PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT DISTINCT ?college WHERE {
  wd:Q134262 wdt:P69 ?college .
  ?college wdt:P31 / wdt:P279 * wd:Q38723 .
}

Got 4 rows and 1 column:
| college                                         |
| ----------------------------------------------- |
| University of Iowa (wd:Q182973)                 |
| University of Missouri (wd:Q579968)             |
| The New School (wd:Q599316)                     |
| Washington University in St. Louis (wd:Q777403) |

Tennessee Williams attended the following colleges and universities: The New School, University of Iowa, Washington University in St. Louis, and University of Missouri.

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

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

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

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

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

[User] where did tennessee williams go to college?

[Assistant] Let's start by looking at some similar examples.

[Tool]
Example 1:
Question:
where did tennessee williams live?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q134262 wdt:P551 ?x .
}

Using entities:
Tennessee Williams, also known as Thomas Lanier Williams III, Thomas Williams (wd:Q134262): American playwright (1911–1983), screenwriter, playwright, novelist, writer, poet

Using properties:
residence, also known as living in, place of residence, resided at, resided in, resident in (wd:P551 as wdt): the place where the person is or has been, resident, location

Example 2:
Question:
where did brian williams go to college?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q459695 wdt:P69 ?x .
  ?x wdt:P31 / wdt:P279 * wd:Q38723 .
}

Using entities:
higher education institution, also known as HEI, Higher Education school, institution of higher education, Superior Institute (wd:Q38723): institution that provides higher education (e.g., universities, ...es, liberal arts colleges, institutes of technology, and others)
Brian Williams (wd:Q459695): American TV news anchor, television presenter, editor-in-chief, news presenter, editing staff, journalist, blogger

Using properties:
subclass of, also known as rdfs:subClassOf, sort of, subcategory of, subgenre of, subtype of (wd:P279 as wdt): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
educated at, also known as graduated from, graduate of, place of education, school attended, schooled at (wd:P69 as wdt): educational institution attended by subject, affiliation

Example 3:
Question:
where did buzz williams go to college?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q5003559 wdt:P69 ?x .
  ?x wdt:P31 / wdt:P279 * wd:Q38723 .
}

Using entities:
higher education institution, also known as HEI, Higher Education school, institution of higher education, Superior Institute (wd:Q38723): institution that provides higher education (e.g., universities, ...es, liberal arts colleges, institutes of technology, and others)
Buzz Williams (wd:Q5003559): American basketball coach, basketball coach

Using properties:
subclass of, also known as rdfs:subClassOf, sort of, subcategory of, subgenre of, subtype of (wd:P279 as wdt): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
educated at, also known as graduated from, graduate of, place of education, school attended, schooled at (wd:P69 as wdt): educational institution attended by subject, affiliation

[Assistant] From the examples, we can see that the property `educated at` (P69) is used to indicate the educational institutions a person has attended. We'll need to identify Tennessee Williams in the knowledge graph and then use the `educated at` property to find out where he went to college.

[Tool]
Top 10 entity alternatives:
1. Tennessee Williams, also known as Thomas Lanier Williams III, Thomas Williams (wd:Q134262): American playwright (1911–1983), screenwriter, playwright, novelist, writer, poet
2. Tennessee Williams (wd:Q55896325)
3. Tennessee Williams (wd:Q47513341): painting by Buffie Johnson
4. Tennessee Williams (wd:Q47510960): painting by Bernard Safran
5. Tennessee Williams (wd:Q33979787): scientific article
6. Williamsport, also known as Williamsport, Tennessee, Williamsport, TN (wd:Q8021260): unincorporated community in Maury County, Tennessee
7. allée Tennessee Williams (wd:Q111588984): alley in Thiais, France
8. Tennessee Williams Canal (wd:Q49269468): canal in St. James Parish, Louisiana, United States of America
9. NOTEBOOKS: TENNESSEE WILLIAMS (wd:Q113218921): scientific article published on 19 May 2022
10. NOTEBOOKS: TENNESSEE WILLIAMS (wd:Q113214712): scientific article published on 05 February 2022

[Assistant]
The first result, `Tennessee Williams` (wd:Q134262), seems to be the correct entity for the American playwright. Now, let's use the `educated at` property (P69) to find out where he went to college.

Let's run a SPARQL query to get this information.

[Tool]
Got 6 rows and 1 column:
| college                                                |
| ------------------------------------------------------ |
| University of Iowa (wd:Q182973)                        |
| University of Missouri (wd:Q579968)                    |
| The New School (wd:Q599316)                            |
| Soldan International Studies High School (wd:Q7557201) |
| Washington University in St. Louis (wd:Q777403)        |
| University City High School (wd:Q7894583)              |

[Assistant]
The query returned several educational institutions that Tennessee Williams attended. To provide a clear and concise answer, we can list these institutions. However, it might be useful to filter out high schools and focus only on colleges and universities.

Let's refine the query to include only higher education institutions.

[Tool]
Got 7 rows and 1 column:
| college                                         |
| ----------------------------------------------- |
| The New School (wd:Q599316)                     |
| University of Iowa (wd:Q182973)                 |
| Washington University in St. Louis (wd:Q777403) |
| University of Missouri (wd:Q579968)             |
| University of Iowa (wd:Q182973)                 |
| University of Missouri (wd:Q579968)             |
| The New School (wd:Q599316)                     |

[Assistant]
The refined query has returned the higher education institutions that Tennessee Williams attended. There are some duplicates, but the distinct institutions are:

- The New School (wd:Q599316)
- University of Iowa (wd:Q182973)
- Washington University in St. Louis (wd:Q777403)
- University of Missouri (wd:Q579968)

Now, let's provide the final answer.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The provided SPARQL query correctly identifies all the institutions where Tennessee Williams studied. The query is well-structured and returns the expected results. No further refinements are necessary.


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT DISTINCT ?coordinates WHERE {
  wd:Q8321 wdt:P625 ?coordinates .
}

The Illinois River is located at the following coordinates: 1. POINT(-90.454200 38.970300) 2. POINT(-88.260300 41.393600)

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

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

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

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

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

[User] where is the illinois river on a map?

[Assistant] Let's start by looking at some similar examples.

[Tool]
Example 1:
Question:
what are the biggest cities in illinois?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?x wdt:P31 / wdt:P279 * wd:Q515 ; wdt:P131 + wd:Q1204 ; wdt:P1082 ?y .
}
ORDER BY DESC ( ?y )
LIMIT 1

Using entities:
Illinois, also known as The Prairie State, Illinois, United States, The Land of Lincoln, US-IL, Prairie State (wd:Q1204): state of the United States of America
city, also known as cities (wd:Q515): large human settlement

Using properties:
population, also known as inhabitants, human population (wd:P1082 as wdt): number of people inhabiting the place; number of people of subject
located in the administrative territorial entity, also known as is in the settlement of, is in the voivodeship of, is in the shire of, is in the state of, is in the territory of (wd:P131 as wdt): the item is located on the territory of the following administra...if the item falls only partially into the administrative entity., location, part of
subclass of, also known as rdfs:subClassOf, sort of, subcategory of, subgenre of, subtype of (wd:P279 as wdt): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)

Example 2:
Question:
where's the hudson river?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q3140 wdt:P131 ?x .
}

Using entities:
Hudson River, also known as Hudson (wd:Q3140): river in New York State, United States

Using properties:
located in the administrative territorial entity, also known as is in the settlement of, is in the voivodeship of, is in the shire of, is in the state of, is in the territory of (wd:P131 as wdt): the item is located on the territory of the following administra...if the item falls only partially into the administrative entity., location, part of

Example 3:
Question:
what states does the missouri river touch?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q5419 wdt:P131 + ?x .
  ?x wdt:P31 / wdt:P279 * wd:Q107390 .
}

Using entities:
federated state, also known as state in a federation, state of a federation, state, federal state, constituent state (wd:Q107390): territorial and constitutional community forming part of a federal union
Missouri River, also known as Missouri, The Missouri (wd:Q5419): major river in central United States

Using properties:
located in the administrative territorial entity, also known as is in the settlement of, is in the voivodeship of, is in the shire of, is in the state of, is in the territory of (wd:P131 as wdt): the item is located on the territory of the following administra...if the item falls only partially into the administrative entity., location, part of
subclass of, also known as rdfs:subClassOf, sort of, subcategory of, subgenre of, subtype of (wd:P279 as wdt): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)

[Assistant] Based on the examples, we need to find the entity for the Illinois River and then use the `located in the administrative territorial entity` property to find where it is located. Let's first search for the entity of the Illinois River.

[Tool]
Top 10 entity alternatives:
1. Illinois River (wd:Q8321): Illinois tributary of the Mississippi River in the United States
2. Illinois River (wd:Q1309881): tributary of the Rogue River in Curry and Josephine counties in Oregon, United States
3. Illinois River (wd:Q5999628): Colorado tributary of the Michigan River
4. Illinois River (wd:Q5999623): river in Arkansas and Oklahoma
5. Chicago River, also known as Chicago river, Chicago (river, Illinois), Chicago River main stem, Chicago River (United States) (wd:Q643284): river in Chicago, Illinois, United States
6. Wood River, also known as Wood River, IL, Wood River (Illinois), Wood River, Illinois (wd:Q185601): city in Illinois, United States
7. River Forest, also known as River Forest, IL, River Forest, Illinois (wd:Q2299339): village in Cook County, Illinois, United States
8. River Grove, also known as River Grove, IL, River Grove, Illinois (wd:Q783421): village in Cook County, Illinois, United States of America
9. Apple River, also known as Apple River, Illinois (wd:Q621346): human settlement in Illinois, United States of America
10. Riverside, also known as Riverside, Illinois, Riverside IL, Riverside, IL (wd:Q2462183): village in Cook County, Illinois, United States

[Assistant]
The top result for the Illinois River is `wd:Q8321`, which is the Illinois tributary of the Mississippi River in the United States. Now, let's use the `located in the administrative territorial entity` property (`wdt:P131`) to find where the Illinois River is located.

I will start with a simple query to get the immediate administrative territorial entity where the Illinois River is located.

[Tool]
Got 1 row and 1 column:
| location            |
| ------------------- |
| Illinois (wd:Q1204) |

[Assistant]
The Illinois River is located in Illinois (wd:Q1204). However, to provide a more precise location on a map, we might want to look for the coordinates of the river. Let's check if there is a property that provides the geographic coordinates of the Illinois River.

I will search for the property `coordinate location` to see if it applies to the Illinois River.

[Tool]
Top 10 property alternatives:
1. coordinate location, also known as point on a map, gps coordinates, gps location, latitude, location (wd:P625 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): geocoordinates of the subject. For Earth, please note that only WGS84 coordinating system is supported at the moment
2. this zoological name is coordinate with, also known as coordinate name (wd:P2743 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): links coordinate zoological names, different from
3. coordinates of depicted place, also known as depicted item geolocation, geolocation of object, object geolocation (wd:P9149 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): coordinates of the place depicted in the work (or Commons image)
4. location map (wd:P1943 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): blank geographic map image suitable for overlaying coordinate points, as defined at c:Category:Location maps, image
5. genomic start, also known as start coordinate, genomic from, Genloc From, from, Genloc Start (wd:P644 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): genomic starting coordinate of the biological sequence (e.g. a gene)
6. set in environment, also known as set in location, takes place in, setting location, playing in, narrative set in (wd:P8411 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): environment (geographic feature, building type, etc.) the narrative work is set in, significant place, depicts
7. narrative location, also known as set in location, set in, setting, setting location, setting of work (wd:P840 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): the narrative of the work is set in this location, significant place, depicts
8. location of discovery, also known as found in, location of discovered object, place of discovery, site of discovery, source (wd:P189 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): where the item was located when discovered, significant place
9. spatial reference system, also known as coordinate reference system, CRS, SRS (wd:P3037 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): frame of reference used to produce a map
10. coordinates of geographic center, also known as coordinates of geographic centre, geographic centre, geographic center, geographical midpoint, geographical centre (wd:P5140 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): coordinates of the center of an area. Use qualifier "determination method" (P459) to indicate how, coordinate location

[Assistant]
The top result for the property search is `coordinate location` (wd:P625), which provides the geocoordinates of the subject. Let's use this property to get the coordinates of the Illinois River.

I will execute a SPARQL query to retrieve the coordinates of the Illinois River.

[Tool]
Got 2 rows and 1 column:
| coordinates                                  |
| -------------------------------------------- |
| POINT(-90.454200 38.970300) (geo:wktLiteral) |
| POINT(-88.260300 41.393600) (geo:wktLiteral) |

[Assistant]
The Illinois River has two sets of coordinates:

1. `POINT(-90.454200 38.970300)`
2. `POINT(-88.260300 41.393600)`

These coordinates represent different points along the river. To provide a more complete answer, we can include both coordinates in the final SPARQL query.

Here is the final SPARQL query to answer the question "Where is the Illinois River on a map?":

```sparql
SELECT DISTINCT ?coordinates WHERE {
  wd:Q8321 wdt:P625 ?coordinates .
}
```

This query returns the coordinates of the Illinois River, which can be used to locate it on a map.

Final answer:
The Illinois River is located at the following coordinates:
1. `POINT(-90.454200 38.970300)`
2. `POINT(-88.260300 41.393600)`

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query correctly retrieves the coordinates of the Illinois River from Wikidata, and the execution result provides the expected points on a map.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT DISTINCT ?work WHERE {
  ?work wdt:P161 wd:Q587156 .
}

Got 12 rows and 1 column, showing the first 5 and last 5 rows below:
| work                                               |
| -------------------------------------------------- |
| The Car (wd:Q1197355)                              |
| Escape to Witch Mountain (wd:Q1212511)             |
| Return from Witch Mountain (wd:Q1925695)           |
| Raid on Entebbe (wd:Q2305094)                      |
| Tuff Turf (wd:Q2362823)                            |
| ...                                                |
| Black Snake Moan (wd:Q468660)                      |
| Hello, Larry (wd:Q5708520)                         |
| No Deposit, No Return (wd:Q7044096)                |
| The Real Housewives of Beverly Hills (wd:Q7759591) |
| Race to Witch Mountain (wd:Q922006)                |

Kim Richards has been in the following works: The Car, Escape to Witch Mountain, Return from Witch Mountain, Raid on Entebbe, Tuff Turf, Black Snake Moan, Hello, Larry, No Deposit, No Return, The Real Housewives of Beverly Hills, and Race to Witch Mountain.

[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 was kim richards in?

[Assistant] Let's start by looking at some similar examples.

[Tool]
Example 1:
Question:
what has lucy hale played in?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?x wdt:P161 wd:Q229940 ; wdt:P31 / wdt:P279 * wd:Q11424 .
}

Using entities:
film, also known as cinematic work, moving picture, movie, motion pictures, motion picture (wd:Q11424): sequence of images that give the impression of movement, stored on film stock
Lucy Hale, also known as Lucy Kate Hale, Karen Lucille Hale (wd:Q229940): American actress and singer, television actor, songwriter, film actor, musician, designer, singer, actor, model

Using properties:
cast member, also known as feat., starring (film/play), film starring, starring, actor (wd:P161 as wdt): actor in the subject production [use "character role" (P453) and...use "recorded participant" (P11108) for non-fiction productions], significant person, performer
subclass of, also known as rdfs:subClassOf, sort of, subcategory of, subgenre of, subtype of (wd:P279 as wdt): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)

Example 2:
Question:
what did shawnee smith star in?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?x wdt:P161 wd:Q233313 ; wdt:P31 / wdt:P279 * wd:Q11424 .
}

Using entities:
film, also known as cinematic work, moving picture, movie, motion pictures, motion picture (wd:Q11424): sequence of images that give the impression of movement, stored on film stock
Shawnee Smith, also known as Shawnee Rebecca Smith (wd:Q233313): singer-songwriter, television actor, American actress, stage actor, film actor, presenter, singer, actor

Using properties:
cast member, also known as feat., starring (film/play), film starring, starring, actor (wd:P161 as wdt): actor in the subject production [use "character role" (P453) and...use "recorded participant" (P11108) for non-fiction productions], significant person, performer
subclass of, also known as rdfs:subClassOf, sort of, subcategory of, subgenre of, subtype of (wd:P279 as wdt): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)

Example 3:
Question:
what does kat dennings star in?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?x wdt:P161 wd:Q231751 ; wdt:P31 / wdt:P279 * wd:Q11424 .
}

Using entities:
film, also known as cinematic work, moving picture, movie, motion pictures, motion picture (wd:Q11424): sequence of images that give the impression of movement, stored on film stock
Kat Dennings, also known as Katherine Victoria Litwack (wd:Q231751): American actress (born 1986), television actor, voice actor, film actor, actor

Using properties:
cast member, also known as feat., starring (film/play), film starring, starring, actor (wd:P161 as wdt): actor in the subject production [use "character role" (P453) and...use "recorded participant" (P11108) for non-fiction productions], significant person, performer
subclass of, also known as rdfs:subClassOf, sort of, subcategory of, subgenre of, subtype of (wd:P279 as wdt): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)

[Assistant] Based on the examples, it seems like we need to find the movies or TV shows that Kim Richards has been in. We can achieve this by finding the works where she is listed as a cast member. Let's first identify the entity for Kim Richards.

[Tool]
Top 10 entity alternatives:
1. Kim Richards, also known as Kimberley Richards, Kim Erica Richards (wd:Q587156): American actress and television personality (born 1964), television actor, film actor, actor
2. Kim Richards, also known as Kim Elizabeth Richards (wd:Q44115773): Australian politician, politician
3. Kim Richards, also known as NanoKim, NanoSounds (wd:Q123745576): content creator, Twitch streamer
4. Kim Richards (wd:Q76151754): Peerage person ID=566462
5. Kim Richardson, also known as Kimberly Ann Richardson (wd:Q19668126): Canadian actor and singer, jazz musician, actor
6. Kim Lahaie Richards, also known as Kim Lahaie (wd:Q26276511): American drag racer, crew chief, racing automobile driver
7. Kim Michele Richardson (wd:Q100598285): American writer, literary critic, writer
8. Sibel Kekilli, also known as Dilara, Sybel, Kim (wd:Q63682): German actress, actor
9. Kim, also known as Kim, CO (wd:Q2275070): Statutory Town in Las Animas County, Colorado, United States
10. Kim (wd:Q589868): picaresque novel by Rudyard Kipling

[Assistant]
The correct entity for Kim Richards is the first one, `wd:Q587156`. Now, let's find the movies or TV shows she has been in by using the property `wdt:P161` (cast member).

Let's construct the SPARQL query to find the works where Kim Richards is listed as a cast member.

[Tool]
Got 12 rows and 1 column, showing the first 5 and last 5 rows below:
| work                                               |
| -------------------------------------------------- |
| The Car (wd:Q1197355)                              |
| Escape to Witch Mountain (wd:Q1212511)             |
| Return from Witch Mountain (wd:Q1925695)           |
| Raid on Entebbe (wd:Q2305094)                      |
| Tuff Turf (wd:Q2362823)                            |
| ...                                                |
| Black Snake Moan (wd:Q468660)                      |
| Hello, Larry (wd:Q5708520)                         |
| No Deposit, No Return (wd:Q7044096)                |
| The Real Housewives of Beverly Hills (wd:Q7759591) |
| Race to Witch Mountain (wd:Q922006)                |

[Assistant]
The SPARQL query has successfully returned the works where Kim Richards has been a cast member. Here are the results:

1. The Car (wd:Q1197355)
2. Escape to Witch Mountain (wd:Q1212511)
3. Return from Witch Mountain (wd:Q1925695)
4. Raid on Entebbe (wd:Q2305094)
5. Tuff Turf (wd:Q2362823)
6. Black Snake Moan (wd:Q468660)
7. Hello, Larry (wd:Q5708520)
8. No Deposit, No Return (wd:Q7044096)
9. The Real Housewives of Beverly Hills (wd:Q7759591)
10. Race to Witch Mountain (wd:Q922006)

Now, let's finalize the answer.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query returns all the works in which Kim Richards has appeared, and the human-readable answer lists them accurately.


PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?chineseWritingSystem WHERE {
  VALUES ?chineseWritingSystem {
    wd:Q8201
  }
}

Got 1 row and 1 column:
| chineseWritingSystem          |
| ----------------------------- |
| Chinese characters (wd:Q8201) |

The Chinese writing system is called Chinese characters (wd:Q8201).

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

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

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

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

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

[User] what do you call the chinese writing system?

[Assistant] Let's start by looking at some similar examples.

[Tool]
Example 1:
Question:
what is the official language of china called?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q148 wdt:P37 ?x .
}

Using entities:
People's Republic of China, also known as Zhōngguó, The PRC, RPC, CHN, Red China (wd:Q148): country in East Asia

Using properties:
official language, also known as language spoken, spoken in, speaking, language official (wd:P37 as wdt): language designated as official by this item, language used

Example 2:
Question:
what language do chinese?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q148 wdt:P37 ?x .
}

Using entities:
People's Republic of China, also known as Zhōngguó, The PRC, RPC, CHN, Red China (wd:Q148): country in East Asia

Using properties:
official language, also known as language spoken, spoken in, speaking, language official (wd:P37 as wdt): language designated as official by this item, language used

Example 3:
Question:
what is the currency name of china?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q148 wdt:P38 ?x .
}

Using entities:
People's Republic of China, also known as Zhōngguó, The PRC, RPC, CHN, Red China (wd:Q148): country in East Asia

Using properties:
currency, also known as base currency, used money (wd:P38 as wdt): currency used by item, uses

[Assistant] From the examples, we can see that the Chinese writing system might be represented as a property of the People's Republic of China (wd:Q148). However, the examples only show the official language and currency. Let's search for the entity representing the Chinese writing system directly.

[Tool]
Top 10 entity alternatives:
1. Journal of Chinese Writing Systems (wd:Q58234452): journal
2. Letter-word Phrases and Chinese Writing System (wd:Q120662500): scholarly article in Journal of Xiamen University (Arts & Social Sciences) no. 6, 2010
3. Image-based anti-interference ro...Chinese character writing system (wd:Q129332745): scholarly article
4. Bibliography of the Chinese language and writing system (wd:Q125242720)
5. writing system, also known as wenzi, ven-tsee, script, graphematics (wd:Q8192): any conventional method of visually representing verbal or signed communication
6. Chinese system, also known as Chinese operating system (wd:Q118876973): program that hooks into an operating system to add the ability to display and input CJK characters
7. Conjectures on Creative Writing ...iplinary System of Chinese Style (wd:Q118678085)
8. Chinese-English biliteracy acqui...uage and writing system transfer (wd:Q52040278): scientific article published on 30 December 2004
9. Chinese writing (wd:Q125357998): Book about Chinese palaeography
10. The Chinese-based writing system of the Zhuang language (wd:Q56915490): article

[Assistant] The most relevant entity seems to be "writing system" (wd:Q8192), but it is a general term for any writing system. We need to find a specific entity for the Chinese writing system. Let's try searching again with a more specific query.

[Tool]
Top 10 entity alternatives:
1. Chinese characters, also known as wenzi, ven-tsee, traditional Vietnamese characters, traditional Korean characters, sinographs (wd:Q8201): logographic writing system with Han origin used in the Sinospher...r Chinese, Japanese, Korean and traditional Vietnamese languages
2. Chinese Script* (wd:Q119947963): painting by unknown artist, Division of Archaeology, University of Cambridge
3. simplified Chinese characters, also known as simplified Chinese script, 簡化字, 简化字, simplified Hanzi, simplified Han characters (wd:Q185614): standardized Chinese characters used in Mainland China, Malaysia and Singapore
4. Chinese cursive script, also known as cǎoshū, grass script, cursive script (wd:Q607543): handwriting, traditional calligraphic script style of East Asian orthography
5. Chinese script style, also known as Chinese script styles (wd:Q11059241): style of writing Chinese characters
6. Chinese script reform (wd:Q2894150)
7. Chinese family of scripts, also known as Chinese scripts (wd:Q117285848): family of writing systems descended from the Chinese oracle bone script
8. Design for writing-paper for Chinese script (wd:Q65574237): print in the National Gallery of Art (NGA 54223)
9. Sound and Script in Chinese Diaspora (wd:Q56455917): article
10. Chinese Buddhist Scriptures and Indian Philosophy (wd:Q119434134)

[Assistant] The most relevant entity here is "Chinese characters" (wd:Q8201), which refers to the logographic writing system used in the Sinosphere. Let's confirm this by checking the properties and values associated with this entity.

[Tool]
Got 794 rows and 3 columns, showing the first 10 rows below:
| subject                       | property                            | object                                                         |
| ----------------------------- | ----------------------------------- | -------------------------------------------------------------- |
| Chinese characters (wd:Q8201) | instance of (wdt:P31)               | writing system (wd:Q8192)                                      |
| Chinese characters (wd:Q8201) | language of work or name (wdt:P407) | Japanese (wd:Q5287)                                            |
| Chinese characters (wd:Q8201) | Commons category (wdt:P373)         | Chinese writing                                                |
| Chinese characters (wd:Q8201) | image (wdt:P18)                     | <http://commons.wikimedia.org/wiki/Special:FilePath/Hanzi.svg> |
| Chinese characters (wd:Q8201) | part of (wdt:P361)                  | Chinese (wd:Q7850)                                             |
| Chinese characters (wd:Q8201) | subclass of (wdt:P279)              | writing system (wd:Q8192)                                      |
| Chinese characters (wd:Q8201) | inception (wdt:P571)                | -1250-01-01T00:00:00Z (xsd:dateTime)                           |
| Chinese characters (wd:Q8201) | has part(s) (wdt:P527)              | Chinese character radical (wd:Q849778)                         |
| Chinese characters (wd:Q8201) | country of origin (wdt:P495)        | China (wd:Q29520)                                              |
| Chinese characters (wd:Q8201) | creator (wdt:P170)                  | Cangjie (wd:Q1033199)                                          |
| ...                           | ...                                 | ...                                                            |

[Assistant]
The entity "Chinese characters" (wd:Q8201) is indeed the correct representation of the Chinese writing system. It is classified as a writing system and has various properties that confirm its relevance.

Now, let's construct a simple SPARQL query to retrieve the IRI of the Chinese writing system.

```sparql
SELECT ?writingSystem WHERE {
  wd:Q8201 wdt:P31 ?writingSystem .
}
```

This query will return the IRI of the writing system that "Chinese characters" (wd:Q8201) is an instance of.

Let's execute this query.

[Tool]
Got 3 rows and 1 column:
| writingSystem                            |
| ---------------------------------------- |
| natural writing system (wd:Q29517555)    |
| logographic writing system (wd:Q3953107) |
| writing system (wd:Q8192)                |

[Assistant]
The query returned multiple types of writing systems that "Chinese characters" (wd:Q8201) is an instance of. However, the most specific and relevant type is "logographic writing system" (wd:Q3953107).

To provide a clear and concise answer, we can modify the query to directly return the IRI of "Chinese characters" (wd:Q8201) and optionally include its label for clarity.

Here is the final SPARQL query:

```sparql
SELECT ?chineseWritingSystem ?label WHERE {
  wd:Q8201 wdt:P31 ?chineseWritingSystem .
  OPTIONAL { wd:Q8201 rdfs:label ?label . FILTER(LANG(?label) = "en") }
}
```

Let's execute this query to get the final result.

[Tool]
Got 3 rows and 2 columns:
| chineseWritingSystem                     | label                   |
| ---------------------------------------- | ----------------------- |
| natural writing system (wd:Q29517555)    | Chinese characters (en) |
| logographic writing system (wd:Q3953107) | Chinese characters (en) |
| writing system (wd:Q8192)                | Chinese characters (en) |

[Assistant]
The query returned the IRI of "Chinese characters" (wd:Q8201) along with its label. However, it also included the types of writing systems it is an instance of, which is not what we want. We need to simplify the query to directly return the IRI of the Chinese writing system.

Here is the simplified final SPARQL query:

```sparql
SELECT ?chineseWritingSystem WHERE {
  VALUES ?chineseWritingSystem { wd:Q8201 }
}
```

This query will directly return the IRI of the Chinese writing system.

Let's execute this query to get the final result.

[Tool]
Got 1 row and 1 column:
| chineseWritingSystem          |
| ----------------------------- |
| Chinese characters (wd:Q8201) |

[Assistant]
The query successfully returned the IRI of the Chinese writing system, which is `wd:Q8201`.

Therefore, the Chinese writing system is referred to as "Chinese characters" (wd:Q8201).

Here is the final answer:

```sparql
SELECT ?chineseWritingSystem WHERE {
  VALUES ?chineseWritingSystem { wd:Q8201 }
}
```

The Chinese writing system is called **Chinese characters** (wd:Q8201).

[Tool] Stopped generation process

[User]
Feedback (status=done):
The answer is correct and complete. The SPARQL query returns the IRI of the Chinese writing system, which is what was asked for. No further refinements are necessary.


PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX pq: <http://www.wikidata.org/prop/qualifier/>
PREFIX p: <http://www.wikidata.org/prop/>
PREFIX ps: <http://www.wikidata.org/prop/statement/>
SELECT ?coach WHERE {
  wd:Q330120 p:P286 ?statement .
  ?statement ps:P286 ?coach .
  ?statement pq:P580 ?startTime .
  ?statement pq:P582 ?endTime .
  FILTER ( ?startTime <= '2013-01-01T00:00:00Z' ^^ xsd:dateTime && ?endTime >= '2013-12-31T00:00:00Z' ^^ xsd:dateTime ) .
}

Ron Rivera was the head coach of the Carolina Panthers in 2013.

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

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

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

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

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

[User] who will coach carolina panthers in 2013?

[Assistant] Let's start by looking at some similar examples.

[Tool]
Example 1:
Question:
what is the name of the pittsburgh steelers head coach?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q191477 wdt:P286 ?x .
}

Using entities:
Pittsburgh Steelers, also known as Pittsburgh Pirates, Rooneymen, Steelers (wd:Q191477): professional American football team based in Pittsburgh, Pennsylvania

Using properties:
head coach, also known as team manager, trainer, senior coach, manager, coached by (wd:P286 as wdt): on-field manager or head coach of a sports club (not to be confu...eneral manager P505, which is not a coaching position) or person

Example 2:
Question:
who is the head coach of the chicago blackhawks?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q209636 wdt:P286 ?x .
}

Using entities:
Chicago Blackhawks, also known as Chicago Black Hawks, Hawks, Chicago Stadium Corporation (wd:Q209636): hockey team of the National Hockey League

Using properties:
head coach, also known as team manager, trainer, senior coach, manager, coached by (wd:P286 as wdt): on-field manager or head coach of a sports club (not to be confu...eneral manager P505, which is not a coaching position) or person

Example 3:
Question:
who has coached the baltimore ravens?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q276539 wdt:P286 ?x .
}

Using entities:
Baltimore Ravens (wd:Q276539): National Football League franchise in Baltimore, Maryland

Using properties:
head coach, also known as team manager, trainer, senior coach, manager, coached by (wd:P286 as wdt): on-field manager or head coach of a sports club (not to be confu...eneral manager P505, which is not a coaching position) or person

[Assistant] Based on the examples, we need to find the entity for the Carolina Panthers and the property for the head coach. Let's start by searching for the Carolina Panthers entity.

[Tool]
Top 10 entity alternatives:
1. Carolina Panthers (wd:Q330120): American football team
2. history of the Carolina Panthers, also known as Carolina Panthers history, the Carolina Panthers history (wd:Q5869201): aspect of history
3. Carolina Panthers Cheerleaders (wd:Q130385213): cheerleading squad of the Carolina Panthers
4. 2021 Carolina Panthers season (wd:Q104869609): 27th season in franchise history
5. 2022 Carolina Panthers season (wd:Q110445399): 28th season in franchise history
6. 2020 Carolina Panthers season (wd:Q80995958): 26th season in franchise history
7. 2018 Carolina Panthers season (wd:Q48843395): 24th season in franchise history
8. 2015 Carolina Panthers season (wd:Q18701993): NFL team 21st season(lost Super Bowl)
9. 2019 Carolina Panthers season (wd:Q60525825): 25th season in franchise history
10. 2023 Carolina Panthers season (wd:Q116170055): 29th season in franchise history

[Assistant]
The first result, `wd:Q330120`, seems to be the correct entity for the Carolina Panthers. Next, let's confirm the property for the head coach, which is likely `wdt:P286`.

Now, let's construct a simple SPARQL query to find the head coach of the Carolina Panthers in 2013.

[Tool]
Got 1 row and 1 column:
| coach                       |
| --------------------------- |
| Dave Canales (wd:Q47456134) |

[Assistant]
It looks like the query returned a coach, but it seems to be incorrect for the year 2013. We need to filter the results to only include the coach in 2013. Let's check the qualifiers for the head coach property to see if there is a specific year associated with it.

We can use the `list` function to inspect the triples for the Carolina Panthers and the head coach property.

[Tool]
Got 1 row and 3 columns:
| subject                        | property              | object                      |
| ------------------------------ | --------------------- | --------------------------- |
| Carolina Panthers (wd:Q330120) | head coach (wdt:P286) | Dave Canales (wd:Q47456134) |

[Assistant]
The list function did not provide any qualifiers for the head coach property. However, we can try to filter the results by adding a time constraint directly in the SPARQL query. We will use the `pq:P580` (start time) and `pq:P582` (end time) qualifiers to filter the results for the year 2013.

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

[Tool] Got no rows and 1 columns

[Assistant]
It seems that the query did not return any results, which suggests that the head coach information for the Carolina Panthers in 2013 might not be structured with explicit start and end times in the way we expected.

Let's try another approach by searching for the head coach of the Carolina Panthers specifically for the year 2013. We can use the `search_object_of_property` function to look for the head coach of the Carolina Panthers and then manually verify the year.

First, let's search for the head coach of the Carolina Panthers.

[Tool]
No entity items found

No other items found

No literal items found

[Assistant]
It seems that the search did not return any results, which indicates that the head coach information for the Carolina Panthers might not be directly linked in the way we expected.

Given this, let's try a different approach by searching for the head coach of the Carolina Panthers and then manually checking the year. We can use the `list` function to inspect the triples for the Carolina Panthers and the head coach property again, but this time we will look for any additional information that might help us filter by year.

Let's try the `list` function again with more details.

[Tool]
Got 8 rows and 3 columns:
| subject                        | property            | object                                         |
| ------------------------------ | ------------------- | ---------------------------------------------- |
| Carolina Panthers (wd:Q330120) | head coach (p:P286) | s:Q330120-2f5b688c-4701-ae35-ef29-51ef69c9de92 |
| Carolina Panthers (wd:Q330120) | head coach (p:P286) | s:Q330120-48e97c3f-4363-828d-db0f-94a68ca39086 |
| Carolina Panthers (wd:Q330120) | head coach (p:P286) | s:Q330120-736700dd-49fa-7abe-2a02-a38de36334ad |
| Carolina Panthers (wd:Q330120) | head coach (p:P286) | s:Q330120-901db853-45eb-301d-3bb0-b14edb6a9c88 |
| Carolina Panthers (wd:Q330120) | head coach (p:P286) | s:Q330120-997ea6c1-4d3d-292e-addd-bbd89937d2c0 |
| Carolina Panthers (wd:Q330120) | head coach (p:P286) | s:Q330120-9f179107-47e5-69c1-ce60-3ecf15100852 |
| Carolina Panthers (wd:Q330120) | head coach (p:P286) | s:Q330120-c511c1f0-48ea-054e-a3bc-89f44d9ff9cc |
| Carolina Panthers (wd:Q330120) | head coach (p:P286) | s:Q330120-eb409d3a-4f10-6ae9-925d-a730cc610f68 |

[Assistant]
The `list` function returned several statements for the head coach of the Carolina Panthers. Each statement has a unique identifier. We need to inspect these statements to find the one that corresponds to the year 2013.

Let's inspect each statement to see if it has a start and end time qualifier. We will use the `list` function again, but this time we will focus on the specific statements.

Let's start with the first statement: `s:Q330120-2f5b688c-4701-ae35-ef29-51ef69c9de92`.

[Tool]
Got 8 rows and 3 columns:
| subject                                        | property              | object                                       |
| ---------------------------------------------- | --------------------- | -------------------------------------------- |
| s:Q330120-2f5b688c-4701-ae35-ef29-51ef69c9de92 | start time (pq:P580)  | 2011-01-01T00:00:00Z (xsd:dateTime)          |
| s:Q330120-2f5b688c-4701-ae35-ef29-51ef69c9de92 | end time (pq:P582)    | 2019-01-01T00:00:00Z (xsd:dateTime)          |
| s:Q330120-2f5b688c-4701-ae35-ef29-51ef69c9de92 | head coach (ps:P286)  | Ron Rivera (wd:Q1991889)                     |
| s:Q330120-2f5b688c-4701-ae35-ef29-51ef69c9de92 | wikibase:rank         | wikibase:NormalRank                          |
| s:Q330120-2f5b688c-4701-ae35-ef29-51ef69c9de92 | rdf:type              | wikibase:Statement                           |
| s:Q330120-2f5b688c-4701-ae35-ef29-51ef69c9de92 | prov:wasDerivedFrom   | ref:e36f8c9d1bd5f4938eddd0f605ee623cff177615 |
| s:Q330120-2f5b688c-4701-ae35-ef29-51ef69c9de92 | start time (pqv:P580) | v:19fb1e607fb895edac44d92bd9141272           |
| s:Q330120-2f5b688c-4701-ae35-ef29-51ef69c9de92 | end time (pqv:P582)   | v:1c9551a398cfb51623c28b80416004d3           |

[Assistant]
The first statement `s:Q330120-2f5b688c-4701-ae35-ef29-51ef69c9de92` shows that Ron Rivera was the head coach of the Carolina Panthers from January 1, 2011, to January 1, 2019. This period includes the year 2013.

Therefore, Ron Rivera was the head coach of the Carolina Panthers in 2013.

Let's finalize the SPARQL query to confirm this result and provide the answer.

```sparql
SELECT ?coach WHERE {
  wd:Q330120 p:P286 ?statement .
  ?statement ps:P286 ?coach .
  ?statement pq:P580 ?startTime .
  ?statement pq:P582 ?endTime .
  FILTER (?startTime <= '2013-01-01T00:00:00Z'^^xsd:dateTime && ?endTime >= '2013-12-31T00:00:00Z'^^xsd:dateTime) .
}
```

Executing this query will confirm our findings.

[Tool]
Got 1 row and 1 column:
| coach                    |
| ------------------------ |
| Ron Rivera (wd:Q1991889) |

[Assistant]
The SPARQL query confirms that Ron Rivera was the head coach of the Carolina Panthers in 2013.

Here is the final SPARQL query and the answer:

```sparql
SELECT ?coach WHERE {
  wd:Q330120 p:P286 ?statement .
  ?statement ps:P286 ?coach .
  ?statement pq:P580 ?startTime .
  ?statement pq:P582 ?endTime .
  FILTER (?startTime <= '2013-01-01T00:00:00Z'^^xsd:dateTime && ?endTime >= '2013-12-31T00:00:00Z'^^xsd:dateTime) .
}
```

The head coach of the Carolina Panthers in 2013 was Ron Rivera.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query effectively retrieves the head coach of the Carolina Panthers for the year 2013, and the answer provided is accurate.


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 DISTINCT ?spouse ?spouseLabel WHERE {
  wd:Q42869 wdt:P26 ?spouse .
  OPTIONAL {
    ?spouse rdfs:label ?spouseLabel .
    FILTER ( LANG( ?spouseLabel ) = 'en' )
  }
}

Got 5 rows and 2 columns:
| spouse                          | spouseLabel             |
| ------------------------------- | ----------------------- |
| Erika Koike (wd:Q109355321)     | Erika Koike (en)        |
| Riko Shibata (wd:Q109355504)    | Riko Shibata (en)       |
| Alice Kim Cage (wd:Q12606061)   | Alice Kim Cage (en)     |
| Patricia Arquette (wd:Q215976)  | Patricia Arquette (en)  |
| Lisa Marie Presley (wd:Q237324) | Lisa Marie Presley (en) |

Nicolas Cage has been married to Erika Koike, Riko Shibata, Alice Kim Cage, Patricia Arquette, and Lisa Marie Presley.

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

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

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

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

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

[User] who is nicolas cage married too?

[Assistant] Let's start by looking at some similar examples.

[Tool]
Example 1:
Question:
who did corey feldman married?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q375419 wdt:P26 ?x .
}

Using entities:
Corey Feldman (wd:Q375419): American actor and musician, television producer, television actor, film producer, film director, voice actor, stage actor, film actor, composer, singer, actor

Using properties:
spouse, also known as married partner, married to, marry, partner, spouses (wd:P26 as wdt): the subject has the object as their spouse (husband, wife, partn...etc.). Use "unmarried partner" (P451) for non-married companions, significant person, relative

Example 2:
Question:
who tom cruise been married to?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q37079 wdt:P26 ?x .
}

Using entities:
Tom Cruise, also known as Thomas Cruise Mapother IV (wd:Q37079): American actor and producer (born 1962), stunt performer, aircraft pilot, film producer, film director, screenwriter, film actor, producer, director, writer, actor

Using properties:
spouse, also known as married partner, married to, marry, partner, spouses (wd:P26 as wdt): the subject has the object as their spouse (husband, wife, partn...etc.). Use "unmarried partner" (P451) for non-married companions, significant person, relative

Example 3:
Question:
who did scarlett johansson married?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q34436 wdt:P26 ?x .
}

Using entities:
Scarlett Johansson, also known as Scarlett Ingrid Johansson (wd:Q34436): American actress (born 1984), recording artist, film director, stage actor, voice actor, film actor, singer, model, actor

Using properties:
spouse, also known as married partner, married to, marry, partner, spouses (wd:P26 as wdt): the subject has the object as their spouse (husband, wife, partn...etc.). Use "unmarried partner" (P451) for non-married companions, significant person, relative

[Assistant] Based on the examples, we need to find Nicolas Cage's spouse. Let's first search for Nicolas Cage's entity.

[Tool]
Top 10 entity alternatives:
1. Nicolas Cage, also known as Nicolas Coppola, Nicolas Kim Coppola, Nic Cage (wd:Q42869): television presenter, television producer, executive producer, character actor, American actor, film director, film producer, screenwriter, voice actor, film actor, director, writer, actor
2. Nicolas Cage/Bobby Brown, also known as Saturday Night Live S18E1, Saturday Night Live, season 18 episode 1 (wd:Q110035758): episode of Saturday Night Live (S18 E1)
3. Weekend Update: Nicolas Cage (wd:Q110060114): sketch from Saturday Night Live
4. Cage's Kiss: The Nicolas Cage Podcast (wd:Q110580467): podcast
5. Cage Rage - A Nicolas Cage Podcast (wd:Q110575223): podcast
6. Weekend Update: Nicolas Cage and Bradley Cooper (wd:Q110060014): sketch from Saturday Night Live
7. cage (wd:Q129211): enclosure used to confine, contain or protect something or someone
8. China Zorrilla, also known as Nicolás (wd:Q458175): Uruguayan actress (1922–2014), stage actor, film actor, actor
9. Frida Kalho, also known as Nicolás (wd:Q458175): Uruguayan actress (1922–2014), stage actor, film actor, actor
10. The Cage, also known as Cage, Cage, The, Pilot: The Cage (wd:Q20909): 1986 episode of Star Trek: The Original Series directed by Robert Butler

[Assistant] The correct entity for Nicolas Cage is `wd:Q42869`. Now let's use the property `wdt:P26` to find out who he is married to.

[Tool]
Got 5 rows and 1 column:
| spouse                          |
| ------------------------------- |
| Erika Koike (wd:Q109355321)     |
| Riko Shibata (wd:Q109355504)    |
| Alice Kim Cage (wd:Q12606061)   |
| Patricia Arquette (wd:Q215976)  |
| Lisa Marie Presley (wd:Q237324) |

[Assistant]
Nicolas Cage has been married to multiple people. Here are the individuals he has been married to:

- Erika Koike (wd:Q109355321)
- Riko Shibata (wd:Q109355504)
- Alice Kim Cage (wd:Q12606061)
- Patricia Arquette (wd:Q215976)
- Lisa Marie Presley (wd:Q237324)

To provide a complete answer, let's include the labels of these individuals in the final SPARQL query.

[Tool]
Got 5 rows and 2 columns:
| spouse                          | spouseLabel             |
| ------------------------------- | ----------------------- |
| Erika Koike (wd:Q109355321)     | Erika Koike (en)        |
| Riko Shibata (wd:Q109355504)    | Riko Shibata (en)       |
| Alice Kim Cage (wd:Q12606061)   | Alice Kim Cage (en)     |
| Patricia Arquette (wd:Q215976)  | Patricia Arquette (en)  |
| Lisa Marie Presley (wd:Q237324) | Lisa Marie Presley (en) |

[Assistant]
Nicolas Cage has been married to the following individuals:

- Erika Koike (wd:Q109355321)
- Riko Shibata (wd:Q109355504)
- Alice Kim Cage (wd:Q12606061)
- Patricia Arquette (wd:Q215976)
- Lisa Marie Presley (wd:Q237324)

Here is the final SPARQL query to answer the question:

```sparql
SELECT DISTINCT ?spouse ?spouseLabel WHERE {
  wd:Q42869 wdt:P26 ?spouse .
  OPTIONAL { ?spouse rdfs:label ?spouseLabel . FILTER(LANG(?spouseLabel) = 'en') }
}
```

The answer to the question "Who is Nicolas Cage married to?" is:

- Erika Koike
- Riko Shibata
- Alice Kim Cage
- Patricia Arquette
- Lisa Marie Presley

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

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query returns all the individuals Nicolas Cage has been married to, along with their labels. The query follows the rules and the answer is accurate.


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?birthPlace WHERE {
  wd:Q206939 wdt:P19 ?birthPlace .
}

Got 1 row and 1 column:
| birthPlace      |
| --------------- |
| London (wd:Q84) |

Elvis Costello was born in London.

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

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

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

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

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

[User] where was elvis costello born?

[Assistant] Let's start by looking at some similar examples.

[Tool]
Example 1:
Question:
what city did elvis presley grew up in?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q303 wdt:P19 ?x .
}

Using entities:
Elvis Presley, also known as The King of Rock'n'Roll, Elvis A. Presley, King of Rock'n'Roll, Elvis, Elvis Aaron Presley (wd:Q303): American singer and actor (1935–1977), military personnel, military officer, philanthropist, film actor, composer, activist, singer, actor

Using properties:
place of birth, also known as POB, location of birth, location born, born in, birthplace (wd:P19 as wdt): most specific known birth location of a person, animal or fictional character, location

Example 2:
Question:
where did elvis presley started his career?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q303 wdt:P19 ?x .
}

Using entities:
Elvis Presley, also known as The King of Rock'n'Roll, Elvis A. Presley, King of Rock'n'Roll, Elvis, Elvis Aaron Presley (wd:Q303): American singer and actor (1935–1977), military personnel, military officer, philanthropist, film actor, composer, activist, singer, actor

Using properties:
place of birth, also known as POB, location of birth, location born, born in, birthplace (wd:P19 as wdt): most specific known birth location of a person, animal or fictional character, location

Example 3:
Question:
where was tommy emmanuel born?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q310298 wdt:P19 ?x .
}

Using entities:
Tommy Emmanuel, also known as William Thomas Emmanuel (wd:Q310298): Australian guitarist, jazz guitarist, percussionist, jazz musician, songwriter, guitarist, composer, lecturer, singer

Using properties:
place of birth, also known as POB, location of birth, location born, born in, birthplace (wd:P19 as wdt): most specific known birth location of a person, animal or fictional character, location

[Assistant] From the examples, we can see that the property `place of birth` (P19) is commonly used to find where someone was born. Let's first find the entity for Elvis Costello.

[Tool]
Top 10 entity alternatives:
1. Elvis Costello, also known as Declan MacManus, Declan Patrick MacManus (wd:Q206939): English singer-songwriter (born 1954), record producer, songwriter, guitarist, singer
2. Elvis Costello (wd:Q107119978): lithograph at the Museum of Modern Art; unknown designer
3. Elvis Costello (wd:Q125462686): written work by Eduardo Margaretto Kohrmann
4. Elvis Costello (wd:Q125462696): 1993 edition of written work by Eduardo Margaretto Kohrmann
5. Elvis Costello (wd:Q28052324): painting by Adam Birtwistle
6. Spectacle: Elvis Costello with... (wd:Q7575075): television series
7. Elvis Costello Band concert (wd:Q99322922): Elvis Costello Band concert performed during Montreux Jazz Festival the 1989-07-11
8. Elvis Costello Solo concert (wd:Q99429372): Elvis Costello Solo concert performed during Montreux Jazz Festival the 1989-07-11
9. Elvis Costello & the Attractions, also known as Elvis Costello and the Attractions, Nick Lowe And His Sound (wd:Q7714866): UK musical group
10. Elvis Costello with Steve Nieve (wd:Q105091759): print by Jenny Gill

[Assistant] The top result for Elvis Costello is `wd:Q206939`. Now let's use the `place of birth` property (P19) to find out where he was born.

[Tool]
Got 1 row and 1 column:
| birthPlace      |
| --------------- |
| London (wd:Q84) |

[Assistant]
Elvis Costello was born in London.

I will now formulate the final SPARQL query and provide the answer.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query correctly identifies the birthplace of Elvis Costello, and the answer provided is accurate based on the execution result.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT DISTINCT ?movie WHERE {
  ?movie wdt:P161 wd:Q187038 ; wdt:P31 / wdt:P279 * wd:Q11424 .
}

Got 111 rows and 1 column, showing the first 5 and last 5 rows below:
| movie                             |
| --------------------------------- |
| Think Big (wd:Q1031705)           |
| Death Race (wd:Q1033191)          |
| The Misfit Brigade (wd:Q10478647) |
| Deathsport (wd:Q1134750)          |
| Crank: High Voltage (wd:Q1138916) |
| ...                               |
| Mean Streets (wd:Q841335)         |
| True Legend (wd:Q910896)          |
| Kill Bill: Volume 2 (wd:Q928019)  |
| Big Stan (wd:Q939728)             |
| Behind Enemy Lines (wd:Q946863)   |

David Carradine played in the following movies: Think Big, Death Race, The Misfit Brigade, Deathsport, Crank: High Voltage, Mean Streets, True Legend, Kill Bill: Volume 2, Big Stan, Behind Enemy Lines, and many others.

[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 movies did david carradine play in?

[Assistant] Let's start by looking at some similar examples.

[Tool]
Example 1:
Question:
what movies did quentin tarantino star in?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?x wdt:P161 wd:Q3772 ; wdt:P31 / wdt:P279 * wd:Q11424 .
}

Using entities:
film, also known as cinematic work, moving picture, movie, motion pictures, motion picture (wd:Q11424): sequence of images that give the impression of movement, stored on film stock
Quentin Tarantino, also known as Quentin Jerome Tarantino, Tarantino (wd:Q3772): American filmmaker, television actor, film director, film producer, screenwriter, film editor, film actor, producer, director, author, writer, actor

Using properties:
cast member, also known as feat., starring (film/play), film starring, starring, actor (wd:P161 as wdt): actor in the subject production [use "character role" (P453) and...use "recorded participant" (P11108) for non-fiction productions], significant person, performer
subclass of, also known as rdfs:subClassOf, sort of, subcategory of, subgenre of, subtype of (wd:P279 as wdt): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)

Example 2:
Question:
what did shawnee smith star in?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?x wdt:P161 wd:Q233313 ; wdt:P31 / wdt:P279 * wd:Q11424 .
}

Using entities:
film, also known as cinematic work, moving picture, movie, motion pictures, motion picture (wd:Q11424): sequence of images that give the impression of movement, stored on film stock
Shawnee Smith, also known as Shawnee Rebecca Smith (wd:Q233313): singer-songwriter, television actor, American actress, stage actor, film actor, presenter, singer, actor

Using properties:
cast member, also known as feat., starring (film/play), film starring, starring, actor (wd:P161 as wdt): actor in the subject production [use "character role" (P453) and...use "recorded participant" (P11108) for non-fiction productions], significant person, performer
subclass of, also known as rdfs:subClassOf, sort of, subcategory of, subgenre of, subtype of (wd:P279 as wdt): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)

Example 3:
Question:
what films did patrick swayze star in?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?x wdt:P161 wd:Q49004 ; wdt:P31 / wdt:P279 * wd:Q11424 .
}

Using entities:
film, also known as cinematic work, moving picture, movie, motion pictures, motion picture (wd:Q11424): sequence of images that give the impression of movement, stored on film stock
Patrick Swayze, also known as Patrick Wayne Swayze (wd:Q49004): American actor, singer and dancer (1952–2009), singer-songwriter, television actor, film producer, choreographer, voice actor, film actor, aikidoka, dancer, singer, actor

Using properties:
cast member, also known as feat., starring (film/play), film starring, starring, actor (wd:P161 as wdt): actor in the subject production [use "character role" (P453) and...use "recorded participant" (P11108) for non-fiction productions], significant person, performer
subclass of, also known as rdfs:subClassOf, sort of, subcategory of, subgenre of, subtype of (wd:P279 as wdt): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)

[Assistant]
Based on the similar examples, we can see that the SPARQL query to find the movies David Carradine played in would look something like this:

```sparql
SELECT DISTINCT ?movie WHERE {
  ?movie wdt:P161 wd:Q191124 ; wdt:P31 / wdt:P279 * wd:Q11424 .
}
```

Where `wd:Q191124` is the identifier for David Carradine. Let's first confirm the identifier for David Carradine.

[Tool]
Top 10 entity alternatives:
1. David Carradine, also known as John Arthur Carradine (wd:Q187038): American actor (1936–2009), television producer, television actor, character actor, film director, film producer, screenwriter, voice actor, film actor, director, actor
2. David Carradine, also known as David M. Carradine, D. Carradine (wd:Q123597734): structural engineer at BRANZ in New Zealand, structural engineer
3. David Carradine, Actor, Is Dead at 72 (wd:Q123708760): The New York Times article (June 5, 2009)
4. David, also known as David, King of Israel, King David, Dawud re d'Israele, Nabi Dawud King of Israel, sweet singer of Israel (wd:Q41370): king of Israel and Judah, instrumentalist, monarch, warlord, herder, ruler, poet
5. Jacques-Louis David, also known as J.-L. David, Louis David, L. David, J.L. David, J. L. David (wd:Q83155): architectural draftsperson, French painter (1748–1825), politician, painter
6. David, also known as David by Michelangelo, Michelangelo's David, David of Michelangelo, Michaelangelo's David (wd:Q179900): sculpture by Michelangelo
7. Saint David, also known as St. David, St David, David of Wales, Dewi, David (wd:Q331697): patron saint of Wales, Catholic bishop, Catholic priest
8. David, also known as David, Chiriquí, San José de David (wd:Q279009): capital of the province of Chiriquí, Panama
9. Gerard David, also known as Gheeraert Davidt, Gheeraedt David, Gheeraert David, Gheeraedt Davit, Gheeraedt Davidt (wd:Q333380): Early Netherlandish painter, (c. 1460–1523), draftsperson, illuminator, painter, artist
10. Armand David, also known as Pere David, Jean-Pierre-Armand David, David, Père David, Jean Pierre Armand David (wd:Q346939): French Catholic priest, zoologist, and botanist (1826–1900), Catholic priest, ornithologist, missionary, zoologist, botanist, explorer

[Assistant] The correct identifier for David Carradine is `wd:Q187038`. Now let's construct and execute the SPARQL query to find the movies he played in.

[Tool]
Got 111 rows and 1 column, showing the first 5 and last 5 rows below:
| movie                             |
| --------------------------------- |
| Think Big (wd:Q1031705)           |
| Death Race (wd:Q1033191)          |
| The Misfit Brigade (wd:Q10478647) |
| Deathsport (wd:Q1134750)          |
| Crank: High Voltage (wd:Q1138916) |
| ...                               |
| Mean Streets (wd:Q841335)         |
| True Legend (wd:Q910896)          |
| Kill Bill: Volume 2 (wd:Q928019)  |
| Big Stan (wd:Q939728)             |
| Behind Enemy Lines (wd:Q946863)   |

[Assistant]
The SPARQL query successfully returned the movies that David Carradine played in. Here are some of the movies:

- Think Big (wd:Q1031705)
- Death Race (wd:Q1033191)
- The Misfit Brigade (wd:Q10478647)
- Deathsport (wd:Q1134750)
- Crank: High Voltage (wd:Q1138916)
- Mean Streets (wd:Q841335)
- True Legend (wd:Q910896)
- Kill Bill: Volume 2 (wd:Q928019)
- Big Stan (wd:Q939728)
- Behind Enemy Lines (wd:Q946863)

Here is the final SPARQL query and the answer:

```sparql
SELECT DISTINCT ?movie WHERE {
  ?movie wdt:P161 wd:Q187038 ; wdt:P31 / wdt:P279 * wd:Q11424 .
}
```

Answer: David Carradine played in the following movies: Think Big, Death Race, The Misfit Brigade, Deathsport, Crank: High Voltage, Mean Streets, True Legend, Kill Bill: Volume 2, Big Stan, Behind Enemy Lines, and many others.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query returns the IRIs of the movies David Carradine played in, and the human-readable answer lists several of these movies, which matches the execution result. No further refinement is needed.