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 ?castMember ?castMemberLabel WHERE {
  wd:Q58815001 wdt:P161 ?castMember .
  OPTIONAL {
    ?castMember rdfs:label ?castMemberLabel FILTER ( LANG( ?castMemberLabel ) = "en" )
  }
}

Got 7 rows and 2 columns:
| castMember                        | castMemberLabel           |
| --------------------------------- | ------------------------- |
| Dwayne Johnson (wd:Q10738)        | Dwayne Johnson (en)       |
| Pascal Petardi (wd:Q16238788)     | Pascal Petardi (en)       |
| Vincenzo Amato (wd:Q1723645)      | Vincenzo Amato (en)       |
| Gal Gadot (wd:Q185654)            | Gal Gadot (en)            |
| Ryan Reynolds (wd:Q192682)        | Ryan Reynolds (en)        |
| Chris Diamantopoulos (wd:Q311980) | Chris Diamantopoulos (en) |
| Ritu Arya (wd:Q98042831)          | Ritu Arya (en)            |

The cast members of the film Red Notice directed by Rawson Marshall Thurber are: Dwayne Johnson, Pascal Petardi, Vincenzo Amato, Gal Gadot, Ryan Reynolds, Chris Diamantopoulos, and Ritu Arya.

Tell me all the cast members of the film Red Notice directed by Rawson Marshall Thurber ?

[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] Tell me all the cast members of the film Red Notice directed by Rawson Marshall Thurber ?

[Assistant]
To answer the question, I need to identify the film "Red Notice" directed by Rawson Marshall Thurber and then find all the cast members of this film.

Step 1: Search for the entity "Red Notice" (film).
Step 2: Verify the director of the film to be Rawson Marshall Thurber.
Step 3: Retrieve all cast members of the film.

I will start by searching for the entity "Red Notice" in the knowledge graph.

[Tool]
Top 10 entity alternatives:
1. Red Notice (wd:Q58815001): 2021 film directed by Rawson Marshall Thurber, instance of film
2. Interpol Red Notice, also known as Red Notice (wd:Q47612352)
3. Red Notice (wd:Q119843350): action comedy film series, instance of film series
4. SAS: Red Notice (wd:Q65119432): 2021 film directed by Magnus Martens, instance of film
5. Bill Browder, Red Notice (wd:Q102079684): scientific article published on 31 December 1969, instance of scholarly article
6. Bill Browder’s ‘Red Notice’ (wd:Q102080235): instance of news article
7. Notice of redundancy (wd:Q57829991): instance of scholarly article
8. Critical notice: Darwinian reductionism (wd:Q107095554): instance of scholarly article, journalArticle
9. Notice of redundant publication (wd:Q35531375): scientific article published on August 2004, instance of scholarly article
10. Notice of redundant publication (wd:Q42117359): scientific article published on September 2013, instance of scholarly article, instance of editorial

[Assistant]
The entity "Red Notice" (wd:Q58815001) is a 2021 film directed by Rawson Marshall Thurber. This matches the film we are interested in.

Next, I will retrieve all the cast members of the film "Red Notice" (wd:Q58815001). The property for cast members in Wikidata is usually "cast member" (wdt:P161). I will query for all cast members of this film.

[Tool]
Got 7 rows and 2 columns:
| castMember                        | castMemberLabel           |
| --------------------------------- | ------------------------- |
| Dwayne Johnson (wd:Q10738)        | Dwayne Johnson (en)       |
| Pascal Petardi (wd:Q16238788)     | Pascal Petardi (en)       |
| Vincenzo Amato (wd:Q1723645)      | Vincenzo Amato (en)       |
| Gal Gadot (wd:Q185654)            | Gal Gadot (en)            |
| Ryan Reynolds (wd:Q192682)        | Ryan Reynolds (en)        |
| Chris Diamantopoulos (wd:Q311980) | Chris Diamantopoulos (en) |
| Ritu Arya (wd:Q98042831)          | Ritu Arya (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 ?founder ?founderLabel WHERE {
  wd:Q308776 wdt:P112 ?founder .
  OPTIONAL {
    ?founder rdfs:label ?founderLabel FILTER ( LANG( ?founderLabel ) = "en" )
  }
}

Got 2 rows and 2 columns:
| founder                        | founderLabel          |
| ------------------------------ | --------------------- |
| Jacques Herzog (wd:Q2745982)   | Jacques Herzog (en)   |
| Pierre de Meuron (wd:Q3174960) | Pierre de Meuron (en) |

The architectural firm Herzog & de Meuron, which planned the Elbphilharmonie, was founded by Jacques Herzog and Pierre de Meuron.

Who founded the architectural firm who planned the Elbphilharmonie?

[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 founded the architectural firm who planned the Elbphilharmonie?

[Assistant]
To answer this question, I need to find the architectural firm that planned the Elbphilharmonie and then find the founder(s) of that firm.

Step 1: Identify the entity for "Elbphilharmonie."
Step 2: Find the architectural firm that planned the Elbphilharmonie.
Step 3: Find the founder(s) of that architectural firm.

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

[Tool]
Top 10 entity alternatives:
1. Elbphilharmonie, also known as Elphi, Kaispeicher A, Elbphilharmonie Hamburg (wd:Q673223): instance of former warehouse, instance of concert hall, instance of hotel, concert hall
2. NDR Elbphilharmonie Orchestra, also known as NWDR-Sinfonieorchester, Orchester des NWDR Hamburg, Orchester Des NWDR Hamburg, North German Radio Symphony Orchestra, NDR Sinfonieorchester (wd:Q706354): instance of symphonic radio orchestra, instance of symphony orchestra, Symphony orchestra in Hamburg, instance of musical group
3. Elbphilharmonie Bau KG (wd:Q811085): instance of enterprise, instance of business
4. Concert Report from the new Elbp..., Hamburg: a Performer's Review. (wd:Q108411337): article from American Choral Review, instance of article

[Assistant] The Elbphilharmonie entity in Wikidata is wd:Q673223. Next, I will find the architectural firm that planned the Elbphilharmonie (wd:Q673223). I will look for properties related to "architect," "architectural firm," or "designer" for this entity.

[Tool]
Got 366 rows and 3 columns, showing the first 10 rows below:
| subject                      | property                                                    | object                                                                                |
| ---------------------------- | ----------------------------------------------------------- | ------------------------------------------------------------------------------------------------- |
| Elbphilharmonie (wd:Q673223) | instance of (wdt:P31)                                       | hotel (wd:Q27686)                                                                                |
| Elbphilharmonie (wd:Q673223) | country (wdt:P17)                                           | Germany (wd:Q183)                                                                                |
| Elbphilharmonie (wd:Q673223) | located in the administrative territorial entity (wdt:P131) | Hamburg-Mitte (wd:Q1626)                                                                          |
| Elbphilharmonie (wd:Q673223) | coordinate location (wdt:P625)                              | POINT(9.984194 53.541417) (geo:wktLiteral)                                                        |
| Elbphilharmonie (wd:Q673223) | Commons category (wdt:P373)                                 | Elbphilharmonie                                                                                |
| Elbphilharmonie (wd:Q673223) | image (wdt:P18)                                             | <http://commons.wikimedia.org/wiki/Special:FilePath/Hamburg%2C%20Barkassenfahrt%20NIK%203536.jpg> |
| Elbphilharmonie (wd:Q673223) | location (wdt:P276)                                         | HafenCity (wd:Q1561)                                                                              |
| Elbphilharmonie (wd:Q673223) | inception (wdt:P571)                                        | 1963-01-01T00:00:00Z (xsd:dateTime)                                                               |
| Elbphilharmonie (wd:Q673223) | described at URL (wdt:P973)                                 | <http://grosseoper-vieltheater.de/elbphilharmonie_hamburg/>                                       |
| Elbphilharmonie (wd:Q673223) | official website (wdt:P856)                                 | <https://www.elbphilharmonie.de/>                                                                 |
| ...                          | ...                                                         | ...                                                                                |

[Assistant] The direct properties for the architectural firm or architect are not immediately visible in the first 10 rows. I will look specifically for properties related to "architect" or "architectural firm" for the Elbphilharmonie entity. I will search for properties related to "architect" or "architectural firm" for the Elbphilharmonie.

[Tool]
Top 10 property alternatives:
1. designed by, also known as has designer, designer, architect (wd:P287 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): person or organization which designed the object, subproperty of creator
2. architect, also known as architecture firm (wd:P84 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): person or architectural firm responsible for designing this building, subproperty of designed by
3. Architectuurgids architect ID (wd:P3058 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): identifier for an architect in the Dutch architectuurgids.nl database
4. architectural style, also known as style of architecture, architecture (wd:P149 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): architectural style of a structure, subproperty of movement
5. instruction set, also known as architecture, Instruction Set Architecture, ISA (wd:P1068 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): set of machine code instructions on which a processor architecture is based
6. landscape architect, also known as landscaper (wd:P7514 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): person or architectural firm that designed this outdoor area, possibly including landmarks and structures, subproperty of creator
7. cover art by, also known as sleeve art by, jacket art by, designer, cover design by, cover artist (wd:P736 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): name of person or team creating cover artwork for book, record album, single record etc.
8. ArchWiki article (wd:P12873 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): identifier for an article on ArchWiki
9. structural engineer, also known as building engineer, engineer (wd:P631 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): person, group or organisation responsible for the structural engineering of a building or structure
10. Prazdne Domy architect ID, also known as prazdnedomy.cz ID (wd:P6811 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): identifier of an architect in the Czech prazdnedomy.cz database

[Assistant] The property "architect" (wdt:P84) is used for the person or architectural firm responsible for designing a building. I will now query the Elbphilharmonie (wd:Q673223) for its architect (wdt:P84).

[Tool]
Got 2 rows and 2 columns:
| architect                       | architectLabel          |
| ------------------------------- | ----------------------- |
| Werner Kallmorgen (wd:Q1456008) | Werner Kallmorgen (en)  |
| Herzog & de Meuron (wd:Q308776) | Herzog & de Meuron (en) |

[Assistant] The architectural firm that planned the Elbphilharmonie is Herzog & de Meuron (wd:Q308776). Now, I will find the founder(s) of Herzog & de Meuron. I will look for properties related to "founder" or "founders" for this entity.

[Tool]
Got 347 rows and 3 columns, showing the first 10 rows below:
| subject                         | property                         | object                                                                      |
| ------------------------------- | -------------------------------- | --------------------------------------------------------------------------- |
| Herzog & de Meuron (wd:Q308776) | instance of (wdt:P31)            | architectural firm (wd:Q4387609)                                            |
| Herzog & de Meuron (wd:Q308776) | country (wdt:P17)                | Switzerland (wd:Q39)                                                        |
| Herzog & de Meuron (wd:Q308776) | Commons category (wdt:P373)      | Herzog & de Meuron                                                          |
| Herzog & de Meuron (wd:Q308776) | image (wdt:P18)                  | <http://commons.wikimedia.org/wiki/Special:FilePath/AllianzArenaSunset.jpg> |
| Herzog & de Meuron (wd:Q308776) | inception (wdt:P571)             | 1978-01-01T00:00:00Z (xsd:dateTime)                                         |
| Herzog & de Meuron (wd:Q308776) | short name (wdt:P1813)           | H&dM (en)                                                                   |
| Herzog & de Meuron (wd:Q308776) | official website (wdt:P856)      | <https://www.herzogdemeuron.com/>                                           |
| Herzog & de Meuron (wd:Q308776) | street address (wdt:P6375)       | Rheinschanze 6 (en)                                                         |
| Herzog & de Meuron (wd:Q308776) | award received (wdt:P166)        | Pritzker Architecture Prize (wd:Q133160)                                    |
| Herzog & de Meuron (wd:Q308776) | headquarters location (wdt:P159) | Basel (wd:Q78)                                                              |
| ...                             | ...                              | ...                                                                         |

[Assistant] I did not see a direct "founder" property in the first 10 rows. I will specifically search for the "founder" property (wdt:P112) for Herzog & de Meuron (wd:Q308776).

[Tool]
Got 2 rows and 3 columns:
| subject                         | property              | object                         |
| ------------------------------- | --------------------- | ------------------------------ |
| Herzog & de Meuron (wd:Q308776) | founded by (wdt:P112) | Jacques Herzog (wd:Q2745982)   |
| Herzog & de Meuron (wd:Q308776) | founded by (wdt:P112) | Pierre de Meuron (wd:Q3174960) |

[Assistant]
The architectural firm Herzog & de Meuron, which planned the Elbphilharmonie, was founded by Jacques Herzog and Pierre de Meuron.

I will now provide the final answer.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?inceptionYear WHERE {
  wd:Q6228 wdt:P571 ?inception .
  BIND( YEAR( ?inception ) AS ?inceptionYear )
}

Got 1 row and 1 column:
| inceptionYear  |
| -------------- |
| 1975 (xsd:int) |

The district of Höxter came into existence in the year 1975.

In what year did the district of Höxter come into existence?

[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] In what year did the district of Höxter come into existence?

[Assistant] To answer the question about the year the district of Höxter came into existence, I need to identify the entity for the district of Höxter in Wikidata and then find the property related to its inception or creation date. I will start by searching for the entity "Höxter district" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Höxter, also known as Höxter (district) (wd:Q6228): instance of rural district of North Rhine-Westphalia, German district in North Rhine-Westphalia
2. Höxter District (wd:Q1351319): former (1816–1974) rural district of Germany, instance of district of Germany
3. Höxter District, also known as District of Hoxter, District of Höxter, Hoxter District (wd:Q1229842): instance of former administrative territorial entity, former district in the Kingdom of Westphalia
4. Alliance 90/The Greens district association Höxter (wd:Q120053297): politische Organisation in North Rhine-Westphalia, instance of Kreisverband
5. district, also known as administrative area (wd:Q149621)
6. neighborhood, also known as subdistrict, quarter, neighbourhood, district, city section (wd:Q123705)
7. Höxter (wd:Q16107): town in North Rhine-Westphalia, Germany, seat of the district of Höxter, instance of urban municipality in Germany, instance of medium-sized district town, instance of district capital, instance of Hanseatic city
8. district of Bangladesh, also known as district, zila (wd:Q152732)
9. district of China, also known as district, district in China, qu, Chinese district, control area of city (wd:Q1065118)
10. Ortsteil, also known as quarter, locality of Germany, district (wd:Q253019)

[Assistant]
There are multiple entities related to "Höxter district." The most relevant one for the current district of Höxter is likely "Höxter (wd:Q6228)" which is described as a rural district of North Rhine-Westphalia, Germany. There are also former districts with similar names, but since the question asks about the district of Höxter in general, I will focus on the current district entity (wd:Q6228).

Next, I will look for the inception or creation date property of this entity. The property for inception or creation date is usually "inception" (P571) in Wikidata. I will check if this property exists for the entity wd:Q6228.

[Tool]
Got 1 row and 3 columns:
| subject           | property             | object                              |
| ----------------- | -------------------- | ----------------------------------- |
| Höxter (wd:Q6228) | inception (wdt:P571) | 1975-01-01T00:00:00Z (xsd:dateTime) |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?inceptionDate WHERE {
  wd:Q1524 wdt:P571 ?inceptionDate .
}

Athens was founded around the year 7000 BCE according to the inception date property in Wikidata.

[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] When was Athens founded?

[Assistant]
To answer the question "When was Athens founded?" I need to find the founding date or inception date of the city Athens in Wikidata.

Step 1: Identify the entity for Athens.
Step 2: Identify properties related to founding or inception date.
Step 3: Query the founding date of Athens using the identified entity and property.

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

[Tool]
Top 10 entity alternatives:
1. Athens, also known as Athenae, Athens city (wd:Q1524): capital and largest city of Greece, instance of largest city, instance of city-state, instance of metropolis, instance of Free city, instance of big city
2. Athens, also known as Athens, GA, The Classic City, Cedar Shoals, Athens, Georgia (wd:Q203263): consolidated city-county and county seat of Clarke County, Georgia, United States, instance of city in the United States, instance of consolidated city-county, instance of municipality of Georgia, instance of county seat, instance of big city
3. Classical Athens, also known as Ancient Athens, Athens (Samian Ware Discovery Site), Athens (wd:Q844930): city-state in ancient Greece, instance of city-state, instance of polis
4. Athens, also known as Athens, Alabama, Athens, AL (wd:Q79439): county seat of Limestone County, Alabama, United States, instance of city in the United States, instance of county seat
5. Athens, also known as Athens, OH, Athens, Ohio (wd:Q755420): instance of city in the United States, city in Ohio, United States, instance of college town, instance of county seat
6. Athens, also known as Athens, Texas, Athens, TX (wd:Q755425): instance of city in the United States, city in Texas, United States, instance of county seat
7. Athens, also known as Athens, Tennessee, Athens, TN (wd:Q3292481): instance of city in the United States, city in Tennessee, United States
8. Athens, also known as Athens, IL, Athens, Illinois (wd:Q578681): city in Menard County, Illinois, United States, instance of city in the United States
9. Athens, also known as Athens, LA, Athens, Louisiana (wd:Q3239684): village in Claiborne Parish, Louisiana, United States, instance of Rural community (local government), instance of village in the United States
10. Athens, also known as Athens, PA, Athens, Pennsylvania (wd:Q1130437): borough in Bradford County, Pennsylvania, United States, instance of borough of Pennsylvania

[Assistant] The entity for Athens as the capital and largest city of Greece is wd:Q1524. Next, I will search for properties related to founding or inception date for this entity. Common properties for founding date include "inception" (P571) or "founding date" (P571). I will verify these properties for Athens (wd:Q1524).

[Tool]
Top 10 property alternatives:
1. 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
2. named after, also known as named in honor of, toponym, namesake, name after, etymology (wd:P138 as wdt/p): entity or event that inspired the subject's name, or namesake (i...fier "applies to name" (P5168) can be used to indicate which one
3. follows, also known as prev, successor to, succeeds to, succeeds, split from (wd:P155 as wdt/p): immediately prior item in a series of which the subject is a par... preceding item, e.g. political offices, use "replaces" (P1365)]

No other items found

[Assistant] The property "inception" (wdt:P571) is used for founding date or inception date. I will now query the inception date of Athens (wd:Q1524) using this property.

[Tool]
Got 1 row and 1 column:
| inceptionDate                        |
| ------------------------------------ |
| -7000-01-01T00:00:00Z (xsd:dateTime) |

[Tool] Stopped generation process


PREFIX pq: <http://www.wikidata.org/prop/qualifier/>
PREFIX ps: <http://www.wikidata.org/prop/statement/>
PREFIX p: <http://www.wikidata.org/prop/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?year1 ?lifeExpectancy1 ?year2 ?lifeExpectancy2 WHERE {
  wd:Q794 p:P2250 ?statement1 , ?statement2 .
  ?statement1 ps:P2250 ?lifeExpectancy1 .
  ?statement1 pq:P585 ?year1 .
  ?statement2 ps:P2250 ?lifeExpectancy2 .
  ?statement2 pq:P585 ?year2 .
  FILTER ( YEAR( ?year1 ) = YEAR( ?year2 ) - 1 && YEAR( ?year1 ) > 1999 && ?lifeExpectancy2 > ?lifeExpectancy1 )
}
LIMIT 1

Yes, life expectancy in Iran has grown in at least one year after 1999. For example, it grew from 76.55 in 2019 to 76.74 in 2020.

has life expectancy in iran grown in any year after 1999?

[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] has life expectancy in iran grown in any year after 1999?

[Assistant]
To answer the question "Has life expectancy in Iran grown in any year after 1999?", I need to:

1. Identify the entity for Iran in Wikidata.
2. Identify the property or data related to life expectancy in Iran.
3. Check the life expectancy values for Iran for years after 1999.
4. Determine if there is any year after 1999 where the life expectancy value is greater than the previous year, indicating growth.

First, I will search for the entity "Iran" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Iran, also known as Islamic Rep. Iran, Islamic Republic of Iran, Persia, IRN, ir (wd:Q794): instance of Islamic Republic, instance of successor state, instance of sovereign state, country in Western Asia, instance of country
2. Qajar Iran, also known as Iran, Sublime State of Persia, Qajar Persia, Qajar Empire, Persia (wd:Q63158027): country in Western Asia (1789–1925), instance of historical country
3. Iran (wd:Q2043606): instance of administrative territorial entity of Russia, village in North Ossetia, Russia, instance of village
4. Iran, also known as Īrān (wd:Q10859757): instance of newspaper, Iranian newspaper
5. IRAN, also known as Iran: Journal of the British Institute of Persian Studies (wd:Q5973372): instance of scientific journal, academic journal
6. Iran (wd:Q96382820): About Iran
7. Iran (wd:Q19880618): 2008 documentary film by Aaron Newman, instance of film
8. Iran, also known as IMO 5327817 (wd:Q101778560): ship built in 1941, instance of ship
9. Iran, also known as Iran (given name), Iran (first name) (wd:Q21286079): instance of male given name, male given name
10. IRAN (wd:Q108434787): instance of herbarium, herbarium

[Assistant] The entity for Iran as a country is wd:Q794. Next, I need to find the property related to life expectancy. I will search for properties related to "life expectancy" in Wikidata.

[Tool]
Top 10 property alternatives:
1. life expectancy, also known as life span, lifespan (wd:P2250 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): average life expectancy for this group or species, subproperty of duration
2. longest observed lifespan, also known as maximum life expectancy, oldest recorded individual, oldest observed, maximum lifespan, maximum life span (wd:P4214 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): age of the oldest specimen of a taxon for which the age can be verified
3. death rate, also known as mortality rate (wd:P10091 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): the total number of persons dead per 1,000 population per year
4. mean lifetime (wd:P2645 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): inverse of exponential decay rate; half-life is ln(2) times this value, subproperty of duration
5. mean age, also known as average age (wd:P4442 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): mean age in a given place
6. discontinued date, also known as EOL, product discontinued, end-of-life date, end-of-life, EOL date (wd:P2669 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): date that the availability of a product or service was discontin...d "service retirement" (P730) for pieces or classes of equipment, subproperty of end time
7. service life (wd:P5069 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): product's total life in use from the point of sale to the point of discard
8. date of death, also known as DOD, deathdate, died, died on, year of death (wd:P570 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): subproperty of dissolved, abolished or demolished date, date on which the subject died, subproperty of end time
9. shelf life, also known as unfit for use after, expired by, expiration, expires in (wd:P4218 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): length of time that a commodity may be stored without becoming unfit for use or consumption
10. cause of death, also known as death cause, murder method, method of murder, die of, die from (wd:P509 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): underlying or immediate cause of death.  Underlying cause (e.g. ...adest category, e.g. natural causes, accident, homicide, suicide

[Assistant] The property "life expectancy" is wd:P2250. Now I want to check if there are statements about life expectancy (wd:P2250) for Iran (wd:Q794) with qualifiers for the year or point in time after 1999. I will query for life expectancy values for Iran with their respective years.

[Tool]
Got 25 rows and 2 columns, showing the first 5 and last 5 rows below:
| year                                | lifeExpectancy       |
| ----------------------------------- | -------------------- |
| 2000-01-01T00:00:00Z (xsd:dateTime) | 70.144 (xsd:decimal) |
| 2001-01-01T00:00:00Z (xsd:dateTime) | 70.526 (xsd:decimal) |
| 2002-01-01T00:00:00Z (xsd:dateTime) | 70.899 (xsd:decimal) |
| 2003-01-01T00:00:00Z (xsd:dateTime) | 71.249 (xsd:decimal) |
| 2004-01-01T00:00:00Z (xsd:dateTime) | 71.582 (xsd:decimal) |
| ...                                 | ...                  |
| 2019-01-01T00:00:00Z (xsd:dateTime) | 76.55 (xsd:decimal)  |
| 2020-01-01T00:00:00Z (xsd:dateTime) | 76.74 (xsd:decimal)  |
| 2020-01-01T00:00:00Z (xsd:dateTime) | 75 (xsd:decimal)     |
| 2021-01-01T00:00:00Z (xsd:dateTime) | 74 (xsd:decimal)     |
| 2022-01-01T00:00:00Z (xsd:dateTime) | 75 (xsd:decimal)     |

[Assistant] There are life expectancy values for Iran for multiple years after 1999. The values seem to generally increase from 70.144 in 2000 to 76.74 in 2020, though there are some fluctuations in the last few years. To confirm if life expectancy has grown in any year after 1999, I will check if there exists at least one year after 1999 where the life expectancy is greater than the previous year. I will write a SPARQL query to check this condition.

[Tool]
Got 1 row and 4 columns:
| year1                               | lifeExpectancy1     | year2                               | lifeExpectancy2     |
| ----------------------------------- | ------------------- | ----------------------------------- | ------------------- |
| 2019-01-01T00:00:00Z (xsd:dateTime) | 76.55 (xsd:decimal) | 2020-01-01T00:00:00Z (xsd:dateTime) | 76.74 (xsd:decimal) |

[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 ?president ?presidentLabel ?father ?fatherLabel WHERE {
  ?president wdt:P39 wd:Q11696 .
  ?president wdt:P22 ?father .
  ?father wdt:P39 wd:Q11696 .
  OPTIONAL {
    ?president rdfs:label ?presidentLabel FILTER ( LANG( ?presidentLabel ) = "en" )
  }
  OPTIONAL {
    ?father rdfs:label ?fatherLabel FILTER ( LANG( ?fatherLabel ) = "en" )
  }
}

Got 2 rows and 4 columns:
| president                     | presidentLabel         | father                        | fatherLabel            |
| ----------------------------- | ---------------------- | ----------------------------- | ---------------------- |
| John Quincy Adams (wd:Q11816) | John Quincy Adams (en) | John Adams (wd:Q11806)        | John Adams (en)        |
| George W. Bush (wd:Q207)      | George W. Bush (en)    | George H. W. Bush (wd:Q23505) | George H. W. Bush (en) |

The American presidents who followed in their father's steps and also became president are John Quincy Adams, whose father was John Adams, and George W. Bush, whose father was George H. W. Bush.

Which American presidents followed in their father‘s step and also became the president?

[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 American presidents followed in their father‘s step and also became the president?

[Assistant]
To answer this question, I need to find American presidents who had a father who was also a president of the United States. This involves identifying entities for American presidents and checking if their father was also an American president.

Step 1: Identify the property for "father" and "president of the United States."
Step 2: Find American presidents (entities with the position "president of the United States").
Step 3: Check if their father is also an American president.

I will start by searching for the property that represents "father" in Wikidata.

[Tool]
Top 10 property alternatives:
1. father, also known as is child of, son of, parent, is son of, is daughter of (wd:P22 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): male parent of the subject. For stepfather, use "stepparent" (P3448), subproperty of parent
2. child, also known as mother of, kids, kid, issue, has sons (wd:P40 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): subject has object as child. Do not use for stepchildren—use "relative" (P1038), qualified with "type of kinship" (P1039), subproperty of significant person, subproperty of relative
3. parent, also known as guardian, parents (wd:P8810 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): parent of the subject. Only use if neither father (P22) nor mother (P25) nor stepparent (P3448) is applicable., subproperty of relative
4. mother, also known as mam, mommy, mom, mum, mummy (wd:P25 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): female parent of the subject. For stepmother, use "stepparent" (P3448), subproperty of parent
5. relative, also known as nephew, nibling, niece, non-binary parent, relation (wd:P1038 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): family member (qualify with "kinship to subject", P1039; for direct family member please use specific property), subproperty of significant person
6. Dictionary of Algorithms and Data Structures ID, also known as DADS ID (wd:P5106 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): identifier for an entry in the Dictionary of Algorithms and Data Structures
7. stepparent, also known as step mother, step-mother, stepmother, step parent, step-parent (wd:P3448 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): subject has the object as their stepparent, subproperty of relative
8. godparent, also known as godfather, godmother (wd:P1290 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): person who is the godparent of a given person, subproperty of relative
9. family, also known as member of family, royal house, noble family, house, dynasty (wd:P53 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): family, including dynasty and nobility houses. Not family name (use P734 for family name)., subproperty of member of
10. generational suffix, also known as Sr., family suffix, Senior, Jr., age suffix (wd:P8017 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): suffix used in name

[Assistant] The property for "father" is wd:P22. Next, I will identify the entity for "president of the United States" and then find American presidents who have a father that was also a president of the United States.

[Tool]
Top 10 entity alternatives:
1. President of the United States, also known as president of United States, the president of the United States, President of USA, President of the USA, U.S. President (wd:Q11696): head of state and head of government of the United States of America, instance of commander-in-chief, instance of head of government, instance of elective office, instance of head of state, instance of president
2. cultural depictions of John F. Kennedy, also known as cultural depictions of John Fitzgerald Kennedy, President of the United States, cultural depictions of President Kennedy, cultural depictions of President John F. Kennedy, cultural depictions of JFK (wd:Q17512211): John F. Kennedy depicted in culture, instance of cultural depiction
3. President of the United States (wd:Q125680992): fictional position in the Grand Theft Auto video game series (HD), instance of fictional office, position, or title
4. Jimmy Carter, also known as James E. Carter, James Earl Carter, Jr., James E. Carter, Jr., James E. Carter Jr., James Carter (wd:Q23685): President of the United States from 1977 to 1981, human rights activist, instance of human, environmentalist, businessperson, peace activist, autobiographer, naval officer, humanitarian, statesperson, submariner, politician, diplomat, engineer, novelist, farmer, writer
5. 2024 United States presidential election, also known as 2024 U.S. presidential race, 60th United States presidential election, 47th President of the United States, Harris vs. Trump, Harris v. Trump (wd:Q101110072): instance of United States presidential election, 60th quadrennial U.S. presidential election
6. Vice President of the United States, also known as US Vice President, US VP, USVP, VP of the United States, VP of the US (wd:Q11699): second-highest constitutional office in the United States, instance of president of the senate, instance of elective office, instance of vice president
7. President of Mexico, also known as President of the United Mexican States (wd:Q628004)
8. Executive Office of the President of the United States, also known as EOP, White House, United States Executive Office of the President, The White House, Executive Office of the President (wd:Q1355327): U.S. federal government office of the president of the United States, instance of United States federal agency, instance of government agency
9. President-elect of the United States, also known as President-Elect (wd:Q1467287)
10. The Presidents of the United States of America, also known as PUSA, The Quitters, The Presidents of the United States, The Presidents, PotUSA (wd:Q176430): instance of musical group, American rock band

[Assistant] The entity for "president of the United States" is wd:Q11696. Now I will write a query to find American presidents who have a father who was also a president of the United States. I will use the property "father" (wdt:P22) to link the president to their father and check if the father is also an instance of "president of the United States" (wd:Q11696).

[Tool]
Got 2 rows and 4 columns:
| president                     | presidentLabel         | father                        | fatherLabel            |
| ----------------------------- | ---------------------- | ----------------------------- | ---------------------- |
| John Quincy Adams (wd:Q11816) | John Quincy Adams (en) | John Adams (wd:Q11806)        | John Adams (en)        |
| George W. Bush (wd:Q207)      | George W. Bush (en)    | George H. W. Bush (wd:Q23505) | George H. W. Bush (en) |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?inception WHERE {
  wd:Q1514848 wdt:P571 ?inception .
}

The University of Saskatchewan was founded in the year 1907.

When was the University of Saskatchewan founded?

[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] When was the University of Saskatchewan founded?

[Assistant] To answer the question about the founding date of the University of Saskatchewan, I will first search for the entity "University of Saskatchewan" in Wikidata to get its identifier. Then, I will look for the property related to the founding date or inception of the university.

[Tool]
Top 10 entity alternatives:
1. University of Saskatchewan, also known as USask, usask.ca (wd:Q1514848): public university in Saskatoon, Saskatchewan, Canada, instance of public research university, instance of open-access publisher
2. University of Saskatchewan (wd:Q35692704): scientific article published on April 1958, instance of scholarly article
3. University of Saskatchewan (wd:Q42426739): scientific article published on April 1961, instance of scholarly article
4. University of Saskatchewan. (wd:Q42222782): scientific article published on April 1960, instance of scholarly article
5. University of Saskatchewan. (wd:Q42858325): scientific article published on April 1962, instance of scholarly article
6. University of Saskatchewan academics (wd:Q16959526)
7. The University of Saskatchewan (wd:Q94355145): instance of scholarly article
8. THE UNIVERSITY OF SASKATCHEWAN. (wd:Q40945864): scientific article published on June 1963, instance of scholarly article
9. University of Saskatchewan Bookstore (wd:Q99433282): bookstore in Saskatoon, SK, Canada, instance of bookstore
10. University of Saskatchewan Library, also known as University of Saskatchewan Libraries (wd:Q57941246): library system of the University of Saskatchewan, instance of academic library

[Assistant] The entity "University of Saskatchewan" is identified as wd:Q1514848. Next, I will search for properties related to the founding date or inception of this university entity. Common properties for founding date include "inception" (P571). I will check if this property is available for this entity.

[Tool]
Got 1 row and 3 columns:
| subject                                  | property             | object                              |
| ---------------------------------------- | -------------------- | ----------------------------------- |
| University of Saskatchewan (wd:Q1514848) | inception (wdt:P571) | 1907-01-01T00:00:00Z (xsd:dateTime) |

[Tool] Stopped generation process