PREFIX ps: <http://www.wikidata.org/prop/statement/>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX pq: <http://www.wikidata.org/prop/qualifier/>
PREFIX p: <http://www.wikidata.org/prop/>
SELECT DISTINCT ?voiceActor WHERE {
  ?work p:P161 ?castStatement .
  ?castStatement ps:P161 ?voiceActor ; 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 find the entity for Chucky and the property for the voice actor. Let's start by searching for the entity Chucky.

[Tool]
Top 10 entity alternatives:
1. Seed of Chucky, also known as Child's Play 5: Seed of Chucky (wd:Q925421): 2004 film directed by Don Mancini
2. Child's Play (wd:Q935945): 1988 film by Tom Holland
3. Child's Play, also known as Childs Play (wd:Q56697304): 2019 film directed by Lars Klevberg
4. Child's Play, also known as Chucky (franchise) (wd:Q2265493): horror film franchise focusing on Chucky, a killer doll
5. Child's Play (wd:Q3966802): 1972 film by Sidney Lumet
6. Child's Play (wd:Q5097661): children's charity
7. Child's Play (wd:Q5097656): episode of Star Trek: Voyager (S6 E19)
8. Child's Play (wd:Q1463844): 1992 film directed by Wolfgang Becker
9. Child's Play (wd:Q5097664): 1994 Marvel Comics storyline
10. Child's Play (wd:Q5097667): 1980s US game show

[Assistant] It seems like the entity for Chucky is not directly listed. However, we can see that "Child's Play" (wd:Q935945) is the original movie where Chucky appears. We can use this to find the character Chucky and then the voice actor. Let's look for the character Chucky in the movie "Child's Play".

[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, the character from the "Child's Play" franchise, is "Chucky" (wd:Q25097). Now, let's find the voice actor for this character.

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

Let's construct the SPARQL query to find the voice actor for Chucky.

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

Now, 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 qualifier `pq:P453` is not necessary or correctly applied. Let's simplify the query and try again without the qualifier.

```sparql
SELECT DISTINCT ?voiceActor WHERE {
  wd:Q25097 p:P725 ?voiceStatement .
  ?voiceStatement ps:P725 ?voiceActor .
}
```

Let's execute this simplified query.

[Tool] Got no rows and 1 columns

[Assistant]
It seems that even the simplified query did not return any results. This suggests that the voice actor information might not be directly linked to the character Chucky (wd:Q25097) in the way we expected.

Let's try another approach by searching for the voice actor of Chucky in the context of the "Child's Play" franchise. We can look for the voice actor of the character in the movies or the TV series.

First, let's find the movies in the "Child's Play" franchise and then check for the voice actor of Chucky in those movies.

Let's search for the movies in the "Child's Play" franchise.

```sparql
SELECT DISTINCT ?movie WHERE {
  wd:Q2265493 wdt:P31 wd:Q11424 .
  wd:Q2265493 wdt:P179 ?movie .
}
```

This query will list the movies in the "Child's Play" franchise. Let's execute this query.

[Tool] Got no rows and 1 columns

[Assistant]
It seems that the query did not return any results, which suggests that the "Child's Play" franchise (wd:Q2265493) might not be directly linked to the movies in the way we expected.

Let's try a different approach by searching for the movies individually and then checking for the voice actor of Chucky in those movies.

First, let's find the original "Child's Play" movie (1988) and then check for the voice actor of Chucky in that movie.

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

This query will find the voice actor of Chucky in the original "Child's Play" movie (1988). Let's execute this query.

[Tool]
Got 1 row and 1 column:
| voiceActor |
| ---------- |

[Assistant]
It seems that the query did not return any results, which suggests that the voice actor information might not be directly linked to the character Chucky in the original "Child's Play" movie (1988) in the way we expected.

Let's try another approach by searching for the voice actor of Chucky in the entire "Child's Play" franchise. We can look for the voice actor of Chucky in the TV series or other media where Chucky appears.

First, let's find the TV series "Chucky" and then check for the voice actor of Chucky in that series.

```sparql
SELECT DISTINCT ?voiceActor WHERE {
  wd:Q106824359 p:P161 ?castStatement .
  ?castStatement ps:P161 ?actor ;
                 pq:P453 wd:Q25097 .
}
```

This query will find the voice actor of Chucky in the TV series "Chucky". Let's execute this query.

[Tool] Got no rows and 1 columns

[Assistant]
It seems that the query did not return any results for the TV series "Chucky" either. This suggests that the voice actor information might not be directly linked to the character Chucky in the TV series in the way we expected.

Given that our previous attempts have not yielded results, let's try a more general approach by searching for the voice actor of Chucky across all media where Chucky appears. We can use the property `voice actor` (wd:P725) and search for the character Chucky (wd:Q25097) in any context.

```sparql
SELECT DISTINCT ?voiceActor WHERE {
  ?work p:P161 ?castStatement .
  ?castStatement ps:P161 ?voiceActor ;
                 pq:P453 wd:Q25097 .
}
```

This query will find the voice actor of Chucky in any work where Chucky appears. Let's execute this query.

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

[Assistant]
The query has returned the voice actor for Chucky, which is Brad Dourif (wd:Q329734).

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 ?voiceActor WHERE {
  ?work p:P161 ?castStatement .
  ?castStatement ps:P161 ?voiceActor ;
                 pq:P453 wd:Q25097 .
}
```

Answer: Brad Dourif

Let's finalize this with the answer function.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT DISTINCT ?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 place. 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.

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

Here is the SPARQL query:

```sparql
SELECT DISTINCT ?timeZone WHERE {
  wd:Q84 wdt:P421 ?timeZone .
}
```

Let's execute this query.

[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 more precise answer, we can focus on the primary time zone used in London, which is typically represented by "Europe/London". This time zone accounts for both standard time (Greenwich Mean Time) and daylight saving time (British Summer Time).

Here is the final SPARQL query and answer:

```sparql
SELECT DISTINCT ?timeZone WHERE {
  wd:Q84 wdt:P421 ?timeZone .
  FILTER(?timeZone = wd:Q16894228)
}
```

The time zone for London, UK is **Europe/London**.

Let's finalize this with the answer function.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT DISTINCT ?year WHERE {
  ?event wdt:P31 / wdt:P279 * wd:Q32096 ; wdt:P1923 wd:Q221878 ; wdt:P585 ?date .
  BIND( STR( YEAR( ?date ) ) AS ?year )
}

The Seattle Seahawks went to the Super Bowl in 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] Based on the examples, we can see that the Super Bowl is represented by the entity `wd:Q32096` and the property `wdt:P585` is used to represent the point in time. We also need to find the entity for the Seattle Seahawks and the property for participating in the Super Bowl. Let's start by searching for the Seattle Seahawks entity.

[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`. Next, let's identify the property that represents participation in the Super Bowl. Based on the examples, it seems that the property `wdt:P1923` is used for this purpose. However, let's verify this by checking the properties of the Seattle Seahawks.

[Tool]
Top 10 property alternatives:
1. sport, also known as play, plays, sport played, sports (wd:P641 as wdt/p): sport that the subject participates or participated in or is associated with
2. home venue, also known as home water, venue, stadium, home ground, home field (wd:P115 as wdt/p): home stadium or venue of a sports team or applicable performing arts organization, location
3. league or competition, also known as tournament, sports league, division, cup, competition (wd:P118 as wdt/p): league or competition in which team or player has played, or in which an event occurs, participant in
4. subreddit, also known as r/, reddit, Reddit community ID, Reddit forum, Reddit sub ID (wd:P3984 as wdt/p): name of the Reddit forum associated to this subject
5. participant in, also known as participated in, present at, takes part, took part, took part in (wd:P1344 as wdt/p): event in which a person or organization was/is a participant; inverse of P710 or P1923, significant event, part of
6. country, also known as sovereign state, state, land, host country (wd:P17 as wdt/p): sovereign state that this item is in (not to be used for human beings), located in the administrative territorial entity
7. topic's main category, also known as article's main category, category, category for this topic, catmain, has category (wd:P910 as wdt/p): main Wikimedia category
8. instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt/p): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
9. head coach, also known as team manager, trainer, senior coach, manager, coached by (wd:P286 as wdt/p): 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
10. FAST ID, also known as Faceted Application of Subject Terminology ID, FAST subject heading, fst, worldcat.org/fast (wd:P2163 as wdtn/wdt/p): authority control identifier in WorldCat's “FAST Linked Data” authority file

No other items found

[Assistant]
It appears that the property `wdt:P1923` (participating team) is not directly listed for the Seattle Seahawks. However, based on the examples, we can use `wdt:P1923` to find the years the Seattle Seahawks participated in the Super Bowl. Let's construct a SPARQL query to find the years the Seattle Seahawks participated in the Super Bowl.

Here is the initial SPARQL query:

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

This query looks for events that are instances or subclasses of the Super Bowl (`wd:Q32096`), where the Seattle Seahawks (`wd:Q221878`) were a participating team, and retrieves the year of the event.

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 query returned the years when the Seattle Seahawks participated in the Super Bowl. The years are:

- 2006-02-05
- 2014-02-02
- 2015-02-01

To provide a more user-friendly answer, we can extract just the year part from these dates. Let's modify the query to format the dates as years.

Here is the updated SPARQL query:

```sparql
SELECT DISTINCT YEAR(?year) AS ?year WHERE {
  ?event wdt:P31 / wdt:P279* wd:Q32096 ;
         wdt:P1923 wd:Q221878 ;
         wdt:P585 ?year .
}
```

Let's execute this updated query.

[Tool]
Failed to call function execute:
failed to parse input: parse error at position 16 for token YEAR with content 'YEAR' (the input most likely does not follow the grammar)

[Assistant]
It seems that the `YEAR` function is not supported in the SPARQL endpoint we are using. Instead, we can use the `STRDT` function to convert the date to a string and then extract the year part using substring functions.

Here is the updated SPARQL query:

```sparql
SELECT DISTINCT STRDT(SUBSTR(STR(?year), 1, 4), xsd:gYear) AS ?year WHERE {
  ?event wdt:P31 / wdt:P279* wd:Q32096 ;
         wdt:P1923 wd:Q221878 ;
         wdt:P585 ?year .
}
```

This query converts the date to a string, extracts the first four characters (which represent the year), and then converts it back to a year type.

Let's execute this updated query.

[Tool]
Failed to call function execute:
failed to parse input: parse error at position 16 for token STRDT with content 'STRDT' (the input most likely does not follow the grammar)

[Assistant]
It seems that the `STRDT` function is also not supported in the SPARQL endpoint we are using. Instead, we can use the `SUBSTR` function directly to extract the year part from the date string.

Here is the updated SPARQL query:

```sparql
SELECT DISTINCT SUBSTR(STR(?year), 1, 4) AS ?year WHERE {
  ?event wdt:P31 / wdt:P279* wd:Q32096 ;
         wdt:P1923 wd:Q221878 ;
         wdt:P585 ?year .
}
```

This query converts the date to a string and extracts the first four characters (which represent the year).

Let's execute this updated query.

[Tool]
Failed to call function execute:
failed to parse input: parse error at position 16 for token SUBSTR with content 'SUBSTR' (the input most likely does not follow the grammar)

[Assistant]
It seems that the `SUBSTR` function is also not supported in the SPARQL endpoint we are using. Instead, we can use the `BIND` function to create a new variable that extracts the year part from the date string.

Here is the updated SPARQL query:

```sparql
SELECT DISTINCT ?year WHERE {
  ?event wdt:P31 / wdt:P279* wd:Q32096 ;
         wdt:P1923 wd:Q221878 ;
         wdt:P585 ?date .
  BIND (STR(YEAR(?date)) AS ?year)
}
```

This query binds the year part of the date to a new variable `?year`.

Let's execute this updated query.

[Tool]
Got 3 rows and 1 column:
| year |
| ---- |
| 2006 |
| 2014 |
| 2015 |

[Assistant]
The query successfully returned the years when the Seattle Seahawks participated in the Super Bowl. The years are:

- 2006
- 2014
- 2015

Now, let's provide the final answer to the user.

Here is the final SPARQL query and the answer:

```sparql
SELECT DISTINCT ?year WHERE {
  ?event wdt:P31 / wdt:P279* wd:Q32096 ;
         wdt:P1923 wd:Q221878 ;
         wdt:P585 ?date .
  BIND (STR(YEAR(?date)) AS ?year)
}
```

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

[Tool] Stopped generation process


PREFIX ps: <http://www.wikidata.org/prop/statement/>
PREFIX p: <http://www.wikidata.org/prop/>
PREFIX pq: <http://www.wikidata.org/prop/qualifier/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?team WHERE {
  wd:Q296403 p:P54 ?statement .
  ?statement ps:P54 ?team .
  OPTIONAL {
    ?statement pq:P580 ?startDate .
  }
  OPTIONAL {
    ?statement pq:P582 ?endDate .
  }
  FILTER ( ( ?startDate <= NOW () || ! BOUND( ?startDate ) ) && ( ?endDate >= NOW () || ! BOUND( ?endDate ) ) )
}

Saski Baskonia (wd:Q54927)

[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 dates of the membership.

To find out which team Lamar Odom is playing for this year, we need to look for the team he is currently a member of, or the team he was a member of during the current year. We'll use the current year in our filter conditions.

First, let's identify the entity for Lamar Odom.

[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 a SPARQL query to find out which team he is playing for this year. We'll use the current year in the filter conditions.

I'll start by writing a simple query to check the teams he has been a member of and their respective start and end dates.

[Tool]
Got 6 rows and 3 columns:
| team                                             | startDate                           | endDate                             |
| ------------------------------------------------ | ----------------------------------- | ----------------------------------- |
| Dallas Mavericks (wd:Q132893)                    | 2011-01-01T00:00:00Z (xsd:dateTime) | 2012-01-01T00:00:00Z (xsd:dateTime) |
| Los Angeles Clippers (wd:Q976396)                | 2012-01-01T00:00:00Z (xsd:dateTime) | 2013-01-01T00:00:00Z (xsd:dateTime) |
| Los Angeles Lakers (wd:Q121783)                  | 2004-01-01T00:00:00Z (xsd:dateTime) | 2011-01-01T00:00:00Z (xsd:dateTime) |
| Saski Baskonia (wd:Q54927)                       |                                     |                                     |
| Rhode Island Rams men's basketball (wd:Q7320948) | 1998-01-01T00:00:00Z (xsd:dateTime) | 1999-01-01T00:00:00Z (xsd:dateTime) |
| Miami Heat (wd:Q169138)                          | 2003-01-01T00:00:00Z (xsd:dateTime) | 2004-01-01T00:00:00Z (xsd:dateTime) |

[Assistant]
Based on the results, it appears that Lamar Odom has not been a member of any team since 2013. However, the last entry shows that he was a member of Saski Baskonia, but without any start or end dates. This could mean that the information is incomplete or outdated.

To find out if he is currently playing for any team, we need to filter the results to include only those where the current date falls between the start and end dates, or where there is no end date (indicating an ongoing membership).

Let's refine the query to include this filter.

[Tool]
Got 1 row and 1 column:
| team                       |
| -------------------------- |
| Saski Baskonia (wd:Q54927) |

[Assistant]
The SPARQL query indicates that Lamar Odom is currently a member of Saski Baskonia.

Therefore, the answer to the question "Who is Lamar Odom playing for this year?" is:

Saski Baskonia (wd:Q54927)

Here is the final SPARQL query:

```sparql
SELECT ?team WHERE {
  wd:Q296403 p:P54 ?statement .
  ?statement ps:P54 ?team .
  OPTIONAL { ?statement pq:P580 ?startDate . }
  OPTIONAL { ?statement pq:P582 ?endDate . }
  FILTER ( ( ?startDate <= NOW() || !BOUND(?startDate) ) && ( ?endDate >= NOW() || !BOUND(?endDate) ) )
}
```

And the answer:

Saski Baskonia (wd:Q54927)

[Tool] Stopped generation process


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?college ?collegeLabel WHERE {
  wd:Q6759529 wdt:P69 ?college .
  OPTIONAL {
    ?college rdfs:label ?collegeLabel .
    FILTER ( LANG( ?collegeLabel ) = "en" )
  }
}

Got 2 rows and 2 columns:
| college                        | collegeLabel           |
| ------------------------------ | ---------------------- |
| Davidson College (wd:Q2902978) | Davidson College (en)  |
| Bryn Mawr College (wd:Q995265) | Bryn Mawr College (en) |

Margaret Hoover went to college at 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 then use the property `educated at` (P69) to find out where she went to college. Let's first search for the entity representing 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] From the search results, it seems that the entity we are looking for is likely "Margaret Hoover, also known as Margaret Claire Hoover" (wd:Q6759529). Let's confirm this by checking the properties associated with this entity to see if it includes information about her education.

[Tool]
Got 170 rows and 3 columns, showing the first 10 rows below:
| subject                       | property                         | object                                                                     |
| ----------------------------- | -------------------------------- | -------------------------------------------------------------------------- |
| Margaret Hoover (wd:Q6759529) | instance of (wdt:P31)            | human (wd:Q5)                                                              |
| Margaret Hoover (wd:Q6759529) | occupation (wdt:P106)            | politician (wd:Q82955)                                                     |
| Margaret Hoover (wd:Q6759529) | sex or gender (wdt:P21)          | female (wd:Q6581072)                                                       |
| Margaret Hoover (wd:Q6759529) | given name (wdt:P735)            | Margaret (wd:Q4963612)                                                     |
| Margaret Hoover (wd:Q6759529) | date of birth (wdt:P569)         | 1977-12-11T00:00:00Z (xsd:dateTime)                                        |
| Margaret Hoover (wd:Q6759529) | Commons category (wdt:P373)      | Margaret Hoover                                                            |
| Margaret Hoover (wd:Q6759529) | image (wdt:P18)                  | <http://commons.wikimedia.org/wiki/Special:FilePath/Margaret%20Hoover.jpg> |
| Margaret Hoover (wd:Q6759529) | country of citizenship (wdt:P27) | United States (wd:Q30)                                                     |
| Margaret Hoover (wd:Q6759529) | family name (wdt:P734)           | Hoover (wd:Q20748947)                                                      |
| Margaret Hoover (wd:Q6759529) | part of (wdt:P361)               | Hoover family (wd:Q62531769)                                               |
| ...                           | ...                              | ...                                                                        |

[Assistant]
The list of properties for Margaret Hoover (wd:Q6759529) does not include information about her education. However, we can still try to use the `educated at` property (P69) to see if there is any information available.

Let's run a SPARQL query to check if there is any data about where Margaret Hoover went to college.

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

Executing this query will help us determine if there is any information about her education.

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

[Assistant]
The SPARQL query results show that Margaret Hoover attended both Davidson College (wd:Q2902978) and Bryn Mawr College (wd:Q995265).

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

```sparql
SELECT ?college ?collegeLabel
WHERE {
  wd:Q6759529 wdt:P69 ?college .
  OPTIONAL { ?college rdfs:label ?collegeLabel . FILTER(LANG(?collegeLabel) = "en") }
}
```

Executing this query will give us the IRIs and labels of the colleges.

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

[Assistant]
The final SPARQL query results show that Margaret Hoover attended both Davidson College and Bryn Mawr College.

Here is the final SPARQL query and the answer:

```sparql
SELECT ?college ?collegeLabel
WHERE {
  wd:Q6759529 wdt:P69 ?college .
  OPTIONAL { ?college rdfs:label ?collegeLabel . FILTER(LANG(?collegeLabel) = "en") }
}
```

Answer: Margaret Hoover went to college at Davidson College and Bryn Mawr College.

[Tool] Stopped generation process


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT 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 entities that influenced Albert Einstein include: Mahatma Gandhi, George Bernard Shaw, Paul Valéry, Thomas Young, Karl Pearson, Riazuddin, James Clerk Maxwell, Isaac Newton, Ernst Mach, Fyodor Dostoyevsky, and 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] 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] Based on the examples, it seems 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 pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/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 pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/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 pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/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 pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): document is amended by specified other document
5. moved by (wd:P6939 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): a person who moves a motion as parliamentary procedure
6. innervated by (wd:P3189 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/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 pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/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 pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/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 pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/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 pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): the subject was dissolved and merged into the object, that existed previously

[Assistant] The property `influenced by` (P737) is indeed the correct property to use for this question. Now let's search for the entity representing 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 entity `Albert Einstein` (Q937) is the correct one to use for this question. Now we can construct a SPARQL query to find out who or what influenced Albert Einstein.

Let's write a simple SPARQL query to retrieve the entities that influenced Albert Einstein.

[Tool]
Got 18 rows and 1 column, showing the first 5 and last 5 rows below:
| influence                       |
| ------------------------------- |
| 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)    |

[Assistant] The SPARQL query successfully retrieved the entities that influenced Albert Einstein. To provide a more complete answer, we can include the labels of these entities in the result. Let's modify the query to include the labels.

[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 now includes the labels of the entities that influenced Albert Einstein. Here is the final SPARQL query and the answer to the user's question:

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

The entities that influenced Albert Einstein include:
- Mahatma Gandhi
- George Bernard Shaw
- Paul Valéry
- Thomas Young
- Karl Pearson
- Riazuddin
- James Clerk Maxwell
- Isaac Newton
- Ernst Mach
- Fyodor Dostoyevsky

And others.

Now, let's provide the final answer.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX pq: <http://www.wikidata.org/prop/qualifier/>
PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
PREFIX ps: <http://www.wikidata.org/prop/statement/>
PREFIX p: <http://www.wikidata.org/prop/>
SELECT ?person ?startDate ?endDate WHERE {
  ?person p:P39 ?positionStatement .
  ?positionStatement ps:P39 wd:Q844587 ; pq:P580 ?startDate ; pq:P582 ?endDate .
  FILTER ( ( ?startDate < "2012-01-01T00:00:00Z" ^^ xsd:dateTime ) && ( ?endDate >= "2011-01-01T00:00:00Z" ^^ xsd:dateTime ) )
}

José Luis Rodríguez Zapatero (wd:Q41395) was the Prime Minister of Spain for most of 2011.

[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 the prime minister of spain 2011?

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

[Tool]
Example 1:
Question:
who is prime minister of japan 2011?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?x p:P39 ?p .
  ?p ps:P39 wd:Q274948 ; 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:
Prime Minister of Japan, also known as Japanese prime minister, Kantei Saigai (wd:Q274948): head of government of Japan

Using properties:
position held, also known as function, held position, holds position, job, office held (wd:P39 as ps/p): subject currently or formerly holds the object position or public office
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:
who is the president of the european union 2011?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?x p:P39 ?p .
  ?p ps:P39 wd:Q740126 ; 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:
President of the European Parliament (wd:Q740126): head of debate oversight in the European Union legislature

Using properties:
position held, also known as function, held position, holds position, job, office held (wd:P39 as ps/p): subject currently or formerly holds the object position or public office
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 is the leader of the dominican republic 2011?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?x p:P39 ?p .
  ?p ps:P39 wd:Q607982 ; 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:
President of the Dominican Republic (wd:Q607982): Dominican head of state and head of government

Using properties:
position held, also known as function, held position, holds position, job, office held (wd:P39 as ps/p): subject currently or formerly holds the object position or public office
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

[Assistant] Based on the examples, we need to identify the Prime Minister of Spain and the relevant time period (2011). Let's first search for the entity representing the Prime Minister of Spain.

[Tool]
Top 10 entity alternatives:
1. Prime Minister of Spain, also known as president of the Government of Spain (wd:Q844587): head of government of Spain
2. Deputy Prime Minister of Spain, also known as First Deputy Prime Minister of Spain, Vice President of the Government of Spain (wd:Q3113035): second-highest position of the Government of Spain
3. Fourth Deputy Prime Minister of Spain (wd:Q83565070): Senior position in the government of Spain
4. Office of the Prime Minister, also known as Presidency of the Council of Ministers (1874-1939), Presidency of the Government (1939-), Prime Minister's Office (Spain) (wd:Q113299788): Office of the prime minister of Spain
5. Third Deputy Prime Minister of Spain (wd:Q6366494)
6. First Deputy Prime Minister of the Government of Spain (wd:Q37936537)
7. minister of Spain (wd:Q20899145)
8. Taoiseach, also known as Irish prime minister, prime minister of Ireland, prime minister of the Republic Ireland, Taoisigh (wd:Q191827): head of government and prime minister of Ireland
9. Prime Minister of Japan, also known as Japanese prime minister, Kantei Saigai (wd:Q274948): head of government of Japan
10. Prime Minister of Australia, also known as Australian Prime Minister (wd:Q319145): Head of Government of Australia

[Assistant]
The correct entity for the Prime Minister of Spain is `wd:Q844587`. Now let's construct a SPARQL query to find who held this position during 2011.

We will use the `position held` property (`wd:P39`) and filter the results based on the start and end times to ensure the person was in office during 2011.

Let's execute the following SPARQL query:

```sparql
SELECT DISTINCT ?person WHERE {
  ?person p:P39 ?positionStatement .
  ?positionStatement ps:P39 wd:Q844587 ;
                     pq:P580 ?startDate ;
                     pq:P582 ?endDate .
  FILTER ( ( ?startDate < "2012-01-01T00:00:00Z"^^xsd:dateTime ) && ( ?endDate >= "2011-01-01T00:00:00Z"^^xsd:dateTime ) )
}
```

This query will return the person who held the position of Prime Minister of Spain during 2011.

Let's execute this query.

[Tool]
Got 2 rows and 1 column:
| person                    |
| ------------------------- |
| Mariano Rajoy (wd:Q10819) |
| wd:Q41395                 |

[Assistant]
The query returned two results, `Mariano Rajoy (wd:Q10819)` and `wd:Q41395`. To provide a clear answer, let's verify the second result and ensure it is relevant. We can do this by checking the label of `wd:Q41395`.

Let's execute a query to get the label of `wd:Q41395`.

```sparql
SELECT ?label WHERE {
  wd:Q41395 rdfs:label ?label .
  FILTER(LANG(?label) = "en")
}
```

Let's execute this query.

[Tool]
Got 1 row and 1 column:
| label                             |
| --------------------------------- |
| José Luis Rodríguez Zapatero (en) |

[Assistant]
The second result, `wd:Q41395`, corresponds to José Luis Rodríguez Zapatero.

Both Mariano Rajoy and José Luis Rodríguez Zapatero were Prime Ministers of Spain during 2011. However, to provide a more precise answer, we need to check the exact dates of their terms to determine who was in office during 2011.

Let's refine our query to include the start and end dates for both individuals to confirm their terms.

```sparql
SELECT ?person ?startDate ?endDate WHERE {
  ?person p:P39 ?positionStatement .
  ?positionStatement ps:P39 wd:Q844587 ;
                     pq:P580 ?startDate ;
                     pq:P582 ?endDate .
  FILTER ( ( ?startDate < "2012-01-01T00:00:00Z"^^xsd:dateTime ) && ( ?endDate >= "2011-01-01T00:00:00Z"^^xsd:dateTime ) )
}
```

Let's execute this refined query.

[Tool]
Got 2 rows and 3 columns:
| person                    | startDate                           | endDate                             |
| ------------------------- | ----------------------------------- | ----------------------------------- |
| Mariano Rajoy (wd:Q10819) | 2011-12-20T00:00:00Z (xsd:dateTime) | 2015-12-21T00:00:00Z (xsd:dateTime) |
| wd:Q41395                 | 2004-04-16T00:00:00Z (xsd:dateTime) | 2011-12-21T00:00:00Z (xsd:dateTime) |

[Assistant]
The results show that:

- Mariano Rajoy (wd:Q10819) was Prime Minister of Spain from December 20, 2011, to December 21, 2015.
- José Luis Rodríguez Zapatero (wd:Q41395) was Prime Minister of Spain from April 16, 2004, to December 21, 2011.

Since the transition occurred on December 21, 2011, José Luis Rodríguez Zapatero was the Prime Minister for the majority of 2011, and Mariano Rajoy took over at the very end of the year.

Therefore, the Prime Minister of Spain for most of 2011 was José Luis Rodríguez Zapatero.

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

```sparql
SELECT ?person ?startDate ?endDate WHERE {
  ?person p:P39 ?positionStatement .
  ?positionStatement ps:P39 wd:Q844587 ;
                     pq:P580 ?startDate ;
                     pq:P582 ?endDate .
  FILTER ( ( ?startDate < "2012-01-01T00:00:00Z"^^xsd:dateTime ) && ( ?endDate >= "2011-01-01T00:00:00Z"^^xsd:dateTime ) )
}
```

The answer to the question "Who is the Prime Minister of Spain in 2011?" is José Luis Rodríguez Zapatero.

[Tool] Stopped generation process