PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?placeOfBirth ?placeOfBirthLabel WHERE {
  wd:Q36844 wdt:P19 ?placeOfBirth .
  OPTIONAL {
    ?placeOfBirth rdfs:label ?placeOfBirthLabel .
    FILTER ( LANG( ?placeOfBirthLabel ) = "en" )
  }
}

Got 1 row and 2 columns:
| ?placeOfBirth              | ?placeOfBirthLabel |
| -------------------------- | ------------------ |
| Saint Michael (wd:Q819170) | Saint Michael      |

Rihanna was raised in Saint Michael, a parish in Barbados.

[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 about where Rihanna was raised, we need to identify the entity for Rihanna and then find the property that indicates her place of upbringing or birth. Let's start by searching for Rihanna in the knowledge graph.

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

[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, which is the correct entity we are looking for. The IRI for this entity is `wd:Q36844`.

Next, we need to find the property that indicates where she was raised or born. We can search for properties related to "birth" or "upbringing" to find the relevant property.

I will use the `search_property` function to find the property for place of birth or upbringing.

[Tool]
Top 10 property alternatives:
1. date of baptism, also known as christened, time of christening, time of baptism, date of christening, date of baptism in early childhood (wd:P1636 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): date when a person was baptized. For times when only baptism rec...were available, this can be a good substitute for date of birth., start time
2. place of birth, also known as POB, location of birth, location born, born in, birthplace (wd:P19 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): most specific known birth location of a person, animal or fictional character, location
3. birth name, also known as née, name at birth, name before marriage, ne, né (wd:P1477 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): full name of a person at birth, if different from their current, generally used name, name
4. birthday, also known as b-day, bday, birth day (wd:P3150 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): item for day and month on which the subject was born. Used when full "date of birth" (P569) isn't known., date of birth
5. date of birth, also known as year of birth, birthdate, DOB, born on, born (wd:P569 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): date on which the subject was born, inception
6. birth rate (wd:P8763 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): the total number of live births per 1,000 population
7. category for births in this time period (wd:P12686 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): category item for people who were born in this time period, point in time
8. derivative work, also known as is source work of, is adapted as, is the basis for, iteration, reboot (wd:P4969 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): new work of art (film, book, software, etc.) derived from major part of this work
9. gestation period (wd:P3063 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): amount of time needed for gestation for this animal taxon, duration
10. inception, also known as initial release date, founded in, founded on, founded on date, founding date (wd:P571 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): time when an entity begins to exist; for date of official opening use P1619, start time

[Assistant]
The property "place of birth" (wd:P19) is the most relevant for our query. Now, let's construct a SPARQL query to find the place of birth for Rihanna (wd:Q36844).

I will execute the following SPARQL query:

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

This query will return the place of birth for Rihanna.

Let's execute the query.

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

[Assistant]
The place of birth for Rihanna is Saint Michael, which is a parish in Barbados.

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

I will execute the following SPARQL query:

```sparql
SELECT ?placeOfBirth ?placeOfBirthLabel WHERE {
  wd:Q36844 wdt:P19 ?placeOfBirth .
  OPTIONAL { ?placeOfBirth rdfs:label ?placeOfBirthLabel . FILTER(LANG(?placeOfBirthLabel) = "en") }
}
```

This query will return the place of birth for Rihanna along with its English label.

Let's execute the query.

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

[Assistant]
The place of birth for Rihanna is Saint Michael, a parish in Barbados.

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

Final SPARQL query:
```sparql
SELECT ?placeOfBirth ?placeOfBirthLabel WHERE {
  wd:Q36844 wdt:P19 ?placeOfBirth .
  OPTIONAL { ?placeOfBirth rdfs:label ?placeOfBirthLabel . FILTER(LANG(?placeOfBirthLabel) = "en") }
}
```

Answer: Rihanna was raised in Saint Michael, a parish in Barbados.

Let's finalize the answer.

[Tool] Stopped generation process


PREFIX pq: <http://www.wikidata.org/prop/qualifier/>
PREFIX ps: <http://www.wikidata.org/prop/statement/>
PREFIX p: <http://www.wikidata.org/prop/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?start ?end WHERE {
  wd:Q91 p:P39 ?statement .
  ?statement ps:P39 wd:Q11696 .
  ?statement pq:P580 ?start .
  ?statement 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 the entity for 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 "term of office".

[Tool]
Top 10 property alternatives:
1. position held by head of the organization, also known as position held by head of the organisation, secretary's position, president's position, position held by head of the organization, office held by head of the organisation (wd:P2388 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): position of the head of this item
2. term length of position, also known as term length, time in position, term of position, position term, length of term (wd:P2097 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): length of time in years (unit: Q577) a person (usually a politician) is to serve in a particular position. Do not add bounds., duration
3. head of state, also known as leader, state headed by, monarch, president, queen (wd:P35 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): official with the highest formal authority in a country/state, director / manager, chairperson
4. rector, also known as chancellor, university president, Rector (academia), president (wd:P1075 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): senior official in an educational institution., director / manager, chairperson
5. chairperson, also known as dean, headed by, leader, president, chairwoman (wd:P488 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): presiding member of an organization, group or body, director / manager, significant person
6. head of government, also known as head of national government, mayor, premier, president, prime minister (wd:P6 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): head of the executive power of this town, city, municipality, state, country, or other governmental body, director / manager, chairperson
7. position holder, also known as incumbent, noble title holder, office holder, position held by (wd:P1308 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): persons who hold and/or held the position
8. oath of office date, also known as start of office date (wd:P1734 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): when person swore the oath and assume or start the office of a position
9. position held, also known as function, held position, holds position, job, office held (wd:P39 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): subject currently or formerly holds the object position or public office
10. office held by head of state, also known as head of state, position held by head of state, state headed by (wd:P1906 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): political office that is fulfilled by the head of state of this item

[Assistant]
The property that indicates the term of office is `wd:P2097` (term length of position). However, this property might not directly give us the start and end dates of the presidency. We should also check the `wd:P39` (position held) property to see if it provides more specific information about the presidency period.

Let's start with a simple query to see what information we can get from `wd:P39` for Abraham Lincoln.

[Tool]
Got 4 rows and 3 columns:
| ?position                                                           | ?start | ?end |
| ------------------------------------------------------------------- | ------ | ---- |
| President of the United States (wd:Q11696)                          |        |      |
| member of the United States House of Representatives (wd:Q13218630) |        |      |
| President-elect of the United States (wd:Q1467287)                  |        |      |
| member of the Illinois House of Representatives (wd:Q17495450)      |        |      |

[Assistant]
The query returned several positions held by Abraham Lincoln, including "President of the United States." However, it did not provide the start and end dates for these positions. We need to refine the query to include the qualifiers for the start and end dates of the presidency.

Let's try a more specific query to get the start and end dates of Abraham Lincoln's presidency.

[Tool] Got no rows and 3 columns

[Assistant]
It seems that the specific qualifiers for the start and end dates of Abraham Lincoln's presidency are not directly linked to the `wd:P39` property. Instead, we should look for the `wd:P580` (start time) and `wd:P582` (end time) properties directly on the statement for the presidency position.

Let's try a different approach to get the start and end dates of Abraham Lincoln's presidency.

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

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

Here is the final SPARQL query and the answer:

```sparql
SELECT ?start ?end WHERE {
  wd:Q91 p:P39 ?statement .
  ?statement ps:P39 wd:Q11696 .
  ?statement pq:P580 ?start .
  ?statement pq:P582 ?end .
}
```

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

[Tool] Stopped generation process


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

The official 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 of a country.

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

[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 country in Northeast Africa and Southwest Asia, which is the correct entity we are looking for. The IRI for this entity is `wd:Q79`.

### Step 2: Identify the property for the language spoken in a country
Next, we need to find the property that indicates the official or commonly spoken language of a country. Let's search for properties related to "language" or "official language".

[Tool]
Top 10 property alternatives:
1. programmed in, also known as language, programming language, written in (wd:P277 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): 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 prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): 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 prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): 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 prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): 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 prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): 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 prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): 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 prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): 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 prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): 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 prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): 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 prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): dialect, graphic or phonetic variety that this lexeme, form or sense is used in

[Assistant]
The property "official language" (P37) seems to be the most relevant for our purpose, as it indicates the language designated as official by a country. We will use this property to find the official language of Egypt.

### Step 3: Construct and execute the SPARQL query
Now, let's construct a SPARQL query to find the official language of Egypt using the identified entity and property.

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

[Assistant]
The SPARQL query returned that the official language of Egypt is Arabic (wd:Q13955).

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

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?vehicle ?vehicleLabel WHERE {
  ?vehicle wdt:P176 wd:Q81965 .
  OPTIONAL {
    ?vehicle rdfs:label ?vehicleLabel .
    FILTER ( LANG( ?vehicleLabel ) = "en" )
  }
}

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

General Motors (GM) manufactures a variety of vehicles, including models such as the Buick Estate, Buick Electra, Buick Invicta, Buick Limited, Buick Master Six, Cadillac ATS, T18 Boarhound, Pontiac Trans Sport, and Isuzu 117 Coupé.

[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 the correct entity (wd:Q81965), which represents the automotive manufacturing corporation based in Detroit, Michigan, USA. Next, we need to find the properties that indicate what products General Motors manufactures. We can search for properties related to "products" or "manufactures" to help us identify the relevant relationships.

Let's search for properties related to "products" and "manufactures".

[Tool]
Top 10 property alternatives:
1. product or material produced, also known as products, product or service provided, product, material, or service produced or provided, product, producer of (wd:P1056 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): 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. 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 prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): 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
3. typically sells, also known as product, sells, sold products, usually sells (wd:P7163 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): type of goods particular type of shops typically sell
4. official shop URL, also known as online store, shop, shop URL, store, store URL (wd:P10225 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): official URL where the agent sells products/merchandise, official website
5. catalog, also known as catalogue, registry, exhibition catalogue, cat. (wd:P972 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): catalog for the item, or, as a qualifier of P528 – catalog for which the 'catalog code' is valid, part of
6. official website, also known as site (official), home page, site official, URL, URL (official) (wd:P856 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): 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
7. award received, also known as win, winner of, recognition title, received award, prize received (wd:P166 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): award or recognition received by a person, organization or creative work
8. grants, also known as awards, bestows, confers, gives out (wd:P5460 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): confers degree, honor, award, prize, title, certificate or medal denoting achievement to a person or organization
9. number of representations, also known as shows, performances, number of shows, number of concerts, no. of shows (wd:P5027 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): number of shows (including the premiere) of a concert tour or a performing arts production
10. depicts, also known as portrays, presents, preserves, quotes, represents (wd:P180 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): entity visually depicted in an image, literarily described in a ...o an audiovisual or other medium; see also P921,  'main subject', has part(s)

[Tool]
Top 10 property alternatives:
1. product or material produced, also known as products, product or service provided, product, material, or service produced or provided, product, producer of (wd:P1056 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): 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. 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 prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): 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
3. manufacturer, also known as manufactured by, provider, provided by, product of, producer (wd:P176 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): (main or final) manufacturer or producer of this product
4. location of creation, also known as manufactured in, manufacturing location, mint, minted at, photographed at (wd:P1071 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): place where the item was conceived or made; where applicable, location of final assembly, significant place
5. made from material, also known as material used, manufactured out of, manufactured from, made of material, made of (wd:P186 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): material the subject or the object is made of or derived from (do not confuse with P10672 which is used for processes), uses
6. industry, also known as branch, economic branch, field of action, field of exercise, sector (wd:P452 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): specific industry of company or organization, part of
7. WMI code, also known as WMI, World Manufacturer Identifier (wd:P6793 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr)
8. fabrication method, also known as process used, method of cooking, preparation method, obtained by, method used (wd:P2079 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): method, process or technique used to grow, cook, weave, build, assemble, manufacture the item, uses
9. creator, also known as created by, visual artist, sculptor, sculpted by, realized by (wd:P170 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): maker of this creative work or other object (where no more specific property exists)
10. unmarried partner, also known as romantic partner, partner of, partners, posslq, relationship (wd:P451 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): someone with whom the person is in a relationship without being married. Use "spouse" (P26) for married couples, significant person

[Assistant]
From the search results, the property `product or material produced` (wd:P1056) seems to be the most relevant for identifying what products General Motors manufactures. Let's use this property to construct a SPARQL query to find out what GM produces.

I'll start with a simple query to retrieve the products associated with General Motors.

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

Let's execute this query.

[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 manufactures. To provide more context, let's also fetch the label for this product.

Here is the updated query:

```sparql
SELECT ?product ?productLabel WHERE {
  wd:Q81965 wdt:P1056 ?product .
  OPTIONAL { ?product rdfs:label ?productLabel . FILTER(LANG(?productLabel) = "en") }
}
```

Let's execute this query to get the label for the product.

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

[Assistant]
The query confirms that General Motors (GM) manufactures "motor cars" (wd:Q1420).

To provide a more comprehensive answer, let's check if there are any specific models or types of vehicles that GM produces. We can look for more detailed information by exploring the relationships of the "motor car" entity.

Let's try to find specific models or types of vehicles produced by GM.

```sparql
SELECT ?model ?modelLabel WHERE {
  wd:Q81965 wdt:P1056 ?product .
  ?product wdt:P31 ?model .
  OPTIONAL { ?model rdfs:label ?modelLabel . FILTER(LANG(?modelLabel) = "en") }
}
```

This query will look for specific models or types of vehicles that are produced by GM.

Let's execute this query.

[Tool] Got no rows and 2 columns

[Assistant]
The query did not return any specific models or types of vehicles directly linked to the "motor car" entity. This suggests that the relationship might be more complex or that the data is structured differently in the knowledge graph.

Instead, let's try a different approach by looking for specific vehicle models that are directly linked to General Motors. We can use the property `manufacturer` (wd:P176) to find vehicles that are produced by GM.

Here is the updated query:

```sparql
SELECT ?vehicle ?vehicleLabel WHERE {
  ?vehicle wdt:P176 wd:Q81965 .
  OPTIONAL { ?vehicle rdfs:label ?vehicleLabel . FILTER(LANG(?vehicleLabel) = "en") }
}
```

This query will find vehicles that are manufactured by General Motors and their labels.

Let's execute this query.

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

[Assistant]
The query returned a list of vehicles that are manufactured by General Motors (GM). Here are some of the specific models:

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

These results show that GM produces a wide range of vehicles, including various models under different brands such as Buick, Cadillac, and Pontiac.

To finalize the answer, I will provide a concise response based on the query results.

```sparql
SELECT ?vehicle ?vehicleLabel WHERE {
  ?vehicle wdt:P176 wd:Q81965 .
  OPTIONAL { ?vehicle rdfs:label ?vehicleLabel . FILTER(LANG(?vehicleLabel) = "en") }
}
```

Based on the SPARQL query results, General Motors (GM) manufactures a variety of vehicles, including models such as 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


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?city ?cityLabel ?population WHERE {
  ?city wdt:P31 wd:Q515 ; wdt:P1082 ?population ; 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 will first search for the entity representing Spain and then look for a property that indicates the population of cities. Afterward, we can construct a SPARQL query to find the city in Spain with the highest population.

Let's start by searching 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 top result for Spain is the entity `wd:Q29`, which represents the modern country of Spain. Next, we need to find a property that indicates the population of cities. Let's search for a property related to population.

[Tool]
Top 10 property alternatives:
1. tabular population, also known as population (wd:P4179 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): same as population (P1082); used when there is data for many different points in time
2. population, also known as inhabitants, human population (wd:P1082 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): number of people inhabiting the place; number of people of subject
3. has census, also known as has human population census (wd:P8701 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): general population census of the country or region
4. urban population (wd:P6343 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): number of people living within the territorial entity who live in its urban parts, population
5. birth rate (wd:P8763 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): the total number of live births per 1,000 population
6. category populated by, also known as category filled by, category has template, cat populated by, component adding into the category, populated by (wd:P4329 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): software component (template, module, extension, MediaWiki core) populating the category
7. rural population (wd:P6344 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): number of people living within the territorial entity who live in its rural parts, population
8. female population (wd:P1539 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): number of female people inhabiting the place; number of female people of subject, population
9. male population, also known as male inhabitants, number of males (wd:P1540 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): number of male people inhabiting the place; number of male people of subject, population
10. number of households, also known as dwellings, households (wd:P1538 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): total number of households in this place, includes dwellings of all types

[Assistant]
The property `wd:P1082` represents the population of a place. Now, we need to find cities in Spain and their populations. We can use the property `wdt:P1082` to filter cities by their population and sort them to find the largest city.

Let's construct a SPARQL query to find the largest city in Spain by population.

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

This query selects the city and its population, filters for cities in Spain (`wd:Q29`), and orders the results by population in descending order, limiting the result to the top entry.

Let's execute this query.

[Tool]
Got 1 row and 3 columns:
| ?city                    | ?cityLabel | ?population |
| ------------------------ | ---------- | ----------- |
| Chisagüés (wd:Q13048734) | Chisagüés  | 22057000200 |

[Assistant]
It seems there was an issue with the query, as the result does not seem correct. The city `Chisagüés` with a population of over 22 billion is not plausible. There might be an issue with the data or the query itself.

Let's try a different approach to ensure we get the correct result. We will explicitly filter for cities and use a more specific property to ensure we are getting the correct population data.

We can use the property `wdt:P31` to filter for instances of cities (`wd:Q515`).

Here is the revised query:

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

This query ensures that we are only considering instances of cities and then sorts them by population to find the largest city in Spain.

Let's execute this revised query.

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

[Assistant]
The largest city in Spain is Madrid, with a population of 3,332,035.

Here is the final SPARQL query and the answer:

```sparql
SELECT ?city ?cityLabel ?population
WHERE {
  ?city wdt:P31 wd:Q515 ;
        wdt:P1082 ?population ;
        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.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?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 |

Eva Perón had cervix uterine 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 the entity for Eva Perón.

[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, let's find the property that indicates the type of cancer she had. We can search for properties related to "cancer" or "disease" to see if there is a relevant property.

[Tool]
Top 10 property alternatives:
1. medical condition, also known as disability, suffers from, suffer from, sickness, paralympic disability (wd:P1050 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): any state relevant to the health of an organism, including diseases and positive conditions
2. National Cancer Institute ID, also known as cancer.gov, NCI ID (wd:P1395 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): identifier at www.cancer.gov
3. date of death, also known as DOD, deathdate, died, died on, year of death (wd:P570 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): dissolved, abolished or demolished date, date on which the subject died, end time
4. Uniform Type Identifier, also known as Apple Uniform Type Identifier, UTI (wd:P3641 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): identifier used on software provided by Apple Inc. to uniquely identify a given class or type of item
5. symptoms and signs, also known as symptoms, has symptom, clinical symptom, clinical sign (wd:P780 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): possible symptoms or signs of a medical condition, has effect
6. religion or worldview, also known as believes, worldview, religious affiliation, religion or world view, religion (wd:P140 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): religion of a person, organization or religious building, or associated with this subject
7. drug or therapy used for treatment, also known as cure, prescribed drug, prescription, treated by, treatment (wd:P2176 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): drug, procedure, or therapy that can be used to treat a medical condition
8. alcohol by volume, also known as alcohol, Alc. vol. (wd:P2665 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): percentage of alcohol in a fluid
9. GHS signal word, also known as signal word, warning:, Globally Harmonized System of Cl...belling of Chemicals signal word, danger: (wd:P1033 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): please use with items "warning" (Q15350847) and "danger" (Q15221217)
10. has effect, also known as has symptom, implies, leads to, led to, outcome (wd:P1542 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): effect of this item, followed by

[Assistant]
The property `wd:P1050` (medical condition) seems to be the most relevant for indicating the type of cancer Eva Perón had. Let's construct a SPARQL query to find the specific type of cancer associated with Eva Perón.

We will query for the medical condition of Eva Perón and then look for the label of that condition to provide a human-readable answer.

[Tool] Got no rows and 2 columns

[Assistant]
It appears that the query did not return any results, which suggests that the property `wd:P1050` might not be the correct one for Eva Perón's medical condition. Let's try another approach by looking at the properties directly associated with Eva Perón to see if there is a more specific property that indicates her type of cancer.

We can list the triples for Eva Perón to see if there is any relevant information.

[Tool]
Got 901 rows and 3 columns, showing the first 10 rows below:
| subject               | property                         | object                                                                         |
| --------------------- | -------------------------------- | ------------------------------------------------------------------------------ |
| Eva Perón (wd:Q40933) | instance of (wdt:P31)            | human (wd:Q5)                                                                  |
| Eva Perón (wd:Q40933) | occupation (wdt:P106)            | politician (wd:Q82955)                                                         |
| Eva Perón (wd:Q40933) | sex or gender (wdt:P21)          | female (wd:Q6581072)                                                           |
| Eva Perón (wd:Q40933) | given name (wdt:P735)            | Eva (wd:Q829665)                                                               |
| Eva Perón (wd:Q40933) | date of birth (wdt:P569)         | 1919-05-07T00:00:00Z                                                           |
| Eva Perón (wd:Q40933) | Commons category (wdt:P373)      | Eva Perón                                                                      |
| Eva Perón (wd:Q40933) | image (wdt:P18)                  | <http://commons.wikimedia.org/wiki/Special:FilePath/Evita%20%28cropped%29.JPG> |
| Eva Perón (wd:Q40933) | country of citizenship (wdt:P27) | Argentina (wd:Q414)                                                            |
| Eva Perón (wd:Q40933) | family name (wdt:P734)           | Duarte (wd:Q18699850)                                                          |
| Eva Perón (wd:Q40933) | place of birth (wdt:P19)         | Junín (wd:Q1020277)                                                            |
| ...                   | ...                              | ...                                                                            |

[Assistant]
From the list of triples, it does not appear that there is a direct property indicating the type of cancer Eva Perón had. However, we can try searching for properties related to "cause of death" to see if that provides the information we need.

Let's search for properties related to "cause of death" and then use that property in our query.

[Tool]
Top 10 property alternatives:
1. cause of death, also known as death cause, murder method, method of murder, die of, die from (wd:P509 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): underlying or immediate cause of death.  Underlying cause (e.g. ...adest category, e.g. natural causes, accident, homicide, suicide
2. manner of death, also known as type of death, nature of death, death manner, death type, circumstance of death (wd:P1196 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): general circumstances of a person's death; e.g. natural causes, ...ysiological mechanism, e.g. heart attack, trauma, pneumonia etc.
3. place of death, also known as POD, location of death, killed in, deathplace, died in (wd:P20 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): most specific known (e.g. city instead of country, or hospital i... city) death location of a person, animal or fictional character, location
4. number of deaths, also known as dead, qty fatalities, qty deaths, number of fatalities, fatalities (wd:P1120 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): total (cumulative) number of people who died since start as a direct result of an event or cause
5. date of death, also known as DOD, deathdate, died, died on, year of death (wd:P570 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): dissolved, abolished or demolished date, date on which the subject died, end time
6. case fatality rate, also known as fatality rate, CFR, case fatality ratio, case fatality, case fatality risk (wd:P3457 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): proportion of patients who die of a particular medical condition out of all who have this condition within a given time frame
7. death rate, also known as mortality rate (wd:P10091 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): the total number of persons dead per 1,000 population per year
8. killed by, also known as slaughtered by, slain by, shot dead by, murderer, murdered by (wd:P157 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): person or organization who killed the subject, significant person
9. has effect, also known as has symptom, implies, leads to, led to, outcome (wd:P1542 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): effect of this item, followed by
10. category for deaths in this time period (wd:P12687 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): category item for people who died in this time period, point in time

[Assistant] The property `wd:P509` (cause of death) seems to be the most relevant for finding the type of cancer Eva Perón had. Let's construct a SPARQL query to find the cause of death for Eva Perón and then look for the label of that cause to provide a human-readable answer.

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

[Assistant]
The SPARQL query returned the cause of death for Eva Perón, which is "cervix uterine cancer" (wd:Q160105).

Therefore, the type of cancer Eva Perón had was cervix uterine cancer.

Here is the final SPARQL query and answer:

[Tool] Stopped generation process