PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?placeOfBirth ?childhoodResidence WHERE {
  wd:Q36844 wdt:P19 ?placeOfBirth .
  OPTIONAL {
    wd:Q36844 wdt:P551 ?childhoodResidence .
  }
}

Rihanna was raised in Saint Michael, Barbados, which is her place of birth. There is no specific information available about a different place where she was raised during her childhood.

[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 was rihanna raised?

[Assistant] To answer the question "Where was Rihanna raised?", we need to identify the entity for Rihanna and then find the location where she was raised. Let's start by searching for Rihanna in the knowledge graph.

[Tool]
Top 10 entity alternatives:
1. Rihanna, also known as Barbadian singer, Rihana, Robyn Fenty, Robyn Rihanna Fenty (wd:Q36844): Barbadian singer and actress, singer
2. Rihanna (wd:Q751012): 2010 photo-book
3. Rihanna (wd:Q17136804): female given name
4. Rihanna (wd:Q65090823): 2017 single by Yxng Bane
5. Rihanna (wd:Q17146308): single  by Orezi
6. Rihanna (wd:Q120757612): original song written by Camille Dhont, and performed by Camille
7. Rihanna (wd:Q119197664)
8. Rihanna (wd:Q119197546)
9. Rihanna (wd:Q120757609): vocal track by Camille; 2023 studio recording
10. Rihanna Twitter account, also known as @rihanna, twitter.com/rihanna (wd:Q65676172): Twitter account of Rihanna

[Assistant] The top result for "Rihanna" is the Barbadian singer and actress, Robyn Rihanna Fenty (wd:Q36844). Now that we have identified the correct entity, let's find the location where she was raised. We can search for properties related to "raised" or "place of birth" for this entity.

[Tool]
Top 10 property alternatives:
1. wears, also known as wore, wearing, uniform, outfit, garment (wd:P3828 as wdt/p): clothing or accessory worn on subject's body, item operated
2. place of birth, also known as POB, location of birth, location born, born in, birthplace (wd:P19 as wdt/p): most specific known birth location of a person, animal or fictional character, location
3. date of birth, also known as year of birth, birthdate, DOB, born on, born (wd:P569 as wdt/p): date on which the subject was born, inception
4. 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
5. height, also known as heighth, size, depth over terrain (wd:P2048 as wdt/p): vertical length of an entity, length
6. award received, also known as win, winner of, recognition title, received award, prize received (wd:P166 as wdt/p): award or recognition received by a person, organization or creative work
7. owner of, also known as owns, owns property, shareholder of (wd:P1830 as wdt/p): entities owned by the subject
8. child, also known as has son, kids, mother of, kid, issue (wd:P40 as wdt/p): subject has object as child. Do not use for stepchildren—use "relative" (P1038), qualified with "type of kinship" (P1039), significant person, relative
9. influenced by, also known as favorite player, has influence, informed by, role model (wd:P737 as wdt/p): this person, idea, etc. is informed by that other person, idea, etc., e.g. “Heidegger was influenced by Aristotle”
10. languages spoken, written or signed, also known as language of expression, language, wrote language, writes language, uses language (wd:P1412 as wdt/p): language(s) that a person or a people speaks, writes or signs, including the native language(s)

No other items found

[Assistant]
The property "place of birth" (wd:P19) seems to be the most relevant for finding where Rihanna was raised. Let's use this property to find the location where Rihanna was born and likely raised.

I will now execute a SPARQL query to retrieve the place of birth for Rihanna.

[Tool]
Got 1 row and 1 column:
| placeOfBirth               |
| -------------------------- |
| Saint Michael (wd:Q819170) |

[Assistant]
The place of birth for Rihanna is Saint Michael (wd:Q819170). Since the place of birth is often the same as the place where someone was raised, it is reasonable to conclude that Rihanna was raised in Saint Michael, Barbados.

Let's confirm this by providing the final answer.

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query correctly identifies Rihanna's place of birth, but the question asks where she was raised. We need to check if there is information about where she grew up, which might be different from her place of birth. Let's try using the property 'place of origin' (P157) or 'residence' (P551) to see if we can find this information.

[Assistant]
Thank you for the feedback. To address the question more accurately, we should look for properties that specifically indicate where Rihanna grew up or resided during her childhood. The properties "place of origin" (P157) and "residence" (P551) are good candidates. Let's search for these properties for Rihanna.

First, I will check if there is any information about her "place of origin" (P157).

[Tool] Got no rows and 1 columns

[Assistant] There is no information about Rihanna's "place of origin" (P157) in the knowledge graph. Next, let's check if there is any information about her "residence" (P551), which might provide insights into where she lived during her childhood or early life.

[Tool] Got no rows and 1 columns

[Assistant]
There is no information about Rihanna's "residence" (P551) in the knowledge graph either. Given that we have checked the most relevant properties and found no specific information about where she was raised, we can infer that the place of birth is likely the most accurate information available.

However, to provide a more comprehensive answer, we can include the place of birth and note that this is the most specific information available. Let's finalize the answer with this information.

Here is the final SPARQL query and answer:

```sparql
SELECT ?placeOfBirth WHERE { wd:Q36844 wdt:P19 ?placeOfBirth . }
```

Answer: Rihanna was raised in Saint Michael, Barbados, which is her place of birth. There is no specific information available about a different place where she was raised.

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The provided answer is correct, but it could be improved by including additional information. Since the question asks where Rihanna was raised, we should also check if there is any information about her childhood or where she grew up, even if it is the same as her place of birth. We can do this by adding an optional clause to the SPARQL query to look for the property 'place of residence during childhood' (P551).

[Assistant]
Thank you for the feedback. To provide a more comprehensive answer, we will include an optional clause to check for the property "place of residence during childhood" (P551) and other relevant properties. If no specific information is found, we will fall back to the place of birth.

Let's update the SPARQL query to include these checks.

[Tool]
Got 1 row and 2 columns:
| placeOfBirth               | childhoodResidence |
| -------------------------- | ------------------ |
| Saint Michael (wd:Q819170) |                    |

[Assistant]
The SPARQL query confirms that there is no specific information about Rihanna's place of residence during childhood (P551) in the knowledge graph. Therefore, the most accurate information we have is that she was born in Saint Michael, Barbados, and it is reasonable to infer that she was raised there.

Here is the final SPARQL query and answer:

```sparql
SELECT ?placeOfBirth ?childhoodResidence WHERE { wd:Q36844 wdt:P19 ?placeOfBirth . OPTIONAL { wd:Q36844 wdt:P551 ?childhoodResidence . } }
```

Answer: Rihanna was raised in Saint Michael, Barbados, which is her place of birth. There is no specific information available about a different place where she was raised during her childhood.

[Tool] Stopped generation process


PREFIX ps: <http://www.wikidata.org/prop/statement/>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX pq: <http://www.wikidata.org/prop/qualifier/>
PREFIX p: <http://www.wikidata.org/prop/>
SELECT ?start ?end WHERE {
  wd:Q91 p:P39 [ ps:P39 wd:Q11696 ; pq:P580 ?start ; pq:P582 ?end ] .
}

Abraham Lincoln was president from March 4, 1861, to April 15, 1865.

[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 was abe lincoln president?

[Assistant] To answer when Abraham Lincoln was president, we need to identify Abraham Lincoln and the property that indicates his presidency period. Let's start by searching for Abraham Lincoln in the knowledge graph.

[Tool]
Top 10 entity alternatives:
1. Abraham Lincoln, also known as President Lincoln, Uncle Abe, Honest Abe, Abe Lincoln (wd:Q91): president of the United States from 1861 to 1865, military officer, statesperson, postmaster, politician, farmer, writer, lawyer
2. USS Abraham Lincoln, also known as CVN-72, Abraham Lincoln, USS Abraham Lincoln CVN-72 (wd:Q587557): 1988 Nimitz-class aircraft carrier
3. Abraham Lincoln (wd:Q166089): 1930 film by D. W. Griffith
4. Statue of Abraham Lincoln, also known as Abraham Lincoln (wd:Q4669005): statue at the Lincoln Memorial, Washington, D.C.
5. Abraham Lincoln, also known as Captain Abraham Lincoln (wd:Q2821841): grandfather of president Abraham Lincoln (1744-1786), military personnel
6. Abraham Lincoln, also known as Abraham Lincoln Statue, Statue of Abraham Lincoln (wd:Q16001435): artwork by Lot Flannery
7. Abraham Lincoln (wd:Q10322725): 1869 painting by George P.A. Healy
8. The Dramatic Life of Abraham Lincoln, also known as Abraham Lincoln, Dramatic Life of Abraham Lincoln (wd:Q7730887): 1924 film by Phil Rosen
9. Abraham Lincoln (wd:Q4668998): 1924 film by J. Searle Dawley
10. Statue of Abraham Lincoln, also known as Abraham Lincoln, Statue of Abraham Lincoln (Lincoln, Nebraska), The Gettysburg Lincoln (wd:Q4669002): statue in Lincoln, Nebraska by Daniel Chester French

[Assistant] The correct entity for Abraham Lincoln is `wd:Q91`. Now, let's find the property that indicates his presidency period. We can search for properties related to "presidency" or "president" for this entity.

[Tool]
Top 10 property alternatives:
1. position held, also known as function, held position, holds position, job, office held (wd:P39 as wdt/p): subject currently or formerly holds the object position or public office
2. candidacy in election, also known as ran in election, running in election, election candidacy, candidate in election, candidate in (wd:P3602 as wdt/p): election where the subject is a candidate, participant in
3. member of political party, also known as political party member, political party, party membership, party, member of party (wd:P102 as wdt/p): the political party of which a person is or has been a member or otherwise affiliated, member of
4. country of citizenship, also known as subject of (country), nation of citizenship, national of, nationality, nation (wd:P27 as wdt/p): the object is a country that recognizes the subject as its citizen, country
5. family, also known as royal house, noble family, member of family, house, dynasty (wd:P53 as wdt/p): family, including dynasty and nobility houses. Not family name (use P734 for family name)., member of
6. influenced by, also known as favorite player, has influence, informed by, role model (wd:P737 as wdt/p): this person, idea, etc. is informed by that other person, idea, etc., e.g. “Heidegger was influenced by Aristotle”
7. significant event, also known as significant occurrence, significant incident, significant achievment, sign. event, outcome (wd:P793 as wdt/p): significant or notable events associated with the subject
8. conflict, also known as war, theatre (military), theater (military), participated in conflict, military conflict (wd:P607 as wdt/p): battles, wars or other military engagements in which the person or item participated, participant in
9. killed by, also known as slaughtered by, slain by, shot dead by, murderer, murdered by (wd:P157 as wdt/p): person or organization who killed the subject, significant person
10. place of birth, also known as POB, location of birth, location born, born in, birthplace (wd:P19 as wdt/p): most specific known birth location of a person, animal or fictional character, location

No other items found

[Assistant] The property `position held` (P39) seems to be the most relevant for identifying Abraham Lincoln's presidency. Let's check the specific positions he held to confirm if it includes his presidency and the corresponding time period.

[Tool]
Got 4 rows and 3 columns:
| subject                  | property                | object                                                              |
| ------------------------ | ----------------------- | ------------------------------------------------------------------- |
| Abraham Lincoln (wd:Q91) | position held (wdt:P39) | President of the United States (wd:Q11696)                          |
| Abraham Lincoln (wd:Q91) | position held (wdt:P39) | President-elect of the United States (wd:Q1467287)                  |
| Abraham Lincoln (wd:Q91) | position held (wdt:P39) | member of the United States House of Representatives (wd:Q13218630) |
| Abraham Lincoln (wd:Q91) | position held (wdt:P39) | member of the Illinois House of Representatives (wd:Q17495450)      |

[Assistant] Abraham Lincoln held several positions, including President of the United States. To get the exact time period during which he was president, we need to look for the qualifiers associated with the `President of the United States` position. Let's check the qualifiers for this position.

[Tool] Only two of subject, property, or object should be provided.

[Assistant] It looks like I made a mistake in the query. Instead of using `p:P39`, I should use `ps:P39` to get the statement and then look for the qualifiers. Let's correct this and try again.

[Tool] Only two of subject, property, or object should be provided.

[Assistant] I see that I made another mistake. Let's try a different approach to get the qualifiers for the position held. We can directly query for the qualifiers using a SPARQL query. Let's write a SPARQL query to get the start and end dates for Abraham Lincoln's presidency.

[Tool]
Got 1 row and 2 columns:
| start                               | end                                 |
| ----------------------------------- | ----------------------------------- |
| 1861-03-04T00:00:00Z (xsd:dateTime) | 1865-04-15T00:00:00Z (xsd:dateTime) |

[Assistant]
The SPARQL query returned the start and end dates for Abraham Lincoln's presidency. According to the results, Abraham Lincoln was president from March 4, 1861, to April 15, 1865.

Now, let's format this into a final answer.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query returns the start and end dates of Abraham Lincoln's presidency accurately.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?language WHERE {
  wd:Q79 wdt:P37 ?language .
}

It appears that there are no results for the languages spoken, written, or signed in Egypt using the property `wd:P1412`. This might be because the data is not available or the property is not commonly used for countries in this context.

Given the results, the official language of Egypt is Arabic. Since we did not find additional languages using `wd:P1412`, we can conclude that Arabic is the primary language spoken in Egypt.

### Final Answer
The language spoken in Egypt is Arabic.

Let's finalize the answer with the SPARQL query that confirms this.

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

Final answer: The language spoken in Egypt is Arabic.

```json
{
  "name": "answer",
  "arguments": {
    "kg": "wikidata",
    "sparql": "SELECT ?language WHERE { wd:Q79 wdt:P37 ?language . }",
    "answer": "The 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 kind of language does egypt speak?

[Assistant]
To answer the question about the language spoken in Egypt, we need to identify the entity for Egypt and then find the property that indicates the official or commonly spoken language(s) of the country.

### Step 1: Identify the Entity for Egypt
First, let's search for the entity representing Egypt in the knowledge graph.

```json
{
  "name": "search_entity",
  "arguments": {
    "kg": "wikidata",
    "query": "Egypt"
  }
}
```

### Step 2: Identify the Property for Language
Once we have the entity for Egypt, we need to find the property that indicates the language(s) spoken in the country. We can search for properties related to "language" or "official language."

```json
{
  "name": "search_property",
  "arguments": {
    "kg": "wikidata",
    "query": "language"
  }
}
```

### Step 3: Build and Execute the SPARQL Query
After identifying the entity and property, we can build and execute a SPARQL query to find the language(s) spoken in Egypt.

Let's start by finding the entity for 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 Republic of Egypt (wd:Q79), which is the correct entity we are looking for.

### Step 2: Identify the Property for Language
Next, let's search for properties related to "language" to find the appropriate property that indicates the language(s) spoken in Egypt.

```json
{
  "name": "search_property",
  "arguments": {
    "kg": "wikidata",
    "query": "language"
  }
}
```

Let's proceed with this search.

[Tool]
Top 10 property alternatives:
1. programmed in, also known as language, programming language, written in (wd:P277 as psv/pqn/p/ps/pq/wdtn/prn/prv/wdt/psn/pr/pqv): the programming language(s) in which the software is developed
2. languages spoken, written or signed, also known as language of expression, language, wrote language, writes language, uses language (wd:P1412 as psv/pqn/p/ps/pq/wdtn/prn/prv/wdt/psn/pr/pqv): language(s) that a person or a people speaks, writes or signs, including the native language(s)
3. language of work or name, also known as language of the reference, language of URL, language of website, language of work, named in language (wd:P407 as psv/pqn/p/ps/pq/wdtn/prn/prv/wdt/psn/pr/pqv): language associated with this creative work (such as books, show...guage" (P103) and "languages spoken, written or signed" (P1412))
4. official language, also known as language spoken, spoken in, speaking, language official (wd:P37 as psv/pqn/p/ps/pq/wdtn/prn/prv/wdt/psn/pr/pqv): language designated as official by this item, language used
5. language used, also known as languages used, medium of instruction, used language, uses language, working language (wd:P2936 as psv/pqn/p/ps/pq/wdtn/prn/prv/wdt/psn/pr/pqv): language widely used (spoken or written) in this place or at this event or organisation
6. language style, also known as manner, term register, obsolete, register, sense register (wd:P6191 as psv/pqn/p/ps/pq/wdtn/prn/prv/wdt/psn/pr/pqv): to denote of the way a sense of a word is used, e.g., slang, vulgarism, babytalk, colloquialism, etc.
7. native language, also known as first language, L1 speaker of, language native, mother tongue (wd:P103 as psv/pqn/p/ps/pq/wdtn/prn/prv/wdt/psn/pr/pqv): language or languages a person has learned from early childhood, languages spoken, written or signed
8. writing language, also known as languages of writings, languages written, language written, writing languages, language of works (wd:P6886 as psv/pqn/p/ps/pq/wdtn/prn/prv/wdt/psn/pr/pqv): language in which the writer has written their work, languages spoken, written or signed
9. Ethnologue language status, also known as EGIDS, language status (wd:P3823 as psv/pqn/p/ps/pq/wdtn/prn/prv/wdt/psn/pr/pqv): language status identifier by Ethnologue.com using EGIDS scale
10. variety of lexeme, form or sense, also known as dialect, used in dialect, variety of form (wd:P7481 as psv/pqn/p/ps/pq/wdtn/prn/prv/wdt/psn/pr/pqv): dialect, graphic or phonetic variety that this lexeme, form or sense is used in

[Assistant]
From the list of properties, the most relevant ones for our purpose are:
- **Official language** (wd:P37): This property indicates the language designated as official by the country.
- **Languages spoken, written or signed** (wd:P1412): This property indicates the language(s) that a person or a people speaks, writes, or signs, including the native language(s).

We will use these properties to find the language(s) spoken in Egypt. Let's start with the official language.

### Step 3: Build and Execute the SPARQL Query
We will first check the official language of Egypt using the property `wd:P37`.

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

Let's execute this query.

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

[Assistant]
The official language of Egypt is Arabic (wd:Q13955).

To provide a more comprehensive answer, let's also check the languages spoken, written, or signed in Egypt using the property `wd:P1412`.

### Step 4: Check Languages Spoken, Written, or Signed
We will now execute a query to find the languages spoken, written, or signed in Egypt.

```sparql
SELECT ?language WHERE {
  wd:Q79 wdt:P1412 ?language .
}
```

Let's execute this query.

[Tool] Got no rows and 1 columns

[Assistant]
It appears that there are no results for the languages spoken, written, or signed in Egypt using the property `wd:P1412`. This might be because the data is not available or the property is not commonly used for countries in this context.

Given the results, the official language of Egypt is Arabic. Since we did not find additional languages using `wd:P1412`, we can conclude that Arabic is the primary language spoken in Egypt.

### Final Answer
The language spoken in Egypt is Arabic.

Let's finalize the answer with the SPARQL query that confirms this.

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

Final answer: The language spoken in Egypt is Arabic.

```json
{
  "name": "answer",
  "arguments": {
    "kg": "wikidata",
    "sparql": "SELECT ?language WHERE { wd:Q79 wdt:P37 ?language . }",
    "answer": "The language spoken in Egypt is Arabic."
  }
}
```


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 ?model ?modelLabel WHERE {
  ?model wdt:P176 wd:Q81965 .
  ?model wdt:P31 wd:Q1420 .
  OPTIONAL {
    ?model rdfs:label ?modelLabel .
    FILTER ( LANG( ?modelLabel ) = 'en' )
  }
}

Got 7 rows and 2 columns:
| model                               | modelLabel                  |
| ----------------------------------- | --------------------------- |
| Buick Regal (wd:Q1002872)           | Buick Regal (en)            |
| Oldsmobile Model 43 (wd:Q1310003)   | Oldsmobile Model 43 (en)    |
| Oldsmobile Series 40 (wd:Q1340552)  | Oldsmobile Series 40 (en)   |
| GMC Yukon (wd:Q1485965)             | GMC Yukon (en)              |
| Buick LaCrosse (wd:Q498843)         | Buick LaCrosse (en)         |
| Cadillac Sixty Special (wd:Q825252) | Cadillac Sixty Special (en) |
| Buick Velite 7 (wd:Q96354603)       | Buick Velite 7 (en)         |

General Motors produces a wide range of vehicle models, including but not limited to the Buick Regal, Oldsmobile Model 43, Oldsmobile Series 40, GMC Yukon, Buick LaCrosse, Cadillac Sixty Special, and Buick Velite 7.

[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 does gm make?

[Assistant] To answer the question "What does GM make?", we need to identify the entity for General Motors (GM) and then find out what products it manufactures. Let's start by searching for the entity representing General Motors in the knowledge graph.

[Tool]
Top 10 entity alternatives:
1. General Motors, also known as GM, General Motors Corporation (wd:Q81965): automotive manufacturing corporation based in Detroit, Michigan, USA, American automotive manufacturing corporation
2. General Motors railway station, also known as General Motors station, General Motors (wd:Q5532064): closed railway station in Dandenong South, Victoria, Australia
3. General Motors (wd:Q16891615): former Rochester Industrial and Rapid Transit Railway station and streetcar carhouse in Rochester, New York
4. General Motors (wd:Q84480751): scientific article published on 01 November 1983
5. General Motors, also known as General Motors (Poland) (wd:Q30338742): Polish subsidary of General Motors
6. General motors (wd:Q84481962): scientific article published on 01 February 1983
7. Grumman TBF Avenger, also known as TBM Avenger, TBM, Grumman Torpedo Bomber Design 1, Grumman TBM, Grumman Tarpon (wd:Q734103): 1941 torpedo bomber aircraft model by Grumman
8. Rogers Arena, also known as GM Place, General Motors Place (wd:Q517318): sports arena in Vancouver, British Columbia, Canada
9. GM Korea, also known as General Motors Korea, GM Daewoo, 한국GM (wd:Q497193): South Korean subsidiary of General Motors
10. General Motors EV1, also known as EV1 (wd:Q152013): electric compact car model

[Assistant]
The top result for "General Motors" is `wd:Q81965`, which represents the automotive manufacturing corporation based in Detroit, Michigan, USA. Now, let's find out what products General Motors makes. We can look for properties related to products or manufacturing.

I will search for properties related to "product" or "manufacture" for the entity `wd:Q81965`.

[Tool]
Top 10 property alternatives:
1. product, material, or service produced or provided, also known as products, product or service provided, product, material, or service produced or provided, product, output (wd:P1056 as wdt/p): material or product produced or sold by an organization, industry, facility, or process, material or product produced by an organization, industry, facility, or process, immediate cause of
2. official shop URL, also known as online store, shop, shop URL, store, store URL (wd:P10225 as wdt/p): official URL where the agent sells products/merchandise, official website
3. official website, also known as site (official), home page, site official, URL, URL (official) (wd:P856 as wdt/p): URL of the official page of an item (current or former). Usage: ...but see the "Hijacked or dead websites" section of the Talk page, URL
4. logo image, also known as masthead, wordmark, trademark, trade mark, sign (wd:P154 as wdt/p): graphic mark or emblem commonly used by commercial enterprises, organizations and products, icon
5. total revenue, also known as turnover, sales, revenue, income, gross revenue (wd:P2139 as wdt/p): income gained by an organization during a given time frame. Not to be confused with fiscal revenue
6. country of origin, also known as origin country, place of origin, originates from, country, CoO (wd:P495 as wdt/p): country of origin of this item (creative work, food, phrase, product, etc.), significant place, country
7. country, also known as sovereign state, state, land, host country (wd:P17 as wdt/p): sovereign state that this item is in (not to be used for human beings), located in the administrative territorial entity
8. 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)
9. official name, also known as long title, name, official title, registered name, full title (wd:P1448 as wdt/p): official name of the subject in its official language(s), name
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]
The property `wdt:P1056` (product, material, or service produced or provided) seems to be the most relevant for finding out what products General Motors makes. Let's use this property in a SPARQL query to find the products manufactured by General Motors.

I will execute the following SPARQL query:

```sparql
SELECT ?product WHERE {
  wd:Q81965 wdt:P1056 ?product .
}
```

This query will return the products that General Motors produces.

[Tool]
Got 1 row and 1 column:
| product              |
| -------------------- |
| motor car (wd:Q1420) |

[Assistant]
The query returned `motor car (wd:Q1420)` as a product that General Motors produces. To provide a more comprehensive answer, let's also check if there are any specific models or types of vehicles that General Motors manufactures. We can do this by looking for more detailed properties or by searching for specific products.

Let's try to find more specific products by using the `wdt:P1056` property again and also include the labels for better readability.

[Tool]
Got 1 row and 2 columns:
| product              | productLabel   |
| -------------------- | -------------- |
| motor car (wd:Q1420) | motor car (en) |

[Assistant]
The query returned `motor car (wd:Q1420)` with the label "motor car". This indicates that General Motors produces motor cars, but it doesn't provide a detailed list of specific models or types of vehicles.

To get a more detailed list of specific products, we can try to find properties that might link General Motors to specific vehicle models. One such property could be `wdt:P361` (part of), which might indicate that certain models are part of General Motors' product line.

Let's execute a query to find specific models or types of vehicles that General Motors produces:

```sparql
SELECT ?model ?modelLabel WHERE {
  ?model wdt:P361 wd:Q81965 .
  OPTIONAL { ?model rdfs:label ?modelLabel . FILTER(LANG(?modelLabel) = 'en') }
}
```

This query will return specific models or types of vehicles that are part of General Motors' product line.

[Tool]
Got 1 row and 2 columns:
| model                       | modelLabel         |
| --------------------------- | ------------------ |
| GM V platform (wd:Q5513904) | GM V platform (en) |

[Assistant]
The query returned `GM V platform (wd:Q5513904)` with the label "GM V platform". This indicates that General Motors produces the GM V platform, which is likely a vehicle platform used for multiple models.

To get a more comprehensive list of specific vehicle models, we can try to find properties that directly link General Motors to specific vehicle models. One such property could be `wdt:P176` (manufacturer), which indicates the manufacturer of a product.

Let's execute a query to find specific vehicle models that General Motors manufactures:

```sparql
SELECT ?model ?modelLabel WHERE {
  ?model wdt:P176 wd:Q81965 .
  OPTIONAL { ?model rdfs:label ?modelLabel . FILTER(LANG(?modelLabel) = 'en') }
}
```

This query will return specific vehicle models that are manufactured by General Motors.

[Tool]
Got 791 rows and 2 columns, showing the first 5 and last 5 rows below:
| model                            | modelLabel                     |
| -------------------------------- | ------------------------------ |
| Buick Estate (wd:Q1002745)       | Buick Estate (en)              |
| Buick Electra (wd:Q1002748)      | Buick Electra (en)             |
| Buick Invicta (wd:Q1002761)      | Buick Invicta (en)             |
| Buick Limited (wd:Q1002771)      | Buick Limited (en)             |
| Buick Master Six (wd:Q1002780)   | Buick Master Six (en)          |
| ...                              | ...                            |
| Cadillac ATS (wd:Q979003)        | Cadillac ATS (en)              |
| T18 Boarhound (wd:Q979464)       | T18 Boarhound (en)             |
| wd:Q991211                       | list of Opel concept cars (en) |
| Pontiac Trans Sport (wd:Q991256) | Pontiac Trans Sport (en)       |
| Isuzu 117 Coupé (wd:Q993674)     | Isuzu 117 Coupé (en)           |

[Assistant]
The query returned a comprehensive list of specific vehicle models that General Motors manufactures. Here are some of the models:

- Buick Estate
- Buick Electra
- Buick Invicta
- Buick Limited
- Buick Master Six
- Cadillac ATS
- T18 Boarhound
- Pontiac Trans Sport
- Isuzu 117 Coupé

Given this information, we can conclude that General Motors produces a wide range of vehicle models across different brands such as Buick, Cadillac, Pontiac, and Isuzu.

Here is the final SPARQL query and answer:

```sparql
SELECT ?model ?modelLabel WHERE {
  ?model wdt:P176 wd:Q81965 .
  OPTIONAL { ?model rdfs:label ?modelLabel . FILTER(LANG(?modelLabel) = 'en') }
}
```

Answer: General Motors produces a wide range of vehicle models, including but not limited to the Buick Estate, Buick Electra, Buick Invicta, Buick Limited, Buick Master Six, Cadillac ATS, T18 Boarhound, Pontiac Trans Sport, and Isuzu 117 Coupé.

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query is mostly correct, but it includes an item that is not a vehicle model (list of Opel concept cars). This item should be filtered out to ensure that only actual vehicle models are returned. Consider adding a filter to exclude non-vehicle items or to specifically include only those items that have a property indicating they are vehicles.

[Assistant]
Thank you for the feedback. To refine the query and ensure that only actual vehicle models are returned, we can add a filter to exclude non-vehicle items. One way to do this is to check if the items have a property indicating they are vehicles, such as `wdt:P31` (instance of) with a value that is a type of vehicle.

Let's add a filter to ensure that the items are instances of a vehicle. We can use the property `wdt:P31` and check if the item is an instance of a vehicle (e.g., `wd:Q1420` for motor car).

Here is the refined SPARQL query:

```sparql
SELECT ?model ?modelLabel WHERE {
  ?model wdt:P176 wd:Q81965 .
  ?model wdt:P31 wd:Q1420 .
  OPTIONAL { ?model rdfs:label ?modelLabel . FILTER(LANG(?modelLabel) = 'en') }
}
```

This query will return only those items that are instances of a motor car and are manufactured by General Motors.

Let's execute this refined query.

[Tool]
Got 7 rows and 2 columns:
| model                               | modelLabel                  |
| ----------------------------------- | --------------------------- |
| Buick Regal (wd:Q1002872)           | Buick Regal (en)            |
| Oldsmobile Model 43 (wd:Q1310003)   | Oldsmobile Model 43 (en)    |
| Oldsmobile Series 40 (wd:Q1340552)  | Oldsmobile Series 40 (en)   |
| GMC Yukon (wd:Q1485965)             | GMC Yukon (en)              |
| Buick LaCrosse (wd:Q498843)         | Buick LaCrosse (en)         |
| Cadillac Sixty Special (wd:Q825252) | Cadillac Sixty Special (en) |
| Buick Velite 7 (wd:Q96354603)       | Buick Velite 7 (en)         |

[Assistant]
The refined query has returned a list of specific vehicle models that are instances of motor cars and are manufactured by General Motors. Here are the models:

- Buick Regal
- Oldsmobile Model 43
- Oldsmobile Series 40
- GMC Yukon
- Buick LaCrosse
- Cadillac Sixty Special
- Buick Velite 7

These results are now more accurate and exclude non-vehicle items.

Here is the final SPARQL query and answer:

```sparql
SELECT ?model ?modelLabel WHERE {
  ?model wdt:P176 wd:Q81965 .
  ?model wdt:P31 wd:Q1420 .
  OPTIONAL { ?model rdfs:label ?modelLabel . FILTER(LANG(?modelLabel) = 'en') }
}
```

Answer: General Motors produces a wide range of vehicle models, including but not limited to the Buick Regal, Oldsmobile Model 43, Oldsmobile Series 40, GMC Yukon, Buick LaCrosse, Cadillac Sixty Special, and Buick Velite 7.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query is well-formed and returns the correct results. It adheres to the guidelines by returning the actual identifiers of the items and their labels. The answer provided is accurate and comprehensive, listing several models produced by General Motors.


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 ?city ?cityLabel ?population WHERE {
  ?city wdt:P31 wd:Q515 .
  ?city wdt:P1082 ?population .
  ?city wdt:P17 wd:Q29 .
  OPTIONAL {
    ?city rdfs:label ?cityLabel .
    FILTER ( LANG( ?cityLabel ) = "en" )
  }
}
ORDER BY DESC ( ?population )
LIMIT 1

The largest city in Spain is Madrid.

[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 the largest city in spain?

[Assistant]
To find the largest city in Spain, we need to identify the relevant entities and properties. We'll start by searching for the entity representing Spain and then look for properties that might help us determine the largest city, such as population or area.

First, let's search for the entity representing Spain.

[Tool]
Top 10 entity alternatives:
1. Spain, also known as Kingdom of Spain (wd:Q29): country in southwestern Europe with territories in Africa
2. Spain men's national football team, also known as La Furia, The Spanish Fury, The Red One, The Red Fury, The Fury (wd:Q42267): men's national association football team representing Spain
3. Second Spanish Republic, also known as Spain (wd:Q178038): political regime established in Spain between April 14, 1931 and April 1, 1939
4. New Spain, also known as Colonial Central America, Central America under Spain, Viceroyalty of New Spain, Spanish Mexico, Spanish Central America (wd:Q170603): viceroyalty of the Spanish Empire (1535-1821)
5. Spain under the Restoration, also known as Bourbon Restoration, Spain, Second Bourbon Restoration, Restoration, Restoration (Spain) (wd:Q1044536): name given to a period between 1874 (end of First Spanish Republ...econd Spanish Republic), and the state of Spain during that time
6. Kingdom of Spain (1820-1823), also known as Kingdom of Spain, Spain, Trienio Liberal (wd:Q238809): 1820–1823 Spanish political period
7. Spain under Joseph Bonaparte, also known as Kingdom of Spain, Napoleonic Spain, Spain (wd:Q1709185): Napoleonic client state (1808–1813)
8. Sexenio Democrático, also known as Kingdom of Spain, Spain (wd:Q12411956): 1868–1874 period of fu under a provisional government
9. Ominous Decade, also known as Kingdom of Spain, second restoration of absolutism, Spain (wd:Q2378204): period in the history of Spain, corresponding to the second rest...reign of Fernando VII from August 31, 1823 to September 29, 1833
10. Hispanic Monarchy, also known as Spains, Hispanic Crown, Spain, Kingdom of Spains, Crown of Spain (wd:Q766543): (1479-1716) set of territories distributed throughout the world,...monarch and corresponding to the period of the House of Habsburg

[Assistant]
The most relevant entity for Spain is "Kingdom of Spain" (wd:Q29). Next, we need to find properties that can help us determine the largest city in Spain. We will search for properties related to cities and their populations.

Let's search for properties related to "city" and "population" for the entity "Spain".

[Tool]
Top 10 property alternatives:
1. urban population (wd:P6343 as wdt/p): number of people living within the territorial entity who live in its urban parts, population
2. population, also known as inhabitants, human population (wd:P1082 as wdt/p): number of people inhabiting the place; number of people of subject
3. contains the administrative territorial entity, also known as has shires, has rural cities, has regions, has municipalities, has members (wd:P150 as wdt/p): (list of) direct subdivisions of an administrative territorial entity, has part(s)
4. capital, also known as administrative seat, seat of government, seat, principal place, location of capital (wd:P36 as wdt/p): seat of government of a country, province, state or other type of administrative territorial entity
5. nominal GDP per capita, also known as GDP per capita, nominal, Gross domestic product per capita, gdp per capita, GDP (nominal) per capita (wd:P2132 as wdt/p): country's total GDP divided by the population
6. rural population (wd:P6344 as wdt/p): number of people living within the territorial entity who live in its rural parts, population
7. nominal GDP, also known as GDP, GDP, nominal, gross domestic product, nominal GDP, nominal gross domestic product (wd:P2131 as wdt/p): market value of all officially recognized final goods and services produced within a country in a given period of time
8. female population (wd:P1539 as wdt/p): number of female people inhabiting the place; number of female people of subject, population
9. head of government, also known as head of national government, mayor, premier, president, prime minister (wd:P6 as wdt/p): head of the executive power of this town, city, municipality, state, country, or other governmental body, director / manager, chairperson
10. male population, also known as male inhabitants, number of males (wd:P1540 as wdt/p): number of male people inhabiting the place; number of male people of subject, population

No other items found

[Assistant]
From the search results, the most relevant properties for determining the largest city in Spain are "contains the administrative territorial entity" (wdt:P150) and "population" (wdt:P1082).

We can use these properties to find the cities in Spain and their populations. Let's construct a SPARQL query to find the largest city in Spain by population.

Here is the initial SPARQL query:

```sparql
SELECT ?city ?cityLabel ?population
WHERE {
  wd:Q29 wdt:P150 ?city .
  ?city wdt:P1082 ?population .
  OPTIONAL { ?city rdfs:label ?cityLabel . FILTER(LANG(?cityLabel) = "en") }
}
ORDER BY DESC(?population)
LIMIT 1
```

This query will return the city with the highest population in Spain along with its label and population.

Let's execute this query.

[Tool]
Got 1 row and 3 columns:
| city                 | cityLabel      | population            |
| -------------------- | -------------- | --------------------- |
| Andalusia (wd:Q5783) | Andalusia (en) | 8476718 (xsd:decimal) |

[Assistant]
It seems that the query returned Andalusia, which is a region rather than a city. This suggests that the property `wdt:P150` might be returning regions instead of cities. To find cities, we need to filter for entities that are specifically classified as cities.

Let's refine the query to include a filter for entities that are instances of a city (using `wdt:P31` to check the instance of the entity).

Here is the refined SPARQL query:

```sparql
SELECT ?city ?cityLabel ?population
WHERE {
  wd:Q29 wdt:P150 ?region .
  ?region wdt:P150 ?city .
  ?city wdt:P31 wd:Q515 .  # Filter for instances of city
  ?city wdt:P1082 ?population .
  OPTIONAL { ?city rdfs:label ?cityLabel . FILTER(LANG(?cityLabel) = "en") }
}
ORDER BY DESC(?population)
LIMIT 1
```

Let's execute this refined query.

[Tool] Got no rows and 3 columns

[Assistant]
It appears that the refined query did not return any results. This might be because the structure of the data in Wikidata is different from what we expected. Instead of going through regions, we can directly query for cities in Spain that are instances of a city and have a population.

Let's try a simpler query that directly looks for cities in Spain:

```sparql
SELECT ?city ?cityLabel ?population
WHERE {
  ?city wdt:P31 wd:Q515 .  # City
  ?city wdt:P1082 ?population .
  ?city wdt:P17 wd:Q29 .  # Located in Spain
  OPTIONAL { ?city rdfs:label ?cityLabel . FILTER(LANG(?cityLabel) = "en") }
}
ORDER BY DESC(?population)
LIMIT 1
```

This query will directly find cities in Spain and order them by population, returning the largest city.

Let's execute this query.

[Tool]
Got 1 row and 3 columns:
| city              | cityLabel   | population            |
| ----------------- | ----------- | --------------------- |
| Madrid (wd:Q2807) | Madrid (en) | 3332035 (xsd:decimal) |

[Assistant]
The query successfully returned the largest city in Spain, which is Madrid, with a population of 3,332,035.

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

```sparql
SELECT ?city ?cityLabel ?population
WHERE {
  ?city wdt:P31 wd:Q515 .  # City
  ?city wdt:P1082 ?population .
  ?city wdt:P17 wd:Q29 .  # Located in Spain
  OPTIONAL { ?city rdfs:label ?cityLabel . FILTER(LANG(?cityLabel) = "en") }
}
ORDER BY DESC(?population)
LIMIT 1
```

The largest city in Spain is Madrid.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query is correct and returns the expected result. The largest city in Spain is indeed Madrid, with a population of 3,332,035.


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 ?causeOfDeath ?causeOfDeathLabel WHERE {
  wd:Q40933 wdt:P509 ?causeOfDeath .
  OPTIONAL {
    ?causeOfDeath rdfs:label ?causeOfDeathLabel .
    FILTER ( LANG( ?causeOfDeathLabel ) = "en" )
  }
}

Got 1 row and 2 columns:
| causeOfDeath                       | causeOfDeathLabel          |
| ---------------------------------- | -------------------------- |
| cervix uterine cancer (wd:Q160105) | cervix uterine cancer (en) |

Eva Perón had cervical cancer.

[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 type of cancer did eva peron have?

[Assistant] To answer the question about the type of cancer Eva Perón had, we need to identify the entity for Eva Perón and then find the property that indicates the type of cancer she had. Let's start by searching for Eva Perón in the knowledge graph.

[Tool]
Top 10 entity alternatives:
1. Eva Perón, also known as Evita Perón, Maria Eva Duarte de Peron, Evita, María Eva Duarte de Perón, Maria Eva Duarte Peron (wd:Q40933): Argentine actress and politician (1919–1952), women's rights activist, trade unionist, stage actor, suffragist, politician, film actor
2. Eva Perón: The True Story, also known as Eva Peron, Eva Perón (wd:Q953441): 1996 film by Juan Carlos Desanzo
3. Fray Luis Beltrán, also known as Eva Perón, Eva Perón tanker, Fray Luis Beltrán tanker (wd:Q113059537): tanker
4. Eva Perón (wd:Q113030601): tanker
5. Hospital Lagomarsino, also known as Eva Perón (wd:Q5902632): hospital in Merlo, Buenos Aires
6. Uruguay, also known as Eva Perón (wd:Q113330807): 1951 passenger ship
7. Eva Perón (wd:Q47512378): painting by Boris Chaliapin
8. Estadio Eva Perón, also known as Estadio Eva Peron (wd:Q5400194): football stadium in Junín, Argentina
9. Puerto Eva Perón (wd:Q972928): ciudad en la provincia de Chaco, Argentina
10. Eva Perón Foundation (wd:Q2004004): former Argentine foundation

[Assistant]
The top result for Eva Perón is `wd:Q40933`, which refers to the Argentine actress and politician. Now, we need to find the property that indicates the type of cancer she had. We can search for properties related to "cancer" or "illness" for this entity.

Let's search for properties related to "cancer" for Eva Perón.

[Tool]
Top 10 property alternatives:
1. date of death, also known as DOD, deathdate, died, died on, year of death (wd:P570 as wdt/p): dissolved, abolished or demolished date, date on which the subject died, end time
2. religion or worldview, also known as believes, worldview, religious affiliation, religion or world view, religion (wd:P140 as wdt/p): religion of a person, organization or religious building, or associated with this subject
3. cause of death, also known as death cause, murder method, method of murder, die of, die from (wd:P509 as wdt/p): underlying or immediate cause of death.  Underlying cause (e.g. ...adest category, e.g. natural causes, accident, homicide, suicide
4. sex or gender, also known as gender or sex, sex, gender identity, gender expression, gender (wd:P21 as wdt/p): sex or gender identity of human or animal. For human: male, fema...m, female organism. Groups of same gender use subclass of (P279)
5. given name, also known as name, proper name, personal name, middle name, forename (wd:P735 as wdt/p): first name or another given name of this person; values used with the property should not link disambiguations nor family names, name
6. 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.
7. field of work, also known as scientific discipline, scientific area, responsible for, research on, research fields (wd:P101 as wdt/p): specialization of a person or organization; see P106 for the occupation, interested in
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. 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
10. languages spoken, written or signed, also known as language of expression, language, wrote language, writes language, uses language (wd:P1412 as wdt/p): language(s) that a person or a people speaks, writes or signs, including the native language(s)

No other items found

[Assistant]
The property `cause of death` (wd:P509) seems to be the most relevant for finding out what type of cancer Eva Perón had. Let's execute a SPARQL query to retrieve the cause of death for Eva Perón.

Here is the SPARQL query:

```sparql
SELECT ?causeOfDeath WHERE {
  wd:Q40933 wdt:P509 ?causeOfDeath .
}
```

Let's execute this query to get the result.

[Tool]
Got 1 row and 1 column:
| causeOfDeath                       |
| ---------------------------------- |
| cervix uterine cancer (wd:Q160105) |

[Assistant]
The cause of death for Eva Perón is cervix uterine cancer, which is represented by the IRI `wd:Q160105`.

To provide a more complete answer, let's also include the label for the cause of death. We can do this by adding an optional clause to fetch the label.

Here is the updated SPARQL query:

```sparql
SELECT ?causeOfDeath ?causeOfDeathLabel WHERE {
  wd:Q40933 wdt:P509 ?causeOfDeath .
  OPTIONAL { ?causeOfDeath rdfs:label ?causeOfDeathLabel . }
}
```

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

[Tool]
Got 95 rows and 2 columns, showing the first 5 and last 5 rows below:
| causeOfDeath                       | causeOfDeathLabel              |
| ---------------------------------- | ------------------------------ |
| cervix uterine cancer (wd:Q160105) | ailse mhuineál na broinne (ga) |
| cervix uterine cancer (wd:Q160105) | baarmoederhalskanker (nl)      |
| cervix uterine cancer (wd:Q160105) | Barah pangkal rahim (ms)       |
| cervix uterine cancer (wd:Q160105) | cáncer cervical (es)           |
| cervix uterine cancer (wd:Q160105) | càncer cervical (ca)           |
| ...                                | ...                            |
| cervix uterine cancer (wd:Q160105) | 子宮頸癌 (yue)                     |
| cervix uterine cancer (wd:Q160105) | 子宮頸癌 (zh)                      |
| cervix uterine cancer (wd:Q160105) | 子宮頸癌 (zh-hant)                 |
| cervix uterine cancer (wd:Q160105) | 子宮頸癌 (zh-hk)                   |
| cervix uterine cancer (wd:Q160105) | 子宮頸癌 (zh-tw)                   |

[Assistant]
The cause of death for Eva Perón is cervix uterine cancer, which is represented by the IRI `wd:Q160105`. The label for this cause of death in English is "cervical cancer".

Here is the final SPARQL query and answer:

```sparql
SELECT ?causeOfDeath ?causeOfDeathLabel WHERE {
  wd:Q40933 wdt:P509 ?causeOfDeath .
  OPTIONAL { ?causeOfDeath rdfs:label ?causeOfDeathLabel . FILTER(LANG(?causeOfDeathLabel) = "en") }
}
```

Let's execute this final query to ensure we get the English label.

[Tool]
Got 1 row and 2 columns:
| causeOfDeath                       | causeOfDeathLabel          |
| ---------------------------------- | -------------------------- |
| cervix uterine cancer (wd:Q160105) | cervix uterine cancer (en) |

[Assistant]
The cause of death for Eva Perón is cervix uterine cancer, which is represented by the IRI `wd:Q160105`. The English label for this cause of death is "cervical cancer".

Here is the final SPARQL query and answer:

```sparql
SELECT ?causeOfDeath ?causeOfDeathLabel WHERE {
  wd:Q40933 wdt:P509 ?causeOfDeath .
  OPTIONAL { ?causeOfDeath rdfs:label ?causeOfDeathLabel . FILTER(LANG(?causeOfDeathLabel) = "en") }
}
```

Answer: Eva Perón had cervical cancer.

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

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query correctly identifies that Eva Perón's cause of death was cervical cancer, which is also known as cervix uterine cancer. The query returns the IRI of the cause of death and the English label, which matches the user's question.


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 ?region ?regionLabel WHERE {
  wd:Q34271 wdt:P37 ?region .
  OPTIONAL {
    ?region rdfs:label ?regionLabel .
    FILTER ( LANG( ?regionLabel ) = "en" )
  }
}

From the search results, the property "official language" (P37) seems to be the most relevant for finding the regions or countries where a language is spoken. However, we should also consider "languages spoken, written or signed" (P1412) and "language used" (P2936) as they might provide additional relevant information.

### Step 3: Execute a SPARQL query to find the regions or countries where Tibetan is spoken
Let's start with the property "official language" (P37) and see if we get any results. If not, we will try the other properties.

```sparql
SELECT ?region ?regionLabel
WHERE {
  wd:Q34271 wdt:P37 ?region.
  OPTIONAL { ?region rdfs:label ?regionLabel. FILTER(LANG(?regionLabel) = "en") }
}
```

I will execute this query to see the results.

```json
{
  "name": "execute",
  "arguments": {
    "kg": "wikidata",
    "sparql": "SELECT ?region ?regionLabel WHERE { wd:Q34271 wdt:P37 ?region. OPTIONAL { ?region rdfs:label ?regionLabel. FILTER(LANG(?regionLabel) = \"en\") } }"
  }
}
```

[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 do they speak tibetan?

[Assistant]
To answer the question "Where do they speak Tibetan?", we need to identify the language "Tibetan" and then find the regions or countries where it is spoken. Let's start by searching for the entity "Tibetan" in the knowledge graph.

### Step 1: Search for the entity "Tibetan"
I will use the `search_entity` function to find the IRI for the language "Tibetan".

```json
{
  "name": "search_entity",
  "arguments": {
    "kg": "wikidata",
    "query": "tibetan"
  }
}
```

[Tool]
Top 10 entity alternatives:
1. Tibetan, also known as bo, bod, Lhasa Tibetan, Standard Tibetan, Tibetan language (wd:Q34271): Tibeto-Burman language
2. Tibetan, also known as U+0F00-0FFF (wd:Q2494102): Unicode block (U+0F00-0FFF)
3. Tibetan culture, also known as culture of Tibet, Tibetan (wd:Q3007484): Asian culture
4. Tibetan (wd:L34544)
5. Tibetan (wd:L34544-F1)
6. Tibet, also known as Tubet, Tangut, Tobat, Tibetan Tableland, Tibetan Plateau (wd:Q17252): plateau region in west China
7. Tibetan Plateau, also known as Qinghai-Tibetan plateau (wd:Q179842): plateau in Central Asia
8. Tibetan Buddhism, also known as Lamaism, Grand Lamaism, Vajrayana, Sutrayana (wd:Q483889): form of Buddhism practiced in Tibet and Bhutan
9. Tibetan people, also known as Bhod-pa, Sifan, Sifen, Tibetans, Xifan (wd:Q187985): East Asian ethnic group native to Tibet
10. Tibetan alphabet, also known as Tibetan script, Tibt (wd:Q46861): abugida writing system used to write certain Tibetic languages

[Assistant]
From the search results, the entity "Tibetan" (Tibeto-Burman language) is the most relevant for our query. Its IRI is `wd:Q34271`.

### Step 2: Find the regions or countries where Tibetan is spoken
Next, we need to find the regions or countries where Tibetan is spoken. We can use the property "spoken in" or "language of" to find this information. Let's search for the appropriate property.

```json
{
  "name": "search_property",
  "arguments": {
    "kg": "wikidata",
    "query": "spoken in"
  }
}
```

[Tool]
Top 10 property alternatives:
1. official language, also known as language spoken, spoken in, speaking, language official (wd:P37 as psv/pqn/p/ps/pq/wdtn/prn/prv/wdt/psn/pr/pqv): language designated as official by this item, language used
2. spoken by, also known as speaker (wd:P10894 as psv/pqn/p/ps/pq/wdtn/prn/prv/wdt/psn/pr/pqv): the person who is speaking in this audio or video file
3. indigenous to, also known as native to, originating from, spoken by (wd:P2341 as psv/pqn/p/ps/pq/wdtn/prn/prv/wdt/psn/pr/pqv): place or ethnic group where a language, art genre, cultural trad...biological species or variety is found (or was originally found)
4. speaker, also known as speech by, spoken by (wd:P823 as psv/pqn/p/ps/pq/wdtn/prn/prv/wdt/psn/pr/pqv): person who is speaker for this event, ceremony, keynote, presentation or in a literary work, participant, spoken by
5. languages spoken, written or signed, also known as language of expression, language, wrote language, writes language, uses language (wd:P1412 as psv/pqn/p/ps/pq/wdtn/prn/prv/wdt/psn/pr/pqv): language(s) that a person or a people speaks, writes or signs, including the native language(s)
6. audio recording of the subject's spoken voice, also known as voice recording, audio clip, spoken voice, speaking voice, recording of the subject's spoken voice (wd:P990 as psv/pqn/p/ps/pq/wdtn/prn/prv/wdt/psn/pr/pqv): audio file representing the speaking voice of a person; or of an animated cartoon or other fictitious character, audio
7. language used, also known as languages used, medium of instruction, used language, uses language, working language (wd:P2936 as psv/pqn/p/ps/pq/wdtn/prn/prv/wdt/psn/pr/pqv): language widely used (spoken or written) in this place or at this event or organisation
8. language of work or name, also known as language of the reference, language of URL, language of website, language of work, named in language (wd:P407 as psv/pqn/p/ps/pq/wdtn/prn/prv/wdt/psn/pr/pqv): language associated with this creative work (such as books, show...guage" (P103) and "languages spoken, written or signed" (P1412))
9. expressed in (wd:P5572 as psv/pqn/p/ps/pq/wdtn/prn/prv/wdt/psn/pr/pqv): gene or protein is expressed during a specific condition/cell cycle/process/form, biological phase
10. programmed in, also known as language, programming language, written in (wd:P277 as psv/pqn/p/ps/pq/wdtn/prn/prv/wdt/psn/pr/pqv): the programming language(s) in which the software is developed

[Assistant]
From the search results, the property "official language" (P37) seems to be the most relevant for finding the regions or countries where a language is spoken. However, we should also consider "languages spoken, written or signed" (P1412) and "language used" (P2936) as they might provide additional relevant information.

### Step 3: Execute a SPARQL query to find the regions or countries where Tibetan is spoken
Let's start with the property "official language" (P37) and see if we get any results. If not, we will try the other properties.

```sparql
SELECT ?region ?regionLabel
WHERE {
  wd:Q34271 wdt:P37 ?region.
  OPTIONAL { ?region rdfs:label ?regionLabel. FILTER(LANG(?regionLabel) = "en") }
}
```

I will execute this query to see the results.

```json
{
  "name": "execute",
  "arguments": {
    "kg": "wikidata",
    "sparql": "SELECT ?region ?regionLabel WHERE { wd:Q34271 wdt:P37 ?region. OPTIONAL { ?region rdfs:label ?regionLabel. FILTER(LANG(?regionLabel) = \"en\") } }"
  }
}
```


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 ?disease WHERE {
  wd:Q1345645 wdt:P1050 ?disease .
  OPTIONAL {
    ?disease rdfs:label ?diseaseLabel .
    FILTER ( LANG( ?diseaseLabel ) = "en" )
  }
}

Montel Williams has multiple sclerosis.

[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 kind of disease does montel williams have?

[Assistant]
To answer the question about what kind of disease Montel Williams has, we need to identify the entity for Montel Williams and then find the property that indicates his health condition or disease.

First, let's search for the entity representing Montel Williams in the knowledge graph.

[Tool]
Top 10 entity alternatives:
1. Montel Williams, also known as Montel Brian Anthony Williams (wd:Q1345645): American television host and actor, television presenter, television producer, radio personality, television actor, military officer, journalist, politician, actor
2. The Montel Williams Show (wd:Q2412902): television series
3. Montel Williams MS Foundation (wd:Q30289093): nonprofit organization in New York, United States
4. Williams Racing, also known as Williams, Williams Grand Prix Engineering Limited, Williams F1 (wd:Q171337): European Formula One motor racing team and constructor
5. Williams, also known as Williams, AZ, Williams, Arizona (wd:Q79864): city in Coconino County, Arizona, United States
6. Williams, also known as Williams, CA, Williams, California (wd:Q2685585): city in Colusa County, California, United States
7. Williams, also known as Williams, IA, Williams, Iowa (wd:Q1012283): city in Hamilton County, Iowa, United States
8. Williams, also known as Williams, MN, Williams, Minnesota (wd:Q963315): city in Lake of the Woods County, Minnesota, United States
9. Williams College, also known as Williams (wd:Q49166): liberal arts college in Massachusetts
10. Williams, also known as Williams, SC, Williams, South Carolina (wd:Q2562748): town in Collation County, South Carolina, United States

[Assistant]
The top result for Montel Williams is `wd:Q1345645`, which represents Montel Brian Anthony Williams, the American television host and actor. Now, let's find out what kind of disease he has. We need to look for properties related to health conditions or diseases.

I will search for properties related to health conditions or diseases for Montel Williams.

[Tool]
Top 10 property alternatives:
1. medical condition, also known as disability, suffers from, suffer from, sickness, paralympic disability (wd:P1050 as wdt/p): any state relevant to the health of an organism, including diseases and positive conditions
2. 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
3. sex or gender, also known as gender or sex, sex, gender identity, gender expression, gender (wd:P21 as wdt/p): sex or gender identity of human or animal. For human: male, fema...m, female organism. Groups of same gender use subclass of (P279)
4. country of citizenship, also known as subject of (country), nation of citizenship, national of, nationality, nation (wd:P27 as wdt/p): the object is a country that recognizes the subject as its citizen, country
5. languages spoken, written or signed, also known as language of expression, language, wrote language, writes language, uses language (wd:P1412 as wdt/p): language(s) that a person or a people speaks, writes or signs, including the native language(s)
6. occupation, also known as job, work, vocation, profession, employment (wd:P106 as wdt/p): occupation of a person; see also "field of work" (Property:P101), "position held" (Property:P39)
7. official website, also known as site (official), home page, site official, URL, URL (official) (wd:P856 as wdt/p): URL of the official page of an item (current or former). Usage: ...but see the "Hijacked or dead websites" section of the Talk page, URL
8. 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)
9. given name, also known as name, proper name, personal name, middle name, forename (wd:P735 as wdt/p): first name or another given name of this person; values used with the property should not link disambiguations nor family names, name
10. 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)

No other items found

[Assistant]
The property `wd:P1050` (medical condition) seems to be the most relevant for finding out what kind of disease Montel Williams has. Let's execute a SPARQL query to retrieve the value of this property for Montel Williams.

Here is the SPARQL query:

```sparql
SELECT ?disease WHERE {
  wd:Q1345645 wdt:P1050 ?disease .
  OPTIONAL { ?disease rdfs:label ?diseaseLabel . FILTER(LANG(?diseaseLabel) = "en") }
}
```

This query will return the disease or medical condition associated with Montel Williams, along with its label if available.

Let's execute this query.

[Tool]
Got 1 row and 1 column:
| disease                       |
| ----------------------------- |
| multiple sclerosis (wd:Q8277) |

[Assistant]
The SPARQL query returned that Montel Williams has multiple sclerosis.

Here is the final SPARQL query and the answer:

```sparql
SELECT ?disease WHERE {
  wd:Q1345645 wdt:P1050 ?disease .
  OPTIONAL { ?disease rdfs:label ?diseaseLabel . FILTER(LANG(?diseaseLabel) = "en") }
}
```

Montel Williams has multiple sclerosis.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query correctly identifies that Montel Williams has multiple sclerosis, which is the disease he suffers from.