PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
ASK WHERE {
  wd:Q5287 wdt:P1098 ?speakers .
  FILTER ( ?speakers > 100000000 )
}

True

[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] Do more than 100000000 people speak Japanese?

[Assistant] To answer this question, we need to find out the number of speakers of the Japanese language and check if it exceeds 100 million. Let's start by searching for the entity representing the Japanese language in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Japanese, also known as Japanese language, Japanesic (wd:Q5287): language spoken in East Asia
2. Japanese language (wd:Q10855582): school subject
3. Japanese Language (wd:Q110262521): Stack Exchange site for students, teachers, and linguists wanting to discuss the finer points of the Japanese language
4. Japanese (language) (wd:L612945-S1)
5. Japanese Wikipedia, also known as ja.wikipedia.org, jpwp, Jp.wikipedia.org, JAWP, jawp (wd:Q177837): Japanese-language edition of Wikipedia
6. Japanese Sign Language, also known as JSL (wd:Q35601): sign language
7. Classical Japanese, also known as Classical Japanese language (wd:Q1332057): literary form of the Japanese language, standard until WW2, based on Heian-period Early Middle Japanese with later influences
8. Japanese language education, also known as teaching Japanese as a foreign language (wd:Q17214937)
9. dictionary of the Japanese language, also known as dictionary of Japanese, Japanese language dictionary, Japanese dictionary (wd:Q6158568): language version of dictionary
10. Japanese language department (wd:Q11086620)

[Assistant] The top result, `wd:Q5287`, represents the Japanese language. Next, we need to find the property that indicates the number of speakers of a language. We can search for this property using the term "number of speakers".

[Tool]
Top 10 property alternatives:
1. number of speakers, writers, or signers, also known as signers of a language, speakers of language, writers of a language (wd:P1098 as prn/pqv/p/pr/psv/prv/pqn/wdt/psn/ps/pq/wdtn): number of people who use a language
2. number of viewers/listeners, also known as plays, watches, watch count, views, view number (wd:P5436 as prn/pqv/p/pr/psv/prv/pqn/wdt/psn/ps/pq/wdtn): number of viewers of a television or broadcasting program; web traffic on websites
3. number of words, also known as word count, words (number of) (wd:P6570 as prn/pqv/p/pr/psv/prv/pqn/wdt/psn/ps/pq/wdtn): number of words in text
4. official language, also known as language spoken, spoken in, speaking, language official (wd:P37 as prn/pqv/p/pr/psv/prv/pqn/wdt/psn/ps/pq/wdtn): language designated as official by this item, language used
5. speaker, also known as speech by, spoken by (wd:P823 as prn/pqv/p/pr/psv/prv/pqn/wdt/psn/ps/pq/wdtn): person who is speaker for this event, ceremony, keynote, presentation or in a literary work, participant, spoken by
6. spoken by, also known as speaker (wd:P10894 as prn/pqv/p/pr/psv/prv/pqn/wdt/psn/ps/pq/wdtn): the person who is speaking in this audio or video file
7. number of aid beneficiaries, also known as number of aid recipients, number of beneficiaries, number of people reached, people reached (wd:P9077 as prn/pqv/p/pr/psv/prv/pqn/wdt/psn/ps/pq/wdtn): number of people reached with international aid programmes, acti...ctivities. Qualify with "point in time" (P585) where appropriate
8. intended public, also known as for, user group, target market, target group, target demographic (wd:P2360 as prn/pqv/p/pr/psv/prv/pqn/wdt/psn/ps/pq/wdtn): this work, product, object or event is intended for, or has been designed to that person or group of people, animals, plants, etc
9. number of registered users/contributors, also known as number of contributors, number of registered users, number of users, users (wd:P1833 as prn/pqv/p/pr/psv/prv/pqn/wdt/psn/ps/pq/wdtn): number of registered users on a website
10. member count, also known as total members, number of members, count of members, amount of members, membership (wd:P2124 as prn/pqv/p/pr/psv/prv/pqn/wdt/psn/ps/pq/wdtn): number of entities that are part of an organization at a particular time

[Assistant]
The property `wd:P1098` represents the number of speakers of a language. Now, let's construct a SPARQL query to check if the number of speakers of Japanese (`wd:Q5287`) exceeds 100 million.

We will use an ASK query to return a boolean value indicating whether the condition is true or false.

[Tool] True

[Assistant]
The ASK query returned `True`, which means that more than 100 million people speak Japanese.

Therefore, the answer to the question is **True**.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query is correct and follows all the rules. It checks if the number of speakers of Japanese is greater than 100,000,000, which is the correct approach for this question. The answer is True, which means more than 100,000,000 people do indeed speak Japanese.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?castMember ?birthDate WHERE {
  VALUES ?castMember {
    wd:Q50379405 wd:Q50377216 wd:Q49729241 wd:Q44870529 wd:Q50379407
  }
  ?castMember wdt:P569 ?birthDate .
}
ORDER BY ASC ( ?birthDate )
LIMIT 1

The oldest cast member of the Netflix show 'Queer Eye' is Karamo Brown.

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

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

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

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

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

[User] Who is the oldest cast member of the Netflix show “Queer Eye” ?

[Assistant]
To find the oldest cast member of the Netflix show "Queer Eye," we need to follow these steps:

1. Identify the entity for the show "Queer Eye."
2. Find the cast members of the show.
3. Determine the birth dates of the cast members.
4. Identify the cast member with the earliest birth date.

Let's start by searching for the entity "Queer Eye" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Queer Eye, also known as Queer Eye for the Straight Guy (wd:Q1418248): 2003 American reality television series
2. Queer Eye (wd:Q48817408): 2018 American television series
3. Queer Eye: Germany, also known as Queer Eye Germany (wd:Q111914878): German reality television series
4. Queer Eye, season 4 (wd:Q67649081): season of television series
5. Queer Eye, season 3 (wd:Q65590894): season of television series
6. Queer Eye, season 2 (wd:Q57313933): season of television series
7. Queer Eye, season 1 (wd:Q57312338): season of television series
8. Queer Eye, season 5 (wd:Q97455984): 5th season of Netflix series
9. Queer Eye, season 6 (wd:Q110791710): 6th season of Netflix series
10. Queer Eyes, Full Hearts (wd:Q18636591): episode of Modern Family

[Assistant]
The correct entity for the Netflix show "Queer Eye" is `wd:Q48817408`. Now, let's find the cast members of this show.

We will look for the property that links the show to its cast members. A common property for this is `P161` (cast member).

Let's list the cast members of the show "Queer Eye."

[Tool]
Got 5 rows and 3 columns:
| subject                  | property               | object                           |
| ------------------------ | ---------------------- | -------------------------------- |
| Queer Eye (wd:Q48817408) | cast member (wdt:P161) | Jonathan Van Ness (wd:Q50379405) |
| Queer Eye (wd:Q48817408) | cast member (wdt:P161) | Antoni Porowski (wd:Q50377216)   |
| Queer Eye (wd:Q48817408) | cast member (wdt:P161) | Bobby Berk (wd:Q49729241)        |
| Queer Eye (wd:Q48817408) | cast member (wdt:P161) | Karamo Brown (wd:Q44870529)      |
| Queer Eye (wd:Q48817408) | cast member (wdt:P161) | Tan France (wd:Q50379407)        |

[Assistant]
The cast members of the Netflix show "Queer Eye" are:
- Jonathan Van Ness (wd:Q50379405)
- Antoni Porowski (wd:Q50377216)
- Bobby Berk (wd:Q49729241)
- Karamo Brown (wd:Q44870529)
- Tan France (wd:Q50379407)

Next, we need to find the birth dates of these cast members. We will use the property `P569` (date of birth) to get this information.

Let's execute a SPARQL query to retrieve the birth dates of these cast members and determine who is the oldest.

[Tool]
Got 1 row and 2 columns:
| castMember                  | birthDate                           |
| --------------------------- | ----------------------------------- |
| Karamo Brown (wd:Q44870529) | 1980-11-02T00:00:00Z (xsd:dateTime) |

[Assistant]
The oldest cast member of the Netflix show "Queer Eye" is Karamo Brown (wd:Q44870529), born on November 2, 1980.

Here is the final SPARQL query and answer:

```sparql
SELECT ?castMember ?birthDate WHERE {
  VALUES ?castMember { wd:Q50379405 wd:Q50377216 wd:Q49729241 wd:Q44870529 wd:Q50379407 }
  ?castMember wdt:P569 ?birthDate .
} ORDER BY ASC(?birthDate) LIMIT 1
```

The oldest cast member of the Netflix show "Queer Eye" is Karamo Brown.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query correctly identifies Karamo Brown as the oldest cast member of the Netflix show 'Queer Eye' based on their birth dates.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?magazine ?establishmentDate WHERE {
  wd:Q696071 wdt:P1433 ?magazine .
  ?magazine wdt:P571 ?establishmentDate .
}

The magazine in which Hunter x Hunter is published, Weekly Shōnen Jump, was established on January 1, 1968.

[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 was the magazine in which hunter x hunter is published established?

[Assistant]
To answer this question, we need to identify the following:

1. The magazine in which Hunter x Hunter is published.
2. The establishment date of that magazine.

Let's start by searching for the entity "Hunter x Hunter" in the knowledge graph and then look for the magazine it is published in.

[Tool]
Top 10 entity alternatives:
1. Hunter × Hunter, also known as HUNTER × HUNTER, Hunter x Hunter, HXH (wd:Q696071): Japanese manga series
2. Hunter × Hunter, also known as H×H, Hunter x Hunter, HxH (wd:Q70209136): 2011 Japanese anime television series
3. Hunter × Hunter, also known as Hunter x Hunter, HxH (wd:Q21232458): 1999 Japanese anime television series
4. Monster Hunter Generations, also known as Monster Hunter X (wd:Q20856979): 2015 Japanese video game
5. Hunter Xcite (wd:Q60767092): sailboat class
6. Xavier Hunter (wd:Q100806788): basketball player
7. Hunter × Hunter: Phantom Rouge, also known as Hunter X Hunter: Phantom Rouge (wd:Q11398237): 2013 film by Yūzō Satō
8. City Hunter XYZ (wd:Q97170832): fan film by Filip Wong
9. Monster Hunter Generations Ultimate, also known as Monster Hunter XX (wd:Q30302563): 2017 video game
10. XX: Beautiful Hunter (wd:Q8042573): 1994 film by Masaru Konuma

[Assistant] The top result for "Hunter x Hunter" is the Japanese manga series (wd:Q696071). We will use this entity to find the magazine in which it is published. Let's search for the property that indicates where a manga is published.

[Tool]
Top 10 property alternatives:
1. published in, also known as song on album, tome of the book, tome of, track of, track on (wd:P1433 as wdt/p): larger work that a given work was published in, like a book, journal or music album, part of
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. 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)
4. author, also known as writer, written by, poet, playwright, maker (wd:P50 as wdt/p): 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
5. title, also known as name, titled, page name, original title, known as (wd:P1476 as wdt/p): published name of a work, such as a newspaper article, a literary work, piece of music, a website, or a performance work, name
6. language of work or name, also known as language of the reference, language of URL, language of website, language of work, named in language (wd:P407 as wdt/p): language associated with this creative work (such as books, show...guage" (P103) and "languages spoken, written or signed" (P1412))
7. country of origin, also known as origin country, place of origin, originates from, country, CoO (wd:P495 as wdt/p): country of origin of this item (creative work, food, phrase, product, etc.), significant place, country
8. 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
9. 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
10. derivative work, also known as is source work of, is adapted as, is the basis for, iteration, reboot (wd:P4969 as wdt/p): new work of art (film, book, software, etc.) derived from major part of this work

No other items found

[Assistant]
The property "published in" (wdt:P1433) seems to be the correct one to find the magazine in which Hunter x Hunter is published. Let's execute a SPARQL query to find the magazine.

### Step 1: Find the magazine in which Hunter x Hunter is published

```sparql
SELECT ?magazine
WHERE {
  wd:Q696071 wdt:P1433 ?magazine .
}
```

Let's execute this query to find the magazine.

[Tool]
Got 1 row and 1 column:
| magazine                        |
| ------------------------------- |
| Weekly Shōnen Jump (wd:Q220015) |

[Assistant]
The magazine in which Hunter x Hunter is published is Weekly Shōnen Jump (wd:Q220015). Now, we need to find the establishment date of this magazine. Let's search for the property that indicates the establishment date of a magazine.

### Step 2: Find the establishment date of Weekly Shōnen Jump

We will use the property "inception" (wdt:P571) to find the establishment date of the magazine.

```sparql
SELECT ?establishmentDate
WHERE {
  wd:Q220015 wdt:P571 ?establishmentDate .
}
```

Let's execute this query to find the establishment date.

[Tool]
Got 1 row and 1 column:
| establishmentDate                   |
| ----------------------------------- |
| 1968-01-01T00:00:00Z (xsd:dateTime) |

[Assistant]
The establishment date of Weekly Shōnen Jump is January 1, 1968.

### Final Answer

The magazine in which Hunter x Hunter is published, Weekly Shōnen Jump, was established on January 1, 1968.

Here is the final SPARQL query that answers the question:

```sparql
SELECT ?magazine ?establishmentDate
WHERE {
  wd:Q696071 wdt:P1433 ?magazine .
  ?magazine wdt:P571 ?establishmentDate .
}
```

Let's provide the final answer.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query correctly identifies the magazine in which Hunter x Hunter is published and retrieves its establishment date. The answer provided is accurate based on the information from the knowledge graph.