PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?castMember ?dateOfBirth WHERE {
  wd:Q48817408 wdt:P161 ?castMember .
  ?castMember wdt:P569 ?dateOfBirth .
}
ORDER BY ASC ( ?dateOfBirth )
LIMIT 1

Karamo Brown (wd:Q44870529) is the oldest cast member of the Netflix show 'Queer Eye'.

[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 answer this question, we need to identify the cast members of the Netflix show "Queer Eye" and then determine their ages. Let's start by finding the entity for the show "Queer Eye" on Wikidata.

I'll search for the show "Queer Eye" using the search_entity function.

[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]
From the search results, it seems that the correct entity for the Netflix show "Queer Eye" is `wd:Q48817408`. Now, let's find the cast members of this show. We can use the list function to look for triples where the show "Queer Eye" is the subject and the predicate is something like `wdt:P161`, which represents "cast member".

Let's execute the list function with the show entity as the subject.

[Tool]
Got 167 rows and 3 columns, showing the first 10 rows below:
| subject                  | property                                        | object                                                                         |
| ------------------------ | ----------------------------------------------- | ------------------------------------------------------------------------------ |
| Queer Eye (wd:Q48817408) | instance of (wdt:P31)                           | television series (wd:Q5398426)                                                |
| Queer Eye (wd:Q48817408) | has part(s) (wdt:P527)                          | Queer Eye, season 1 (wd:Q57312338)                                             |
| Queer Eye (wd:Q48817408) | country of origin (wdt:P495)                    | United States (wd:Q30)                                                         |
| Queer Eye (wd:Q48817408) | official website (wdt:P856)                     | <https://www.netflix.com/title/80160037>                                       |
| Queer Eye (wd:Q48817408) | genre (wdt:P136)                                | reality television (wd:Q182415)                                                |
| Queer Eye (wd:Q48817408) | award received (wdt:P166)                       | Primetime Emmy Award for Outstanding Structured Reality Program (wd:Q18153300) |
| Queer Eye (wd:Q48817408) | cast member (wdt:P161)                          | Jonathan Van Ness (wd:Q50379405)                                               |
| Queer Eye (wd:Q48817408) | start time (wdt:P580)                           | 2018-02-07T00:00:00Z                                                           |
| Queer Eye (wd:Q48817408) | social media followers (wdt:P8687)              | 430422                                                                         |
| Queer Eye (wd:Q48817408) | original language of film or TV show (wdt:P364) | American English (wd:Q7976)                                                    |
| ...                      | ...                                             | ...                                                                            |

[Assistant]
From the list function results, we can see that the cast members of the show "Queer Eye" are listed under the property `wdt:P161` (cast member). One of the cast members listed is Jonathan Van Ness (`wd:Q50379405`).

Next, we need to find the age of each cast member. To do this, we can search for the property related to date of birth for each cast member. We will then calculate the age based on the current date and the date of birth.

Let's start by listing the cast members and their dates of birth.

I will construct a SPARQL query to retrieve the cast members and their dates of birth.

```sparql
SELECT ?castMember ?dateOfBirth WHERE {
  wd:Q48817408 wdt:P161 ?castMember .
  ?castMember wdt:P569 ?dateOfBirth .
}
```

Now, I will execute this SPARQL query.

[Tool]
Got 5 rows and 2 columns:
| ?castMember                      | ?dateOfBirth         |
| -------------------------------- | -------------------- |
| Karamo Brown (wd:Q44870529)      | 1980-11-02T00:00:00Z |
| Bobby Berk (wd:Q49729241)        | 1982-08-25T00:00:00Z |
| Antoni Porowski (wd:Q50377216)   | 1984-03-14T00:00:00Z |
| Jonathan Van Ness (wd:Q50379405) | 1987-03-28T00:00:00Z |
| Tan France (wd:Q50379407)        | 1983-04-20T00:00:00Z |

[Assistant]
We now have the dates of birth for the cast members of "Queer Eye". To determine who is the oldest, we need to compare these dates. The cast members and their respective dates of birth are:

- Karamo Brown (wd:Q44870529): 1980-11-02
- Bobby Berk (wd:Q49729241): 1982-08-25
- Antoni Porowski (wd:Q50377216): 1984-03-14
- Jonathan Van Ness (wd:Q50379405): 1987-03-28
- Tan France (wd:Q50379407): 1983-04-20

Based on these dates, Karamo Brown is the oldest cast member since his date of birth is the earliest.

Let's finalize the SPARQL query to confirm this and provide the answer.

```sparql
SELECT ?castMember ?dateOfBirth WHERE {
  wd:Q48817408 wdt:P161 ?castMember .
  ?castMember wdt:P569 ?dateOfBirth .
} ORDER BY ASC(?dateOfBirth)
LIMIT 1
```

This query will order the results by ascending date of birth and limit the output to the oldest cast member.

Executing this query:

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

[Assistant]
The SPARQL query confirms that Karamo Brown (wd:Q44870529) is the oldest cast member of the Netflix show "Queer Eye".

Finalizing the answer:

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?inception WHERE {
  wd:Q220015 wdt:P571 ?inception .
}

The magazine in which Hunter × 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 follow these steps:

1. Identify the entity representing the manga "Hunter × Hunter".
2. Find the publication medium of "Hunter × Hunter".
3. Identify when the publication medium was established.

Let's start by searching for the entity representing "Hunter × Hunter".

[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. Hunter × Hunter, also known as Hunter×Hunter (wd:Q132543073): video game series
5. Hunter × Hunter, also known as Hunter×Hunter (wd:Q132542678): media franchise
6. Centipede × And × Memory, also known as Hunter × Hunter Episode 23, Mukade × to × Omoide, Hunter × Hunter ep 23, Hunter × Hunter ep. 23, Hunter × Hunter ep23 (wd:Q116849908): Episode 23 of Hunter × Hunter
7. Carnage × And × Devastation, also known as Gekitou × de × Gekimetsu, Hunter × Hunter ep 97, Hunter × Hunter ep. 97, Hunter × Hunter ep97, Hunter × Hunter Episode 97 (wd:Q116849877): Episode 97 of Hunter × Hunter
8. Beware × Of × Prisoners, also known as Shuujin × ni × Goyoujin, Hunter × Hunter Episode 9, Hunter × Hunter ep. 9, Hunter × Hunter ep 9, Hunter × Hunter ep9 (wd:Q116849538): Episode 9 of Hunter × Hunter
9. Bid × And × Haste, also known as Hunter × Hunter ep. 59, Hunter × Hunter ep 59, Seri × to × Aseri, Hunter × Hunter ep59, Hunter × Hunter Episode 59 (wd:Q116849600): Episode 59 of Hunter × Hunter
10. Big × Time × Interview, also known as Hunter × Hunter ep 18, Hunter × Hunter ep. 18, Hunter × Hunter ep18, Hunter × Hunter Episode 18, Taisetsu × na × Mensetsu (wd:Q116849550): Episode 18 of Hunter × Hunter

[Assistant]
From the search results, it appears that "Hunter × Hunter" has multiple entries, including the manga series and various anime adaptations. We should focus on the manga series, which is represented by `wd:Q696071`.

Next, we need to find the publication medium of the manga series "Hunter × Hunter". Let's search for properties related to publication of this manga series.

[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 p/wdt): 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 p/wdt): organization or person responsible for publishing books, periodicals, printed music, podcasts, games or software
3. author, also known as writer, written by, poet, playwright, maker (wd:P50 as p/wdt): main creator(s) of a written work (use on works, not humans); us...hor name string) when Wikidata item is unknown or does not exist, creator
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. official website, also known as site (official), home page, site official, URL, URL (official) (wd:P856 as p/wdt): 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
6. characters, also known as appears in this work (wd:P674 as p/wdt): characters which appear in this item (like plays, operas, operettas, books, comics, films, TV series, video games)
7. topic's main template, also known as topic has template, topic's main module, topic's main infobox, topic infobox, main template (wd:P1424 as p/wdt): the main template relating to a topic
8. logo image, also known as masthead, wordmark, trademark, trade mark, sign (wd:P154 as p/wdt): graphic mark or emblem commonly used by commercial enterprises, organizations and products, icon
9. derivative work, also known as is source work of, is adapted as, is the basis for, iteration, reboot (wd:P4969 as p/wdt): new work of art (film, book, software, etc.) derived from major part of this work
10. 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

No other items found

[Assistant]
From the search results, the property `P1433` (published in) seems relevant for finding the publication medium of "Hunter × Hunter". Let's use this property to find the publication medium of the manga series and then identify when that publication medium was established.

First, let's find the publication medium of "Hunter × Hunter" using the property `P1433`.

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

[Assistant]
We have determined that "Hunter × Hunter" is published in "Weekly Shōnen Jump" (wd:Q220015). Now, we need to find out when "Weekly Shōnen Jump" was established.

Let's search for properties related to establishment or founding of "Weekly Shōnen Jump".

[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. short name, also known as short title, shortname, abbreviation, shortened name, initialism (wd:P1813 as p/wdt): short name of a place, organisation, person, journal, wikidata property, etc. Used by some Wikipedia templates., name
3. field of work, also known as scientific discipline, scientific area, responsible for, research on, research fields (wd:P101 as p/wdt): specialization of a person or organization; see P106 for the occupation, interested in
4. country of origin, also known as origin country, place of origin, originates from, country, CoO (wd:P495 as p/wdt): country of origin of this item (creative work, food, phrase, product, etc.), significant place, country
5. official website, also known as site (official), home page, site official, URL, URL (official) (wd:P856 as p/wdt): 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
6. instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as p/wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)

No other items found

[Assistant] From the search results, the property `P571` (inception) seems relevant for finding the establishment date of "Weekly Shōnen Jump". Let's use this property to find the establishment date of "Weekly Shōnen Jump".

[Tool]
Got 1 row and 1 column:
| ?inception           |
| -------------------- |
| 1968-01-01T00:00:00Z |

[Assistant]
The magazine "Weekly Shōnen Jump" (wd:Q220015) was established on January 1, 1968.

Therefore, the magazine in which "Hunter × Hunter" is published was established on January 1, 1968.

Now, I will provide the final answer.

[Tool] Stopped generation process