PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?country WHERE {
  wd:Q9441 wdt:P19 ?birthplace .
  ?birthplace wdt:P17 ?country .
}

Got 1 row and 1 column:
| country         |
| --------------- |
| Nepal (wd:Q837) |

Buddha is generally considered to have come from Nepal (wd:Q837).

[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 country did buddha come from?

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

[Tool]
Example 1:
Question:
what are buddhist gods?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?x wdt:P31 wd:Q6058157 ; wdt:P1049 wd:Q748 .
}

Using entities:
god (wd:Q6058157): male deity, in contrast to female deities
Buddhism, also known as Fo-Kiao, Bauddha Dharma, Fojiao, Fo-chiao, Dharmavinaya (wd:Q748): Indian religion

Using properties:
worshipped by, also known as deity of (wd:P1049 as wdt): religion or group/civilization that worships a given deity, part of
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 country did vasco da gama came from?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q7328 wdt:P27 ?x .
}

Using entities:
Vasco da Gama, also known as Count of Vidigueira (wd:Q7328): Portuguese explorer, world traveler, naval officer, explorer, seafarer

Using properties:
country of citizenship, also known as subject of (country), nation of citizenship, national of, nationality, nation (wd:P27 as wdt): the object is a country that recognizes the subject as its citizen, country

Example 3:
Question:
what country did vasco da gama come from?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q7328 wdt:P27 ?x .
}

Using entities:
Vasco da Gama, also known as Count of Vidigueira (wd:Q7328): Portuguese explorer, world traveler, naval officer, explorer, seafarer

Using properties:
country of citizenship, also known as subject of (country), nation of citizenship, national of, nationality, nation (wd:P27 as wdt): the object is a country that recognizes the subject as its citizen, country

[Assistant]
Based on the examples, we need to find the entity for Buddha and then use the property `country of citizenship` (P27) to find the country he came from.
First, let's search for the entity representing Buddha.

[Tool]
Top 10 entity alternatives:
1. The Buddha, also known as Trigyesh, Shakyamuni, Sakyasinha, Shakyasimha, Siddhartha Gautama (wd:Q9441): Indian philosopher and the founder of Buddhism (623 or 563 BCE – 543 or 483 BCE), spiritual teacher, religious leader, social reformer, father of faith, psychologist, psychiatrist, philosopher, preacher, bhikkhu, writer
2. The Buddha, also known as Lord Buddha, Mahatma Buddha, Siddhartha Gautama, Munish, Shakyasimha (wd:Q9441): Indian philosopher and the founder of Buddhism (623 or 563 BCE – 543 or 483 BCE), spiritual teacher, religious leader, father of faith, social reformer, psychologist, psychiatrist, philosopher, preacher, bhikkhu, writer
3. Buddha, also known as Arhat, Vijjācaraṇasampanna, Vidyācaraṇasaṃpanna, Tathārāja, Sugata (wd:Q7055): being who attains perfect enlightenment
4. buddhahood, also known as Buddha, fóguǒ, Chengfo, bukkai, buddhatta (wd:Q56247062): condition or rank of fully awakened enlightenment
5. Buddharupa, also known as Buddha, Buddharūpa (wd:Q1000809): statues of beings who have obtained Buddhahood
6. Buddha (wd:Q945140): manga series
7. Ulf Ekberg, also known as Ulf Buddha Ekberg, Ulf Gunnar Ekberg, Buddha (wd:Q2656667): television producer, Swedish musician, record producer, businessperson, film producer, songwriter, composer, musician, singer
8. Buddha (wd:Q851370): 1994 demo album by Blink-182
9. Buddha, also known as Buddhaa-Rajaon Ka Raja, Buddhaa - The King of Kings (wd:Q16954312): TV series
10. Huberht Taylor Hudson, also known as Buddha, Hubert Hudson (wd:Q3787383): British sailor who took part in Shackleton's Imperial Trans-Antarctic Expedition, navigator, sailor

[Assistant]
The top result is the entity for Buddha, which is `wd:Q9441`. Now, let's use this entity and the property `country of citizenship` (P27) to find the country Buddha came from.
I'll construct a simple SPARQL query to retrieve this information.

[Tool]
Got 1 row and 1 column:
| country              |
| -------------------- |
| Shakya (wd:Q1062748) |

[Assistant]
The result indicates that Buddha's country of citizenship is `Shakya` (wd:Q1062748). However, Shakya was a clan and not a modern country. To provide a more accurate answer, we should check if Shakya is located within a specific region or country.

Let's look for the location of Shakya to determine the modern-day country it corresponds to.
I'll search for the property `located in the administrative territorial entity` (P131) to find the broader region where Shakya was located.

[Tool] Got no rows and 1 columns

[Assistant]
It seems that there is no direct information about the administrative region where Shakya was located. However, we can try to find the broader geographical context by checking the `country` (P17) property of Shakya.

Let's execute a query to find the country associated with Shakya.

[Tool]
Got 2 rows and 1 column:
| country         |
| --------------- |
| India (wd:Q668) |
| Nepal (wd:Q837) |

[Assistant]
The result shows that Shakya is associated with both India (wd:Q668) and Nepal (wd:Q837). This makes sense because the region where Buddha lived is now split between these two countries.

To provide a more precise answer, we can say that Buddha came from the region that is now part of both India and Nepal. However, since the exact location of Shakya is often considered to be in present-day Nepal, we can conclude that Buddha is generally considered to have come from Nepal.

Here is the final SPARQL query and answer:

```sparql
SELECT ?country WHERE {
  wd:Q9441 wdt:P27 ?shakya .
  ?shakya wdt:P17 ?country .
}
```

Answer: Buddha is generally considered to have come from Nepal (wd:Q837).

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

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query returns both India and Nepal as potential countries where Buddha came from. However, historically, Buddha is generally considered to have been born in Lumbini, which is now part of modern-day Nepal. The query should be refined to ensure that it only returns Nepal as the answer. One way to do this is to specifically look for the birthplace of Buddha and then determine the country associated with that location.

[Assistant]
Thank you for the feedback. Let's refine the query to specifically look for the birthplace of Buddha and then determine the country associated with that location.

First, I'll search for the property `place of birth` (P19) to find Buddha's birthplace.

[Tool]
Got 1 row and 1 column:
| birthplace         |
| ------------------ |
| Lumbini (wd:Q9213) |

[Assistant]
The result shows that Buddha's birthplace is Lumbini (wd:Q9213). Now, let's find the country associated with Lumbini.

I'll execute a query to find the country where Lumbini is located using the `country` (P17) property.

[Tool]
Got 1 row and 1 column:
| country         |
| --------------- |
| Nepal (wd:Q837) |

[Assistant]
The result confirms that Lumbini, the birthplace of Buddha, is located in Nepal (wd:Q837).

Here is the refined SPARQL query and answer:

```sparql
SELECT ?country WHERE {
  wd:Q9441 wdt:P19 ?birthplace .
  ?birthplace wdt:P17 ?country .
}
```

Answer: Buddha is generally considered to have come from Nepal (wd:Q837).

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 effectively retrieves the country of origin for Buddha, which is Nepal (wd:Q837).


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?county WHERE {
  wd:Q486472 wdt:P131 ?county .
}

Wyandotte County

[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 county is kansas city kansas?

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

[Tool]
Example 1:
Question:
what famous people are from kansas city?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?x wdt:P19 wd:Q41819 .
}

Using entities:
Kansas City, also known as Kansas City, Missouri, Kansas City, MO, K.C., KC, KCMO (wd:Q41819): city in Missouri, United States

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 do the kansas city chiefs play?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q223522 wdt:P159 ?x .
}

Using entities:
Kansas City Chiefs, also known as Chiefs, Dallas Texans, KC Chiefs (wd:Q223522): National Football League franchise in Kansas City, Missouri

Using properties:
headquarters location, also known as headquarters locality, seat, headquarters town, HQ, HQ location (wd:P159 as wdt): city or town where an organization's headquarters is or has been situated. Use P276 qualifier for specific building, significant place

Example 3:
Question:
what time is kansas in?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q1558 wdt:P421 ?x .
}

Using entities:
Kansas, also known as Kan., Kans., Kansas, United States of America, US-KS, KS (wd:Q1558): state of the United States of America

Using properties:
located in time zone, also known as TZ, time zone, timezone (wd:P421 as wdt): time zone for this item

[Assistant] None of these examples directly answer the question about the county of Kansas City, Kansas. However, we can infer that we need to look for the county where Kansas City, Kansas is located. Let's first identify the entity for Kansas City, Kansas.

[Tool]
Top 10 entity alternatives:
1. Kansas City, also known as Kansas City, Missouri, Kansas City, MO, K.C., KC, KCMO (wd:Q41819): city in Missouri, United States
2. Kansas City, also known as Kansas City, Kansas, KCK, Kansas, Kansas, Kansas City, KS (wd:Q486472): city and county seat of Wyandotte County, State of Kansas, United States
3. Kansas City, also known as Kansas City, Kansas, KCK, Kansas City, KS, Kansas, Kansas (wd:Q486472): city and county seat of Wyandotte County, State of Kansas, United States
4. Kansas City (wd:Q977996): 1996 film by Robert Altman
5. Kansas City, also known as K. C. Loving (wd:Q1723915): original song written and composed by Jerry Leiber and Mike Stoller
6. Kansas City, also known as Kansas City, Oregon, Kansas City, OR (wd:Q3709254): unincorporated community in Washington County, Oregon, United States
7. Kansas City/Hey, Hey, Hey, Hey, also known as Kansas City (wd:Q10543339): vocal track by The Beatles
8. Kansas City (wd:Q6364626): US basketball team
9. Kansas City (wd:Q6364628): 2008 single by Sneaky Sound System
10. Kansas City, also known as Kansas City (Oklahoma!) (wd:Q6364632): original song written and composed by Richard Rodgers (music) and Oscar Hammerstein II; from the musical Oklahoma!

[Assistant]
The correct entity for Kansas City, Kansas appears to be `wd:Q486472`. Now, let's find out which county it is located in. We can use the property "located in administrative territorial entity" (`wdt:P131`) to find the county.

Let's execute a SPARQL query to find the county.

[Tool]
Got 1 row and 1 column:
| county                        |
| ----------------------------- |
| Wyandotte County (wd:Q375652) |

[Assistant]
The SPARQL query successfully returned the county where Kansas City, Kansas is located. The county is Wyandotte County.

Here is the final SPARQL query and answer:

```sparql
SELECT ?county WHERE {
  wd:Q486472 wdt:P131 ?county .
}
```

Answer: Wyandotte County

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

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query correctly identifies Wyandotte County as the county where Kansas City, Kansas is located.


PREFIX pq: <http://www.wikidata.org/prop/qualifier/>
PREFIX ps: <http://www.wikidata.org/prop/statement/>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX p: <http://www.wikidata.org/prop/>
SELECT DISTINCT ?actor WHERE {
  wd:Q935945 p:P161 ?statement .
  ?statement ps:P161 ?actor ; pq:P453 wd:Q25097 .
}

Brad Dourif

[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 does the voice for chucky in child's play?

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

[Tool]
Example 1:
Question:
who the voice of jack skellington?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?p p:P725 ?y .
  ?y ps:P725 ?x ; pq:P453 wd:Q2708262 .
}

Using entities:
Jack Skellington, also known as The Pumpkin King (wd:Q2708262): fictional character from the film The Nightmare Before Christmas

Using properties:
character role, also known as role, plays, performer of, as, acting role (wd:P453 as pq): specific role played or filled by subject -- use only as qualifier of "cast member" (P161), "voice actor" (P725)
voice actor, also known as voice actors, voice dubber, voice actress, voice actresses, voiced by (wd:P725 as ps/p): performer of a spoken role in a creative work such as animation,... (P453) as qualifier] [use "cast member" (P161) for live acting], film crew member, cast member

Example 2:
Question:
who played andy in toy story 3?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q187278 p:P725 ?p .
  ?p ps:P725 ?x ; pq:P453 wd:Q1808466 .
}

Using entities:
Andy Davis (wd:Q1808466): Toy Story character
Toy Story 3 (wd:Q187278): 2010 American animated film

Using properties:
character role, also known as role, plays, performer of, as, acting role (wd:P453 as pq): specific role played or filled by subject -- use only as qualifier of "cast member" (P161), "voice actor" (P725)
voice actor, also known as voice actors, voice dubber, voice actress, voice actresses, voiced by (wd:P725 as ps/p): performer of a spoken role in a creative work such as animation,... (P453) as qualifier] [use "cast member" (P161) for live acting], film crew member, cast member

Example 3:
Question:
who has the voice of stewie griffin?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?p p:P725 ?y .
  ?y ps:P725 ?x ; pq:P453 wd:Q837909 .
}

Using entities:
Stewie Griffin, also known as Stewart Gilligan Griffin, Stewart Gilligan "Stewie" Griffin (wd:Q837909): fictional character from the Family Guy franchise

Using properties:
character role, also known as role, plays, performer of, as, acting role (wd:P453 as pq): specific role played or filled by subject -- use only as qualifier of "cast member" (P161), "voice actor" (P725)
voice actor, also known as voice actors, voice dubber, voice actress, voice actresses, voiced by (wd:P725 as ps/p): performer of a spoken role in a creative work such as animation,... (P453) as qualifier] [use "cast member" (P161) for live acting], film crew member, cast member

[Assistant] Based on the examples, we need to identify the character Chucky and the film Child's Play, then find the voice actor for that character in that film. Let's start by searching for the character Chucky.

[Tool]
Top 10 entity alternatives:
1. character, also known as fictional character (wd:Q95074): fictional human or non-human character in a narrative work of art
2. character (wd:Q366773): relatively permanent traits of an individual relates to various kinds of stimuli, and to the environment
3. Hirving Lozano, also known as Chucky, Hirving Rodrigo Lozano Bahena, Hirving Lozano Bahena, Chucky Lozano (wd:Q17633812): Mexican association football player, association football player
4. character, also known as char, Char, rune (wd:Q32483): primitive data type
5. Character (wd:Q928545): 1997 film directed by Mike van Diem
6. Chucky, also known as The Lakeshore Strangler, Lakeshore Strangler, Good Guy doll, Chucky the murderdoll, Charles Ray (wd:Q25097): horror film villain, serial killer
7. disposition, also known as nature, personage, natural inclination, character (wd:Q1149305): tendency of a person to act in a specified way
8. Chucky (wd:Q106824359): American horror television series
9. character, also known as computer character, computer symbol, sign, symbol, typographical mark (wd:Q3241972): symbol or sign
10. Character (wd:Q1062911): album by Dark Tranquillity

[Assistant] The correct entity for Chucky is the horror film villain, serial killer (wd:Q25097). Now let's search for the film Child's Play.

[Tool]
Top 10 entity alternatives:
1. Child's Play (wd:Q935945): 1988 film by Tom Holland
2. Child's Play, also known as Childs Play (wd:Q56697304): 2019 film directed by Lars Klevberg
3. Child's Play, also known as Chucky (franchise) (wd:Q2265493): horror film franchise focusing on Chucky, a killer doll
4. Child's Play (wd:Q3966802): 1972 film by Sidney Lumet
5. Child's Play (wd:Q5097661): children's charity
6. Child's Play (wd:Q5097656): episode of Star Trek: Voyager (S6 E19)
7. Child's Play (wd:Q1463844): 1992 film directed by Wolfgang Becker
8. Child's Play (wd:Q5097664): 1994 Marvel Comics storyline
9. Child's Play (wd:Q5097667): 1980s US game show
10. Child's Play (wd:Q5097668): D&D module

[Assistant]
The correct entity for the original Child's Play film is 1988 film by Tom Holland (wd:Q935945). Now let's find the voice actor for Chucky in this film.

We will use the property `voice actor` (wd:P725) and the qualifier `character role` (wd:P453).

Let's construct the SPARQL query:

```sparql
SELECT DISTINCT ?voiceActor WHERE {
  wd:Q935945 p:P725 ?statement .
  ?statement ps:P725 ?voiceActor ;
             pq:P453 wd:Q25097 .
}
```

This query selects the distinct voice actor for the character Chucky (wd:Q25097) in the film Child's Play (wd:Q935945).

Let's execute this query.

[Tool] Got no rows and 1 columns

[Assistant]
It seems that the query did not return any results. This might be because the specific combination of properties and qualifiers is not present in the knowledge graph. Let's try a different approach by first finding the cast members of the film and then checking if any of them are associated with the character Chucky.

We will use the property `cast member` (wd:P161) to find the cast members of the film and then check if any of them have the role of Chucky.

Let's construct the SPARQL query:

```sparql
SELECT DISTINCT ?actor WHERE {
  wd:Q935945 p:P161 ?statement .
  ?statement ps:P161 ?actor ;
             pq:P453 wd:Q25097 .
}
```

This query selects the distinct actor who played the role of Chucky (wd:Q25097) in the film Child's Play (wd:Q935945).

Let's execute this query.

[Tool]
Got 1 row and 1 column:
| actor                    |
| ------------------------ |
| Brad Dourif (wd:Q329734) |

[Assistant]
The query returned Brad Dourif (wd:Q329734) as the voice actor for Chucky in the film Child's Play.

Therefore, the answer to the question "Who does the voice for Chucky in Child's Play?" is Brad Dourif.

Here is the final SPARQL query and answer:

```sparql
SELECT DISTINCT ?actor WHERE {
  wd:Q935945 p:P161 ?statement .
  ?statement ps:P161 ?actor ;
             pq:P453 wd:Q25097 .
}
```

Answer: Brad Dourif

Let's finalize this with the answer function.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query successfully identified Brad Dourif as the voice actor for Chucky in Child's Play.


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?timeZone WHERE {
  wd:Q84 wdt:P421 ?timeZone .
  FILTER ( ?timeZone = wd:Q16894228 )
}

The time zone for London, UK is Europe/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] what is london uk time zone?

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

[Tool]
Example 1:
Question:
what time zone is it in london england?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q84 wdt:P421 ?x .
}

Using entities:
London, also known as Loniniensi, Trinovantum, the Big Smoke, Lundenwic, Lundenburh (wd:Q84): capital and largest city of England and the United Kingdom

Using properties:
located in time zone, also known as TZ, time zone, timezone (wd:P421 as wdt): time zone for this item

Example 2:
Question:
what time zone is london in right now?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q84 wdt:P421 ?x .
}

Using entities:
London, also known as Loniniensi, Trinovantum, the Big Smoke, Lundenwic, Lundenburh (wd:Q84): capital and largest city of England and the United Kingdom

Using properties:
located in time zone, also known as TZ, time zone, timezone (wd:P421 as wdt): time zone for this item

Example 3:
Question:
what time zone in the uk?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q145 wdt:P421 ?x .
}

Using entities:
United Kingdom, also known as U.K., UK, U. K., U K, United Kingdom of Great Britain and Northern Ireland (wd:Q145): country in north-west Europe

Using properties:
located in time zone, also known as TZ, time zone, timezone (wd:P421 as wdt): time zone for this item

[Assistant]
Based on the examples, we can see that the property `located in time zone` (P421) is used to find the time zone of a location. We need to identify the entity for London, UK, and then use this property to find the time zone.

Let's first confirm the entity for London, UK.

[Tool]
Top 10 entity alternatives:
1. London, also known as Loniniensi, Trinovantum, the Big Smoke, Lundenwic, Lundenburh (wd:Q84): capital and largest city of England and the United Kingdom
2. Transport UK London Bus (wd:Q4666716): bus company operating services in Greater London
3. Case-Based Reasoning Research an...ondon, UK, September 12-15, 2011, also known as ICCBR 2011: London, UK (wd:Q106067800): academic conference
4. Biomedical Simulation, 4th Inter...2008, London, UK, July 7-8, 2008, also known as ISBMS 2008: London, UK (wd:Q106245166): academic conference
5. ACM SIGCOMM '94 Conference on Co...K, August 31 - September 2, 1994, also known as SIGCOMM 1994: London, UK (wd:Q106304259): academic conference
6. 2014 International Conference on...ed Kingdom, September 8-12, 2014, also known as ICCAC 2014: London, UK (wd:Q106335891): academic conference
7. 2017 International Conference On...United Kingdom, June 19-20, 2017, also known as CyberSA 2017: London, UK (wd:Q106336151): academic conference
8. 21st ACM SIGPLAN/SIGBED Internat... 2020, London, UK, June 16, 2020, also known as LCTES 2020: London, UK (wd:Q106303884): academic conference
9. 7th 2016 International Conferenc..., London, UK, July 11 - 13, 2016, also known as SMSociety 2016: London, UK (wd:Q106337600): academic conference
10. 5th International Conference on ...ondon, UK, September 29-30, 2008, also known as iPRES 2008: London, UK (wd:Q106336198): academic conference

[Assistant]
The top result for "London, UK" is `wd:Q84`, which represents the city of London, the capital and largest city of England and the United Kingdom. This is the entity we need to use to find the time zone.

Next, let's use the property `located in time zone` (P421) to find the time zone for London, UK.

I'll construct and execute the SPARQL query to get the time zone for London, UK.

[Tool]
Got 4 rows and 1 column:
| timeZone                        |
| ------------------------------- |
| Europe/London (wd:Q16894228)    |
| Greenwich Mean Time (wd:Q30192) |
| UTC±00:00 (wd:Q6574)            |
| UTC+01:00 (wd:Q6655)            |

[Assistant]
The query returned multiple time zones associated with London, UK. These include:

- `Europe/London` (wd:Q16894228)
- `Greenwich Mean Time` (wd:Q30192)
- `UTC±00:00` (wd:Q6574)
- `UTC+01:00` (wd:Q6655)

To provide a clear and concise answer, we can focus on the primary time zone, which is `Europe/London`. This time zone is commonly used to represent the time in London, UK, and it accounts for both standard time (GMT) and daylight saving time (BST).

Here is the final SPARQL query and answer:

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query returns multiple time zones, which might confuse the user. We should refine the query to return only the most relevant time zone for London, UK, which is 'Europe/London'. Consider adding a filter to exclude other time zones.

[Assistant]
Thank you for the feedback. To refine the query, we will add a filter to specifically return the `Europe/London` time zone, which is the most relevant and commonly used time zone for London, UK.

Let's update the SPARQL query to include this filter.

[Tool]
Got 1 row and 1 column:
| timeZone                     |
| ---------------------------- |
| Europe/London (wd:Q16894228) |

[Assistant]
The refined query has successfully returned only the most relevant time zone for London, UK, which is `Europe/London`.

Here is the final SPARQL query and answer:

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query correctly identifies the time zone for London, UK as Europe/London.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT DISTINCT ?year WHERE {
  ?superbowl wdt:P31 / wdt:P279 * wd:Q32096 ; wdt:P1923 wd:Q221878 ; wdt:P585 ?year .
}

The Seattle Seahawks went to the Super Bowl in the years 2006, 2014, and 2015.

[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 year did seattle seahawks go to the super bowl?

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

[Tool]
Example 1:
Question:
what year did arizona cardinals go to superbowl?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?y wdt:P31 / wdt:P279 * wd:Q32096 ; wdt:P1923 wd:Q224164 ; wdt:P585 ?x .
}

Using entities:
Arizona Cardinals, also known as St. Louis Cardinals, Racine Cardinals, Morgan Athletic Club, Chicago Cardinals, Phoenix Cardinals (wd:Q224164): National Football League franchise in Glendale, Arizona
Super Bowl, also known as AFL–NFL World Championship Game (wd:Q32096): National Football League championship game

Using properties:
participating team, also known as teams (wd:P1923 as wdt): like 'Participant' (P710) but for teams. For an event like a cyc...member of sports team' (P54) as a qualifier for the individuals), participant
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)
point in time, also known as at time, as of, year, when, time of event (wd:P585 as wdt): date something took place, existed or a statement was true; for providing time use the "refine date" property (P4241)

Example 2:
Question:
when did miami dolphins win super bowl?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?y wdt:P31 / wdt:P279 * wd:Q32096 ; wdt:P1346 wd:Q223243 ; wdt:P585 ?x .
}

Using entities:
Miami Dolphins, also known as Dolphins, Miami (wd:Q223243): National Football League franchise in Miami, Florida
Super Bowl, also known as AFL–NFL World Championship Game (wd:Q32096): National Football League championship game

Using properties:
winner, also known as nominee, prizewinner, titleholder, top dog, victor (wd:P1346 as wdt): winner of a competition or similar event, not to be used for awa...ssibly qualified with "for work" (P1686)) or for wars or battles, participating team, participant
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)
point in time, also known as at time, as of, year, when, time of event (wd:P585 as wdt): date something took place, existed or a statement was true; for providing time use the "refine date" property (P4241)

Example 3:
Question:
what year did baltimore ravens win the superbowl?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?y wdt:P31 / wdt:P279 * wd:Q32096 ; wdt:P1346 wd:Q276539 ; wdt:P585 ?x .
}

Using entities:
Baltimore Ravens (wd:Q276539): National Football League franchise in Baltimore, Maryland
Super Bowl, also known as AFL–NFL World Championship Game (wd:Q32096): National Football League championship game

Using properties:
winner, also known as nominee, prizewinner, titleholder, top dog, victor (wd:P1346 as wdt): winner of a competition or similar event, not to be used for awa...ssibly qualified with "for work" (P1686)) or for wars or battles, participating team, participant
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)
point in time, also known as at time, as of, year, when, time of event (wd:P585 as wdt): date something took place, existed or a statement was true; for providing time use the "refine date" property (P4241)

[Assistant]
From these examples, we can see that the Super Bowl is represented by the entity `wd:Q32096`. We also see that the property `wdt:P1923` is used to represent the participating team in the Super Bowl, and `wdt:P585` represents the point in time when the event occurred.

However, the Seattle Seahawks might have participated in multiple Super Bowls, so we need to ensure we capture all instances. Let's first identify the entity for the Seattle Seahawks.

[Tool]
Top 10 entity alternatives:
1. Seattle Seahawks (wd:Q221878): American football team
2. Seattle Seahawks (wd:Q1431833): ice hockey team
3. history of the Seattle Seahawks, also known as Seattle Seahawks history, the Seattle Seahawks history (wd:Q3973903): aspect of history
4. 2021 Seattle Seahawks season (wd:Q104869618): 46th season in franchise history
5. 2022 Seattle Seahawks season (wd:Q110445415): 47th season in franchise history
6. 2023 Seattle Seahawks season (wd:Q116170066): 48th season in franchise history
7. 2020 Seattle Seahawks season (wd:Q81002024): 45th season in franchise history
8. 2018 Seattle Seahawks season (wd:Q47191924): 43rd season in franchise history; final one under ownership of Paul Allen
9. 2015 Seattle Seahawks season (wd:Q19255692): 40th season in franchise history
10. 2024 Seattle Seahawks season (wd:Q124167235): 49th season in franchise history

[Assistant]
The correct entity for the Seattle Seahawks is `wd:Q221878`. Now, let's construct a SPARQL query to find out the years when the Seattle Seahawks participated in the Super Bowl.

We will use the property `wdt:P1923` to find the Super Bowl events where the Seattle Seahawks were a participating team, and then extract the year using `wdt:P585`.

Let's execute this query.

[Tool]
Got 3 rows and 1 column:
| year                                |
| ----------------------------------- |
| 2006-02-05T00:00:00Z (xsd:dateTime) |
| 2014-02-02T00:00:00Z (xsd:dateTime) |
| 2015-02-01T00:00:00Z (xsd:dateTime) |

[Assistant]
The Seattle Seahawks participated in the Super Bowl in the years 2006, 2014, and 2015.

To provide a clear and concise answer, let's format the years in a more readable way.

Here is the final SPARQL query and the answer:

```sparql
SELECT DISTINCT ?year WHERE {
  ?superbowl wdt:P31 / wdt:P279* wd:Q32096 ;
             wdt:P1923 wd:Q221878 ;
             wdt:P585 ?year .
}
```

The Seattle Seahawks went to the Super Bowl in the years 2006, 2014, and 2015.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query returns the years when the Seattle Seahawks participated in the Super Bowl, and the human-readable answer accurately reflects the results.


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/>
ASK WHERE {
  wd:Q296403 p:P54 ?statement .
  ?statement ps:P54 ?team ; pq:P580 ?startDate ; pq:P582 ?endDate .
  FILTER ( ( ?startDate < "2023-01-01T00:00:00Z" ^^ xsd:dateTime ) && ( ?endDate >= "2023-12-31T23:59:59Z" ^^ xsd:dateTime ) )
}

Lamar Odom is not playing for any team in 2023.

[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 lamar odom playing for this year?

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

[Tool]
Example 1:
Question:
who is lamar odom playing for in 2012?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q296403 p:P54 ?p .
  ?p ps:P54 ?x ; pq:P580 ?y ; pq:P582 ?z .
  FILTER ( ( ?y < "2013-01-01T00:00:00Z" ^^ xsd:dateTime ) && ( ?z >= "2012-01-01T00:00:00Z" ^^ xsd:dateTime ) )
}

Using entities:
Lamar Odom, also known as Lamar Joseph Odom, The Goods, L.O., Candy Man (wd:Q296403): American basketball player, basketball player

Using properties:
member of sports team, also known as plays for, played for, sport team, team, team played for (wd:P54 as ps/p): sports teams or clubs that the subject represents or represented, member of
start time, also known as start date, since, valid, join time, joined (wd:P580 as pq): time an entity begins to exist or a statement starts being valid, point in time
end time, also known as ends, stop time, endtime, fall date, ineffective (wd:P582 as pq): moment when an entity ceases to exist and a statement stops being entirely valid or no longer be true, point in time

Example 2:
Question:
what team does lamar odom play for 2011?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q296403 p:P54 ?p .
  ?p ps:P54 ?x ; pq:P580 ?y ; pq:P582 ?z .
  FILTER ( ( ?y < "2012-01-01T00:00:00Z" ^^ xsd:dateTime ) && ( ?z > "2011-01-01T00:00:00Z" ^^ xsd:dateTime ) )
}

Using entities:
Lamar Odom, also known as Lamar Joseph Odom, The Goods, L.O., Candy Man (wd:Q296403): American basketball player, basketball player

Using properties:
member of sports team, also known as plays for, played for, sport team, team, team played for (wd:P54 as ps/p): sports teams or clubs that the subject represents or represented, member of
start time, also known as start date, since, valid, join time, joined (wd:P580 as pq): time an entity begins to exist or a statement starts being valid, point in time
end time, also known as ends, stop time, endtime, fall date, ineffective (wd:P582 as pq): moment when an entity ceases to exist and a statement stops being entirely valid or no longer be true, point in time

Example 3:
Question:
who did lamar odom sign with?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q296403 wdt:P54 ?x .
}

Using entities:
Lamar Odom, also known as Lamar Joseph Odom, The Goods, L.O., Candy Man (wd:Q296403): American basketball player, basketball player

Using properties:
member of sports team, also known as plays for, played for, sport team, team, team played for (wd:P54 as wdt): sports teams or clubs that the subject represents or represented, member of

[Assistant]
From the examples, we can see that the property `P54` is used to represent the team a player is a member of, and the qualifiers `P580` and `P582` are used to specify the start and end times of the membership.

To answer the question "Who is Lamar Odom playing for this year?", we need to find the team he was a member of during the current year. Let's first identify the current year and then construct the SPARQL query accordingly.

I'll start by searching for Lamar Odom's entity.

[Tool]
Top 10 entity alternatives:
1. Lamar Odom, also known as Lamar Joseph Odom, The Goods, L.O., Candy Man (wd:Q296403): American basketball player, basketball player
2. Lamar Odom: Reborn, also known as Lamar Odom Re/born, Lamar Odom Reborn (wd:Q130267969): 2023 American documetary about addiction and recovery
3. Lamar, also known as Lamar, CO, Lamar, Colorado (wd:Q994475): home rule municipality in and county seat of Prowers County, Colorado, United States
4. Lamar, also known as Lamar, Missouri, Lamar, MO (wd:Q572172): city in Missouri, United States
5. Lamar, also known as Lamar, AR, Lamar, Arkansas (wd:Q79276): city in Johnson County, Arkansas, United States
6. Lamar, also known as Lamar, OK, Lamar, Oklahoma (wd:Q3287546): town in Hughes County, Oklahoma, United States
7. Lamar, also known as Lamar, SC, Lamar, South Carolina (wd:Q2083536): town in South Carolina
8. Lamar, also known as Lamar, NE, Lamar, Nebraska (wd:Q1809547): village in Nebraska
9. Lamar, also known as Lamar, Texas, Lamar, TX (wd:Q972793): census-designated place in Aransas County, Texas, United States
10. Lamar, also known as Lamar, Pennsylvania (wd:Q6480823): census-designated place in Clinton County, Pennsylvania, USA

[Assistant]
The correct entity for Lamar Odom is `wd:Q296403`. Now, let's construct the SPARQL query to find out which team Lamar Odom is playing for this year. We need to filter the results based on the current year.

First, I'll check the current year using a simple SPARQL query.

[Tool]
Failed to call function execute:
failed to parse input: parse error at position 36 for token EOF with content '' (the input most likely does not follow the grammar)

[Assistant]
It seems that the `NOW()` function is not supported in the SPARQL endpoint I'm using. Instead, I'll hard-code the current year into the query. As of now, the current year is 2023.

Let's construct the SPARQL query to find out which team Lamar Odom is playing for in 2023.

```sparql
SELECT DISTINCT ?team WHERE {
  wd:Q296403 p:P54 ?statement .
  ?statement ps:P54 ?team ;
             pq:P580 ?startDate ;
             pq:P582 ?endDate .
  FILTER ( ( ?startDate < "2023-01-01T00:00:00Z"^^xsd:dateTime ) && ( ?endDate >= "2023-12-31T23:59:59Z"^^xsd:dateTime ) )
}
```

This query will find the team that Lamar Odom was a member of during the year 2023. Let's execute it.

[Tool] Got no rows and 1 columns

[Assistant]
It appears that there are no results for Lamar Odom playing for any team in 2023. This could mean that he is not currently active in any professional basketball league or that his team affiliation is not recorded in the knowledge graph for this year.

To provide a more comprehensive answer, let's check if there are any recent team affiliations for Lamar Odom, even if they don't cover the entire year 2023. We can look for the most recent team he was associated with.

Let's modify the query to find the most recent team affiliation:

```sparql
SELECT DISTINCT ?team ?endDate WHERE {
  wd:Q296403 p:P54 ?statement .
  ?statement ps:P54 ?team ;
             pq:P580 ?startDate ;
             pq:P582 ?endDate .
  FILTER ( ?endDate >= "2022-01-01T00:00:00Z"^^xsd:dateTime )
}
ORDER BY DESC(?endDate)
LIMIT 1
```

This query will find the most recent team affiliation for Lamar Odom starting from 2022. Let's execute it.

[Tool] Got no rows and 2 columns

[Assistant]
It seems that there are no recent team affiliations for Lamar Odom recorded in the knowledge graph starting from 2022. This suggests that Lamar Odom is not currently playing for any professional basketball team.

Given this information, the answer to the question "Who is Lamar Odom playing for this year?" is that he is not playing for any team in 2023.

Let's finalize the answer with an ASK query to confirm this.

```sparql
ASK WHERE {
  wd:Q296403 p:P54 ?statement .
  ?statement ps:P54 ?team ;
             pq:P580 ?startDate ;
             pq:P582 ?endDate .
  FILTER ( ( ?startDate < "2023-01-01T00:00:00Z"^^xsd:dateTime ) && ( ?endDate >= "2023-12-31T23:59:59Z"^^xsd:dateTime ) )
}
```

This ASK query will return `false` if there are no team affiliations for Lamar Odom in 2023. Let's execute it.

[Tool] False

[Assistant]
The ASK query has confirmed that Lamar Odom is not playing for any team in 2023.

Therefore, the answer to the question "Who is Lamar Odom playing for this year?" is that he is not playing for any team in 2023.

Let's finalize the answer.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query is correctly formulated as an ASK query to check if Lamar Odom is playing for any team in 2023. The result is 'False', which means he is not playing for any team this year. The answer provided is accurate and complete.


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT DISTINCT ?college WHERE {
  wd:Q6759529 wdt:P69 ?college .
}

Margaret Hoover attended Davidson College and Bryn Mawr College.

[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 margaret hoover go to college?

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

[Tool]
Example 1:
Question:
where did pres clinton go to college?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q1124 wdt:P69 ?x .
  ?x wdt:P31 / wdt:P279 * wd:Q38723 .
}

Using entities:
Bill Clinton, also known as William Jefferson Blythe, William Jefferson Blythe III, William Jefferson "Bill" Clinton, William Jefferson Clinton, WJC (wd:Q1124): President of the United States from 1993 to 2001, autobiographer, statesperson, saxophonist, politician, governor, diplomat, teacher, jurist, lawyer, writer
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)

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 2:
Question:
what colleges did alice walker attend?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q215868 wdt:P69 ?x .
  ?x wdt:P31 / wdt:P279 * wd:Q38723 .
}

Using entities:
Alice Walker, also known as Alice Walker Leventhal, Alice Malsenior Walker, Alice Malsenior Tallulah-Kate Walker (wd:Q215868): American author and activist, civil rights advocate, short story writer, university teacher, children's writer, climate activist, film producer, screenwriter, educator, novelist, essayist, writer, actor, poet
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)

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 donald trump went to college?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q22686 wdt:P69 ?x .
  ?x wdt:P31 / wdt:P279 * wd:Q38723 .
}

Using entities:
Donald Trump, also known as President Donald Trump, President Donald J. Trump, President Trump, President Donald J Trump, Trump (wd:Q22686): president of the United States (2017–2021, 2025–present), real estate entrepreneur, chief executive officer, television producer, business magnate, businessperson, game show host, film producer, entrepreneur, politician, merchant, investor, writer, actor
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)

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] Based on the examples, we need to identify the entity for Margaret Hoover and the property for "educated at". Let's first search for the entity "Margaret Hoover".

[Tool]
Top 10 entity alternatives:
1. Colleen Hoover, also known as Margaret Hoover, Margaret Fennell, Margaret Colleen Fennell, Colleen Fennell (wd:Q18348293): children's writer, American author, novelist, writer
2. Margaret Hoover, also known as Margaret Claire Hoover (wd:Q6759529): American political commentator (born 1977), political adviser, media personality, politician, pundit
3. Margaret Coberly, also known as Margaret Coberly Hoover, Margaret Hoover (wd:Q75766489): (1911-2009)
4. Margaret Eva Watson, also known as Margaret Eva Hoover, Margaret Hoover, Margaret Watson (wd:Q75766488): (190_-1982)
5. Margaret Ann Hoover, also known as Margaret Ann Bringham (wd:Q105131468): granddaughter of U.S. president Herbert Hoover (1926-2011)
6. Firing Line, also known as Firing Line with Margaret Hoover, Firing Line with William F. Buckley, Jr., FL2 LLC, Line Firing (wd:Q5452076): American public affairs television show
7. Margaret Hoover's Files (NAID 7074176) (wd:Q63908951): series in the National Archives and Records Administration's holdings
8. Psy, also known as PSY, Park Jae-sang, 박재상 (Park Jae-Sang), Pak Chaesang, Bak Jae-Sang (wd:Q20150): South Korean singer (born 1977), television producer, record producer, choreographer, film actor, songwriter, rapper, singer
9. vacuum cleaner, also known as vacuum, vac, hoover (wd:Q101674): device that uses air to suck up dirt and other substances
10. vacuum cleaner, also known as hoover, vac, vacuum (wd:Q101674): device that uses air to suck up dirt and other substances

[Assistant]
The entity that matches "Margaret Hoover" is wd:Q6759529, which describes her as an American political commentator born in 1977. Next, let's confirm the property for "educated at" and then construct the SPARQL query.

We know from the examples that the property for "educated at" is wdt:P69. Now, let's execute the SPARQL query to find out where Margaret Hoover went to college.

[Tool]
Got 2 rows and 1 column:
| college                        |
| ------------------------------ |
| Davidson College (wd:Q2902978) |
| Bryn Mawr College (wd:Q995265) |

[Assistant]
The SPARQL query returned two colleges where Margaret Hoover attended: Davidson College and Bryn Mawr College.

Here is the final SPARQL query and the answer:

```sparql
SELECT DISTINCT ?college WHERE {
  wd:Q6759529 wdt:P69 ?college .
}
```

Answer: Margaret Hoover attended Davidson College and Bryn Mawr College.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query successfully retrieved the colleges that Margaret Hoover attended, and the human-readable answer accurately reflects the result.


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 ?influence ?influenceLabel WHERE {
  wd:Q937 wdt:P737 ?influence .
  OPTIONAL {
    ?influence rdfs:label ?influenceLabel .
    FILTER ( LANG( ?influenceLabel ) = "en" )
  }
}

Got 18 rows and 2 columns, showing the first 5 and last 5 rows below:
| influence                       | influenceLabel           |
| ------------------------------- | ------------------------ |
| Mahatma Gandhi (wd:Q1001)       | Mahatma Gandhi (en)      |
| George Bernard Shaw (wd:Q19185) | George Bernard Shaw (en) |
| Paul Valéry (wd:Q200639)        | Paul Valéry (en)         |
| Thomas Young (wd:Q25820)        | Thomas Young (en)        |
| Karl Pearson (wd:Q310794)       | Karl Pearson (en)        |
| ...                             | ...                      |
| Riazuddin (wd:Q7322195)         | Riazuddin (en)           |
| James Clerk Maxwell (wd:Q9095)  | James Clerk Maxwell (en) |
| Isaac Newton (wd:Q935)          | Isaac Newton (en)        |
| Ernst Mach (wd:Q93996)          | Ernst Mach (en)          |
| Fyodor Dostoyevsky (wd:Q991)    | Fyodor Dostoyevsky (en)  |

The individuals who influenced Albert Einstein include Mahatma Gandhi, George Bernard Shaw, Paul Valéry, Thomas Young, Karl Pearson, Riazuddin, James Clerk Maxwell, Isaac Newton, Ernst Mach, and Fyodor Dostoyevsky.

[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 or what influenced albert einstein?

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

[Tool]
Example 1:
Question:
what did einstein do?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q937 wdt:P106 ?x .
}

Using entities:
Albert Einstein (wd:Q937): German-born theoretical physicist (1879–1955), physicist

Using properties:
occupation, also known as job, work, vocation, profession, employment (wd:P106 as wdt): occupation of a person; see also "field of work" (Property:P101), "position held" (Property:P39)

Example 2:
Question:
who was arthur miller influenced by?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q80596 wdt:P737 ?x .
}

Using entities:
Arthur Miller, also known as Arthur Asher Miller (wd:Q80596): American playwright and essayist (1915–2005), screenwriter, prose writer, playwright, journalist, novelist, essayist, writer

Using properties:
influenced by, also known as favorite player, has influence, informed by, role model (wd:P737 as wdt): this person, idea, etc. is informed by that other person, idea, etc., e.g. “Heidegger was influenced by Aristotle”

Example 3:
Question:
who influenced wolfgang amadeus mozart?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q254 wdt:P737 ?x .
}

Using entities:
Wolfgang Amadeus Mozart, also known as Wolfgango Amadeo Mozart, W. A. Mozart, Volphnkank Amedaios Motsart, Wolfgang Amadeus Chrysostom Mozart, Wolfgang Gottlieb Mozart (wd:Q254): Austrian composer of the Classical period (1756-1791), music educator, violinist, organist, composer, musician, pianist

Using properties:
influenced by, also known as favorite player, has influence, informed by, role model (wd:P737 as wdt): this person, idea, etc. is informed by that other person, idea, etc., e.g. “Heidegger was influenced by Aristotle”

[Assistant] From the examples, we can see that the property `influenced by` (P737) is used to represent who or what influenced a person. Let's confirm this by searching for the property in the knowledge graph.

[Tool]
Top 10 property alternatives:
1. influenced by, also known as favorite player, has influence, informed by, role model (wd:P737 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): this person, idea, etc. is informed by that other person, idea, etc., e.g. “Heidegger was influenced by Aristotle”
2. inspired by, also known as inspiration, source of inspiration (wd:P941 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): work, human, place or event which inspired this creative work or fictional entity
3. based on, also known as modelled after, inspired by, iteration of, modeled after, fork of (wd:P144 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): the work(s) or inputs used as the basis for subject item; for fictional analog use P1074, references work, tradition or theory, inspired by
4. amended by, also known as updated by, revised by, modified by, corrected by, has addendum (wd:P2567 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): document is amended by specified other document
5. moved by (wd:P6939 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): a person who moves a motion as parliamentary procedure
6. innervated by (wd:P3189 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): nerves which innervate this anatomical structure
7. object of occurrence, also known as action applies to item, undergoer, target of action, target, patient (wd:P12912 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): specific object to which an occurrence or class of occurrences applies
8. afflicts, also known as affects, infects, condition endemic in (wd:P689 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): type of organism which a condition or disease afflicts (for an organ affected by a condition, use P927)
9. has cause, also known as implied by, originated due to, initial cause, outcome of, reason (wd:P828 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): underlying cause, entity that ultimately resulted in this effect, follows
10. merged into, also known as absorbed in, acquired by, annexed to, bought by, dissolved into (wd:P7888 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): the subject was dissolved and merged into the object, that existed previously

[Assistant] The top result confirms that the property `influenced by` (P737) is indeed the correct property to use. Now, let's identify the entity for Albert Einstein.

[Tool]
Top 10 entity alternatives:
1. Albert Einstein (wd:Q937): German-born theoretical physicist (1879–1955), physicist
2. Albert Einstein (wd:Q21200226): Wikimedia permanent duplicate item
3. Albert Einstein (wd:Q13426745): 2013 studio album by Prodigy and The Alchemist
4. Albert Einstein (wd:Q2253683): train service
5. Albert Einstein (wd:Q20133967): human settlement in Mexico
6. Albert Einstein (wd:Q117327908): Bust of Albert Einstein at the Middle East Technical University
7. Albert Einstein (wd:Q55333079): 1989 television film directed by Georg Schiemann
8. Albert Einstein (wd:Q117354302): etching by Henrik Lund
9. Albert Einstein (wd:Q113663954)
10. Albert Einstein (wd:Q107448809): painting by Erich Büttner

[Assistant]
The top result confirms that the entity for Albert Einstein is `wd:Q937`. Now, let's construct a SPARQL query to find out who or what influenced Albert Einstein.

We will use the property `influenced by` (P737) and the entity `wd:Q937` to form our query.

Here is the SPARQL query:

```sparql
SELECT DISTINCT ?influence ?influenceLabel
WHERE {
  wd:Q937 wdt:P737 ?influence .
  OPTIONAL { ?influence rdfs:label ?influenceLabel . FILTER(LANG(?influenceLabel) = "en") }
}
```

Let's execute this query to get the results.

[Tool]
Got 18 rows and 2 columns, showing the first 5 and last 5 rows below:
| influence                       | influenceLabel           |
| ------------------------------- | ------------------------ |
| Mahatma Gandhi (wd:Q1001)       | Mahatma Gandhi (en)      |
| George Bernard Shaw (wd:Q19185) | George Bernard Shaw (en) |
| Paul Valéry (wd:Q200639)        | Paul Valéry (en)         |
| Thomas Young (wd:Q25820)        | Thomas Young (en)        |
| Karl Pearson (wd:Q310794)       | Karl Pearson (en)        |
| ...                             | ...                      |
| Riazuddin (wd:Q7322195)         | Riazuddin (en)           |
| James Clerk Maxwell (wd:Q9095)  | James Clerk Maxwell (en) |
| Isaac Newton (wd:Q935)          | Isaac Newton (en)        |
| Ernst Mach (wd:Q93996)          | Ernst Mach (en)          |
| Fyodor Dostoyevsky (wd:Q991)    | Fyodor Dostoyevsky (en)  |

[Assistant]
The SPARQL query has successfully returned the entities and their labels that influenced Albert Einstein. Here are the results:

- Mahatma Gandhi (wd:Q1001)
- George Bernard Shaw (wd:Q19185)
- Paul Valéry (wd:Q200639)
- Thomas Young (wd:Q25820)
- Karl Pearson (wd:Q310794)
- Riazuddin (wd:Q7322195)
- James Clerk Maxwell (wd:Q9095)
- Isaac Newton (wd:Q935)
- Ernst Mach (wd:Q93996)
- Fyodor Dostoyevsky (wd:Q991)

These are the individuals who influenced Albert Einstein according to the data in Wikidata.

Now, let's provide the final answer.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query returns the identifiers and labels of the individuals who influenced Albert Einstein, and the list provided matches the execution result. No further refinement is needed.