PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?instrument WHERE {
  wd:Q154216 wdt:P1303 ?instrument .
}

Cat Stevens 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.

First, let's search for the entity "Cat Stevens" in the knowledge graph.

[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

[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 out which instruments he plays.

Next, we need to identify the property that indicates the instruments played by a musician. Let's search for properties related to "instruments" or "plays".

[Tool]
Top 10 property alternatives:
1. instrumentation, also known as scoring, scored for, orchestration, instruments employed, orchestrated for (wd:P870 as psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): combination of musical instruments employed in a composition or accompanying a (folk) dance
2. instrument, also known as uses this instrument, teaches instrument, instruments, plays this instrument, plays instrument (wd:P1303 as psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): musical instrument that a person plays or teaches or used in a music occupation, uses
3. orchestration, also known as scoring, score notation, orchestration notation, orchestration in notated form, notated scoring (wd:P11862 as psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): an enumeration of the instruments used in a musical composition or arrangement described using any system of score notation
4. uses, also known as uses trope, used, using, wielded, wields (wd:P2283 as psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): item or concept used by the subject or in the operation (see also instrument [P1303] and armament [P520])
5. captured with, also known as camera used, taken with, recording device, recorded with, photographed with (wd:P4082 as psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): equipment (e.g. model of camera, lens, microphone), used to capture this image, video, audio, or data
6. setlist, also known as set list, works performed, songs performed, performances, performance list (wd:P9793 as psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): performances of songs or other music at an event; concert program, presented works
7. avionics, also known as sensors, electronics (wd:P878 as psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): notable sensors, electronics, and other avionics installed in the subject aircraft, has facility
8. issuing agent of work, also known as work issued by, organ of, official publication of, official organ of, official journal of (wd:P9901 as psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): organization, agency, or other entity responsible for issuing a ...f the entity; distinct from commercial publisher of the resource
9. carries scientific instrument, also known as metering equipment, scientific equipment (wd:P1202 as psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): scientific instruments carried by a vessel, satellite, or device that are not required for propelling or navigating, has facility
10. performer, also known as actor, sung by, singer, recording by, recorded by (wd:P175 as psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): actor, musician, band or other performer associated with this role or musical work, contributor to the creative work or subject

[Assistant]
The property "instrument" (P1303) seems to be the most relevant for indicating the instruments played by a musician. Let's use this property to find out which instruments Cat Stevens plays.

We will construct a SPARQL query to retrieve the instruments played by Cat Stevens (Yusuf Islam).

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

[Assistant]
The SPARQL query returned the instruments that Cat Stevens plays. These include:

- Voice (wd:Q17172850)
- Bass guitar (wd:Q46185)
- Piano (wd:Q5994)
- Guitar (wd:Q6607)

Now, let's format the answer and provide the final SPARQL query.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?org ?orgLabel WHERE {
  ?org wdt:P276 wd:Q31 .
  OPTIONAL {
    ?org rdfs:label ?orgLabel .
    FILTER ( LANG( ?orgLabel ) = 'en' )
  }
}
LIMIT 100

Got 100 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?org                                                                 | ?orgLabel                                            |
| -------------------------------------------------------------------- | ---------------------------------------------------- |
| bandy in Belgium (wd:Q10425949)                                      | bandy in Belgium                                     |
| motorsports in Belgium (wd:Q10589700)                                | motorsports in Belgium                               |
| Européen transporté dans un hamac (wd:Q107330944)                    | Européen transporté dans un hamac                    |
| wd:Q107331070                                                        |                                                      |
| 2021–22 Belgian Women's Super League (wd:Q108253013)                 | 2021–22 Belgian Women's Super League                 |
| ...                                                                  | ...                                                  |
| Dieu sera avec nous dans cette cause juste, Albert I (wd:Q131425100) | Dieu sera avec nous dans cette cause juste, Albert I |
| Dieu sera avec nous dans cette cause juste, Albert I (wd:Q131425101) | Dieu sera avec nous dans cette cause juste, Albert I |
| Dieu sera avec nous dans cette cause juste, Albert I (wd:Q131425102) | Dieu sera avec nous dans cette cause juste, Albert I |
| voor de eer van 't vaderland, Albert I (wd:Q131425103)               | voor de eer van 't vaderland, Albert I               |
| pour l'honneur de la patrie, Albert I (wd:Q131425105)                | pour l'honneur de la patrie, Albert I                |

No telecommunications organizations located in Belgium were found in the Wikidata knowledge graph.

[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:

1. **Telecommunications Organizations**: We need to find entities that are classified as telecommunications organizations.
2. **Location**: We need to find entities that are located in Belgium.

Let's start by searching for the entity representing Belgium and the property for location.

### Step 1: Search for Belgium
We will use the `search_entity` function to find the entity for Belgium.

### Step 2: Search for the property for location
We will use the `search_property` function to find the property that represents the location of an organization.

Let's start with the first step.

[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

[Assistant]
The top result for Belgium is `wd:Q31`, which represents the country of Belgium. We will use this entity in our query.

### Step 2: Search for the property for location
Now, let's search for the property that represents the location of an organization.

[Tool]
Top 10 property alternatives:
1. work location, also known as work place, working from, working at, worked from, worked at (wd:P937 as psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): location where persons or organisations were actively participating in employment, business or other work, significant place, location
2. location, also known as neighbourhood, whereabouts, vilage, venue, town (wd:P276 as psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): location of the object, structure or event. In the case of an ad...ity use P706. Use P7153 for locations associated with the object
3. coordinate location, also known as point on a map, gps coordinates, gps location, latitude, location (wd:P625 as psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): geocoordinates of the subject. For Earth, please note that only WGS84 coordinating system is supported at the moment
4. occupant, also known as tenant, resident, renter, occupiers, lessee (wd:P466 as psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): person or organization occupying property, used by
5. recorded at studio or venue, also known as venue, studio, recording venue, recording studio, recorded at event (wd:P483 as psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): studio or location where a musical composition/release was recorded, recording location
6. home venue, also known as home water, venue, stadium, home ground, home field (wd:P115 as psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): home stadium or venue of a sports team or applicable performing arts organization, location
7. published in, also known as song on album, tome of the book, tome of, track of, track on (wd:P1433 as psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): larger work that a given work was published in, like a book, journal or music album, part of
8. located in the present-day administrative territorial entity, also known as present-day administrative territorial entity, located in the current administrative territorial entity, located in the modern-day administrative territorial entity, located in the present-day administrative territorial entity, would today be located in administrative territorial entity (wd:P3842 as psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): the item was located in the territory of this present-day administrative unit; however the two did not at any point coexist in time, location, part of
9. directions, also known as locality or place (wd:P2795 as psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): describe how to find the subject - directions, objects along way, comments
10. ranking, also known as peak position, place, placing, position, rank (wd:P1352 as psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): subject's ordinal position as qualitatively evaluated relative to other members of a group

[Assistant]
The property `wd:P276` (location) seems to be the most appropriate for our needs. It represents the location of an object, structure, or event.

### Step 3: Identify Telecommunications Organizations
Next, we need to find the entity that represents telecommunications organizations. We can search for this entity using the `search_entity` function.

Let's search for "telecommunications organization" or "telecom company".

[Tool]
Top 10 entity alternatives:
1. Intelsat, also known as International Telecommunications Satellite Organization, International Telecommunications Satellite Corporation, Intelsat S.A. (wd:Q778126): Luxembourgish communications satellite services provider
2. OTE, also known as Organismos Tilepikinonion Ellados AE, HTO, Hellenic Telecommunications Organization S.A. (wd:Q206678): Greek telecommunication company
3. International Telecommunications Satellite Organization, also known as Agreement relating to the Intern...nications Satellite Organization (wd:Q15546770): intergovernmental organization
4. European Telecommunications Satellite Organization, also known as Eutelsat IGO (wd:Q47537350): European intergovernmental organisation
5. Globalization, International Organizations, and Telecommunications (wd:Q60140176): scholarly article by Kirsten Rodine-Hardy published September 2015 in Review of Policy Research
6. Intelsat headquarters, also known as 3400 International Drive, 4000 Connecticut Avenue, INTELSAT Headquarter Building, INTELSAT Headquarters Building, International Telecommunications...ganization Headquarters Building (wd:Q6044259): space age style building in Washington, DC
7. organization, also known as organizations, organization in sociology, organisation, institution, organisations (wd:Q43229): social entity established to meet needs or pursue goals
8. .org, also known as dot-org, .organization, .org domain (wd:Q32131): generic top-level domain
9. The Organization, also known as Organization (wd:Q1986084): 1971 film by Don Medford
10. 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

[Assistant]
The entity `wd:Q2401742` represents the telecommunications industry or providers of telecommunications networks and services. However, it is not a specific organization but rather a broader category. To find specific telecommunications organizations, we can use the property `wd:P31` (instance of) to filter for organizations that are instances of telecommunications organizations.

### Step 4: Build the SPARQL Query
We will now build a SPARQL query to find telecommunications organizations located in Belgium. We will use the following properties:
- `wd:P31` to filter for organizations that are instances of telecommunications organizations.
- `wd:P276` to filter for organizations located in Belgium (`wd:Q31`).

Let's execute the query.

[Tool] Got no rows and 2 columns

[Assistant]
It seems that the query did not return any results. This could be due to the fact that the entity `wd:Q2401742` might not be the correct one for identifying specific telecommunications organizations. Let's try another approach.

### Step 5: Search for Specific Telecommunications Organizations
We can search for specific telecommunications organizations and then check their locations. Let's search for "telecom company" to find more specific entities.

Let's search for "telecom company".

[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. Saudi Telecom Company, also known as STC, STC International, STC Group, Saudi Telecom Group, Saudi Telecom (wd:Q1535171): Saudi Arabia-based telecommunications company
4. Telecom Egypt, also known as Egypt Telecom Company, TE, Telecom Egypt Company, We (wd:Q1583794): main and leading Internet Service Provider (ISP) and mobile carrier in Egypt
5. Integrated Telecom Company (wd:Q16965386)
6. CMC Telecom Company, also known as CMC Telecom (wd:Q50480350): Vietnamese international telecommunications infrastructure company
7. Mongolia Telecom Company (wd:Q6899579)
8. telecommunications company (wd:L779293-S1)
9. 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
10. Omantel, also known as Oman Telecommunications Company (wd:Q1535581): telecommunication company in Oman

[Assistant]
The entity `wd:Q1266169` (telephone company) and `wd:Q2401749` (telecommunication company) seem to be more specific and appropriate for identifying telecommunications organizations. We will use these entities to filter for organizations that are instances of these types.

### Step 6: Refine the SPARQL Query
We will now refine the SPARQL query to include both `wd:Q1266169` and `wd:Q2401749` as possible instances of telecommunications organizations.

Let's execute the refined query.

[Tool] Got no rows and 2 columns

[Assistant]
It seems that even with the refined query, we are still not getting any results. This suggests that there might be a different way to identify telecommunications organizations in Belgium.

### Step 7: Search for Specific Belgian Telecommunications Organizations
Let's try a different approach by searching for specific Belgian telecommunications organizations directly. We can use the `search_entity` function to find entities that are telecommunications organizations in Belgium.

Let's search for "Belgian telecom company" or "Belgian telecommunications organization".

[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. Saudi Telecom Company, also known as STC, STC International, STC Group, Saudi Telecom Group, Saudi Telecom (wd:Q1535171): Saudi Arabia-based telecommunications company
4. Telecom Egypt, also known as Egypt Telecom Company, TE, Telecom Egypt Company, We (wd:Q1583794): main and leading Internet Service Provider (ISP) and mobile carrier in Egypt
5. Crescent (Belgian company), also known as Crescent, Option N.V. (wd:Q1949398): technology company
6. Integrated Telecom Company (wd:Q16965386)
7. CMC Telecom Company, also known as CMC Telecom (wd:Q50480350): Vietnamese international telecommunications infrastructure company
8. Mongolia Telecom Company (wd:Q6899579)
9. Belgian state-owned company (wd:Q117406600): company owned by the Belgian government
10. telecommunications company (wd:L779293-S1)

[Assistant]
The search results did not provide any specific Belgian telecommunications organizations. However, we can try a more direct approach by looking for organizations that are located in Belgium and are instances of telecommunications companies.

### Step 8: Directly Query for Belgian Telecommunications Organizations
We will directly query for organizations that are located in Belgium and are instances of telecommunications companies. We will use the properties `wd:P31` and `wd:P276` to filter for organizations that are instances of telecommunications companies and are located in Belgium.

Let's execute the query again, but this time we will use a broader approach to ensure we capture all relevant organizations.

[Tool] Got no rows and 2 columns

[Assistant]
It seems that even with a broader approach, we are still not getting any results. This suggests that the data in Wikidata might not be structured in a way that allows us to easily find Belgian telecommunications organizations using the properties and entities we have tried.

### Step 9: Try a Different Approach
Let's try a different approach by searching for organizations that are located in Belgium and then checking if they are telecommunications organizations. We will use the property `wd:P31` to filter for organizations and then check if they are instances of telecommunications companies.

Let's execute the query to find organizations located in Belgium and then filter for telecommunications organizations.

[Tool] Got no rows and 2 columns

[Assistant]
It appears that even with this approach, we are not finding any Belgian telecommunications organizations. This could be due to the data in Wikidata not being structured in a way that allows us to easily identify these organizations using the properties and entities we have tried.

### Step 10: Final Attempt
As a final attempt, let's try a more general query to find organizations located in Belgium and then manually check if they are telecommunications organizations. We will use the property `wd:P276` to filter for organizations located in Belgium and then check if they are instances of any type of organization.

Let's execute the query to find organizations located in Belgium and then manually inspect the results.

[Tool]
Got 100 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?org                                                                 | ?orgLabel                                            |
| -------------------------------------------------------------------- | ---------------------------------------------------- |
| bandy in Belgium (wd:Q10425949)                                      | bandy in Belgium                                     |
| motorsports in Belgium (wd:Q10589700)                                | motorsports in Belgium                               |
| Européen transporté dans un hamac (wd:Q107330944)                    | Européen transporté dans un hamac                    |
| wd:Q107331070                                                        |                                                      |
| 2021–22 Belgian Women's Super League (wd:Q108253013)                 | 2021–22 Belgian Women's Super League                 |
| ...                                                                  | ...                                                  |
| Dieu sera avec nous dans cette cause juste, Albert I (wd:Q131425100) | Dieu sera avec nous dans cette cause juste, Albert I |
| Dieu sera avec nous dans cette cause juste, Albert I (wd:Q131425101) | Dieu sera avec nous dans cette cause juste, Albert I |
| Dieu sera avec nous dans cette cause juste, Albert I (wd:Q131425102) | Dieu sera avec nous dans cette cause juste, Albert I |
| voor de eer van 't vaderland, Albert I (wd:Q131425103)               | voor de eer van 't vaderland, Albert I               |
| pour l'honneur de la patrie, Albert I (wd:Q131425105)                | pour l'honneur de la patrie, Albert I                |

[Assistant]
The results from the query show a variety of organizations and entities located in Belgium, but none of them appear to be telecommunications organizations. This suggests that the data in Wikidata might not have the specific information we are looking for in the way we are querying it.

### Conclusion
Given the lack of results from multiple approaches, it seems that the data in Wikidata does not provide a straightforward way to identify Belgian telecommunications organizations using the properties and entities we have tried.

### Final Answer
Since we were unable to find any Belgian telecommunications organizations in the data, the answer to the question "Which telecommunications organizations are located in Belgium?" is that there are no results available in the Wikidata knowledge graph with the current query methods.

I will provide the final SPARQL query and answer.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
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 which awards Douglas Hofstadler won, we need to identify the entity for Douglas Hofstadter and then find the awards associated with him. Let's start by searching for the entity for Douglas Hofstadter.

[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

[Assistant]
The correct entity for Douglas Hofstadter is `wd:Q319308`. Now, let's find the awards associated with this entity. We will look for properties related to awards and then use these properties to construct a SPARQL query.

First, let's search for properties related to awards.

[Tool]
Top 10 property alternatives:
1. award received, also known as win, winner of, recognition title, received award, prize received (wd:P166 as psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): award or recognition received by a person, organization or creative work
2. conferred by, also known as presented by, officiated by, issued by, granted by, given by (wd:P1027 as psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): person or organization who grants an award, certification, grant, or role
3. winner, also known as nominee, prizewinner, titleholder, top dog, victor (wd:P1346 as psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): winner of a competition or similar event, not to be used for awa...ssibly qualified with "for work" (P1686)) or for wars or battles, participating team, participant
4. grants, also known as awards, bestows, confers, gives out (wd:P5460 as psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): confers degree, honor, award, prize, title, certificate or medal denoting achievement to a person or organization
5. nominated for, also known as nominee for, shortlisted for, selected for award, selected for, nominee for award (wd:P1411 as psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): award nomination received by a person, organisation or creative work (inspired from "award received" (Property:P166))
6. for work, also known as awarded for work, citation for award, for their work on, nominated for work (wd:P1686 as psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): qualifier of award received (P166) to specify the work that an award was given to the creator for
7. award rationale, also known as award citation, rationale, award reason (wd:P6208 as psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): qualifier to add short citation explaining the official reason f... or add comments about a person's award. No quote marked needed.
8. nominee, also known as award nominee (wd:P2453 as psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): qualifier used with “nominated for” to specify which person or organization was nominated
9. eligible recipient, also known as eligible recipient for award, type of recipient who is eligible for this award (wd:P8115 as psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): party that is eligible to receive something, like an award, grant or prize
10. awarded for period (wd:P4566 as psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): period during which the achievement must have been made to be eligible for an award

[Assistant]
The property `wd:P166` (award received) is the most relevant for finding the awards Douglas Hofstadter has won. Let's construct a SPARQL query to retrieve the awards associated with `wd:Q319308`.

Here is the initial SPARQL query:

```sparql
SELECT ?award
WHERE {
  wd:Q319308 wdt:P166 ?award .
}
```

Let's execute this query to see the results.

[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 that Douglas Hofstadter has won. 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)

Now, let's provide the final answer.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?areaCode WHERE {
  wd:Q64 wdt:P473 ?areaCode .
}

Got 1 row and 1 column:
| ?areaCode |
| --------- |
| 030       |

The area code of Berlin 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 a property that represents the area code. Let's start by searching for the entity "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

[Assistant] The top result for "Berlin" is the capital and largest city of Germany, which is what we are looking for. The corresponding entity is `wd:Q64`. Next, we need to find a property that represents the area code. Let's search for a property related to "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 psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): identifier dedicated to subject city by the area communication network
2. CDDA designationTypeCode, also known as designated area code, CDDA type ID, CDDA type, CDDA, CDDA code (wd:P9800 as psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): code for the national designation type used in the Common Database on Designated Areas inventory (CDDA)
3. country calling code, also known as calling code, country code, country dialing code, dialing code, phone code (wd:P474 as psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): identifier for a country - dialed on phone after the international dialing prefix (precede value by +)
4. location code (wd:P13262 as psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): an assigned code for the location (physical or virtual) within a given system (please use more specific property if available), catalog code
5. mobile country code, also known as ITU-T country identifier, MCC (wd:P2258 as psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): identifier for countries in ITU-T Recommendation E.212 for use in describing mobile networks
6. postal code, also known as pincode, PIN Code, postal index number, post code, postcode (wd:P281 as psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): identifier assigned by postal authorities for the subject area or building
7. official number of town quarter, also known as Chak number, district number, region number, village number (wd:P5901 as psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): official number for the administrative division or section, region or part of a town
8. ATC code, also known as ATCC (wd:P267 as psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): therapeutic chemical identification code per ATC
9. operating area, also known as province served, serves jurisdiction, serves geographic region, serves geographic area, serves area (wd:P2541 as psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): geographic area or jurisdiction an organisation or industry operates in, serves, or has responsibility for, location
10. station code, also known as station identifier, station number, station numbering, telegraph code (wd:P296 as psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): generic identifier for a railway station, when possible, use spe... certain coding system (e.g. P1378 for China Railway TMIS codes), location code

[Assistant]
The property `wd:P473` (local dialing code) seems to be the most relevant for finding the area code of Berlin. Let's construct a SPARQL query to retrieve the area code for Berlin using this property.

Here is the initial SPARQL query:

```sparql
SELECT ?areaCode
WHERE {
  wd:Q64 wdt:P473 ?areaCode .
}
```

Let's execute this query to see if it returns the correct area code for Berlin.

[Tool]
Got 1 row and 1 column:
| ?areaCode |
| --------- |
| 030       |

[Assistant]
The SPARQL query returned the area code for Berlin, which is `030`.

Therefore, the area code of Berlin is **030**.

Here is the final SPARQL query and answer:

```sparql
SELECT ?areaCode
WHERE {
  wd:Q64 wdt:P473 ?areaCode .
}
```

Answer: The area code of Berlin is 030.

[Tool] Stopped generation process


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

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/>      |

The websites of companies with more than 500,000 employees include: DHL Group (<https://www.dpdhl.com>), United States Department of Defense (<https://www.defense.gov/>), United States Navy (<https://www.navy.mil/>), Police of Russia (<https://mvd.ru/>), Diakonisches Werk (<http://www.diakonie.de/>), China National Petroleum Corporation (<http://www.cnpc.com.cn/es/> and <http://www.cnpc.com.cn/ru/>), Russian Railways (<https://eng.rzd.ru> and <https://www.rzd.ru/>), and United States Postal Service (<https://www.usps.com/>).

[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, we need to identify the following elements:
1. The property for the company's website.
2. The property for the number of employees.
3. A way to filter out companies with more than 500,000 employees.

Let's start by searching for the relevant properties in the knowledge graph.

[Tool]
Top 10 property alternatives:
1. official shop URL, also known as online store, shop, shop URL, store, store URL (wd:P10225 as psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): 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/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): 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/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): 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/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): 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/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): 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/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): 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/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): 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/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): 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/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): 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/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): location of a resource

[Tool]
Top 10 property alternatives:
1. employees, also known as workers, staff, personnel, number of employees, employee count (wd:P1128 as psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): 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/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): 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/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): 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/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): 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/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): 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/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): 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/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): 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/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): 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/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): employment divided by sector
10. in work (wd:P2553 as psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): qualifier of award received and nominated for (P1411) to specify...ominated creative work appeared causing the nomination/award win

[Assistant]
From the search results, we can see that:
1. The property for the company's website is `wdt:P856`.
2. The property for the number of employees is `wdt:P1128`.

Now, let's construct a SPARQL query to find companies with more than 500,000 employees and their websites.

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

This query selects the company and its website where the number of employees is greater than 500,000. Let's execute this query to see the 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 SPARQL query has returned the websites of companies with more than 500,000 employees. Here are some of the results:

- **DHL Group**: <https://www.dpdhl.com>
- **United States Department of Defense**: <https://www.defense.gov/>
- **United States Navy**: <https://www.navy.mil/>
- **Police of Russia**: <https://mvd.ru/>
- **Diakonisches Werk**: <http://www.diakonie.de/>
- **China National Petroleum Corporation**: <http://www.cnpc.com.cn/es/> and <http://www.cnpc.com.cn/ru/>
- **Russian Railways**: <https://eng.rzd.ru> and <https://www.rzd.ru/>
- **United States Postal Service**: <https://www.usps.com/>

These are the websites of companies that have more than 500,000 employees.

Here is the final SPARQL query and the answer:

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

The answer to the question is the list of companies and their websites as shown above.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?cosmonaut ?cosmonautLabel WHERE {
  ?cosmonaut wdt:P106 wd:Q12634815 .
  OPTIONAL {
    ?cosmonaut rdfs:label ?cosmonautLabel FILTER ( LANG( ?cosmonautLabel ) = "en" ) .
  }
}

Got 7 rows and 2 columns:
| ?cosmonaut                                      | ?cosmonautLabel                 |
| ----------------------------------------------- | ------------------------------- |
| Void (wd:Q106373153)                            | Void                            |
| Svetlana Oktyabrevna Omelchenko (wd:Q108169808) | Svetlana Oktyabrevna Omelchenko |
| Marina Vasilevskaya (wd:Q118185958)             | Marina Vasilevskaya             |
| Ural Sultanov (wd:Q15210574)                    | Ural Sultanov                   |
| Anna Kikina (wd:Q18352451)                      | Anna Kikina                     |
| Vladimír Remek (wd:Q254985)                     | Vladimír Remek                  |
| Arnaldo Tamayo Méndez (wd:Q362902)              | Arnaldo Tamayo Méndez           |

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 Wikidata. A common approach is to look for a property like "occupation" or "profession" and see if it links to a specific item for cosmonauts. Let's start by searching for the term "cosmonaut" to find the relevant entity.

[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)

[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 "occupation" (`wdt:P106`). Let's list some examples of how this property is used to confirm our assumption.

[Tool]
Got more than 131,072 rows and 3 columns, showing the first 10 rows below:
| subject                              | property              | object                              |
| ------------------------------------ | --------------------- | ----------------------------------- |
| Mahatma Gandhi (wd:Q1001)            | occupation (wdt:P106) | civil rights advocate (wd:Q1021386) |
| Nicole Cattell (wd:Q100793542)       | occupation (wdt:P106) | film (wd:Q11424)                    |
| wd:Q100170717                        | occupation (wdt:P106) | Tbilisi (wd:Q994)                   |
| Eliška Hladilová (wd:Q10000101)      | occupation (wdt:P106) | actor (wd:Q33999)                   |
| Paul Mohr (wd:Q100000811)            | occupation (wdt:P106) | physician (wd:Q39631)               |
| Luigi Mauri (wd:Q100035856)          | occupation (wdt:P106) | teacher (wd:Q37226)                 |
| Adrián Juan Pelacchi (wd:Q100774821) | occupation (wdt:P106) | police force (wd:Q35535)            |
| Carlos Hobert (wd:Q100147541)        | occupation (wdt:P106) | military (wd:Q8473)                 |
| Frederik X of Denmark (wd:Q1004037)  | occupation (wdt:P106) | king (wd:Q12097)                    |
| Tóbiás Ferenczi (wd:Q1000552)        | occupation (wdt:P106) | priest (wd:Q42603)                  |
| ...                                  | ...                   | ...                                 |

[Assistant]
The property `wdt:P106` (occupation) is indeed used to link people to their professions. Now, let's construct a SPARQL query to find all individuals whose occupation is `wd:Q12634815` (cosmonaut).

Here is the initial SPARQL query:

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

Let's execute this query to see the results.

[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 cosmonauts. To provide a more complete and user-friendly answer, we can include the labels of these cosmonauts in the results. We will use the `rdfs:label` property to fetch the labels.

Here is the updated SPARQL query:

```sparql
SELECT ?cosmonaut ?cosmonautLabel WHERE {
  ?cosmonaut wdt:P106 wd:Q12634815 .
  OPTIONAL { ?cosmonaut rdfs:label ?cosmonautLabel . }
}
```

Let's execute this query to get the labels as well.

[Tool]
Got 196 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?cosmonaut                                      | ?cosmonautLabel                 |
| ----------------------------------------------- | ------------------------------- |
| Void (wd:Q106373153)                            | Void                            |
| Void (wd:Q106373153)                            | "Войд"@ru                       |
| Svetlana Oktyabrevna Omelchenko (wd:Q108169808) | Svetlana Oktyabrevna Omelchenko |
| Marina Vasilevskaya (wd:Q118185958)             | "Marina Vaciliouskaia"@pt       |
| Marina Vasilevskaya (wd:Q118185958)             | "Marina Vaciliouskaia"@pt-br    |
| ...                                             | ...                             |
| Arnaldo Tamayo Méndez (wd:Q362902)              | "ارنالدو تامايو منديز"@arz      |
| Arnaldo Tamayo Méndez (wd:Q362902)              | "อาร์นัลโด ตามาโย เมนเดซ"@th    |
| Arnaldo Tamayo Méndez (wd:Q362902)              | "아르날도 타마요 멘데즈"@ko               |
| Arnaldo Tamayo Méndez (wd:Q362902)              | "アルナルド・タマヨ・メンデス"@ja             |
| Arnaldo Tamayo Méndez (wd:Q362902)              | "阿纳尔多·塔马约·门德斯"@zh               |

[Assistant]
The query has returned the cosmonauts along with their labels in various languages. However, to provide a more concise and user-friendly answer, we should filter the labels to a single language, such as English, if available.

Here is the refined SPARQL query to get the labels in English:

```sparql
SELECT ?cosmonaut ?cosmonautLabel WHERE {
  ?cosmonaut wdt:P106 wd:Q12634815 .
  OPTIONAL { ?cosmonaut rdfs:label ?cosmonautLabel FILTER(LANG(?cosmonautLabel) = "en") . }
}
```

Let's execute this query to get the labels in English.

[Tool]
Got 7 rows and 2 columns:
| ?cosmonaut                                      | ?cosmonautLabel                 |
| ----------------------------------------------- | ------------------------------- |
| Void (wd:Q106373153)                            | Void                            |
| Svetlana Oktyabrevna Omelchenko (wd:Q108169808) | Svetlana Oktyabrevna Omelchenko |
| Marina Vasilevskaya (wd:Q118185958)             | Marina Vasilevskaya             |
| Ural Sultanov (wd:Q15210574)                    | Ural Sultanov                   |
| Anna Kikina (wd:Q18352451)                      | Anna Kikina                     |
| Vladimír Remek (wd:Q254985)                     | Vladimír Remek                  |
| Arnaldo Tamayo Méndez (wd:Q362902)              | Arnaldo Tamayo Méndez           |

[Assistant]
The refined SPARQL query has successfully returned the cosmonauts along with their English labels. Here is the final SPARQL query and the answer to the user's question:

```sparql
SELECT ?cosmonaut ?cosmonautLabel WHERE {
  ?cosmonaut wdt:P106 wd:Q12634815 .
  OPTIONAL { ?cosmonaut rdfs:label ?cosmonautLabel FILTER(LANG(?cosmonautLabel) = "en") . }
}
```

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 using the `answer` function.

[Tool] Stopped generation process


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

The owners of Rolls-Royce Holdings plc are ValueAct Capital (wd:Q22032185), Credit Suisse (wd:Q372657), and Capital Group Companies (wd:Q505275).

[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 find out who owns Rolls-Royce, we need to identify the entity representing Rolls-Royce and then look for a property that indicates ownership. Let's start by searching for the entity "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

[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 a property that indicates ownership. Let's search for a property related to "owner" or "ownership".

[Tool]
Top 10 property alternatives:
1. owned by, also known as belongs to, stockholders, stockholder, shareholders, shareholder (wd:P127 as psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): owner of the subject
2. owner of, also known as owns, owns property, shareholder of (wd:P1830 as psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): entities owned by the subject
3. has subsidiary, also known as subordinate body, subsidiary body, subordinate unit, subsidiary, subsidiary company (wd:P355 as psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): subsidiary of a company or organization; generally a fully owned...siness division" (P199). Opposite of parent organization (P749)., has part(s), owner of
4. copyright holder, also known as licensor, proprietor, copyright owner, copyright ‘owner’ (wd:P3931 as psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): person or organisation who holds the copyright of a work according to the Berne Convention, owned by
5. beneficial owner, also known as beneficial ownership, UBO (wd:P12621 as psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): natural person or persons who ultimately owns or controls a company or organisation
6. 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 psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): when this subject is used as object of “instance of”, the following properties normally apply
7. Wikidata property, also known as main property, property for this item, property, has property (wd:P1687 as psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): main Wikidata property for this item
8. has characteristic, also known as has quality, inherent property, object has characteristic, parameter, quality (wd:P1552 as psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): inherent or distinguishing quality or feature of the entity. Use a more specific property when possible
9. afterward owned by, also known as subsequently owned by, purchased by, owned after by, new owner, bought by (wd:P11812 as psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): person or institution that owned the subject after stated event....ficant event (P793)" for events like sales, auctions, gifts, etc
10. leased to, also known as lessee (wd:P13047 as psv/pqn/prn/pr/wdtn/p/pqv/prv/pq/wdt/ps/psn): person or organisation that holds or was granted a lease on the subject, occupant

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

Here is the initial SPARQL query:

```sparql
SELECT ?owner
WHERE {
  wd:Q243278 wdt:P127 ?owner .
}
```

Let's execute this query to see the results.

[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 are listed as owners of Rolls-Royce Holdings plc (wd:Q243278):

1. ValueAct Capital (wd:Q22032185)
2. Credit Suisse (wd:Q372657)
3. Capital Group Companies (wd:Q505275)

These entities are likely major shareholders or institutional investors in Rolls-Royce Holdings plc. To provide a more complete answer, we can include the labels of these entities in the query. However, since the user did not explicitly request labels, we will keep the query simple and return the IRIs.

Here is the final SPARQL query:

```sparql
SELECT ?owner
WHERE {
  wd:Q243278 wdt:P127 ?owner .
}
```

The answer to the question "Who is the owner of Rolls-Royce?" is:

- ValueAct Capital (wd:Q22032185)
- Credit Suisse (wd:Q372657)
- Capital Group Companies (wd:Q505275)

Now, let's finalize the answer.

[Tool] Stopped generation process