PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?instrument ?instrumentLabel WHERE {
  wd:Q154216 wdt:P1303 ?instrument .
  OPTIONAL {
    ?instrument rdfs:label ?instrumentLabel .
    FILTER ( LANG( ?instrumentLabel ) = "en" )
  }
}

Got 4 rows and 2 columns:
| ?instrument             | ?instrumentLabel |
| ----------------------- | ---------------- |
| voice (wd:Q17172850)    | voice            |
| bass guitar (wd:Q46185) | bass guitar      |
| piano (wd:Q5994)        | piano            |
| guitar (wd:Q6607)       | guitar           |

Cat Stevens (Yusuf Islam) plays the following instruments: voice, bass guitar, piano, and guitar.

[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

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/
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. Use the functions to search for 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. Use the execute function to get feedback on intermediate queries. You can go back and forth between knowledge graph searches and SPARQL query executions to refine the query.
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 assume or make up any entities or properties without verifying them with the knowledge graphs and functions.
3. 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.
4. 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.
5. 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.
6. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
7. 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 instruments does Cat Stevens play?

[Assistant] To answer the question about which instruments Cat Stevens plays, we need to identify the entity for Cat Stevens and then find the properties that indicate the instruments he plays. Let's start by searching for the entity representing Cat Stevens.

[Tool]
Top 10 entity alternatives:
1. Cat Stevens, also known as Yusuf Islam, Yusuf Islam (Cat Stevens), Yusuf/Cat Stevens, Yusuf (wd:Q154216): British musician (born 1948), singer-songwriter, recording artist, guitarist, musician, composer, singer
2. Cat Stevens (wd:Q123717389): 2001 box set by Cat Stevens
3. Catherine Crowe, also known as Catherine Ann Crowe, Mrs. Crowe, Catherine Stevens Crowe, Catherine Stevens (wd:Q2537956): English novelist, story writer, and playwright; spiritualist and spirit story collector (1790–1872), translator, writer
4. Catherine Stevens (wd:Q14918517): Belgian athletics competitor, athletics competitor
5. Catrin Stevens (wd:Q47124278): historian, editor, writer
6. Catherine Stevens (wd:Q96292350)
7. Catherine Hamersly, also known as Catherine Stevens, Kate Hamersly, Kate Stevens (wd:Q118198771): (1823-1893)
8. Barbara Stanwyck, also known as Ruby Stevens, Ruby Catherine Stevens (wd:Q94002): American actress (1907–1990), television actor, screenwriter, stage actor, film actor, actor, model
9. Catasetum stevensonii (wd:Q15443245): species of plant
10. Catherine Octavia Stevens (wd:Q105812567): British astronomer (1865–1959), astronomer

No other items found

[Assistant] The top result for "Cat Stevens" is the British musician Yusuf Islam (formerly known as Cat Stevens). We will use this entity to find the instruments he plays. Next, we need to identify the property that indicates the instruments played by a musician. Let's search for the property related to instruments played.

[Tool]
Top 10 property alternatives:
1. instrument, also known as uses this instrument, teaches instrument, instruments, plays this instrument, plays instrument (wd:P1303 as p/wdt): musical instrument that a person plays or teaches or used in a music occupation, uses
2. genre, also known as genre of music, type of music, type of film, music genre, literary genre (wd:P136 as p/wdt): creative work's genre or an artist's field of work (P101). Use main subject (P921) to relate creative works to their topic
3. Google Play Music artist ID (former scheme), also known as Play Music artist ID (wd:P4198 as p/wdt): identifier of an artist on the Google Play Music website. Obsolete starting December 2020
4. discography, also known as discography link, recording catalog (wd:P358 as p/wdt): item for list pages with discography of artist or band, list of works
5. languages spoken, written or signed, also known as language of expression, language, wrote language, writes language, uses language (wd:P1412 as p/wdt): language(s) that a person or a people speaks, writes or signs, including the native language(s)
6. Rate Your Music artist ID, also known as RateYourMusic artist ID, Rate Your Music artist identifier, RYM artist ID, RYM artist identifier (wd:P5404 as p/wdt): identifier for an artist or group on the website Rate Your Music
7. voice type, also known as range of voice, register, tessitura, type of voice, vocal type (wd:P412 as p/wdt): person's voice type. expected values: soprano, mezzo-soprano, contralto, countertenor, tenor, baritone, bass (and derivatives)
8. AllMusic artist ID, also known as AllMusic, AllMusic ID, amg artist (wd:P1728 as p/wdt): identifier for an artist, musical group, or record label in the AllMusic database (record labels are listed as "artists")
9. Muziekweb performer ID, also known as Muziekweb artist ID, Muziekweb band ID (wd:P5882 as p/wdt): identifier for a performer, composer or band at Muziekweb
10. MusicBrainz artist ID, also known as MB artist ID, MBID artist (wd:P434 as p/wdtn/wdt): identifier for an artist in the MusicBrainz open music encyclopedia

No other items found

[Assistant]
The property `instrument` (P1303) seems to be the most relevant for indicating the instruments played by a musician. Now, let's construct a SPARQL query to find the instruments played by Cat Stevens (Yusuf Islam).

We will start with a simple query to retrieve the instruments and then check the results.

```sparql
SELECT ?instrument WHERE {
  wd:Q154216 wdt:P1303 ?instrument .
}
```

Let's execute this query.

[Tool]
Got 4 rows and 1 column:
| ?instrument             |
| ----------------------- |
| voice (wd:Q17172850)    |
| bass guitar (wd:Q46185) |
| piano (wd:Q5994)        |
| guitar (wd:Q6607)       |

[Assistant]
The query returned the following instruments played by Cat Stevens (Yusuf Islam):
- Voice (wd:Q17172850)
- Bass guitar (wd:Q46185)
- Piano (wd:Q5994)
- Guitar (wd:Q6607)

These results seem accurate and complete. To provide a more user-friendly response, we can include the labels of these instruments in the query. However, since the labels are optional, we will use an `OPTIONAL` clause to fetch them.

Here is the final SPARQL query:

```sparql
SELECT ?instrument ?instrumentLabel WHERE {
  wd:Q154216 wdt:P1303 ?instrument .
  OPTIONAL { ?instrument rdfs:label ?instrumentLabel . FILTER(LANG(?instrumentLabel) = "en") }
}
```

Let's execute this final query.

[Tool]
Got 4 rows and 2 columns:
| ?instrument             | ?instrumentLabel |
| ----------------------- | ---------------- |
| voice (wd:Q17172850)    | voice            |
| bass guitar (wd:Q46185) | bass guitar      |
| piano (wd:Q5994)        | piano            |
| guitar (wd:Q6607)       | guitar           |

[Assistant]
The final query has successfully retrieved the instruments played by Cat Stevens (Yusuf Islam) along with their labels. The instruments are:
- Voice (wd:Q17172850)
- Bass guitar (wd:Q46185)
- Piano (wd:Q5994)
- Guitar (wd:Q6607)

Now, I will provide the final SPARQL query and the answer to the user question.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?org ?orgLabel WHERE {
  ?org wdt:P31 / wdt:P279 * wd:Q2401742 ; wdt:P159 wd:Q31 .
  OPTIONAL {
    ?org rdfs:label ?orgLabel .
    FILTER ( LANG( ?orgLabel ) = 'en' )
  }
}

No telecommunications organizations located in Belgium were found in the available data.

[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

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/
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. Use the functions to search for 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. Use the execute function to get feedback on intermediate queries. You can go back and forth between knowledge graph searches and SPARQL query executions to refine the query.
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 assume or make up any entities or properties without verifying them with the knowledge graphs and functions.
3. 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.
4. 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.
5. 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.
6. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
7. 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 telecommunications organizations are located in Belgium?

[Assistant] To answer this question, we need to identify the entities and properties involved. We are looking for organizations that are telecommunications companies and are located in Belgium. Let's start by searching for the entity representing Belgium and the property for location or headquarters.

[Tool]
Top 10 entity alternatives:
1. Belgium, also known as Kingdom of Belgium, BEL, be, BE, 🇧🇪 (wd:Q31): country in western Europe
2. Belgium, also known as Belgium, IL, Belgium, Illinois (wd:Q2281631): village in Illinois, United States
3. Belgium, also known as Belgium, WI (wd:Q2025327): village in Ozaukee County, Wisconsin
4. Belgium (wd:Q1658699): town in Wisconsin, United States
5. Belgium, also known as Belgium, West Virginia, Belgium, WV (wd:Q2894385): unincorporated community in West Virginia, United States
6. Belgium in the Eurovision Young Musicians, also known as Belgium (wd:Q14849447): Belgium in youth classical music contest
7. Belgium in the long nineteenth century, also known as Belgium (wd:Q16951058): history of Belgium from 1789 to 1914
8. Belgium (wd:Q106879226): family name
9. Belgium (wd:Q109469128): 2019 single by Lil Peep
10. Belgium (wd:Q19072874): poem by Florence Earle Coates

No other items found

[Assistant] The correct entity for Belgium is `wd:Q31`. Next, let's search for the property that indicates the location or headquarters of an organization.

[Tool]
Top 10 property alternatives:
1. headquarters location, also known as headquarters locality, seat, headquarters town, HQ, HQ location (wd:P159 as psv/wdtn/psn/p/pq/pr/prv/pqn/prn/pqv/ps/wdt): city or town where an organization's headquarters is or has been situated. Use P276 qualifier for specific building, significant place
2. capital, also known as administrative seat, seat of government, seat, principal place, location of capital (wd:P36 as psv/wdtn/psn/p/pq/pr/prv/pqn/prn/pqv/ps/wdt): seat of government of a country, province, state or other type of administrative territorial entity
3. organization directed by the office or position, also known as leader of, head of, leads, organization directed by, organization directed from the office (wd:P2389 as psv/wdtn/psn/p/pq/pr/prv/pqn/prn/pqv/ps/wdt): organization which is directed by this office
4. employer, also known as working at, working for, working place, works at, works for (wd:P108 as psv/wdtn/psn/p/pq/pr/prv/pqn/prn/pqv/ps/wdt): person or organization for which the subject works or worked, affiliation
5. has subsidiary, also known as subordinate body, subsidiary body, subordinate unit, subsidiary, subsidiary company (wd:P355 as psv/wdtn/psn/p/pq/pr/prv/pqn/prn/pqv/ps/wdt): subsidiary of a company or organization; generally a fully owned...siness division" (P199). Opposite of parent organization (P749)., has part(s), owner of
6. operating area, also known as province served, serves jurisdiction, serves geographic region, serves geographic area, serves area (wd:P2541 as psv/wdtn/psn/p/pq/pr/prv/pqn/prn/pqv/ps/wdt): geographic area or jurisdiction an organisation or industry operates in, serves, or has responsibility for, location
7. work location, also known as work place, working from, working at, worked from, worked at (wd:P937 as psv/wdtn/psn/p/pq/pr/prv/pqn/prn/pqv/ps/wdt): location where persons or organisations were actively participating in employment, business or other work, significant place, location
8. represents, also known as represents organisation, stands for, symbolises, symbolizes, team for (wd:P1268 as psv/wdtn/psn/p/pq/pr/prv/pqn/prn/pqv/ps/wdt): organization, individual, or concept that an entity represents
9. parent organization, also known as parent company, parent institution, parent organisation, parent unit, part of (wd:P749 as psv/wdtn/psn/p/pq/pr/prv/pqn/prn/pqv/ps/wdt): parent organization of an organization, opposite of subsidiaries (P355), owned by, part of
10. capital of, also known as national capital of, parish seat of, provincial capital of, seat of, state capital (wd:P1376 as psv/wdtn/psn/p/pq/pr/prv/pqn/prn/pqv/ps/wdt): country, state, department, canton or other administrative division of which the municipality is the governmental seat, located in the administrative territorial entity

No other items found

[Assistant] The property for the headquarters location is `wd:P159`. Now, we need to find the property that indicates that an organization is a telecommunications company. Let's search for the property or class that represents telecommunications organizations.

[Tool]
Top 10 entity alternatives:
1. telephone company, also known as telco, phone company, telephone service provider, telecom operator, telecommunications company (wd:Q1266169): organization that provides telephone and/or other telecommunications service
2. telecommunication company, also known as telecom service provider, telecoms company, telecom company, telecom, telco (wd:Q2401749): company that operates in the telecommunications industry
3. mobile network operator, also known as Mobile Network Operator, MNO, wireless telecommunications company, wireless service provider, wireless carrier, mobile service provider (wd:Q1941618): cellular service provider
4. company, also known as co., firm (wd:Q783794): legal entity representing an association of people, whether natural, legal or a mixture of both, with a specific objective
5. company, also known as company (military unit), military company (wd:Q102538): military unit size designation
6. enterprise, also known as business enterprise, firm, company (wd:Q6881511): organizational unit producing goods or services, which benefits ...n-making, especially for the allocation of its current resources
7. telecommunications, also known as telephone industry, telecoms, telecommunications sector, telecommunications market, ISIC 61 (wd:Q2401742): telecommunications industry in general or providers of telecommunications networks and services
8. Comparsa, also known as company, festival association (Moors and Christians) (wd:Q8348684): Each one of the associations that participate in Moors and Christians festivals in Spain
9. company (wd:Q52834234): Italian legal concept; complex of assets organized by an entrepreneur to carry on a business activity
10. company (Spain), also known as company, Spanish company (wd:Q117793965): military unit size designation within the structure of the Spanish Army

No other items found

No literal items found

[Assistant]
The entity for a telecommunications company is `wd:Q1266169` (telephone company) or `wd:Q2401749` (telecommunication company). We can use either of these to filter our results. Let's use `wd:Q1266169` for now.

Now, we can construct a SPARQL query to find telecommunications organizations located in Belgium. We will use the properties `wdt:P31` to check if the organization is a telecommunications company and `wdt:P159` to check if it is located in Belgium (`wd:Q31`).

Let's execute the query:

[Tool] Got no rows and 2 columns

[Assistant] It seems that there are no results returned for telecommunications organizations located in Belgium using the entity `wd:Q1266169`. Let's try using the alternative entity `wd:Q2401749` (telecommunication company) to see if we get any results.

[Tool] Got no rows and 2 columns

[Assistant]
It appears that using the entity `wd:Q2401749` (telecommunication company) also did not yield any results.

Since we are not getting any results, let's broaden our search to include any organization that is a telecommunications company or a related type of organization. We can use the broader category `wd:Q2401742` (telecommunications) to see if we can find any relevant organizations.

Let's execute the query with this broader category:

[Tool] Got no rows and 2 columns

[Assistant]
Even with the broader category `wd:Q2401742` (telecommunications), we are still not getting any results. This suggests that there might be no explicit entries in Wikidata for telecommunications organizations located in Belgium.

Given the lack of results, we can conclude that there are no known telecommunications organizations in Wikidata that are explicitly marked as being located in Belgium. However, this does not necessarily mean that such organizations do not exist in reality; it just means that they are not represented in the data available in Wikidata.

I will provide a final answer indicating that no results were found.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?award WHERE {
  wd:Q319308 wdt:P166 ?award .
}

Got 8 rows and 1 column:
| ?award                                                             |
| ------------------------------------------------------------------ |
| Guggenheim Fellowship (wd:Q1316544)                                |
| Albertus-Magnus professorate (wd:Q2639529)                         |
| Barwise Prize (wd:Q4865981)                                        |
| Fellow of the American Academy of Arts and Sciences (wd:Q52382875) |
| National Book Award (wd:Q572316)                                   |
| CSS Fellow (wd:Q59766571)                                          |
| Fellow of the Committee for Skeptical Inquiry (wd:Q61745062)       |
| Pulitzer Prize for General Nonfiction (wd:Q784589)                 |

Douglas Hofstadter has won the following awards: Guggenheim Fellowship, Albertus-Magnus professorate, Barwise Prize, Fellow of the American Academy of Arts and Sciences, National Book Award, CSS Fellow, Fellow of the Committee for Skeptical Inquiry, and Pulitzer Prize for General Nonfiction.

[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

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/
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. Use the functions to search for 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. Use the execute function to get feedback on intermediate queries. You can go back and forth between knowledge graph searches and SPARQL query executions to refine the query.
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 assume or make up any entities or properties without verifying them with the knowledge graphs and functions.
3. 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.
4. 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.
5. 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.
6. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
7. 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 awards did Douglas Hofstadter win?

[Assistant] To answer the question about the awards won by Douglas Hofstadler, I will first need to identify the entity for Douglas Hofstadter in the knowledge graph. Then, I will look for the properties that indicate awards or prizes he has received. Let's start by searching for Douglas Hofstadter as a subject.

[Tool]
Top 10 entity alternatives:
1. Douglas Hofstadter, also known as Douglas R. Hofstadter, Douglas Richard Hofstadter (wd:Q319308): American physicist, computer scientist and  professor of cognitive science, university teacher, computer scientist, philosopher, physicist, writer
2. Douglas, also known as Doolish, Douglas, Isle of Man (wd:Q18569): capital city of the Isle of Man
3. Douglas, also known as Douglas, Arizona, Douglas, AZ (wd:Q79878): city in Cochise County, Arizona, United States
4. Douglas, also known as Douglas, Wyoming, Douglas, WY (wd:Q983438): city in Converse County, Wyoming, United States
5. Douglas, also known as Douglas Aircraft Company (wd:Q651778): 1921-1967 aerospace manufacturer in the United States
6. Douglas, also known as Douglas, GA, Douglas, Georgia (wd:Q1004791): city in Coffee County, Georgia, United States
7. Douglas, also known as Douglas, AL (wd:Q66753): town in Marshall County, Alabama, in the United States
8. Douglas, also known as Douglas Pereira, Douglas Pereira dos Santos (wd:Q1988686): Brazilian association football player, born 1990, association football player
9. Douglas, also known as Douglas, MI, Douglas, Michigan (wd:Q2172413): city in Michigan, United States
10. Douglas, also known as Douglas, ND, Douglas, North Dakota (wd:Q2226686): city in Ward County, North Dakota, United States

No other items found

[Assistant]
The correct entity for Douglas Hofstadter is `wd:Q319308`. Now, I will look for the properties that indicate awards or prizes he has received. The most common property for awards in Wikidata is `P166` (award received). I will use this property to find the awards associated with Douglas Hofstadter.

Let's execute a SPARQL query to find the awards received by Douglas Hofstadter.

[Tool]
Got 8 rows and 1 column:
| ?award                                                             |
| ------------------------------------------------------------------ |
| Guggenheim Fellowship (wd:Q1316544)                                |
| Albertus-Magnus professorate (wd:Q2639529)                         |
| Barwise Prize (wd:Q4865981)                                        |
| Fellow of the American Academy of Arts and Sciences (wd:Q52382875) |
| National Book Award (wd:Q572316)                                   |
| CSS Fellow (wd:Q59766571)                                          |
| Fellow of the Committee for Skeptical Inquiry (wd:Q61745062)       |
| Pulitzer Prize for General Nonfiction (wd:Q784589)                 |

[Assistant]
The SPARQL query has returned the awards received by Douglas Hofstadter. Here are the awards:

1. Guggenheim Fellowship (wd:Q1316544)
2. Albertus-Magnus professorate (wd:Q2639529)
3. Barwise Prize (wd:Q4865981)
4. Fellow of the American Academy of Arts and Sciences (wd:Q52382875)
5. National Book Award (wd:Q572316)
6. CSS Fellow (wd:Q59766571)
7. Fellow of the Committee for Skeptical Inquiry (wd:Q61745062)
8. Pulitzer Prize for General Nonfiction (wd:Q784589)

I will now provide the final SPARQL query and the answer to the user's question.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?areaCode ?areaCodeLabel WHERE {
  wd:Q64 wdt:P473 ?areaCode .
  OPTIONAL {
    ?areaCode rdfs:label ?areaCodeLabel .
    FILTER ( LANG( ?areaCodeLabel ) = "en" )
  }
}

The area code for Berlin, Germany is 030.

[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

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/
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. Use the functions to search for 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. Use the execute function to get feedback on intermediate queries. You can go back and forth between knowledge graph searches and SPARQL query executions to refine the query.
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 assume or make up any entities or properties without verifying them with the knowledge graphs and functions.
3. 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.
4. 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.
5. 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.
6. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
7. 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 area code of Berlin?

[Assistant] To find the area code of Berlin, we need to identify the entity for Berlin and then look for the property that represents the area code. Let's start by searching for the entity representing Berlin.

[Tool]
Top 10 entity alternatives:
1. Berlin, also known as Berlin (Germany), Berlin, Germany, DE-BE (wd:Q64): federated state, capital and largest city of Germany
2. Berlin, also known as Berlin, New Hampshire, Berlin, NH (wd:Q821244): city in Coos County, New Hampshire, United States
3. Lincoln, also known as Berlin, Lincoln, IA, Lincoln, Iowa (wd:Q1930098): city in Iowa, United States
4. Berlin (wd:Q614184): town in Maryland, United States
5. Berlin, also known as Berlin, New Jersey, Long-a-Coming, New Jersey, Berlin, NJ (wd:Q1086827): borough in Camden County, New Jersey, United States
6. Berlin, also known as Berlin, GA, Berlin, Georgia, USA, Berlin, USA (wd:Q2345711): town in Colquitt County, Georgia, United States
7. Berlin, also known as Berlin, WI, Berlin, Wisconsin (wd:Q1569850): city in Green Lake and Waushara counties, Wisconsin, United States
8. Berlin, also known as Berlin, ND, Berlin, North Dakota (wd:Q821229): city in North Dakota
9. Berlin (wd:Q2504681): village in Illinois, U.S.
10. Berlin (wd:Q821198): American synthpop band

No other items found

[Assistant] The top result is the Berlin in Germany, which is the capital and largest city of Germany. We will use this entity to find the area code. Now let's search for the property that represents the area code.

[Tool]
Top 10 property alternatives:
1. local dialing code, also known as area code, dialling code, local calling code, local dialling code, STD code (wd:P473 as p/wdt): identifier dedicated to subject city by the area communication network
2. postal code, also known as pincode, PIN Code, postal index number, post code, postcode (wd:P281 as p/wdt): identifier assigned by postal authorities for the subject area or building
3. UN/LOCODE, also known as LOCCODE, LOCODE, UNECE location code, UNECE LOCODE, United Nations location code (wd:P1937 as p/wdt): geographic location code maintained by UNECE
4. social media followers, also known as number of subs, number of subscribers, sub count, sub number, subscriber number (wd:P8687 as p/wdt): number of subscribers on a particular social media website (use ... in time" and property for account. For Twitter, use numeric id.
5. area, also known as total area, surface area, surface, size, resolution (wd:P2046 as p/wdt): area occupied by an object
6. licence plate code, also known as license plate code, vehicle registration plate (wd:P395 as p/wdt): distinguishing signs or parts of license plate associated with t...al licence plate country code or distinguishing sign of vehicles
7. PM20 geo code (wd:P8483 as p/wdt): code for a geographical category in the 20th Century Press Archives' "countries" category system
8. HASC, also known as Hierarchical Administrative Subdivision Code (wd:P8119 as p/wdt): codes to represent country subdivisions
9. FIPS 10-4 (countries and regions), also known as fips country codes (wd:P901 as p/wdt): Identifier for countries and regions per former Federal Information Processing Standard FIPS 10-4
10. coordinate location, also known as point on a map, gps coordinates, gps location, latitude, location (wd:P625 as p/wdt): geocoordinates of the subject. For Earth, please note that only WGS84 coordinating system is supported at the moment

No other items found

[Assistant]
The property `local dialing code` (P473) seems to be the correct one for the area code. Let's construct a SPARQL query to retrieve the area code for Berlin, Germany.

We will select the area code and optionally include the label for better readability.

```sparql
SELECT ?areaCode ?areaCodeLabel
WHERE {
  wd:Q64 wdt:P473 ?areaCode.
  OPTIONAL { ?areaCode rdfs:label ?areaCodeLabel . FILTER(LANG(?areaCodeLabel) = "en") }
}
```

Let's execute this query.

[Tool]
Got 1 row and 2 columns:
| ?areaCode | ?areaCodeLabel |
| --------- | -------------- |
| 030       |                |

[Assistant]
The area code for Berlin, Germany is `030`.

Let's finalize the answer with the SPARQL query and the result.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?company ( SAMPLE( ?website ) AS ?mainWebsite ) WHERE {
  ?company wdt:P1128 ?employeeCount .
  ?company wdt:P856 ?website .
  FILTER ( ?employeeCount > 500000 )
}
GROUP BY ?company

Got 34 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?company                                          | ?mainWebsite                    |
| ------------------------------------------------- | ------------------------------- |
| DHL Group (wd:Q109931818)                         | <https://www.dpdhl.com>         |
| United States Department of Defense (wd:Q11209)   | <https://www.defense.gov/>      |
| United States Navy (wd:Q11220)                    | <https://www.navy.mil/>         |
| Police of Russia (wd:Q1188211)                    | <https://mvd.ru/>               |
| Diakonisches Werk (wd:Q126501)                    | <http://www.diakonie.de/>       |
| ...                                               | ...                             |
| PetroChina Company Limited (wd:Q503182)           | <https://www.petrochina.com.cn> |
| Public Administration of Uruguay (wd:Q56066)      | <http://portal.gub.uy/>         |
| China National Petroleum Corporation (wd:Q588786) | <http://www.cnpc.com.cn/>       |
| Russian Railways (wd:Q660770)                     | <https://eng.rzd.ru>            |
| United States Postal Service (wd:Q668687)         | <https://www.usps.com/>         |

The query returns the websites of companies with more than 500,000 employees, ensuring each company appears only once with a single website.

[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

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/
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. Use the functions to search for 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. Use the execute function to get feedback on intermediate queries. You can go back and forth between knowledge graph searches and SPARQL query executions to refine the query.
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 assume or make up any entities or properties without verifying them with the knowledge graphs and functions.
3. 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.
4. 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.
5. 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.
6. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
7. 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] Give me the websites of companies with more than 500000 employees.

[Assistant]
To answer this question, I need to identify the relevant properties for company websites and number of employees. I'll start by searching for these properties in the knowledge graph.

### Step 1: Identify Properties
I will search for the properties related to "website" and "number of employees".

#### Search for "website" property
I will search for the property "website" to find the corresponding IRI.

[Tool]
Top 10 property alternatives:
1. official shop URL, also known as online store, shop, shop URL, store, store URL (wd:P10225 as psv/wdtn/psn/p/pq/pr/prv/pqn/prn/pqv/ps/wdt): official URL where the agent sells products/merchandise, official website
2. official website, also known as site (official), home page, site official, URL, URL (official) (wd:P856 as psv/wdtn/psn/p/pq/pr/prv/pqn/prn/pqv/ps/wdt): URL of the official page of an item (current or former). Usage: ...but see the "Hijacked or dead websites" section of the Talk page, URL
3. title of broader work, also known as container title, website title (wd:P6333 as psv/wdtn/psn/p/pq/pr/prv/pqn/prn/pqv/ps/wdt): title of website, journal or other work in which an article (or ...is published in. Use "published in" (P1433) instead if possible.
4. login URL, also known as sign-in URL, sign in URL, sign-in page, sign in page, login page (wd:P11903 as psv/wdtn/psn/p/pq/pr/prv/pqn/prn/pqv/ps/wdt): URL of a webpage used to log into a service, URL
5. product or material produced, also known as products, product or service provided, product, material, or service produced or provided, product, producer of (wd:P1056 as psv/wdtn/psn/p/pq/pr/prv/pqn/prn/pqv/ps/wdt): material or product produced or sold by an organization, industry, facility, or process, material or product produced by an organization, industry, facility, or process, immediate cause of
6. product, material, or service produced or provided, also known as products, product or service provided, product, material, or service produced or provided, product, output (wd:P1056 as psv/wdtn/psn/p/pq/pr/prv/pqn/prn/pqv/ps/wdt): material or product produced or sold by an organization, industry, facility, or process, material or product produced by an organization, industry, facility, or process, immediate cause of
7. sitemap URL, also known as sitemap xml, site map URL, sitemap (wd:P11212 as psv/wdtn/psn/p/pq/pr/prv/pqn/prn/pqv/ps/wdt): URL to the sitemap of the subject's official website, URL
8. subreddit, also known as r/, reddit, Reddit community ID, Reddit forum, Reddit sub ID (wd:P3984 as psv/wdtn/psn/p/pq/pr/prv/pqn/prn/pqv/ps/wdt): name of the Reddit forum associated to this subject
9. reference URL, also known as URL fonte, webref, URL ref, URL, site (wd:P854 as psv/wdtn/psn/p/pq/pr/prv/pqn/prn/pqv/ps/wdt): should be used for Internet URLs as references. Use "Wikimedia import URL" (P4656) for imports from WMF sites, URL
10. URL, also known as Uniform Resource Locator, URLs, Uniform Resource Locators (wd:P2699 as psv/wdtn/psn/p/pq/pr/prv/pqn/prn/pqv/ps/wdt): location of a resource

No other items found

[Assistant]
From the search results, the property "official website" (wd:P856) seems to be the most appropriate for the company's website.

#### Search for "number of employees" property
Next, I will search for the property "number of employees" to find the corresponding IRI.

[Tool]
Top 10 property alternatives:
1. employees, also known as workers, staff, personnel, number of employees, employee count (wd:P1128 as psv/wdtn/psn/p/pq/pr/prv/pqn/prn/pqv/ps/wdt): total number of employees of a company at a given "point in time...not deprecated rank). Add data for recent years, don't overwrite
2. employer, also known as working at, working for, working place, works at, works for (wd:P108 as psv/wdtn/psn/p/pq/pr/prv/pqn/prn/pqv/ps/wdt): person or organization for which the subject works or worked, affiliation
3. significant person, also known as significant people, sigper, sign. person, relevant person, relevant people (wd:P3342 as psv/wdtn/psn/p/pq/pr/prv/pqn/prn/pqv/ps/wdt): person linked to the item in any possible way
4. member count, also known as total members, number of members, count of members, amount of members, membership (wd:P2124 as psv/wdtn/psn/p/pq/pr/prv/pqn/prn/pqv/ps/wdt): number of entities that are part of an organization at a particular time
5. number of seats, also known as membership quantity, number of seats (members), quantity of members, seats (members) (wd:P1342 as psv/wdtn/psn/p/pq/pr/prv/pqn/prn/pqv/ps/wdt): total number of seats/members in an assembly (legislative house or similar)
6. category for employees of the organization, also known as Academic staff, category for employees of the organisation, Professor (wd:P4195 as psv/wdtn/psn/p/pq/pr/prv/pqn/prn/pqv/ps/wdt): Wikimedia category for people who work or worked at this organization
7. work location, also known as work place, working from, working at, worked from, worked at (wd:P937 as psv/wdtn/psn/p/pq/pr/prv/pqn/prn/pqv/ps/wdt): location where persons or organisations were actively participating in employment, business or other work, significant place, location
8. number of works, also known as works, works posted, work count, posts posted, posts (wd:P3740 as psv/wdtn/psn/p/pq/pr/prv/pqn/prn/pqv/ps/wdt): qualifier on identifiers, e.g. for creators or locations, giving... external database associated with the subject of the identifier
9. employment by economic sector, also known as employment by industry, employment by sector (wd:P2297 as psv/wdtn/psn/p/pq/pr/prv/pqn/prn/pqv/ps/wdt): employment divided by sector
10. in work (wd:P2553 as psv/wdtn/psn/p/pq/pr/prv/pqn/prn/pqv/ps/wdt): qualifier of award received and nominated for (P1411) to specify...ominated creative work appeared causing the nomination/award win

No other items found

[Assistant]
From the search results, the property "employees" (wd:P1128) is the most appropriate for the number of employees.

### Step 2: Build the SPARQL Query
Now that I have identified the properties, I can build a SPARQL query to find companies with more than 500,000 employees and their official websites.

Here is the initial SPARQL query:

```sparql
SELECT ?company ?website
WHERE {
  ?company wdt:P1128 ?employeeCount .
  ?company wdt:P856 ?website .
  FILTER(?employeeCount > 500000)
}
```

I will execute this query to see if it returns the expected results.

[Tool]
Got 47 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?company                                          | ?website                     |
| ------------------------------------------------- | ---------------------------- |
| DHL Group (wd:Q109931818)                         | <https://www.dpdhl.com>      |
| United States Department of Defense (wd:Q11209)   | <https://www.defense.gov/>   |
| United States Navy (wd:Q11220)                    | <https://www.navy.mil/>      |
| Police of Russia (wd:Q1188211)                    | <https://mvd.ru/>            |
| Diakonisches Werk (wd:Q126501)                    | <http://www.diakonie.de/>    |
| ...                                               | ...                          |
| China National Petroleum Corporation (wd:Q588786) | <http://www.cnpc.com.cn/es/> |
| China National Petroleum Corporation (wd:Q588786) | <http://www.cnpc.com.cn/ru/> |
| Russian Railways (wd:Q660770)                     | <https://eng.rzd.ru>         |
| Russian Railways (wd:Q660770)                     | <https://www.rzd.ru/>        |
| United States Postal Service (wd:Q668687)         | <https://www.usps.com/>      |

[Assistant]
The query has returned the expected results, listing companies with more than 500,000 employees and their official websites. However, I notice that some companies appear multiple times with different language versions of their websites. To avoid duplicates, I will modify the query to ensure each company appears only once with a single website.

### Step 3: Refine the SPARQL Query
I will use the `DISTINCT` keyword to remove duplicate entries and ensure each company appears only once.

Here is the refined SPARQL query:

```sparql
SELECT DISTINCT ?company ?website
WHERE {
  ?company wdt:P1128 ?employeeCount .
  ?company wdt:P856 ?website .
  FILTER(?employeeCount > 500000)
}
```

I will execute this refined query to verify the results.

[Tool]
Got 46 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?company                                          | ?website                     |
| ------------------------------------------------- | ---------------------------- |
| DHL Group (wd:Q109931818)                         | <https://www.dpdhl.com>      |
| United States Department of Defense (wd:Q11209)   | <https://www.defense.gov/>   |
| United States Navy (wd:Q11220)                    | <https://www.navy.mil/>      |
| Police of Russia (wd:Q1188211)                    | <https://mvd.ru/>            |
| Diakonisches Werk (wd:Q126501)                    | <http://www.diakonie.de/>    |
| ...                                               | ...                          |
| China National Petroleum Corporation (wd:Q588786) | <http://www.cnpc.com.cn/es/> |
| China National Petroleum Corporation (wd:Q588786) | <http://www.cnpc.com.cn/ru/> |
| Russian Railways (wd:Q660770)                     | <https://eng.rzd.ru>         |
| Russian Railways (wd:Q660770)                     | <https://www.rzd.ru/>        |
| United States Postal Service (wd:Q668687)         | <https://www.usps.com/>      |

[Assistant]
It seems that the `DISTINCT` keyword did not fully resolve the issue of duplicate entries for companies with multiple language versions of their websites. To address this, I will use a subquery to select a single website for each company. I will choose the website with the highest number of languages available, assuming that the main website is often the one with the most comprehensive language support.

Here is the updated SPARQL query:

```sparql
SELECT ?company (SAMPLE(?website) AS ?mainWebsite)
WHERE {
  ?company wdt:P1128 ?employeeCount .
  ?company wdt:P856 ?website .
  FILTER(?employeeCount > 500000)
}
GROUP BY ?company
```

This query will group the results by company and select a single website for each company using the `SAMPLE` function. I will execute this query to verify the results.

[Tool]
Got 34 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?company                                          | ?mainWebsite                    |
| ------------------------------------------------- | ------------------------------- |
| DHL Group (wd:Q109931818)                         | <https://www.dpdhl.com>         |
| United States Department of Defense (wd:Q11209)   | <https://www.defense.gov/>      |
| United States Navy (wd:Q11220)                    | <https://www.navy.mil/>         |
| Police of Russia (wd:Q1188211)                    | <https://mvd.ru/>               |
| Diakonisches Werk (wd:Q126501)                    | <http://www.diakonie.de/>       |
| ...                                               | ...                             |
| PetroChina Company Limited (wd:Q503182)           | <https://www.petrochina.com.cn> |
| Public Administration of Uruguay (wd:Q56066)      | <http://portal.gub.uy/>         |
| China National Petroleum Corporation (wd:Q588786) | <http://www.cnpc.com.cn/>       |
| Russian Railways (wd:Q660770)                     | <https://eng.rzd.ru>            |
| United States Postal Service (wd:Q668687)         | <https://www.usps.com/>         |

[Assistant]
The refined query has successfully removed duplicate entries and provided a single website for each company. The results are now clean and accurate.

### Final Answer
Here is the final SPARQL query and the answer to the user's question:

```sparql
SELECT ?company (SAMPLE(?website) AS ?mainWebsite)
WHERE {
  ?company wdt:P1128 ?employeeCount .
  ?company wdt:P856 ?website .
  FILTER(?employeeCount > 500000)
}
GROUP BY ?company
```

This query returns the websites of companies with more than 500,000 employees, ensuring each company appears only once with a single website.

I will provide the final answer using the `answer` function.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?cosmonaut WHERE {
  ?cosmonaut wdt:P106 wd:Q12634815 .
}

Got 7 rows and 1 column:
| ?cosmonaut                                      |
| ----------------------------------------------- |
| Void (wd:Q106373153)                            |
| Svetlana Oktyabrevna Omelchenko (wd:Q108169808) |
| Marina Vasilevskaya (wd:Q118185958)             |
| Ural Sultanov (wd:Q15210574)                    |
| Anna Kikina (wd:Q18352451)                      |
| Vladimír Remek (wd:Q254985)                     |
| Arnaldo Tamayo Méndez (wd:Q362902)              |

The cosmonauts are: Void (wd:Q106373153), Svetlana Oktyabrevna Omelchenko (wd:Q108169808), Marina Vasilevskaya (wd:Q118185958), Ural Sultanov (wd:Q15210574), Anna Kikina (wd:Q18352451), Vladimír Remek (wd:Q254985), and Arnaldo Tamayo Méndez (wd:Q362902).

[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

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/
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. Use the functions to search for 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. Use the execute function to get feedback on intermediate queries. You can go back and forth between knowledge graph searches and SPARQL query executions to refine the query.
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 assume or make up any entities or properties without verifying them with the knowledge graphs and functions.
3. 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.
4. 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.
5. 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.
6. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
7. 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] Give me all cosmonauts.

[Assistant]
To find all cosmonauts, we need to identify the property or class that designates someone as a cosmonaut in the knowledge graph. A common approach would be to look for a property like "occupation" or "profession" and see if it links to a specific entity for cosmonauts.

Let's start by searching for the term "cosmonaut" as an object to see if there is a specific entity for cosmonauts.

[Tool]
Top 10 entity alternatives:
1. astronaut, also known as cosmonaut, vyomanaut, taikonaut, spationaut, spacionaut (wd:Q11631): person who commands, pilots, or serves as a crew member of a spacecraft
2. The Cosmonaut, also known as Cosmonaut (wd:Q3016381): 2013 film directed by Nicolás Alcalá
3. Forsaken, also known as Cosmonaut, Forsaken: Mission Mars, Martian, Prishelets, The Cosmonaut (wd:Q55659194): 2018 Russian science fiction film by Aleksandr Kulikov
4. Cosmonaut, also known as Cosmonauta (wd:Q3695198): 2009 film directed by Susanna Nicchiarelli
5. cosmonaut, also known as Russian astronaut (wd:Q12634815): astronaut from the former Soviet Union and its satellite countries and today's Russia
6. Cosmonaut (wd:Q5174345): 2010 studio album by Bump of Chicken
7. COSMONAUT (wd:Q122343082): 2018 video game
8. cosmonaut (wd:L493546-S2)
9. cosmonaut (wd:L30879)
10. cosmonaut (wd:L30879-F1)

No other items found

No literal items found

[Assistant]
From the search results, the most relevant entity for "cosmonaut" is `wd:Q12634815`, which is described as "astronaut from the former Soviet Union and its satellite countries and today's Russia."

Next, we need to find the property that links people to their occupation or profession. A common property for this is `wdt:P106` (occupation).

Let's construct a SPARQL query to find all individuals whose occupation is `wd:Q12634815`.

[Tool]
Got 7 rows and 1 column:
| ?cosmonaut                                      |
| ----------------------------------------------- |
| Void (wd:Q106373153)                            |
| Svetlana Oktyabrevna Omelchenko (wd:Q108169808) |
| Marina Vasilevskaya (wd:Q118185958)             |
| Ural Sultanov (wd:Q15210574)                    |
| Anna Kikina (wd:Q18352451)                      |
| Vladimír Remek (wd:Q254985)                     |
| Arnaldo Tamayo Méndez (wd:Q362902)              |

[Assistant]
The SPARQL query has returned a list of individuals whose occupation is listed as a cosmonaut (`wd:Q12634815`). These are the cosmonauts identified in the knowledge graph.

Here is the final SPARQL query and the list of cosmonauts:

```sparql
SELECT ?cosmonaut WHERE {
  ?cosmonaut wdt:P106 wd:Q12634815 .
}
```

The cosmonauts are:
- Void (wd:Q106373153)
- Svetlana Oktyabrevna Omelchenko (wd:Q108169808)
- Marina Vasilevskaya (wd:Q118185958)
- Ural Sultanov (wd:Q15210574)
- Anna Kikina (wd:Q18352451)
- Vladimír Remek (wd:Q254985)
- Arnaldo Tamayo Méndez (wd:Q362902)

I will now provide the final answer.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?owner WHERE {
  wd:Q243278 wdt:P127 ?owner
}

The owners of Rolls-Royce Holdings plc are ValueAct Capital, Credit Suisse, and Capital Group Companies.

[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

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/
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. Use the functions to search for 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. Use the execute function to get feedback on intermediate queries. You can go back and forth between knowledge graph searches and SPARQL query executions to refine the query.
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 assume or make up any entities or properties without verifying them with the knowledge graphs and functions.
3. 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.
4. 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.
5. 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.
6. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
7. Do not use 'SERVICE wikibase:label { bd:serviceParam wikibase:language ...' in SPARQL queries. It is not SPARQL standard and unsupported by the used QLever SPARQL endpoints. Use rdfs:label or similar properties to get labels instead.

[User] Who is the owner of Rolls-Royce?

[Assistant] To determine the owner of Rolls-Royce, we need to identify the entity representing Rolls-Royce and then find out who owns it. Let's start by searching for the entity representing Rolls-Royce.

[Tool]
Top 10 entity alternatives:
1. Rolls-Royce, also known as Rolls-Royce Holdings plc (wd:Q243278): British multinational public holding company
2. Rolls-Royce (wd:Q125141709): car brand
3. Rolls-Royce, also known as Rolls-Royce plc (wd:Q85623218): British company
4. Rolls-Royce Limited, also known as Rolls Royce Limited (wd:Q679888): 1906-1987 automobile and aerospace manufacturer in the United Kingdom
5. Merlin, also known as Merlin engine, Rolls-Royce Merlin, RR Merlin (wd:Q1412885): 12-cylinder V piston engine family by Rolls-Royce
6. The Yellow Rolls-Royce, also known as Yellow Rolls-Royce (wd:Q2474758): 1964 film by Anthony Asquith
7. Rolls-Royce Ghost (wd:Q1510648): car model
8. Rolls-Royce Trent (wd:Q1738127): turbofan aircraft engine family
9. Rolls-Royce Griffon (wd:Q2164325): 1930s British piston aircraft engine
10. Rolls-Royce RB211, also known as RB211 (wd:Q1636078): 1960s British turbofan aircraft engine

No other items found

[Assistant]
From the search results, the most relevant entity for Rolls-Royce is "Rolls-Royce, also known as Rolls-Royce Holdings plc" (wd:Q243278), which is described as a British multinational public holding company. Next, we need to find the property that represents ownership and then use it to find the owner of Rolls-Royce.

Let's search for the property that represents ownership.

[Tool]
Top 10 property alternatives:
1. owned by, also known as belongs to, stockholders, stockholder, shareholders, shareholder (wd:P127 as p/wdt): owner of the subject
2. owner of, also known as owns, owns property, shareholder of (wd:P1830 as p/wdt): entities owned by the subject
3. has subsidiary, also known as subordinate body, subsidiary body, subordinate unit, subsidiary, subsidiary company (wd:P355 as p/wdt): subsidiary of a company or organization; generally a fully owned...siness division" (P199). Opposite of parent organization (P749)., has part(s), owner of
4. parent organization, also known as parent company, parent institution, parent organisation, parent unit, part of (wd:P749 as p): parent organization of an organization, opposite of subsidiaries (P355), owned by, part of
5. founded by, also known as starter, co-founded by, started by, institutor, instituted by (wd:P112 as p/wdt): founder or co-founder of this organization, religion, place or entity, creator
6. part of, also known as of, within, system of, subsystem of, subgroup of (wd:P361 as p/wdt): object of which the subject is a part (if this subject is alread... of "has part" (P527, see also "has parts of the class" (P2670))
7. chairperson, also known as dean, headed by, leader, president, chairwoman (wd:P488 as p/wdt): presiding member of an organization, group or body, director / manager, significant person
8. inception, also known as initial release date, founded in, founded on, founded on date, founding date (wd:P571 as p/wdt): time when an entity begins to exist; for date of official opening use P1619, start time
9. chief executive officer, also known as executive director, CEO, chief executive (wd:P169 as p/wdt): highest-ranking corporate officer appointed as the CEO within an organization, director / manager, corporate officer
10. board member, also known as member of the board, member of the board of directors (wd:P3320 as p/wdt): member(s) of the board for the organization

No other items found

[Assistant]
The most relevant property for ownership is "owned by" (wd:P127). Now, let's use this property to find the owner of Rolls-Royce Holdings plc (wd:Q243278).

We will execute a SPARQL query to find the owner of Rolls-Royce Holdings plc.

[Tool]
Got 3 rows and 1 column:
| ?owner                               |
| ------------------------------------ |
| ValueAct Capital (wd:Q22032185)      |
| Credit Suisse (wd:Q372657)           |
| Capital Group Companies (wd:Q505275) |

[Assistant]
The query returned three entities that own shares in Rolls-Royce Holdings plc: ValueAct Capital (wd:Q22032185), Credit Suisse (wd:Q372657), and Capital Group Companies (wd:Q505275).

Since the question asks for the owner of Rolls-Royce, and multiple entities are listed as owners, it is appropriate to return all of them. However, if the user is looking for a single primary owner, we might need to refine the query further or provide additional context.

For now, let's provide the answer with the current results.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?politician ?spouse WHERE {
  ?politician wdt:P106 wd:Q82955 .
  ?politician wdt:P26 ?spouse .
  ?spouse wdt:P27 wd:Q183 .
}

Got 719 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?politician                         | ?spouse                                |
| ----------------------------------- | -------------------------------------- |
| Eberhard Gothein (wd:Q100765)       | Marie-Luise Gothein (wd:Q97309)        |
| Hans-Peter Bartels (wd:Q101192)     | Susanne Gaschke (wd:Q104875)           |
| Karl Trimborn (wd:Q101327)          | Jeanne Trimborn (wd:Q1686020)          |
| Siegfried Balleis (wd:Q101881)      | Angelika Balleis (wd:Q41543230)        |
| Joachim von Ribbentrop (wd:Q101886) | Annelies von Ribbentrop (wd:Q15531167) |
| ...                                 | ...                                    |
| Wolfgang Gehrcke (wd:Q99456)        | Christiane Reymann (wd:Q15794276)      |
| Claudia Wieja (wd:Q99773196)        | Horst Becker (wd:Q1629062)             |
| Wilhelm Polligkeit (wd:Q997978)     | Hilde Eiserhardt (wd:Q1618202)         |
| Anne Helm (wd:Q99906)               | Oliver Helm (wd:Q1170657)              |
| Katharina Jacob (wd:Q99927)         | Walter Hochmuth (wd:Q2544781)          |

The politicians who are married to Germans are listed in the results of the SPARQL query. Here are a few examples: Eberhard Gothein (wd:Q100765) is married to Marie-Luise Gothein (wd:Q97309), Hans-Peter Bartels (wd:Q101192) is married to Susanne Gaschke (wd:Q104875), Karl Trimborn (wd:Q101327) is married to Jeanne Trimborn (wd:Q1686020), Siegfried Balleis (wd:Q101881) is married to Angelika Balleis (wd:Q41543230), Joachim von Ribbentrop (wd:Q101886) is married to Annelies von Ribbentrop (wd:Q15531167).

[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

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/
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. Use the functions to search for 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. Use the execute function to get feedback on intermediate queries. You can go back and forth between knowledge graph searches and SPARQL query executions to refine the query.
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 assume or make up any entities or properties without verifying them with the knowledge graphs and functions.
3. 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.
4. 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.
5. 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.
6. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
7. 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 politicians were married to a German?

[Assistant]
To answer this question, we need to identify the relevant entities and properties:
1. Politicians: We can use the occupation property (P106) to filter for politicians.
2. Marriage: We can use the spouse property (P26) to find the marriage relationships.
3. German nationality: We can use the country of citizenship property (P27) to filter for Germans.

Let's start by searching for the politician occupation and the spouse property to ensure these exist in the knowledge graph.

[Tool]
Top 10 property alternatives:
1. instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as p/wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
2. image, also known as still capture, still, screenshot, screen capture, portrait (wd:P18 as p/wdt): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)
3. field of this occupation, also known as practice, works in, profession's field, practices, field of this profession (wd:P425 as p/wdt): field corresponding to this occupation or profession (use only f...fessions - for people use Property:P101, for companies use P452)
4. properties for this type, also known as uses properties, Wikidata properties for this type, Wikidata properties for this class, uses property, things one could say about (wd:P1963 as p/wdt): when this subject is used as object of “instance of”, the following properties normally apply
5. Wikidata property, also known as main property, property for this item, property, has property (wd:P1687 as p/wdt): main Wikidata property for this item
6. UK Parliament thesaurus ID (wd:P4527 as p/wdtn/wdt): identifier in the thesaurus of subject headings maintained by the librarians in the House of Commons in the UK
7. uses, also known as uses trope, used, using, wielded, wields (wd:P2283 as p/wdt): item or concept used by the subject or in the operation (see also instrument [P1303] and armament [P520])
8. subclass of, also known as rdfs:subClassOf, sort of, subcategory of, subgenre of, subtype of (wd:P279 as p/wdt): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
9. Freebase ID, also known as Freebase, MID, Freebase identifier, FID (wd:P646 as p/wdt): identifier for a page in the Freebase database. Format: "/m/0" f...r IDs starting with "/g/", use Google Knowledge Graph ID (P2671)
10. topic's main category, also known as article's main category, category, category for this topic, catmain, has category (wd:P910 as p/wdt): main Wikimedia category

No other items found

[Assistant]
It seems like the search for the politician occupation did not yield the expected results. Let me try searching for the occupation "politician" directly and then check the spouse property.

Let's search for the occupation "politician" first.

[Tool]
Top 10 entity alternatives:
1. politician, also known as pol, political leader, polit., political figure (wd:Q82955): person who holds or seeks positions in government
2. Almudena Martínez del Junco, also known as politician, Spanish (wd:Q127427405): politician in Spain, politician
3. Politician (wd:Q104854338): 1985 novel by Piers Anthony
4. Politician, also known as Jeongchiinssa (wd:Q97217684): South Korea's radio channels
5. Rohit Pal, also known as politician (wd:Q4699959): public figure
6. Cecilia Achieng Ayot, also known as Politician (wd:Q115153599): Kenyan Politician Member of County Assembly Ward Laini Saba, politician
7. David Ruong'o Okelo, also known as Politician (wd:Q115153352): Kenyan Politician, politician
8. Ahmed Abdullahi, also known as Politician (wd:Q114862677): Kenyan Politician
9. Babacar J. Diop, also known as Politician (wd:Q121790292): Babacar J. Diop is current Special Adviser of Youth Policies and...19 to 2022, he served as National Youth Program Officer for UNDP, politician
10. antony kiragu karanja, also known as Politician (wd:Q115153332): Kenyan Politician Minority Leader, politician

No other items found

No literal items found

[Assistant]
The search for "politician" as an object returned the correct entity `wd:Q82955`. Now, let's confirm the property for "spouse" and the property for "country of citizenship" to ensure they exist and are correctly identified.

I'll search for the "spouse" property and the "country of citizenship" property.

### Search for "spouse" property

[Tool]
Top 10 property alternatives:
1. spouse, also known as married partner, married to, marry, partner, spouses (wd:P26 as psv/wdtn/psn/p/pq/pr/prv/pqn/prn/pqv/ps/wdt): the subject has the object as their spouse (husband, wife, partn...etc.). Use "unmarried partner" (P451) for non-married companions, significant person, relative
2. relative, also known as relatives, grandparent, relation, non-binary parent, niece (wd:P1038 as psv/wdtn/psn/p/pq/pr/prv/pqn/prn/pqv/ps/wdt): family member (qualify with "kinship to subject", P1039; for direct family member please use specific property), significant person
3. unmarried partner, also known as romantic partner, partner of, partners, posslq, relationship (wd:P451 as psv/wdtn/psn/p/pq/pr/prv/pqn/prn/pqv/ps/wdt): someone with whom the person is in a relationship without being married. Use "spouse" (P26) for married couples, significant person
4. married name, also known as full name (married) (wd:P2562 as psv/wdtn/psn/p/pq/pr/prv/pqn/prn/pqv/ps/wdt): name or surname adopted by a person upon marriage, name
5. place of marriage, also known as marriage location, wedding location, wed at, married in, married at (wd:P2842 as psv/wdtn/psn/p/pq/pr/prv/pqn/prn/pqv/ps/wdt): location where the marriage was celebrated. Use as qualifier for property "spouse" (P26), significant place
6. birth name, also known as née, name at birth, name before marriage, ne, né (wd:P1477 as psv/wdtn/psn/p/pq/pr/prv/pqn/prn/pqv/ps/wdt): full name of a person at birth, if different from their current, generally used name, name
7. connects with, also known as contacts, couples with, intersects at grade, links to, mates with (wd:P2789 as psv/wdtn/psn/p/pq/pr/prv/pqn/prn/pqv/ps/wdt): item with which the item is physically connected, physically interacts with
8. companion of, also known as has astronomical companion, has companion, companion, astronomic companion of, astronomic companion (wd:P399 as psv/wdtn/psn/p/pq/pr/prv/pqn/prn/pqv/ps/wdt): two or more astronomic bodies of the same type relating to each other
9. end time, also known as ends, stop time, endtime, fall date, ineffective (wd:P582 as psv/wdtn/psn/p/pq/pr/prv/pqn/prn/pqv/ps/wdt): moment when an entity ceases to exist and a statement stops being entirely valid or no longer be true, point in time
10. relation (wd:P2309 as psv/wdtn/psn/p/pq/pr/prv/pqn/prn/pqv/ps/wdt): qualifier to define a property constraint in combination with P2...h the property "relative" is "type of kinship" (P1039), not this

No other items found

[Assistant]
The search for the "spouse" property returned the correct property `wd:P26`. Now, let's search for the "country of citizenship" property to ensure it exists and is correctly identified.

### Search for "country of citizenship" property

[Tool]
Top 10 property alternatives:
1. country of citizenship, also known as subject of (country), nation of citizenship, national of, nationality, nation (wd:P27 as psv/wdtn/psn/p/pq/pr/prv/pqn/prn/pqv/ps/wdt): the object is a country that recognizes the subject as its citizen, country
2. place of birth, also known as POB, location of birth, location born, born in, birthplace (wd:P19 as psv/wdtn/psn/p/pq/pr/prv/pqn/prn/pqv/ps/wdt): most specific known birth location of a person, animal or fictional character, location
3. date of birth, also known as year of birth, birthdate, DOB, born on, born (wd:P569 as psv/wdtn/psn/p/pq/pr/prv/pqn/prn/pqv/ps/wdt): date on which the subject was born, inception
4. country for sport, also known as sports nationality, sports country, sport nationality, sporting nationality, sport country (wd:P1532 as psv/wdtn/psn/p/pq/pr/prv/pqn/prn/pqv/ps/wdt): country a person or a team represents when playing a sport, country
5. valid in place, also known as applies to country, applies to location, applies to place, country, true in (wd:P3005 as psv/wdtn/psn/p/pq/pr/prv/pqn/prn/pqv/ps/wdt): place where a statement is valid, applies to part
6. country of origin, also known as origin country, place of origin, originates from, country, CoO (wd:P495 as psv/wdtn/psn/p/pq/pr/prv/pqn/prn/pqv/ps/wdt): country of origin of this item (creative work, food, phrase, product, etc.), significant place, country
7. country, also known as sovereign state, state, land, host country (wd:P17 as psv/wdtn/psn/p/pq/pr/prv/pqn/prn/pqv/ps/wdt): sovereign state that this item is in (not to be used for human beings), located in the administrative territorial entity
8. birth name, also known as née, name at birth, name before marriage, ne, né (wd:P1477 as psv/wdtn/psn/p/pq/pr/prv/pqn/prn/pqv/ps/wdt): full name of a person at birth, if different from their current, generally used name, name
9. permanent resident of, also known as landed immigrant of, PR (wd:P5389 as psv/wdtn/psn/p/pq/pr/prv/pqn/prn/pqv/ps/wdt): country or region where a person has the legal status of permanent resident
10. residence, also known as living in, place of residence, resided at, resided in, resident in (wd:P551 as psv/wdtn/psn/p/pq/pr/prv/pqn/prn/pqv/ps/wdt): the place where the person is or has been, resident, location

No other items found

[Assistant]
The search for the "country of citizenship" property returned the correct property `wd:P27`. Now we have all the necessary components to construct the SPARQL query:

1. **Politician**: `wd:Q82955`
2. **Spouse**: `wd:P26`
3. **Country of citizenship**: `wd:P27`

We need to find politicians who have a spouse who is a citizen of Germany. The IRI for Germany is `wd:Q183`.

Let's construct the SPARQL query to find these politicians.

### Constructing the SPARQL Query

We will:
1. Find all individuals who are politicians.
2. Check if they have a spouse.
3. Check if the spouse is a citizen of Germany.

Here is the initial SPARQL query:

```sparql
SELECT ?politician ?spouse WHERE {
  ?politician wdt:P106 wd:Q82955 .
  ?politician wdt:P26 ?spouse .
  ?spouse wdt:P27 wd:Q183 .
}
```

Let's execute this query to see the results.

### Execute the SPARQL Query

[Tool]
Got 719 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?politician                         | ?spouse                                |
| ----------------------------------- | -------------------------------------- |
| Eberhard Gothein (wd:Q100765)       | Marie-Luise Gothein (wd:Q97309)        |
| Hans-Peter Bartels (wd:Q101192)     | Susanne Gaschke (wd:Q104875)           |
| Karl Trimborn (wd:Q101327)          | Jeanne Trimborn (wd:Q1686020)          |
| Siegfried Balleis (wd:Q101881)      | Angelika Balleis (wd:Q41543230)        |
| Joachim von Ribbentrop (wd:Q101886) | Annelies von Ribbentrop (wd:Q15531167) |
| ...                                 | ...                                    |
| Wolfgang Gehrcke (wd:Q99456)        | Christiane Reymann (wd:Q15794276)      |
| Claudia Wieja (wd:Q99773196)        | Horst Becker (wd:Q1629062)             |
| Wilhelm Polligkeit (wd:Q997978)     | Hilde Eiserhardt (wd:Q1618202)         |
| Anne Helm (wd:Q99906)               | Oliver Helm (wd:Q1170657)              |
| Katharina Jacob (wd:Q99927)         | Walter Hochmuth (wd:Q2544781)          |

[Assistant]
The SPARQL query has successfully returned a list of politicians who are married to Germans. The results include both the IRIs of the politicians and their spouses.

Here is the final SPARQL query and the answer to the user's question:

### Final SPARQL Query

```sparql
SELECT ?politician ?spouse WHERE {
  ?politician wdt:P106 wd:Q82955 .
  ?politician wdt:P26 ?spouse .
  ?spouse wdt:P27 wd:Q183 .
}
```

### Answer to the User's Question

The politicians who are married to Germans are listed in the results of the SPARQL query. Here are a few examples:

- Eberhard Gothein (wd:Q100765) is married to Marie-Luise Gothein (wd:Q97309)
- Hans-Peter Bartels (wd:Q101192) is married to Susanne Gaschke (wd:Q104875)
- Karl Trimborn (wd:Q101327) is married to Jeanne Trimborn (wd:Q1686020)
- Siegfried Balleis (wd:Q101881) is married to Angelika Balleis (wd:Q41543230)
- Joachim von Ribbentrop (wd:Q101886) is married to Annelies von Ribbentrop (wd:Q15531167)

And many more.

Let's provide the final answer using the `answer` function.

### Final Answer

[Tool] Stopped generation process