PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?date WHERE {
  wd:Q211386 wdt:P585 ?date .
}

The San Francisco earthquake occurred on April 18, 1906.

[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] when did the san francisco earthquake occur?

[Assistant]
To answer the question about when the San Francisco earthquake occurred, we need to identify the entity representing the San Francisco earthquake and then find the date associated with it.

First, let's search for the entity representing the San Francisco earthquake.

[Tool]
Top 10 entity alternatives:
1. 1906 San Francisco earthquake, also known as Great San Francisco Earthquake and Fire, San Francisco earthquake of 1906, San Francisco earthquake and fire, 1906, Great 1906 San Francisco Earthquake, earthquake of 1906 (wd:Q211386): major earthquake that struck San Francisco and the coast of Northern California
2. 1906 San Francisco earthquake, also known as 1906 San Francisco earthquake and fire, earthquake of 1906, San Francisco earthquake of 1906, Great 1906 San Francisco Earthquake, Great San Francisco Earthquake and Fire (wd:Q211386): major earthquake that struck San Francisco and the coast of Northern California
3. 1989 Loma Prieta earthquake, also known as 1989 San Francisco earthquake, Loma Prieta earthquake (wd:Q1053476): major earthquake in northern California
4. 1957 San Francisco earthquake (wd:Q20312370)
5. Earthquake waves at San Francisco (wd:Q80986050): scientific article published on 01 February 1884
6. The Great San Francisco Earthquake (wd:Q125816606): scientific article published in 1988
7. The San Francisco earthquake of 1906 (wd:Q58176314): scholarly article by Dennis R. Dean published November 1993 in Annals of Science
8. Saints and the San Francisco earthquake (wd:Q57138167): scientific article published on 01 January 1983
9. The Big Picture: San Francisco Earthquake (wd:Q110056932): sketch from Saturday Night Live
10. Psychic Effects of the San Francisco Earthquake (wd:Q57143755): scholarly article published 4 August 1906 in The Hospital

[Assistant]
The top result is the 1906 San Francisco earthquake, which is the event we are interested in. Let's use this entity and find the date when it occurred.

Next, we need to identify the property that represents the date of occurrence for this earthquake. We can search for properties related to "date" or "time" for this specific entity.

Let's search for properties related to the date of the 1906 San Francisco earthquake.

[Tool]
Top 10 property alternatives:
1. point in time, also known as at time, as of, year, when, time of event (wd:P585 as wdt/p): date something took place, existed or a statement was true; for providing time use the "refine date" property (P4241)
2. location, also known as neighbourhood, whereabouts, vilage, venue, town (wd:P276 as wdt/p): 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. instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt/p): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
4. located in the administrative territorial entity, also known as is in the settlement of, is in the voivodeship of, is in the shire of, is in the state of, is in the territory of (wd:P131 as wdt/p): the item is located on the territory of the following administra...if the item falls only partially into the administrative entity., location, part of
5. video, also known as gif, animation, trailer, media (wd:P10 as wdt/p): relevant video. For images, use the property P18. For film trailers, qualify with "object has role" (P3831)="trailer" (Q622550)
6. has cause, also known as implied by, originated due to, initial cause, outcome of, reason (wd:P828 as wdt/p): underlying cause, entity that ultimately resulted in this effect, follows
7. described by source, also known as mentioned in, source of item, reviewed in, mentioned in news article, talked about in (wd:P1343 as wdt/p): work where this item is described, attested in
8. country, also known as sovereign state, state, land, host country (wd:P17 as wdt/p): sovereign state that this item is in (not to be used for human beings), located in the administrative territorial entity
9. image, also known as still capture, still, screenshot, screen capture, portrait (wd:P18 as wdt/p): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)
10. coordinate location, also known as point on a map, gps coordinates, gps location, latitude, location (wd:P625 as wdt/p): geocoordinates of the subject. For Earth, please note that only WGS84 coordinating system is supported at the moment

No other items found

[Assistant]
The top result for the property related to the date of the 1906 San Francisco earthquake is "point in time" (P585). This property is used to specify the date when an event occurred.

Now, let's construct a SPARQL query to retrieve the date of the 1906 San Francisco earthquake using the identified entity and property.

```sparql
SELECT ?date
WHERE {
  wd:Q211386 wdt:P585 ?date .
}
```

Let's execute this query to get the date of the earthquake.

[Tool]
Got 1 row and 1 column:
| ?date                |
| -------------------- |
| 1906-04-18T00:00:00Z |

[Assistant]
The 1906 San Francisco earthquake occurred on April 18, 1906.

Here is the final SPARQL query and the answer:

```sparql
SELECT ?date
WHERE {
  wd:Q211386 wdt:P585 ?date .
}
```

Answer: The San Francisco earthquake occurred on April 18, 1906.

[Tool] Stopped generation process


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?placeOfBirth ?placeOfBirthLabel WHERE {
  wd:Q13603754 wdt:P19 ?placeOfBirth .
  OPTIONAL {
    ?placeOfBirth rdfs:label ?placeOfBirthLabel .
  }
}

Got 101 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?placeOfBirth          | ?placeOfBirthLabel |
| ---------------------- | ------------------ |
| Salisbury (wd:Q160642) | "Caersallog"@cy    |
| Salisbury (wd:Q160642) | "Karsallek"@kw     |
| Salisbury (wd:Q160642) | "Salesbire"@nrm    |
| Salisbury (wd:Q160642) | "Salisbury"@af     |
| Salisbury (wd:Q160642) | "Salisbury"@ast    |
| ...                    | ...                |
| Salisbury (wd:Q160642) | "索尔兹伯里"@wuu        |
| Salisbury (wd:Q160642) | "索尔兹伯里"@zh-cn      |
| Salisbury (wd:Q160642) | "索尔兹伯里"@zh-hans    |
| Salisbury (wd:Q160642) | "索尔兹伯里"@zh-sg      |
| Salisbury (wd:Q160642) | "索爾茲伯里"@zh         |

Robert Irvine comes from Salisbury, England.

[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] where does robert irvine come from?

[Assistant]
To answer the question "Where does Robert Irvine come from?", we need to identify the entity for Robert Irvine and then find his place of birth or origin.

### Step 1: Identify the entity for Robert Irvine
Let's start by searching for Robert Irvine in the knowledge graph.

#### Function Call: Search for Robert Irvine

[Tool]
Top 10 entity alternatives:
1. Bobby Irvine, also known as Robert Irvine, Robert James Irvine (wd:Q4935169): Northern Irish association footballer (born 1942), association football player
2. Robert Irvine (wd:Q29561444): association football player
3. Robert Irvine, also known as Robert Paul Irvine (wd:Q13603754): British celebrity chef, chef
4. Irvine, Robert (wd:Q94829234): entry in Alumni Oxonienses: the Members of the University of Oxford, 1715-1886
5. Robert Irvine (wd:Q7345808): New Zealand rugby league footballer, rugby league player
6. Robert Brian Irvine, also known as Robert Irvine (wd:Q75949893): Peerage person ID=423923
7. Robert Irvine (wd:Q93942944)
8. Robert Irvine (wd:Q76346164): Peerage person ID=703910
9. Robert Irvine (wd:Q76170766): Peerage person ID=579126
10. Robert Irvine (wd:Q76037914): Peerage person ID=485894

[Assistant]
From the search results, the most relevant entity for Robert Irvine is likely **Robert Irvine (wd:Q13603754)**, who is a British celebrity chef.

### Step 2: Find the place of birth or origin for Robert Irvine
Next, we need to find the place of birth or origin for Robert Irvine. We can use the property `P19` (place of birth) or `P153` (country of citizenship) to find this information.

#### Function Call: List triples for Robert Irvine with place of birth or country of citizenship

[Tool]
Got 1 row and 3 columns:
| subject                      | property                 | object                 |
| ---------------------------- | ------------------------ | ---------------------- |
| Robert Irvine (wd:Q13603754) | place of birth (wdt:P19) | Salisbury (wd:Q160642) |

[Assistant]
The place of birth for Robert Irvine is Salisbury (wd:Q160642).

### Step 3: Build the SPARQL query
Now, let's build the SPARQL query to confirm this information and return the place of birth.

#### SPARQL Query: Get the place of birth for Robert Irvine

```sparql
SELECT ?placeOfBirth ?placeOfBirthLabel
WHERE {
  wd:Q13603754 wdt:P19 ?placeOfBirth.
  OPTIONAL { ?placeOfBirth rdfs:label ?placeOfBirthLabel. }
}
```

#### Function Call: Execute the SPARQL query

[Tool]
Got 101 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?placeOfBirth          | ?placeOfBirthLabel |
| ---------------------- | ------------------ |
| Salisbury (wd:Q160642) | "Caersallog"@cy    |
| Salisbury (wd:Q160642) | "Karsallek"@kw     |
| Salisbury (wd:Q160642) | "Salesbire"@nrm    |
| Salisbury (wd:Q160642) | "Salisbury"@af     |
| Salisbury (wd:Q160642) | "Salisbury"@ast    |
| ...                    | ...                |
| Salisbury (wd:Q160642) | "索尔兹伯里"@wuu        |
| Salisbury (wd:Q160642) | "索尔兹伯里"@zh-cn      |
| Salisbury (wd:Q160642) | "索尔兹伯里"@zh-hans    |
| Salisbury (wd:Q160642) | "索尔兹伯里"@zh-sg      |
| Salisbury (wd:Q160642) | "索爾茲伯里"@zh         |

[Assistant]
The SPARQL query confirms that Robert Irvine was born in Salisbury (wd:Q160642). The results include various language labels for Salisbury, but the most common English label is "Salisbury".

### Final Answer
Robert Irvine comes from Salisbury, England.

#### Final SPARQL Query and Answer

```sparql
SELECT ?placeOfBirth ?placeOfBirthLabel
WHERE {
  wd:Q13603754 wdt:P19 ?placeOfBirth.
  OPTIONAL { ?placeOfBirth rdfs:label ?placeOfBirthLabel. }
}
```

Answer: Robert Irvine comes from Salisbury, England.

[Tool] Stopped generation process


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?headquarters ?headquartersLabel WHERE {
  wd:Q505922 wdt:P159 ?headquarters .
  OPTIONAL {
    ?headquarters rdfs:label ?headquartersLabel .
    FILTER ( LANG( ?headquartersLabel ) = "en" )
  }
}

Got 1 row and 2 columns:
| ?headquarters        | ?headquartersLabel |
| -------------------- | ------------------ |
| Stockholm (wd:Q1754) | Stockholm          |

Electrolux is based in Stockholm.

[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] where is electrolux based?

[Assistant] To find out where Electrolux is based, we need to identify the entity for Electrolux and then look for the property that indicates its headquarters or location. Let's start by searching for the entity "Electrolux" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Electrolux, also known as AB Electrolux (wd:Q505922): Swedish multinational manufacturer and marketer of home appliances
2. Electrolux (wd:Q2923843): genus of fishes
3. ELECTROLUX, also known as ELECTROLUX, s.r.o. (wd:Q58218446): Czech company
4. Electrolux addisoni (wd:Q2387882): species of fish
5. Electrolux Assistent, also known as Electrolux Ankarsrum Assistent (wd:Q17496378): stand mixer introduced by Electrolux in 1940
6. Electrolux Trilobite (wd:Q5358060): first commercially available robotic vacuum cleaner
7. Electrolux (Italy), also known as Electrolux Italia S.p.A., Electrolux Rex (wd:Q106045223)
8. Electrolux F.C. (wd:Q96377107): football club
9. Electrolux Professional (wd:Q109974149): Swedish professional appliance manufacturer
10. Lux Norge AS, also known as Electrolux-Euroclean AS, Electrolux AS, Electrolux Rengjøringsmaskiner AS (wd:Q15729564)

[Assistant]
The top result for "Electrolux" is the Swedish multinational manufacturer and marketer of home appliances, which is likely the entity we are looking for. The IRI for this entity is `wd:Q505922`.

Next, we need to find the property that indicates the headquarters or location of a company. We can search for properties related to "headquarters" or "location" to find the appropriate property.

[Tool]
Top 10 property alternatives:
1. headquarters location, also known as headquarters locality, seat, headquarters town, HQ, HQ location (wd:P159 as pqn/ps/pq/prn/psv/wdt/pr/pqv/p/prv/psn/wdtn): 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 pqn/ps/pq/prn/psv/wdt/pr/pqv/p/prv/psn/wdtn): 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 pqn/ps/pq/prn/psv/wdt/pr/pqv/p/prv/psn/wdtn): 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 pqn/ps/pq/prn/psv/wdt/pr/pqv/p/prv/psn/wdtn): 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 pqn/ps/pq/prn/psv/wdt/pr/pqv/p/prv/psn/wdtn): 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 pqn/ps/pq/prn/psv/wdt/pr/pqv/p/prv/psn/wdtn): 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 pqn/ps/pq/prn/psv/wdt/pr/pqv/p/prv/psn/wdtn): 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 pqn/ps/pq/prn/psv/wdt/pr/pqv/p/prv/psn/wdtn): 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 pqn/ps/pq/prn/psv/wdt/pr/pqv/p/prv/psn/wdtn): 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 pqn/ps/pq/prn/psv/wdt/pr/pqv/p/prv/psn/wdtn): country, state, department, canton or other administrative division of which the municipality is the governmental seat, located in the administrative territorial entity

[Assistant]
The property "headquarters location" (wd:P159) seems to be the most relevant for finding the location of a company's headquarters. Let's use this property to construct a SPARQL query to find the headquarters of Electrolux.

We will start with a simple query to retrieve the headquarters location of Electrolux.

```sparql
SELECT ?headquarters WHERE {
  wd:Q505922 wdt:P159 ?headquarters .
}
```

Let's execute this query to see the results.

[Tool]
Got 1 row and 1 column:
| ?headquarters        |
| -------------------- |
| Stockholm (wd:Q1754) |

[Assistant]
The SPARQL query returned the headquarters location of Electrolux as Stockholm, which is represented by the IRI `wd:Q1754`.

To provide a more complete answer, we can include the label of the headquarters location in the query. However, since the label is optional, we will use an `OPTIONAL` clause to fetch it.

```sparql
SELECT ?headquarters ?headquartersLabel WHERE {
  wd:Q505922 wdt:P159 ?headquarters .
  OPTIONAL { ?headquarters rdfs:label ?headquartersLabel . }
}
```

Let's execute this refined query to get both the IRI and the label of the headquarters location.

[Tool]
Got 267 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?headquarters        | ?headquartersLabel  |
| -------------------- | ------------------- |
| Stockholm (wd:Q1754) | "Estocolm"@ca       |
| Stockholm (wd:Q1754) | "Estocòlme"@oc      |
| Stockholm (wd:Q1754) | "Estocolmo"@an      |
| Stockholm (wd:Q1754) | "Estocolmo"@cbk-zam |
| Stockholm (wd:Q1754) | "Estocolmo"@es      |
| ...                  | ...                 |
| Stockholm (wd:Q1754) | "斯德哥爾摩"@yue         |
| Stockholm (wd:Q1754) | "斯德哥爾摩"@zh-hant     |
| Stockholm (wd:Q1754) | "斯德哥爾摩"@zh-hk       |
| Stockholm (wd:Q1754) | "斯德哥爾摩"@zh-mo       |
| Stockholm (wd:Q1754) | "斯德哥爾摩"@zh-tw       |

[Assistant]
The query returned multiple rows with different language labels for Stockholm. To simplify the result, we can limit the query to return only the English label for the headquarters location. We can do this by adding a filter to specify the language.

Here is the updated SPARQL query:

```sparql
SELECT ?headquarters ?headquartersLabel WHERE {
  wd:Q505922 wdt:P159 ?headquarters .
  OPTIONAL { ?headquarters rdfs:label ?headquartersLabel . FILTER(LANG(?headquartersLabel) = "en") }
}
```

Let's execute this query to get the English label for the headquarters location.

[Tool]
Got 1 row and 2 columns:
| ?headquarters        | ?headquartersLabel |
| -------------------- | ------------------ |
| Stockholm (wd:Q1754) | Stockholm          |

[Assistant]
The SPARQL query has successfully returned the headquarters location of Electrolux as Stockholm, along with its English label.

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

```sparql
SELECT ?headquarters ?headquartersLabel WHERE {
  wd:Q505922 wdt:P159 ?headquarters .
  OPTIONAL { ?headquarters rdfs:label ?headquartersLabel . FILTER(LANG(?headquartersLabel) = "en") }
}
```

Answer: Electrolux is based in Stockholm.

Now, I will 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 ?zipCode WHERE {
  wd:Q793929 wdt:P281 ?zipCode .
}

The zip code for Azle, Texas is 76020.

[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 zip code for azle texas?

[Assistant] To find the zip code for Azle, Texas, we need to identify the entity representing Azle, Texas, and then look for a property that corresponds to the zip code. Let's start by searching for the entity representing Azle, Texas.

[Tool]
Top 10 entity alternatives:
1. Azle, also known as Azle, Texas, Azle, TX (wd:Q793929): city in Texas, United States
2. Texas Health Resources Azle heliport (wd:Q61719753): heliport in Texas, United States
3. Causal factors for seismicity near Azle, Texas (wd:Q35541092): scientific article published on 21 April 2015
4. Texas, also known as Tex., US-TX, Lone Star State, Texas, United States, State of Texas (wd:Q1439): state of the United States of America
5. Texas, also known as Lone Star State, US-TX, TX, Texas, United States, Tex. (wd:Q1439): state of the United States of America
6. Texas (wd:Q916484): Scottish musical group; alternative rock band
7. Texas, also known as Texas, WI (wd:Q1887280): town in Wisconsin, United States
8. Texas (wd:Q7707414): 1941 Western film directed by George Marshall
9. Texas, also known as Texas, Queensland, Texas, Queensland, Australia (wd:Q144229): town in Queensland, Australia
10. Texas, also known as Texas, NY, Texas, New York (wd:Q3816828): town in Oswego County, New York

[Assistant] The top result for "Azle, Texas" is `wd:Q793929`, which represents the city of Azle, Texas. Next, we need to find a property that corresponds to the zip code. Let's search for properties related to "zip code" or "postal code" for this entity.

[Tool]
Top 10 property alternatives:
1. postal code, also known as pincode, PIN Code, postal index number, post code, postcode (wd:P281 as wdt/p): identifier assigned by postal authorities for the subject area or building
2. local dialing code, also known as area code, dialling code, local calling code, local dialling code, STD code (wd:P473 as wdt/p): identifier dedicated to subject city by the area communication network
3. FIPS 55-3 (locations in the US), also known as fips code for US locations, Federal Information Processing Standard 55-3, FIPS 55-3 (wd:P774 as wdt/p): identifier for places in the United States per former Federal Information Processing Standard FIPS 55-3
4. social media followers, also known as number of subs, number of subscribers, sub count, sub number, subscriber number (wd:P8687 as wdt/p): number of subscribers on a particular social media website (use ... in time" and property for account. For Twitter, use numeric id.
5. UN/LOCODE, also known as LOCCODE, LOCODE, UNECE location code, UNECE LOCODE, United Nations location code (wd:P1937 as wdt/p): geographic location code maintained by UNECE
6. coordinate location, also known as point on a map, gps coordinates, gps location, latitude, location (wd:P625 as wdt/p): geocoordinates of the subject. For Earth, please note that only WGS84 coordinating system is supported at the moment
7. area, also known as total area, surface area, surface, size, resolution (wd:P2046 as wdt/p): area occupied by an object
8. FactGrid item ID, also known as FG ID, FactGrid ID (wd:P8168 as wdtn/wdt/p): identifier for an item in FactGrid
9. located in time zone, also known as TZ, time zone, timezone (wd:P421 as wdt/p): time zone for this item
10. GeoNames ID, also known as GeoNames, Geonames Id (wd:P1566 as wdtn/wdt/p): identifier in the GeoNames geographical database

No other items found

[Assistant]
The property `wdt:P281` (postal code) is the most relevant for finding the zip code. Let's construct a SPARQL query to retrieve the zip code for Azle, Texas using this property.

```sparql
SELECT ?zipCode WHERE {
  wd:Q793929 wdt:P281 ?zipCode .
}
```

Let's execute this query to get the zip code for Azle, Texas.

[Tool]
Got 1 row and 1 column:
| ?zipCode |
| -------- |
| 76020    |

[Assistant]
The zip code for Azle, Texas is 76020.

Here is the final SPARQL query and answer:

```sparql
SELECT ?zipCode WHERE {
  wd:Q793929 wdt:P281 ?zipCode .
}
```

Answer: The zip code for Azle, Texas is 76020.

[Tool] Stopped generation process


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?language ?languageLabel WHERE {
  wd:Q79 wdt:P37 ?language .
  OPTIONAL {
    ?language rdfs:label ?languageLabel .
    FILTER ( LANG( ?languageLabel ) = "en" )
  }
}

The main language spoken in Egypt is Arabic.

[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 language is mainly spoken in egypt?

[Assistant] To determine the main language spoken in Egypt, we need to identify the entity for Egypt and then find the property that indicates the official or main language of a country. Let's start by searching for the entity representing Egypt.

[Tool]
Top 10 entity alternatives:
1. Egypt, also known as Republic of Egypt, Rep. Egypt, EGY, eg, Arab Republic of Egypt (wd:Q79): country in Northeast Africa and Southwest Asia
2. Khedivate of Egypt, also known as Egypt, Egyptian Khedivate (wd:Q127861): 1867-1914 monarchy in Northeastern Africa
3. Egypt Eyalet, also known as QASR of Egypt, Pashalik of Egypt, Egypt, Mısır Eyalet, Beylerbeyilik of Egypt (wd:Q491507): Egyptian State (1517–1867)
4. Egypt, also known as Egypt, AK, Egypt, AR (wd:Q2083973): town in Arkansas
5. Egypt, also known as Egypt, Etowah County, Alabama, Egypt, AL (wd:Q5348296): census-designated place in Etowah County, Alabama
6. Egypt, also known as Egypt, Pennsylvania (wd:Q5348306): human settlement in Pennsylvania, United States
7. Egypt 1156 B.C., also known as Egypt, Egypt 1156 B.C.: Tomb of the Pharaoh (wd:Q3586212): 1999 video game
8. Egypt, also known as Egypt, WV (wd:Q5348310): unincorporated community in Jefferson County, West Virginia
9. Egypt (wd:Q5348317): 2004 album by Youssou N'Dour
10. Egypt, also known as Egypt, IN, Egypt, Indiana (wd:Q5348297): unincorporated community in Jasper County, Minnesota

[Assistant] The top result for "Egypt" is the correct one, which is the Republic of Egypt (wd:Q79). Now, let's find the property that indicates the official or main language of a country. We can search for properties related to "language" and see if any of them are used for Egypt.

[Tool]
Top 10 property alternatives:
1. official language, also known as language spoken, spoken in, speaking, language official (wd:P37 as wdt/p): language designated as official by this item, language used
2. language used, also known as languages used, medium of instruction, used language, uses language, working language (wd:P2936 as wdt/p): language widely used (spoken or written) in this place or at this event or organisation
3. instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt/p): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
4. native label, also known as official title, original label, original language label, original language name, original language title (wd:P1705 as wdt/p): label for the items in their official language  or their original language, name
5. culture, also known as archaeological culture, civilisation, civilization (wd:P2596 as wdt/p): human culture or people (or several cultures) associated with this item, part of
6. ethnic group, also known as ethnic or cultural origin, tribal affiliation, race, people, ethnicity (wd:P172 as wdt/p): subject's ethnicity (consensus is that a VERY high standard of p...agreed on by scholars, or 3) is fictional and portrayed as such)
7. name in native language, also known as native name (wd:P1559 as wdt/p): name of a person in their native language, name
8. country, also known as sovereign state, state, land, host country (wd:P17 as wdt/p): sovereign state that this item is in (not to be used for human beings), located in the administrative territorial entity
9. described by source, also known as mentioned in, source of item, reviewed in, mentioned in news article, talked about in (wd:P1343 as wdt/p): work where this item is described, attested in
10. Libraries Australia ID, also known as CNLKIN, NLA Kinetica Service identifier, NLA (Australia) identifier, NLA (Australia) ID, Libraries Australia identifier (wd:P409 as wdt/p): identifier issued by the National Library of Australia (see also...F component. Format: 1-12 digits, removing leading zero-padding.

No other items found

[Assistant]
The property "official language" (wd:P37) seems to be the most relevant for our purpose. Let's use this property to find the main language spoken in Egypt.

I will now construct a SPARQL query to retrieve the official language of Egypt.

```sparql
SELECT ?language WHERE {
  wd:Q79 wdt:P37 ?language .
}
```

Let's execute this query to get the result.

[Tool]
Got 1 row and 1 column:
| ?language          |
| ------------------ |
| Arabic (wd:Q13955) |

[Assistant]
The main language spoken in Egypt is Arabic (wd:Q13955).

To provide a more complete answer, we can also include the label of the language. Let's modify the SPARQL query to include the label of the language.

```sparql
SELECT ?language ?languageLabel WHERE {
  wd:Q79 wdt:P37 ?language .
  OPTIONAL { ?language rdfs:label ?languageLabel . FILTER(LANG(?languageLabel) = "en") }
}
```

Let's execute this updated query.

[Tool]
Got 1 row and 2 columns:
| ?language          | ?languageLabel |
| ------------------ | -------------- |
| Arabic (wd:Q13955) | Arabic         |

[Assistant]
The main language spoken in Egypt is Arabic (wd:Q13955).

Here is the final SPARQL query and answer:

```sparql
SELECT ?language ?languageLabel WHERE {
  wd:Q79 wdt:P37 ?language .
  OPTIONAL { ?language rdfs:label ?languageLabel . FILTER(LANG(?languageLabel) = "en") }
}
```

Answer: The main language spoken in Egypt is Arabic.

[Tool] Stopped generation process


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?language ?languageLabel WHERE {
  wd:Q45 wdt:P37 ?language .
  OPTIONAL {
    ?language rdfs:label ?languageLabel .
    FILTER ( LANG( ?languageLabel ) = "en" )
  }
}

Got 2 rows and 2 columns:
| ?language             | ?languageLabel |
| --------------------- | -------------- |
| Mirandese (wd:Q13330) | Mirandese      |
| Portuguese (wd:Q5146) | Portuguese     |

Portuguese people primarily speak Portuguese and some also speak Mirandese.

[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 do portuguese people speak?

[Assistant]
To answer the question "What do Portuguese people speak?", we need to identify the language(s) spoken by people in Portugal. Let's start by searching for the entity representing Portugal and then look for properties related to languages spoken there.

First, I will search for the entity "Portugal" in the knowledge graph.

[Tool]
Top 10 entity alternatives:
1. Portugal, also known as POR, Portuguese Republic, PRT (wd:Q45): country in Southwestern Europe
2. Portugal (wd:Q7232531): constituency of the European Parliament
3. Portugal (wd:Q14110517): family name
4. Portugal (wd:Q21087356): comic book Cyril Pedrosa
5. Portugal, also known as Portugal, Oca, Coristanco (wd:Q31283096)
6. Portugal, also known as IMO 9147083 (wd:Q83609413): ship built in 1998
7. Portugal (wd:Q10638037): single
8. Portugal (wd:Q66987006): Populated place in Alforja (Baix Camp)
9. Portugal (wd:Q61400880): 2018 film directed by Lauri Lagle
10. Portugal (wd:Q35267157): stream in Sofala Province, Mozambique

[Assistant]
The top result for "Portugal" is `wd:Q45`, which represents the country Portugal. Next, I will search for properties related to languages spoken in Portugal. I will use the `search_property_of_entity` function to find relevant properties.

Let's search for properties related to "language" for the entity `wd:Q45`.

[Tool]
Top 10 property alternatives:
1. official language, also known as language spoken, spoken in, speaking, language official (wd:P37 as wdt/p): language designated as official by this item, language used
2. language used, also known as languages used, medium of instruction, used language, uses language, working language (wd:P2936 as wdt/p): language widely used (spoken or written) in this place or at this event or organisation
3. instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt/p): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
4. native label, also known as official title, original label, original language label, original language name, original language title (wd:P1705 as wdt/p): label for the items in their official language  or their original language, name
5. culture, also known as archaeological culture, civilisation, civilization (wd:P2596 as wdt/p): human culture or people (or several cultures) associated with this item, part of
6. name in native language, also known as native name (wd:P1559 as wdt/p): name of a person in their native language, name
7. country, also known as sovereign state, state, land, host country (wd:P17 as wdt/p): sovereign state that this item is in (not to be used for human beings), located in the administrative territorial entity
8. described by source, also known as mentioned in, source of item, reviewed in, mentioned in news article, talked about in (wd:P1343 as wdt/p): work where this item is described, attested in
9. Unicode character, also known as emoji, emote, pictograph, Unicode symbol (wd:P487 as wdt/p): Unicode character representing the item (only if this is not a c...acter or a compatiblity character: in that case, use only P4213)
10. topic's main category, also known as article's main category, category, category for this topic, catmain, has category (wd:P910 as wdt/p): main Wikimedia category

No other items found

[Assistant]
The top relevant property for languages spoken in Portugal is `wdt:P37` (official language). This property indicates the official language of a country. Let's use this property to find the language(s) spoken in Portugal.

I will execute a SPARQL query to retrieve the language(s) spoken in Portugal using the `wdt:P37` property.

```sparql
SELECT ?language WHERE {
  wd:Q45 wdt:P37 ?language .
}
```

Let's run this query.

[Tool]
Got 2 rows and 1 column:
| ?language             |
| --------------------- |
| Mirandese (wd:Q13330) |
| Portuguese (wd:Q5146) |

[Assistant]
The query returned two languages spoken in Portugal: Mirandese (`wd:Q13330`) and Portuguese (`wd:Q5146`). To provide a more complete answer, let's also include the labels for these languages. We can do this by adding an optional clause to fetch the labels.

Here is the updated SPARQL query:

```sparql
SELECT ?language ?languageLabel WHERE {
  wd:Q45 wdt:P37 ?language .
  OPTIONAL { ?language rdfs:label ?languageLabel . FILTER(LANG(?languageLabel) = "en") }
}
```

Let's run this query to get the labels for the languages.

[Tool]
Got 2 rows and 2 columns:
| ?language             | ?languageLabel |
| --------------------- | -------------- |
| Mirandese (wd:Q13330) | Mirandese      |
| Portuguese (wd:Q5146) | Portuguese     |

[Assistant]
The query returned the languages spoken in Portugal along with their labels:

- Mirandese (`wd:Q13330`)
- Portuguese (`wd:Q5146`)

Therefore, the answer to the question "What do Portuguese people speak?" is that they primarily speak Portuguese, and some also speak Mirandese.

Here is the final SPARQL query and answer:

```sparql
SELECT ?language ?languageLabel WHERE {
  wd:Q45 wdt:P37 ?language .
  OPTIONAL { ?language rdfs:label ?languageLabel . FILTER(LANG(?languageLabel) = "en") }
}
```

Answer: Portuguese people primarily speak Portuguese and some also speak Mirandese.

[Tool] Stopped generation process


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?player ?playerLabel WHERE {
  ?player wdt:P647 wd:Q162990 .
  OPTIONAL {
    ?player rdfs:label ?playerLabel .
    FILTER ( LANG( ?playerLabel ) = "en" )
  }
}

Got 117 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?player                       | ?playerLabel     |
| ----------------------------- | ---------------- |
| Larry Sanders (wd:Q1034012)   | Larry Sanders    |
| LeBron James (wd:Q108870659)  | LeBron James     |
| Eddie Jordan (wd:Q1134403)    | Eddie Jordan     |
| Reggie Geary (wd:Q115509)     | Reggie Geary     |
| DeSagana Diop (wd:Q1179612)   | DeSagana Diop    |
| ...                           | ...              |
| Austin Carr (wd:Q933952)      | Austin Carr      |
| Winston Bennett (wd:Q943368)  | Winston Bennett  |
| Mickey Dillard (wd:Q962929)   | Mickey Dillard   |
| Ben McDonald (wd:Q967072)     | Ben McDonald     |
| Tristan Thompson (wd:Q973212) | Tristan Thompson |

The Cleveland Cavaliers have drafted several players, including Larry Sanders, LeBron James, Eddie Jordan, Reggie Geary, DeSagana Diop, Austin Carr, Winston Bennett, Mickey Dillard, Ben McDonald, and Tristan Thompson.

[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 did the cleveland cavaliers draft?

[Assistant]
To answer the question about who the Cleveland Cavaliers drafted, we need to identify the following:

1. The entity representing the Cleveland Cavaliers.
2. The property that indicates drafting a player.
3. The players drafted by the Cleveland Cavaliers.

Let's start by searching for the entity representing the Cleveland Cavaliers.

[Tool]
Top 10 entity alternatives:
1. Cleveland Cavaliers, also known as Cavaliers, Cleveland Cavs, Cavs (wd:Q162990): National Basketball Association team in Cleveland, Ohio
2. history of the Cleveland Cavaliers, also known as Cleveland Cavaliers history, the Cleveland Cavaliers history (wd:Q20710370): aspect of history
3. 2022–23 Cleveland Cavaliers season (wd:Q112133040): NBA professional basketball team season
4. 2023–24 Cleveland Cavaliers season (wd:Q118908694): 2023–24 NBA season by team
5. 2015–16 Cleveland Cavaliers season (wd:Q20020792): NBA professional basketball team season (won championship)
6. 2018–19 Cleveland Cavaliers season (wd:Q54958273): season of National Basketball Association team the Cleveland Cavaliers
7. 2020–21 Cleveland Cavaliers season (wd:Q98553687): NBA professional basketball team season
8. 2021–22 Cleveland Cavaliers season (wd:Q107570641): NBA season for the Cleveland Cavaliers (2021-2022)
9. 2017–18 Cleveland Cavaliers season (wd:Q30641924): NBA professional basketball team season
10. 2019–20 Cleveland Cavaliers season (wd:Q63859185): NBA professional basketball team season

[Assistant] The top result for the Cleveland Cavaliers is `wd:Q162990`, which represents the National Basketball Association team in Cleveland, Ohio. Next, we need to find the property that indicates drafting a player. Let's search for properties related to "draft" or "drafted" for the Cleveland Cavaliers.

[Tool]
Top 10 property alternatives:
1. inception, also known as initial release date, founded in, founded on, founded on date, founding date (wd:P571 as wdt/p): time when an entity begins to exist; for date of official opening use P1619, start time
2. sport, also known as play, plays, sport played, sports (wd:P641 as wdt/p): sport that the subject participates or participated in or is associated with
3. image, also known as still capture, still, screenshot, screen capture, portrait (wd:P18 as wdt/p): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)
4. league or competition, also known as tournament, sports league, division, cup, competition (wd:P118 as wdt/p): league or competition in which team or player has played, or in which an event occurs, participant in
5. coordinate location, also known as point on a map, gps coordinates, gps location, latitude, location (wd:P625 as wdt/p): geocoordinates of the subject. For Earth, please note that only WGS84 coordinating system is supported at the moment
6. home venue, also known as home water, venue, stadium, home ground, home field (wd:P115 as wdt/p): home stadium or venue of a sports team or applicable performing arts organization, location
7. instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt/p): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
8. category for members of a team, also known as category for players, category for team members (wd:P6112 as wdt/p): Wikimedia category for players playing for a sports team, topic's main category
9. Freebase ID, also known as Freebase, MID, Freebase identifier, FID (wd:P646 as wdt/p): identifier for a page in the Freebase database. Format: "/m/0" f...r IDs starting with "/g/", use Google Knowledge Graph ID (P2671)
10. head coach, also known as team manager, trainer, senior coach, manager, coached by (wd:P286 as wdt/p): on-field manager or head coach of a sports club (not to be confu...eneral manager P505, which is not a coaching position) or person

No other items found

[Assistant]
It seems that the search for properties related to "draft" or "drafted" for the Cleveland Cavaliers did not yield any relevant results. Let's try a different approach by searching for the general property related to drafting in the knowledge graph.

I will search for the property "draft" or "drafted" in the knowledge graph.

[Tool]
Top 10 property alternatives:
1. draft, also known as draught (wd:P2262 as pqn/ps/pq/prn/psv/wdt/pr/pqv/p/prv/psn/wdtn): depth below the water line to the bottom of a vessel's hull; summer draft is implied unless stated otherwise.
2. study or design for, also known as design for, draft for, first version of, preparatory work for, study for (wd:P6606 as pqn/ps/pq/prn/psv/wdt/pr/pqv/p/prv/psn/wdtn): finished work of art for which the subject is a preliminary study, design, or conceptual drawing
3. drafted by (wd:P647 as pqn/ps/pq/prn/psv/wdt/pr/pqv/p/prv/psn/wdtn): which team the player was drafted by
4. draft pick number, also known as draft number (wd:P1836 as pqn/ps/pq/prn/psv/wdt/pr/pqv/p/prv/psn/wdtn): overall pick number with which a player is selected in a sports draft, series ordinal
5. The Draft Review ID, also known as Draft Review identifier, The Draft Review identifier, Draft Review ID (wd:P9514 as pqn/ps/pq/prn/psv/wdt/pr/pqv/p/prv/psn/wdtn): identifier for a basketball player in the database of The Draft Review
6. DraftExpress ID, also known as draftexpress.com ID (wd:P3533 as pqn/ps/pq/prn/psv/wdt/pr/pqv/p/prv/psn/wdtn): identifier for a basketball player at draftexpress.com
7. Homebrew formula name, also known as brew (wd:P8443 as pqn/ps/pq/prn/psv/wdt/pr/pqv/p/prv/psn/wdtn): identifier of a package (formula) in the Homebrew package manager
8. edition/version, also known as designation of edition, designation of version, edition, edition designation, edition or version (wd:P9767 as pqn/ps/pq/prn/psv/wdt/pr/pqv/p/prv/psn/wdtn): the official edition or version name of a resource; this name is not considered part of the title or subtitle of the resource
9. has edition or translation, also known as version, translation(s), released as, modified as, has version (wd:P747 as pqn/ps/pq/prn/psv/wdt/pr/pqv/p/prv/psn/wdtn): item that is an edition or translation of this item
10. distribution format, also known as book format, type of media, publication format, method of distribution, medium (wd:P437 as pqn/ps/pq/prn/psv/wdt/pr/pqv/p/prv/psn/wdtn): method (or type) of distribution for the subject

[Assistant]
The property `drafted by` (wd:P647) seems to be the most relevant for our query. Now, let's construct a SPARQL query to find the players drafted by the Cleveland Cavaliers (wd:Q162990).

We will start with a simple query to list the players drafted by the Cleveland Cavaliers.

[Tool]
Got 117 rows and 1 column, showing the first 5 and last 5 rows below:
| ?player                       |
| ----------------------------- |
| Larry Sanders (wd:Q1034012)   |
| LeBron James (wd:Q108870659)  |
| Eddie Jordan (wd:Q1134403)    |
| Reggie Geary (wd:Q115509)     |
| DeSagana Diop (wd:Q1179612)   |
| ...                           |
| Austin Carr (wd:Q933952)      |
| Winston Bennett (wd:Q943368)  |
| Mickey Dillard (wd:Q962929)   |
| Ben McDonald (wd:Q967072)     |
| Tristan Thompson (wd:Q973212) |

[Assistant]
The SPARQL query successfully returned a list of players drafted by the Cleveland Cavaliers. To provide a more complete answer, we can include the labels of the players in the results. Let's modify the query to include the labels of the players.

Here is the updated SPARQL query:

```sparql
SELECT ?player ?playerLabel
WHERE {
  ?player wdt:P647 wd:Q162990 .
  OPTIONAL { ?player rdfs:label ?playerLabel . FILTER(LANG(?playerLabel) = "en") }
}
```

Let's execute this query to get the player names along with their labels.

[Tool]
Got 117 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?player                       | ?playerLabel     |
| ----------------------------- | ---------------- |
| Larry Sanders (wd:Q1034012)   | Larry Sanders    |
| LeBron James (wd:Q108870659)  | LeBron James     |
| Eddie Jordan (wd:Q1134403)    | Eddie Jordan     |
| Reggie Geary (wd:Q115509)     | Reggie Geary     |
| DeSagana Diop (wd:Q1179612)   | DeSagana Diop    |
| ...                           | ...              |
| Austin Carr (wd:Q933952)      | Austin Carr      |
| Winston Bennett (wd:Q943368)  | Winston Bennett  |
| Mickey Dillard (wd:Q962929)   | Mickey Dillard   |
| Ben McDonald (wd:Q967072)     | Ben McDonald     |
| Tristan Thompson (wd:Q973212) | Tristan Thompson |

[Assistant]
The SPARQL query has successfully returned the players drafted by the Cleveland Cavaliers along with their labels. Here are the results:

| Player IRI                    | Player Name        |
| ----------------------------- | ------------------ |
| [Larry Sanders](wd:Q1034012)  | Larry Sanders      |
| [LeBron James](wd:Q108870659) | LeBron James       |
| [Eddie Jordan](wd:Q1134403)   | Eddie Jordan       |
| [Reggie Geary](wd:Q115509)    | Reggie Geary       |
| [DeSagana Diop](wd:Q1179612)  | DeSagana Diop      |
| ...                           | ...                |
| [Austin Carr](wd:Q933952)     | Austin Carr        |
| [Winston Bennett](wd:Q943368) | Winston Bennett    |
| [Mickey Dillard](wd:Q962929)  | Mickey Dillard     |
| [Ben McDonald](wd:Q967072)    | Ben McDonald       |
| [Tristan Thompson](wd:Q973212)| Tristan Thompson   |

These are the players drafted by the Cleveland Cavaliers.

Now, I will 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 ?movement WHERE {
  wd:Q156386 wdt:P135 ?movement .
}

Henri Rousseau worked in the artistic styles of post-impressionism, naïve art, and primitivism.

[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 artistic style did henri rousseau work in?

[Assistant]
To answer the question about the artistic style Henri Rousseau worked in, we need to identify the entity for Henri Rousseau and then find the property that links him to his artistic style.

First, let's search for Henri Rousseau in the knowledge graph.

[Tool]
Top 10 entity alternatives:
1. Henri Rousseau, also known as le Douanier Rousseau, Henri rousseau, Le Douanier, le Douanier, h. rousseau (wd:Q156386): French painter (1844–1910), printmaker, painter, artist
2. Henri Rousseau (wd:Q106847442): French illustrator, illustrator
3. Henri Rousseau (wd:Q110034910): Righteous Among the Nations, farmer
4. Henri Rousseau (wd:Q110890688): 1942 exhibition at Museum of Modern Art
5. Henri Rousseau (wd:Q110893138): 1985 exhibition at Museum of Modern Art
6. Henri Emilien Rousseau, also known as Henri Émilien Rousseau, Henri E. Rousseau (wd:Q3132420): French painter (1875-1933), painter
7. Henri-Paul Rousseau (wd:Q3130451): Canadian businessperson, university teacher, businessperson
8. Rousseau, Henri(-Julien-Félix) (wd:Q103964446): encyclopedia article
9. Portrait of Henri Rousseau (wd:Q52083876): underglass painting by Franz Marc
10. Charles Henri Joseph Rousseau (wd:Q125622073): artisan (1732-1820), artisan

[Assistant] The top result for Henri Rousseau is the French painter Henri Rousseau (wd:Q156386). Now, we need to find the property that links Henri Rousseau to his artistic style. Let's search for properties related to "artistic style" for this entity.

[Tool]
Top 10 property alternatives:
1. genre, also known as genre of music, type of music, type of film, music genre, literary genre (wd:P136 as wdt/p): creative work's genre or an artist's field of work (P101). Use main subject (P921) to relate creative works to their topic
2. movement, also known as subculture, trend, scientific movement, school, philosophical movement (wd:P135 as wdt/p): literary, artistic, scientific or philosophical movement or scen...ed with this person or work. For political ideologies use P1142.
3. notable work, also known as artwork, wrote, writer of, works, work (wd:P800 as wdt/p): notable scientific, artistic or literary work, or other work of significance among subject's works
4. Artsy artist ID, also known as Artsy artist identifier, Artsy id (wd:P2042 as wdt/p): identifier at the Artsy website
5. pseudonym, also known as nom de plume, nom-de-plume, nome de guerre, pen name, ring name (wd:P742 as wdt/p): alias used by someone (for nickname use P1449), name
6. artist files at, also known as has artist files in, has artist files at, has artist file in, has artist file at, artist files in (wd:P9493 as wdt/p): institution or collection that holds artist files about the subject, documentation files at
7. image, also known as still capture, still, screenshot, screen capture, portrait (wd:P18 as wdt/p): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)
8. Web Gallery of Art artist ID, also known as WGA artist id, WGA ID, Web Gallery of Art identifier, Web Gallery of Art ID (wd:P1882 as wdt/p): identifier for an artist in the Web Gallery of Art
9. signature, also known as signature image, image of signature, facsimiles, autograph (wd:P109 as wdt/p): image of a person's signature, image
10. instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt/p): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)

No other items found

[Assistant]
From the search results, the property "genre" (wdt:P136) seems to be the most relevant for identifying the artistic style of Henri Rousseau. Let's construct a SPARQL query to find the artistic style(s) associated with Henri Rousseau.

Here is the initial SPARQL query:

```sparql
SELECT ?style WHERE {
  wd:Q156386 wdt:P136 ?style .
}
```

Let's execute this query to see the results.

[Tool]
Got 3 rows and 1 column:
| ?style                          |
| ------------------------------- |
| still life (wd:Q170571)         |
| landscape painting (wd:Q191163) |
| figure painting (wd:Q5448026)   |

[Assistant]
The SPARQL query returned three artistic styles associated with Henri Rousseau: still life (wd:Q170571), landscape painting (wd:Q191163), and figure painting (wd:Q5448026).

However, these styles are more specific genres rather than the broader artistic movement he is known for. Let's try another property, "movement" (wdt:P135), which might give us the broader artistic style or movement.

Here is the updated SPARQL query:

```sparql
SELECT ?movement WHERE {
  wd:Q156386 wdt:P135 ?movement .
}
```

Let's execute this query to see the results.

[Tool]
Got 3 rows and 1 column:
| ?movement                       |
| ------------------------------- |
| post-impressionism (wd:Q166713) |
| naïve art (wd:Q281108)          |
| primitivism (wd:Q848365)        |

[Assistant]
The SPARQL query returned three artistic movements associated with Henri Rousseau: post-impressionism (wd:Q166713), naïve art (wd:Q281108), and primitivism (wd:Q848365).

These results provide a broader and more accurate representation of the artistic style Henri Rousseau worked in. Therefore, we can conclude that Henri Rousseau worked in the styles of post-impressionism, naïve art, and primitivism.

Here is the final SPARQL query and answer:

```sparql
SELECT ?movement WHERE {
  wd:Q156386 wdt:P135 ?movement .
}
```

Henri Rousseau worked in the artistic styles of post-impressionism, naïve art, and primitivism.

[Tool] Stopped generation process


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?deity ?deityLabel WHERE {
  wd:Q2095353 rdfs:label ?deityLabel .
  FILTER ( LANG( ?deityLabel ) = "en" )
}

Got 1 row and 2 columns:
| ?deity | ?deityLabel  |
| ------ | ------------ |
|        | God in Islam |

Islamic people pray to God in Islam, represented by the entity `wd:Q2095353`.

[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 do islamic people pray to?

[Assistant]
To answer the question "Who do Islamic people pray to?", we need to identify the entity that represents the deity or supreme being in Islam. In Islam, this is typically referred to as Allah. Let's start by searching for the entity representing Allah in the knowledge graph.

I will use the `search_entity` function to find the entity for Allah.

[Tool]
Top 10 entity alternatives:
1. God, also known as All-Knowing, The One, The Lord, The All, supreme being (wd:Q190): principal object of faith in monotheistic religions, a divine entity that created and typically supervises all existence
2. Allah, also known as Allāh, ﷲ, الله, Allaah, ʾAllāh (wd:Q234801): Arabic word for God
3. Allah, also known as ﷲ, الله, ʾAllāh, ʾallāh, God in Arabic (wd:Q234801): Arabic word for God
4. God in Islam, also known as Allah, God (Islam), God (wd:Q2095353): Islamic conception of God
5. Clarence 13X, also known as Father Allah, Clarence Edward Smith, Allah the Father, Allah (wd:Q1095354): founder of The Nation of Gods and Earths (1928–1969), black supremacist, religious leader
6. Allah, also known as Allah, Arizona, Allah, AZ (wd:Q22058587): populated place located in Maricopa County, Arizona
7. Allah Allah / Hülya, also known as Allah Allah, Hülya (wd:Q6043103): 1987 studio album by İbrahim Tatlıses
8. Allah (wd:Q37516973): family name
9. Allah (wd:Q30064506): book
10. Allah (wd:Q126126270): painting by Isma'il Gulgee

[Assistant]
From the search results, the most relevant entity for Allah in the context of Islam is `wd:Q234801`, which is the Arabic word for God. Another relevant entity is `wd:Q2095353`, which specifically refers to the concept of God in Islam.

To ensure we are capturing the correct entity, let's use `wd:Q2095353` (God in Islam) in our SPARQL query. We will construct a simple query to confirm that this entity is indeed the one that Islamic people pray to.

Let's execute a simple SPARQL query to verify this:

```sparql
SELECT ?deity WHERE {
  wd:Q2095353 rdfs:label ?deity .
  FILTER(LANG(?deity) = "en")
}
```

This query will return the label of the entity `wd:Q2095353` in English.

[Tool]
Got 1 row and 1 column:
| ?deity       |
| ------------ |
| God in Islam |

[Assistant]
The query confirms that the entity `wd:Q2095353` is labeled as "God in Islam" in English. This is the correct entity that represents the deity that Islamic people pray to.

Now, let's construct the final SPARQL query to answer the user's question. Since the user asked "Who do Islamic people pray to?", we can directly return the entity `wd:Q2095353` along with its label.

Here is the final SPARQL query:

```sparql
SELECT ?deity ?deityLabel WHERE {
  wd:Q2095353 rdfs:label ?deityLabel .
  FILTER(LANG(?deityLabel) = "en")
}
```

This query will return the IRI and the English label of the entity representing the deity that Islamic people pray to.

Let's execute this final query:

[Tool]
Got 1 row and 2 columns:
| ?deity | ?deityLabel  |
| ------ | ------------ |
|        | God in Islam |

[Assistant]
The final query execution confirms that the entity `wd:Q2095353` is labeled as "God in Islam" in English. This is the correct answer to the user's question "Who do Islamic people pray to?".

Therefore, the answer to the user's question is that Islamic people pray to God in Islam, represented by the entity `wd:Q2095353`.

Here is the final SPARQL query and the answer:

```sparql
SELECT ?deity ?deityLabel WHERE {
  wd:Q2095353 rdfs:label ?deityLabel .
  FILTER(LANG(?deityLabel) = "en")
}
```

Answer: Islamic people pray to God in Islam, represented by the entity `wd:Q2095353`.

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

[Tool] Stopped generation process