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:Q479361 wdt:P112 ?founder .
  ?founder rdfs:label ?founderLabel
  FILTER ( LANG( ?founderLabel ) = "en" )
}

Got 3 rows and 2 columns:
| founder                      | founderLabel         |
| ---------------------------- | -------------------- |
| Fritz Mackensen (wd:Q448400) | Fritz Mackensen (en) |
| Hans am Ende (wd:Q561091)    | Hans am Ende (en)    |
| Otto Modersohn (wd:Q71425)   | Otto Modersohn (en)  |

The Worpswede artist colony was founded by Otto Modersohn, Fritz Mackensen, and Hans am Ende.

Who founded the Worpswede artist colony ?

[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 Worpswede artist colony ?

[Assistant] To answer the question "Who founded the Worpswede artist colony?" I will first search for the entity "Worpswede artist colony" in Wikidata to identify its corresponding entity ID. Then, I will look for properties related to its founder or founding persons.

[Tool]
Top 10 entity alternatives:
1. Worpswede artist colony, also known as Worpswede circle (wd:Q479361): Artist's colony founded in Worpswede in 1889, instance of art colony
2. art colony, also known as artist colony, artists' colony (wd:Q1558054)
3. Worpswede colony (wd:Q103974815): instance of encyclopedia article, encyclopedia article
4. Nagybánya art colony, also known as Nagybanya art colony, Nagybánya artists' colony, Nagybanya School, Nagybánya School (wd:Q591229): instance of art colony
5. Önningeby artists colony, also known as Önningebykolonin (wd:Q2687905): instance of art colony, Finnish artists colony
6. Ekensund Artists' Colony (wd:Q1669854): instance of art colony
7. Ein Hod artist colony, also known as Artists Village of Ein Hod, Ein Hod artists village (wd:Q124390656): instance of art colony
8. Nishimui Art Village, also known as Nishimui Art Colony, Nishimui Artists' Colony (wd:Q38277516): instance of art colony
9. New Rochelle artist colony (wd:Q19877130): instance of art colony
10. Kirkcudbright Artists' Colony (wd:Q20713695): instance of art society, artists' community

[Assistant] The entity "Worpswede artist colony" corresponds to wd:Q479361 in Wikidata. Next, I will look for properties related to the founder or founding persons of this artist colony. I will check the properties of this entity to find relevant information about its founder.

[Tool]
Got 109 rows and 3 columns, showing the first 10 rows below:
| subject                              | property                                                    | object                                                                                |
| ------------------------------------ | ----------------------------------------------------------- | ----------------------------------------------------------------------------------------- |
| Worpswede artist colony (wd:Q479361) | instance of (wdt:P31)                                       | art colony (wd:Q1558054)                                                                  |
| Worpswede artist colony (wd:Q479361) | country (wdt:P17)                                           | Germany (wd:Q183)                                                                         |
| Worpswede artist colony (wd:Q479361) | located in the administrative territorial entity (wdt:P131) | Worpswede (wd:Q269174)                                                                    |
| Worpswede artist colony (wd:Q479361) | coordinate location (wdt:P625)                              | POINT(8.932220 53.215600) (geo:wktLiteral)                                                |
| Worpswede artist colony (wd:Q479361) | Commons category (wdt:P373)                                 | Worpswede in art                                                                          |
| Worpswede artist colony (wd:Q479361) | image (wdt:P18)                                             | <http://commons.wikimedia.org/wiki/Special:FilePath/Heinrich%20Vogeler%20Sommerabend.jpg> |
| Worpswede artist colony (wd:Q479361) | inception (wdt:P571)                                        | 1989-01-01T00:00:00Z (xsd:dateTime)                                                       |
| Worpswede artist colony (wd:Q479361) | has part(s) (wdt:P527)                                      | Heinrich Vogeler (wd:Q213734)                                                             |
| Worpswede artist colony (wd:Q479361) | different from (wdt:P1889)                                  | Künstlerhäuser Worpswede (wd:Q1797150)                                                    |
| Worpswede artist colony (wd:Q479361) | founded by (wdt:P112)                                       | Otto Modersohn (wd:Q71425)                                                                |
| ...                                  | ...                                                         | ...                                                                                |

[Assistant] The property "founded by" (wdt:P112) is present for the entity Worpswede artist colony (wd:Q479361) and the value is Otto Modersohn (wd:Q71425). I will now retrieve the label of Otto Modersohn to confirm the founder's name.

[Tool]
Got 3 rows and 2 columns:
| founder                      | founderLabel         |
| ---------------------------- | -------------------- |
| Fritz Mackensen (wd:Q448400) | Fritz Mackensen (en) |
| Hans am Ende (wd:Q561091)    | Hans am Ende (en)    |
| Otto Modersohn (wd:Q71425)   | Otto Modersohn (en)  |

[Assistant] The Worpswede artist colony was founded by Otto Modersohn, Fritz Mackensen, and Hans am Ende. I will now provide this as 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 ?founder1 ?founder1Label ?founder2 ?founder2Label ?university ?universityLabel WHERE {
  VALUES ?founder1 {
    wd:Q15934341 wd:Q1739008 wd:Q18653685 wd:Q18659347 wd:Q20689208
  }
  VALUES ?founder2 {
    wd:Q15934341 wd:Q1739008 wd:Q18653685 wd:Q18659347 wd:Q20689208
  }
  FILTER ( ?founder1 != ?founder2 ) ?founder1 wdt:P69 ?university .
  ?founder2 wdt:P69 ?university .
  ?university rdfs:label ?universityLabel
  FILTER ( LANG( ?universityLabel ) = "en" ) OPTIONAL {
    ?founder1 rdfs:label ?founder1Label FILTER ( LANG( ?founder1Label ) = "en" )
  }
  OPTIONAL {
    ?founder2 rdfs:label ?founder2Label FILTER ( LANG( ?founder2Label ) = "en" )
  }
}
LIMIT 10

Got 10 rows and 6 columns:
| founder1                     | founder1Label      | founder2                     | founder2Label      | university                           | universityLabel             |
| ---------------------------- | ------------------ | ---------------------------- | ------------------ | ------------------------------------ | --------------------------- |
| Ma Huateng (wd:Q1739008)     | Ma Huateng (en)    | Zhang Zhidong (wd:Q15934341) | Zhang Zhidong (en) | Shenzhen University (wd:Q1945756)    | Shenzhen University (en)    |
| Daniel Xu (wd:Q18659347)     | Daniel Xu (en)     | Zhang Zhidong (wd:Q15934341) | Zhang Zhidong (en) | Shenzhen University (wd:Q1945756)    | Shenzhen University (en)    |
| Chen Yidan (wd:Q20689208)    | Chen Yidan (en)    | Zhang Zhidong (wd:Q15934341) | Zhang Zhidong (en) | Shenzhen University (wd:Q1945756)    | Shenzhen University (en)    |
| Zhang Zhidong (wd:Q15934341) | Zhang Zhidong (en) | Ma Huateng (wd:Q1739008)     | Ma Huateng (en)    | Shenzhen University (wd:Q1945756)    | Shenzhen University (en)    |
| Daniel Xu (wd:Q18659347)     | Daniel Xu (en)     | Ma Huateng (wd:Q1739008)     | Ma Huateng (en)    | Shenzhen University (wd:Q1945756)    | Shenzhen University (en)    |
| Chen Yidan (wd:Q20689208)    | Chen Yidan (en)    | Ma Huateng (wd:Q1739008)     | Ma Huateng (en)    | Shenzhen University (wd:Q1945756)    | Shenzhen University (en)    |
| Chen Yidan (wd:Q20689208)    | Chen Yidan (en)    | Ma Huateng (wd:Q1739008)     | Ma Huateng (en)    | Shenzhen Middle School (wd:Q7494378) | Shenzhen Middle School (en) |
| Zhang Zhidong (wd:Q15934341) | Zhang Zhidong (en) | Daniel Xu (wd:Q18659347)     | Daniel Xu (en)     | Shenzhen University (wd:Q1945756)    | Shenzhen University (en)    |
| Ma Huateng (wd:Q1739008)     | Ma Huateng (en)    | Daniel Xu (wd:Q18659347)     | Daniel Xu (en)     | Shenzhen University (wd:Q1945756)    | Shenzhen University (en)    |
| Chen Yidan (wd:Q20689208)    | Chen Yidan (en)    | Daniel Xu (wd:Q18659347)     | Daniel Xu (en)     | Shenzhen University (wd:Q1945756)    | Shenzhen University (en)    |

Yes, at least two of the founders of Tencent were educated at the same university. Zhang Zhidong, Ma Huateng, Daniel Xu, and Chen Yidan were all educated at Shenzhen University.

Were at least two of the founders of tencent educated at the same university?

[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] Were at least two of the founders of tencent educated at the same university?

[Assistant]
Step 1: Identify the entity "Tencent" in Wikidata to find its founders. Then, identify the founders and check their educational background to see if at least two of them were educated at the same university.

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

[Tool]
Top 10 entity alternatives:
1. Tencent, also known as Tencent Holdings Ltd., Tencent Holdings Limited, Tengxun, Téngxùn, Teng xun (firma) (wd:Q860580): Chinese multinational investment holding conglomerate, instance of conglomerate, instance of business
2. Tencent QQ, also known as QICQ, OPEN-ICQ, Qq, QQ, KYOUKYOU (wd:Q857640): instance of customer relationship management, instance of social networking service, instant messaging software service, instance of online community, instance of mobile app, instance of website
3. Tencent Video, also known as TencentVideo, WeTV, v.qq.com (wd:Q28136991): instance of Internet audio-visual programming service provider, instance of video streaming service, video streaming service from China, instance of mobile app
4. Tencent Pictures (wd:Q22080095): instance of film production company, Chinese film production company
5. Tencent Games, also known as Tencent Game (wd:Q39057776): division of Tencent Holdings, instance of business
6. Tencent Music, also known as TME, Tencent Music Entertainment Group, Tencent Music Entertainment, tencentmusic.com (wd:Q56011958): Chinese music distribution company, instance of public company, instance of record label, instance of company
7. Tencent Weibo (wd:Q3983400): Chinese microblogging website launched by Tencent in April 2010, instance of website
8. WeGame, also known as TGP, Tencent WeGame, Tencent Gaming Platform, Tencent Games Platform, Tencent Game Platform (wd:Q10852223): instance of digital distribution platform, video gaming platform of Tencent, instance of software repository
9. QQ browser, also known as TBS, X5 blink, WeChatWeb.exe, wechatweb.exe, Tencent Traveler (wd:Q6862273): instance of web browser, web browser
10. Tencent Cloud, also known as cloud.tencent.com, tencentcloud.com, QCloud (wd:Q17500218): Cloud computing platform provided by Tencent, instance of dot-com company, instance of cloud computing, instance of subsidiary

[Assistant] The entity for Tencent is wd:Q860580. Next, I will find the founders of Tencent (wd:Q860580) and then check their educational background to see if at least two of them were educated at the same university.

[Tool]
Got 5 rows and 2 columns:
| founder                      | founderLabel       |
| ---------------------------- | ------------------ |
| Zhang Zhidong (wd:Q15934341) | Zhang Zhidong (en) |
| Ma Huateng (wd:Q1739008)     | Ma Huateng (en)    |
| Zeng Liqing (wd:Q18653685)   | Zeng Liqing (en)   |
| Daniel Xu (wd:Q18659347)     | Daniel Xu (en)     |
| Chen Yidan (wd:Q20689208)    | Chen Yidan (en)    |

[Assistant] I found five founders of Tencent: Zhang Zhidong, Ma Huateng, Zeng Liqing, Daniel Xu, and Chen Yidan. Now I will check the educational background (universities attended) of each of these founders to see if at least two of them were educated at the same university. I will start with Zhang Zhidong.

[Tool]
Got 2 rows and 2 columns:
| university                                        | universityLabel                           |
| ------------------------------------------------- | ----------------------------------------- |
| Shenzhen University (wd:Q1945756)                 | Shenzhen University (en)                  |
| South China University of Technology (wd:Q835366) | South China University of Technology (en) |

[Assistant] Zhang Zhidong was educated at Shenzhen University and South China University of Technology. Next, I will check the educational background of Ma Huateng.

[Tool]
Got 2 rows and 2 columns:
| university                           | universityLabel             |
| ------------------------------------ | --------------------------- |
| Shenzhen University (wd:Q1945756)    | Shenzhen University (en)    |
| Shenzhen Middle School (wd:Q7494378) | Shenzhen Middle School (en) |

[Assistant] Ma Huateng was educated at Shenzhen University and Shenzhen Middle School. Both Zhang Zhidong and Ma Huateng were educated at Shenzhen University. I will check the other founders to confirm if there are more overlaps. Next, I will check the educational background of Zeng Liqing.

[Tool]
Got 1 row and 2 columns:
| university                     | universityLabel        |
| ------------------------------ | ---------------------- |
| Xidian University (wd:Q649906) | Xidian University (en) |

[Assistant] Zeng Liqing was educated at Xidian University. Next, I will check the educational background of Daniel Xu.

[Tool]
Got 2 rows and 2 columns:
| university                        | universityLabel          |
| --------------------------------- | ------------------------ |
| Shenzhen University (wd:Q1945756) | Shenzhen University (en) |
| Nanjing University (wd:Q683580)   | Nanjing University (en)  |

[Assistant] Daniel Xu was educated at Shenzhen University and Nanjing University. So far, Zhang Zhidong, Ma Huateng, and Daniel Xu were educated at Shenzhen University. Finally, I will check the educational background of Chen Yidan.

[Tool]
Got 2 rows and 2 columns:
| university                           | universityLabel             |
| ------------------------------------ | --------------------------- |
| Shenzhen University (wd:Q1945756)    | Shenzhen University (en)    |
| Shenzhen Middle School (wd:Q7494378) | Shenzhen Middle School (en) |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ( COUNT( DISTINCT ?airport ) AS ?count ) WHERE {
  ?airport wdt:P31 wd:Q1248784 ; wdt:P131 ?location .
  ?location wdt:P131 * wd:Q1055 .
  ?airport wdt:P31 / wdt:P279 * wd:Q644371 .
}

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

According to the available data in Wikidata, there are no international airports explicitly listed as located within the city or state of Hamburg.

How many international airports are located within the city of Hamburg ?

[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 international airports are located within the city of Hamburg ?

[Assistant]
Step 1: Identify the entity for the city of Hamburg.
Step 2: Identify the property for airports located in a city.
Step 3: Identify the property or qualifier that indicates an airport is international.
Step 4: Construct a SPARQL query to count the number of international airports located within Hamburg.

I will start by searching for the entity Hamburg in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Hamburg, also known as Hamburg, Germany, Hamburg, Freie und Hansestadt, Free and Hanseatic City of Hamburg (wd:Q1055): instance of urban municipality in Germany, instance of Einheitsgemeinde of Germany, instance of federated state of Germany, city and state in the North of Germany, instance of urban district of Hamburg, instance of free imperial city, instance of Hanseatic city, instance of city-state, instance of metropolis, instance of port city, instance of big city
2. Hamburger SV, also known as HamburgerSV, Hamburg Sport Club, Hamburg SV, H.S.V., HSV (wd:Q51974): German sports club based in Hamburg, with its largest branch being its football department, instance of association football club, instance of sports club
3. Hamburg, also known as Hamburg, Minnesota, Hamburg, MN (wd:Q1005682): city in Carver County, Minnesota, United States, instance of city in the United States
4. Hamburg, also known as Hamburg, IA, Hamburg, Iowa (wd:Q998718): city in Fremont County, Iowa, United States, instance of city in the United States
5. Hamburg, also known as Hamburg, Arkansas, Hamburg, AR (wd:Q79657): city in Ashley County, Arkansas, United States, instance of city in the United States
6. Hamburg, also known as Hamburg, New Jersey, Hamburg, NJ (wd:Q1082931): borough in Sussex County, New Jersey, United States, instance of borough of New Jersey
7. Hamburg, also known as Hamburg, New York, Hamburg, NY (wd:Q742785): town in Erie County, New York, United States, instance of town in the United States, instance of town of New York
8. Hamburg, also known as Hamburg, IL (wd:Q2672139): village in Calhoun County, Illinois, United States, instance of village of Illinois
9. Hamburg, also known as Hamburg, PA, Hamburg, Pennsylvania (wd:Q1181264): borough in Berks County, Pennsylvania, United States, instance of borough of Pennsylvania
10. Hamburg, also known as Hamburgh (wd:Q939795)

[Assistant]
The entity for the city of Hamburg is wd:Q1055.

Next, I will search for the property that indicates an airport is located in a city or municipality. I will search for properties related to "location" or "located in" for airports.

[Tool]
Top 10 property alternatives:
1. location, also known as neighborhood, locale, neighbourhood, place, place held (wd:P276 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): location of the object, structure or event. In the case of an ad...ity use P706. Use P7153 for locations associated with the object
2. located in the administrative territorial entity, also known as is in the shire of, is in the voivodeship of, is in the state of, is in the territory of, is in the town of (wd:P131 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): the item is located on the territory of the following administra...if the item falls only partially into the administrative entity., subproperty of location, subproperty of part of
3. occupant, also known as location of, tenant, resident, renter, occupiers (wd:P466 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): person or organization occupying property, subproperty of used by
4. headquarters location, also known as headquarters locality, headquarters town, HQ, HQ location, location of headquarters (wd:P159 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): city or town where an organization's headquarters is or has been situated. Use P276 qualifier for specific building, subproperty of significant place
5. adjacent building, also known as adjoining building, next to, neighboring building, located next to (wd:P3032 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): building adjacent to the item, subproperty of connects with
6. operating area, also known as operating region, serves geographic region, serves geographic area, serves area, region served (wd:P2541 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): geographic area or jurisdiction an organisation or industry operates in, serves, or has responsibility for, subproperty of location
7. set in environment, also known as set in location, takes place in, setting location, playing in, narrative set in (wd:P8411 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): environment (geographic feature, building type, etc.) the narrative work is set in, subproperty of significant place, subproperty of depicts
8. narrative location, also known as set in location, setting of work, setting, setting location, setting place (wd:P840 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): the narrative of the work is set in this location, subproperty of significant place, subproperty of depicts
9. located in the present-day administrative territorial entity, also known as present-day administrative territorial entity, located in the current administrative territorial entity, located in the modern-day administrative territorial entity, located in the present-day administrative territorial entity, located in present day administrative territorial entity (wd:P3842 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): the item was located in the territory of this present-day administrative unit; however the two did not at any point coexist in time, subproperty of location, subproperty of part of
10. work location, also known as worked from, work place, working from, working at, worked at (wd:P937 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): location where persons or organisations were actively participating in employment, business or other work, subproperty of significant place, subproperty of location

[Assistant]
The property "located in the administrative territorial entity" (wdt:P131) is suitable to indicate that an airport is located within a city or municipality.

Next, I will search for the property or qualifier that indicates an airport is international. I will search for properties related to "international airport" or "airport type".

[Tool]
Top 10 property alternatives:
1. airline hub, also known as hub, hub airport (wd:P113 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): airport that serves as a hub for an airline
2. IATA airport code, also known as International Air Transport Association airport code, uio, IATA station code, IATA location identifier (wd:P238 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): three-letter identifier for designating airports, railway stations or cities (for airlines, see P229)
3. aerodrome reference point, also known as ARP, airport reference point, airfield reference point (wd:P2786 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): official coordinates for airport. Aeronautical publications use format with degrees, minutes and seconds concatenated, subproperty of coordinate location
4. image of design plans, also known as floor plan, plan view image, plan view, plan image, design plan (wd:P3311 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): image representing the plan of a building or place, subproperty of detail map
5. ICAO airport code, also known as ICAO aerodrome code, ICAO airfield code, International Civil Aviation Organization airport code (wd:P239 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): four-character alphanumeric identifier for designating airports (for airlines, see P230)
6. FAA airport code, also known as FAA LID (wd:P240 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): three-letter or four-letter alphanumeric code identifying United States airports
7. Ex-USSR internal airport code, also known as russian airport codes (wd:P5851 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): airport code similar to IATA codes but only for Russian and CEI airports
8. UTC date of spacecraft landing, also known as date of spacecraft landing, time of spacecraft landing, spacecraft landing time, landing time, landing date (wd:P620 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): date of spacecraft landing in UTC
9. location of landing, also known as landing site, landing location (wd:P1158 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): subproperty of significant place, location where the craft landed, subproperty of location
10. Tierstimmenarchiv ID, also known as TSA, TSA ID (wd:P5397 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): identifier for a species in the Animal Sound Archive Berlin

[Assistant]
There is no direct property explicitly named "international airport." I will try to find the entity for "international airport" to check if airports are classified as instances of international airports.

I will search for the entity "international airport" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. international airport (wd:Q644371)
2. International Airport (wd:Q55614529): 1985 film directed by Don Chaffey, instance of film
3. Domodedovo International Airport, also known as Moscow Domodedovo Airport, Moscow-Domodedovo Airport, Moscow International Airport, UUDD, DME (wd:Q186614): international airport serving Moscow, Russia, instance of commercial traffic aerodrome, instance of international airport, instance of federal aeroport
4. John F. Kennedy International Airport, also known as JFK International Airport, Idlewild International Airport, Kennedy International, Kennedy International Airport, KJFK (wd:Q8685): international airport in New York, United States, instance of commercial traffic aerodrome, instance of international airport
5. Sheremetyevo International Airport, also known as Sheremetyevo Airport, SVO, UUEE, Moscow Airport, Moscow International Airport (wd:Q192733): international airport serving Moscow, Russia, instance of commercial traffic aerodrome, instance of international airport, instance of federal aeroport, instance of airport
6. Istanbul-Atatürk, also known as LTBA, Istanbul Atatürk Airport, Istanbul Ataturk Airport, İstanbul Atatürk, Atatürk International Airport (wd:Q86473): international airport in Istanbul, Turkey, instance of commercial traffic aerodrome, instance of international airport
7. Dubai International Airport, also known as DXB, OMDB (wd:Q193439): international airport in Dubai, United Arab Emirates, instance of commercial traffic aerodrome, instance of international airport
8. Singapore Changi Airport, also known as Changi Airport, WSSS, SIN, Changi International Airport, Changi Airport Singapore (wd:Q32159): instance of commercial traffic aerodrome, international airport in Singapore, instance of international airport, instance of airport
9. Tokyo International Airport, also known as Haneda Airport, RJTT, HND (wd:Q204853): international airport serving Tokyo, Japan, instance of commercial traffic aerodrome, instance of international airport, instance of airport
10. Narita International Airport, also known as Narita Airport, 成田, Tokyo Narita Airport, RJAA, NRT (wd:Q36454): instance of commercial traffic aerodrome, instance of international airport, airport serving Tokyo, Japan, instance of airport

[Assistant]
The entity for "international airport" is wd:Q644371.

Now I will construct a SPARQL query to count the number of airports that are instances of international airports (wd:Q644371) and are located in the administrative territorial entity Hamburg (wd:Q1055).

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

[Assistant]
The query returned a count of 0, which means no airports are directly classified as instances of "international airport" located in Hamburg.

I will try a broader approach by checking airports (wd:Q1248784) located in Hamburg and then filter those that have an IATA airport code (wdt:P238), which usually indicates commercial airports including international ones. This might give a better indication of international airports in Hamburg.

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

[Assistant]
The query returned a count of 0, indicating no airports with an IATA code are directly located in Hamburg according to the data.

I will now check for airports that are located in the state or city Hamburg (wd:Q1055) or its administrative parts, as airports might be located in districts or boroughs of Hamburg rather than directly in the city entity. I will try to include airports located in any administrative territorial entity within Hamburg.

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

[Assistant]
The query still returns 0, indicating no airports with IATA codes are located within Hamburg or its administrative parts according to the data.

I will now try to find airports that have the label "Hamburg" in their name, which might help identify the main international airport serving Hamburg. Then I can check its location and classification.

[Tool]
Got 3 rows and 2 columns:
| airport                              | airportLabel               |
| ------------------------------------ | -------------------------- |
| Novo Hamburgo Airport (wd:Q12694211) | Novo Hamburgo Airport (en) |
| Hamburg Inc Airport (wd:Q34959585)   | Hamburg Inc Airport (en)   |
| Hamburg Aerodrome (wd:Q3914758)      | Hamburg Aerodrome (en)     |

[Assistant]
There are three airports with "Hamburg" in their English label, but none explicitly named "Hamburg International Airport."

I will check the location of these airports to see if any are located in the city of Hamburg (wd:Q1055).

[Tool]
Got 4 rows and 4 columns:
| airport                              | airportLabel               | location                      | locationLabel          |
| ------------------------------------ | -------------------------- | ----------------------------- | ---------------------- |
| Hamburg Inc Airport (wd:Q34959585)   | Hamburg Inc Airport (en)   | Erie County (wd:Q114843)      | Erie County (en)       |
| Hamburg Aerodrome (wd:Q3914758)      | Hamburg Aerodrome (en)     | Alberta (wd:Q1951)            | Alberta (en)           |
| Novo Hamburgo Airport (wd:Q12694211) | Novo Hamburgo Airport (en) | Novo Hamburgo (wd:Q321331)    | Novo Hamburgo (en)     |
| Novo Hamburgo Airport (wd:Q12694211) | Novo Hamburgo Airport (en) | Rio Grande do Sul (wd:Q40030) | Rio Grande do Sul (en) |

[Assistant]
None of the airports with "Hamburg" in their name are located in the city of Hamburg (wd:Q1055). They are located in other places named Hamburg or similar.

I will now directly search for airports located in Hamburg (wd:Q1055) without filtering by name or type to see if any airports are listed there.

[Tool] Got no rows and 2 columns

[Assistant]
There are no airports listed as located directly in the city of Hamburg (wd:Q1055) in the data.

I will now check for airports located in the state or federal state of Hamburg, as Hamburg is also a city-state. I will check for airports located in any administrative territorial entity that is Hamburg or part of Hamburg (wd:Q1055).

[Tool] Got no rows and 2 columns

[Assistant]
There are no airports listed as located in Hamburg or its administrative parts in the data.

Given the data limitations, it appears that the knowledge graph does not have explicit entries for international airports located within the city or state of Hamburg.

I will now provide the answer based on this finding.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?discoveryDate WHERE {
  wd:Q15787 wdt:P575 ?discoveryDate .
}

HIV was first identified in the year 1983 according to 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 HIV first identified?

[Assistant]
To answer the question "When was HIV first identified?", I need to find the entity for HIV in Wikidata and then look for properties related to the date or year it was first identified or discovered. This could be represented by properties like "discovery date," "inception," or "date of discovery."

Step 1: Search for the entity "HIV" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. HIV, also known as human immunodeficiency virus, AIDS virus (wd:Q15787): instance of organisms known by a particular common name, human retrovirus, cause of AIDS
2. Hiv (wd:Q5959052): instance of village, village in Iran
3. .hiv (wd:Q20573587): instance of top-level domain, top level domain
4. human immunodeficiency virus infectious disease, also known as HIV-positive, HIV infections, HIV infection, HIV+ (wd:Q18556697)
5. HIV/AIDS in Ethiopia, also known as HIV (wd:Q65409016)
6. HIV. (wd:Q53613054): instance of scholarly article, scientific article
7. HIV. (wd:Q45982420): scientific article published in February 2001, instance of scholarly article
8. HIV (wd:Q36928794): scientific article published on December 2006, instance of scholarly article, instance of case report
9. Hiv, also known as Haploinviable, unnamed (wd:Q29729262): genetic element in the species Drosophila melanogaster, instance of gene
10. [HIV] (wd:Q54246952): instance of scholarly article, scientific article

[Assistant] The entity "HIV" corresponding to the human immunodeficiency virus is wd:Q15787. Next, I will look for properties related to the date or year when HIV was first identified or discovered for this entity. I will search for properties related to "discovery date" or "inception" for this entity.

[Tool]
Top 10 property alternatives:
1. time of discovery or invention, also known as invented in, time of invention, time of discovery, invention time, invented on (wd:P575 as wdt/p): date or point in time when the item was discovered or invented
2. 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
3. ScienceDirect topic ID, also known as Science Direct topic ID, Science Direct topic identifier, ScienceDirect topic identifier (wd:P10376 as wdt/p): identifier for a topic on the research publication website ScienceDirect
4. Library of Congress authority ID, also known as LCSH Authority File ID, Library of Congress Control Number (Subject Authority), LCSH ID, LCSH LCCN, LC Subject Heading ID (wd:P244 as wdt/p/wdtn): Library of Congress name authority (persons, families, corporate...lowed by 8-10 digits (see regex). For manifestations, use P1144]
5. GND ID, also known as GND identifier, Universal Authority File, Integrated Authority File, IndividualisedGnd, GND-IDN (wd:P227 as wdt/p/wdtn): identifier from an international authority file of names, subjects, and organizations
6. FAST ID, also known as Faceted Application of Subject Terminology ID, FAST subject heading, fst, worldcat.org/fast (wd:P2163 as wdt/p/wdtn): authority control identifier in WorldCat's “FAST Linked Data” authority file
7. 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)
8. has part(s), also known as has ingredient, has ingredients, had part, has member, has part (wd:P527 as wdt/p): part of this subject; inverse property of "part of" (P361). See also "has parts of the class" (P2670).
9. has effect, also known as results in, result, outcome, led to, leads to (wd:P1542 as wdt/p): subproperty of followed by, effect of this item
10. NL CR AUT ID, also known as Czech National Authority Database, NLCR AUT ID (wd:P691 as wdt/p): identifier in the Czech National Authority Database of National Library of the Czech Republic (NL CR)

No other items found

[Assistant] The property "time of discovery or invention" (wdt:P575) seems to be the most relevant to find when HIV was first identified. I will now query the discovery or invention time for the entity HIV (wd:Q15787).

[Tool]
Got 1 row and 1 column:
| discoveryDate                       |
| ----------------------------------- |
| 1983-01-01T00:00:00Z (xsd:dateTime) |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?developer ?developerLabel WHERE {
  wd:Q533822 wdt:P178 ?developer .
  OPTIONAL {
    ?developer rdfs:label ?developerLabel FILTER ( LANG( ?developerLabel ) = "en" )
  }
}

According to Wikidata, Christiane Fellbaum is listed as a developer of WordNet. However, the original developer of WordNet is George A. Miller, who is widely recognized as the creator of WordNet.

[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 developed WordNet?

[Assistant] To answer the question "Who developed WordNet?", I will first search for the entity "WordNet" in Wikidata to identify its identifier. Then, I will look for properties related to the developer or creator of WordNet.

[Tool]
Top 10 entity alternatives:
1. WordNet, also known as Princeton WordNet (wd:Q533822): Princeton University's computational lexicon of English, a seman...nodes connected by hypernymy, hyponymy, holonymy, meronymy, etc., instance of lexical database
2. word net, also known as wordnet (wd:Q2594143)
3. WordNet (wd:Q125949755): instance of edited volume
4. WordNet (wd:Q98000414): instance of scholarly article, chapter in book from 2010
5. WordNet: An Electronic Lexical Database, also known as WordNet.  An  Electronic Lexical Database, WordNet (wd:Q35839029): book edition published in 1998 with Fellbaum as editor, instance of version, edition or translation
6. Arabic WordNet, also known as AWN (wd:Q28136431): instance of word net
7. Estonian Wordnet (wd:Q31277898): instance of word net
8. Malayalam WordNet (wd:Q25112033): instance of dictionary of the Malayalam language, WordNet for Malayalam language by CUSAT, instance of word net
9. Chinese WordNet, also known as CWN (wd:Q65924663): Huang et al. wordnet in Chinese, instance of word net
10. GF WordNet (wd:Q112258758): a lexical resource which combines WordNet resources from many languages and makes them compatible within GF, instance of lexicographic database, instance of free software

[Assistant] The entity "WordNet" is identified as wd:Q533822 in Wikidata, which is Princeton University's computational lexicon of English and a lexical database. Next, I will look for the developer or creator of WordNet using properties related to creator or developer for this entity.

[Tool]
Got 1 row and 2 columns:
| creator                           | creatorLabel             |
| --------------------------------- | ------------------------ |
| Christiane Fellbaum (wd:Q4558744) | Christiane Fellbaum (en) |

[Tool]
Got 1 row and 2 columns:
| developer                         | developerLabel           |
| --------------------------------- | ------------------------ |
| Christiane Fellbaum (wd:Q4558744) | Christiane Fellbaum (en) |

[Tool] Got no rows and 2 columns

[Tool] Got no rows and 2 columns

[Tool]
Got 1 row and 2 columns:
| developer                         | developerLabel           |
| --------------------------------- | ------------------------ |
| Christiane Fellbaum (wd:Q4558744) | Christiane Fellbaum (en) |

[Assistant] The Wikidata entity for WordNet (wd:Q533822) lists Christiane Fellbaum as a developer (wdt:P178). However, Christiane Fellbaum is known as one of the key contributors but not the sole developer. The original developer of WordNet is George A. Miller. I will check if George A. Miller is also linked to WordNet as a developer or creator.

[Tool]
Got 1 row and 2 columns:
| person                            | personLabel              |
| --------------------------------- | ------------------------ |
| Christiane Fellbaum (wd:Q4558744) | Christiane Fellbaum (en) |

[Tool]
Top 10 entity alternatives:
1. George Armitage Miller, also known as George Miller, George A. Miller (wd:Q670658): American psychologist (1920–2012), university teacher, instance of human, psychologist
2. George Arthur Miller (wd:Q2495778): British polo player (1867–1935), instance of human, polo player
3. George Abram Miller, also known as G. A. Miller (wd:Q1506815): American mathematician (1863–1951), historian of mathematics, university teacher, instance of human, mathematician
4. Alf Miller, also known as Alfred George Abraham Miller (wd:Q23461809): association football player (1917-1999), association football player, instance of human
5. George Albert Miller (wd:Q94487076): instance of human
6. George A. Miller (1920-2012). (wd:Q30353436): instance of scholarly article, scientific article
7. George Alfred Miller (wd:Q118153037): graduated from University of Washington with a Ph. D. in Sociology, instance of human
8. George Miller Armstrong, also known as G. M. Armstrong, G.M.Armstr. (wd:Q21505333): American botanist and plant pathologist (1893-1986), botanical collector, instance of human, phytopathologist, botanist
9. George Miller Andrews, also known as George Andrews (wd:Q75636713): Peerage person ID=262014, instance of human
10. George Augustus Miller Jr. (wd:Q62592140): 1894-1983 , armorer, instance of human, inventor, armourer, engineer

[Tool]
Got 1 row and 2 columns:
| developer                         | developerLabel           |
| --------------------------------- | ------------------------ |
| Christiane Fellbaum (wd:Q4558744) | Christiane Fellbaum (en) |

[Tool] Got no rows and 2 columns

[Tool] Got no rows and 2 columns

[Assistant]
The Wikidata entity for WordNet (wd:Q533822) only lists Christiane Fellbaum as a developer (wdt:P178). There is no direct property for the original creator or developer George A. Miller linked to WordNet in Wikidata. However, it is well-known that George A. Miller developed WordNet.

I will provide the answer based on the information from Wikidata and general knowledge that George A. Miller developed WordNet, and Christiane Fellbaum is also a notable contributor.

[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 ?highestPoint ?highestPointLabel WHERE {
  wd:Q20 wdt:P610 ?highestPoint .
  OPTIONAL {
    ?highestPoint rdfs:label ?highestPointLabel FILTER ( LANG( ?highestPointLabel ) = "en" )
  }
}

Got 1 row and 2 columns:
| highestPoint              | highestPointLabel |
| ------------------------- | ----------------- |
| Galdhøpiggen (wd:Q203942) | Galdhøpiggen (en) |

The highest point of Norway is Galdhøpiggen.

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

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

No general notes for all knowledge graphs available

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

You should follow a step-by-step approach to generate the SPARQL query:
1. Determine possible entities and properties implied by the user question.
2. Search for the entities and properties in the knowledge graphs. Where applicable, constrain the searches with already identified entities and properties.
3. Gradually build up the SPARQL query using the identified entities and properties. Start with simple queries and add more complexity as needed. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
4. Use the answer or cancel function to finalize your answer and stop the generation process.

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

[User] What is the highest point of Norway?

[Assistant] To answer the question "What is the highest point of Norway?", I need to find the geographical entity that is the highest point in Norway. This is typically a mountain or peak. I will start by searching for the entity representing Norway in Wikidata and then look for the property that indicates the highest point or highest mountain in Norway.

[Tool]
Top 10 entity alternatives:
1. Norway, also known as Kingdom of Norway (wd:Q20): instance of sovereign state, country in Northern Europe, instance of country
2. Norway, also known as Norway, Iowa, Norway, IA (wd:Q1913264): instance of city in the United States, city in Iowa, United States
3. Norway, also known as Norway, MI, Norway, Michigan (wd:Q2480177): city in Dickinson County, Michigan, United States, instance of city in the United States
4. Norway, also known as Norway, South Carolina, Norway, SC (wd:Q3243583): instance of town in the United States, town in South Carolina
5. Kingdom of Norway, also known as Norway, Norwegian Empire (wd:Q2196956): former Kingdom from 872 to 1397, instance of historical country
6. Norway, also known as Norway, Maine, Norway, ME (wd:Q2734330): instance of town in the United States, town in Maine, USA
7. Norway, also known as Norway, IN, Norway, Indiana (wd:Q2118814): instance of census-designated place in the United States, census-designated place in Indiana
8. Kingdom of Norway, also known as Norwegian Kingdom, Norway (wd:Q2597352): short-lived monarchy in Northern Europe (1814), instance of historical country
9. Norway, also known as Norway, New York, Norway, NY (wd:Q944765): instance of town in the United States, instance of town of New York, town in New York, USA
10. Norway, also known as Norway, WI (wd:Q3046928): instance of civil town of Wisconsin, town in Wisconsin, USA

[Assistant] The entity for Norway as a country is wd:Q20. Next, I will look for the property that indicates the highest point or highest mountain in Norway. I will search for properties related to "highest point" or "highest mountain" for the entity Norway (wd:Q20).

[Tool]
Top 10 property alternatives:
1. highest point, also known as zenith, tallest point, summit, pinnacle, highest peak (wd:P610 as wdt/p): point with highest elevation in a region or path, subproperty of has part(s)
2. coordinate location, also known as gps co-ordinates, wgs 84, gps coordinates, gps location, latitude (wd:P625 as wdt/p): geocoordinates of the subject. For Earth, please note that only WGS84 coordinating system is supported at the moment
3. coordinates of northernmost point, also known as wikibase:geoLatitude ?max, northernmost point coordinates, northerly point, northerly, farthest north (wd:P1332 as wdt/p): northernmost point of a location. For an administrative entity this includes offshore islands
4. coordinates of westernmost point, also known as wikibase:geoLongitude ?W, westernmost point coordinates, westerly point, extreme west, most western (wd:P1335 as wdt/p): westernmost point of a location
5. coordinates of easternmost point, also known as wikibase:geoLongitude ?E, most eastern, farthest east, easternmost point coordinates, easterly (wd:P1334 as wdt/p): easternmost point of a location
6. lowest point, also known as deepest point, extreme point deepest, extreme point lowest (wd:P1589 as wdt/p): point with lowest elevation in a region or path
7. coordinates of southernmost point, also known as coordinate of southernmost point, extreme point south, farthest south, most south, southerly (wd:P1333 as wdt/p): southernmost point of a place. For administrative entities this includes offshore islands
8. located in/on physical feature, also known as on geographical feature, on, loc (terr), located on the terrain feature, location (terrain feature) (wd:P706 as wdt/p): located on the specified (geo)physical feature. Should not be us...nly political/administrative (P131) or a mountain range (P4552)., subproperty of location, subproperty of part of
9. highest judicial authority, also known as court, has supreme court, highest court, judicial branch, judiciary (wd:P209 as wdt/p): supreme judicial body within a country, administrative division, or other organization
10. The Top Tens ID, also known as The Top Tens identifier, Top Ten Lists at TheTopTens ID, TTT ID (wd:P7157 as wdt/p): item's ID on The Top Tens website

No other items found

[Assistant] The property "highest point" (wdt:P610) is the relevant property for the highest point of a region. I will now query for the highest point of Norway (wd:Q20) using this property.

[Tool]
Got 1 row and 2 columns:
| highestPoint              | highestPointLabel |
| ------------------------- | ----------------- |
| Galdhøpiggen (wd:Q203942) | Galdhøpiggen (en) |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?inceptionDate WHERE {
  wd:Q41304 wdt:P571 ?inceptionDate .
}

The Weimar Republic started on November 9, 1918.

[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 did the Weimar Republic start?

[Assistant]
To answer the question about the start date of the Weimar Republic, I need to identify the entity representing the Weimar Republic in Wikidata and find the property that indicates its start date. Typically, the start date of a historical period or political entity is represented by the property "inception" (P571) in Wikidata.

I will first search for the entity "Weimar Republic" in Wikidata to get its identifier. Then I will query for its inception date.

[Tool]
Top 10 entity alternatives:
1. Weimar Republic, also known as Germany, Weimar Germany, Republic of Germany, German Republic, German Reich (wd:Q41304): Germany in the years 1918/1919–1933, instance of historical country, instance of historical period
2. flag of the Weimar Republic, also known as Weimar Republic flag, the Weimar Republic flag (wd:Q4485721): instance of national flag, flag
3. Landtag (Weimar Republic) (wd:Q1453030)
4. historian of the Weimar Republic, also known as Weimar Republic historian, historian of Weimar Republic (wd:Q104730830)
5. history of the Weimar Republic, also known as Weimar Republic history, the Weimar Republic history (wd:Q104731094)
6. hyperinflation in the Weimar Republic (wd:Q695052): occurrence of hyperinflation in early 20th century Germany, instance of hyperinflation, instance of occurrence
7. states of the Weimar Republic (wd:Q3712151)
8. Reichsadler of the Weimar Republic, also known as coat of arms of the Weimar Republic (wd:Q25401250): instance of coat of arms, instance of Reichsadler
9. Landtag of Bavaria (Weimar Republic) (wd:Q812353): Parliament of the Free State of Bavaria during the period of the Weimar Republic, instance of Landtag (Weimar Republic)
10. аrt of the Weimar Republic, also known as Weimar аrt (wd:Q5551325)

[Assistant] The Weimar Republic is represented by the entity wd:Q41304 in Wikidata. Now I will query for the inception (start) date of this entity.

[Tool]
Got 1 row and 1 column:
| inceptionDate                       |
| ----------------------------------- |
| 1918-11-09T00:00:00Z (xsd:dateTime) |

[Tool] Stopped generation process