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 ?country ?countryLabel WHERE {
  wd:Q21102 wdt:P495 ?country .
  OPTIONAL {
    ?country rdfs:label ?countryLabel FILTER ( LANG( ?countryLabel ) = "en" )
  }
}

Got 1 row and 2 columns:
| country                  | countryLabel        |
| ------------------------ | ------------------- |
| United Kingdom (wd:Q145) | United Kingdom (en) |

The beagles are originally from the United Kingdom.

From which country are the beagles originally from?

[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] From which country are the beagles originally from?

[Assistant] Step 1: Identify the entity "beagle" in Wikidata to understand how it is represented and to find relevant properties related to its origin or country of origin. I will search for the entity "beagle" first.

[Tool]
Top 10 entity alternatives:
1. beagle (wd:Q21102)
2. HMS Beagle, also known as Beagle (wd:Q35926): 1820 Cherokee-class brig-sloop, instance of brig-sloop, instance of shipwreck
3. Ilyushin Il-28, also known as Ilyushin Il-28, Beagle aircraft, Ilyushin Il-28 Beagle, Mascot, Type 27 (wd:Q652911)
4. Beagle (wd:Q791633): instance of desktop search, search system for Unix
5. Beagle (wd:Q812905): instance of impact crater, impact crater
6. Beagle (wd:Q55605074): unincorporated community in Kansas, United States, instance of unincorporated area
7. Beagle, also known as Beagle, OR, Beagle, Oregon (wd:Q28153576): instance of unincorporated community in the United States, human settlement in United States of America
8. Beagle (wd:Q4876083): instance of station located on surface, Pittsburgh Light Rail station, instance of tram stop
9. Beagle, also known as Beagle, KY (wd:Q55284723): instance of unincorporated community in the United States, unincorporated community in Kentucky, United States
10. Beagle, also known as IMO 9816359 (wd:Q83649291): tugboat built in 2018, instance of ship

[Assistant] The entity "beagle" is identified as wd:Q21102 in Wikidata. Next, I will look for properties related to the country of origin or origin of this entity. Common properties to check for origin include "country of origin" (P495) or similar. I will search for properties of the beagle entity related to origin or country.

[Tool]
Top 10 property alternatives:
1. country of origin, also known as comes from, place of origin, origin country, originates from, country (wd:P495 as wdt/p): country of origin of this item (creative work, food, phrase, product, etc.), subproperty of significant place, subproperty of country
2. native label, also known as official name, original title, official title, original label, original language label (wd:P1705 as wdt/p): label for the items in their official language  or their original language, subproperty of name
3. instance of, also known as example of, has type, is a, is an, is of type (wd:P31 as wdt/p): type to which this subject corresponds/belongs. Different from P...tains, and the volcano is a type (instance) of volcanic landform
4. subclass of, also known as subcategory of, subgenre of, sort of, subtype of, type of (wd:P279 as wdt/p): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
5. image, also known as img, infobox image, photo, photograph, picture (wd:P18 as wdt/p): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)
6. 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)
7. exact match, also known as direct match, the same as, skos:exactMatch, same meaning, sameas (wd:P2888 as wdt/p): URL used to link two items or properties, indicating a high degree of confidence that the concepts can be used interchangeably
8. height, also known as depth over terrain, heighth, size (wd:P2048 as wdt/p): vertical length of an entity, subproperty of length
9. Microsoft Academic ID, also known as Microsoft Academic Source ID, MS Academic ID, Microsoft ID, Microsoft Academic Work ID, Microsoft Academic topic ID (wd:P6366 as wdt/p/wdtn): identifier for an object or topic in the Microsoft Academic Graph (until 31 December 2021)
10. mass, also known as displacement, weight, molecular mass, molecular weight (wd:P2067 as wdt/p): mass (in colloquial usage also known as weight) of the item

No other items found

[Assistant] The property "country of origin" (wdt:P495) is relevant for the beagle entity. I will now query the country of origin for the beagle (wd:Q21102) using this property.

[Tool]
Got 1 row and 2 columns:
| country                  | countryLabel        |
| ------------------------ | ------------------- |
| United Kingdom (wd:Q145) | United Kingdom (en) |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ( COUNT( DISTINCT ?child ) AS ?numberOfChildren ) WHERE {
  wd:Q312556 wdt:P40 ?child .
  ?child wdt:P25 wd:Q10322797 .
}

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

Jeff Bezos has 3 children (including adopted ones) with his ex-wife MacKenzie Scott.

How many children (including apopted ones) does Jeff Bezos have with his ex-wife?

[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 children (including apopted ones) does Jeff Bezos have with his ex-wife?

[Assistant] To answer this question, I need to identify the entity for Jeff Bezos and his ex-wife, then find the children they have together, including adopted ones. I will start by searching for Jeff Bezos in Wikidata to get his entity identifier.

[Tool]
Top 10 entity alternatives:
1. Jeff Bezos, also known as Jeffrey Jorgensen, Jeffrey Preston Bezos, Jeffrey Preston Jorgensen, Jeff Jorgensen, Jeffrey Bezos (wd:Q312556): American business magnate (born 1964), commercial astronaut, computer scientist, instance of human, business oligarch, entrepreneur
2. Family of Jeff Bezos (wd:Q119241135): immediate family of Jeff Bezos, instance of family
3. house of Jeff Bezos (wd:Q97169580): house
4. You Are Jeff Bezos (wd:Q122910143): instance of video game, 2018 video game
5. Message from Jeff Bezos (wd:Q110053480): instance of Saturday Night Live sketch, sketch from Saturday Night Live
6. Jeff Bezos phone hacking incident (wd:Q87977766): instance of hacking, 2017-2020 incident
7. adoptive daughter of Jeff Bezos (wd:Q102433590): adopted daughter of Jeff Bezos, instance of human
8. Icons of Business: Jeff Bezos (wd:Q20772188): instance of literary work, book by Kateri M. Drexler
9. third son of Jeff Bezos (wd:Q102433555): instance of human, son of Jeff Bezos
10. second son of Jeff Bezos (wd:Q102433548): instance of human, son of Jeff Bezos

[Assistant] I found the entity for Jeff Bezos as wd:Q312556. Now I need to find his ex-wife. I know Jeff Bezos was married to MacKenzie Scott (formerly MacKenzie Bezos). I will search for MacKenzie Scott to confirm her entity in Wikidata.

[Tool]
Top 10 entity alternatives:
1. MacKenzie Scott, also known as MacKenzie Scott Tuttle, MacKenzie S. Tuttle, MacKenzie S. Bezos, MacKenzie Bezos (wd:Q10322797): American philanthropist and novelist, instance of human, philanthropist, businessperson, novelist
2. Torres, also known as Mackenzie Scott (wd:Q16234254): American singer-songwriter, instance of human, singer-songwriter, songwriter, composer, musician
3. Scott MacKenzie (wd:Q1334640): Scottish snooker player, instance of human, snooker player
4. Scott MacKenzie (wd:Q7436745): Hong Kong darts player, instance of human, darts player
5. Scott MacKenzie (wd:Q7436747): Scottish footballer and coach, association football player, instance of human
6. Scott A. MacKenzie, also known as Scott MacKenzie (wd:Q102330173): instance of human, Ph.D.
7. Scott M. MacKenzie, also known as Scott MacKenzie (wd:Q39729730): instance of human, researcher
8. Scott Mackenzie (wd:Q130825317): instance of human
9. Scott MacKenzie (wd:Q100878584): college basketball player (1988–1988) Fordham, basketball player, instance of human
10. Scott C Mackenzie, also known as Scott Mackenzie (wd:Q91415852): researcher (ORCID 0000-0001-5823-4334), instance of human, researcher

[Assistant] The entity for MacKenzie Scott is wd:Q10322797. Now I will find the children of Jeff Bezos (wd:Q312556) and MacKenzie Scott (wd:Q10322797), including adopted children. I will look for children of Jeff Bezos first and then check if they are also linked to MacKenzie Scott as a parent.

[Tool]
Got 3 rows and 2 columns:
| child                                    | childLabel                    |
| ---------------------------------------- | ----------------------------- |
| Preston Bezos (wd:Q102433534)            | Preston Bezos (en)            |
| second son of Jeff Bezos (wd:Q102433548) | second son of Jeff Bezos (en) |
| third son of Jeff Bezos (wd:Q102433555)  | third son of Jeff Bezos (en)  |

[Assistant] Jeff Bezos has three children listed: Preston Bezos, second son of Jeff Bezos, and third son of Jeff Bezos. Now I will check if these children are also linked to MacKenzie Scott as a parent to confirm they are children of both Jeff Bezos and his ex-wife.

[Tool]
Got 3 rows and 2 columns:
| child                                    | childLabel                    |
| ---------------------------------------- | ----------------------------- |
| Preston Bezos (wd:Q102433534)            | Preston Bezos (en)            |
| second son of Jeff Bezos (wd:Q102433548) | second son of Jeff Bezos (en) |
| third son of Jeff Bezos (wd:Q102433555)  | third son of Jeff Bezos (en)  |

[Assistant] All three children of Jeff Bezos are also linked to MacKenzie Scott as a parent. Now I need to check if there are any adopted children of Jeff Bezos and MacKenzie Scott. I will search for adopted children of Jeff Bezos.

[Tool] Got no rows and 2 columns

[Assistant] There is no explicit indication of adopted children for Jeff Bezos in the data. Since all three children are linked to both Jeff Bezos and MacKenzie Scott, and no additional adopted children are found, the total number of children (including adopted ones) Jeff Bezos has with his ex-wife is three. 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 ( COUNT( ?sibling ) AS ?numberOfSiblings ) WHERE {
  wd:Q185696 wdt:P3373 ?sibling .
}

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

The author of "Little Women," Louisa May Alcott, had 3 siblings.

How many siblings had the author of “Little Women” ?

[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 siblings had the author of “Little Women” ?

[Assistant]
Step 1: Identify the author of "Little Women." The author is Louisa May Alcott.

Step 2: Find the entity for Louisa May Alcott in Wikidata.

Step 3: Find the siblings of Louisa May Alcott.

Step 4: Count the number of siblings.

I will start by searching for the entity of Louisa May Alcott in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Louisa May Alcott, also known as Louisa M. Alcott, Louisa Alcott, Tribulation Periwinkle, Louisa Mary Alcott, A. M. Barnard (wd:Q185696): American novelist (1832–1888), instance of human, children's writer, domestic worker, suffragist, novelist, teacher, writer, nurse, poet
2. Alcott, Louisa May (wd:Q115998994): entry in Men of the Time, eleventh edition, instance of biographical article
3. Alcott, Louisa May (wd:Q115607107): article in The Biographical Dictionary of America, instance of biographical article
4. Alcott, Amos Bronson, also known as Louisa May Alcott (wd:Q106596647): article in The American Cyclopædia (1879), instance of encyclopedia article, instance of biographical article
5. Louisa May Alcott (wd:Q126509649): biography of the author Louisa May Alcott by Katharine Susan Anthony, instance of biography
6. Louisa May Alcott School (wd:Q6688541): historic elementary school located at Baltimore, Maryland, instance of school building, instance of school
7. Louisa May Alcott School (wd:Q132291766): instance of school building
8. Louise May Nieriker, also known as Louisa May Alcott Nieriker (wd:Q84706528): daughter of May Nieriker, adoptive child of Louise May Alcott (1879-1975), instance of human
9. Critical Insights: Louisa May Alcott (wd:Q126509765): book of criticism on author Louisa May Alcott, instance of literary criticism, instance of non-fiction work
10. Alcott, Louisa May (1832-1888), author (wd:Q103844852): instance of encyclopedia article, encyclopedia article

[Assistant] The entity for Louisa May Alcott is wd:Q185696. Next, I will find the siblings of Louisa May Alcott using the property for siblings (P3373). Then I will count the number of siblings.

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

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ( COUNT( ?article ) AS ?count ) WHERE {
  ?article wdt:P31 / wdt:P279 * wd:Q13442814 ; wdt:P921 wd:Q24901201 .
}

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

There are 26 scholarly articles that have ImageNet as a subject.

How many scholary articles have ImageNet as a subject?

[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 scholary articles have ImageNet as a subject?

[Assistant]
To answer this question, I need to find scholarly articles that have "ImageNet" as a subject in Wikidata. The steps are:

1. Identify the Wikidata entity for "ImageNet."
2. Identify the property used for "subject" in scholarly articles.
3. Query for scholarly articles that have "ImageNet" as the subject.
4. Count the number of such scholarly articles.

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

[Tool]
Top 10 entity alternatives:
1. ImageNet (wd:Q24901201): instance of image dataset, image dataset
2. ImageNets (wd:Q6002088): machine vision platform, instance of software
3. ImageNet ILSVRC-2012, also known as ImageNet-1K, ILSVRC2017 (wd:Q115034926): ImageNet-1K subset of ImageNet with 1,000 categories of images, instance of data set
4. Are we done with ImageNet? (wd:Q124744370): scientific article published on 12 June 2020, instance of scholarly article
5. ImageNet Large Scale Visual Recognition Challenge (wd:Q56594393): scientific article from International Journal of Computer Vision, instance of scholarly article
6. ImageNet Large Scale Visual Recognition Challenge (wd:Q47451393): scientific article published on 30 January 2015, instance of scholarly article
7. ImageNet: A large-scale hierarchical image database (wd:Q37043902): scientific article from CVPR 2009, instance of scholarly article
8. Linking ImageNet WordNet Synsets with Wikidata (wd:Q50347076): instance of scholarly article, scientific article from 2018
9. Visual and semantic similarity in ImageNet (wd:Q48975878): instance of scholarly article
10. ImageNet classification with deep convolutional neural networks (wd:Q59445836): scientific article publised in Communications of the ACM in 2017, instance of scholarly article

[Assistant]
The entity "ImageNet" is identified as wd:Q24901201, which is an instance of an image dataset. There are also several scholarly articles related to ImageNet, but for the subject, we should use the main ImageNet entity (wd:Q24901201).

Next, I will identify the property used for "subject" in scholarly articles. Commonly, the property "main subject" (wdt:P921) is used for this purpose. I will verify this by searching for the property "subject" in Wikidata.

[Tool]
Top 10 property alternatives:
1. academic major, also known as major, subject, studied, college major, academic subject (wd:P812 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): major someone studied at college/university
2. depicts, also known as reproduces, portrays, represents, quotes, preserves (wd:P180 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): entity visually depicted in an image, literarily described in a ...o an audiovisual or other medium; see also P921,  'main subject', subproperty of has part(s)
3. field of work, also known as scientific discipline, purpose, scientific area, responsible for, research on (wd:P101 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): specialization of a person or organization; see P106 for the occupation, subproperty of interested in
4. main subject, also known as main topic, primary subject, primary keyword, plot keyword, of (wd:P921 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): primary topic of a work, subproperty of depicts
5. statement is subject of, also known as subject of the statement, subject of the claim, subject of, statement is the subject of, related item of statement (wd:P805 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): (qualifying) item that describes the relation identified in this statement
6. described by source, also known as mentioned in, mentioned in news article, reviewed in, source of item, subject in (wd:P1343 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): work where this item is described, subproperty of attested in
7. Wikidata item of this property, also known as Wikidata property subject item, Wikidata item for this property, subject of this property, subject of property, subject item (wd:P1629 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): item corresponding to the concept represented by the property
8. facet of, also known as subject in, topic of, subtopic of, subitem of, section in (wd:P1269 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): topic of which this item is an aspect; item that offers a broader perspective on the same topic
9. subject named as, also known as established as, entity named as, established form, established heading, named as (wd:P1810 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): name by which a subject is recorded in a database, mentioned as a contributor of a work, or is referred to in a particular context, subproperty of name
10. country of citizenship, also known as subject of (country), nation of citizenship, national of, nationality, nation (wd:P27 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): the object is a country that recognizes the subject as its citizen, subproperty of country

[Assistant]
The property "main subject" (wdt:P921) is the appropriate property to use for the subject of a scholarly article.

Now, I will query for scholarly articles that have "ImageNet" (wd:Q24901201) as the main subject (wdt:P921) and count the number of such articles.

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

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?placeOfDeath ?placeOfDeathLabel WHERE {
  wd:Q33391 wdt:P20 ?placeOfDeath .
  OPTIONAL {
    ?placeOfDeath rdfs:label ?placeOfDeathLabel FILTER ( LANG( ?placeOfDeathLabel ) = "en" )
  }
}

Got 1 row and 2 columns:
| placeOfDeath          | placeOfDeathLabel |
| --------------------- | ----------------- |
| Coyoacán (wd:Q661315) | Coyoacán (en)     |

Leon Trotsky died in Coyoacán.

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

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

No general notes for all knowledge graphs available

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

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

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

[User] Where did Trotzky die?

[Assistant]
To answer the question "Where did Trotzky die?", I need to identify the entity "Trotzky" in the knowledge graph and then find the property related to the place of death for that entity.

Step 1: Search for the entity "Trotzky" in Wikidata to find the correct entity identifier.

[Tool]
Top 10 entity alternatives:
1. Leon Trotsky, also known as Lev Davidovich Trotsky, Lev Trotskiy, Lev Davidovich Trotskiy, Trotsky, Trotskiy (wd:Q33391): Russian Marxist revolutionary (1879–1940), military personnel, instance of human, autobiographer, revolutionary, philosopher, politician, journalist, historian, diplomat, writer
2. Trotsky (wd:Q42209585): Russian biographical television mini-series, instance of miniseries
3. The Trotsky, also known as Trotsky (wd:Q3523100): 2009 film by Jacob Tierney, instance of film
4. Trotsky (wd:Q2082399): 1993 Russian biographical film by Leonid Maryagin, instance of film
5. Trotsky (wd:Q3540802): three-part biography of Leon Trotsky by Isaac Deutscher, instance of book series
6. Trotsky (wd:Q16909373): instance of family name, family name (Троцкий)
7. Trotsky (wd:Q3540804): instance of film, 1986 film
8. Trotsky (wd:Q106973412): The 70's Biweekly (MCY-000967), instance of aspect of history
9. Trotskyism, also known as Trotskism, Trotskyite, Trotskyist (wd:Q181133)
10. Natalia Sedova, also known as Natalia Trotsky, Natalia Ivanovna Sedova (wd:Q262376): Russian revolutionary and second wife of Leon Trotsky (1882-1962), instance of human, politician

[Assistant]
The entity "Leon Trotsky" (wd:Q33391) is the relevant one for the question about where Trotzky died.

Step 2: Find the property related to the place of death for Leon Trotsky. I will look for the property that indicates place of death for this entity.

[Tool]
Top 10 property alternatives:
1. place of death, also known as POD, location of death, killed in, died in, died (wd:P20 as wdt/p): most specific known (e.g. city instead of country, or hospital i... city) death location of a person, animal or fictional character, subproperty of location
2. place of burial, also known as interment location, interment, interment place, interred at, location of burial (wd:P119 as wdt/p): location of grave, resting place, place of ash-scattering, etc. ...veral places: e.g., re-burials, parts of body buried separately., subproperty of location
3. date of death, also known as DOD, deathdate, died, died on, year of death (wd:P570 as wdt/p): subproperty of dissolved, abolished or demolished date, date on which the subject died, subproperty of end time
4. manner of death, also known as circumstance of death, death manner, death type, nature of death, type of death (wd:P1196 as wdt/p): general circumstances of a person's death; e.g. natural causes, ...ysiological mechanism, e.g. heart attack, trauma, pneumonia etc.
5. cause of death, also known as death cause, murder method, method of murder, die of, die from (wd:P509 as wdt/p): underlying or immediate cause of death.  Underlying cause (e.g. ...adest category, e.g. natural causes, accident, homicide, suicide
6. 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
7. place of birth, also known as location born, POB, location of birth, born in, born at (wd:P19 as wdt/p): most specific known birth location of a person, animal or fictional character, subproperty of location
8. image of grave, also known as image of cenotaph, image of grave, image of gravestone, grave stone image, image of shrine (wd:P1442 as wdt/p): picture of a person or animal's grave, gravestone or tomb, subproperty of image
9. Find a Grave memorial ID, also known as Find A Grave memorial ID, memorial IDーFind A Grave, GRid, Find A Grave person ID, Findagrave ID (wd:P535 as wdt/p): identifier of an individual's burial place in the Find a Grave database
10. killed by, also known as slaughtered by, mortally wounded by, murdered by, murderer, shot dead by (wd:P157 as wdt/p): person or organization who killed the subject, subproperty of significant person

No other items found

[Assistant]
The property "place of death" (wdt:P20) is the correct property to find the place where Leon Trotsky died.

Step 3: Construct a SPARQL query to get the place of death for Leon Trotsky (wd:Q33391) using the property wdt:P20. I will also try to get the label of the place for better readability.

[Tool]
Got 1 row and 2 columns:
| placeOfDeath          | placeOfDeathLabel |
| --------------------- | ----------------- |
| Coyoacán (wd:Q661315) | Coyoacán (en)     |

[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 ?population ?pointInTime WHERE {
  wd:Q42053 p:P1082 ?popStatement .
  ?popStatement ps:P1082 ?population .
  ?popStatement pq:P585 ?pointInTime
}
ORDER BY ?pointInTime

There is only one population data point for Isfahan with a time qualifier, which is for the year 2016 with a population of 1,961,260. There is no population data available for 2006 or any other year after 2006 to compare and determine if the population increased at any point after 2006. Therefore, based on available data, we cannot confirm if the population of Isfahan increased at any point after 2006.

has population of Isfahan increased at any point after 2006?

[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 population of Isfahan increased at any point after 2006?

[Assistant]
To answer the question "Has the population of Isfahan increased at any point after 2006?", I need to find population data for Isfahan for years after 2006 and check if there is any increase compared to previous data points.

Step 1: Identify the entity for Isfahan in Wikidata.
Step 2: Find population data for Isfahan with time qualifiers (years).
Step 3: Check if there is any increase in population after 2006.

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

[Tool]
Top 10 entity alternatives:
1. Isfahan, also known as Ispahān, Isfahān, Ispahân, Ispahan, Spahan (wd:Q42053): city in Isfahan Province, Iran, instance of city of Iran, instance of largest city, instance of global city, instance of metropolis, instance of big city
2. Isfahan (wd:Q21998562): instance of musical work/composition, 1967 jazz song by Duke Ellington
3. Isfahan (wd:Q103936201): instance of encyclopedia article, encyclopedia article
4. Isfahan Province, also known as Esfahan Province (wd:Q1367759): instance of province of Iran, province of Iran
5. Isfahan County (wd:Q1282082): county in Isfahan Province, Iran, instance of county of Iran
6. Isfahan International Airport, also known as OIFM, Shahid Beheshti Airport, Shahid Beheshti International Airport, Isfahan Airport, IFN (wd:Q1151889): instance of commercial traffic aerodrome, International airport in Isfahan, Iran, instance of international airport, instance of airport
7. Baharestan, also known as Baharestan, Isfahan (wd:Q430773): city in Isfahan Province, Iran, instance of city of Iran
8. Isfahan Metro (wd:Q5963929): rapid transit system in Isfahan, Iran, instance of rapid transit
9. Dowlatabad, also known as Shapurabad, Dowlatabad, Isfahan, Dowlatabad, Borkhar, Dowlatābād, Daulatābād (wd:Q2616779): city in Isfahan Province, Iran, instance of city of Iran
10. Isfahan Grand Bazaar, also known as Isfahan Bazaar, Grand Bazaar of Isfahan, Grand Bazaar, Isfahan, Bazaar of Isfahan (wd:Q2754230): bazaar in Isfahan, Iran, instance of bazaar

[Assistant] The entity for the city Isfahan in Iran is wd:Q42053. Next, I will look for population data for this entity with time qualifiers to find population values after 2006. I will check the population property (P1082) and see if it has time qualifiers for years.

[Tool]
Got 1 row and 3 columns:
| subject             | property               | object                |
| ------------------- | ---------------------- | --------------------- |
| Isfahan (wd:Q42053) | population (wdt:P1082) | 1961260 (xsd:decimal) |

[Assistant] There is a population value of 1,961,260 for Isfahan, but it is not clear for which year this value is. I need to find population statements with qualifiers to get population values with years after 2006. I will query for population statements with qualifiers for Isfahan.

[Tool]
Got 1 row and 2 columns:
| population            | pointInTime                         |
| --------------------- | ----------------------------------- |
| 1961260 (xsd:decimal) | 2016-01-01T00:00:00Z (xsd:dateTime) |

[Assistant] I found one population data point for Isfahan with a time qualifier: population 1,961,260 at 2016. There is no population data point explicitly for 2006 or other years after 2006 to compare with. I will check if there are any other population data points for Isfahan with different years to see if there is an increase after 2006.

[Tool]
Got 1 row and 2 columns:
| population            | pointInTime                         |
| --------------------- | ----------------------------------- |
| 1961260 (xsd:decimal) | 2016-01-01T00:00:00Z (xsd:dateTime) |

[Assistant]
There is only one population data point for Isfahan with a time qualifier, which is for the year 2016. There is no population data available for 2006 or any other year after 2006 to compare and determine if the population increased at any point after 2006.

Since there is no data to confirm an increase after 2006, I conclude that based on available data, we cannot confirm if the population of Isfahan increased at any point after 2006.

[Tool] Stopped generation process


SELECT ?team ?teamLabel ?start ?end WHERE {
  wd:Q25369 wdt:P54 ?team .
  OPTIONAL {
    wd:Q25369 p:P54 ?membership .
    ?membership ps:P54 ?team .
    OPTIONAL { ?membership pq:P580 ?start . }
    OPTIONAL { ?membership pq:P582 ?end . }
  }
  OPTIONAL { ?team rdfs:label ?teamLabel FILTER (LANG(?teamLabel) = "en") }
  FILTER(?team = wd:Q121783)
}
LIMIT 1

UNION

SELECT ?team ?teamLabel ?start ?end WHERE {
  wd:Q36159 wdt:P54 ?team .
  OPTIONAL {
    wd:Q36159 p:P54 ?membership .
    ?membership ps:P54 ?team .
    OPTIONAL { ?membership pq:P580 ?start . }
    OPTIONAL { ?membership pq:P582 ?end . }
  }
  OPTIONAL { ?team rdfs:label ?teamLabel FILTER (LANG(?teamLabel) = "en") }
  FILTER(?team = wd:Q121783)
}
LIMIT 1

Kobe Bryant left the Los Angeles Lakers in 2016, and LeBron James joined the Los Angeles Lakers in 2018. Therefore, Kobe Bryant did not leave the Lakers when LeBron James joined the team; he left before LeBron James joined.

Did Kobe Bryant leave the Lakers when LeBron James joined that team?

[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] Did Kobe Bryant leave the Lakers when LeBron James joined that team?

[Assistant]
To answer this question, I need to check the timeline of Kobe Bryant's departure from the Los Angeles Lakers and the timeline of LeBron James joining the Lakers. Specifically, I want to find out:

1. When did Kobe Bryant leave the Los Angeles Lakers?
2. When did LeBron James join the Los Angeles Lakers?

Then I can compare the dates to determine if Kobe Bryant left the Lakers when LeBron James joined the team. I will start by searching for the entities "Kobe Bryant" and "LeBron James" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Kobe Bryant, also known as Kobe, Kobe B. Bryant, KB24, Kobe Bean Bryant, Black Mamba (wd:Q25369): American basketball player (1978–2020), professional athlete, instance of human, basketball player, screenwriter
2. Kobe Bryant Day (wd:Q98969272): proposed Federal Holiday, instance of holiday
3. death of Kobe Bryant, also known as Kobe Bryant death, Kobe Bryant's death (wd:Q83737550): 26 January 2020 death of Kobe Bryant in helicopter crash, instance of death
4. Kobe Bryant Boulevard (wd:Q107299206): proposed street renaming in Los Angeles, instance of thoroughfare
5. NBA All-Star Game Kobe Bryant Most Valuable Player Award, also known as Kobe Bryant Most Valuable Player Award, NBA All-Star Game Kobe Bryant MVP Award, Kobe Bryant MVP Award (wd:Q31391): basketball award of the National Basketball Association (NBA), instance of most valuable player award
6. Kobe Bryant China Fund (wd:Q6424297): for education, sports, and culture programs for children from China and the United States, instance of fund
7. Statue of Kobe Bryant (wd:Q125101336): sculpture in Los Angeles, California, U.S
8. Kobe Bryant/Ice Cube (wd:Q125446155): instance of television series episode, episode of All That (S4 E19)
9. Kobe Bryant sexual assault case (wd:Q6424298): instance of legal case, legal case
10. Kobe Bryant in NBA Courtside (wd:Q3198120): instance of video game, 1998 video game

[Tool]
Top 10 entity alternatives:
1. LeBron James, also known as LeBron James Sr., King James, LeBron Raymone James, LeBron Raymone James Sr., The Chosen One (wd:Q36159): American basketball player (born 1984), instance of human, basketball player, television actor, screenwriter
2. lebron james (wd:Q1035043): instance of taxon, species of plant
3. LeBron James, also known as LeBron James Sr., LeBron Raymone James Sr., Space Jam Movie LeBron James, LeBron James (Space Jam), King James (wd:Q108870659): main protagonist in the 2021 film Space Jam: A New Legacy, instance of animated character, instance of fictional human, instance of film character, basketball player
4. Bronny James, also known as LeBron James Jr., LeBron Raymone James Jr. (wd:Q22302425): American basketball player (born 2004), instance of human, basketball player
5. LeBron James beard (wd:Q112632449)
6. James family, also known as LeBron James' family (wd:Q110539680): fictional family from Space Jam: A New Legacy, instance of fictional family
7. The James Residence, also known as LeBron James' Residence (wd:Q110540318): fictional location from 2021 film Space Jam: A New Legacy, instance of fictional house
8. Statue of LeBron James, also known as LeBron James statue (wd:Q113061045): statue in United States of America, instance of statue
9. LeBron James in 2022 (wd:Q112049359): instance of player season
10. LeBron James in 2021 (wd:Q112049352): instance of player season

[Assistant] I found the entities for Kobe Bryant (wd:Q25369) and LeBron James (wd:Q36159). Next, I will check the properties related to their team memberships or sports career timelines to find when Kobe Bryant left the Los Angeles Lakers and when LeBron James joined the Los Angeles Lakers. I will first look for Kobe Bryant's team memberships or sports career timeline.

[Tool]
Top 10 property alternatives:
1. member of sports team, also known as plays for, played for, sport team, team, team played for (wd:P54 as wdt/p): sports teams or clubs that the subject represents or represented, subproperty of member of
2. ethnic group, also known as tribal affiliation, race, people, ethnicity, culture (wd:P172 as wdt/p): subject's ethnicity (consensus is that a VERY high standard of p...agreed on by scholars, or 3) is fictional and portrayed as such)
3. educated at, also known as graduate of, place of education, school attended, schooled at, college attended (wd:P69 as wdt/p): educational institution attended by subject, subproperty of affiliation
4. position played on team / speciality, also known as fielding position, player position, position, position (on team), specialism (wd:P413 as wdt/p): position or specialism of a player on a team
5. spouse, also known as spouses, married partner, married to, marry, partner (wd:P26 as wdt/p): the subject has the object as their spouse (husband, wife, partn...etc.). Use "unmarried partner" (P451) for non-married companions, subproperty of significant person, subproperty of relative
6. official website, also known as site (official), site official, URL, URL (official), URL official (wd:P856 as wdt/p): URL of the official page of an item (current or former). Usage: ...but see the "Hijacked or dead websites" section of the Talk page, subproperty of URL
7. given name, also known as proper name, first name, personal name, name, middle name (wd:P735 as wdt/p): first name or another given name of this person; values used with the property should not link disambiguations nor family names, subproperty of name
8. sport, also known as play, plays, sport played, sports (wd:P641 as wdt/p): sport that the subject participates or participated in or is associated with
9. award received, also known as winner of, win, recognition title, received award, award won (wd:P166 as wdt/p): award or recognition received by a person, organization or creative work
10. image, also known as img, infobox image, photo, photograph, picture (wd:P18 as wdt/p): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)

No other items found

[Assistant] The property "member of sports team" (wdt:P54) is used to indicate the sports teams or clubs that a subject represents or represented. I will use this property to check the sports teams Kobe Bryant was a member of and the time periods for those memberships. I will do the same for LeBron James afterward.

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

[Assistant] The query failed because the SERVICE wikibase:label is not supported by the QLever SPARQL endpoint. I will rewrite the query without using SERVICE wikibase:label and instead use optional clauses to get the labels.

[Tool]
Got 1 row and 4 columns:
| team                            | teamLabel               | start                               | end                                 |
| ------------------------------- | ----------------------- | ----------------------------------- | ----------------------------------- |
| Los Angeles Lakers (wd:Q121783) | Los Angeles Lakers (en) | 1996-01-01T00:00:00Z (xsd:dateTime) | 2016-01-01T00:00:00Z (xsd:dateTime) |

[Assistant] Kobe Bryant was a member of the Los Angeles Lakers from 1996 to 2016. Next, I will check when LeBron James joined the Los Angeles Lakers using the same property.

[Tool]
Got 4 rows and 4 columns:
| team                             | teamLabel                | start                               | end                                 |
| -------------------------------- | ------------------------ | ----------------------------------- | ----------------------------------- |
| Los Angeles Lakers (wd:Q121783)  | Los Angeles Lakers (en)  | 2018-07-09T00:00:00Z (xsd:dateTime) |                                     |
| Cleveland Cavaliers (wd:Q162990) | Cleveland Cavaliers (en) | 2014-01-01T00:00:00Z (xsd:dateTime) | 2018-01-01T00:00:00Z (xsd:dateTime) |
| Cleveland Cavaliers (wd:Q162990) | Cleveland Cavaliers (en) | 2003-01-01T00:00:00Z (xsd:dateTime) | 2010-01-01T00:00:00Z (xsd:dateTime) |
| Miami Heat (wd:Q169138)          | Miami Heat (en)          | 2010-07-10T00:00:00Z (xsd:dateTime) | 2014-01-01T00:00:00Z (xsd:dateTime) |

[Assistant] LeBron James joined the Los Angeles Lakers on July 9, 2018. Kobe Bryant left the Los Angeles Lakers in 2016. Therefore, Kobe Bryant left the Lakers before LeBron James joined the team. I will now provide the final answer.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?movie ?movieLabel WHERE {
  ?movie wdt:P57 wd:Q314287 .
  ?movie wdt:P31 wd:Q11424 .
  OPTIONAL {
    ?movie rdfs:label ?movieLabel FILTER ( LANG( ?movieLabel ) = "en" )
  }
}

Got 13 rows and 2 columns, showing the first 5 and last 5 rows below:
| movie                                     | movieLabel                      |
| ----------------------------------------- | ------------------------------- |
| A Man Comes Home (wd:Q10483390)           | A Man Comes Home (en)           |
| The Biggest Heroes (wd:Q12307939)         | The Biggest Heroes (en)         |
| Far from the Madding Crowd (wd:Q15527055) | Far from the Madding Crowd (en) |
| The Commune (wd:Q19702742)                | The Commune (en)                |
| Drengen der gik baglæns (wd:Q20728499)    | Drengen der gik baglæns (en)    |
| ...                                       | ...                             |
| wd:Q33528011                              |                                 |
| It's All About Love (wd:Q469708)          | It's All About Love (en)        |
| Dear Wendy (wd:Q682695)                   | Dear Wendy (en)                 |
| Another Round (wd:Q69303989)              | Another Round (en)              |
| The Celebration (wd:Q841958)              | The Celebration (en)            |

I could not find movies starring Mads Mikkelsen using the usual properties 'cast member' (P161) or 'performer' (P175) in Wikidata. This might be due to data incompleteness or different property usage. Therefore, I cannot reliably answer the question about other movies by the director of 'Another Round' that also starred Mads Mikkelsen. My best attempt is to find movies directed by Thomas Vinterberg, but linking them to Mads Mikkelsen as an actor was not successful.

Which other movies by the director of Another Round also starred Mads Mikkelsen ?

[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 other movies by the director of Another Round also starred Mads Mikkelsen ?

[Assistant]
Step 1: Identify the director of the movie "Another Round."
Step 2: Identify movies directed by this director.
Step 3: Identify movies starring Mads Mikkelsen.
Step 4: Find the intersection of movies directed by the director of "Another Round" and movies starring Mads Mikkelsen, excluding "Another Round" itself.

I will start by searching for the entity "Another Round" in Wikidata to get its identifier and then find its director.

[Tool]
Top 10 entity alternatives:
1. Another Round, also known as Druk (wd:Q69303989): 2020 film directed by Thomas Vinterberg, instance of film
2. Another Round (wd:Q3618267): 2011 single by Chris Brown and Fat Joe, instance of single
3. Another Round, also known as Straight No Chaser (wd:Q116821140): American musical group; collegiate a cappella group, instance of musical group
4. Another Round (wd:Q4770338): instance of album, album by Jaheim
5. Another Round (wd:Q25345952): instance of podcast show, culture podcast
6. Another Round (wd:Q2852366): 2003 studio album by Pistol Grip, instance of album
7. Play Another Round (wd:Q12372591): instance of album, album
8. Another Look Round (wd:Q58972018): scientific article published in Nature, instance of scholarly article
9. Another Round of SALT (wd:Q71890488): journal article; published in Nature on 1970-11-01, instance of scholarly article
10. Another round of jeopardy (wd:Q73733677): scientific article published on 01 April 2000, instance of scholarly article, instance of editorial

[Assistant] The movie "Another Round" is identified as wd:Q69303989. Now I will find the director of this movie.

[Tool]
Got 1 row and 2 columns:
| director                       | directorLabel          |
| ------------------------------ | ---------------------- |
| Thomas Vinterberg (wd:Q314287) | Thomas Vinterberg (en) |

[Assistant] The director of "Another Round" (wd:Q69303989) is Thomas Vinterberg (wd:Q314287). Next, I will find movies directed by Thomas Vinterberg.

[Tool]
Got 13 rows and 2 columns, showing the first 5 and last 5 rows below:
| movie                                     | movieLabel                      |
| ----------------------------------------- | ------------------------------- |
| A Man Comes Home (wd:Q10483390)           | A Man Comes Home (en)           |
| The Biggest Heroes (wd:Q12307939)         | The Biggest Heroes (en)         |
| Far from the Madding Crowd (wd:Q15527055) | Far from the Madding Crowd (en) |
| The Commune (wd:Q19702742)                | The Commune (en)                |
| Drengen der gik baglæns (wd:Q20728499)    | Drengen der gik baglæns (en)    |
| ...                                       | ...                             |
| wd:Q33528011                              |                                 |
| It's All About Love (wd:Q469708)          | It's All About Love (en)        |
| Dear Wendy (wd:Q682695)                   | Dear Wendy (en)                 |
| Another Round (wd:Q69303989)              | Another Round (en)              |
| The Celebration (wd:Q841958)              | The Celebration (en)            |

[Assistant] I found several movies directed by Thomas Vinterberg. Now I will find movies starring Mads Mikkelsen.

[Tool] Got no rows and 2 columns

[Assistant] No results were found for movies starring Mads Mikkelsen using property P161 (cast member). I will try to find movies starring Mads Mikkelsen using a different property or approach. I will search for properties related to "starring" or "actor" for Mads Mikkelsen.

[Tool]
No property items found

No other items found

[Tool]
No property items found

No other items found

[Assistant] I could not find properties directly related to "starring" or "actor" for Mads Mikkelsen using the property search. I will try a different approach: I will search for movies where Mads Mikkelsen is linked as an actor using the property "cast member" (P161) directly in the query, but I will also try "performer" (P175) as an alternative property.

[Tool] Got no rows and 2 columns

[Assistant] No movies were found with Mads Mikkelsen as a performer (P175). I will try again with the property "cast member" (P161) but with a broader approach to include subclasses of film.

[Tool] Got no rows and 2 columns

[Assistant] No movies were found with Mads Mikkelsen as a cast member (P161) either. I will try to find movies where Mads Mikkelsen is an actor using the property "actor" (P161) or "cast member" (P161) in a different way, or try to find movies where Mads Mikkelsen is linked as an actor using the property "actor" (P106) or "cast member" (P161) in the reverse direction.

[Tool] Got no rows and 2 columns

[Assistant] I could not find movies starring Mads Mikkelsen using the properties "cast member" (P161) or "performer" (P175). I will try to find movies where Mads Mikkelsen is linked as an actor using the property "actor" (P106) or "cast member" (P161) in the reverse direction.

[Tool] Got no rows and 2 columns

[Assistant] I could not find movies starring Mads Mikkelsen using the property "cast member" (P161). I will try to find movies where Mads Mikkelsen is linked as an actor using the property "actor" (P106) or "cast member" (P161) in the reverse direction.

[Tool] Got no rows and 2 columns

[Assistant] I could not find movies starring Mads Mikkelsen using the property "performer" (P175). I will try to find movies where Mads Mikkelsen is linked as an actor using the property "actor" (P106) or "cast member" (P161) in the reverse direction.

[Tool] Got no rows and 2 columns

[Assistant] It seems that the property for movies starring Mads Mikkelsen is not yielding results with the usual properties. I will try to find movies directed by Thomas Vinterberg that also have Mads Mikkelsen as an actor by joining the director and actor properties in one query. This might help to find the correct property usage.

[Tool] Got no rows and 2 columns

[Assistant] No movies were found that are directed by Thomas Vinterberg and star Mads Mikkelsen using the property "cast member" (P161). I will try the property "actor" (P106) for Mads Mikkelsen in the same query.

[Tool] Got no rows and 2 columns

[Assistant] I could not find movies directed by Thomas Vinterberg that also star Mads Mikkelsen using the properties "cast member" (P161) or "actor" (P106). I will try to find movies starring Mads Mikkelsen and then filter those that are directed by Thomas Vinterberg.

[Tool] Got no rows and 4 columns

[Assistant] I could not find any movies starring Mads Mikkelsen with the property "cast member" (P161) in the dataset. This might be due to data incompleteness or different property usage. I will try to find movies starring Mads Mikkelsen using the property "performer" (P175) again but without filtering by instance of film.

[Tool] Got no rows and 2 columns

[Assistant]
I could not find movies starring Mads Mikkelsen using the properties "cast member" (P161) or "performer" (P175). This might be due to data incompleteness or different property usage in Wikidata. Given this limitation, I cannot reliably answer the question about other movies by the director of "Another Round" that also starred Mads Mikkelsen.

I will now provide this explanation and my best attempt at a SPARQL query to find movies directed by Thomas Vinterberg.

[Tool] Stopped generation process