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 ?location ?locationLabel ?fictionalUniverse ?fictionalUniverseLabel WHERE {
  OPTIONAL {
    wd:Q15228 wdt:P840 ?location .
    ?location rdfs:label ?locationLabel
    FILTER ( LANG( ?locationLabel ) = "en" )
  }
  OPTIONAL {
    wd:Q15228 wdt:P1434 ?fictionalUniverse .
    ?fictionalUniverse rdfs:label ?fictionalUniverseLabel
    FILTER ( LANG( ?fictionalUniverseLabel ) = "en" )
  }
}
LIMIT 10

Got 1 row and 4 columns:
| location                 | locationLabel     | fictionalUniverse                 | fictionalUniverseLabel     |
| ------------------------ | ----------------- | --------------------------------- | -------------------------- |
| Middle-earth (wd:Q79734) | Middle-earth (en) | Tolkien's legendarium (wd:Q81738) | Tolkien's legendarium (en) |

The novels of the franchise The Lord of the Rings take place in the narrative location Middle-earth and the fictional universe called Tolkien's legendarium.

Where do the novels of the franchise The Lord of the Rings take place?

[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 do the novels of the franchise The Lord of the Rings take place?

[Assistant]
The user is asking about the setting of the novels of the franchise "The Lord of the Rings." Specifically, they want to know where the novels take place.

Step 1: Identify the entity "The Lord of the Rings" in Wikidata.
Step 2: Identify the novels of this franchise.
Step 3: Identify the location or setting where these novels take place.

I will start by searching for the entity "The Lord of the Rings" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. The Lord of the Rings, also known as Lord of the Rings, TLotR, LotR (wd:Q15228): 1954–1955 fantasy novel trilogy by J. R. R. Tolkien, instance of series of creative works, instance of literary trilogy, instance of novel series
2. The Lord of the Rings, also known as Lord of the Rings, The Lord of the Rings trilogy, LotR trilogy, LotR, Lord of the Rings trilogy (wd:Q190214): 2001-2003 three films directed by Peter Jackson, instance of film trilogy
3. Sauron, also known as Gorthaur, Lord of the Rings, Mairon, Necromancer, Artano (wd:Q2281): fictional character from J.R.R. Tolkien's legendarium, instance of fantasy film character, instance of Maiar, fictional tyrant, necromancer
4. The Lord of the Rings, also known as Lord of the Rings (wd:Q378913): 1978 animated film by Ralph Bakshi, instance of animated film
5. Lord of the Rings (wd:Q905691): instance of dramatico-musical work, stage musical
6. Lord of the Rings (wd:Q1194258): instance of cooperative board game, instance of board game, board game
7. The Lord of the Rings (wd:Q3227050): instance of radio adaptation of the Lord of the Ring, 1981 British radio drama series, instance of radio program
8. Symphony No. 1 "The Lord of the Rings", also known as The Lord of the Rings (wd:Q268133): musical work; symphony in five movements composed by Johan de Meij, instance of musical work/composition
9. Themes of The Lord of the Rings (wd:Q7781266): Academic analyses of The Lord of the Rings
10. The Lord of the Rings (wd:Q3227048): 1955 radio adaptation of the novel by J. R. R. Tolkien, instance of radio adaptation of the Lord of the Ring, instance of radio serial

[Assistant]
The most relevant entity for the novels is wd:Q15228, which is the 1954–1955 fantasy novel trilogy by J. R. R. Tolkien, known as "The Lord of the Rings."

Next, I want to find the setting or location where these novels take place. Usually, the setting of a fictional work is linked by a property like "setting" (P840) or "fictional universe" (P1080).

I will check the properties of this entity to find the setting or fictional universe.

[Tool]
Top 10 property alternatives:
1. narrative location, also known as set in location, setting of work, setting, setting location, setting place (wd:P840 as wdt/p): the narrative of the work is set in this location, subproperty of significant place, subproperty of depicts
2. title, also known as page name, titled, original title, name, known as (wd:P1476 as wdt/p): published name of a work, such as a newspaper article, a literary work, piece of music, a website, or a performance work, subproperty of name
3. derivative work, also known as is source work of, is the basis for, iteration, reboot, remake (wd:P4969 as wdt/p): new work of art (film, book, software, etc.) derived from major part of this work
4. has edition or translation, also known as released as, version, translation(s), modified as, has version (wd:P747 as wdt/p): item that is an edition or translation of this item
5. genre, also known as type of music, type of film, music genre, literary genre, kind of music (wd:P136 as wdt/p): creative work's genre or an artist's field of work (P101). Use main subject (P921) to relate creative works to their topic
6. uses, also known as instrument played, item used, made use of, makes use of, trope used (wd:P2283 as wdt/p): item or concept used by the subject or in the operation (see also instrument [P1303] and armament [P520])
7. 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
8. takes place in fictional universe, also known as universe featured, universe described, set in fictional universe, fictional universe described, describes fictional universe (wd:P1434 as wdt/p): the subject is a work describing a fictional universe, i.e. whose plot occurs in this universe
9. Freebase ID, also known as FID, Freebase, Freebase identifier, MID (wd:P646 as wdt/p): identifier for a page in the Freebase database. Format: "/m/0" f...r IDs starting with "/g/", use Google Knowledge Graph ID (P2671)
10. has part(s), also known as has ingredient, has ingredients, had part, has member, has part (wd:P527 as wdt/p): part of this subject; inverse property of "part of" (P361). See also "has parts of the class" (P2670).

No other items found

[Assistant]
The property "narrative location" (wdt:P840) and "takes place in fictional universe" (wdt:P1434) seem relevant to find the setting of the novels.

I will query the narrative location and fictional universe for the entity "The Lord of the Rings" novel series (wd:Q15228).

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

[Assistant]
The query failed due to the use of SERVICE wikibase:label which is not supported by the QLever endpoint. I will rewrite the query without using SERVICE wikibase:label and instead use optional rdfs:label for the location.

I will query the narrative location (wdt:P840) and fictional universe (wdt:P1434) for the entity "The Lord of the Rings" novel series (wd:Q15228) and get their labels with optional rdfs:label.

[Tool]
Got 1 row and 4 columns:
| location                 | locationLabel     | fictionalUniverse                 | fictionalUniverseLabel     |
| ------------------------ | ----------------- | --------------------------------- | -------------------------- |
| Middle-earth (wd:Q79734) | Middle-earth (en) | Tolkien's legendarium (wd:Q81738) | Tolkien's legendarium (en) |

[Assistant]
The novels of the franchise "The Lord of the Rings" take place in the narrative location Middle-earth (wd:Q79734) and the fictional universe is Tolkien's legendarium (wd:Q81738).

I will now provide the final answer with this information.

[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 ?product ?productLabel WHERE {
  wd:Q653941 wdt:P101 ?product .
  OPTIONAL {
    ?product rdfs:label ?productLabel FILTER ( LANG( ?productLabel ) = "en" )
  }
}

Ray-Ban primarily produces eyewear.

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

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

No general notes for all knowledge graphs available

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

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

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

[User] What does Ray-Ban produce primarily?

[Assistant] To answer the question about what Ray-Ban primarily produces, I will first search for the entity "Ray-Ban" in Wikidata to find its identifier. Then, I will look for properties related to the products or industry of Ray-Ban to identify its primary production.

[Tool]
Top 10 entity alternatives:
1. Ray-Ban, also known as RayBan (wd:Q653941): brand of luxury sunglasses and eyeglasses, instance of enterprise, instance of business, instance of company
2. Ray-Ban Wayfarer (wd:Q2487557)
3. Ray-Ban Stories (wd:Q108803484)
4. Ray-Ban Clubmaster, also known as Clubmaster (wd:Q55957409): instance of glasses model, glasses model
5. Outdoorsman, also known as Ray-Ban Outdoorsman (wd:Q30898918): instance of glasses model, model of glasses
6. The Vergecast - Facebook debuts ...s first-ever lineup of smart TVs, also known as Facebook debuts Ray-Ban smart gl...s first-ever lineup of smart TVs (wd:Q109501085): instance of podcast episode, episode of the Vergecast

[Assistant] I found the entity for Ray-Ban as a brand of luxury sunglasses and eyeglasses (wd:Q653941). Now I will look for properties related to the products or industry of Ray-Ban to identify what it primarily produces.

[Tool]
Got 281 rows and 3 columns, showing the first 10 rows below:
| subject              | property                         | object                                                                 |
| -------------------- | -------------------------------- | ---------------------------------------------------------------------- |
| Ray-Ban (wd:Q653941) | instance of (wdt:P31)            | business (wd:Q4830453)                                                 |
| Ray-Ban (wd:Q653941) | country (wdt:P17)                | Italy (wd:Q38)                                                         |
| Ray-Ban (wd:Q653941) | Commons category (wdt:P373)      | Ray-Ban                                                                |
| Ray-Ban (wd:Q653941) | image (wdt:P18)                  | <http://commons.wikimedia.org/wiki/Special:FilePath/RayBanAviator.jpg> |
| Ray-Ban (wd:Q653941) | inception (wdt:P571)             | 1937-01-01T00:00:00Z (xsd:dateTime)                                    |
| Ray-Ban (wd:Q653941) | official website (wdt:P856)      | <https://www.ray-ban.com/>                                             |
| Ray-Ban (wd:Q653941) | field of work (wdt:P101)         | Eyewear (wd:Q3267544)                                                  |
| Ray-Ban (wd:Q653941) | native label (wdt:P1705)         | Ray-Ban (en)                                                           |
| Ray-Ban (wd:Q653941) | owned by (wdt:P127)              | Luxottica (wd:Q1878364)                                                |
| Ray-Ban (wd:Q653941) | headquarters location (wdt:P159) | Milan (wd:Q490)                                                        |
| ...                  | ...                              | ...                                                                    |

[Assistant] The property "field of work" (wdt:P101) for Ray-Ban (wd:Q653941) is listed as "Eyewear" (wd:Q3267544). This indicates that Ray-Ban primarily produces eyewear. I will now formulate the final SPARQL query to confirm this and retrieve the label of the primary product.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?child WHERE {
  wd:Q2599 wdt:P40 ?child .
  ?child wdt:P25 wd:Q228899 .
}

Got 4 rows and 1 column:
| child                           |
| ------------------------------- |
| Mary McCartney (wd:Q201739)     |
| Stella McCartney (wd:Q232812)   |
| Heather McCartney (wd:Q3129093) |
| James McCartney (wd:Q498390)    |

Paul McCartney had 4 children together with his ex-wife Linda McCartney.

How many children had Paul McCartney together with his ex-wife Linda McCartney?

[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 had Paul McCartney together with his ex-wife Linda McCartney?

[Assistant]
To answer the question about how many children Paul McCartney had together with his ex-wife Linda McCartney, I need to:

1. Identify the Wikidata entity for Paul McCartney.
2. Identify the Wikidata entity for Linda McCartney.
3. Find the children of Paul McCartney.
4. Filter those children to only those whose mother is Linda McCartney.
5. Count the number of such children.

I will start by searching for the entities of Paul McCartney and Linda McCartney.

[Tool]
Top 10 entity alternatives:
1. Paul McCartney, also known as Sir James Paul McCartney, Sir Paul McCartney, James Paul McCartney, Macca (wd:Q2599): English musician and member of the Beatles (born 1942), television director, film score composer, instance of human, singer-songwriter, recording artist, television actor, bass guitarist, film producer, film director, screenwriter, film actor, guitarist, musician, lyricist, composer, pianist, drummer, painter, singer, actor
2. Paul McCartney (wd:Q28052090): painting by Humphrey Ocean (Humphrey Anthony Erdeswick Butler-Bowdon), instance of painting
3. Paul McCartney (wd:Q47512209): painting by Peter Max, instance of painting
4. Paul McCartney (wd:Q47513089): painting by Peter Max (NPG NPG.78.TC571), instance of painting
5. Bill McCartney, also known as William Paul McCartney (wd:Q4910081): American football player, coach, American football coach, instance of human
6. James Paul McCartney (wd:Q797787): instance of television special, television special
7. Paul McCartney Band (wd:Q110442670): Studio and touring band of Paul McCartney, instance of musical group
8. Paula McCartney (wd:Q106272781): American photographer, instance of human, photographer
9. Paul Rudd/Paul McCartney, also known as Saturday Night Live S36E9, Saturday Night Live, season 36 episode 9 (wd:Q110037121): episode of Saturday Night Live (S36 E9), instance of television series episode
10. Paul Mccartney Swingle (wd:Q102147773): Ph.D. University of Michigan 1929, instance of human

[Tool]
Top 10 entity alternatives:
1. Linda McCartney, also known as Linda Louise McCartney, McCartney, Linda, 1941-1998, Linda Eastman McCartney, Linda Eastman (wd:Q228899): American photographer, musician and animal rights activist (1941–1998), instance of human, recording artist, film director, photographer, keyboardist, songwriter, singer, artist
2. Linda McCartney Foods (wd:Q6551813): British brand of vegetarian food, instance of business
3. The Linda McCartney Story, also known as Linda McCartney Story (wd:Q7747397): 2000 film directed by Armand Mastroianni, instance of television film
4. Linda McCartney Racing Team (wd:Q1825885): instance of professional cycling team, instance of UCI Trade Team II, cycling team
5. Linda McCartney's Sixties (wd:Q6551812): instance of literary work, book by Linda McCartney
6. Paul & Linda McCartney, also known as Paul and Linda McCartney (wd:Q62302446): musical duo, married couple, instance of married couple, instance of musical duo
7. Linda McCartney Memorial Garden (wd:Q124386219): memorial in Campbeltown, Scotland, United Kingdom, instance of memorial, instance of garden
8. Collaborations - Brian Clarke, Linda McCartney (wd:Q55351845): exhibition at the Swiss Museum of Stained Glass of works by Brian Clarke and Linda McCartney, instance of temporary exhibition, instance of art exhibition
9. Steve Martin/Paul & Linda McCartney/3-D, also known as Saturday Night Live, season 5 episode 19 (wd:Q110712888): episode of Saturday Night Live (S5 E19), instance of television series episode
10. The Glass Wall (Dedicated to Linda McCartney), also known as The Glass Wall (wd:Q110632063): 1998 stained glass artwork and homonymous exhibition, by artist Brian Clarke, instance of installation artwork, instance of temporary structure, instance of physical object, instance of art exhibition, instance of work of art

[Assistant] The Wikidata entities for Paul McCartney (wd:Q2599) and Linda McCartney (wd:Q228899) have been identified. Next, I will find the children of Paul McCartney and check which of them have Linda McCartney as their mother. Then I will count the number of such children.

[Tool]
Got 4 rows and 1 column:
| child                           |
| ------------------------------- |
| Mary McCartney (wd:Q201739)     |
| Stella McCartney (wd:Q232812)   |
| Heather McCartney (wd:Q3129093) |
| James McCartney (wd:Q498390)    |

[Tool] Stopped generation process


PREFIX ps: <http://www.wikidata.org/prop/statement/>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX pq: <http://www.wikidata.org/prop/qualifier/>
PREFIX p: <http://www.wikidata.org/prop/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?boilingPoint ?unitLabel WHERE {
  wd:Q283 p:P2102 ?statement .
  ?statement ps:P2102 ?boilingPoint .
  OPTIONAL {
    ?statement pq:P398 ?unit .
    ?unit rdfs:label ?unitLabel
    FILTER ( LANG( ?unitLabel ) = "en" )
  }
}
LIMIT 1

The boiling point of water is approximately 99.9839 degrees Celsius according to Wikidata.

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

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

No general notes for all knowledge graphs available

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

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

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

[User] What is the boiling point of water?

[Tool]
Top 10 entity alternatives:
1. water, also known as hydrogen hydroxide, hydrogen oxide, oxidane, pure water, dihydridooxygen (wd:Q283)
2. ice, also known as solid water, water, water ice, frozen water, crystalline water (wd:Q23392)
3. drinking water, also known as improved drinking water, water, potable water, drinkable water (wd:Q7892)
4. Water, also known as Water (2005 movie), Water (2005) (wd:Q617395): 2005 film by Deepa Mehta, instance of film
5. water (wd:Q2637515): classical element in philosophy and cosmology, instance of classical element
6. Water (wd:Q123169436): instance of musical work/composition, 2023 single by Tyla
7. liquid water, also known as water (wd:Q29053744)
8. Water, also known as Water (Elitsa & Stoyan song) (wd:Q2735297): song by Elitsa Todorova and Stoyan Yankoulov, instance of musical work/composition
9. Water, also known as Water (Chinese astrology), Water (element), Water (Wu Xing) (wd:Q1400698): fifth of the five elements in the Wu Xing cycle in Chinese astrology, instance of element in Chinese astrology
10. Water, also known as Great Mystery of Water, The Great Mystery of Water (wd:Q4106371): 2006 Russian pseudo-documentary film by Anastasia Popova, instance of television film

[Tool]
Top 10 property alternatives:
1. boiling point (wd:P2102 as wdt/p): temperature at which a substance changes its phase from liquid to gas (indicate the corresponding pressure as qualifier)
2. melting point, also known as freezing point (wd:P2101 as wdt/p): temperature at which a solid changes its state from solid to liquid at atmospheric pressure
3. molar enthalpy of vaporization, also known as enthalpy of vaporization, heat of evaporation, heat of vaporization, latent heat of vaporization, molar enthalpy of vaporisation (wd:P2116 as wdt/p): vaporization enthalpy of a substance with temperature as qualifier
4. ionization energy, also known as IE, ionisation energy, ionisation potential, ionization potential, IP (wd:P2260 as wdt/p): minimum amount of energy required to remove an electron from an atom, ion or molecule in the gaseous state
5. chemical structure, also known as crystallographic structure, image of chemical structure, structural formula (wd:P117 as wdt/p): image of a representation of the structure for a chemical compound, subproperty of image
6. standard enthalpy of formation, also known as standard heat of formation (wd:P3078 as wdt/p): change of enthalpy during the formation of 1 mole of the compoun..., with all substances in their standard states at 1 bar and 25°C
7. specific heat capacity, also known as heat capacity, specific heat, thermal capacity (wd:P2056 as wdt/p): specific heat capacity of a substance, with phase of matter and temperature as qualifiers
8. image of molecular model or crystal lattice model, also known as crystal lattice model, molecular model (wd:P8224 as wdt/p): graphical representation of a molecule or crystal lattice, subproperty of image
9. phase point (wd:P873 as wdt/p): phase point to describe critical point and triple point (see talk page for an example)
10. standard molar entropy (wd:P3071 as wdt/p): entropy content of one mole of substance under standard conditions (25°C, 1 bar)

No other items found

[Tool] Got no rows and 2 columns

[Tool]
Got 1 row and 2 columns:
| boilingPoint          | unitLabel |
| --------------------- | --------- |
| 99.9839 (xsd:decimal) |           |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?desert ?desertLabel ?area WHERE {
  VALUES ?desert {
    wd:Q6583 wd:Q208299
  }
  ?desert wdt:P2046 ?area .
  OPTIONAL {
    ?desert rdfs:label ?desertLabel FILTER ( LANG( ?desertLabel ) = "en" )
  }
}

The Sahara Desert has an area of 9,200,000 square kilometers, while the Arabian Desert has an area of 2,330,000 square kilometers. Therefore, the Sahara Desert is bigger than the Arabian Desert.

which desert is bigger, sahara desert or arabian desert?

[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 desert is bigger, sahara desert or arabian desert?

[Assistant]
To answer the question of which desert is bigger, the Sahara Desert or the Arabian Desert, I need to find the area of both deserts and compare them.

Step 1: Identify the entities for Sahara Desert and Arabian Desert in Wikidata.
Step 2: Find the property for the area of these deserts.
Step 3: Retrieve the area values for both deserts.
Step 4: Compare the areas to determine which desert is bigger.

I will start by searching for the entities of Sahara Desert and Arabian Desert.

[Tool]
Top 10 entity alternatives:
1. Sahara, also known as Sahara Desert, Saharra, Great Desert, the Desart, the Desert (wd:Q6583): desert on the African continent, instance of desert
2. Sahara Desert (wd:Q3889421): instance of WWF ecoregion, instance of ecoregion, ecoregion
3. Sahara (Desert) (wd:Q104765639): painting by Jan Ciągliński (MNK MNK II-b-419), instance of painting
4. Sahara (Desert) (wd:Q104765619): painting by Jan Ciągliński (MNK MNK II-b-426), instance of painting
5. Sahara (Desert) (wd:Q104765585): painting by Jan Ciągliński, instance of painting
6. Sahara (Desert) (wd:Q104765650): painting by Jan Ciągliński (MNK MNK II-b-422), instance of painting
7. Sahara desert (wd:L643007-S1)
8. Sahara Desert (wd:L1411251-S1)
9. Sahara desert (wd:L300591-S1)
10. Cataglyphis bicolor, also known as Sahara desert ant (wd:Q1717126): instance of taxon, species of insect

[Tool]
Top 10 entity alternatives:
1. Arabian Desert (wd:Q208299): desert located in Western Asia, instance of desert
2. Eastern Desert, also known as Arabian Desert (wd:Q624170): Sahara desert east of the Nile river, instance of desert
3. Badia of Hejaz, also known as Arabian badiyah, Badiyat al-Hejaz, Hejaz Badiyah, Badiyah of Hejaz, steppe of arabia (wd:Q104785744)
4. Arabian Desert Ecoregion (wd:Q124361827): instance of One Earth Ecoregion, One Earth Ecoregion
5. Arabian Sand Desert Ecoregion (wd:Q124361716): instance of One Earth Ecoregion, One Earth Ecoregion
6. North Arabian Desert Ecoregion (wd:Q124361858): instance of One Earth Ecoregion, One Earth Ecoregion
7. Sunlit Cliff, Arabian Desert (wd:Q119724623): painting by Keith Henderson (1883–1982), Worthing Museum and Art Gallery, instance of painting
8. Arabian Peninsula coastal fog desert (wd:Q17002300): instance of WWF ecoregion, instance of ecoregion, Ecoregion (WWF)
9. Immunology in the Arabian desert (wd:Q81477978): scientific article published on 01 July 2008, instance of scholarly article
10. Recollections from the Arabian Desert (wd:Q70163572): scientific article published on 01 March 1991, instance of scholarly article

[Assistant] I identified the entities for the Sahara Desert (wd:Q6583) and the Arabian Desert (wd:Q208299). Next, I will find the property for the area of these deserts. The property for area is commonly "area" (wdt:P2046) in Wikidata. I will now query the area values for both deserts.

[Tool]
Got 2 rows and 3 columns:
| desert                      | desertLabel         | area                  |
| --------------------------- | ------------------- | --------------------- |
| Arabian Desert (wd:Q208299) | Arabian Desert (en) | 2330000 (xsd:decimal) |
| Sahara (wd:Q6583)           | Sahara (en)         | 9200000 (xsd:decimal) |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?population WHERE {
  wd:Q2833 wdt:P1082 ?population .
}

The population of Freiburg im Breisgau is 237,244.

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

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

No general notes for all knowledge graphs available

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

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

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

[User] What is the population in Freiburg ?

[Assistant]
Step 1: Identify the entity "Freiburg" in Wikidata. There are multiple Freiburgs (e.g., Freiburg im Breisgau in Germany, Freiburg in Switzerland). The most commonly referred Freiburg is Freiburg im Breisgau in Germany, so I will search for that entity first.

Step 2: Identify the property for population in Wikidata. The common property for population is "population" (P1082).

I will first search for the entity "Freiburg im Breisgau" to get its Wikidata ID.

[Tool]
Top 10 entity alternatives:
1. Freiburg im Breisgau, also known as Freiburg i. B., Freiburg i. Br., Freiburg/Breisgau, Freiburg (wd:Q2833): instance of City district in Baden-Württemberg, instance of urban municipality in Germany, large city in Baden-Württemberg, Germany, instance of state capital in Germany, instance of district capital, instance of college town, instance of big city, instance of spa town
2. Freiburg im Breisgau (wd:Q19196967): German article in Die Gartenlaube, 1875, no. 32, instance of article
3. Freiburg im Breisgau (wd:Q32017471): instance of second-level administrative division
4. Freiburg im Breisgau (wd:Q103920694): instance of encyclopedia article, encyclopedia article
5. Freiburg im Breisgau, also known as Mint of Freiburg im Breisgau (wd:Q127430253): The mint(s) at the city of Freiburg, National State of Baden-Württemberg, Germany, instance of mint
6. Freiburg im Breisgau (wd:L452977)
7. Freiburg im Breisgau (wd:L452977-F1)
8. Bismarckturm (Freiburg im Breisgau) (wd:Q335847): historic site in Baden-Württemberg, Germany, instance of Bismarck tower
9. E-Werk (Freiburg im Breisgau) (wd:Q1453881): instance of drama school
10. Justizvollzugsanstalt Freiburg im Breisgau (wd:Q1714380): building in Freiburg im Breisgau, Freiburg Government Region, Bade-Württemberg, Germany, instance of correctional facility

[Assistant]
The entity for Freiburg im Breisgau is wd:Q2833.

Next, I will query the population (property P1082) of Freiburg im Breisgau (wd:Q2833).

[Tool]
Got 1 row and 1 column:
| population           |
| -------------------- |
| 237244 (xsd:decimal) |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?company ?companyLabel ?inception WHERE {
  VALUES ?company {
    wd:Q20716 wd:Q41187
  }
  ?company wdt:P571 ?inception .
  OPTIONAL {
    ?company rdfs:label ?companyLabel FILTER ( LANG( ?companyLabel ) = "en" )
  }
}
ORDER BY ?inception

Samsung Group was founded in 1938, and Sony Group was founded in 1946. Therefore, Sony was founded later than Samsung.

which company is founded later, samsung or sony?

[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 company is founded later, samsung or sony?

[Assistant]
To answer the question "Which company is founded later, Samsung or Sony?", I need to find the founding dates of both Samsung and Sony companies and then compare them.

Step 1: Identify the entities for Samsung and Sony.
Step 2: Find the property for the founding date of these companies.
Step 3: Retrieve the founding dates for both companies.
Step 4: Compare the dates to determine which company was founded later.

I will start by searching for the entities Samsung and Sony in the knowledge graph.

[Tool]
Top 10 entity alternatives:
1. Samsung Electronics, also known as Samsung Electronics Co., Ltd., Samsung Electronics Co., Ltd. (Suwon-si), Samsung Electronics Ltd, SΛMSUNG, Samsung Electric Industries (wd:Q20718): South Korean multinational electronics corporation, instance of public company, instance of organization, instance of enterprise, instance of business
2. .samsung (wd:Q20576393): instance of generic top-level domain, top-level domain
3. Seonseong, also known as Samsung (wd:Q12600201): instance of doctrine, Buddhist doctrine
4. Samsung, also known as Samsung brand (wd:Q124989916): instance of trademark, brand of electronics, instance of brand
5. Samsung (wd:Q132198249): creative work by Marc Voge, Young-Hae Chang, Young-Hae Chang Heavy Industries, instance of creative work, instance of application
6. Samsung Group, also known as Samsung chaebol (wd:Q20716): South Korean multinational conglomerate, instance of chaebol
7. Samsung Galaxy, also known as Samsung Galaxy series, SG (wd:Q493064)
8. Samsung Galaxy S7, also known as Samsung S7, S7, Galaxy S7 (wd:Q22893875)
9. Samsung Galaxy S6, also known as Samsung GS6, Samsung S6, SG 6, SG6, SGS6 (wd:Q18941713)
10. Renault Korea, also known as Samsung Motors, Renault Samsung Motors, Renault Korea Motors (wd:Q494168): instance of automobile manufacturer, South Korean car manufacturer, instance of enterprise, instance of business

[Assistant]
The most relevant entity for Samsung as a company is "Samsung Electronics" (wd:Q20718) or "Samsung Group" (wd:Q20716). Since the question is about the company Samsung in general, I will consider "Samsung Group" (wd:Q20716) as it represents the conglomerate.

Next, I will search for the entity Sony.

[Tool]
Top 10 entity alternatives:
1. Sony Group, also known as Tokyo Telecommunications Engineering Corporation, Sony, Tokyo Tsushin Kogyo K.K., Totsuko, Sony Corp. (wd:Q41187): Japanese multinational conglomerate corporation, instance of multinational corporation, instance of camera manufacturer, instance of holding company, instance of public company, instance of conglomerate, instance of brand
2. Sony (wd:Q3490722): commune in Kayes Region, Mali, instance of commune of Mali
3. .sony (wd:Q20576928): instance of top-level domain, top-level domain
4. Sony Corporation, also known as SONY (wd:Q6014034): instance of business, Japanese business
5. Abdullah Al Durrani Sony, also known as Sony (wd:Q51587925): Bangladeshi independent filmmaker and writer, instance of human, filmmaker, writer
6. Sony, also known as Sonny (wd:Q12642424): instance of elephant, elephant
7. Sony BMG, also known as Flash Music, Sony (wd:Q56826329): major record label between 2004–2008; owned by Sony BMG Music Entertainment, Inc., instance of major label
8. Sony (wd:Q125216441): instance of trademark, brand of electronics, instance of brand
9. Sony (wd:Q65177437): instance of given name, male given name
10. Sony (wd:Q37521663): instance of family name, family name

[Assistant]
The most relevant entity for Sony as a company is "Sony Group" (wd:Q41187), which is a Japanese multinational conglomerate corporation.

Now I will search for the founding dates of Samsung Group (wd:Q20716) and Sony Group (wd:Q41187). The property for founding date is usually "inception" (wdt:P571). I will query the founding dates for both companies.

[Tool]
Got 2 rows and 3 columns:
| company                   | companyLabel       | inception                           |
| ------------------------- | ------------------ | ----------------------------------- |
| Samsung Group (wd:Q20716) | Samsung Group (en) | 1938-01-01T00:00:00Z (xsd:dateTime) |
| Sony Group (wd:Q41187)    | Sony Group (en)    | 1946-05-07T00:00:00Z (xsd:dateTime) |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ( COUNT( DISTINCT ?party ) AS ?count ) WHERE {
  ?person wdt:P39 wd:Q1939555 ; wdt:P102 ?party .
  ?party wdt:P31 wd:Q7278 .
}

Got 1 row and 1 column:
| count        |
| ------------ |
| 44 (xsd:int) |

44 political parties have ever had seats in the German Bundestag.

How many political parties have ever had seats in the German Bundestag ?

[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 political parties have ever had seats in the German Bundestag ?

[Assistant]
To answer the question "How many political parties have ever had seats in the German Bundestag?", I need to:

1. Identify the entity for the German Bundestag.
2. Identify the property or relation that links political parties to having seats in the Bundestag.
3. Count the distinct political parties that have had seats in the Bundestag.

Step 1: Search for the entity representing the German Bundestag.

[Tool]
Top 10 entity alternatives:
1. German Bundestag, also known as Bundestag of Germany, German parliament (wd:Q154797): instance of representative assembly, legislature of Germany; parliament, instance of lower house, instance of legislature
2. 1st German Bundestag, also known as First German Bundestag (wd:Q30542687): legislative term from 1949 to 1953, instance of legislative term
3. 21st German Bundestag, also known as Twenty-first German Bundestag (wd:Q124661964): legislative term from 2025 to 2029 (expected), instance of legislative term
4. 20th German Bundestag, also known as Twentieth German Bundestag (wd:Q33091469): legislative term from 2021 to 2025, instance of legislative term
5. 2nd German Bundestag, also known as Second German Bundestag (wd:Q30542689): legislative term from 1953 to 1957, instance of legislative term
6. 19th German Bundestag, also known as Nineteenth German Bundestag (wd:Q30579723): legislative term from 2017 to 2021, instance of legislative term
7. 17th German Bundestag, also known as Seventeenth German Bundestag (wd:Q30542764): legislative term from 2009 to 2013, instance of legislative term
8. 16th German Bundestag, also known as Sixteenth German Bundestag (wd:Q30542763): legislative term from 2005 to 2009, instance of legislative term
9. 18th German Bundestag, also known as Eighteenth German Bundestag (wd:Q15081430): legislative term from 2013 to 2017, instance of legislative term
10. 10th German Bundestag, also known as Tenth German Bundestag (wd:Q30542755): legislative term from 1983 to 1987, instance of legislative term

[Assistant] The entity for the German Bundestag is wd:Q154797. Next, I need to find the property or relation that links political parties to having seats in the Bundestag. This could be a property indicating that a party has seats in a legislative body or a membership relation. I will search for properties related to "seat" or "parliament" for the German Bundestag entity.

[Tool]
Top 10 property alternatives:
1. headquarters location, also known as headquarters locality, headquarters town, HQ, HQ location, location of headquarters (wd:P159 as wdt/p): city or town where an organization's headquarters is or has been situated. Use P276 qualifier for specific building, subproperty of significant place
2. chairperson, also known as dean, headed by, leader, chairman, president (wd:P488 as wdt/p): presiding member of an organization, group or body, subproperty of director / manager, subproperty of significant person
3. number of seats, also known as quantity of members, seats (members), number of seats (members), membership quantity (wd:P1342 as wdt/p): total number of seats/members in an assembly (legislative house or similar)
4. has part(s), also known as has ingredient, has ingredients, had part, has member, has part (wd:P527 as wdt/p): part of this subject; inverse property of "part of" (P361). See also "has parts of the class" (P2670).
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. topic's main category, also known as category for this topic, Wikipedia category, Wikimedia category, Wiki category, subject category (wd:P910 as wdt/p): main Wikimedia category
7. country, also known as state, sovereign state, land, host country (wd:P17 as wdt/p): sovereign state that this item is in (not to be used for human beings), subproperty of located in the administrative territorial entity
8. applies to jurisdiction, also known as country of jurisdiction, of, jurisdiction linked, jurisdiction, belongs to jurisdiction (wd:P1001 as wdt/p): the item (institution, law, public office, public register...) o...a territorial jurisdiction: a country, state, municipality, ...), subproperty of characteristic of, subproperty of significant place, subproperty of operating area
9. disabled accessibility, also known as step-free access, wheelchair accessibility, easy access, ADA, accessibility for wheelchair users (wd:P2846 as wdt/p): describes disabled accessibility of location or event
10. position held by head of the organization, also known as position held by head of the organisation, secretary's position, president's position, position held by head of the organization, office held by head of the organisation (wd:P2388 as wdt/p): position of the head of this item

No other items found

[Assistant]
The property "number of seats" (wdt:P1342) indicates the total number of seats in an assembly, but it does not directly link political parties to having seats. I need to find a property that links political parties to the German Bundestag or to having seats in it.

I will now search for properties related to "political party" and "Bundestag" to find a relation that indicates a party has had seats in the Bundestag.

[Tool]
Top 10 property alternatives:
1. biography at the Bundestag of Germany URL, also known as biography at the Bundestag of Germany (wd:P1713 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): link to the biography pages of the German parliament
2. member of political party, also known as member of party, political party member, political party, party membership, party (wd:P102 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): the political party of which a person is or has been a member or otherwise affiliated, subproperty of member of
3. Swiss parliament ID, also known as parliament.ch, Swiss parliament identifier, parlement.ch, parlament.ch, id parliament.ch (wd:P1307 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): identifier for a member of the Swiss Parliament since 1848
4. abgeordnetenwatch.de politician ID, also known as abgeordnetenwatch.de politician identifier (wd:P5355 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): identifier for a German politician, in the abgeordnetenwatch.de database
5. political coalition, also known as alliance, political bloc, political alliance, electoral list, electoral block (wd:P5832 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): agreement for cooperation between different political parties, subproperty of member of
6. position held, also known as function, held position, holds position, job, office held (wd:P39 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): subject currently or formerly holds the object position or public office
7. electoral district, also known as constituency, ward, seat, electorate, electoral area (wd:P768 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): electoral district this person is representing, or of the office... or "office contested" (P541) or "candidacy in election" (P3602)
8. Tagesschau election ID (wd:P4247 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): identifier of a parliamentary election in the elections archive on tagesschau.de
9. Politiklexikon ID, also known as Politiklexikon article ID, Politiklexikon article identifier (wd:P9437 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): identifier for an article in the Politiklexikon
10. biography at the Landtag of Mecklenburg-Vorpommern, also known as Landtag of Mecklenburg-Vorpommern ID, Mecklenburg-Vorpommern Landtag ID (wd:P3290 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): link to the biography pages at the Landtag of Mecklenburg-Vorpommern

[Assistant]
The property "position held" (wdt:P39) can indicate a position or office held by a person, which might be linked to membership in the Bundestag. However, this is about persons, not parties.

I will try a different approach: I will search for political parties that have "seat in the German Bundestag" or similar statements. I will search for political parties with statements about the German Bundestag as a parliamentary body where they have seats.

I will search for political parties that have the German Bundestag (wd:Q154797) as a "parliament" or "legislative body" or similar property.

[Tool]
Top 10 property alternatives:
1. legislative body, also known as aboriginal council, Zila, Tehsil, rural council, representative body (wd:P194 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): legislative body governing this entity; political institution wi...ted representatives, such as a parliament/legislature or council
2. Swiss parliament ID, also known as parliament.ch, Swiss parliament identifier, parlement.ch, parlament.ch, id parliament.ch (wd:P1307 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): identifier for a member of the Swiss Parliament since 1848
3. parliamentary term, also known as legislative term (wd:P2937 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): term of a parliament or any deliberative assembly
4. parliamentary group, also known as faction, party, party organization (wd:P4100 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): parliamentary group to which a member of a parliament belongs, subproperty of member of
5. UK Parliament ID, also known as UK parliament data, UK Parliament identifier, UK parliament linked-data (wd:P6213 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): identifier used by the UK Parliament linked-data system
6. History of Parliament ID, also known as HoP ID (wd:P1614 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): identifier on the History of Parliament website
7. parliament.bg MP ID (wd:P11873 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): identifier for a Member of Parliament at parliament.bg
8. number of seats, also known as quantity of members, seats (members), number of seats (members), membership quantity (wd:P1342 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): total number of seats/members in an assembly (legislative house or similar)
9. Parliamentary record ID, also known as Parliamentary record identifier (wd:P2172 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): Parliamentary record identifier for British MPs
10. The Parliamentary Newspaper ID, also known as pnp.ru person ID (wd:P10131 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): identifier for a politician on "The Parliamentary Newspaper"

[Assistant]
The property "legislative body" (wdt:P194) seems relevant as it links an entity to a legislative body such as a parliament. I will check if political parties have this property pointing to the German Bundestag (wd:Q154797).

I will query for political parties that have the German Bundestag as their legislative body. This should give me parties that have had seats in the Bundestag.

[Tool] Got no rows and 1 columns

[Assistant]
No political parties have the property "legislative body" set to the German Bundestag. I will try another approach: I will look for political parties that have members who held a position in the German Bundestag. This can be done by finding political parties with members who held a position (wdt:P39) that is an instance of member of the Bundestag.

I will first find the position entity for "Member of the German Bundestag" or similar.

[Tool]
Top 10 entity alternatives:
1. member of the German Bundestag, also known as MdB, member of the Bundestag of Germany (wd:Q1939555)
2. Members of the Bundestag Act, also known as Act on the Legal Status of Members of the German  Bundestag (wd:Q320253): instance of Federal Act (Germany)
3. Library of the German Bundestag, also known as Bibliothek des Deutschen Bundestages (wd:Q856510): Library that serves the Members of the Bundestag (Germany parliament), the parliamentary groups and the Bundestag Administration, instance of parliamentary library
4. Berlin member of the Bundestag, also known as Berlin member of the Bundestag (1949–1990), West-Berlin member of the Bundestag (wd:Q821448)
5. member of the German Reichstag, also known as MdR, Member of the Reichstag (wd:Q1939564)
6. Council of Elders of the German Bundestag (wd:Q131193756): instance of Council of Elders (Germany), committee of the German Bundestag
7. Member of the Bundesrat of Germany (wd:Q15835370)
8. chauffeur service of the German Bundestag (wd:Q1392283): instance of government agency
9. Rules of Procedure of the German Bundestag, also known as Geschäftsordnung (wd:Q1518204): instance of parliamentary rules of procedure
10. President by age, also known as Most senior member of the Bundestag (wd:Q15303678)

[Assistant]
The entity for "member of the German Bundestag" is wd:Q1939555. Now, I will find political parties that have members who held the position "member of the German Bundestag" (wd:Q1939555). This can be done by finding persons who held this position and then finding their political party.

I will query for distinct political parties of persons who held the position "member of the German Bundestag".

[Tool]
Got 44 rows and 1 column, showing the first 5 and last 5 rows below:
| party                                                  |
| ------------------------------------------------------ |
| Germany Alliance (wd:Q115801973)                       |
| Yugoslav Radical Union (wd:Q116009)                    |
| Democratic Party of Germany (wd:Q1186255)              |
| Values Union (wd:Q124624267)                           |
| National Democratic Party (wd:Q1272750)                |
| ...                                                    |
| Bavarian Peasants' League (wd:Q812330)                 |
| Social Democratic Party of Saarland (wd:Q876465)       |
| Internationaler Sozialistischer Kampfbund (wd:Q882940) |
| Danish Social Liberal Party (wd:Q916161)               |
| Liberal Democrats (wd:Q9624)                           |

[Assistant]
I found 44 distinct political parties that have had members who held the position "member of the German Bundestag." This means 44 political parties have ever had seats in the German Bundestag.

I will now finalize the answer.

[Tool] Stopped generation process