PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?country WHERE {
  wd:Q34404 wdt:P17 ?country .
}

Got 1 row and 1 column:
| ?country               |
| ---------------------- |
| United States (wd:Q30) |

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

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

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

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

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

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

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

[Assistant]
To answer the question about the country where the United Fruit Company had its headquarters, we need to follow these steps:

1. Identify the entity for "United Fruit Company".
2. Find the property that represents "headquarters location" or something similar.
3. Search for the country associated with the headquarters of the United Fruit Company.

Let's start by identifying the entity for "United Fruit Company".

I will use the `search_entity` function to find the entity for "United Fruit Company".

[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]
From the search results, the correct entity for "United Fruit Company" is `wd:Q1771942`.

Next, I will identify the property that represents the headquarters location of a company. I will use the `search_property_of_entity` function to find relevant properties for the entity `wd:Q1771942`.

[Tool]
Top 10 property alternatives:
1. headquarters location, also known as headquarters locality, seat, headquarters town, HQ, HQ location (wd:P159 as p/wdt): 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 p/wdt): 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 p/wdt): 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 p/wdt): 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 p/wdt): 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/p/wdtn): 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 p/wdt): 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 p/wdt): 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 p/wdt): 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 p/wdt): main Wikimedia category

No other items found

[Assistant]
Based on the search results, the property `wd:P159` (headquarters location) seems to be the most relevant for finding the headquarters of the United Fruit Company.

Now, I will construct a SPARQL query to find the headquarters location of the United Fruit Company (`wd:Q1771942`) and then determine the country where the headquarters is located using the `wd:P17` property (country).

Let's first create a SPARQL query to find the headquarters location of the United Fruit Company.

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

I will now execute this query to see the result.

[Tool]
Got 1 row and 1 column:
| ?headquarters           |
| ----------------------- |
| New Orleans (wd:Q34404) |

[Assistant]
The headquarters of the United Fruit Company is located in New Orleans, which has the identifier `wd:Q34404`. Now, we need to find the country where New Orleans is located. We can use the `wd:P17` property (country) to achieve this.

Let's construct and execute a new SPARQL query to find the country of New Orleans.

```sparql
SELECT ?country WHERE {
  wd:Q34404 wdt:P17 ?country .
}
```

I will now execute this query to determine the country where New Orleans is located.

[Tool]
Got 1 row and 1 column:
| ?country               |
| ---------------------- |
| United States (wd:Q30) |

[Assistant]
The SPARQL query has returned the result that New Orleans is located in the United States, which has the identifier `wd:Q30`.

Therefore, the country where the United Fruit Company had its headquarters is the United States.

I will now provide the final answer using the `answer` function.

[Tool] Stopped generation process


PREFIX ps: <http://www.wikidata.org/prop/statement/>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX pr: <http://www.wikidata.org/prop/reference/>
SELECT ?crime ?reference WHERE {
  wd:Q201377 wdt:P1399 ?crime ; ps:P1399 / pr:P248 ?reference .
}
LIMIT 10

The information about the number of people Oscar Pistorius killed is not directly available in the current dataset.

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

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

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

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

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

[User] How many people did Oscar Pistorius kill?

[Assistant]
To answer this question, we need to identify the entity corresponding to Oscar Pistorius and then look for properties related to the number of people he killed. Let's start by searching for the entity corresponding to Oscar Pistorius.

I'll use the `search_entity` function to find the entity ID for Oscar Pistorius.

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

[Assistant]
From the search results, the correct entity for Oscar Pistorius is `wd:Q201377`. Now that we have the entity ID, let's proceed to find out how many people he killed. We need to look for properties related to the number of victims.

I will use the `search_property_of_entity` function to find relevant properties for Oscar Pistorius related to the number of people he killed.

[Tool]
Top 10 property alternatives:
1. sport, also known as play, plays, sport played, sports (wd:P641 as p/wdt): sport that the subject participates or participated in or is associated with
2. penalty, also known as judicial sentence, punishment, sentence (wd:P1596 as p/wdt): penalty imposed by an authority
3. given name, also known as name, proper name, personal name, middle name, forename (wd:P735 as p/wdt): 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 p/wdt): 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 p/wdt): 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 p/wdt): informal name (for a pseudonym use P742), name
7. occupation, also known as job, work, vocation, profession, employment (wd:P106 as p/wdt): 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 p/wdt): 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 p/wdt): 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]
None of the suggested properties directly relate to the number of people Oscar Pistorius killed. However, we can try to find statements related to his criminal case where such information might be recorded. Let's list some triples involving Oscar Pistorius to see if we can find any relevant information.

I will use the `list` function to find triples involving Oscar Pistorius.

[Tool]
Got 501 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                                                                                |
| 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]
The listed triples do not provide direct information about the number of people Oscar Pistorius killed. However, there might be specific properties or qualifiers that contain this information. Let's search for properties related to his criminal case.

I will use the `search_property_of_entity` function again, this time with a query related to "murder" or "criminal case" to find more relevant properties.

[Tool]
Top 10 property alternatives:
1. convicted of, also known as conviction, found guilty of, guilty of (wd:P1399 as p/wdt): crime a person was convicted of
2. penalty, also known as judicial sentence, punishment, sentence (wd:P1596 as p/wdt): penalty imposed by an authority
3. place of detention, also known as internment at, internment place, jail, jailed at, penal (wd:P2632 as p/wdt): place where this person is or was detained, residence
4. country of citizenship, also known as subject of (country), nation of citizenship, national of, nationality, nation (wd:P27 as p/wdt): the object is a country that recognizes the subject as its citizen, country
5. sport, also known as play, plays, sport played, sports (wd:P641 as p/wdt): sport that the subject participates or participated in or is associated with
6. significant event, also known as significant occurrence, significant incident, significant achievment, sign. event, outcome (wd:P793 as p/wdt): significant or notable events associated with the subject
7. described by source, also known as mentioned in, source of item, reviewed in, mentioned in news article, talked about in (wd:P1343 as p/wdt): work where this item is described, attested in
8. sex or gender, also known as gender or sex, sex, gender identity, gender expression, gender (wd:P21 as p/wdt): sex or gender identity of human or animal. For human: male, fema...m, female organism. Groups of same gender use subclass of (P279)
9. place of birth, also known as POB, location of birth, location born, born in, birthplace (wd:P19 as p/wdt): most specific known birth location of a person, animal or fictional character, location
10. languages spoken, written or signed, also known as language of expression, language, wrote language, writes language, uses language (wd:P1412 as p/wdt): language(s) that a person or a people speaks, writes or signs, including the native language(s)

No other items found

[Assistant]
The property `convicted of (wd:P1399)` seems to be the most relevant for our query. This property indicates the crimes a person was convicted of. We can use this property to find out what Oscar Pistorius was convicted of and then check if there is any information about the number of people involved.

Let's construct a SPARQL query to find the crime Oscar Pistorius was convicted of and see if it provides the required information.

Here is the initial SPARQL query:

```sparql
SELECT ?crime
WHERE {
  wd:Q201377 wdt:P1399 ?crime .
}
```

I will now execute this query to see the results.

[Tool]
Got 1 row and 1 column:
| ?crime                           |
| -------------------------------- |
| culpable homicide (wd:Q16973098) |

[Assistant]
The query returned that Oscar Pistorius was convicted of culpable homicide (wd:Q16973098). However, this does not directly provide the number of people he killed. To get more details, we need to check if there are any qualifiers or references that might contain this information.

Let's extend the query to include qualifiers and references to see if there is any additional information about the number of victims.

Here is the extended SPARQL query:

```sparql
SELECT ?crime ?reference
WHERE {
  wd:Q201377 wdt:P1399 ?crime ;
             ps:P1399/pr:P248 ?reference .
}
```

This query will fetch the crime Oscar Pistorius was convicted of along with any references that might provide more context.

I will now execute this query to see the results.

[Tool] Got no rows and 2 columns

[Assistant]
The extended query did not return any additional references or qualifiers that provide the number of people Oscar Pistorius killed. Given the available data, we cannot directly determine the exact number of people he killed from the current SPARQL query results.

Since we have exhausted the available information and cannot find a direct answer to the question, I will provide a response indicating that the information is not available in the current dataset.

I will now finalize the answer using the `answer` function.

[Tool] Stopped generation process


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

Got 1 row and 1 column:
| ?count |
| ------ |
| 5008   |

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

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

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

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

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

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

[Assistant]
To answer this question, we need to follow these steps:

1. Identify the entity for "Dead by Daylight".
2. Find the year it was released.
3. Search for other video games released in the same year.

Let's start by identifying the entity for "Dead by Daylight".

I'll use the `search_entity` function to find the entity for "Dead by Daylight".

[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]
From the search results, the correct entity for "Dead by Daylight" is `wd:Q24514682`.

Next, I will find the release year of "Dead by Daylight". To do this, I will list the triples where `wd:Q24514682` is the subject and look for properties related to release dates.

I'll use the `list` function to find relevant properties for `wd:Q24514682`.

[Tool]
Got 747 rows and 3 columns, showing the first 10 rows below:
| subject                         | property                               | object                            |
| ------------------------------- | -------------------------------------- | --------------------------------- |
| Dead by Daylight (wd:Q24514682) | instance of (wdt:P31)                  | video game (wd:Q7889)             |
| Dead by Daylight (wd:Q24514682) | publication date (wdt:P577)            | 2016-06-14T00:00:00Z              |
| Dead by Daylight (wd:Q24514682) | language of work or name (wdt:P407)    | English (wd:Q1860)                |
| Dead by Daylight (wd:Q24514682) | Commons category (wdt:P373)            | Dead by Daylight                  |
| Dead by Daylight (wd:Q24514682) | copyright status (wdt:P6216)           | copyrighted (wd:Q50423863)        |
| Dead by Daylight (wd:Q24514682) | copyright license (wdt:P275)           | proprietary license (wd:Q3238057) |
| Dead by Daylight (wd:Q24514682) | country of origin (wdt:P495)           | Canada (wd:Q16)                   |
| Dead by Daylight (wd:Q24514682) | official website (wdt:P856)            | <http://www.deadbydaylight.com>   |
| Dead by Daylight (wd:Q24514682) | genre (wdt:P136)                       | survival horror (wd:Q333967)      |
| Dead by Daylight (wd:Q24514682) | software version identifier (wdt:P348) | 8.3.2                             |
| ...                             | ...                                    | ...                               |

[Assistant]
From the list of triples, we can see that the publication date of "Dead by Daylight" is `2016-06-14T00:00:00Z`, which means it was released in 2016.

Now, I will search for other video games released in the same year, 2016. To do this, I will use the `search_object_of_property` function to find video games released in 2016.

I'll use the `wdt:P577` property to find video games released in 2016.

Let's construct the SPARQL query to find video games released in 2016 and count them.

First, let's confirm the property `wdt:P577` is the correct one for release date and then proceed with the SPARQL query construction.

Since we know the property `wdt:P577` is for the publication date, we can directly construct the SPARQL query.

Here's the SPARQL query to find video games released in 2016 and count them:

```sparql
SELECT (COUNT(?video_game) AS ?count)
WHERE {
  ?video_game wdt:P31 wd:Q7889 . # instance of video game
  ?video_game wdt:P577 ?date .
  FILTER(YEAR(?date) = 2016)
}
```

Let's execute this query to get the count of video games released in 2016.

[Tool]
Failed to call function execute:
failed to parse input: parse error at position 159 for token EOF with content '' (the input most likely does not follow the grammar)

[Assistant]
It seems there was a syntax error in the SPARQL query due to the comment inside the query. Comments are not allowed in SPARQL queries in this context. Let's remove the comment and re-execute the query.

Here's the corrected SPARQL query:

```sparql
SELECT (COUNT(?video_game) AS ?count)
WHERE {
  ?video_game wdt:P31 wd:Q7889 .
  ?video_game wdt:P577 ?date .
  FILTER(YEAR(?date) = 2016)
}
```

Let's execute this query again to get the count of video games released in 2016.

[Tool]
Got 1 row and 1 column:
| ?count |
| ------ |
| 5008   |

[Assistant]
The SPARQL query returned the count of video games released in 2016, which is 5008.

Therefore, the number of other video games that began the same year as "Dead by Daylight" is 5008.

I will now provide the final answer.

[Tool] Stopped generation process


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

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

There are 71 different occupations held by the spouses of Nobel Prize in Literature winners.

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

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

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

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

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

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

[Assistant]
To answer this question, we need to break it down into several steps:

1. Identify the entities representing the Nobel Prize in Literature winners.
2. Find their spouses.
3. Determine the occupations of these spouses.
4. Count the number of unique occupations.

Let's start by finding the entities representing the Nobel Prize in Literature winners.

### Step 1: Find Nobel Prize in Literature Winners
We can use `search_entity` to find entities related to Nobel Prize in Literature winners.

```json
{
  "name": "search_entity",
  "arguments": {
    "kg": "wikidata",
    "query": "Nobel Prize in Literature winner"
  }
}
```

### Step 2: Find Spouses of Nobel Prize in Literature Winners
Once we have the entities for the Nobel Prize in Literature winners, we can find their spouses using the `wdt:P26` property, which represents "spouse".

### Step 3: Determine Occupations of These Spouses
Next, we can find the occupations of these spouses using the `wdt:P106` property, which represents "occupation".

### Step 4: Count Unique Occupations
Finally, we can count the number of unique occupations held by these spouses.

Let's start with the first step.

[Tool]
Top 10 entity alternatives:
1. Selma Lagerlöf (1858-1940), auth...Nobel prize winner in literature (wd:Q113483533): sculpture of Selma Lagerlöf by Knut Gustaf Emil Malmqvist
2. Nobel Prize in Literature, also known as Nobel Literature Prize (wd:Q37922): one of five prizes established by Alfred Nobel
3. Polish Nobel Prize Winners in Literature: Are They Really Polish? (wd:Q64039039)
4. 2022 Nobel Prize in Literature (wd:Q112594135): literary award to French author Annie Ernaux
5. 1901 Nobel Prize in Literature (wd:Q15918052)
6. 2023 Nobel Prize in Literature (wd:Q115937544)
7. 2012 Nobel Prize in Literature (wd:Q10845886)
8. 1905 Nobel Prize in Literature (wd:Q112731128)
9. 1908 Nobel Prize in Literature (wd:Q112731708)
10. 2003 Nobel Prize in Literature (wd:Q113212896)

[Assistant]
From the search results, we see that the entity representing the Nobel Prize in Literature itself is `wd:Q37922`. However, we need the specific individuals who won the Nobel Prize in Literature. We can filter out the relevant entities by looking for those that are directly associated with the Nobel Prize in Literature.

Let's list the triples where the subject is a person (`wdt:P31` value `wd:Q5`, indicating a human) and the object is the Nobel Prize in Literature (`wd:Q37922`).

```json
{
  "name": "list",
  "arguments": {
    "kg": "wikidata",
    "property": "wdt:P166",
    "object": "wd:Q37922"
  }
}
```

This will give us the entities representing the Nobel Prize in Literature winners. Let's proceed with this query.

[Tool]
Got 122 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]
We now have a list of entities representing Nobel Prize in Literature winners. Next, we need to find their spouses using the `wdt:P26` property, which represents "spouse". We will then determine the occupations of these spouses using the `wdt:P106` property, which represents "occupation".

Let's construct a SPARQL query to find the spouses of these Nobel Prize in Literature winners and their occupations.

Here's the initial SPARQL query to find the spouses and their occupations:

```sparql
SELECT DISTINCT ?spouse ?occupation
WHERE {
  ?winner wdt:P166 wd:Q37922 .
  ?winner wdt:P26 ?spouse .
  ?spouse wdt:P106 ?occupation .
}
```

Let's execute this query to get the results.

[Tool]
Got 185 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?spouse                              | ?occupation                   |
| ------------------------------------ | ----------------------------- |
| John Dranow (wd:Q100234047)          | lector (wd:Q1553123)          |
| John Dranow (wd:Q100234047)          | writer (wd:Q36180)            |
| John Dranow (wd:Q100234047)          | businessperson (wd:Q43845)    |
| Ilda Reis (wd:Q10301028)             | painter (wd:Q1028181)         |
| Isabel da Nóbrega (wd:Q10303821)     | prose writer (wd:Q12144794)   |
| ...                                  | ...                           |
| Irene Eucken (wd:Q94748608)          | painter (wd:Q1028181)         |
| Irene Eucken (wd:Q94748608)          | handicrafter (wd:Q11479517)   |
| Anna Grass (wd:Q95208466)            | dancer (wd:Q5716684)          |
| Emilie von Heidenstam (wd:Q98545639) | autobiographer (wd:Q18814623) |
| Emilie von Heidenstam (wd:Q98545639) | writer (wd:Q36180)            |

[Assistant]
The query has returned a list of spouses of Nobel Prize in Literature winners along with their occupations. Now, we need to count the number of unique occupations held by these spouses.

We can modify our SPARQL query to include a `GROUP BY` clause to count the distinct occupations.

Here's the updated SPARQL query:

```sparql
SELECT (COUNT(DISTINCT ?occupation) AS ?uniqueOccupations)
WHERE {
  ?winner wdt:P166 wd:Q37922 .
  ?winner wdt:P26 ?spouse .
  ?spouse wdt:P106 ?occupation .
}
```

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

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

[Assistant]
The SPARQL query has returned the count of unique occupations held by the spouses of Nobel Prize in Literature winners. The result indicates that there are 71 different occupations among these spouses.

Now, let's finalize the answer using the `answer` function.

[Tool] Stopped generation process


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

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

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

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

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

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

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

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

[Assistant]
To answer this question, we need to identify the establishment dates of both the University of Oxford and the University of Cambridge. We'll start by searching for the entities corresponding to these universities.

Let's begin by searching for the entities:

1. Search for the entity corresponding to the University of Oxford.
2. Search for the entity corresponding to the University of Cambridge.

I will now search for these entities.

[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]
From the search results, it is clear that the primary entities we are interested in are:

- University of Oxford: `wd:Q34433`
- University of Cambridge: `wd:Q35794`

Next, we need to find the establishment date for each university. To do this, we can search for properties related to the establishment date of these universities.

Let's search for properties related to the establishment date for each university:

1. Search for properties related to the establishment date for the University of Oxford.
2. Search for properties related to the establishment date for the University of Cambridge.

I will now search for these properties.

[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 p/wdt): time when an entity begins to exist; for date of official opening use P1619, start time
2. ranking, also known as peak position, place, placing, position, rank (wd:P1352 as p/wdt): subject's ordinal position as qualitatively evaluated relative to other members of a group
3. headquarters location, also known as headquarters locality, seat, headquarters town, HQ, HQ location (wd:P159 as p/wdt): city or town where an organization's headquarters is or has been situated. Use P276 qualifier for specific building, significant place
4. location, also known as neighbourhood, whereabouts, vilage, venue, town (wd:P276 as p/wdt): location of the object, structure or event. In the case of an ad...ity use P706. Use P7153 for locations associated with the object
5. affiliation, also known as association, sister society, affiliated with, affiliated to, associated with (wd:P1416 as p/wdt): organization that a person or organization is affiliated with (not necessarily member of or employed by)
6. described by source, also known as mentioned in, source of item, reviewed in, mentioned in news article, talked about in (wd:P1343 as p/wdt): work where this item is described, attested in
7. has subsidiary, also known as subordinate body, subsidiary body, subordinate unit, subsidiary, subsidiary company (wd:P355 as p/wdt): subsidiary of a company or organization; generally a fully owned...siness division" (P199). Opposite of parent organization (P749)., has part(s), owner of
8. located in the administrative territorial entity, also known as is in the settlement of, is in the voivodeship of, is in the shire of, is in the state of, is in the territory of (wd:P131 as p/wdt): the item is located on the territory of the following administra...if the item falls only partially into the administrative entity., location, part of
9. has part(s), also known as has component, incorporates, has ingredient, has ingredients, has member (wd:P527 as p/wdt): part of this subject; inverse property of "part of" (P361). See also "has parts of the class" (P2670).
10. Commons Institution page, also known as Institution template (wd:P1612 as p/wdt): name of the institutions's page on Wikimedia Commons (without the prefix "Institution")

No other items found

[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 p/wdt): time when an entity begins to exist; for date of official opening use P1619, start time
2. ranking, also known as peak position, place, placing, position, rank (wd:P1352 as p/wdt): subject's ordinal position as qualitatively evaluated relative to other members of a group
3. headquarters location, also known as headquarters locality, seat, headquarters town, HQ, HQ location (wd:P159 as p/wdt): city or town where an organization's headquarters is or has been situated. Use P276 qualifier for specific building, significant place
4. location, also known as neighbourhood, whereabouts, vilage, venue, town (wd:P276 as p/wdt): location of the object, structure or event. In the case of an ad...ity use P706. Use P7153 for locations associated with the object
5. affiliation, also known as association, sister society, affiliated with, affiliated to, associated with (wd:P1416 as p/wdt): organization that a person or organization is affiliated with (not necessarily member of or employed by)
6. described by source, also known as mentioned in, source of item, reviewed in, mentioned in news article, talked about in (wd:P1343 as p/wdt): work where this item is described, attested in
7. has subsidiary, also known as subordinate body, subsidiary body, subordinate unit, subsidiary, subsidiary company (wd:P355 as p/wdt): subsidiary of a company or organization; generally a fully owned...siness division" (P199). Opposite of parent organization (P749)., has part(s), owner of
8. located in the administrative territorial entity, also known as is in the settlement of, is in the voivodeship of, is in the shire of, is in the state of, is in the territory of (wd:P131 as p/wdt): the item is located on the territory of the following administra...if the item falls only partially into the administrative entity., location, part of
9. has part(s), also known as has component, incorporates, has ingredient, has ingredients, has member (wd:P527 as p/wdt): part of this subject; inverse property of "part of" (P361). See also "has parts of the class" (P2670).
10. Commons Institution page, also known as Institution template (wd:P1612 as p/wdt): name of the institutions's page on Wikimedia Commons (without the prefix "Institution")

No other items found

[Assistant]
Both searches returned the property `wdt:P571` (inception/founding date) as the relevant property for finding the establishment date of the universities.

Now, let's construct a SPARQL query to retrieve the establishment dates of the University of Oxford (`wd:Q34433`) and the University of Cambridge (`wd:Q35794`). We will select the establishment date (`wdt:P571`) for each university.

Here is the SPARQL query:

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

Let's execute this query to get the establishment dates.

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

[Assistant]
Based on the SPARQL query execution, we have the following establishment dates:

- **University of Oxford**: January 1, 1096
- **University of Cambridge**: January 1, 1209

Therefore, the University of Oxford was established earlier than the University of Cambridge.

Let's provide the final answer:

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?artMovement WHERE {
  wd:Q117012 wdt:P135 ?artMovement
}

Yoko Ono was part of the Fluxus art movement.

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

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

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

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

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

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

[Assistant]
To answer this question, we need to identify the Wikidata ID for Yoko Ono and then find out which art movements she was associated with. Let's start by searching for Yoko Ono's Wikidata ID.

I'll use the `search_entity` function to find Yoko Ono.

[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]
From the search results, the correct entity for Yoko Ono is `wd:Q117012`. Now that we have the Wikidata ID for Yoko Ono, we need to find the art movements she was part of. We can do this by searching for properties related to art movements for Yoko Ono.

Let's use the `search_property_of_entity` function to find such properties.

[Tool]
Top 10 property alternatives:
1. movement, also known as subculture, trend, scientific movement, school, philosophical movement (wd:P135 as p/wdt): 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 p/wdt): 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 p/wdt): 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 p/wdt): 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 p/wdt): 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 p/wdt): 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 p/wdt/wdtn): identifier from the Getty Union List of Artist Names
8. image, also known as still capture, still, screenshot, screen capture, portrait (wd:P18 as p/wdt): 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 p/wdt): 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 p/wdt): identifier for an artist, musical group, or record label in the AllMusic database (record labels are listed as "artists")

No other items found

[Assistant]
Based on the search results, the property `wdt:P135` seems to be the most relevant for finding the art movements Yoko Ono was part of. This property is labeled as "movement" and is used to indicate the artistic, scientific, or philosophical movement associated with a person or work.

Now, let's construct a SPARQL query to find the art movements Yoko Ono was part of using the `wdt:P135` property.

I'll write the SPARQL query and then execute it to get the results.
```json
{
  "sparql": "SELECT ?artMovement WHERE { wd:Q117012 wdt:P135 ?artMovement }"
}
```
Let me execute this query now.

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

[Assistant]
The SPARQL query returned that Yoko Ono was part of the Fluxus art movement.

Now, I will provide the final SPARQL query and the answer to the question.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?startDate WHERE {
  wd:Q19798744 wdt:P580 ?startDate
}

The OA was first released on December 16, 2016.

[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] When did The OA was first released?

[Assistant]
To answer this question, we need to identify the entity corresponding to "The OA" and then find the release date of its first season or episode. Let's start by searching for the entity "The OA".

I'll use the `search_entity` function to find the entity for "The OA".

[Tool]
Top 10 entity alternatives:
1. The OA (wd:Q19798744): 2016 American mystery drama television series
2. The Oa, also known as The Oa RSPB Nature Reserve (wd:Q1325866): peninsula in the southwest of Islay, Argyll and Bute, Scotland, UK
3. The Oa (wd:Q126204501): Special Protection Area in Scotland, UK
4. The Oath, also known as Oath, Eiðurinn (wd:Q27834781): 2016 film by Baltasar Kormákur
5. Di Shvue, also known as Di Bundishe Shvue, The Oath (wd:Q5270055): Jewish song, anthem of the Bund
6. Il giuramento, also known as The Oath (wd:Q1469058): opera by Saverio Mercadante
7. The Oak, also known as Oak (wd:Q2328850): 1992 film
8. Epsom Oaks, also known as Oaks, The Oaks, The Oaks Stakes (wd:Q3347856): British Group 1 horse race tor 3-year-old filles over 1 mile, 4 furlongs and 10 yards (2,423 metres)
9. The Oatmeal, also known as theoatmeal.com (wd:Q2015830): humor website
10. The Oath (wd:Q39071891): television series

[Assistant]
From the search results, the correct entity for "The OA" is likely `wd:Q19798744`, which is described as "2016 American mystery drama television series". Now, let's find out when it was first released. We need to look for a property related to the release date of the series.

I'll use the `search_property_of_entity` function to find properties related to release dates for this entity.

[Tool]
Top 10 property alternatives:
1. distribution format, also known as book format, type of media, publication format, method of distribution, medium (wd:P437 as p/wdt): method (or type) of distribution for the subject
2. distributed by, also known as distributor (wd:P750 as p/wdt): distributor of a creative work; distributor for a record label; news agency; film distributor
3. genre, also known as genre of music, type of music, type of film, music genre, literary genre (wd:P136 as p/wdt): creative work's genre or an artist's field of work (P101). Use main subject (P921) to relate creative works to their topic
4. title, also known as name, titled, page name, original title, known as (wd:P1476 as p/wdt): published name of a work, such as a newspaper article, a literary work, piece of music, a website, or a performance work, name
5. start time, also known as start date, since, valid, join time, joined (wd:P580 as p/wdt): time an entity begins to exist or a statement starts being valid, point in time
6. end time, also known as ends, stop time, endtime, fall date, ineffective (wd:P582 as p/wdt): moment when an entity ceases to exist and a statement stops being entirely valid or no longer be true, point in time
7. creator, also known as created by, visual artist, sculptor, sculpted by, realized by (wd:P170 as p/wdt): maker of this creative work or other object (where no more specific property exists)
8. production company, also known as broadcasting company, theatrical troupe, theatre company, theater company, studio (wd:P272 as p/wdt): company that produced this film, audio or performing arts work
9. producer, also known as music producer, record producer, produced by, film producer, creative producer (wd:P162 as p/wdt): person(s) who produced the film, musical work, theatrical produc...etc.) [for production company, use P272, video games - use P178]
10. has part(s), also known as has component, incorporates, has ingredient, has ingredients, has member (wd:P527 as p/wdt): part of this subject; inverse property of "part of" (P361). See also "has parts of the class" (P2670).

No other items found

[Assistant]
From the search results, the property `start time (wd:P580)` seems relevant as it indicates when something began to exist or became valid. This property might give us the release date of "The OA".

Let's construct a SPARQL query to find the value of `wdt:P580` for the entity `wd:Q19798744`.

I'll now execute the SPARQL query to get the release date.

[Tool]
Got 1 row and 1 column:
| ?startDate           |
| -------------------- |
| 2016-12-16T00:00:00Z |

[Assistant]
The SPARQL query returned the start date of "The OA" as December 16, 2016. Therefore, "The OA" was first released on December 16, 2016.

I will now provide the final answer.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ( COUNT( DISTINCT ?headOfState ) AS ?count ) WHERE {
  wd:Q794 wdt:P35 ?headOfState .
}

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

[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 head of the state does iran have?

[Assistant]
To answer the question "How many heads of state does Iran have?", we need to identify the entity for Iran and then find the relevant properties that link to the heads of state. Let's start by searching for the entity representing Iran.

I'll use the `search_entity` function to find the entity for Iran.

[Tool]
Top 10 entity alternatives:
1. Iran, also known as ir, IRI, IRN, Islamic Rep. Iran, Islamic Republic of Iran (wd:Q794): country in Western Asia
2. Qajar Iran, also known as Guarded Domains of Persia, Sublime State of Persia, Qajar Persia, Persia, Iran (wd:Q63158027): country in Western Asia (1789–1925)
3. Iran (wd:Q2043606): village in North Ossetia, Russia
4. Iran, also known as Īrān (wd:Q10859757): Iranian newspaper
5. IRAN, also known as Iran: Journal of the British Institute of Persian Studies (wd:Q5973372): academic journal
6. Irane Man, also known as Iran (wd:Q100939484): 2018 studio by Homayoun Shajarian & Sohrab Pournazeri
7. IRAN (wd:Q108434787): herbarium
8. Iran, also known as Iran (first name), Iran (given name) (wd:Q21286079): male given name
9. Iran, also known as IMO 5327817 (wd:Q101778560): ship built in 1941
10. Iran (wd:Q96382820): About Iran

[Assistant]
From the search results, the correct entity for Iran is `wd:Q794`. Now, we need to find the property that links to the heads of state for countries. We can use the `search_property_of_entity` function to find properties related to heads of state for Iran.

Let's proceed with finding the relevant properties.

[Tool]
Top 10 property alternatives:
1. office held by head of state, also known as head of state, position held by head of state, state headed by (wd:P1906 as p/wdt): political office that is fulfilled by the head of state of this item
2. head of state, also known as leader, state headed by, monarch, president, queen (wd:P35 as p/wdt): official with the highest formal authority in a country/state, director / manager, chairperson
3. head of government, also known as head of national government, mayor, premier, president, prime minister (wd:P6 as p/wdt): head of the executive power of this town, city, municipality, state, country, or other governmental body, director / manager, chairperson
4. office held by head of government, also known as title of head of government, position of head of government, office of head of government, head of government's office, position held by head of government (wd:P1313 as p/wdt): political office that is fulfilled by the head of the government of this item
5. country, also known as sovereign state, state, land, host country (wd:P17 as p/wdt): sovereign state that this item is in (not to be used for human beings), located in the administrative territorial entity
6. diplomatic relation, also known as political relation, political relations, foreign relations, foreign relation, diplomatic relations (wd:P530 as p/wdt): diplomatic relations of the country
7. executive body, also known as executive branch (wd:P208 as p/wdt): branch of government for the daily administration of the territorial entity
8. basic form of government, also known as form of government, government, type of government (wd:P122 as p/wdt): subject's government
9. capital, also known as administrative seat, seat of government, seat, principal place, location of capital (wd:P36 as p/wdt): seat of government of a country, province, state or other type of administrative territorial entity
10. native label, also known as official title, original label, original language label, original language name, original language title (wd:P1705 as p/wdt): label for the items in their official language  or their original language, name

No other items found

[Assistant]
Based on the search results, the property `wdt:P35` seems to be the most relevant for linking to the heads of state of a country. This property is labeled as "head of state".

Now, let's construct a SPARQL query to count the number of distinct heads of state for Iran (`wd:Q794`).

Here's the initial SPARQL query:

```sparql
SELECT (COUNT(DISTINCT ?headOfState) AS ?count)
WHERE {
  wd:Q794 wdt:P35 ?headOfState .
}
```

Let's execute this query to see the result.

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

[Assistant]
The SPARQL query returned that there is 1 distinct head of state for Iran. Therefore, the answer to the question "How many heads of state does Iran have?" is 1.

I will now provide the final answer using the `answer` function.

[Tool] Stopped generation process