PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT DISTINCT ?country ?countryLabel WHERE {
  wd:Q1771942 wdt:P159 ?headquartersLocation .
  ?headquartersLocation wdt:P17 ?country .
  OPTIONAL {
    ?country rdfs:label ?countryLabel FILTER ( LANG( ?countryLabel ) = "en" )
  }
}

Got 1 row and 2 columns:
| country                | countryLabel       |
| ---------------------- | ------------------ |
| United States (wd:Q30) | United States (en) |

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

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

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

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

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

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

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

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

[Tool]
Example 1:
Question:
Give me all Danish films.

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?type wdt:P279 * wd:Q11424 .
  ?uri wdt:P31 ?type ; wdt:P495 wd:Q35 .
}

Using entities:
film, also known as cinematic work, moving picture, movie, motion pictures, motion picture (wd:Q11424): sequence of images that give the impression of movement, stored on film stock
Denmark, also known as 🇩🇰, Danmark, DEK, TAN, DEN (wd:Q35): country in Northern Europe

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)
country of origin, also known as origin country, place of origin, originates from, country, CoO (wd:P495 as wdt): country of origin of this item (creative work, food, phrase, product, etc.), significant place, country

Example 2:
Question:
How many children does Eddie Murphy have?

SPARQL:
SELECT ( COUNT( DISTINCT ?uri ) AS ?c ) WHERE {
  wd:Q43874 wdt:P40 ?uri .
}

Using entities:
Eddie Murphy, also known as Edward Regan Murphy, Edward Murphy (wd:Q43874): American actor and comedian, television producer, television actor, film producer, film director, screenwriter, voice actor, film actor, songwriter, director, comedian, producer, singer, writer, actor

Using properties:
child, also known as has son, kids, mother of, kid, issue (wd:P40 as wdt): subject has object as child. Do not use for stepchildren—use "relative" (P1038), qualified with "type of kinship" (P1039), significant person, relative

Example 3:
Question:
Which movies did Kurosawa direct?

SPARQL:
SELECT DISTINCT * WHERE {
  ?uri wdt:P57 wd:Q8006 .
}

Using entities:
Akira Kurosawa, also known as Kurosawa Akira, Kurosawa (wd:Q8006): Japanese filmmaker (1910–1998), film producer, film director, screenwriter, film editor, producer, director, painter, writer

Using properties:
director, also known as film director, movie director, game director, directed by (wd:P57 as wdt): director(s) of film, TV-series, stageplay, video game or similar, contributor to the creative work or subject, creator

[Assistant] The examples show how to find the country of origin or headquarters for an entity. Now, I will search for the entity "United Fruit Company" in Wikidata to get its identifier.

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

[Assistant] The entity "United Fruit Company" is identified as wd:Q1771942 in Wikidata. Now I will search for the property that indicates the headquarters or country of the United Fruit Company. I will search for properties related to "headquarters" or "country" for this entity.

[Tool]
Top 10 property alternatives:
1. headquarters location, also known as headquarters locality, seat, headquarters town, HQ, HQ location (wd:P159 as wdt/p): city or town where an organization's headquarters is or has been situated. Use P276 qualifier for specific building, significant place
2. inception, also known as initial release date, founded in, founded on, founded on date, founding date (wd:P571 as wdt/p): time when an entity begins to exist; for date of official opening use P1619, start time
3. industry, also known as branch, economic branch, field of action, field of exercise, sector (wd:P452 as wdt/p): specific industry of company or organization, part of
4. founded by, also known as starter, co-founded by, started by, institutor, instituted by (wd:P112 as wdt/p): founder or co-founder of this organization, religion, place or entity, creator
5. replaced by, also known as heir, superseded by, successor, succeeded by, next job holder (wd:P1366 as wdt/p): other person or item which continues the item by replacing it in... nor identical, but adds to the series (e.g. books in a series)., followed by
6. Library of Congress authority ID, also known as LCSH Authority File ID, LC Name Authority ID, LCSH ID, LCSH LCCN, LC Subject Heading ID (wd:P244 as wdt/wdtn/p): Library of Congress name authority (persons, families, corporate...lowed by 8-10 digits (see regex). For manifestations, use P1144]
7. 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
8. dissolved, abolished or demolished date, also known as ended, publication end date, final issue, date repealed, final year (wd:P576 as wdt/p): point in time at which the subject (organisation, building) ceas...g equipment,  "discontinued date" (P2669) for stopping a product, end time
9. archives at, also known as archive location, correspondence at, papers at (wd:P485 as wdt/p): the institution holding the subject's archives, significant place
10. 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

No other items found

[Assistant] The property "headquarters location" (wdt:P159) is the most relevant for finding the headquarters of the United Fruit Company. However, the user asked for the country where the headquarters was located, so I will also consider the property "country" (wdt:P17) for the headquarters location. I will now write a SPARQL query to find the country of the headquarters location of the United Fruit Company.

[Tool]
Got 1 row and 2 columns:
| country                | countryLabel       |
| ---------------------- | ------------------ |
| United States (wd:Q30) | United States (en) |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves the country where the United Fruit Company had its headquarters by first getting the headquarters location and then the country of that location. The query returns the United States, which is accurate. The query also optionally retrieves the English label of the country, which is helpful for readability. The answer is correct and complete, and the query follows the rules for SPARQL queries. No further refinement is necessary.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?crimeLabel WHERE {
  wd:Q201377 wdt:P1399 ?crime .
  OPTIONAL {
    ?crime rdfs:label ?crimeLabel FILTER ( LANG( ?crimeLabel ) = "en" )
  }
}

Got 1 row and 1 column:
| crimeLabel             |
| ---------------------- |
| culpable homicide (en) |

According to Wikidata, Oscar Pistorius is convicted of culpable homicide, but the knowledge graph does not contain information about the number of people he killed.

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

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

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

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

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

[User] How many people did Oscar Pistorius kill?

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

[Tool]
Example 1:
Question:
Give me all soccer clubs in Spain.

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P31 wd:Q476028 .
  ?uri wdt:P17 wd:Q29 .
}

Using entities:
Spain, also known as Kingdom of Spain (wd:Q29): country in southwestern Europe with territories in Africa
association football club, also known as association soccer club, F.C., FC, football club, soccer club (wd:Q476028): sports club devoted to association football (soccer)

Using properties:
country, also known as sovereign state, state, land, host country (wd:P17 as wdt): sovereign state that this item is in (not to be used for human beings), located in the administrative territorial entity
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)

Example 2:
Question:
Which countries have places with more than two caves?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?cave wdt:P31 wd:Q35509 ; wdt:P17 ?uri .
}
GROUP BY ?uri
HAVING ( COUNT( ?cave ) > 2 )

Using entities:
cave, also known as cavern (wd:Q35509): natural void under the Earth's surface

Using properties:
country, also known as sovereign state, state, land, host country (wd:P17 as wdt): sovereign state that this item is in (not to be used for human beings), located in the administrative territorial entity
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)

Example 3:
Question:
What were the names of the three ships by Columbus?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P31 / wdt:P279 * wd:Q11446 .
  ?uri wdt:P1344 / wdt:P710 wd:Q7322 .
}

Using entities:
ship, also known as water vessel, vessel, marine vessel, ships (wd:Q11446): large buoyant watercraft
Christopher Columbus, also known as Cristóbal Colón, Cristoforo Colombo, Don Cristobál Colón, 'Christopher Columbus', Cristobal Colon, Christofer Columbus (wd:Q7322): Italian navigator, explorer, and colonist (1451–1506), explorer, seafarer, traveler, inventor, sailor

Using properties:
participant in, also known as participated in, present at, takes part, took part, took part in (wd:P1344 as wdt): event in which a person or organization was/is a participant; inverse of P710 or P1923, significant event, part of
subclass of, also known as rdfs:subClassOf, sort of, subcategory of, subgenre of, subtype of (wd:P279 as wdt): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
participant, also known as suspect, player, party, participants, has participant (wd:P710 as wdt): person, group of people or organization (object) that actively t...ct has role" (P3831). Use P1923 for participants that are teams., significant person

[Assistant]
The question is about how many people Oscar Pistorius killed. This relates to the concept of a person (Oscar Pistorius) and an event or fact about killing or murder. We need to find the entity for Oscar Pistorius and then find a property related to killing or murder.

Step 1: Search for the entity "Oscar Pistorius" in Wikidata.

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

[Assistant] The entity for Oscar Pistorius is wd:Q201377. Now, I need to find properties related to killing or murder that are associated with this entity or generally used to describe such facts. I will search for properties related to "kill" or "murder".

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

[Assistant] The property "number of victims of killer" (wdt:P1345) seems to be the most relevant for the question about how many people Oscar Pistorius killed. I will now check if this property is used for Oscar Pistorius (wd:Q201377).

[Tool] Got no rows and 3 columns

[Assistant] There is no direct statement for Oscar Pistorius with the property "number of victims of killer" (wdt:P1345). I will try to find if there is any statement about the number of people he killed using other properties or qualifiers. I will check the statements of Oscar Pistorius to see if there is any relevant information about killing or murder.

[Tool]
Got 504 rows and 3 columns, showing the first 10 rows below:
| subject                      | property                         | object                                                                                |
| ---------------------------- | -------------------------------- | -------------------------------------------------------------------------------------------------------- |
| Oscar Pistorius (wd:Q201377) | instance of (wdt:P31)            | human (wd:Q5)                                                                                |
| Oscar Pistorius (wd:Q201377) | occupation (wdt:P106)            | athletics competitor (wd:Q11513337)                                                                      |
| Oscar Pistorius (wd:Q201377) | sex or gender (wdt:P21)          | male (wd:Q6581097)                                                                                |
| Oscar Pistorius (wd:Q201377) | given name (wdt:P735)            | Leonard (wd:Q948418)                                                                                |
| Oscar Pistorius (wd:Q201377) | date of birth (wdt:P569)         | 1986-11-22T00:00:00Z (xsd:dateTime)                                                                      |
| Oscar Pistorius (wd:Q201377) | Commons category (wdt:P373)      | Oscar Pistorius                                                                                |
| Oscar Pistorius (wd:Q201377) | image (wdt:P18)                  | <http://commons.wikimedia.org/wiki/Special:FilePath/Oscar%20Pistorius%20in%20Warsaw%20%28cropped%29.jpg> |
| Oscar Pistorius (wd:Q201377) | country of citizenship (wdt:P27) | South Africa (wd:Q258)                                                                                |
| Oscar Pistorius (wd:Q201377) | family name (wdt:P734)           | Pistorius (wd:Q21448653)                                                                                |
| Oscar Pistorius (wd:Q201377) | place of birth (wdt:P19)         | Sandton (wd:Q1026420)                                                                                |
| ...                          | ...                              | ...                                                                                |

[Assistant] There are many statements about Oscar Pistorius, but none of the first ones seem to directly address the number of people he killed. I will now specifically search for properties related to "murder" or "kill" that are used for Oscar Pistorius.

[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. penalty, also known as judicial sentence, punishment, sentence (wd:P1596 as wdt/p): penalty imposed by an authority
3. given name, also known as name, proper name, personal name, middle name, forename (wd:P735 as wdt/p): first name or another given name of this person; values used with the property should not link disambiguations nor family names, name
4. significant event, also known as significant occurrence, significant incident, significant achievment, sign. event, outcome (wd:P793 as wdt/p): significant or notable events associated with the subject
5. medical condition, also known as disability, suffers from, suffer from, sickness, paralympic disability (wd:P1050 as wdt/p): any state relevant to the health of an organism, including diseases and positive conditions
6. nickname, also known as informal name, moniker, other name, othername, other_names (wd:P1449 as wdt/p): informal name (for a pseudonym use P742), name
7. occupation, also known as job, work, vocation, profession, employment (wd:P106 as wdt/p): occupation of a person; see also "field of work" (Property:P101), "position held" (Property:P39)
8. languages spoken, written or signed, also known as language of expression, language, wrote language, writes language, uses language (wd:P1412 as wdt/p): language(s) that a person or a people speaks, writes or signs, including the native language(s)
9. sex or gender, also known as gender or sex, sex, gender identity, gender expression, gender (wd:P21 as wdt/p): sex or gender identity of human or animal. For human: male, fema...m, female organism. Groups of same gender use subclass of (P279)
10. official website, also known as site (official), home page, site official, URL, URL (official) (wd:P856 as p): URL of the official page of an item (current or former). Usage: ...but see the "Hijacked or dead websites" section of the Talk page, URL

No other items found

[Assistant] There is a property "penalty" (wdt:P1596) which might be related to judicial sentences, but no direct property for the number of people killed is found for Oscar Pistorius. Since Oscar Pistorius is known as a convicted murderer, I will check if there is any statement about the number of victims or murder related to him using the property "number of victims of killer" (wdt:P1345) again but more broadly. I will try to find the value of this property for Oscar Pistorius.

[Tool] Got no rows and 1 columns

[Assistant] There is no direct statement for the number of victims of Oscar Pistorius using the property "number of victims of killer" (wdt:P1345). I will try to find if there is any other property or statement that indicates the number of people he killed. I will check if there is any statement about the murder or killing event related to Oscar Pistorius.

[Tool]
Got 2 rows and 3 columns:
| subject                      | property                     | object                                  |
| ---------------------------- | ---------------------------- | --------------------------------------- |
| Oscar Pistorius (wd:Q201377) | significant event (wdt:P793) | shooting (wd:Q2252077)                  |
| Oscar Pistorius (wd:Q201377) | significant event (wdt:P793) | trial of Oscar Pistorius (wd:Q16328520) |

[Assistant] Oscar Pistorius is linked to two significant events: a shooting and his trial. The shooting event might be related to the killing. I will now check the details of the shooting event (wd:Q2252077) to see if it mentions the number of people killed.

[Tool]
Got 333 rows and 3 columns, showing the first 10 rows below:
| subject                | property                         | object                                                                                |
| ---------------------- | -------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| shooting (wd:Q2252077) | Commons category (wdt:P373)      | Shooting                                                                                |
| shooting (wd:Q2252077) | image (wdt:P18)                  | <http://commons.wikimedia.org/wiki/Special:FilePath/Print%2C%201866%2C%20by%20Edward%20Hacker%20%281813-1905%29%2C%20after%20Abraham%20Cooper%2C%20RA%2C%20%281787%E2%80%931868%29%2C%20shooting%20scene%2C%20UK.jpg> |
| shooting (wd:Q2252077) | subclass of (wdt:P279)           | technical activity (wd:Q119822504)                                                                                |
| shooting (wd:Q2252077) | described by source (wdt:P1343)  | Encyclopædia Britannica 11th edition (wd:Q867541)                                                                                |
| shooting (wd:Q2252077) | follows (wdt:P155)               | aiming (wd:Q3858684)                                                                                |
| shooting (wd:Q2252077) | different from (wdt:P1889)       | shooting sport (wd:Q206989)                                                                                |
| shooting (wd:Q2252077) | topic's main category (wdt:P910) | wd:Q10095528                                                                                |
| shooting (wd:Q2252077) | facet of (wdt:P1269)             | combat (wd:Q650711)                                                                                |
| shooting (wd:Q2252077) | has use (wdt:P366)               | hunting (wd:Q36963)                                                                                |
| shooting (wd:Q2252077) | uses (wdt:P2283)                 | gunshot (wd:Q2992372)                                                                                |
| ...                    | ...                              | ...                                                                                |

[Assistant] The shooting event (wd:Q2252077) is a general concept of shooting and does not contain specific information about the number of people killed in the context of Oscar Pistorius. I will now check the trial of Oscar Pistorius (wd:Q16328520) to see if it contains information about the number of people he killed.

[Tool]
Got 47 rows and 3 columns, showing the first 10 rows below:
| subject                                 | property                 | object                                                                                |
| --------------------------------------- | ------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| trial of Oscar Pistorius (wd:Q16328520) | instance of (wdt:P31)    | legal case (wd:Q2334719)                                                                                |
| trial of Oscar Pistorius (wd:Q16328520) | country (wdt:P17)        | South Africa (wd:Q258)                                                                                |
| trial of Oscar Pistorius (wd:Q16328520) | image (wdt:P18)          | <http://commons.wikimedia.org/wiki/Special:FilePath/Cape%20Times%20newspaper%20poster%20dated%2015%20February%202013%20about%20the%20arrest%20of%20Oscar%20Pistorius.jpg> |
| trial of Oscar Pistorius (wd:Q16328520) | point in time (wdt:P585) | 2014-01-01T00:00:00Z (xsd:dateTime)                                                                                |
| trial of Oscar Pistorius (wd:Q16328520) | court (wdt:P4884)        | High Court of South Africa (wd:Q5755253)                                                                                |
| trial of Oscar Pistorius (wd:Q16328520) | defendant (wdt:P1591)    | Oscar Pistorius (wd:Q201377)                                                                                |
| trial of Oscar Pistorius (wd:Q16328520) | Freebase ID (wdt:P646)   | /m/0_x7rv5                                                                                |
| trial of Oscar Pistorius (wd:Q16328520) | wdt:P31279               | legal case (wd:Q2334719)                                                                                |
| trial of Oscar Pistorius (wd:Q16328520) | schema:description       | adjudication of murder and gun c... athlete Oscar Pistorius in 2014 (en)                                                                                |
| trial of Oscar Pistorius (wd:Q16328520) | schema:name              | Hofsaak van Oscar Pistorius (af)                                                                                |
| ...                                     | ...                      | ...                                                                                |

[Assistant] The trial of Oscar Pistorius (wd:Q16328520) is a legal case where he was the defendant. The description mentions adjudication of murder and gun charges. I will check if there is any property or statement about the number of victims or people killed in this trial entity.

[Tool] Got no rows and 3 columns

[Assistant] There is no direct statement about the number of victims in the trial entity either. Given the information available, it is known that Oscar Pistorius was convicted of murder, but the exact number of people he killed is not explicitly stated in the data. Based on general knowledge, he killed one person. I will now formulate the final answer with this information.

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query used the property 'number of victims of killer' (wdt:P1345) to find how many people Oscar Pistorius killed, but this property is not populated for him in Wikidata, resulting in no rows returned. The answer given is based on general knowledge, not on the knowledge graph data. To improve, the system should either confirm the absence of this data explicitly or attempt to find related information such as convictions or incidents linked to Oscar Pistorius that might indicate the number of victims. Additionally, the query could be refined to check for statements about convictions or charges related to homicide. Since the user asked for a number, the system should clarify that the knowledge graph does not contain this information rather than providing a possibly misleading number from external knowledge.

[Assistant]
Thank you for the feedback. I understand that the property "number of victims of killer" (wdt:P1345) is not populated for Oscar Pistorius in Wikidata, and I should explicitly confirm the absence of this data. Additionally, I should try to find related information such as convictions or charges related to homicide to clarify the situation. Since the user asked for a number, I will clarify that the knowledge graph does not contain this information rather than providing a number based on external knowledge.

I will now refine the approach:
1. Confirm explicitly that the property "number of victims of killer" is not present for Oscar Pistorius.
2. Check for any statements about convictions or charges related to homicide or murder.
3. Provide a clear answer that the knowledge graph does not contain the number of people he killed.

I will start by confirming the absence of the property "number of victims of killer" for Oscar Pistorius with a query that returns a boolean result.

[Tool] False

[Assistant] The property "number of victims of killer" (wdt:P1345) is not present for Oscar Pistorius in Wikidata. I will now check if there are any statements about convictions or charges related to homicide or murder for Oscar Pistorius. I will search for properties related to "conviction" or "charge" for him.

[Tool]
Top 10 property alternatives:
1. convicted of, also known as conviction, found guilty of, guilty of (wd:P1399 as wdt/p): crime a person was convicted of
2. penalty, also known as judicial sentence, punishment, sentence (wd:P1596 as wdt/p): penalty imposed by an authority
3. place of detention, also known as internment at, internment place, jail, jailed at, penal (wd:P2632 as wdt/p): place where this person is or was detained, residence
4. significant event, also known as significant occurrence, significant incident, significant achievment, sign. event, outcome (wd:P793 as wdt/p): significant or notable events associated with the subject
5. country of citizenship, also known as subject of (country), nation of citizenship, national of, nationality, nation (wd:P27 as wdt/p): the object is a country that recognizes the subject as its citizen, country
6. place of birth, also known as POB, location of birth, location born, born in, birthplace (wd:P19 as wdt/p): most specific known birth location of a person, animal or fictional character, location
7. 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
8. date of birth, also known as year of birth, birthdate, DOB, born on, born (wd:P569 as wdt/p): date on which the subject was born, inception
9. medical condition, also known as disability, suffers from, suffer from, sickness, paralympic disability (wd:P1050 as wdt/p): any state relevant to the health of an organism, including diseases and positive conditions
10. image, also known as still capture, still, screenshot, screen capture, portrait (wd:P18 as wdt/p): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)

No other items found

[Assistant] There is a property "convicted of" (wdt:P1399) which indicates the crime a person was convicted of. I will check if Oscar Pistorius has any statements with this property.

[Tool]
Got 1 row and 3 columns:
| subject                      | property                 | object                           |
| ---------------------------- | ------------------------ | -------------------------------- |
| Oscar Pistorius (wd:Q201377) | convicted of (wdt:P1399) | culpable homicide (wd:Q16973098) |

[Assistant]
Oscar Pistorius is convicted of "culpable homicide" (wd:Q16973098) according to Wikidata. This indicates he was found guilty of causing death but does not specify the number of people killed.

I will now provide the final answer clarifying that the knowledge graph does not contain the number of people he killed but confirms his conviction for culpable homicide.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The system correctly identified that Oscar Pistorius was convicted of culpable homicide according to Wikidata. It also correctly noted that the knowledge graph does not provide information about the number of people he killed, which is the specific information requested by the user. The SPARQL query is appropriate for retrieving the crimes he was convicted of, and the answer is accurate and complete given the available data. No further refinement is necessary.


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

There are 4790 other video games that began in the same year (2016) as Dead by Daylight.

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

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

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

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

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

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

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

[Tool]
Example 1:
Question:
What is the name of the university where Obama's wife studied?

SPARQL:
SELECT DISTINCT ?uri ?institute WHERE {
  wd:Q76 wdt:P26 / wdt:P69 / wdt:P361 * ?uri .
  ?uri wdt:P31 * / wdt:P279 * wd:Q3918 .
}

Using entities:
university, also known as uni, universities (wd:Q3918): academic institution for further education
Barack Obama, also known as Honorable Barack Obama, BHO, Obama, Barry O Bomber, President Obama (wd:Q76): president of the United States from 2009 to 2017, community organizer, political writer, statesperson, politician, podcaster, memoirist, academic, lawyer, jurist

Using properties:
spouse, also known as married partner, married to, marry, partner, spouses (wd:P26 as wdt): the subject has the object as their spouse (husband, wife, partn...etc.). Use "unmarried partner" (P451) for non-married companions, significant person, relative
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)
part of, also known as of, within, system of, subsystem of, subgroup of (wd:P361 as wdt): object of which the subject is a part (if this subject is alread... of "has part" (P527, see also "has parts of the class" (P2670))
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:
Show me Hemingway's autobiography.

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P50 wd:Q23434 .
  ?uri wdt:P136 wd:Q4184 .
}

Using entities:
Ernest Hemingway, also known as Ernest Miller Hemingway, Ernest Miller Hemmingway, Hemingway, Papa, E. Hemingway (wd:Q23434): American author and journalist (1899–1961), short story writer, war correspondent, screenwriter, prose writer, playwright, journalist, novelist, writer, poet
autobiography, also known as Autobiography as Topic, self-biography, autobiographies, autobiographical writing (wd:Q4184): genre and publication type, biography written by the subject

Using properties:
genre, also known as genre of music, type of music, type of film, music genre, literary genre (wd:P136 as wdt): creative work's genre or an artist's field of work (P101). Use main subject (P921) to relate creative works to their topic
author, also known as writer, written by, poet, playwright, maker (wd:P50 as wdt): main creator(s) of a written work (use on works, not humans); us...hor name string) when Wikidata item is unknown or does not exist, creator

Example 3:
Question:
Give me all Apollo 14 astronauts.

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q178871 wdt:P1029 ?uri
}

Using entities:
Apollo 14, also known as Apollo XIV (wd:Q178871): third Moon landing and eighth crewed flight of the United States Apollo program

Using properties:
crew member, also known as crewmember (wd:P1029 as wdt): person that participated operating or serving aboard this vehicle

[Assistant] The examples did not directly relate to video games or their release years. Next, I will search for the entity "Dead by Daylight" to find its Wikidata identifier. This will help us find the year it began (release year). Then, we can find other video games that began in the same year.

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

[Assistant] The entity for the video game "Dead by Daylight" is wd:Q24514682. Next, I will find the property that indicates the year it began or was released. Usually, the property for publication or release date is used for video games. I will search for properties related to "start year" or "publication date" for this entity.

[Tool]
Top 10 property alternatives:
1. publication date, also known as launched, published, publication time, publication, pubdate (wd:P577 as wdt/p): date or point in time when a work was first published or released, point in time
2. publisher, also known as video game publisher, software publisher, comic book publisher, sheet music publisher, publishing house (wd:P123 as wdt/p): organization or person responsible for publishing books, periodicals, printed music, podcasts, games or software
3. distribution format, also known as book format, type of media, publication format, method of distribution, medium (wd:P437 as wdt/p): method (or type) of distribution for the subject
4. platform, also known as computing platform, runtime, published on, platforms, hardware (wd:P400 as wdt/p): platform for which a work was developed or released, or the specific platform version of a software product
5. developer, also known as written by, video game developer, video game dev, game developer, game dev (wd:P178 as wdt/p): organization or person that developed the item, creator
6. software version identifier, also known as software release number, software version number, software release version, software update identifier, software update number (wd:P348 as wdt/p): numeric or nominal identifier of a version of a software program or file format, current or past, has edition or translation, edition number
7. logo image, also known as masthead, wordmark, trademark, trade mark, sign (wd:P154 as wdt/p): graphic mark or emblem commonly used by commercial enterprises, organizations and products, icon
8. characters, also known as appears in this work (wd:P674 as wdt/p): characters which appear in this item (like plays, operas, operettas, books, comics, films, TV series, video games)
9. copyright status, also known as copyrighted, copyright restriction (wd:P6216 as wdt/p): copyright status for intellectual creations like works of art, publications, software, etc.
10. PROSPEROPatches game ID, also known as PP game ID, PP title ID, PROSPEROPatches title ID (wd:P12437 as wdt/p): identifier of a video game or application in the PROSPEROPatches database

No other items found

[Assistant] The property "publication date" (wdt:P577) is the relevant property for the release or start year of the video game "Dead by Daylight." Next, I will query the publication date of Dead by Daylight to determine the year it began.

[Tool]
Got 1 row and 1 column:
| date                                |
| ----------------------------------- |
| 2016-06-14T00:00:00Z (xsd:dateTime) |

[Assistant] The video game "Dead by Daylight" began in the year 2016. Now, I will find how many other video games also began in the year 2016. I will query for video games with the publication date in 2016 and count them, excluding "Dead by Daylight" itself.

[Tool]
Got 1 row and 1 column:
| count          |
| -------------- |
| 4790 (xsd:int) |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly identifies the publication year of Dead by Daylight as 2016 and counts all other video games published in the same year, excluding Dead by Daylight itself. The query uses appropriate properties and filters, and the result is consistent with the question. The answer is clearly stated and matches the query result. No further refinement is needed.


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

Got 1 row and 1 column:
| count        |
| ------------ |
| 72 (xsd:int) |

There are 72 different occupations held by spouses of winners of the Nobel Prize in Literature.

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

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

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

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

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

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

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

[Tool]
Example 1:
Question:
What is the smallest city by area in Germany?

SPARQL:
SELECT ?res WHERE {
  ?res wdt:P17 wd:Q183 .
  ?res wdt:P31 / wdt:P279 * wd:Q515 .
  ?res wdt:P1082 ?inhabitants .
}
ORDER BY ?inhabitants
LIMIt 1

Using entities:
Germany, also known as BR Deutschland, Bundesrepublik Deutschland, Deutschland, Federal Republic of Germany (wd:Q183): country in Central Europe
city, also known as cities (wd:Q515): large human settlement

Using properties:
population, also known as inhabitants, human population (wd:P1082 as wdt): number of people inhabiting the place; number of people of subject
country, also known as sovereign state, state, land, host country (wd:P17 as wdt): sovereign state that this item is in (not to be used for human beings), located in the administrative territorial entity
subclass of, also known as rdfs:subClassOf, sort of, subcategory of, subgenre of, subtype of (wd:P279 as wdt): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)

Example 2:
Question:
How many films did Hal Roach produce?

SPARQL:
SELECT ( COUNT( ?uri ) as ?c ) WHERE {
  ?uri wdt:P162 wd:Q72792 .
}

Using entities:
Hal Roach (wd:Q72792): American filmmaker (1892–1992), television producer, film director, film producer, screenwriter, director, actor

Using properties:
producer, also known as music producer, record producer, produced by, film producer, creative producer (wd:P162 as wdt): person(s) who produced the film, musical work, theatrical produc...etc.) [for production company, use P272, video games - use P178]

Example 3:
Question:
Which airports does Yeti Airlines serve?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q1480118 wdt:P113 ?uri .
}

Using entities:
Yeti Airlines, also known as Yeti Airlines Domestic Pvt. Ltd. (wd:Q1480118): Airline based in Kathmandu, Nepal.

Using properties:
airline hub, also known as hub airport, hub (wd:P113 as wdt): airport that serves as a hub for an airline

[Assistant]
The question is about the occupations of spouses of winners of the Nobel Prize in Literature.

Step 1: Identify the entity for "Nobel Prize in Literature".
Step 2: Find the winners of the Nobel Prize in Literature.
Step 3: Find the spouses of these winners.
Step 4: Find the occupations of these spouses.
Step 5: Count the number of different occupations.

I will start by searching for the entity "Nobel Prize in Literature".

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

[Assistant] The entity for "Nobel Prize in Literature" is wd:Q37922. Next, I will find the winners of the Nobel Prize in Literature. Usually, the winners are linked by the property "award received" (wdt:P166). I will check if this property is used with the Nobel Prize in Literature entity.

[Tool]
Got 121 rows and 3 columns, showing the first 10 rows below:
| subject                           | property                  | object                                |
| --------------------------------- | ------------------------- | ------------------------------------- |
| Winston Churchill (wd:Q8016)      | award received (wdt:P166) | Nobel Prize in Literature (wd:Q37922) |
| Rabindranath Tagore (wd:Q7241)    | award received (wdt:P166) | Nobel Prize in Literature (wd:Q37922) |
| Ernest Hemingway (wd:Q23434)      | award received (wdt:P166) | Nobel Prize in Literature (wd:Q37922) |
| Albert Camus (wd:Q34670)          | award received (wdt:P166) | Nobel Prize in Literature (wd:Q37922) |
| Gabriel García Márquez (wd:Q5878) | award received (wdt:P166) | Nobel Prize in Literature (wd:Q37922) |
| Jean-Paul Sartre (wd:Q9364)       | award received (wdt:P166) | Nobel Prize in Literature (wd:Q37922) |
| Rudyard Kipling (wd:Q34743)       | award received (wdt:P166) | Nobel Prize in Literature (wd:Q37922) |
| Bob Dylan (wd:Q392)               | award received (wdt:P166) | Nobel Prize in Literature (wd:Q37922) |
| Bertrand Russell (wd:Q33760)      | award received (wdt:P166) | Nobel Prize in Literature (wd:Q37922) |
| George Bernard Shaw (wd:Q19185)   | award received (wdt:P166) | Nobel Prize in Literature (wd:Q37922) |
| ...                               | ...                       | ...                                   |

[Assistant] I found winners of the Nobel Prize in Literature using the property "award received" (wdt:P166) with the object Nobel Prize in Literature (wd:Q37922). Now, I will find the spouses of these winners. The property for spouse is "spouse" (wdt:P26). I will write a query to get the spouses of winners of the Nobel Prize in Literature.

[Tool]
Got 159 rows and 1 column, showing the first 5 and last 5 rows below:
| spouse                           |
| -------------------------------- |
| John Dranow (wd:Q100234047)      |
| Ilda Reis (wd:Q10301028)         |
| Isabel da Nóbrega (wd:Q10303821) |
| Richard J. Walsh (wd:Q103841872) |
| Harold Morrison (wd:Q108646506)  |
| ...                              |
| _:bn145031                       |
| _:bn411647                       |
| _:bn415492                       |
| _:bn1256057                      |
| _:bn1471091                      |

[Assistant] I found 159 spouses of winners of the Nobel Prize in Literature. Some results are blank nodes, which might indicate missing or anonymous data. Next, I will find the occupations of these spouses. The property for occupation is "occupation" (wdt:P106). I will write a query to get the distinct occupations of spouses of winners of the Nobel Prize in Literature and count them.

[Tool]
Got 1 row and 1 column:
| count        |
| ------------ |
| 72 (xsd:int) |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly identifies winners of the Nobel Prize in Literature and retrieves their spouses' occupations, counting the distinct occupations. The query uses appropriate properties (award received, spouse, occupation) and correctly counts distinct occupations. The answer '72' is consistent with the query result. The query follows the rules by returning identifiers and does not perform extra computation outside SPARQL. No further refinement is necessary.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?university ?universityLabel ?inception WHERE {
  VALUES ?university {
    wd:Q34433 wd:Q35794
  }
  ?university wdt:P571 ?inception .
  OPTIONAL {
    ?university rdfs:label ?universityLabel FILTER ( LANG( ?universityLabel ) = "en" )
  }
}
ORDER BY ?inception

The University of Oxford was established earlier in 1096, while the University of Cambridge was established later in 1209.

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

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

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

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

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

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

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

[Tool]
Example 1:
Question:
What are the official languages of the Philippines?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q928 wdt:P37 ?uri .
}

Using entities:
Philippines, also known as ph, The Philippines, RP, Republic of the Philippines, Philippine Islands (wd:Q928): archipelagic country in Southeast Asia

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

Example 2:
Question:
How many seats does the home stadium of FC Porto have?

SPARQL:
SELECT DISTINCT ?num WHERE {
  wd:Q128446 wdt:P115 / wdt:P1083 ?num
}

Using entities:
FC Porto, also known as Porto, Futebol Clube do Porto, Football Club of Porto, FCPorto, F.C. Porto (wd:Q128446): professional association football club based in Porto, Portugal

Using properties:
maximum capacity, also known as capacity, complement, crew, number of seats, seating capacity (wd:P1083 as wdt): number of people allowed for a venue or vehicle
home venue, also known as home water, venue, stadium, home ground, home field (wd:P115 as wdt): home stadium or venue of a sports team or applicable performing arts organization, location

Example 3:
Question:
What is the birth name of Angela Merkel?

SPARQL:
SELECT DISTINCT ?name WHERE {
  wd:Q567 wdt:P1477 ?name .
}

Using entities:
Angela Merkel, also known as Angela Dorothea Merkel, leader of the free world, Angela Kasner (wd:Q567): chancellor of Germany from 2005 to 2021, politician, physicist

Using properties:
birth name, also known as née, name at birth, name before marriage, ne, né (wd:P1477 as wdt): full name of a person at birth, if different from their current, generally used name, name

[Assistant] I have seen examples of how to query properties of entities such as official languages, stadium capacities, and birth names. Now, I will identify the entities for "University of Oxford" and "University of Cambridge" in Wikidata. Then, I will find the property related to the establishment date of these universities.

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

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

[Assistant]
The entities for the universities are:
- University of Oxford: wd:Q34433
- University of Cambridge: wd:Q35794

Next, I will search for the property related to the establishment date of these universities. I will search for properties related to "established" or "inception" for the University of Oxford first.

[Tool]
Top 10 property alternatives:
1. inception, also known as initial release date, founded in, founded on, founded on date, founding date (wd:P571 as wdt/p): time when an entity begins to exist; for date of official opening use P1619, start time
2. affiliation, also known as association, sister society, affiliated with, affiliated to, associated with (wd:P1416 as wdt/p): organization that a person or organization is affiliated with (not necessarily member of or employed by)
3. official website, also known as site (official), home page, site official, URL, URL (official) (wd:P856 as wdt/p): URL of the official page of an item (current or former). Usage: ...but see the "Hijacked or dead websites" section of the Talk page, URL
4. headquarters location, also known as headquarters locality, seat, headquarters town, HQ, HQ location (wd:P159 as wdt/p): city or town where an organization's headquarters is or has been situated. Use P276 qualifier for specific building, significant place
5. location, also known as neighbourhood, whereabouts, vilage, venue, town (wd:P276 as wdt/p): location of the object, structure or event. In the case of an ad...ity use P706. Use P7153 for locations associated with the object
6. owner of, also known as owns, owns property, shareholder of (wd:P1830 as wdt/p): entities owned by the subject
7. has subsidiary, also known as subordinate body, subsidiary body, subordinate unit, subsidiary, subsidiary company (wd:P355 as wdt/p): subsidiary of a company or organization; generally a fully owned...siness division" (P199). Opposite of parent organization (P749)., has part(s), owner of
8. funder, also known as benefactor, sponsored by, sponsor, has funder, has donor (wd:P8324 as wdt/p): entity that gives money to a person, organization, or project for a specific purpose, sponsor
9. native label, also known as official title, original label, original language label, original language name, original language title (wd:P1705 as wdt/p): label for the items in their official language  or their original language, name
10. described by source, also known as mentioned in, source of item, reviewed in, mentioned in news article, talked about in (wd:P1343 as wdt/p): work where this item is described, attested in

No other items found

[Assistant] The property "inception" (wdt:P571) corresponds to the establishment or founding date of an entity. I will now write a SPARQL query to get the inception dates of the University of Oxford and the University of Cambridge and compare which one was established earlier.

[Tool]
Got 2 rows and 3 columns:
| university                          | universityLabel              | inception                           |
| ----------------------------------- | ---------------------------- | ----------------------------------- |
| University of Oxford (wd:Q34433)    | University of Oxford (en)    | 1096-01-01T00:00:00Z (xsd:dateTime) |
| University of Cambridge (wd:Q35794) | University of Cambridge (en) | 1209-01-01T00:00:00Z (xsd:dateTime) |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves the inception dates of both the University of Oxford and the University of Cambridge. The results show that the University of Oxford was established in 1096 and the University of Cambridge in 1209, which directly answers the user's question. The query uses appropriate properties and entities, and the answer is clearly stated and accurate. No further refinement is needed.


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

Yoko Ono was part of the art movement called Fluxus.

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

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

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

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

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

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

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

[Tool]
Example 1:
Question:
How many films did Leonardo DiCaprio star in?

SPARQL:
SELECT ( COUNT( DISTINCT ?uri ) AS ?c ) WHERE {
  ?uri wdt:P31 wd:Q11424 ; wdt:P161 wd:Q38111 .
}

Using entities:
film, also known as cinematic work, moving picture, movie, motion pictures, motion picture (wd:Q11424): sequence of images that give the impression of movement, stored on film stock
Leonardo DiCaprio (wd:Q38111): American actor and film producer (born 1974), television actor, environmentalist, film producer, screenwriter, stage actor, film actor, actor

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

Example 2:
Question:
List the children of Margaret Thatcher.

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q7416 wdt:P40 ?uri .
}

Using entities:
Margaret Thatcher, also known as Margaret Hilda Thatcher, Margaret Roberts, Margaret Hilda Roberts, Mrs. T, Mrs. Thatcher (wd:Q7416): British stateswoman and prime minister (1925–2013), research scientist, business executive, autobiographer, statesperson, politician, barrister, chemist

Using properties:
child, also known as has son, kids, mother of, kid, issue (wd:P40 as wdt): subject has object as child. Do not use for stepchildren—use "relative" (P1038), qualified with "type of kinship" (P1039), significant person, relative

Example 3:
Question:
Who are the parents of the wife of Juan Carlos I?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q19943 wdt:P26 ?spouse .
  ?uri wdt:P40 ?spouse .
}

Using entities:
Juan Carlos I of Spain, also known as Juan Carlos I, Juan Carlos Alfonso Víctor María de Borbón y Borbón-Dos Sicilias (wd:Q19943): King of Spain from 1975 to 2014, military officer, aristocrat, royalty, monarch

Using properties:
spouse, also known as married partner, married to, marry, partner, spouses (wd:P26 as wdt): the subject has the object as their spouse (husband, wife, partn...etc.). Use "unmarried partner" (P451) for non-married companions, significant person, relative
child, also known as has son, kids, mother of, kid, issue (wd:P40 as wdt): subject has object as child. Do not use for stepchildren—use "relative" (P1038), qualified with "type of kinship" (P1039), significant person, relative

[Assistant] I have seen examples of how to query for properties of a person or entity. Now I will search for the entity "Yoko Ono" in Wikidata to get her identifier.

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

[Assistant] The entity for Yoko Ono as a Japanese artist and activist is wd:Q117012. Next, I will search for properties related to art movements or artistic movements for this entity.

[Tool]
Top 10 property alternatives:
1. movement, also known as subculture, trend, scientific movement, school, philosophical movement (wd:P135 as wdt/p): literary, artistic, scientific or philosophical movement or scen...ed with this person or work. For political ideologies use P1142.
2. genre, also known as genre of music, type of music, type of film, music genre, literary genre (wd:P136 as wdt/p): creative work's genre or an artist's field of work (P101). Use main subject (P921) to relate creative works to their topic
3. notable work, also known as artwork, wrote, writer of, works, work (wd:P800 as wdt/p): notable scientific, artistic or literary work, or other work of significance among subject's works
4. Artsy artist ID, also known as Artsy artist identifier, Artsy id (wd:P2042 as wdt/p): identifier at the Artsy website
5. pseudonym, also known as nom de plume, nom-de-plume, nome de guerre, pen name, ring name (wd:P742 as wdt/p): alias used by someone (for nickname use P1449), name
6. has works in the collection, also known as has items in the collection, works in collection, work in collection, museum housing this person's work, items in the collection (wd:P6379 as wdt/p): collection that has works of this person or organisation (use archives at [P485] for archives)
7. Union List of Artist Names ID, also known as Union List of Artist Names, ULAN identifier, ULAN, ULAN ID, Getty ULAN ID (wd:P245 as wdt/wdtn/p): identifier from the Getty Union List of Artist Names
8. image, also known as still capture, still, screenshot, screen capture, portrait (wd:P18 as wdt/p): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)
9. ARTIC artist ID, also known as Art Institute of Chicago artist ID (wd:P6295 as wdt/p): identifier for an artist on the Art Institute of Chicago website
10. AllMusic artist ID, also known as AllMusic, AllMusic ID, amg artist (wd:P1728 as wdt/p): identifier for an artist, musical group, or record label in the AllMusic database (record labels are listed as "artists")

No other items found

[Assistant] The property "movement" (wdt:P135) corresponds to literary, artistic, scientific, or philosophical movements. This is likely the property we want to use to find the art movement Yoko Ono was part of. I will now write a SPARQL query to find the art movement(s) for Yoko Ono (wd:Q117012) using this property.

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

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves the art movement associated with Yoko Ono using the property P135 (movement). The query returns the identifier and English label of the movement, which is Fluxus, matching the user's question. The query follows best practices by including an optional clause for the label and filtering for English labels. The answer is accurate and complete based on the data retrieved from Wikidata. No further refinement is necessary.