PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?company ?companyLabel WHERE {
  wd:Q1361648 wdt:P31 wd:Q4830453 .
  OPTIONAL {
    wd:Q1361648 rdfs:label ?companyLabel FILTER ( LANG( ?companyLabel ) = "en" )
  }
}

Got 1 row and 2 columns:
| company | companyLabel                |
| ------- | --------------------------- |
|         | Allegheny Technologies (en) |

Allegheny Technologies, also known as ATI, has no recorded dissolution date and no end time, indicating that the ATI Company still exists.

[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 with notes:
1. When direct properties are missing, chain related properties (e.g., child→child for grandchildren) or search for indirect connections to answer complex relationship queries.
2. For set/class queries (e.g., all cities, all holidays), use instance/subclass chains (P31/P279) for completeness and relevant filters. Be aware of data incompleteness and schema inconsistencies in Wikidata.
3. If queries return too many irrelevant or too few results, try varying properties (country, location, observed in, etc.) and subclass chains, but be ready to report data/model limitations if results remain incomplete or noisy.
4. When a direct property or class is missing or inconsistently used, try related or broader properties/classes (e.g., use inception for independence date, or musical work/composition for musicals) to increase recall.
5. For calculated properties (e.g., population density), combine relevant direct properties (e.g., population, area) in SPARQL queries, as Wikidata may not store the derived value directly.
6. When entity search returns multiple candidates, use context (description, instance type, location, etc.) to disambiguate and select the most relevant entity for the query.

No general notes for all knowledge graphs available

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/
qlss: <https://qlever.cs.uni-freiburg.de/spatialSearch/
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. Search for the 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. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
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 just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. 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] Step 1: Identify the entity "ATI Company" in Wikidata to find its exact identifier and relevant information about its existence status. I will search for the entity "ATI Company" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. company, also known as firm, co. (wd:Q783794)
2. company, also known as company (military unit), military company (wd:Q102538)
3. enterprise, also known as business enterprise, company, firm (wd:Q6881511)
4. Ati (wd:Q755791): instance of subprefecture of Chad, instance of municipality of Chad, instance of city, city
5. Aashaadha, also known as Ashadha, Āṭi (wd:Q1105198)
6. Artigas Airport, also known as ATI, SUAG (wd:Q1430852): instance of airport, airport
7. Company (wd:Q225093): 2002 film directed by Ram Gopal Varma, instance of film
8. Company (wd:Q2915810): 1970 musical comedy by Stephen Sondheim, instance of dramatico-musical work
9. Ati, also known as Ati language, Ati (Philippines) (wd:Q3217458)
10. Company (wd:Q23723998): instance of musical work/composition, 2016 single by Justin Bieber

[Assistant] The search for "ATI Company" did not return a direct match for a company named "ATI Company." It returned general entities related to "company" and other unrelated entities. To refine the search, I will try searching for "ATI" as a company or business to see if there is a relevant company entity with that name or abbreviation.

[Tool]
Top 10 entity alternatives:
1. Ati (wd:Q755791): instance of subprefecture of Chad, instance of municipality of Chad, instance of city, city
2. Aashaadha, also known as Ashadha, Āṭi (wd:Q1105198)
3. Artigas Airport, also known as ATI, SUAG (wd:Q1430852): instance of airport, airport
4. Ati, also known as Ati language, Ati (Philippines) (wd:Q3217458)
5. Alan Turing Institute, also known as ATI, turing.ac.uk, Turing Institute (wd:Q16826821): instance of research institute, research institute in Britain
6. Boneh-ye Ati, also known as Boneh-ye ‘Āţī, ‘Āţī (wd:Q4941616): instance of village, village in Iran
7. Allegheny Technologies, also known as ATI, ATI Metals, Allegheny Technologies Incorporated (wd:Q1361648): instance of public company, instance of enterprise, instance of business, company
8. Computer Engineers Association of Spain, also known as ATI (wd:Q5709317): instance of professional association, instance of nonprofit organization, Spanish non-profit association
9. Aati, also known as Ati (wd:Q106432253)
10. Airmen Training Institution, also known as ATI (wd:Q110130809): Bangladesh Air Force Airmen Training and Education Institution, instance of military school

[Assistant]
Among the search results for "ATI," the entity "Allegheny Technologies" (wd:Q1361648) is also known as ATI, ATI Metals, Allegheny Technologies Incorporated, and is an instance of a public company. This seems to be a relevant candidate for the "ATI Company."

Step 2: Check if Allegheny Technologies (ATI) still exists by looking for its dissolution date or current status. I will query for the existence or dissolution date of this company.

[Tool] Got no rows and 1 columns

[Assistant] There is no dissolution date for Allegheny Technologies (ATI) in Wikidata, which suggests that the company likely still exists. To further confirm, I will check if the company has a "inception" date and no "end time" or "dissolved" statement, which would indicate it is still active. I will also check for a "instance of" public company and see if there is any indication of it being defunct.

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

[Assistant]
Allegheny Technologies (ATI) has an inception date of January 1, 1996, and no end time or dissolution date is recorded. This indicates that the company still exists.

I will now provide the final answer.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?instrument ?instrumentLabel WHERE {
  wd:Q1779 wdt:P1303 ?instrument .
  OPTIONAL {
    ?instrument rdfs:label ?instrumentLabel FILTER ( LANG( ?instrumentLabel ) = "en" )
  }
}

Got 3 rows and 2 columns:
| instrument           | instrumentLabel |
| -------------------- | --------------- |
| voice (wd:Q17172850) | voice (en)      |
| cornet (wd:Q202027)  | cornet (en)     |
| trumpet (wd:Q8338)   | trumpet (en)    |

Louis Armstrong played the instruments voice, cornet, and trumpet.

What instruments did Louis Armstrong play?

[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 with notes:
1. When direct properties are missing, chain related properties (e.g., child→child for grandchildren) or search for indirect connections to answer complex relationship queries.
2. For set/class queries (e.g., all cities, all holidays), use instance/subclass chains (P31/P279) for completeness and relevant filters. Be aware of data incompleteness and schema inconsistencies in Wikidata.
3. If queries return too many irrelevant or too few results, try varying properties (country, location, observed in, etc.) and subclass chains, but be ready to report data/model limitations if results remain incomplete or noisy.
4. When a direct property or class is missing or inconsistently used, try related or broader properties/classes (e.g., use inception for independence date, or musical work/composition for musicals) to increase recall.
5. For calculated properties (e.g., population density), combine relevant direct properties (e.g., population, area) in SPARQL queries, as Wikidata may not store the derived value directly.
6. When entity search returns multiple candidates, use context (description, instance type, location, etc.) to disambiguate and select the most relevant entity for the query.

No general notes for all knowledge graphs available

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/
qlss: <https://qlever.cs.uni-freiburg.de/spatialSearch/
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. Search for the 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. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
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 just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. 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] To answer the question about which instruments Louis Armstrong played, I will first identify the entity for Louis Armstrong in Wikidata. Then, I will look for properties related to musical instruments he played. Typically, the property "instrument played" (P1303) is used for this kind of information. I will verify the existence of this property for Louis Armstrong and retrieve the instruments.

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

[Assistant] The entity for Louis Armstrong is wd:Q1779. Next, I will check the properties related to the instruments he played, specifically looking for the property "instrument played" (P1303) for this entity.

[Tool]
Got 3 rows and 3 columns:
| subject                    | property               | object               |
| -------------------------- | ---------------------- | -------------------- |
| Louis Armstrong (wd:Q1779) | instrument (wdt:P1303) | voice (wd:Q17172850) |
| Louis Armstrong (wd:Q1779) | instrument (wdt:P1303) | cornet (wd:Q202027)  |
| Louis Armstrong (wd:Q1779) | instrument (wdt:P1303) | trumpet (wd:Q8338)   |

[Tool] Stopped generation process


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

Got 1 row and 1 column:
| count        |
| ------------ |
| 47 (xsd:int) |

There are 47 unique musical scales in Wikidata, considering both instances and subclasses of the entity 'musical scale' (wd:Q179651).

[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 with notes:
1. When direct properties are missing, chain related properties (e.g., child→child for grandchildren) or search for indirect connections to answer complex relationship queries.
2. For set/class queries (e.g., all cities, all holidays), use instance/subclass chains (P31/P279) for completeness and relevant filters. Be aware of data incompleteness and schema inconsistencies in Wikidata.
3. If queries return too many irrelevant or too few results, try varying properties (country, location, observed in, etc.) and subclass chains, but be ready to report data/model limitations if results remain incomplete or noisy.
4. When a direct property or class is missing or inconsistently used, try related or broader properties/classes (e.g., use inception for independence date, or musical work/composition for musicals) to increase recall.
5. For calculated properties (e.g., population density), combine relevant direct properties (e.g., population, area) in SPARQL queries, as Wikidata may not store the derived value directly.
6. When entity search returns multiple candidates, use context (description, instance type, location, etc.) to disambiguate and select the most relevant entity for the query.

No general notes for all knowledge graphs available

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/
qlss: <https://qlever.cs.uni-freiburg.de/spatialSearch/
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. Search for the 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. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
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 just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. 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]
To answer the question "How many musical scales are there?" we need to clarify what is meant by "musical scales." In Wikidata, musical scales are likely represented as instances or subclasses of a class related to musical scales. We can try to find the entity for "musical scale" and then count how many instances or subclasses it has.

Step 1: Search for the entity "musical scale" in Wikidata.

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

[Assistant] The entity "scale, also known as gamut, musical scale" (wd:Q179651) seems to be the main entity representing musical scales. Now, I will check how many instances or subclasses of this entity exist to determine the number of musical scales. I will first check for instances of this entity.

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

[Assistant] There are 22 instances of the entity "musical scale" (wd:Q179651) in Wikidata. This suggests that there are 22 musical scales recorded as instances in the knowledge graph. Now, I will check if there are any subclasses of "musical scale" that might represent additional scales.

[Tool]
Got 1 row and 1 column:
| count        |
| ------------ |
| 26 (xsd:int) |

[Assistant] There are 26 subclasses of the entity "musical scale" (wd:Q179651) in Wikidata. To get a comprehensive count of musical scales, I should consider both instances and subclasses. However, some subclasses might also have instances, so I will count all unique entities that are either instances or subclasses of "musical scale."

[Tool]
Got 1 row and 1 column:
| count        |
| ------------ |
| 47 (xsd:int) |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?partner ?partnerLabel ?gender ?genderLabel WHERE {
  VALUES ?person {
    wd:Q6711
  }
  {
    ?person wdt:P26 ?partner .
  } UNION {
    ?person wdt:P451 ?partner .
  }
  ?partner wdt:P21 ?gender .
  OPTIONAL {
    ?partner rdfs:label ?partnerLabel FILTER ( LANG( ?partnerLabel ) = "en" )
  }
  OPTIONAL {
    ?gender rdfs:label ?genderLabel FILTER ( LANG( ?genderLabel ) = "en" )
  }
}

The partner of Allen Ginsberg is Peter Orlovsky, whose binary gender is male.

What is the binary gender of the partner of Allen Ginsberg ?

[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 with notes:
1. When direct properties are missing, chain related properties (e.g., child→child for grandchildren) or search for indirect connections to answer complex relationship queries.
2. For set/class queries (e.g., all cities, all holidays), use instance/subclass chains (P31/P279) for completeness and relevant filters. Be aware of data incompleteness and schema inconsistencies in Wikidata.
3. If queries return too many irrelevant or too few results, try varying properties (country, location, observed in, etc.) and subclass chains, but be ready to report data/model limitations if results remain incomplete or noisy.
4. When a direct property or class is missing or inconsistently used, try related or broader properties/classes (e.g., use inception for independence date, or musical work/composition for musicals) to increase recall.
5. For calculated properties (e.g., population density), combine relevant direct properties (e.g., population, area) in SPARQL queries, as Wikidata may not store the derived value directly.
6. When entity search returns multiple candidates, use context (description, instance type, location, etc.) to disambiguate and select the most relevant entity for the query.

No general notes for all knowledge graphs available

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/
qlss: <https://qlever.cs.uni-freiburg.de/spatialSearch/
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. Search for the 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. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
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 just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. 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]
Step 1: Identify the entity "Allen Ginsberg" in Wikidata.
Step 2: Identify the property for "partner" in Wikidata.
Step 3: Identify the property for "binary gender" or "sex or gender" in Wikidata.
Step 4: Query the partner of Allen Ginsberg and then get the binary gender of that partner.

I will start by searching for the entity "Allen Ginsberg" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Allen Ginsberg, also known as Irwin Allen Ginsberg (wd:Q6711): American poet and writer (1926–1997), instance of human, autobiographer, photographer, screenwriter, playwright, musician, diarist, teacher, writer, poet
2. Allen Ginsberg (wd:Q102249265): Ph.D. Rutgers University, New Brunswick 1986, instance of human
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, instance of library
4. Untitled (Allen Ginsberg) (wd:Q76558858): print in the National Gallery of Art (NGA 155538), instance of work of art
5. Allen Ginsberg no number (wd:Q64163104): photograph in the National Gallery of Art (NGA 88333), instance of photograph
6. Allen Ginsberg no number (wd:Q64163625): photograph in the National Gallery of Art (NGA 88331), instance of photograph
7. Allen Ginsberg no number (wd:Q64163102): photograph in the National Gallery of Art (NGA 88336), instance of photograph
8. Allen Ginsberg no number (wd:Q64163079): photograph in the National Gallery of Art (NGA 88334), instance of photograph
9. Allen Ginsberg no number (wd:Q64162725): photograph in the National Gallery of Art (NGA 88330), instance of photograph
10. Allen Ginsberg no number (wd:Q64162264): photograph in the National Gallery of Art (NGA 88332), instance of photograph

[Assistant] The entity for Allen Ginsberg is wd:Q6711. Next, I will search for the property "partner" in Wikidata to find the correct property to use in the query.

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

[Assistant]
The property for partner in a romantic or unmarried sense is wd:P451 ("unmarried partner"). There is also wd:P26 for "spouse" which is a married partner. Since the question asks for "partner" without specifying marriage, I will consider both properties to cover all possibilities.

Next, I will search for the property related to "binary gender" or "sex or gender" in Wikidata.

[Tool]
Top 10 property alternatives:
1. grammatical gender, also known as gender (wd:P5185 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): grammatical gender of the word
2. sex or gender, also known as biological sex, gender, gender expression, gender identity, gender or sex (wd:P21 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/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 psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/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 psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/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 psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): personal pronoun(s) this person goes by
6. semantic gender, also known as natural gender (wd:P10339 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/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 psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): female form of name or title (for male use P3321)
8. gender educated (wd:P7419 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/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 psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): male form of name or title (for female use P2521)
10. given name version for other gender, also known as female form of name, female version of this male given name, gender inflection of given name, male form of name, male version of this female given name (wd:P1560 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/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 property for "sex or gender" is wd:P21. This property is used to indicate the binary gender of a human.

Now I will write a SPARQL query to get the partner(s) of Allen Ginsberg (wd:Q6711) using both "spouse" (wdt:P26) and "unmarried partner" (wdt:P451) properties and retrieve their binary gender (wdt:P21). I will also include labels for readability.

[Tool]
Got 1 row and 4 columns:
| partner                      | partnerLabel        | gender             | genderLabel |
| ---------------------------- | ------------------- | ------------------ | ----------- |
| Peter Orlovsky (wd:Q1983102) | Peter Orlovsky (en) | male (wd:Q6581097) | male (en)   |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?member ?memberLabel ?birthDate WHERE {
  wd:Q51885404 wdt:P527 ?member .
  ?member wdt:P569 ?birthDate .
  OPTIONAL {
    ?member rdfs:label ?memberLabel FILTER ( LANG( ?memberLabel ) = "en" )
  }
}
ORDER BY DESC ( ?birthDate )
LIMIT 1

The youngest member of (G)I-DLE is Shuhua, born on January 6, 2000.

[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 with notes:
1. When direct properties are missing, chain related properties (e.g., child→child for grandchildren) or search for indirect connections to answer complex relationship queries.
2. For set/class queries (e.g., all cities, all holidays), use instance/subclass chains (P31/P279) for completeness and relevant filters. Be aware of data incompleteness and schema inconsistencies in Wikidata.
3. If queries return too many irrelevant or too few results, try varying properties (country, location, observed in, etc.) and subclass chains, but be ready to report data/model limitations if results remain incomplete or noisy.
4. When a direct property or class is missing or inconsistently used, try related or broader properties/classes (e.g., use inception for independence date, or musical work/composition for musicals) to increase recall.
5. For calculated properties (e.g., population density), combine relevant direct properties (e.g., population, area) in SPARQL queries, as Wikidata may not store the derived value directly.
6. When entity search returns multiple candidates, use context (description, instance type, location, etc.) to disambiguate and select the most relevant entity for the query.

No general notes for all knowledge graphs available

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/
qlss: <https://qlever.cs.uni-freiburg.de/spatialSearch/
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. Search for the 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. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
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 just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. 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] To answer the question "Who is the youngest member in (G)I-DLE?", I will first identify the entity for the music group (G)I-DLE in Wikidata. Then, I will find the members of this group and their birth dates to determine the youngest member.

[Tool]
Top 10 entity alternatives:
1. (G)I-dle, also known as (G)I-DLE (wd:Q51885404): instance of musical group, South Korean girl group, instance of 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, instance of Korean variety show, instance of film
3. Hwaa, also known as Hwaa (song), Hwaa ((G)I-dle song) (wd:Q104600483): instance of musical work/composition, 2021 single by (G)I-dle
4. Moon, also known as Moon ((G)I-dle song), Moon (G-Idle song) (wd:Q104969991): instance of musical work/composition, 2021 single by (G)I-dle
5. Last Dance, also known as Last Dance (G-Idle song), Last Dance ((G)I-dle song) (wd:Q106584541): song written and composed by Kriz and Soyeon, adapted by GroovyRoom; recorded 2021 by (G)I-dle, instance of musical work/composition
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, instance of concert
7. (G)I-dle Just Me ( )I-dle in Seoul, June 2022 (wd:Q124058812): instance of concert
8. (G)I-dle at 2020 Tainan Christmas & New Year Eve Party (wd:Q124058809)
9. (G)I-dle Just Me ( )I-dle in Monterrey, 14 August 2022 (wd:Q124058808): instance of concert
10. (G)I-dle Just Me ( )I-dle in San Francisco, 24 July 2022 (wd:Q124058817): instance of concert

[Assistant] The entity for the music group (G)I-DLE in Wikidata is wd:Q51885404. Next, I will find the members of this group and their birth dates to determine the youngest member.

[Tool]
SPARQL execution failed:
Error while executing a SERVICE request to <http://wikiba.se:80/ontology#label>: SERVICE responded with HTTP status code: 301, Moved Permanently. First 100 bytes of the response: '<html>
<head><title>301 Moved Permanently</title></head>
<body>
<center><h1>301 Moved Permanently'

[Assistant] The query failed due to the unsupported SERVICE wikibase:label clause on this endpoint. I will rewrite the query without using SERVICE wikibase:label and instead use optional rdfs:label to get the member's name.

[Tool]
Got 1 row and 3 columns:
| member                | memberLabel | birthDate                           |
| --------------------- | ----------- | ----------------------------------- |
| Shuhua (wd:Q55287305) | Shuhua (en) | 2000-01-06T00:00:00Z (xsd:dateTime) |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?author ?authorLabel WHERE {
  wd:Q3521239 wdt:P50 ?author .
  OPTIONAL {
    ?author rdfs:label ?authorLabel FILTER ( LANG( ?authorLabel ) = "en" )
  }
}

The author of The Hellbound Heart is Clive Barker.

Who's the author of The Hellbound Heart ?

[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 with notes:
1. When direct properties are missing, chain related properties (e.g., child→child for grandchildren) or search for indirect connections to answer complex relationship queries.
2. For set/class queries (e.g., all cities, all holidays), use instance/subclass chains (P31/P279) for completeness and relevant filters. Be aware of data incompleteness and schema inconsistencies in Wikidata.
3. If queries return too many irrelevant or too few results, try varying properties (country, location, observed in, etc.) and subclass chains, but be ready to report data/model limitations if results remain incomplete or noisy.
4. When a direct property or class is missing or inconsistently used, try related or broader properties/classes (e.g., use inception for independence date, or musical work/composition for musicals) to increase recall.
5. For calculated properties (e.g., population density), combine relevant direct properties (e.g., population, area) in SPARQL queries, as Wikidata may not store the derived value directly.
6. When entity search returns multiple candidates, use context (description, instance type, location, etc.) to disambiguate and select the most relevant entity for the query.

No general notes for all knowledge graphs available

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/
qlss: <https://qlever.cs.uni-freiburg.de/spatialSearch/
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. Search for the 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. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
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 just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. 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] Step 1: Identify the entity "The Hellbound Heart" in Wikidata to get its identifier. Then, find the property that relates a literary work to its author. Typically, the property "author" (wdt:P50) is used for this purpose. I will first search for the entity "The Hellbound Heart" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. The Hellbound Heart (wd:Q3521239): instance of literary work, novella by Clive Barker
2. The Hellbound Heart (wd:Q131862610): instance of version, edition or translation, 2008 HarperVoyager edition
3. Holiday Peak, also known as The Heart (wd:Q5880456): mountain in the Antarctic, instance of mountain
4. The Heart, also known as Heart, Kokoro (wd:Q19567789): 1955 Japanese film directed by Kon Ichikawa, instance of film
5. Das Herz, also known as The Heart (wd:Q49139357): instance of dramatico-musical work, opera by Hans Pfitzner
6. The Heart, also known as Srdce (wd:Q12055804): instance of dramatico-musical work, opera by Josef Bohuslav Foerster
7. The Heart, also known as Hjärtat (wd:Q10524935): instance of film, 1987 film
8. The Heart (wd:Q124421885): instance of written work, work by Hippocrates
9. The Heart (wd:Q110895729): film by Kaneto Shindo, instance of film
10. THE HEART (wd:Q11248834): instance of musical group

[Assistant] I found the entity "The Hellbound Heart" with the identifier wd:Q3521239, which is an instance of a literary work, specifically a novella by Clive Barker. Now I will query the author (wdt:P50) of this entity.

[Tool]
Got 1 row and 2 columns:
| author                    | authorLabel       |
| ------------------------- | ----------------- |
| Clive Barker (wd:Q298761) | Clive Barker (en) |

[Tool] Stopped generation process


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

No, After Eight is not manufactured by Unilever according to Wikidata.

Is After Eight manufactured by Unilever?

[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 with notes:
1. When direct properties are missing, chain related properties (e.g., child→child for grandchildren) or search for indirect connections to answer complex relationship queries.
2. For set/class queries (e.g., all cities, all holidays), use instance/subclass chains (P31/P279) for completeness and relevant filters. Be aware of data incompleteness and schema inconsistencies in Wikidata.
3. If queries return too many irrelevant or too few results, try varying properties (country, location, observed in, etc.) and subclass chains, but be ready to report data/model limitations if results remain incomplete or noisy.
4. When a direct property or class is missing or inconsistently used, try related or broader properties/classes (e.g., use inception for independence date, or musical work/composition for musicals) to increase recall.
5. For calculated properties (e.g., population density), combine relevant direct properties (e.g., population, area) in SPARQL queries, as Wikidata may not store the derived value directly.
6. When entity search returns multiple candidates, use context (description, instance type, location, etc.) to disambiguate and select the most relevant entity for the query.

No general notes for all knowledge graphs available

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/
qlss: <https://qlever.cs.uni-freiburg.de/spatialSearch/
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. Search for the 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. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
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 just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. 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]
Step 1: Identify the entities and properties involved in the question.
- Entity: "After Eight" (a product, likely a confectionery or chocolate brand)
- Entity: "Unilever" (a company)
- Property: Manufacturer or producer of the product

Step 2: Search for the entity "After Eight" in Wikidata to find its identifier.
I will search for "After Eight" to find the correct entity.

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

[Assistant]
The entity "After Eight" referring to the mint chocolate product is wd:Q388973.

Step 3: Search for the entity "Unilever" in Wikidata to find its identifier.

[Tool]
Top 10 entity alternatives:
1. Unilever (wd:Q157062): British multinational consumer goods company, instance of multinational corporation, instance of dual-listed company, instance of public company, instance of trademark, instance of business
2. Unilever, also known as PT Unilever  Tbk, UNVR, Unilever Tbk, Lever Zeepfabrieken N.V. (wd:Q7885208): instance of public company, instance of business, British company in
3. Unilever Headquarters, also known as Unilever, Weena 455 (wd:Q125679337): office building at Weena 455, Centrum, Rotterdam, the Netherlands, instance of office building
4. T.H. Estabrooks Co. Ltd., also known as Brooke Bond, Unilever (wd:Q116708935): instance of organization, Canadian business.
5. Hindustan Unilever, also known as Hindustan Unilever Limited, Unilever (India), HUL (wd:Q1619376): instance of multi-level marketing company, instance of dual-listed company, Indiconsumer goods company, instance of subsidiary
6. Unilever House (wd:Q2494434): grade II listed office building in City of London, United kingdom, instance of office building
7. Unilever Philippines, also known as Unilever (Philippines) (wd:Q18159894): Philippine subsidiary of Unilever, instance of dual-listed company, instance of business
8. Unilever Pakistan, also known as Unilever Pakistan Limited (wd:Q7885211): Pakistani subsidiary of a foreign company, instance of business
9. Unilever Gloucester, also known as Walls Ice Cream Factory (wd:Q96412295): food manufacturing site in Gloucester, England, UK, instance of factory
10. Unilever Israel (wd:Q30526669): Israeli branch of Unilever, instance of company

[Assistant]
The entity "Unilever" as the British multinational consumer goods company is wd:Q157062.

Step 4: Find the property that links a product to its manufacturer or producer. I will search for properties related to "manufacturer" or "producer" to find the appropriate property.

[Tool]
Top 10 property alternatives:
1. manufacturer, also known as built by, provider, provided by, product of, producer (wd:P176 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): (main or final) manufacturer or producer of this product
2. product or material produced, also known as products, product or service provided, product, material, or service produced or provided, product, produces (wd:P1056 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): material or product produced by an organization, industry, facility, or process, subproperty of immediate cause of
3. location of creation, also known as place made, manufactured in, place built, photographed at, minted at (wd:P1071 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): place where the item was conceived or made; where applicable, location of final assembly, subproperty of significant place
4. made from material, also known as made of, material used, manufactured out of, manufactured from, made of material (wd:P186 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): material the subject or the object is made of or derived from (do not confuse with P10672 which is used for processes), subproperty of uses
5. fabrication method, also known as process used, preparation method, obtained by, method used, method of preparation (wd:P2079 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): method, process or technique used to grow, cook, weave, build, assemble, manufacture the item, subproperty of uses
6. creator, also known as art by, visual artist, sculptor, sculpted by, realized by (wd:P170 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): maker of this creative work or other object (where no more specific property exists)
7. brand, also known as main brand (wd:P1716 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): commercial brand associated with the item
8. author, also known as co-author, written by, writer, poet, playwright (wd:P50 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): 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, subproperty of creator
9. serial number, also known as c/n, construction number, constructor's number, manufacturer serial number, MSN (wd:P2598 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): an identifier for a specific object among the same product. Not a product code or model number
10. WMI code, also known as World Manufacturer Identifier, WMI (wd:P6793 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn)

[Assistant]
The property "manufacturer" (wdt:P176) is the main or final manufacturer or producer of a product.

Step 5: Construct a SPARQL query to check if "After Eight" (wd:Q388973) has "Unilever" (wd:Q157062) as its manufacturer (wdt:P176).

[Tool] False

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?business ?businessLabel WHERE {
  ?business wdt:P112 wd:Q317521 .
  OPTIONAL {
    ?business rdfs:label ?businessLabel FILTER ( LANG( ?businessLabel ) = "en" )
  }
}

Got 14 rows and 2 columns, showing the first 5 and last 5 rows below:
| business                                                | businessLabel                                |
| ------------------------------------------------------- | -------------------------------------------- |
| Texas Institute of Technology & Science (wd:Q109332244) | Texas Institute of Technology & Science (en) |
| Thud (wd:Q115815194)                                    | Thud (en)                                    |
| X Corp. (wd:Q117617480)                                 | X Corp. (en)                                 |
| X Holdings Corp (wd:Q117705742)                         | X Holdings Corp (en)                         |
| xAI (wd:Q120599684)                                     | xAI (en)                                     |
| ...                                                     | ...                                          |
| Zip2 (wd:Q28222602)                                     | Zip2 (en)                                    |
| The Boring Company (wd:Q28874479)                       | The Boring Company (en)                      |
| Neuralink (wd:Q29043471)                                | Neuralink (en)                               |
| X.com (wd:Q30686532)                                    | X.com (en)                                   |
| PayPal (wd:Q483959)                                     | PayPal (en)                                  |

The businesses founded by Elon Musk, the person in charge of Tesla, Inc., include Texas Institute of Technology & Science, Thud, X Corp., X Holdings Corp, xAI, Astra Nova School, America PAC, SpaceX, OpenAI, Zip2, and others.

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

[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 with notes:
1. When direct properties are missing, chain related properties (e.g., child→child for grandchildren) or search for indirect connections to answer complex relationship queries.
2. For set/class queries (e.g., all cities, all holidays), use instance/subclass chains (P31/P279) for completeness and relevant filters. Be aware of data incompleteness and schema inconsistencies in Wikidata.
3. If queries return too many irrelevant or too few results, try varying properties (country, location, observed in, etc.) and subclass chains, but be ready to report data/model limitations if results remain incomplete or noisy.
4. When a direct property or class is missing or inconsistently used, try related or broader properties/classes (e.g., use inception for independence date, or musical work/composition for musicals) to increase recall.
5. For calculated properties (e.g., population density), combine relevant direct properties (e.g., population, area) in SPARQL queries, as Wikidata may not store the derived value directly.
6. When entity search returns multiple candidates, use context (description, instance type, location, etc.) to disambiguate and select the most relevant entity for the query.

No general notes for all knowledge graphs available

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/
qlss: <https://qlever.cs.uni-freiburg.de/spatialSearch/
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. Search for the 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. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
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 just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. 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]
To answer this question, I need to identify the person in charge of Tesla and then find the businesses founded by that person.

Step 1: Identify the person in charge of Tesla.
- Tesla is a company, so I will search for the entity "Tesla" in Wikidata.
- Then, I will find the property that indicates the person in charge, such as CEO or founder.

Step 2: Find the businesses founded by that person.
- Once I have the person entity, I will look for properties indicating businesses they founded.

I will start by searching for the entity "Tesla" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Nikola Tesla, also known as Tesla, Nicola Tesla (wd:Q9036): Serbian-American inventor (1856–1943), electrical engineer, instance of human, electrician, physicist, inventor
2. Tesla, Inc., also known as Tesla, Tesla Inc. (wd:Q478214): American automotive, energy storage and solar power company, instance of automobile manufacturer, instance of corporation, instance of enterprise, instance of business
3. tesla, also known as T (wd:Q163343): instance of unit of magnetic flux density, instance of SI unit with special name, SI unit of magnetic flux density, instance of UCUM derived unit
4. Tesla (wd:Q1428953): American hard rock band, instance of rock band
5. Nvidia Tesla, also known as Tesla, NVIDIA Tesla, NVidia Tesla (wd:Q16258100)
6. Tesla (wd:Q88053410): 2020 film directed by Michael Almereyda, instance of film
7. Tesla (wd:Q2406220): instance of impact crater, instance of lunar crater, lunar crater
8. Tesla, also known as TESLA, Tesla (Czech Republic) (wd:Q1548225): Czech manufacturer of electronical and electrical equipment (est.1991), instance of enterprise, instance of business
9. Tesla, also known as NVIDIA Tesla, NVidia Tesla, Nvidia Tesla (wd:Q1050485): GPU microarchitecture designed by NVIDIA, instance of microarchitecture
10. Tesla, also known as Tesla, West Virginia, Tesla, WV (wd:Q7705502): instance of unincorporated community in the United States, unincorporated community in Braxton County, West Virginia

[Assistant] The entity for Tesla as a company is "Tesla, Inc." with the identifier wd:Q478214. Next, I will find the person in charge of Tesla, Inc. by looking for properties like CEO or founder related to this entity.

[Tool]
Got 1 row and 3 columns:
| subject                  | property                           | object                 |
| ------------------------ | ---------------------------------- | ---------------------- |
| Tesla, Inc. (wd:Q478214) | chief executive officer (wdt:P169) | Elon Musk (wd:Q317521) |

[Assistant] The person in charge of Tesla, Inc. as the chief executive officer (CEO) is Elon Musk (wd:Q317521). Now, I will find the businesses founded by Elon Musk.

[Tool] Got no rows and 3 columns

[Assistant] There are no direct statements for Elon Musk founding businesses using property P112 (founded by). I will try another property related to founding or founding organizations for Elon Musk. I will search for properties related to "found" for Elon Musk.

[Tool]
Top 10 property alternatives:
1. described by source, also known as mentioned in, mentioned in news article, reviewed in, source of item, subject in (wd:P1343 as wdt/p): work where this item is described, subproperty of attested in
2. medical condition, also known as disorder, suffers from, suffer from, sickness, paralympic disability (wd:P1050 as wdt/p): any state relevant to the health of an organism, including diseases and positive conditions
3. religion or worldview, also known as believes, worldview, religious affiliation, religion or world view, religion (wd:P140 as wdt/p): religion of a person, organization or religious building, or associated with this subject
4. owner of, also known as owns property, shareholder of, owns (wd:P1830 as wdt/p): entities owned by the subject
5. employer, also known as working at, working for, working place, works at, works for (wd:P108 as wdt/p): person or organization for which the subject works or worked, subproperty of affiliation
6. languages spoken, written or signed, also known as language written, wrote language, writes language, uses language, used language (wd:P1412 as wdt/p): language(s) that a person or a people speaks, writes or signs, including the native language(s)
7. described at URL, also known as d@url, descURL, discussed at URL, info URL, mentioned at URL (wd:P973 as wdt/p): item is described at the following URL, subproperty of URL
8. spouse, also known as spouses, married partner, married to, marry, partner (wd:P26 as wdt/p): the subject has the object as their spouse (husband, wife, partn...etc.). Use "unmarried partner" (P451) for non-married companions, subproperty of significant person, subproperty of relative
9. place of birth, also known as location born, POB, location of birth, born in, born at (wd:P19 as wdt/p): most specific known birth location of a person, animal or fictional character, subproperty of location
10. date of birth, also known as year of birth, DOB, born on, birthyear, born (wd:P569 as wdt/p): date on which the subject was born, subproperty of inception

No other items found

[Assistant] There is no direct property for Elon Musk indicating businesses he founded. I will try a broader approach by searching for entities that have Elon Musk as their founder or co-founder. The property for "founder" is P112. I will search for entities with P112 pointing to Elon Musk.

[Tool]
Got 14 rows and 3 columns, showing the first 10 rows below:
| subject                                                 | property              | object                 |
| ------------------------------------------------------- | --------------------- | ---------------------- |
| Texas Institute of Technology & Science (wd:Q109332244) | founded by (wdt:P112) | Elon Musk (wd:Q317521) |
| Thud (wd:Q115815194)                                    | founded by (wdt:P112) | Elon Musk (wd:Q317521) |
| X Corp. (wd:Q117617480)                                 | founded by (wdt:P112) | Elon Musk (wd:Q317521) |
| X Holdings Corp (wd:Q117705742)                         | founded by (wdt:P112) | Elon Musk (wd:Q317521) |
| xAI (wd:Q120599684)                                     | founded by (wdt:P112) | Elon Musk (wd:Q317521) |
| Astra Nova School (wd:Q123376376)                       | founded by (wdt:P112) | Elon Musk (wd:Q317521) |
| America PAC (wd:Q127790371)                             | founded by (wdt:P112) | Elon Musk (wd:Q317521) |
| SpaceX (wd:Q193701)                                     | founded by (wdt:P112) | Elon Musk (wd:Q317521) |
| OpenAI (wd:Q21708200)                                   | founded by (wdt:P112) | Elon Musk (wd:Q317521) |
| Zip2 (wd:Q28222602)                                     | founded by (wdt:P112) | Elon Musk (wd:Q317521) |
| ...                                                     | ...                   | ...                    |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?genre ?genreLabel ( STRLEN( ?genreLabel ) AS ?length ) WHERE {
  wd:Q68061 wdt:P136 ?genre .
  ?genre rdfs:label ?genreLabel .
  FILTER ( LANG( ?genreLabel ) = "en" )
}
ORDER BY ?length
LIMIT 1

The genre that Cage the Elephant belongs to with the fewest English characters is 'blues' with 5 characters.

Which genre that Cage the Elephant belongs to has the fewest english characters?

[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 with notes:
1. When direct properties are missing, chain related properties (e.g., child→child for grandchildren) or search for indirect connections to answer complex relationship queries.
2. For set/class queries (e.g., all cities, all holidays), use instance/subclass chains (P31/P279) for completeness and relevant filters. Be aware of data incompleteness and schema inconsistencies in Wikidata.
3. If queries return too many irrelevant or too few results, try varying properties (country, location, observed in, etc.) and subclass chains, but be ready to report data/model limitations if results remain incomplete or noisy.
4. When a direct property or class is missing or inconsistently used, try related or broader properties/classes (e.g., use inception for independence date, or musical work/composition for musicals) to increase recall.
5. For calculated properties (e.g., population density), combine relevant direct properties (e.g., population, area) in SPARQL queries, as Wikidata may not store the derived value directly.
6. When entity search returns multiple candidates, use context (description, instance type, location, etc.) to disambiguate and select the most relevant entity for the query.

No general notes for all knowledge graphs available

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/
qlss: <https://qlever.cs.uni-freiburg.de/spatialSearch/
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. Search for the 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. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
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 just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. 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 genre that Cage the Elephant belongs to has the fewest english characters?

[Assistant]
To answer this question, I need to:

1. Identify the entity for "Cage the Elephant."
2. Find the genres associated with Cage the Elephant.
3. Retrieve the English labels of these genres.
4. Determine the genre with the fewest English characters in its label.

I will start by searching for the entity "Cage the Elephant."

[Tool]
Top 10 entity alternatives:
1. Cage the Elephant (wd:Q68061): American rock band from Bowling Green, Kentucky, instance of musical group
2. Cage the Elephant (wd:Q1950243): debut album by the American rock band Cage the Elephant, instance of album
3. The Cage, also known as Cage, Pilot: The Cage, Cage, The (wd:Q20909): 1986 episode of Star Trek: The Original Series directed by Robert Butler, instance of Star Trek episode, instance of television film
4. The Cage, also known as La Cage (wd:Q3206978): 1975 film by Pierre Granier-Deferre, instance of film
5. The Cage, also known as Cage (wd:Q41016691): 2017 film by Akram Khan, instance of film
6. The Cage, also known as Cage, La Cage (wd:Q7720935): 1963 film by Robert Darène, instance of film
7. West Fourth Street Courts, also known as The Cage, West 4th Street Courts (wd:Q7985184): athletic venue in Manhattan, New York, instance of outdoor basketball court
8. The Cage (wd:Q3520180): album by Tygers of Pan Tang, instance of album
9. The Cage, also known as Colivia (wd:Q29581150): instance of film, Romanian film
10. The Cage (wd:Q73295242): painting by Berthe Morisot, instance of painting

[Assistant] I found the entity for "Cage the Elephant" as wd:Q68061, which is an American rock band. Next, I will find the genres associated with this band.

[Tool]
Got 6 rows and 2 columns:
| genre                         | genreLabel            |
| ----------------------------- | --------------------- |
| alternative rock (wd:Q11366)  | alternative rock (en) |
| indie rock (wd:Q183504)       | indie rock (en)       |
| psychedelic rock (wd:Q206159) | psychedelic rock (en) |
| garage rock (wd:Q211573)      | garage rock (en)      |
| punk rock (wd:Q3071)          | punk rock (en)        |
| blues (wd:Q9759)              | blues (en)            |

[Assistant] I retrieved the genres associated with Cage the Elephant: alternative rock, indie rock, psychedelic rock, garage rock, punk rock, and blues. Now I will find the genre with the fewest English characters in its label.

[Tool]
Got 1 row and 3 columns:
| genre            | genreLabel | length      |
| ---------------- | ---------- | ----------- |
| blues (wd:Q9759) | blues (en) | 5 (xsd:int) |

[Tool] Stopped generation process