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 ?team ?teamLabel WHERE {
  wd:Q46569648 wdt:P1923 ?team .
  OPTIONAL {
    ?team rdfs:label ?teamLabel FILTER ( LANG( ?teamLabel ) = "en" )
  }
}

Got 2 rows and 2 columns:
| team                                | teamLabel                   |
| ----------------------------------- | --------------------------- |
| FC Bayern Munich (wd:Q15789)        | FC Bayern Munich (en)       |
| Paris Saint-Germain FC (wd:Q483020) | Paris Saint-Germain FC (en) |

The teams that participated in the 2020 UEFA Champions League Final are FC Bayern Munich and Paris Saint-Germain FC.

Which teams participated in the 2020 UEFA Champions League Final ?

[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 teams participated in the 2020 UEFA Champions League Final ?

[Assistant] To answer the question about which teams participated in the 2020 UEFA Champions League Final, I will first identify the entity for the 2020 UEFA Champions League Final in Wikidata. Then, I will look for properties that link this event to the participating teams. Typically, the property "participant" or similar is used to indicate teams or players involved in an event. I will start by searching for the entity of the 2020 UEFA Champions League Final.

[Tool]
Top 10 entity alternatives:
1. 2020 UEFA Champions League Final (wd:Q46569648): The final of the 2019–20 season of the UEFA Champions League, instance of UEFA Libertadores Final
2. 2020 UEFA Women's Champions League Final (wd:Q48839950): instance of association football final, association football match, instance of final
3. 2020–21 UEFA Champions League, also known as 2020-21 UEFA Champions League, UEFA Champions League 2020-2021, UEFA Champions League 2020–2021 (wd:Q63088790): 66th season of the UEFA club football tournament, instance of YEYE
4. 2014-2015 UEFA Champions League Final, also known as UEFA Champions League 2014-15 Final (wd:Q13557593): Final match of UEFA Champions League 2014/15 season, instance of UEFA Libertadores Final
5. 2012 UEFA Champions League Final, also known as Finale dahoam (wd:Q719820): instance of association football club match, instance of UEFA Libertadores Final, association football match
6. 2014 UEFA Champions League Final, also known as UEFA Champions League 2013-14 Final (wd:Q2614328): final match of the 2013–14 UEFA Champions League, instance of UEFA Libertadores Final
7. 2016 UEFA Champions League Final, also known as Finale della UEFA Champions League 2015-2016, UEFA Champions League 2015-16 Final (wd:Q18084793): final of the 2015–16 edition of the UEFA Champions League, instance of UEFA Libertadores Final
8. 2008 UEFA Champions League Final (wd:Q645861): instance of UEFA Libertadores Final, association football match
9. 2017 UEFA Champions League Final, also known as UEFA Champions League 2016-17 Final (wd:Q20650304): final of the 2016–17 edition of the UEFA Champions League, instance of UEFA Libertadores Final
10. 2018 UEFA Champions League Final, also known as UEFA Champions League 2017-18 Final, UEFA Champions League Final 2018 (wd:Q26897857): association football match at NSC Olimpiyskyi during 2017–18 UEFA Champions League with score 3:1, instance of UEFA Libertadores Final

[Assistant] The entity for the 2020 UEFA Champions League Final is wd:Q46569648. Next, I will look for the properties that link this event to the participating teams. I will check for properties like "participant" or similar that indicate the teams involved in this final.

[Tool]
Got 222 rows and 3 columns, showing the first 10 rows below:
| subject                                         | property                 | object                                                                |
| ----------------------------------------------- | ------------------------ | --------------------------------------------------------------------- |
| 2020 UEFA Champions League Final (wd:Q46569648) | instance of (wdt:P31)    | UEFA Libertadores Final (wd:Q80716240)                                |
| 2020 UEFA Champions League Final (wd:Q46569648) | country (wdt:P17)        | Portugal (wd:Q45)                                                     |
| 2020 UEFA Champions League Final (wd:Q46569648) | image (wdt:P18)          | <http://commons.wikimedia.org/wiki/Special:FilePath/S7000272%202.JPG> |
| 2020 UEFA Champions League Final (wd:Q46569648) | part of (wdt:P361)       | 2019–20 UEFA Champions League (wd:Q53164830)                          |
| 2020 UEFA Champions League Final (wd:Q46569648) | location (wdt:P276)      | Estádio da Luz (wd:Q7875112)                                          |
| 2020 UEFA Champions League Final (wd:Q46569648) | sport (wdt:P641)         | association football (wd:Q2736)                                       |
| 2020 UEFA Champions League Final (wd:Q46569648) | follows (wdt:P155)       | 2019 UEFA Champions League Final (wd:Q28842926)                       |
| 2020 UEFA Champions League Final (wd:Q46569648) | followed by (wdt:P156)   | 2021 UEFA Champions League Final (wd:Q59667065)                       |
| 2020 UEFA Champions League Final (wd:Q46569648) | point in time (wdt:P585) | 2020-08-23T00:00:00Z (xsd:dateTime)                                   |
| 2020 UEFA Champions League Final (wd:Q46569648) | participant (wdt:P710)   | Neymar (wd:Q142794)                                                   |
| ...                                             | ...                      | ...                                                                   |

[Assistant] The property "participant" (wdt:P710) is used for the 2020 UEFA Champions League Final, but it seems to list individual players like Neymar, not teams. I will check if there is a property that links the final to the teams directly. I will search for properties related to "team" or "club" for this event.

[Tool]
Top 10 property alternatives:
1. participating team, also known as teams (wd:P1923 as wdt/p): like 'Participant' (P710) but for teams. For an event like a cyc...member of sports team' (P54) as a qualifier for the individuals), subproperty of participant
2. location, also known as neighborhood, locale, neighbourhood, place, place held (wd:P276 as wdt/p): location of the object, structure or event. In the case of an ad...ity use P706. Use P7153 for locations associated with the object
3. sport, also known as play, plays, sport played, sports (wd:P641 as wdt/p): sport that the subject participates or participated in or is associated with
4. participant, also known as suspect, player, party, participants, has participant (wd:P710 as wdt/p): person, group of people or organization (object) that actively t...ct has role" (P3831). Use P1923 for participants that are teams., subproperty of significant person
5. image, also known as img, infobox image, photo, photograph, picture (wd:P18 as wdt/p): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)
6. winner, also known as nominee, prizewinner, titleholder, top dog, victor (wd:P1346 as wdt/p): winner of a competition or similar event, not to be used for awa...ssibly qualified with "for work" (P1686)) or for wars or battles, subproperty of participating team, subproperty of participant
7. country, also known as state, sovereign state, land, host country (wd:P17 as wdt/p): sovereign state that this item is in (not to be used for human beings), subproperty of located in the administrative territorial entity
8. competition class, also known as disability sport classifications, weight class, rated at, qualifies for, qualification class (wd:P2094 as wdt/p): official classification by a regulating body under which the sub...ents, teams, participants, or equipment) qualifies for inclusion
9. point in time, also known as year, when, time of event, time, during (wd:P585 as wdt/p): date something took place, existed or a statement was true; for providing time use the "refine date" property (P4241)
10. instance of, also known as example of, has type, is a, is an, is of type (wd:P31 as wdt/p): type to which this subject corresponds/belongs. Different from P...tains, and the volcano is a type (instance) of volcanic landform

No other items found

[Assistant] There is a property "participating team" (wdt:P1923) which is described as like 'Participant' (P710) but for teams. This seems to be the appropriate property to find the teams that participated in the 2020 UEFA Champions League Final. I will query the teams linked to the final using this property.

[Tool]
Got 2 rows and 2 columns:
| team                                | teamLabel                   |
| ----------------------------------- | --------------------------- |
| FC Bayern Munich (wd:Q15789)        | FC Bayern Munich (en)       |
| Paris Saint-Germain FC (wd:Q483020) | Paris Saint-Germain FC (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 ?causeOfDeath ?causeOfDeathLabel WHERE {
  wd:Q19837 wdt:P509 ?causeOfDeath .
  OPTIONAL {
    ?causeOfDeath rdfs:label ?causeOfDeathLabel FILTER ( LANG( ?causeOfDeathLabel ) = "en" )
  }
}

Got 1 row and 2 columns:
| causeOfDeath                   | causeOfDeathLabel      |
| ------------------------------ | ---------------------- |
| pancreatic cancer (wd:Q212961) | pancreatic cancer (en) |

Yes, the cause of death of Steve Jobs, who was the CEO of Apple, was pancreatic cancer.

Was it the cancer that caused the death of Steve Jobs who was the CEO of Apple?

[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] Was it the cancer that caused the death of Steve Jobs who was the CEO of Apple?

[Assistant] To answer this question, I need to check the cause of death of Steve Jobs, who was the CEO of Apple. First, I will identify the Wikidata entity for Steve Jobs and verify his role as CEO of Apple. Then, I will look for the cause of death property for Steve Jobs to see if it was cancer.

[Tool]
Top 10 entity alternatives:
1. Steve Jobs, also known as Steven Paul Jobs, Abdul Lateef Jandali, T. Lateef, Steven Jobs (wd:Q19837): American entrepreneur; co-founder of Apple Inc. (1955–2011), technology entrepreneur, executive producer, computer scientist, instance of human, film producer, entrepreneur, financier, inventor, designer
2. Steve Jobs (wd:Q18754959): 2015 film directed by Danny Boyle, instance of film
3. Steve Jobs, also known as '' Steve Jobs '', Steve Jobs (book) (wd:Q625645): 2011 authorized biography by Walter Isaacson, instance of literary work
4. Steve Jobs (wd:Q111953476): 2015 soundtrack album by Daniel Pemberton, instance of album
5. Steve Jobs (wd:Q48966678): Italian clothing company
6. Steve Jobs (wd:Q52986630): middle school in Latina, Latina, Lazio, Italy [school id: LTTDZG5007], instance of middle school
7. Steve Jobs (wd:Q52987978): middle school in Gioia Tauro, Reggio Calabria, Calabria, Italy, instance of middle school
8. Steve Jobs (wd:Q52951676): middle school in Latina, Latina, Lazio, Italy [school id: LTPSLH500H], instance of middle school
9. Steve Jobs (wd:Q52946669): middle school in Latina, Latina, Lazio, Italy [school id: LTPMSO5002], instance of middle school
10. Steve Jobs (wd:Q47510529): painting by Alan Magee, instance of painting

[Assistant] The Wikidata entity for Steve Jobs, the American entrepreneur and co-founder of Apple Inc., is wd:Q19837. Next, I will check the cause of death property for this entity to see if it was cancer.

[Tool]
Got 1 row and 3 columns:
| subject                | property                  | object                         |
| ---------------------- | ------------------------- | ------------------------------ |
| Steve Jobs (wd:Q19837) | cause of death (wdt:P509) | pancreatic cancer (wd:Q212961) |

[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 ?treatment ?treatmentLabel WHERE {
  wd:Q133087 wdt:P2176 ?treatment .
  OPTIONAL {
    ?treatment rdfs:label ?treatmentLabel FILTER ( LANG( ?treatmentLabel ) = "en" )
  }
}

Got 13 rows and 2 columns, showing the first 5 and last 5 rows below:
| treatment                       | treatmentLabel          |
| ------------------------------- | ----------------------- |
| naproxen (wd:Q1215575)          | naproxen (en)           |
| aspirin (wd:Q18216)             | aspirin (en)            |
| ibuprofen (wd:Q186969)          | ibuprofen (en)          |
| Diclofenac (wd:Q244408)         | Diclofenac (en)         |
| (S)-(−)-colchicine (wd:Q326224) | (S)-(−)-colchicine (en) |
| ...                             | ...                     |
| febuxostat (wd:Q417296)         | febuxostat (en)         |
| diet (wd:Q474191)               | diet (en)               |
| pegloticase (wd:Q7160794)       | pegloticase (en)        |
| probenecid (wd:Q900898)         | probenecid (en)         |
| sulindac (wd:Q963093)           | sulindac (en)           |

The treatments that can be done against gout include various drugs and therapies such as naproxen, aspirin, ibuprofen, diclofenac, (S)-(−)-colchicine, febuxostat, diet, pegloticase, probenecid, and sulindac. These are medications and approaches used to manage and treat gout.

[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 can be done against gout?

[Assistant]
To answer the question "What can be done against gout?" I need to find information about treatments, remedies, or preventive measures related to gout in the knowledge graph.

Step 1: Identify the entity "gout" in Wikidata.
Step 2: Look for properties or related entities that describe treatments, medications, or preventive measures for gout.

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

[Tool]
Top 10 entity alternatives:
1. gout, also known as gouty arthropathy, podagra, articular gout (wd:Q133087)
2. Gout Gout (wd:Q123274543): athletics competitor, Australian athlete, instance of human
3. Ruisseau du Goût, also known as Goût, Ry du Goût (wd:Q21769075): instance of river, river in Belgium
4. [Gout] (wd:Q68959346): scientific article published on 01 February 1973, instance of scholarly article
5. [Gout.] (wd:Q75912746): scientific article published on 01 September 1951, instance of scholarly article
6. [Gout.] (wd:Q78597255): scientific article published on 01 December 1954, instance of scholarly article
7. [Gout.] (wd:Q78630216): scientific article published on 01 January 1955, instance of scholarly article
8. [Gout.] (wd:Q78646359): scientific article published on 01 May 1959, instance of scholarly article
9. [Gout.] (wd:Q78734222): scientific article published on 01 June 1955, instance of scholarly article
10. [Gout.] (wd:Q78963506): scientific article published on 01 March 1960, instance of scholarly article

[Assistant] The entity "gout" in Wikidata is wd:Q133087. Now I will look for properties or related entities that describe treatments, medications, or preventive measures for gout. I will start by checking the properties of the gout entity to find relevant information about treatments or remedies.

[Tool]
Got 749 rows and 3 columns, showing the first 10 rows below:
| subject           | property                                       | object                                                                    |
| ----------------- | ---------------------------------------------- | ------------------------------------------------------------------------- |
| gout (wd:Q133087) | instance of (wdt:P31)                          | class of disease (wd:Q112193867)                                          |
| gout (wd:Q133087) | Commons category (wdt:P373)                    | Gout                                                                      |
| gout (wd:Q133087) | image (wdt:P18)                                | <http://commons.wikimedia.org/wiki/Special:FilePath/Case%2030-bottom.jpg> |
| gout (wd:Q133087) | subclass of (wdt:P279)                         | disease (wd:Q12136)                                                       |
| gout (wd:Q133087) | described at URL (wdt:P973)                    | <https://versusarthritis.org/about-arthritis/conditions/gout/>            |
| gout (wd:Q133087) | described by source (wdt:P1343)                | Small Brockhaus and Efron Encyclopedic Dictionary (wd:Q19180675)          |
| gout (wd:Q133087) | on focus list of Wikimedia project (wdt:P5008) | wd:Q4099686                                                               |
| gout (wd:Q133087) | topic's main category (wdt:P910)               | wd:Q7450206                                                               |
| gout (wd:Q133087) | social media followers (wdt:P8687)             | 18143 (xsd:decimal)                                                       |
| gout (wd:Q133087) | named after (wdt:P138)                         | drop (wd:Q185789)                                                         |
| ...               | ...                                            | ...                                                                       |

[Assistant] The direct properties of gout (wd:Q133087) do not explicitly show treatments or remedies. I will now search for properties related to treatment or medication for the entity gout to find relevant properties that might describe what can be done against gout.

[Tool]
Top 10 property alternatives:
1. drug or therapy used for treatment, also known as cure, prescribed drug, prescription, treated by, treatment (wd:P2176 as wdt/p): drug, procedure, or therapy that can be used to treat a medical condition
2. symptoms and signs, also known as clinical sign, clinical symptom, has symptom, symptoms (wd:P780 as wdt/p): possible symptoms or signs of a medical condition, subproperty of has effect
3. afflicts, also known as affects, infects, condition endemic in (wd:P689 as wdt/p): type of organism which a condition or disease afflicts (for an organ affected by a condition, use P927)
4. WikiProjectMed ID, also known as mdwiki ID, WikiProjectMed article, WikiProjectMed identifier, WikiProjectMed page, WikiProjectMed topic ID (wd:P11143 as wdt/p): identifier of a topic in WikiProjectMed
5. health specialty, also known as medical field, medical speciality, medical specialty (wd:P1995 as wdt/p): main specialty relevant to this medical condition or treatment
6. age of onset (wd:P2841 as wdt/p): age group in which disease manifestations appear
7. image, also known as img, infobox image, photo, photograph, picture (wd:P18 as wdt/p): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)
8. instance of, also known as example of, has type, is a, is an, is of type (wd:P31 as wdt/p): type to which this subject corresponds/belongs. Different from P...tains, and the volcano is a type (instance) of volcanic landform
9. prevalence (wd:P1193 as wdt/p): portion in percent of a population with a given disease or disorder
10. 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

No other items found

[Assistant] The property "drug or therapy used for treatment" (wdt:P2176) is relevant for describing treatments or therapies for gout. I will now query for drugs, therapies, or treatments that are used for treating gout using this property.

[Tool]
Got 13 rows and 2 columns, showing the first 5 and last 5 rows below:
| treatment                       | treatmentLabel          |
| ------------------------------- | ----------------------- |
| naproxen (wd:Q1215575)          | naproxen (en)           |
| aspirin (wd:Q18216)             | aspirin (en)            |
| ibuprofen (wd:Q186969)          | ibuprofen (en)          |
| Diclofenac (wd:Q244408)         | Diclofenac (en)         |
| (S)-(−)-colchicine (wd:Q326224) | (S)-(−)-colchicine (en) |
| ...                             | ...                     |
| febuxostat (wd:Q417296)         | febuxostat (en)         |
| diet (wd:Q474191)               | diet (en)               |
| pegloticase (wd:Q7160794)       | pegloticase (en)        |
| probenecid (wd:Q900898)         | probenecid (en)         |
| sulindac (wd:Q963093)           | sulindac (en)           |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ( COUNT( ?work ) AS ?count ) WHERE {
  ?work wdt:P50 wd:Q6711 .
  ?work wdt:P31 / wdt:P279 * wd:Q5185279 .
}

Got 1 row and 1 column:
| count       |
| ----------- |
| 4 (xsd:int) |

Allen Ginsberg published 4 poems according to Wikidata.

How many poems did Allen Ginsberg published?

[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 poems did Allen Ginsberg published?

[Assistant]
To answer the question "How many poems did Allen Ginsberg publish?" I need to:

1. Identify the entity for Allen Ginsberg in Wikidata.
2. Find the property or relation that links Allen Ginsberg to poems he authored or published.
3. Count the number of such poems.

First, I will search 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 as an American poet and writer is wd:Q6711. Next, I will look for properties that link Allen Ginsberg (wd:Q6711) to poems he authored or published. I will search for properties related to "poem" or "work" for this entity.

[Tool]
Top 10 property alternatives:
1. notable work, also known as representative of artwork type, representative work, significance, significant works, they created (wd:P800 as wdt/p): notable scientific, artistic or literary work, or other work of significance among subject's works
2. Poetry Archive poet ID (wd:P5392 as wdt/p): identifier for a poet on the Poetry Archive website
3. Poetry International Web poet ID, also known as Poetry International ID, Poetry International poet ID, Poetry International Web ID (wd:P5430 as wdt/p): identifier for a poet on the Poetry International Web website
4. Poetry In Voice poet ID (wd:P5506 as wdt/p): identifier for a poet on the Poetry In Voice website
5. MAPS poet ID (wd:P5509 as wdt/p): identifier for a poet on the Modern American Poetry website
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. genre, also known as type of music, type of film, music genre, literary genre, kind of music (wd:P136 as wdt/p): creative work's genre or an artist's field of work (P101). Use main subject (P921) to relate creative works to their topic
8. Poetry Foundation ID (wd:P5341 as wdt/p): identifier for a poet on the Poetry Foundation website
9. 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
10. image, also known as img, infobox image, photo, photograph, picture (wd:P18 as wdt/p): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)

No other items found

[Assistant] The property "notable work" (wdt:P800) is used to link Allen Ginsberg to his notable works, which may include poems. I will query the number of notable works of Allen Ginsberg that are instances of "poem" or subclasses of poem. I will first check the class for poem in Wikidata.

[Tool]
Top 10 entity alternatives:
1. poem, also known as poetry, poems (wd:Q5185279)
2. long poem, also known as poem (wd:Q1782649)
3. NPNT, also known as EGFL6L, POEM, nephronectin (wd:Q18053814)
4. Poem, also known as Poetry (wd:Q19423189): Poem written by Abdullah ibn al-Mu'tazz, instance of poem
5. Poem (wd:Q7207343): song by nu metal band Taproot, instance of single
6. Poem (wd:Q7207339): album by industrial/electronic music group Delerium, instance of album
7. Poem (wd:Q56525947): instance of musical group, Greek band
8. Poem (wd:Q2893205): 1981 album by Bridges, instance of album
9. poem (wd:Q19415001): poem written by Mostafa Saadeq Al-Rafe'ie, instance of poem
10. The Sorrow of the Winds, also known as Poem (wd:Q19099527): 1912

[Assistant] The entity for poem is wd:Q5185279. Now I will query the number of notable works of Allen Ginsberg (wd:Q6711) that are instances of poem (wd:Q5185279) or its subclasses.

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

[Assistant]
The query returned 0 notable works of Allen Ginsberg that are explicitly classified as poems. This might be because not all poems are marked as notable works or not all poems are classified as instances of poem in Wikidata.

I will try a different approach: I will look for works authored by Allen Ginsberg (wd:Q6711) and count those that are instances of poem (wd:Q5185279) or its subclasses. The property for author is wdt:P50.

[Tool]
Got 1 row and 1 column:
| count       |
| ----------- |
| 4 (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 ?school ?schoolLabel WHERE {
  wd:Q231121 wdt:P69 ?school .
  OPTIONAL {
    ?school rdfs:label ?schoolLabel FILTER ( LANG( ?schoolLabel ) = "en" )
  }
}

Yayoi Kusama was educated at Kyoto City University of Arts.

At which school was Yayoi Kusama educated at?

[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] At which school was Yayoi Kusama educated at?

[Assistant]
Step 1: Identify the entity "Yayoi Kusama" in Wikidata to get her entity ID. Then, find the property related to education or schools attended for this entity. Common properties for education are "educated at" (P69).

I will first search for the entity "Yayoi Kusama" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Yayoi Kusama, also known as Kusama Yayoi (wd:Q231121): Japanese painter, performance, and installation artist, architectural draftsperson, installation artist, performance artist, instance of human, conceptual artist, fashion designer, visual artist, photographer, video artist, draftsperson, collagist, novelist, sculptor, painter, writer
2. Kusama, Yayoi (wd:Q103982697): instance of encyclopedia article, encyclopedia article
3. Yayoi Kusama (wd:Q84569253): Art exhibition at Tate Modern, instance of art exhibition
4. Yayoi Kusama Museum (wd:Q65941212): instance of art museum, Tokyo | Japan
5. Yayoi Kusama: A Retrospective (wd:Q125850156): instance of written work, book by Yayoi Kusama
6. Yayoi Kusama (Contemporary Artists) (wd:Q22813200): instance of version, edition or translation, monograph
7. Yayoi Kusama: Infinity Mirrors (wd:Q125016988): instance of exhibition, exhibition
8. Yayoi Kusama: mirrored years (wd:Q104634755): instance of temporary exhibition, exhibition in 2008
9. Love Forever: Yayoi Kusama 1958–1968 (wd:Q110893622): 1998 exhibition at Museum of Modern Art, instance of art exhibition, instance of event
10. Yayoi Kusama, the Modern Alice (wd:Q126423954): instance of scholarly article, scholarly article

[Assistant] The entity for Yayoi Kusama is wd:Q231121. Next, I will check the property "educated at" (P69) for this entity to find the school(s) where Yayoi Kusama was educated.

[Tool]
Got 1 row and 3 columns:
| subject                   | property              | object                                     |
| ------------------------- | --------------------- | ------------------------------------------ |
| Yayoi Kusama (wd:Q231121) | educated at (wdt:P69) | Kyoto City University of Arts (wd:Q749884) |

[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 ?work ?workLabel ?location ?locationLabel WHERE {
  VALUES ?work {
    wd:Q476871 wd:Q152267
  }
  ?work wdt:P840 ?location .
  OPTIONAL {
    ?work rdfs:label ?workLabel FILTER ( LANG( ?workLabel ) = "en" )
  }
  OPTIONAL {
    ?location rdfs:label ?locationLabel FILTER ( LANG( ?locationLabel ) = "en" )
  }
}
ORDER BY ?work ?locationLabel

Got 7 rows and 4 columns:
| work                              | workLabel                 | location                    | locationLabel       |
| --------------------------------- | ------------------------- | --------------------------- | ------------------- |
| White Fang (wd:Q152267)           | White Fang (en)           | Alaska (wd:Q797)            | Alaska (en)         |
| White Fang (wd:Q152267)           | White Fang (en)           | Yukon (wd:Q2009)            | Yukon (en)          |
| The Call of the Wild (wd:Q476871) | The Call of the Wild (en) | Alaska (wd:Q797)            | Alaska (en)         |
| The Call of the Wild (wd:Q476871) | The Call of the Wild (en) | Canada (wd:Q16)             | Canada (en)         |
| The Call of the Wild (wd:Q476871) | The Call of the Wild (en) | Klondike River (wd:Q286340) | Klondike River (en) |
| The Call of the Wild (wd:Q476871) | The Call of the Wild (en) | United States (wd:Q30)      | United States (en)  |
| The Call of the Wild (wd:Q476871) | The Call of the Wild (en) | Yukon (wd:Q2009)            | Yukon (en)          |

Both The Call of the Wild (wd:Q476871) and White Fang (wd:Q152267), the two most famous works of Jack London, are set in Alaska (wd:Q797) and Yukon (wd:Q2009). Additionally, The Call of the Wild is also set in the United States (wd:Q30), Canada (wd:Q16), and Klondike River (wd:Q286340).

Where are both The Call of the Wild and White Fang set, the most two famous works of Jack London?

[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] Where are both The Call of the Wild and White Fang set, the most two famous works of Jack London?

[Assistant]
Step 1: Identify the entities "The Call of the Wild" and "White Fang" as works by Jack London. Then find the property that indicates the setting location of these works.

Step 2: Search for the entities "The Call of the Wild" and "White Fang" in Wikidata to get their identifiers.

Step 3: Find the property that indicates the setting of a literary work.

Step 4: Query the setting locations for both works and check if they share the same setting.

I will start by searching for the entities "The Call of the Wild" and "White Fang" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. The Call of the Wild, also known as The Call of the Wolf, Call of the Wild (wd:Q476871): 1903 novel by Jack London, instance of literary work
2. The Call of the Wild (wd:Q57982258): 2020 film directed by Chris Sanders, instance of film
3. The Call of the Wild, also known as Call of the Wild (wd:Q3202192): 1908 film by D. W. Griffith, instance of short film
4. The Call of the Wild, also known as Call of the Wild (wd:Q2358112): 1935 film by William A. Wellman, instance of film
5. The Call of the Wild, also known as Call of the Wild (wd:Q153039): 1972 film by Ken Annakin, instance of film
6. Call of the Wild (wd:Q108545510): 2021 studio album by Powerwolf, instance of album
7. The Call of the Wild, also known as Call of the Wild (wd:Q3795543): 1923 film by Fred Jackman, instance of film
8. Call of the Wild, also known as Buck, Call of the Wild 3D (wd:Q728236): instance of film, 2009 film
9. Call of the Wild (wd:Q3141256): 1987 single by Deep Purple, instance of single
10. Call of the Wild (wd:Q2934076): album by Ted Nugent & The Amboy Dukes, instance of album

[Tool]
Top 10 entity alternatives:
1. White Fang (wd:Q152267): instance of literary work, 1906 novel by Jack London
2. White Fang (wd:Q135491): 1991 American adventure film directed by Randal Kleiser, instance of film
3. The White Fang, also known as White Fang (wd:Q4083077): 1946 film by Alexandre Zgouridi, instance of film
4. White Fang (wd:Q51163619): 2018 film directed by Alexandre Espigares, instance of animated film
5. White Fang, also known as Zanna Bianca (wd:Q1628844): 1973 film directed by Lucio Fulci, instance of film
6. White Fang (wd:Q20073193): 1936 film by David Butler, instance of film
7. White Wolf, also known as White Fang (wd:Q52015286): 1990 animated film directed by Tsuneo Maeda, instance of animated film
8. White Fang (wd:Q3656675): instance of television series, television series
9. White Fang (wd:Q60847778): 1925 American film directed by Laurence Trimble, instance of film
10. White Fang (wd:Q60603682): instance of version, edition or translation, 1906 edition

[Assistant]
I found the entities for the two works by Jack London:
- The Call of the Wild: wd:Q476871
- White Fang: wd:Q152267

Next, I will find the property that indicates the setting location of a literary work. I will search for properties related to "setting" or "location" for the entity "The Call of the Wild" (wd:Q476871).

[Tool]
Top 10 property alternatives:
1. narrative location, also known as set in location, setting of work, setting, setting location, setting place (wd:P840 as wdt/p): the narrative of the work is set in this location, subproperty of significant place, subproperty of depicts
2. main subject, also known as main topic, primary subject, primary keyword, plot keyword, of (wd:P921 as wdt/p): primary topic of a work, subproperty of depicts
3. title, also known as page name, titled, original title, name, known as (wd:P1476 as wdt/p): published name of a work, such as a newspaper article, a literary work, piece of music, a website, or a performance work, subproperty of name
4. inception, also known as inception date, founding date, founded in, founded on date, founded on (wd:P571 as wdt/p): time when an entity begins to exist; for date of official opening use P1619, subproperty of start time
5. derivative work, also known as is source work of, is the basis for, iteration, reboot, remake (wd:P4969 as wdt/p): new work of art (film, book, software, etc.) derived from major part of this work
6. has edition or translation, also known as released as, version, translation(s), modified as, has version (wd:P747 as wdt/p): item that is an edition or translation of this item
7. genre, also known as type of music, type of film, music genre, literary genre, kind of music (wd:P136 as wdt/p): creative work's genre or an artist's field of work (P101). Use main subject (P921) to relate creative works to their topic
8. country of origin, also known as comes from, place of origin, origin country, originates from, country (wd:P495 as wdt/p): country of origin of this item (creative work, food, phrase, product, etc.), subproperty of significant place, subproperty of country
9. Freebase ID, also known as FID, Freebase, Freebase identifier, MID (wd:P646 as wdt/p): identifier for a page in the Freebase database. Format: "/m/0" f...r IDs starting with "/g/", use Google Knowledge Graph ID (P2671)
10. language of work or name, also known as used language, named in language, language of work, language of website, language of URL (wd:P407 as wdt/p): language associated with this creative work (such as books, show...guage" (P103) and "languages spoken, written or signed" (P1412))

No other items found

[Assistant]
The property "narrative location" (wdt:P840) indicates the setting of a work.

Now I will query the narrative locations for both "The Call of the Wild" (wd:Q476871) and "White Fang" (wd:Q152267) to find where they are set.

[Tool]
SPARQL execution failed:
HTTPSConnectionPool(host='qlever.cs.uni-freiburg.de', port=443): Read timed out. (read timeout=30)

[Assistant] The query to get the narrative locations timed out. I will try to simplify the query by only querying the narrative locations for each work separately to reduce complexity and then combine the results. First, I will query the narrative location for "The Call of the Wild" (wd:Q476871).

[Tool]
SPARQL execution failed:
HTTPSConnectionPool(host='qlever.cs.uni-freiburg.de', port=443): Read timed out. (read timeout=30)

[Assistant] The query for the narrative location of "The Call of the Wild" also timed out. I will try to check if there is any narrative location property at all for "The Call of the Wild" by listing some triples with subject wd:Q476871 and property wdt:P840.

[Tool]
Got 5 rows and 3 columns:
| subject                           | property                      | object                      |
| --------------------------------- | ----------------------------- | --------------------------- |
| The Call of the Wild (wd:Q476871) | narrative location (wdt:P840) | United States (wd:Q30)      |
| The Call of the Wild (wd:Q476871) | narrative location (wdt:P840) | Canada (wd:Q16)             |
| The Call of the Wild (wd:Q476871) | narrative location (wdt:P840) | Alaska (wd:Q797)            |
| The Call of the Wild (wd:Q476871) | narrative location (wdt:P840) | Yukon (wd:Q2009)            |
| The Call of the Wild (wd:Q476871) | narrative location (wdt:P840) | Klondike River (wd:Q286340) |

[Assistant]
The Call of the Wild is set in multiple locations: United States, Canada, Alaska, Yukon, and Klondike River.

Now I will check the narrative location for White Fang (wd:Q152267) by listing triples with subject wd:Q152267 and property wdt:P840.

[Tool]
Got 2 rows and 3 columns:
| subject                 | property                      | object           |
| ----------------------- | ----------------------------- | ---------------- |
| White Fang (wd:Q152267) | narrative location (wdt:P840) | Alaska (wd:Q797) |
| White Fang (wd:Q152267) | narrative location (wdt:P840) | Yukon (wd:Q2009) |

[Assistant]
White Fang is set in Alaska and Yukon.

Both The Call of the Wild and White Fang are set in Alaska and Yukon. The Call of the Wild has additional settings in the United States, Canada, and Klondike River.

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 ?author ?authorLabel WHERE {
  wd:Q1545259 wdt:P50 ?author .
  OPTIONAL {
    ?author rdfs:label ?authorLabel FILTER ( LANG( ?authorLabel ) = "en" )
  }
}

The poem 'Howl' was written by 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] Who wrote the poem Howl ?

[Assistant]
Step 1: Identify the entities and properties involved in the question.
- Entity: "Howl" (a poem)
- Property: author or writer of the poem

Step 2: Search for the entity "Howl" in the knowledge graph to find its identifier.
Step 3: Search for properties related to authorship or writers of literary works.

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

[Tool]
Top 10 entity alternatives:
1. Howl (wd:Q1545259): 1955 poem by Allen Ginsberg, part of the Beat Generation movement, instance of literary work
2. Howl (wd:Q1632040): 2010 film by Jeffrey Friedman, Rob Epstein, instance of film
3. howl, also known as howling, Howling, howling sound (wd:Q1755925)
4. Howl (wd:Q21198251): instance of film, 2015 film
5. Glitterhouse Records, also known as Stag-O-Lee, Plattenbau, Howl, Glitterhouse (wd:Q786296): German independent record label based in Beverungen, instance of record label
6. The Howl, also known as Howl, L'urlo (wd:Q3820113): 1968 film by Tinto Brass, instance of film
7. Howl (wd:Q10575): 2005 album by Black Rebel Motorcycle Club., instance of album
8. Howl (wd:Q131956046): 2025 film by Domini Marshall, instance of short film, instance of film
9. Howl (wd:Q5922189): instance of musical group, Norwegian band
10. Hunting Grounds, also known as Howl (wd:Q5944904): former six piece Australian punk band, instance of musical group

[Assistant]
The poem "Howl" is identified as wd:Q1545259 in Wikidata. Now I will search for the author or writer of this poem using relevant properties. Typically, the property for author is "wdt:P50" (author).

I will query the author of the poem "Howl" (wd:Q1545259).

[Tool]
Got 1 row and 2 columns:
| author                    | authorLabel         |
| ------------------------- | ------------------- |
| Allen Ginsberg (wd:Q6711) | Allen Ginsberg (en) |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ( COUNT( DISTINCT ?business ) AS ?count ) WHERE {
  ?business wdt:P112 wd:Q19837 .
}

Got 1 row and 1 column:
| count       |
| ----------- |
| 3 (xsd:int) |

Steve Jobs founded 3 distinct businesses according to Wikidata.

How many businesses did Steve Jobs found?

[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 businesses did Steve Jobs found?

[Assistant]
To answer the question "How many businesses did Steve Jobs found?" we need to:

1. Identify the entity for Steve Jobs in Wikidata.
2. Find properties related to founding or creating businesses or companies associated with Steve Jobs.
3. Count the number of distinct businesses he founded.

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

[Tool]
Top 10 entity alternatives:
1. Steve Jobs, also known as Steven Paul Jobs, Abdul Lateef Jandali, T. Lateef, Steven Jobs (wd:Q19837): American entrepreneur; co-founder of Apple Inc. (1955–2011), technology entrepreneur, executive producer, computer scientist, instance of human, film producer, entrepreneur, financier, inventor, designer
2. Steve Jobs (wd:Q18754959): 2015 film directed by Danny Boyle, instance of film
3. Steve Jobs, also known as '' Steve Jobs '', Steve Jobs (book) (wd:Q625645): 2011 authorized biography by Walter Isaacson, instance of literary work
4. Steve Jobs (wd:Q111953476): 2015 soundtrack album by Daniel Pemberton, instance of album
5. Steve Jobs (wd:Q48966678): Italian clothing company
6. Steve Jobs (wd:Q52986630): middle school in Latina, Latina, Lazio, Italy [school id: LTTDZG5007], instance of middle school
7. Steve Jobs (wd:Q52987978): middle school in Gioia Tauro, Reggio Calabria, Calabria, Italy, instance of middle school
8. Steve Jobs (wd:Q52951676): middle school in Latina, Latina, Lazio, Italy [school id: LTPSLH500H], instance of middle school
9. Steve Jobs (wd:Q52946669): middle school in Latina, Latina, Lazio, Italy [school id: LTPMSO5002], instance of middle school
10. Steve Jobs (wd:Q47510529): painting by Alan Magee, instance of painting

[Assistant] The entity for Steve Jobs as the American entrepreneur and co-founder of Apple Inc. is wd:Q19837. Next, I will look for properties related to founding businesses or companies for this entity. I will search for properties related to "found" or "founder" for Steve Jobs.

[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. date of death, also known as DOD, deathdate, died, died on, year of death (wd:P570 as wdt/p): subproperty of dissolved, abolished or demolished date, date on which the subject died, subproperty of end time
3. present in work, also known as appeared in, is shown in, in work, in narrative, from work (wd:P1441 as wdt/p): this (fictional or fictionalized) entity, place, or person appea...r works, P1343 for entities described in non-fictional accounts)
4. 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
5. notable work, also known as representative of artwork type, representative work, significance, significant works, they created (wd:P800 as wdt/p): notable scientific, artistic or literary work, or other work of significance among subject's works
6. place of death, also known as POD, location of death, killed in, died in, died (wd:P20 as wdt/p): most specific known (e.g. city instead of country, or hospital i... city) death location of a person, animal or fictional character, subproperty of location
7. 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
8. owner of, also known as owns property, shareholder of, owns (wd:P1830 as wdt/p): entities owned by the subject
9. ELNET ID, also known as ERRR ID (wd:P6394 as wdt/p): identifier assigned by the ELNET consortium of Estonian libraries. Format: 7 digits, "x" or another digit
10. Oxford Reference overview ID, also known as OR ID, ORO ID, Overview Oxford Reference ID, Overview Reference Oxford ID, Oxford Reference ID (wd:P12582 as wdt/p): identifier for an overview page for a topic in the Oxford Reference

No other items found

[Assistant] The direct search for a property related to "found" or "founder" for Steve Jobs did not yield a direct property. I will try a more general search for properties related to "founder" or "founding" in Wikidata to find the relevant property that links a person to the businesses they founded.

[Tool]
Top 10 property alternatives:
1. founded by, also known as starter, started by, institutor, instituted by, founders (wd:P112 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): founder or co-founder of this organization, religion, place or entity, subproperty of creator
2. notable work, also known as representative of artwork type, representative work, significance, significant works, they created (wd:P800 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): notable scientific, artistic or literary work, or other work of significance among subject's works
3. inception, also known as inception date, founding date, founded in, founded on date, founded on (wd:P571 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): time when an entity begins to exist; for date of official opening use P1619, subproperty of start time
4. collection creator, also known as archive creator, creator, collector, collected by, archive of (wd:P6241 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): entity (person, organization, etc.) that caused a record or collection to be produced or gathered
5. 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)
6. 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
7. location of formation, also known as originates from, place of incorporation, place of establishment, place of formation, place of foundation (wd:P740 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): location where a group or organization was formed, subproperty of location of creation
8. discoverer or inventor, also known as first studied by, inventor or discoverer, inventor, invented by, invented (wd:P61 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): subject who discovered, first described, invented, or developed this discovery or invention or scientific hypothesis or theory
9. author name string, also known as writer named as, writer name, author string, writer indicated as, songwriting credits string (wd:P2093 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): stores unspecified author or editor name for publications; use i...or editor (P98) does not exist or is not known. Do not use both.
10. place of birth, also known as location born, POB, location of birth, born in, born at (wd:P19 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): most specific known birth location of a person, animal or fictional character, subproperty of location

[Assistant]
The property "founded by" (wdt:P112) is the relevant property that links an organization or business to its founder(s). To find the businesses Steve Jobs founded, we need to find entities where Steve Jobs (wd:Q19837) is the value of the "founded by" property.

I will now query for the count of distinct businesses or organizations that have Steve Jobs as their founder.

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

[Tool] Stopped generation process