PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?son ?profession WHERE {
  wd:Q1203 wdt:P40 ?son .
  ?son wdt:P106 ?profession .
}

Got 16 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?son                       | ?profession                     |
| -------------------------- | ------------------------------- |
| Sean Lennon (wd:Q311238)   | singer (wd:Q177220)             |
| Sean Lennon (wd:Q311238)   | screenwriter (wd:Q28389)        |
| Sean Lennon (wd:Q311238)   | actor (wd:Q33999)               |
| Sean Lennon (wd:Q311238)   | composer (wd:Q36834)            |
| Sean Lennon (wd:Q311238)   | pianist (wd:Q486748)            |
| ...                        | ...                             |
| Julian Lennon (wd:Q357974) | composer (wd:Q36834)            |
| Julian Lennon (wd:Q357974) | singer-songwriter (wd:Q488205)  |
| Julian Lennon (wd:Q357974) | poet (wd:Q49757)                |
| Julian Lennon (wd:Q357974) | recording artist (wd:Q55960555) |
| Julian Lennon (wd:Q357974) | guitarist (wd:Q855091)          |

The professions of John Lennon's sons are as follows:
- **Sean Lennon**: Singer, Screenwriter, Actor, Composer, Pianist
- **Julian Lennon**: Composer, Singer-songwriter, Poet, Recording artist, Guitarist
The other two sons, John Ono Lennon and John Lennon III, do not have recorded professions in the knowledge graph.

[System]
You are a question answering assistant. Your job is to generate a SPARQL query to answer a given user question.

You have access to the following knowledge graphs:
wikidata at https://qlever.cs.uni-freiburg.de/api/wikidata-sebastian

You can use the following SPARQL prefixes implicitly in all functions:
cc: <http://creativecommons.org/ns#
data: <https://www.wikidata.org/wiki/Special:EntityData/
dct: <http://purl.org/dc/terms/
geo: <http://www.opengis.net/ont/geosparql#
geof: <http://www.opengis.net/def/function/geosparql/
imdb: <https://www.imdb.com/
math: <http://www.w3.org/2005/xpath-functions/math#
ontolex: <http://www.w3.org/ns/lemon/ontolex#
owl: <http://www.w3.org/2002/07/owl#
p: <http://www.wikidata.org/prop/
pq: <http://www.wikidata.org/prop/qualifier/
pqn: <http://www.wikidata.org/prop/qualifier/value-normalized/
pqv: <http://www.wikidata.org/prop/qualifier/value/
pr: <http://www.wikidata.org/prop/reference/
prn: <http://www.wikidata.org/prop/reference/value-normalized/
prov: <http://www.w3.org/ns/prov#
prv: <http://www.wikidata.org/prop/reference/value/
ps: <http://www.wikidata.org/prop/statement/
psn: <http://www.wikidata.org/prop/statement/value-normalized/
psv: <http://www.wikidata.org/prop/statement/value/
qfn: <http://qlever.cs.uni-freiburg.de/function#
ql: <http://qlever.cs.uni-freiburg.de/builtin-functions/
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
ref: <http://www.wikidata.org/reference/
s: <http://www.wikidata.org/entity/statement/
schema: <http://schema.org/
skos: <http://www.w3.org/2004/02/skos/core#
v: <http://www.wikidata.org/value/
wd: <http://www.wikidata.org/entity/
wdno: <http://www.wikidata.org/prop/novalue/
wdt: <http://www.wikidata.org/prop/direct/
wdtn: <http://www.wikidata.org/prop/direct-normalized/
wikibase: <http://wikiba.se/ontology#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
dc: <http://purl.org/dc/elements/1.1/
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
qb: <http://purl.org/linked-data/cube#
void: <http://rdfs.org/ns/void#

You should follow a step-by-step approach to generate the SPARQL query:
1. Determine possible entities and properties implied by the user question.
2. Use the functions to search for entities and properties in the knowledge graphs. Where applicable, constrain the searches with already identified entities and properties.
3. Gradually build up the SPARQL query using the identified entities and properties. Start with simple queries and add more complexity as needed. Use the execute function to get feedback on intermediate queries. You can go back and forth between knowledge graph searches and SPARQL query executions to refine the query.
4. Use the answer or cancel function to finalize your answer and stop the generation process.

Additional rules:
1. Explain your thought process before and after each step and function call.
2. Do not assume or make up any entities or properties without verifying them with the knowledge graphs and functions.
3. The SPARQL query should always return the actual identifiers / IRIs of the items in its result. It additionally may return labels or other human-readable information, but they are optional and should be put within optional clauses unless explicitly requested by the user.
4. Do not stop early if there are still obvious improvements to be made to the SPARQL query. For example, keep refining your SPARQL query if its result contains irrelevant items or is missing items you expected.
5. Do not perform additional computation (e.g. filtering, sorting, calculations) on the result of the SPARQL query to determine the answer. All computation should be done solely within SPARQL.
6. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
7. Do not use 'SERVICE wikibase:label { bd:serviceParam wikibase:language ...' in SPARQL queries. It is not SPARQL standard and unsupported by the used QLever SPARQL endpoints. Use rdfs:label or similar properties to get labels instead.

[User] What are the professions of John Lennon’s sons?

[Assistant]
To answer the question about the professions of John Lennon's sons, we need to follow these steps:

1. Identify John Lennon in the knowledge graph.
2. Find his sons.
3. Determine the professions of those sons.

Let's start by identifying John Lennon in the knowledge graph.

[Tool]
Top 10 entity alternatives:
1. John Lennon, also known as John Winston Ono Lennon, John Ono Lennon (wd:Q1203): British musician (1940–1980), political activist, singer-songwriter, recording artist, record producer, peace activist, public figure, film director, film producer, prose writer, film actor, guitarist, composer, painter, pianist, singer, writer, actor, poet
2. Liverpool John Lennon Airport, also known as John Lennon, John Lennon airport, Liverpool Airport, Liverpool (John Lennon), LPL (wd:Q8727): airport in Liverpool, England
3. John Lennon Silva Santos, also known as John Lennon (wd:Q6244563): association football player, Brazilian footballer
4. John Brown Lennon, also known as John B. Lennon, John Lennon (wd:Q6223555): American labor leader (1850–1923), trade unionist
5. John Lennon, also known as J. John Lennon, Justin John Lennon (wd:Q11979004): Scottish tourism researcher, university teacher, tourism scholar
6. John Lennon (wd:Q65548791): Irish hurler born 1996
7. John Lennon (wd:Q6244553): British naval captain, military personnel
8. John Lennon (wd:Q12871620): 1855 - 3 Aug 1921
9. John Lennon (wd:Q125643070): 2005 edition of written work by Jordi Sierra i Fabra
10. John Lennon (wd:Q106781630): 2015 song by SCH

[Assistant]
The correct entity for John Lennon, the British musician, is `wd:Q1203`. Now, let's find his sons.

We need to look for the property that indicates children or offspring. Let's search for the property related to children.

[Tool]
Top 10 property alternatives:
1. number of children, also known as children, children (number), how many children, issue, kids (wd:P1971 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): number of children of the person, animal, or character
2. child, also known as has son, kids, mother of, kid, issue (wd:P40 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): subject has object as child. Do not use for stepchildren—use "relative" (P1038), qualified with "type of kinship" (P1039), significant person, relative
3. relative, also known as relatives, grandparent, relation, non-binary parent, niece (wd:P1038 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): family member (qualify with "kinship to subject", P1039; for direct family member please use specific property), significant person
4. ASF KID Cave Tag Number, also known as Australian Speleological Federation KID, KID, Karst Index Database (wd:P2792 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): unique identifier for caves within the Karst Index Database of the Australian Speleological Federation
5. Kunstindeks Danmark Artist ID, also known as KID, KID artist ID, KID ID (wd:P1138 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): unique artist identifier used by Kunstindeks Danmark
6. child astronomical body, also known as child body, secondary, satellite, orbited by, has planet (wd:P398 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): minor body that belongs to the item
7. sibling, also known as is sister of, is the brother of, is the sibling of, is the sister of, sib (wd:P3373 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): the subject and the object have at least one common parent (brot...law, etc.) and step-siblings (step-brothers, step-sisters, etc.), relative
8. student, also known as teacher to, teacher of, had student, students, pupils (wd:P802 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): notable student(s) of the subject individual, significant person
9. parent, also known as guardian, parents (wd:P8810 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): parent of the subject. Only use if neither father (P22) nor mother (P25) nor stepparent (P3448) is applicable., relative
10. number of out-of-school children (wd:P2573 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): number of out-of-school children reported for a place

[Assistant]
The property for children is `wdt:P40`. Now, let's find John Lennon's sons using this property.

We will execute a SPARQL query to find the sons of John Lennon and then determine their professions.

### Step 1: Find John Lennon's sons
```sparql
SELECT ?son WHERE {
  wd:Q1203 wdt:P40 ?son .
}
```

Let's execute this query to find the sons of John Lennon.

[Tool]
Got 4 rows and 1 column:
| ?son                            |
| ------------------------------- |
| John Ono Lennon (wd:Q124306663) |
| John Lennon III (wd:Q124306740) |
| Sean Lennon (wd:Q311238)        |
| Julian Lennon (wd:Q357974)      |

[Assistant]
John Lennon has four sons: John Ono Lennon, John Lennon III, Sean Lennon, and Julian Lennon. Next, we need to find the professions of these sons.

### Step 2: Find the professions of John Lennon's sons
We will use the property `wdt:P106` to find the occupations or professions of these individuals.

Let's execute a SPARQL query to find the professions of John Lennon's sons.

```sparql
SELECT ?son ?profession WHERE {
  wd:Q1203 wdt:P40 ?son .
  ?son wdt:P106 ?profession .
}
```

Let's execute this query.

[Tool]
Got 16 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?son                       | ?profession                     |
| -------------------------- | ------------------------------- |
| Sean Lennon (wd:Q311238)   | singer (wd:Q177220)             |
| Sean Lennon (wd:Q311238)   | screenwriter (wd:Q28389)        |
| Sean Lennon (wd:Q311238)   | actor (wd:Q33999)               |
| Sean Lennon (wd:Q311238)   | composer (wd:Q36834)            |
| Sean Lennon (wd:Q311238)   | pianist (wd:Q486748)            |
| ...                        | ...                             |
| Julian Lennon (wd:Q357974) | composer (wd:Q36834)            |
| Julian Lennon (wd:Q357974) | singer-songwriter (wd:Q488205)  |
| Julian Lennon (wd:Q357974) | poet (wd:Q49757)                |
| Julian Lennon (wd:Q357974) | recording artist (wd:Q55960555) |
| Julian Lennon (wd:Q357974) | guitarist (wd:Q855091)          |

[Assistant]
The professions of John Lennon's sons are as follows:

- **Sean Lennon**:
  - Singer (wd:Q177220)
  - Screenwriter (wd:Q28389)
  - Actor (wd:Q33999)
  - Composer (wd:Q36834)
  - Pianist (wd:Q486748)

- **Julian Lennon**:
  - Composer (wd:Q36834)
  - Singer-songwriter (wd:Q488205)
  - Poet (wd:Q49757)
  - Recording artist (wd:Q55960555)
  - Guitarist (wd:Q855091)

It appears that the other two sons, John Ono Lennon and John Lennon III, do not have recorded professions in the knowledge graph.

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

```sparql
SELECT ?son ?profession WHERE {
  wd:Q1203 wdt:P40 ?son .
  ?son wdt:P106 ?profession .
}
```

Final answer:
- **Sean Lennon**: Singer, Screenwriter, Actor, Composer, Pianist
- **Julian Lennon**: Composer, Singer-songwriter, Poet, Recording artist, Guitarist

The other two sons, John Ono Lennon and John Lennon III, do not have recorded professions in the knowledge graph.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?stadium WHERE {
  wd:Q213417 wdt:P115 ?stadium .
}

The New York Yankees play in Yankee Stadium.

[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] In which stadium do the New York Yankees play?

[Assistant]
To answer the question about the stadium where the New York Yankees play, we need to identify the entity for the New York Yankees and then find the property that links them to their home stadium.

First, let's search for the entity representing the New York Yankees.

[Tool]
Top 10 entity alternatives:
1. New York Yankees, also known as New York Highlanders, Yankees, NY Yankees, NYY, Highlanders (wd:Q213417): baseball team and Major League Baseball franchise in the Bronx, New York, United States
2. New York Yankees (wd:Q979646): defunct American football team
3. New York Yankees (wd:Q1275088): American football team from 1946 to 1949
4. New York Yankees (wd:Q7014865): American football team in 1936
5. New York Yankees (wd:Q16845944): association football (soccer) team
6. New York Yankees (wd:Q7014868): American football team in 1940
7. New York Yankees Museum (wd:Q7014872): baseball museum in The Bronx
8. New York Black Yankees, also known as Black Yankees (wd:Q7013072): Negro League Baseball team active from 1931-1948; record: 243-462-19 (.345)
9. history of the New York Yankees, also known as New York Yankees history, the New York Yankees history (wd:Q5870090): Sports team history
10. Saratoga Harlem Yankees, also known as Glens Falls-Saratoga, New York Harlem Yankees, Schenectady Yankees (wd:Q7423363): defunct American basketball team

[Assistant]
The top result for the New York Yankees is `wd:Q213417`, which represents the baseball team and Major League Baseball franchise in the Bronx, New York, United States. This is the correct entity we need.

Next, we need to find the property that links the New York Yankees to their home stadium. Let's search for properties related to "stadium" or "home ground".

[Tool]
Top 10 property alternatives:
1. home venue, also known as home water, venue, stadium, home ground, home field (wd:P115 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): home stadium or venue of a sports team or applicable performing arts organization, location
2. PlaymakerStats.com stadium ID, also known as www.playmakerstats.com/estadio.php?id=, https://www.playmakerstats.com/estadio.php?id=, playmakerstats.com/estadio.php?id= (wd:P10917 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): identifier for a association football (soccer) stadium in the PlayakerStats.com database
3. has facility, also known as has amenity, has feature, features, amenity, facility (wd:P912 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): the subject item has this type of facility, e.g. toilet, car park, has part(s) of the class
4. UEFA stadium category (wd:P9803 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): Category of the sports facility according to the UEFA Stadium Infrastructure Regulations
5. recorded at studio or venue, also known as venue, studio, recording venue, recording studio, recorded at event (wd:P483 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): studio or location where a musical composition/release was recorded, recording location
6. location, also known as neighbourhood, whereabouts, vilage, venue, town (wd:P276 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): location of the object, structure or event. In the case of an ad...ity use P706. Use P7153 for locations associated with the object
7. published in, also known as song on album, tome of the book, tome of, track of, track on (wd:P1433 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): larger work that a given work was published in, like a book, journal or music album, part of
8. Stadium 64 ID, also known as S64 ID (wd:P12220 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): identifier of a video game in the Stadium 64 database
9. EU-Football.info stadium ID (wd:P8320 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): identifier for a association football (soccer) stadium in the EU-Football.info database
10. StadiumDB ID, also known as StadiumDB.com ID, StadiumDB.com identifier, StadiumDB identifier (wd:P5288 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): identifier for a stadium on StadiumDB.com

[Assistant]
The top result for the property related to "stadium" is `wd:P115`, which represents the home venue or stadium of a sports team. This is the property we need to use to find the home stadium of the New York Yankees.

Now, let's construct a SPARQL query to find the home stadium of the New York Yankees.

```sparql
SELECT ?stadium WHERE {
  wd:Q213417 wdt:P115 ?stadium .
}
```

Let's execute this query to get the result.

[Tool]
Got 1 row and 1 column:
| ?stadium                    |
| --------------------------- |
| Yankee Stadium (wd:Q753529) |

[Assistant]
The SPARQL query returned the home stadium of the New York Yankees, which is `Yankee Stadium` (wd:Q753529).

Therefore, the New York Yankees play in Yankee Stadium.

Here is the final SPARQL query and answer:

```sparql
SELECT ?stadium WHERE {
  wd:Q213417 wdt:P115 ?stadium .
}
```

Answer: The New York Yankees play in Yankee Stadium.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?headquarters ?country WHERE {
  wd:Q1771942 wdt:P159 ?headquarters .
  ?headquarters wdt:P17 ?country .
}

The United Fruit Company had its headquarters in the United States.

[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] In which country did the United Fruit Company have their headquarters?

[Assistant]
To answer the question about the country where the United Fruit Company had their headquarters, we need to identify the entity for the United Fruit Company and then find the location of its headquarters. We can start by searching for the entity representing the United Fruit Company in the knowledge graph.

Let's search for the entity "United Fruit Company" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. United Fruit Company (wd:Q1771942): American corporation
2. United Fruit Company Building (wd:Q79310992): historic building in Wellington, New Zealand
3. United Fruit Company Infrastructure (wd:Q65591387): Tentative World Heritage Site in Colombia
4. Kossick v. United Fruit Company (wd:Q19089122): United States Supreme Court case
5. United Fruit Company Medal (obverse) (wd:Q122934149): bronze medal-sample by Paul Manship
6. United Fruit Company Medal (reverse) (wd:Q122934151): bronze medal-sample by Paul Manship
7. American Banana Company v. United Fruit Company (wd:Q19037455)
8. United Fruit Company strike of 1913 (wd:Q105962401)
9. United States v. Fruit Growers' Express Company (wd:Q19030798): United States Supreme Court case
10. United Surety Company v. American Fruit Product Company (wd:Q19041555)

[Assistant] The top result for "United Fruit Company" is `wd:Q1771942`, which represents the American corporation. Next, we need to find the property that indicates the location of the company's headquarters. Let's search for properties related to "headquarters" in Wikidata.

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

[Assistant]
The property `wd:P159` (headquarters location) is the most relevant for finding the location of the United Fruit Company's headquarters. Now, let's construct a SPARQL query to find the country where the United Fruit Company had its headquarters.

We will first find the city or town where the headquarters was located and then find the country associated with that city or town.

Here is the initial SPARQL query:

```sparql
SELECT ?headquarters ?country WHERE {
  wd:Q1771942 wdt:P159 ?headquarters .
  ?headquarters wdt:P17 ?country .
}
```

Let's execute this query to see the results.

[Tool]
Got 1 row and 2 columns:
| ?headquarters           | ?country               |
| ----------------------- | ---------------------- |
| New Orleans (wd:Q34404) | United States (wd:Q30) |

[Assistant]
The SPARQL query returned the headquarters location of the United Fruit Company as New Orleans, which is in the United States. Therefore, the United Fruit Company had its headquarters in the United States.

Here is the final SPARQL query and the answer to the question:

```sparql
SELECT ?headquarters ?country WHERE {
  wd:Q1771942 wdt:P159 ?headquarters .
  ?headquarters wdt:P17 ?country .
}
```

The answer to the question is that the United Fruit Company had its headquarters in the United States.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?victim WHERE {
  ?victim wdt:P157 wd:Q201377 .
}

Oscar Pistorius killed 1 person.

[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] How many people did Oscar Pistorius kill?

[Assistant]
To answer this question, we need to identify the entity for Oscar Pistorius and then find out the number of people he killed. We will start by searching for Oscar Pistorius in the knowledge graph.

Let's search for Oscar Pistorius.

[Tool]
Top 10 entity alternatives:
1. Oscar Pistorius, also known as Oscar Leonard Carl Pistorius, Fastest man on no legs, Blade Runner (wd:Q201377): South African sprinter and convicted murderer (born 1986), athletics competitor, murderer
2. trial of Oscar Pistorius, also known as The State v Oscar Pistorius (wd:Q16328520): adjudication of murder and gun charges brought against athlete Oscar Pistorius in 2014
3. Oscar Pistorius: Blade Runner Killer (wd:Q55698496): 2017 television film
4. Oscar Pistorius, enhancement and post-humans. (wd:Q47375029): scientific article published in September 2008
5. Mechanics of Oscar Pistorius' running blades (wd:Q6804597): blades used by South African Paralympic runner Oscar Pistorius
6. Oscar Pistorius and the melancholy of intersectionality (wd:Q58274689)
7. O, also known as letter 15, o, O, Oscar (wd:Q9941): letter of the Latin alphabet
8. Oscar, also known as Oscar dos Santos Emboaba Junior, Oscar dos Santos Emboaba Júnior (wd:Q19497): Brazilian association football player (born 1991), association football player
9. Astronotus ocellatus, also known as marble cichlid, oscar, Oscar (fish), tiger oscar, velvet cichlid (wd:Q3442): species of fish from the cichlid family
10. Nakajima Ki-43 Hayabusa, also known as Oscar, Ki-43, Hayabusa, Army Zero, Army Type 1 Fighter (wd:Q700703): Japanese fighter aircraft

[Assistant]
The correct entity for Oscar Pistorius is `wd:Q201377`. Now, we need to find out the number of people he killed. This information might be linked through a property such as "victim" or "killed." Let's search for relevant properties.

Let's search for properties related to "kill" or "victim."

[Tool]
Top 10 property alternatives:
1. number of victims of killer, also known as number of persons killed, number of killed persons, number of homicide victims, kills, kill count (wd:P1345 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): people killed by the subject, both legal and illegal
2. perpetrator, also known as robber, perpetrators, perpetrated by, perp, offender (wd:P8031 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): person or organization who carried out this harmful, illegal, or immoral act, participant
3. killed by, also known as slaughtered by, slain by, shot dead by, murderer, murdered by (wd:P157 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): person or organization who killed the subject, significant person
4. cause of death, also known as death cause, murder method, method of murder, die of, die from (wd:P509 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): underlying or immediate cause of death.  Underlying cause (e.g. ...adest category, e.g. natural causes, accident, homicide, suicide
5. date of death, also known as DOD, deathdate, died, died on, year of death (wd:P570 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): dissolved, abolished or demolished date, date on which the subject died, end time
6. place of death, also known as POD, location of death, killed in, deathplace, died in (wd:P20 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): most specific known (e.g. city instead of country, or hospital i... city) death location of a person, animal or fictional character, location
7. enemy, also known as opponent, rival, archvillain, archrival, archnemesis (wd:P7047 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): opponent character or group of this fictive character or group, significant person
8. hairstyle / hairlength, also known as hair style, style of hair, hair styling, hairdo, haircut (wd:P8839 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): style or length of cutting, arranging, or combing the hair on the subject's scalp
9. award received, also known as win, winner of, recognition title, received award, prize received (wd:P166 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): award or recognition received by a person, organization or creative work
10. powered by, also known as propulsion, prime mover, power source, powerplant, drive (wd:P516 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): equipment or engine used by the subject to convert a source or energy into mechanical energy, has part(s)

[Assistant]
The property `wd:P1345` (number of victims of killer) seems to be the most relevant for our query. Let's check if Oscar Pistorius has a value for this property.

We will execute a SPARQL query to find the number of victims associated with Oscar Pistorius.

```sparql
SELECT ?numVictims
WHERE {
  wd:Q201377 wdt:P1345 ?numVictims .
}
```

Let's run this query.

[Tool] Got no rows and 1 columns

[Assistant]
It appears that Oscar Pistorius does not have a value for the `wd:P1345` (number of victims of killer) property. However, we can try another approach by looking at the people who were killed by him using the `wd:P157` (killed by) property.

Let's execute a SPARQL query to find the people who were killed by Oscar Pistorius.

```sparql
SELECT ?victim
WHERE {
  ?victim wdt:P157 wd:Q201377 .
}
```

Let's run this query.

[Tool]
Got 1 row and 1 column:
| ?victim                       |
| ----------------------------- |
| Reeva Steenkamp (wd:Q4584302) |

[Assistant]
The query returned one victim, Reeva Steenkamp, who was killed by Oscar Pistorius. Therefore, the number of people Oscar Pistorius killed is 1.

Here is the final SPARQL query and the answer:

```sparql
SELECT ?victim
WHERE {
  ?victim wdt:P157 wd:Q201377 .
}
```

Answer: Oscar Pistorius killed 1 person.

Let's provide the final answer.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ( COUNT( ?game ) AS ?count ) WHERE {
  ?game wdt:P31 wd:Q7889 ; wdt:P577 ?releaseDate .
  FILTER ( YEAR( ?releaseDate ) = 2016 && ?game != wd:Q24514682 )
}

Got 1 row and 1 column:
| ?count |
| ------ |
| 5007   |

5007 other video games began the same year as Dead by Daylight.

[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] How many other video games began the same year as Dead by Daylight?

[Assistant] To answer this question, we need to identify the release year of the video game "Dead by Daylight" and then count how many other video games were released in the same year. Let's start by finding the entity for "Dead by Daylight" in the knowledge graph.

[Tool]
Top 10 entity alternatives:
1. Dead by Daylight, also known as dbd (wd:Q24514682): 2016 asymmetric survival horror video game developed by Behaviour Interactive
2. Dead by Daylight, also known as DBD (wd:Q116976554): upcoming horror film based in the videogame
3. Dead by Daylight universe (wd:Q125569668): narrative universe of the 2016 video game Dead by Daylight
4. Dead by Daylight: Bloodstained Sack (wd:Q98802595): 2016 downloadable content
5. Hooked on You: A Dead by Daylight Dating Sim, also known as A Dead by Daylight Dating Sim, Hooked on You (wd:Q113468031): 2022 video game
6. Dead by Daylight - Hellraiser chapter (wd:Q125760779): 2021 downloadable content
7. Dead by Daylight - All-Kill Chapter (wd:Q125760955): 2021 downloadable content
8. Dead by Daylight - Terror Expansion Pack (wd:Q125760940): 2021 downloadable content
9. Dead by Daylight - Sadako Rising Chapter (wd:Q125570431): 2022 downloadable content
10. Dead by Daylight - Resident Evil Chapter (wd:Q125760789): 2021 downloadable content

[Assistant]
The top result for "Dead by Daylight" is `wd:Q24514682`, which is the main video game. Next, we need to find the release year of this game. We can use the `list` function to look for the property that indicates the release year.

Let's search for the property that represents the release date or year of a video game.

[Tool]
Top 10 property alternatives:
1. date of commercialization, also known as availability date, commercialization date, product release date, release date (wd:P5204 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): date when a product was first commercialized
2. publication date, also known as launched, published, publication time, publication, pubdate (wd:P577 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): date or point in time when a work was first published or released, point in time
3. announcement date, also known as released, date teased, made public on, public announcement date, published (wd:P6949 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): time of the first public presentation of a subject by the creator, of information by the media, point in time
4. inception, also known as initial release date, founded in, founded on, founded on date, founding date (wd:P571 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): time when an entity begins to exist; for date of official opening use P1619, start time
5. day of regular release (wd:P6437 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): day(s) when parts of this creative work are released, aired, published, etc
6. service entry, also known as commissioned, time of service entry, service introduction, launch date, in service (wd:P729 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): date or point in time on which a piece or class of equipment entered operational service, start time
7. date of official opening, also known as launch date, launched, officially opened on, official opening, official opening time (wd:P1619 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): date or point in time a place, organization, or event officially opened, start time, inception
8. UTC date of spacecraft launch, also known as time of spacecraft launch, spacecraft launch time, spacecraft launch date, launch time, launch date (wd:P619 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): date of spacecraft launch in UTC
9. production date, also known as date of production, filming date, filming time, production date, year of production (wd:P2754 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): period of active production of (creative) work; the only date st... publication date (P577); in general cases, use inception (P571)
10. place of publication, also known as published in (place), where published, release region, released in, published from (wd:P291 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): geographical place of publication of the edition (use 1st edition when referring to works), significant place

[Assistant]
The most relevant property for the release date of a video game appears to be `wdt:P577`. Let's use this property to find the release date of "Dead by Daylight" (`wd:Q24514682`).

I will execute a SPARQL query to retrieve the release date of "Dead by Daylight".

[Tool]
Got 1 row and 1 column:
| ?releaseDate         |
| -------------------- |
| 2016-06-14T00:00:00Z |

[Assistant]
The release date of "Dead by Daylight" is June 14, 2016. Now, we need to count how many other video games were released in the year 2016. To do this, we will query for all video games that have a release date in 2016 and exclude "Dead by Daylight" itself.

Let's construct and execute the SPARQL query to count the number of video games released in 2016, excluding "Dead by Daylight".

[Tool]
Got 1 row and 1 column:
| ?count |
| ------ |
| 5007   |

[Assistant]
The number of other video games that began the same year as "Dead by Daylight" (2016) is 5007.

Here is the final SPARQL query and answer:

```sparql
SELECT (COUNT(?game) AS ?count)
WHERE {
  ?game wdt:P31 wd:Q7889 ;
  wdt:P577 ?releaseDate .
  FILTER(YEAR(?releaseDate) = 2016 && ?game != wd:Q24514682)
}
```

Answer: 5007 other video games began the same year as "Dead by Daylight".

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ( COUNT( DISTINCT ?occupation ) AS ?unique_occupations ) WHERE {
  ?winner wdt:P166 wd:Q37922 .
  ?winner wdt:P26 ?spouse .
  ?spouse wdt:P106 ?occupation .
}

Got 1 row and 1 column:
| ?unique_occupations |
| ------------------- |
| 71                  |

[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] How many different occupations did/do spouses of winners of the Nobel Prize in Literature hold?

[Assistant]
To answer this question, we need to identify the following elements:

1. **Nobel Prize in Literature Winners**: We need to find entities that have won the Nobel Prize in Literature.
2. **Spouses of Nobel Prize Winners**: For each winner, we need to find their spouses.
3. **Occupations of Spouses**: For each spouse, we need to find their occupations.
4. **Count Unique Occupations**: Finally, we need to count the unique occupations held by these spouses.

Let's start by identifying the property for the Nobel Prize in Literature and the property for spouses.

### Step 1: Identify Properties
First, let's search for the property related to the Nobel Prize in Literature and the property for spouses.

#### Search for Nobel Prize in Literature Property
We will search for the property related to the Nobel Prize in Literature.

[Tool]
Top 10 property alternatives:
1. Nobel Prize People Nomination ID (wd:P3360 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): identifier for a person who nominated someone, or who was nominated, for a Nobel Prize
2. movement, also known as subculture, trend, scientific movement, school, philosophical movement (wd:P135 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): literary, artistic, scientific or philosophical movement or scen...ed with this person or work. For political ideologies use P1142.
3. genre, also known as genre of music, type of music, type of film, music genre, literary genre (wd:P136 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): creative work's genre or an artist's field of work (P101). Use main subject (P921) to relate creative works to their topic
4. notable work, also known as artwork, wrote, writer of, works, work (wd:P800 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): notable scientific, artistic or literary work, or other work of significance among subject's works
5. form of creative work, also known as work type, work form, type of work, artform, type of reference work (wd:P7937 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): structure of a creative work
6. award received, also known as win, winner of, recognition title, received award, prize received (wd:P166 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): award or recognition received by a person, organization or creative work
7. winner, also known as nominee, prizewinner, titleholder, top dog, victor (wd:P1346 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): winner of a competition or similar event, not to be used for awa...ssibly qualified with "for work" (P1686)) or for wars or battles, participating team, participant
8. National Humanities Medal winner ID (wd:P5657 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): identifier for a National Humanities Medal winner on the National Endowment for the Humanities website
9. Nobel Laureate API ID (wd:P8024 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): REST API service with info about Nobel laureates
10. nominee, also known as award nominee (wd:P2453 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): qualifier used with “nominated for” to specify which person or organization was nominated

[Assistant]
From the search results, the property **"award received" (wd:P166)** seems to be the most relevant for identifying Nobel Prize winners. We will use this property to find winners of the Nobel Prize in Literature.

Next, let's search for the property related to spouses.

#### Search for Spouse Property
We will search for the property related to spouses.

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

[Assistant]
From the search results, the property **"spouse" (wd:P26)** is the most relevant for identifying the spouses of Nobel Prize winners.

### Step 2: Identify Nobel Prize in Literature Winners
Now, let's write a SPARQL query to find the winners of the Nobel Prize in Literature. We will use the property `wd:P166` and the specific award `wd:Q8108` (Nobel Prize in Literature).

#### Query for Nobel Prize in Literature Winners
We will execute a query to find the winners of the Nobel Prize in Literature.

```sparql
SELECT ?winner
WHERE {
  ?winner wdt:P166 wd:Q8108 .
}
```

Let's execute this query to get the list of winners.

[Tool] Got no rows and 1 columns

[Assistant]
It seems that the query did not return any results. This could be due to a few reasons, such as the specific award IRI being incorrect or the data not being available in the knowledge graph. Let's verify the IRI for the Nobel Prize in Literature.

#### Verify Nobel Prize in Literature IRI
We will search for the entity "Nobel Prize in Literature" to ensure we have the correct IRI.

[Tool]
Top 10 entity alternatives:
1. Nobel Prize in Literature, also known as Nobel Literature Prize (wd:Q37922): one of five prizes established by Alfred Nobel
2. 2022 Nobel Prize in Literature (wd:Q112594135): literary award to French author Annie Ernaux
3. 1901 Nobel Prize in Literature (wd:Q15918052)
4. 2023 Nobel Prize in Literature (wd:Q115937544)
5. 2012 Nobel Prize in Literature (wd:Q10845886)
6. 1905 Nobel Prize in Literature (wd:Q112731128)
7. 1908 Nobel Prize in Literature (wd:Q112731708)
8. 2003 Nobel Prize in Literature (wd:Q113212896)
9. 1938 Nobel Prize in Literature (wd:Q112811579)
10. 1954 Nobel Prize in Literature (wd:Q107359944): award

[Assistant]
The correct IRI for the Nobel Prize in Literature is `wd:Q37922`. Let's update our query to use this IRI and try again.

#### Updated Query for Nobel Prize in Literature Winners
We will execute the updated query to find the winners of the Nobel Prize in Literature.

```sparql
SELECT ?winner
WHERE {
  ?winner wdt:P166 wd:Q37922 .
}
```

Let's execute this query to get the list of winners.

[Tool]
Got 122 rows and 1 column, showing the first 5 and last 5 rows below:
| ?winner                        |
| ------------------------------ |
| Jaroslav Seifert (wd:Q102483)  |
| Annie Ernaux (wd:Q1153825)     |
| Władysław Reymont (wd:Q121180) |
| Sinclair Lewis (wd:Q123469)    |
| José Echegaray (wd:Q127349)    |
| ...                            |
| Mo Yan (wd:Q8998)              |
| Pär Lagerkvist (wd:Q93137)     |
| Eugene O'Neill (wd:Q93157)     |
| Seamus Heaney (wd:Q93356)      |
| Jean-Paul Sartre (wd:Q9364)    |

[Assistant]
The query returned 122 winners of the Nobel Prize in Literature. Now, we need to find the spouses of these winners and their occupations.

### Step 3: Find Spouses of Nobel Prize Winners
We will extend the query to include the spouses of the Nobel Prize winners using the property `wd:P26`.

#### Query for Spouses of Nobel Prize Winners
```sparql
SELECT ?winner ?spouse
WHERE {
  ?winner wdt:P166 wd:Q37922 .
  ?winner wdt:P26 ?spouse .
}
```

Let's execute this query to get the list of spouses.

[Tool]
Got 158 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?winner                            | ?spouse                                       |
| ---------------------------------- | --------------------------------------------- |
| Jaroslav Seifert (wd:Q102483)      | Marie Seifertová (wd:Q108714106)              |
| Władysław Reymont (wd:Q121180)     | Aurelia Szacnajder Szabłowska (wd:Q108696389) |
| Sinclair Lewis (wd:Q123469)        | Dorothy Thompson (wd:Q275545)                 |
| Roger Martin du Gard (wd:Q129155)  | Hélène Foucault (wd:Q108667581)               |
| Verner von Heidenstam (wd:Q129173) | Olga Wiberg (wd:Q108680177)                   |
| ...                                | ...                                           |
| Pär Lagerkvist (wd:Q93137)         | Karen Sørensen (wd:Q108760591)                |
| Pär Lagerkvist (wd:Q93137)         | Elaine Lagerkvist (wd:Q108760600)             |
| Eugene O'Neill (wd:Q93157)         | Agnes Boulton (wd:Q4693019)                   |
| Eugene O'Neill (wd:Q93157)         | Carlotta Monterey (wd:Q5042834)               |
| Seamus Heaney (wd:Q93356)          | Marie Devlin (wd:Q14756963)                   |

[Assistant]
The query returned 158 rows, indicating that some Nobel Prize winners have multiple spouses. Next, we need to find the occupations of these spouses.

### Step 4: Find Occupations of Spouses
We will extend the query to include the occupations of the spouses using the property `wd:P106`.

#### Query for Occupations of Spouses
```sparql
SELECT ?winner ?spouse ?occupation
WHERE {
  ?winner wdt:P166 wd:Q37922 .
  ?winner wdt:P26 ?spouse .
  ?spouse wdt:P106 ?occupation .
}
```

Let's execute this query to get the list of occupations.

[Tool]
Got 185 rows and 3 columns, showing the first 5 and last 5 rows below:
| ?winner                             | ?spouse                              | ?occupation                   |
| ----------------------------------- | ------------------------------------ | ----------------------------- |
| Louise Glück (wd:Q2344210)          | John Dranow (wd:Q100234047)          | lector (wd:Q1553123)          |
| Louise Glück (wd:Q2344210)          | John Dranow (wd:Q100234047)          | writer (wd:Q36180)            |
| Louise Glück (wd:Q2344210)          | John Dranow (wd:Q100234047)          | businessperson (wd:Q43845)    |
| José Saramago (wd:Q37060)           | Ilda Reis (wd:Q10301028)             | painter (wd:Q1028181)         |
| José Saramago (wd:Q37060)           | Isabel da Nóbrega (wd:Q10303821)     | prose writer (wd:Q12144794)   |
| ...                                 | ...                                  | ...                           |
| Rudolf Christoph Eucken (wd:Q47695) | Irene Eucken (wd:Q94748608)          | painter (wd:Q1028181)         |
| Rudolf Christoph Eucken (wd:Q47695) | Irene Eucken (wd:Q94748608)          | handicrafter (wd:Q11479517)   |
| Günter Grass (wd:Q6538)             | Anna Grass (wd:Q95208466)            | dancer (wd:Q5716684)          |
| Verner von Heidenstam (wd:Q129173)  | Emilie von Heidenstam (wd:Q98545639) | autobiographer (wd:Q18814623) |
| Verner von Heidenstam (wd:Q129173)  | Emilie von Heidenstam (wd:Q98545639) | writer (wd:Q36180)            |

[Assistant]
The query returned 185 rows, indicating that some spouses have multiple occupations. Now, we need to count the unique occupations held by these spouses.

### Step 5: Count Unique Occupations
We will modify the query to count the distinct occupations.

#### Query to Count Unique Occupations
```sparql
SELECT (COUNT(DISTINCT ?occupation) AS ?unique_occupations)
WHERE {
  ?winner wdt:P166 wd:Q37922 .
  ?winner wdt:P26 ?spouse .
  ?spouse wdt:P106 ?occupation .
}
```

Let's execute this query to get the count of unique occupations.

[Tool]
Got 1 row and 1 column:
| ?unique_occupations |
| ------------------- |
| 71                  |

[Assistant]
The query returned that there are 71 unique occupations held by the spouses of Nobel Prize in Literature winners.

### Final Answer
The number of different occupations held by the spouses of Nobel Prize in Literature winners is 71.

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

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?university ?establishmentDate WHERE {
  VALUES ?university {
    wd:Q34433 wd:Q35794
  }
  ?university wdt:P571 ?establishmentDate .
}

The University of Oxford was established earlier than the University of Cambridge.

[System]
You are a question answering assistant. Your job is to generate a SPARQL query to answer a given user question.

You have access to the following knowledge graphs:
wikidata at https://qlever.cs.uni-freiburg.de/api/wikidata-sebastian

You can use the following SPARQL prefixes implicitly in all functions:
cc: <http://creativecommons.org/ns#
data: <https://www.wikidata.org/wiki/Special:EntityData/
dct: <http://purl.org/dc/terms/
geo: <http://www.opengis.net/ont/geosparql#
geof: <http://www.opengis.net/def/function/geosparql/
imdb: <https://www.imdb.com/
math: <http://www.w3.org/2005/xpath-functions/math#
ontolex: <http://www.w3.org/ns/lemon/ontolex#
owl: <http://www.w3.org/2002/07/owl#
p: <http://www.wikidata.org/prop/
pq: <http://www.wikidata.org/prop/qualifier/
pqn: <http://www.wikidata.org/prop/qualifier/value-normalized/
pqv: <http://www.wikidata.org/prop/qualifier/value/
pr: <http://www.wikidata.org/prop/reference/
prn: <http://www.wikidata.org/prop/reference/value-normalized/
prov: <http://www.w3.org/ns/prov#
prv: <http://www.wikidata.org/prop/reference/value/
ps: <http://www.wikidata.org/prop/statement/
psn: <http://www.wikidata.org/prop/statement/value-normalized/
psv: <http://www.wikidata.org/prop/statement/value/
qfn: <http://qlever.cs.uni-freiburg.de/function#
ql: <http://qlever.cs.uni-freiburg.de/builtin-functions/
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
ref: <http://www.wikidata.org/reference/
s: <http://www.wikidata.org/entity/statement/
schema: <http://schema.org/
skos: <http://www.w3.org/2004/02/skos/core#
v: <http://www.wikidata.org/value/
wd: <http://www.wikidata.org/entity/
wdno: <http://www.wikidata.org/prop/novalue/
wdt: <http://www.wikidata.org/prop/direct/
wdtn: <http://www.wikidata.org/prop/direct-normalized/
wikibase: <http://wikiba.se/ontology#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
dc: <http://purl.org/dc/elements/1.1/
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
qb: <http://purl.org/linked-data/cube#
void: <http://rdfs.org/ns/void#

You should follow a step-by-step approach to generate the SPARQL query:
1. Determine possible entities and properties implied by the user question.
2. Use the functions to search for entities and properties in the knowledge graphs. Where applicable, constrain the searches with already identified entities and properties.
3. Gradually build up the SPARQL query using the identified entities and properties. Start with simple queries and add more complexity as needed. Use the execute function to get feedback on intermediate queries. You can go back and forth between knowledge graph searches and SPARQL query executions to refine the query.
4. Use the answer or cancel function to finalize your answer and stop the generation process.

Additional rules:
1. Explain your thought process before and after each step and function call.
2. Do not assume or make up any entities or properties without verifying them with the knowledge graphs and functions.
3. The SPARQL query should always return the actual identifiers / IRIs of the items in its result. It additionally may return labels or other human-readable information, but they are optional and should be put within optional clauses unless explicitly requested by the user.
4. Do not stop early if there are still obvious improvements to be made to the SPARQL query. For example, keep refining your SPARQL query if its result contains irrelevant items or is missing items you expected.
5. Do not perform additional computation (e.g. filtering, sorting, calculations) on the result of the SPARQL query to determine the answer. All computation should be done solely within SPARQL.
6. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
7. Do not use 'SERVICE wikibase:label { bd:serviceParam wikibase:language ...' in SPARQL queries. It is not SPARQL standard and unsupported by the used QLever SPARQL endpoints. Use rdfs:label or similar properties to get labels instead.

[User] which university is established earlier, university of oxford or university of cambridge?

[Assistant]
To answer this question, we need to identify the entities for the University of Oxford and the University of Cambridge, and then find their establishment dates. We can use these dates to determine which university was established earlier.

Let's start by searching for the entities of the University of Oxford and the University of Cambridge.

[Tool]
Top 10 entity alternatives:
1. University of Oxford, also known as Univ. of Oxford, Universitas Oxoniensis, The University of Oxford, Oxon., Oxford (wd:Q34433): collegiate research university in Oxford, England
2. University of Oxford (wd:Q58944020): scholarly article
3. WikiProject University of Oxford (wd:Q21831045): Wikimedia subject-area collaboration
4. ISCA, University of Oxford (wd:Q52666568): Academic publisher based in United Kingdom
5. The University of Oxford (wd:Q61960212): scientific article published in March 2016
6. University of Oxford Finance (wd:Q102523083): business school
7. University of Oxford: Erratum (wd:Q94059932): scholarly article published in The BMJ
8. University of Oxford Latin American Centre, also known as University of Oxford LAC (wd:Q85960644): research centre in the UK
9. University of Oxford Blackfriars (wd:Q86020251): academic department in the UK
10. University of Oxford University College Oxford (wd:Q86020246): academic department in the UK

[Assistant] The correct entity for the University of Oxford is `wd:Q34433`. Now, let's search for the entity of the University of Cambridge.

[Tool]
Top 10 entity alternatives:
1. University of Cambridge, also known as Cambridge University, The Chancellor, Masters, and Sch...s of the University of Cambridge, Cambridge, The University of Cambridge, Universitas Cantabrigiensis (wd:Q35794): collegiate public research university in Cambridge, England, United Kingdom
2. University of Cambridge, also known as The University of Cambridge, Universitas Cantabrigiensis, The Chancellor, Masters, and Sch...s of the University of Cambridge, Cambridge University, Cambridge (wd:Q35794): collegiate public research university in Cambridge, England, United Kingdom
3. University of Cambridge (wd:Q60109853): scholarly article published in Nature 148, November 1941
4. University of Cambridge (wd:Q60080699): scholarly article published in Nature 161, June 1948
5. University of Cambridge (wd:Q110095020): engraving by Nathaniel Parr
6. Cambridge University Constabulary, also known as University of Cambridge Constabulary (wd:Q304220)
7. University of Cambridge Museums & Botanic Garden, also known as University of Cambridge Museums (wd:Q17028042): consortium of museums of the University of Cambridge, United Kigdom
8. University of Cambridge legends (wd:Q7895218)
9. WikiProject University of Cambridge (wd:Q21831036): Wikimedia subject-area collaboration
10. Eur.Th.Group Of Cambridge University (wd:Q54909497): UK organization

[Assistant]
The correct entity for the University of Cambridge is `wd:Q35794`.

Next, we need to find the establishment dates for both universities. Let's search for the property that represents the establishment date.

[Tool]
Top 10 property alternatives:
1. inception, also known as initial release date, founded in, founded on, founded on date, founding date (wd:P571 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): time when an entity begins to exist; for date of official opening use P1619, start time
2. service entry, also known as commissioned, time of service entry, service introduction, launch date, in service (wd:P729 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): date or point in time on which a piece or class of equipment entered operational service, start time
3. subject named as, also known as established as, established form, established heading, named as, spelled as (wd:P1810 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): name by which a subject is recorded in a database, mentioned as a contributor of a work, or is referred to in a particular context, name
4. foundational text, also known as main text, treaty, organic law, founding document, establishing document (wd:P457 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): text through which an institution or object has been created or established
5. founded by, also known as starter, co-founded by, started by, institutor, instituted by (wd:P112 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): founder or co-founder of this organization, religion, place or entity, creator
6. notable work, also known as artwork, wrote, writer of, works, work (wd:P800 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): notable scientific, artistic or literary work, or other work of significance among subject's works
7. date of incorporation, also known as date incorporated, incorporated, incorporated on date, incorporation date, year incorporated (wd:P10786 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): date when company, organization or city was incorporated
8. location of formation, also known as place of incorporation, originates from, place of establishment, place of formation, place of foundation (wd:P740 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): location where a group or organization was formed, location of creation
9. main regulatory text, also known as regulated by, statutory authorization, statutory authority, legally established by, legal basis (wd:P92 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): text setting the main rules by which the subject is regulated
10. date of official opening, also known as launch date, launched, officially opened on, official opening, official opening time (wd:P1619 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): date or point in time a place, organization, or event officially opened, start time, inception

[Assistant]
The property that represents the establishment date is `wd:P571` (inception).

Now, let's write a SPARQL query to retrieve the establishment dates for both the University of Oxford and the University of Cambridge.

```sparql
SELECT ?university ?establishmentDate
WHERE {
  VALUES ?university { wd:Q34433 wd:Q35794 }
  ?university wdt:P571 ?establishmentDate .
}
```

This query will return the establishment dates for both universities. Let's execute it.

[Tool]
Got 2 rows and 2 columns:
| ?university                         | ?establishmentDate   |
| ----------------------------------- | -------------------- |
| University of Oxford (wd:Q34433)    | 1096-01-01T00:00:00Z |
| University of Cambridge (wd:Q35794) | 1209-01-01T00:00:00Z |

[Assistant]
The query results show that the University of Oxford was established in 1096, and the University of Cambridge was established in 1209. Therefore, the University of Oxford was established earlier than the University of Cambridge.

Here is the final SPARQL query and the answer:

```sparql
SELECT ?university ?establishmentDate
WHERE {
  VALUES ?university { wd:Q34433 wd:Q35794 }
  ?university wdt:P571 ?establishmentDate .
}
```

Answer: The University of Oxford was established earlier than the University of Cambridge.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?movement ?movementLabel WHERE {
  wd:Q117012 wdt:P135 ?movement .
  OPTIONAL {
    ?movement rdfs:label ?movementLabel .
    FILTER ( LANG( ?movementLabel ) = "en" )
  }
}

Yoko Ono was part of the Fluxus art movement.

[System]
You are a question answering assistant. Your job is to generate a SPARQL query to answer a given user question.

You have access to the following knowledge graphs:
wikidata at https://qlever.cs.uni-freiburg.de/api/wikidata-sebastian

You can use the following SPARQL prefixes implicitly in all functions:
cc: <http://creativecommons.org/ns#
data: <https://www.wikidata.org/wiki/Special:EntityData/
dct: <http://purl.org/dc/terms/
geo: <http://www.opengis.net/ont/geosparql#
geof: <http://www.opengis.net/def/function/geosparql/
imdb: <https://www.imdb.com/
math: <http://www.w3.org/2005/xpath-functions/math#
ontolex: <http://www.w3.org/ns/lemon/ontolex#
owl: <http://www.w3.org/2002/07/owl#
p: <http://www.wikidata.org/prop/
pq: <http://www.wikidata.org/prop/qualifier/
pqn: <http://www.wikidata.org/prop/qualifier/value-normalized/
pqv: <http://www.wikidata.org/prop/qualifier/value/
pr: <http://www.wikidata.org/prop/reference/
prn: <http://www.wikidata.org/prop/reference/value-normalized/
prov: <http://www.w3.org/ns/prov#
prv: <http://www.wikidata.org/prop/reference/value/
ps: <http://www.wikidata.org/prop/statement/
psn: <http://www.wikidata.org/prop/statement/value-normalized/
psv: <http://www.wikidata.org/prop/statement/value/
qfn: <http://qlever.cs.uni-freiburg.de/function#
ql: <http://qlever.cs.uni-freiburg.de/builtin-functions/
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
ref: <http://www.wikidata.org/reference/
s: <http://www.wikidata.org/entity/statement/
schema: <http://schema.org/
skos: <http://www.w3.org/2004/02/skos/core#
v: <http://www.wikidata.org/value/
wd: <http://www.wikidata.org/entity/
wdno: <http://www.wikidata.org/prop/novalue/
wdt: <http://www.wikidata.org/prop/direct/
wdtn: <http://www.wikidata.org/prop/direct-normalized/
wikibase: <http://wikiba.se/ontology#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
dc: <http://purl.org/dc/elements/1.1/
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
qb: <http://purl.org/linked-data/cube#
void: <http://rdfs.org/ns/void#

You should follow a step-by-step approach to generate the SPARQL query:
1. Determine possible entities and properties implied by the user question.
2. Use the functions to search for entities and properties in the knowledge graphs. Where applicable, constrain the searches with already identified entities and properties.
3. Gradually build up the SPARQL query using the identified entities and properties. Start with simple queries and add more complexity as needed. Use the execute function to get feedback on intermediate queries. You can go back and forth between knowledge graph searches and SPARQL query executions to refine the query.
4. Use the answer or cancel function to finalize your answer and stop the generation process.

Additional rules:
1. Explain your thought process before and after each step and function call.
2. Do not assume or make up any entities or properties without verifying them with the knowledge graphs and functions.
3. The SPARQL query should always return the actual identifiers / IRIs of the items in its result. It additionally may return labels or other human-readable information, but they are optional and should be put within optional clauses unless explicitly requested by the user.
4. Do not stop early if there are still obvious improvements to be made to the SPARQL query. For example, keep refining your SPARQL query if its result contains irrelevant items or is missing items you expected.
5. Do not perform additional computation (e.g. filtering, sorting, calculations) on the result of the SPARQL query to determine the answer. All computation should be done solely within SPARQL.
6. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
7. Do not use 'SERVICE wikibase:label { bd:serviceParam wikibase:language ...' in SPARQL queries. It is not SPARQL standard and unsupported by the used QLever SPARQL endpoints. Use rdfs:label or similar properties to get labels instead.

[User] Which art movement was Yoko Ono part of?

[Assistant] To answer the question about which art movement Yoko Ono was part of, we need to identify the entity for Yoko Ono and then find the property that links her to an art movement. Let's start by searching for Yoko Ono in the knowledge graph.

[Tool]
Top 10 entity alternatives:
1. Yoko Ono, also known as Yōko Ono, Ono Yōko, Yoko Ono Lennon, Ĭoko Ono, Ono (wd:Q117012): Japanese artist and activist based in the United States, performance artist, conceptual artist, recording artist, peace activist, visual artist, film director, photographer, filmmaker, sculptor, composer, painter, singer
2. Yōko Ōno, also known as Ono Yoko, Youko Ouno, Yoko Ono, Ōno Yōko (wd:Q16264827): Japanese judoka, judoka
3. Yoko Ono (wd:Q1867590): song by Die Ärzte
4. Yōko Ono, also known as Ono Yoko, Youko Ono, Yoko Ono, Ono Yōko (wd:Q11464149): Japanese actress born in 1954, actor, seiyū
5. Ono, Yoko (wd:Q103989374): encyclopedia article
6. Yoko Ono/Plastic Ono Band (wd:Q507672): album
7. Plastic Ono Band, also known as Plastic Ono Elephant's Memory Band, Plastic Ono Nuclear Band, Plastic U.F. Ono Band, The Plastic Ono Band, Yoko Ono Plastic Ono Band (wd:Q731721): UK musical group; rock band
8. Be My Yoko Ono (wd:Q4875557): 1992 single by Barenaked Ladies
9. Yoko Ono Lennon Centre, also known as Tung Auditorium (wd:Q130417289): teaching and performance facility in Liverpool
10. John Lennon and Yoko Ono, also known as Yoko & John, Yoko Ono & John Lennon, Yoko Ono and John Lennon, John & Yoko, John Lennon & Yoko Ono (wd:Q47122619): musical duo; couple

[Assistant] The top result for Yoko Ono is `wd:Q117012`, which describes her as a Japanese artist and activist based in the United States. This is the correct entity for Yoko Ono. Next, we need to find the property that links her to an art movement. Let's search for properties related to "art movement" or "movement" to see if we can find a relevant property.

[Tool]
Top 10 property alternatives:
1. movement, also known as subculture, trend, scientific movement, school, philosophical movement (wd:P135 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): literary, artistic, scientific or philosophical movement or scen...ed with this person or work. For political ideologies use P1142.
2. form of creative work, also known as work type, work form, type of work, artform, type of reference work (wd:P7937 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): structure of a creative work
3. genre, also known as genre of music, type of music, type of film, music genre, literary genre (wd:P136 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): creative work's genre or an artist's field of work (P101). Use main subject (P921) to relate creative works to their topic
4. main subject, also known as primary topic, primary subject, primary keyword, plot keyword, of (wd:P921 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): primary topic of a work (see also P180: depicts), depicts
5. expression, gesture or body pose, also known as action, expression, facial expression, gesture, movement (wd:P6022 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): qualifier on "depicts" (P180) for human position of an individual in an artwork
6. creator, also known as created by, visual artist, sculptor, sculpted by, realized by (wd:P170 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): maker of this creative work or other object (where no more specific property exists)
7. notable work, also known as artwork, wrote, writer of, works, work (wd:P800 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): notable scientific, artistic or literary work, or other work of significance among subject's works
8. after a work by, also known as inspirational artist, inspiring artist, copy of a work by, based on a work by, artist who inspired this (wd:P1877 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): artist whose work strongly inspired / was copied in this item, significant person
9. art name, also known as artistic name, hao, hiệu (wd:P1787 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): type of pseudonym of modern and historical artists as well as sp...ically traditionally adopted by writers and artists in East Asia
10. exhibited creator, also known as artist exhibited, exhibited artist (wd:P10661 as pqv/psn/psv/pqn/prn/pq/prv/p/ps/wdt/wdtn/pr): intellectual or creative entity (person or organization) responsible for the elements exhibited, creator

[Assistant]
The property `wd:P135` (movement) seems to be the most relevant for linking an artist to an art movement. Let's check if Yoko Ono is linked to any art movements through this property.

I will execute a SPARQL query to find the art movement associated with Yoko Ono.

```sparql
SELECT ?movement WHERE {
  wd:Q117012 wdt:P135 ?movement .
}
```

Let's run this query.

[Tool]
Got 1 row and 1 column:
| ?movement           |
| ------------------- |
| Fluxus (wd:Q108908) |

[Assistant]
The SPARQL query returned that Yoko Ono was part of the Fluxus art movement. To provide a more complete answer, let's also include the label of the art movement in the result.

Here is the final SPARQL query:

```sparql
SELECT ?movement ?movementLabel WHERE {
  wd:Q117012 wdt:P135 ?movement .
  OPTIONAL { ?movement rdfs:label ?movementLabel . FILTER(LANG(?movementLabel) = "en") }
}
```

Let's execute this query to get the label of the art movement.

[Tool]
Got 1 row and 2 columns:
| ?movement           | ?movementLabel |
| ------------------- | -------------- |
| Fluxus (wd:Q108908) | Fluxus         |

[Assistant]
The SPARQL query has confirmed that Yoko Ono was part of the Fluxus art movement. Here is the final answer:

Yoko Ono was part of the **Fluxus** art movement.

Final SPARQL query:
```sparql
SELECT ?movement ?movementLabel WHERE {
  wd:Q117012 wdt:P135 ?movement .
  OPTIONAL { ?movement rdfs:label ?movementLabel . FILTER(LANG(?movementLabel) = "en") }
}
```

Answer:
Yoko Ono was part of the Fluxus art movement.

[Tool] Stopped generation process