity for the K-pop group G-IDLE is `wd:Q51885404`. Now, let's find the members of G-IDLE and their dates of birth. We will then sort them by their date of birth to find the youngest member.

First, let's list the members of G-IDLE.

[Tool]
Got 6 rows and 3 columns:
| subject                 | property               | object                     |
| ----------------------- | ---------------------- | -------------------------- |
| (G)I-dle (wd:Q51885404) | has part(s) (wdt:P527) | Jeon Soyeon (wd:Q26707915) |
| (G)I-dle (wd:Q51885404) | has part(s) (wdt:P527) | Cho Mi-yeon (wd:Q56259927) |
| (G)I-dle (wd:Q51885404) | has part(s) (wdt:P527) | Song Yuqi (wd:Q55287306)   |
| (G)I-dle (wd:Q51885404) | has part(s) (wdt:P527) | Soojin (wd:Q61100297)      |
| (G)I-dle (wd:Q51885404) | has part(s) (wdt:P527) | Minnie (wd:Q56761923)      |
| (G)I-dle (wd:Q51885404) | has part(s) (wdt:P527) | Shuhua (wd:Q55287305)      |

[Assistant]
We have identified the members of G-IDLE. Now, let's find their dates of birth and determine the youngest member.

We will use the `wdt:P569` property to get the date of birth for each member and then sort them to find the youngest.

Here is the SPARQL query to achieve this:

```sparql
SELECT ?member ?dateOfBirth WHERE {
  wd:Q51885404 wdt:P527 ?member .
  ?member wdt:P569 ?dateOfBirth .
}
ORDER BY DESC(?dateOfBirth)
LIMIT 1
```

Let's execute this query.

[Tool]
Got 1 row and 2 columns:
| member                | dateOfBirth                         |
| --------------------- | ----------------------------------- |
| Shuhua (wd:Q55287305) | 2000-01-06T00:00:00Z (xsd:dateTime) |

[Assistant]
The youngest member of G-IDLE is Shuhua, born on January 6, 2000.

Here is the final SPARQL query and answer:

```sparql
SELECT ?member ?dateOfBirth WHERE {
  wd:Q51885404 wdt:P527 ?member .
  ?member wdt:P569 ?dateOfBirth .
}
ORDER BY DESC(?dateOfBirth)
LIMIT 1
```

Answer: The youngest member of G-IDLE is Shuhua.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query is correct and returns the youngest member of G-IDLE along with their date of birth. The answer provided is accurate based on the query results.


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?author WHERE {
  wd:Q3521239 wdt:P50 ?author .
}

Clive Barker

[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's the author of The Hellbound Heart ?

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

[Tool]
Example 1:
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 2:
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 3:
Question:
Who wrote the book The Pillars of the Earth?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q1163227 wdt:P50 ?uri .
}

Using entities:
The Pillars of the Earth, also known as The Pillars of the Earth (Kingsbridge, #1) (wd:Q1163227): 1989 novel by Ken Follett

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

[Assistant] Based on the examples, we need to find the entity for "The Hellbound Heart" and then use the property P50 (author) to find the author of the book. Let's first search for the entity "The Hellbound Heart".

[Tool]
Top 10 entity alternatives:
1. The Hellbound Heart (wd:Q3521239): novella by Clive Barker
2. The Hellbound Heart (wd:Q131862610): 2008 HarperVoyager edition
3. Holiday Peak, also known as The Heart (wd:Q5880456): mountain in the Antarctic
4. The Heart, also known as Heart, Kokoro (wd:Q19567789): 1955 Japanese film directed by Kon Ichikawa
5. Das Herz, also known as The Heart (wd:Q49139357): opera by Hans Pfitzner
6. The Heart (wd:Q110895729): film by Kaneto Shindo
7. The Heart (wd:Q124421885): work by Hippocrates
8. The Heart, also known as Hjärtat (wd:Q10524935): 1987 film
9. The Heart, also known as Srdce (wd:Q12055804): opera by Josef Bohuslav Foerster
10. The Heart, also known as Hjärtat (wd:Q61672477): 2018 film

[Assistant]
The correct entity for "The Hellbound Heart" is wd:Q3521239. Now let's use the property P50 (author) to find the author of the book.

I will now construct a SPARQL query to find the author of "The Hellbound Heart".

[Tool]
Got 1 row and 1 column:
| author                    |
| ------------------------- |
| Clive Barker (wd:Q298761) |

[Assistant]
The author of "The Hellbound Heart" is Clive Barker.

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

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query correctly identifies the author of 'The Hellbound Heart' as Clive Barker, and the result includes the IRI of the author.


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
ASK {
  wd:Q388973 wdt:P176 wd:Q157062
}

False

[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 After Eight manufactured by Unilever?

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

[Tool] No examples found

[Assistant] Since no similar examples were found, let's proceed by searching for the entities mentioned in the question: "After Eight" and "Unilever".

[Tool]
Top 10 entity alternatives:
1. After Eight, also known as After Eight mint chocolate (wd:Q388973): chocolate brand of Nestle
2. Polar Bear, also known as After Eight, Peppermint Paddy (wd:Q7209010): mint chocolate cocktail
3. After Eight (wd:Q60786577): 1982 debut studio album by Taco
4. Dahlia 'Twyning's After Eight', also known as Twyning's After Eight (wd:Q83544826): Dahlia cultivar
5. 8 Man After, also known as Eightman After (wd:Q115948406): 1993 original video animation series
6. DeltaD, also known as after eight protein, delta-like protein D, dld, aei (wd:Q29825141): protein found in Danio rerio
7. Quarter After Eight (wd:Q15762559): journal
8. Twelve after Eight (wd:Q131628059): journal article from 'Journal of Medical Humanities' published in 2015
9. Chernobyl: eight years after (wd:Q56608399): scientific article published on 01 October 1994
10. After Eighty Study (wd:Q63818230): clinical trial

[Assistant]
From the search results, it seems that the first option, "After Eight, also known as After Eight mint chocolate (wd:Q388973)", is the correct entity for the chocolate brand we're interested in.

Now, let's search for "Unilever".

[Tool]
Top 10 entity alternatives:
1. Unilever (wd:Q157062): British multinational consumer goods company
2. Unilever, also known as UNVR, Unilever Tbk, Lever Zeepfabrieken N.V., PT Unilever  Tbk (wd:Q7885208): British company in
3. Unilever Headquarters, also known as Unilever, Weena 455 (wd:Q125679337): office building at Weena 455, Centrum, Rotterdam, the Netherlands
4. T.H. Estabrooks Co. Ltd., also known as Brooke Bond, Unilever (wd:Q116708935): Canadian business.
5. Hindustan Unilever, also known as Unilever (India), HUL, Hindustan Unilever Limited (wd:Q1619376): Indian consumer goods company
6. Unilever House (wd:Q2494434): grade II listed office building in City of London, United kingdom
7. Unilever Philippines, also known as Unilever (Philippines) (wd:Q18159894): Philippine subsidiary of Unilever
8. Unilever Pakistan, also known as Unilever Pakistan Limited (wd:Q7885211): Pakistani subsidiary of a foreign company
9. Unilever Gloucester, also known as Walls Ice Cream Factory (wd:Q96412295): food manufacturing site in Gloucester, England, UK
10. Unilever (Bydgoszcz) (wd:Q25415517): business enterprise

[Assistant]
The first option, "Unilever (wd:Q157062)", is the correct entity for the multinational consumer goods company.

Next, we need to check if "After Eight" (wd:Q388973) is manufactured by "Unilever" (wd:Q157062). We can do this by checking if there is a direct relationship between these two entities using the `manufacturer` property (P176).

Let's run a SPARQL query to check this.

[Tool] False

[Assistant]
The SPARQL query returned `False`, indicating that "After Eight" (wd:Q388973) is not manufactured by "Unilever" (wd:Q157062).

To provide a complete answer, let's confirm who actually manufactures "After Eight". We can do this by querying the `manufacturer` property (P176) for "After Eight".

Let's run another SPARQL query to find out the manufacturer of "After Eight".

[Tool]
Got 1 row and 1 column:
| manufacturer        |
| ------------------- |
| Nestlé (wd:Q160746) |

[Assistant]
The manufacturer of "After Eight" is Nestlé (wd:Q160746).

Therefore, the answer to the question "Is After Eight manufactured by Unilever?" is **False**.

Here is the final SPARQL query and answer:

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query is correctly formulated as an ASK query and accurately checks whether After Eight is manufactured by Unilever. The answer is False, which is consistent with the information provided, as After Eight is a chocolate brand of Nestle, not Unilever.


Failed to generate SPARQL query

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

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

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

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

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

[User] Which businesses are founded by the person in charge of Tesla?

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

[Tool]
Example 1:
Question:
How many companies were founded by the founder of Facebook?

SPARQL:
SELECT ( COUNT( DISTINCT ?uri ) AS ?c ) WHERE {
  ?uri wdt:P112 wd:Q36215 .
  ?uri wdt:P31 / wdt:P279 * wd:Q783794 .
}

Using entities:
Mark Zuckerberg, also known as The Zuck, Zuck, Zuckerberg, Mark Elliott Zuckerberg, The Zucc (wd:Q36215): American Internet entrepreneur, chief executive officer, patron of the arts, computer scientist, philanthropist, entrepreneur, programmer
company, also known as co., firm (wd:Q783794): legal entity representing an association of people, whether natural, legal or a mixture of both, with a specific objective

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

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q243278 wdt:P127 ?uri .
}

Using entities:
Rolls-Royce, also known as Rolls-Royce Holdings plc (wd:Q243278): British multinational public holding company

Using properties:
owned by, also known as belongs to, stockholders, stockholder, shareholders, shareholder (wd:P127 as wdt): owner of the subject

Example 3:
Question:
Who is the owner of Universal Studios?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q168383 wdt:P127 ?uri .
}

Using entities:
Universal Pictures, also known as Universal City Studios LLC, Universal Film Manufacturing Company, Universal-International Pictures Inc., Universal Studios (wd:Q168383): American film studio

Using properties:
owned by, also known as belongs to, stockholders, stockholder, shareholders, shareholder (wd:P127 as wdt): owner of the subject

[Assistant] From these examples, we can see that we need to identify the person in charge of Tesla and then find the businesses they have founded. Let's start by identifying the person in charge of Tesla.

[Tool]
Top 10 entity alternatives:
1. Nikola Tesla, also known as Tesla, Nicola Tesla (wd:Q9036): Serbian-American inventor (1856–1943), mechanical engineer, electrical engineer, electrician, physicist, futurist, inventor
2. Tesla, Inc., also known as Tesla, Tesla Inc. (wd:Q478214): American automotive, energy storage and solar power company
3. tesla, also known as T (wd:Q163343): SI unit of magnetic flux density
4. Tesla (wd:Q1428953): American hard rock band
5. Nvidia Tesla, also known as NVidia Tesla, NVIDIA Tesla, Tesla (wd:Q16258100): Nvidia's brand name for their products targeting stream processing and/or general purpose GPU
6. Tesla (wd:Q88053410): 2020 film directed by Michael Almereyda
7. Tesla (wd:Q2406220): lunar crater
8. Tesla, also known as TESLA, Tesla (Czech Republic) (wd:Q1548225): Czech manufacturer of electronical and electrical equipment (est.1991)
9. Tesla, also known as NVIDIA Tesla, Nvidia Tesla, NVidia Tesla (wd:Q1050485): GPU microarchitecture designed by NVIDIA
10. Tesla, also known as Tesla, West Virginia, Tesla, WV (wd:Q7705502): unincorporated community in Braxton County, West Virginia

[Assistant] The correct entity for Tesla, the automotive and energy company, is `wd:Q478214`. Now let's find out who is in charge of Tesla. We can look for properties such as "CEO" or "founder" to identify the person in charge.

[Tool]
Top 10 property alternatives:
1. chief executive officer, also known as executive director, CEO, chief executive (wd:P169 as wdt/p): highest-ranking corporate officer appointed as the CEO within an organization, director / manager, corporate officer
2. chairperson, also known as dean, headed by, leader, president, chairwoman (wd:P488 as wdt/p): presiding member of an organization, group or body, director / manager, significant person
3. founded by, also known as starter, co-founded by, started by, institutor, instituted by (wd:P112 as wdt/p): founder or co-founder of this organization, religion, place or entity, creator
4. board member, also known as member of the board, member of the board of directors (wd:P3320 as wdt/p): member(s) of the board for the organization
5. headquarters location, also known as headquarters locality, seat, headquarters town, HQ, HQ location (wd:P159 as wdt/p): city or town where an organization's headquarters is or has been situated. Use P276 qualifier for specific building, significant place
6. owned by, also known as belongs to, stockholders, stockholder, shareholders, shareholder (wd:P127 as wdt/p): owner of the subject
7. inception, also known as initial release date, founded in, founded on, founded on date, founding date (wd:P571 as wdt/p): time when an entity begins to exist; for date of official opening use P1619, start time
8. owner of, also known as owns, owns property, shareholder of (wd:P1830 as wdt/p): entities owned by the subject
9. country of origin, also known as origin country, place of origin, originates from, country, CoO (wd:P495 as wdt/p): country of origin of this item (creative work, food, phrase, product, etc.), significant place, country
10. has subsidiary, also known as subordinate body, subsidiary body, subordinate unit, subsidiary, subsidiary company (wd:P355 as wdt/p): subsidiary of a company or organization; generally a fully owned...siness division" (P199). Opposite of parent organization (P749)., has part(s), owner of

No other items found

[Assistant] The property "chief executive officer" (`wdt:P169`) is the most relevant for identifying the person in charge of Tesla. Let's use this property to find the CEO of Tesla.

[Tool]
Got 1 row and 1 column:
| ceo                    |
| ---------------------- |
| Elon Musk (wd:Q317521) |

[Assistant]
The CEO of Tesla is Elon Musk (`wd:Q317521`). Next, we need to find the businesses that Elon Musk has founded. We can use the property "founded by" (`wdt:P112`) to find these businesses.

Let's construct a SPARQL query to find the businesses founded by Elon Musk.

[Tool]
Got 14 rows and 1 column, showing the first 5 and last 5 rows below:
| business                                                |
| ------------------------------------------------------- |
| Texas Institute of Technology & Science (wd:Q109332244) |
| Thud (wd:Q115815194)                                    |
| X Corp. (wd:Q117617480)                                 |
| X Holdings Corp (wd:Q117705742)                         |
| xAI (wd:Q120599684)                                     |
| ...                                                     |
| Zip2 (wd:Q28222602)                                     |
| The Boring Company (wd:Q28874479)                       |
| Neuralink (wd:Q29043471)                                |
| X.com (wd:Q30686532)                                    |
| PayPal (wd:Q483959)                                     |

[Assistant]
The businesses founded by Elon Musk include:

- Texas Institute of Technology & Science (wd:Q109332244)
- Thud (wd:Q115815194)
- X Corp. (wd:Q117617480)
- X Holdings Corp (wd:Q117705742)
- xAI (wd:Q120599684)
- Zip2 (wd:Q28222602)
- The Boring Company (wd:Q28874479)
- Neuralink (wd:Q29043471)
- X.com (wd:Q30686532)
- PayPal (wd:Q483959)

Here is the final SPARQL query and the answer to the question:

```sparql
SELECT ?business WHERE {
  ?business wdt:P112 wd:Q317521
}
```

The businesses founded by Elon Musk, the person in charge of Tesla, are listed above.

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query is almost correct, but it only considers businesses that Elon Musk is the founder of. To fully answer the question, we need to identify the person in charge of Tesla, which might not necessarily be the founder, and then find the businesses that this person has founded. We should add a step to identify the person in charge of Tesla using the appropriate property, such as 'position held' or 'CEO of', and then use that person to find the businesses they have founded.

[Assistant]
Thank you for the feedback. Let's refine the query to ensure we correctly identify the person in charge of Tesla and then find the businesses they have founded.

First, let's confirm the property for identifying the person in charge of Tesla. We can use "position held" (`wdt:P39`) or "CEO of" (`wdt:P169`). Since we already know that Elon Musk is the CEO of Tesla, we can use `wdt:P169` to confirm this.

Next, we will use the identified person to find the businesses they have founded using the "founded by" property (`wdt:P112`).

Let's start by confirming the CEO of Tesla again using `wdt:P169`.