PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ( COUNT( ?film ) AS ?count ) WHERE {
  ?film wdt:P31 wd:Q11424 ; wdt:P577 ?date .
  FILTER ( YEAR( ?date ) = 1978 ) ?film wdt:P136 ?genre .
  ?genre rdfs:label ?genreLabel .
  FILTER ( LANG( ?genreLabel ) = "en" ) FILTER ( CONTAINS( LCASE( ?genreLabel ) , "musical" ) ) FILTER ( ?film != wd:Q267721 )
}

Got 1 row and 1 column:
| count        |
| ------------ |
| 69 (xsd:int) |

[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 musical films were launched the same year as Grease?

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

[Tool]
Example 1:
Question:
Who are the developers of DBpedia?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q465 wdt:P112 ?uri .
}

Using entities:
DBpedia, also known as dbpedia.org (wd:Q465): online database project of structured data extracted from Wikipedia

Using properties:
founded by, also known as starter, co-founded by, started by, institutor, instituted by (wd:P112 as wdt): founder or co-founder of this organization, religion, place or entity, creator

Example 2:
Question:
Which U.S. states are in the same timezone as Utah?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q829 wdt:P421 ?timezone .
  ?uri wdt:P31 wd:Q35657 ; wdt:P421 ?timezone .
  FILTER ( ?uri != wd:Q829 )
}

Using entities:
U.S. state, also known as U.S. states, US state, USA state, U.S.A. state, United States states (wd:Q35657): constituent political entity of the United States of America
Utah, also known as Beehive State, State of Utah, Utah, United States, US-UT, UT (wd:Q829): state of the United States of America

Using properties:
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
located in time zone, also known as TZ, time zone, timezone (wd:P421 as wdt): time zone for this item

Example 3:
Question:
How many languages are spoken in Turkmenistan?

SPARQL:
SELECT ( COUNT( DISTINCT ?uri ) as ?c ) WHERE {
  wd:Q874 wdt:P2936 ?uri .
}

Using entities:
Turkmenistan, also known as TKM, tm, Turkmenia (wd:Q874): country in Central Asia

Using properties:
language used, also known as languages used, medium of instruction, used language, uses language, working language (wd:P2936 as wdt): language widely used (spoken or written) in this place or at this event or organisation

[Assistant] Based on the examples, we need to identify the film "Grease" and the year it was released. Then we can look for other musical films that were released in the same year. Let's start by searching for the entity "Grease".

[Tool]
Top 10 entity alternatives:
1. fat, also known as grease, fats and oils, fat or oil, dietary fats, dietary fat (wd:Q127980): esters of three fatty acid chains and the alcohol glycerol, one of the three main macronutrients, also known as triglycerides
2. Grease (wd:Q267721): 1978 film by Randal Kleiser
3. grease, also known as lubricating grease, grease lubricant (wd:Q629823): semi-solid, oily lubricant
4. Grease (wd:Q611626): 1972 musical
5. Grease (wd:Q728791): original song written and composed by Barry Gibb; from the 1978 film "Grease", performed by Frankie Valli
6. Grease (wd:Q3776108): 2010 video game
7. Grease (wd:Q104861673): American media franchise
8. Grease (wd:Q114518793): episode of Cheers (S9 E6)
9. Grease (wd:Q116013684): sardana by Martí de Joan
10. Grease (wd:Q126836847): vocal track by Mina; 1986 studio recording; cover version

[Assistant] The second option, "Grease" (wd:Q267721), is the 1978 film directed by Randal Kleiser. We'll use this entity to find out the release year and then look for other musical films from the same year. First, let's confirm the release year of "Grease".

[Tool]
Got 2 rows and 1 column:
| year           |
| -------------- |
| 1978 (xsd:int) |
| 1978 (xsd:int) |

[Assistant]
The film "Grease" was released in 1978. Now, we need to find other musical films that were also released in 1978. To do this, we will first identify the property for "musical" and then construct a query to find films that match these criteria.

Let's search for the property "musical" to see if there is a specific property that indicates a film is a musical.

[Tool]
Top 10 property alternatives:
1. sheet music, also known as musical score (wd:P3030 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): media file containing the musical score (sheet music) for this item
2. setlist, also known as set list, works performed, songs performed, performances, performance list (wd:P9793 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): performances of songs or other music at an event; concert program, presented works
3. musical quotation or excerpt, also known as musical excerpt, musical quotation (wd:P6670 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): quotation or excerpt from this musical work, in LilyPond syntax
4. All Musicals lyrics ID, also known as All Musicals ID (wd:P12941 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): identifier for lyrics for musicals and songs from musicals at All Musicals
5. composer, also known as music by, written by, songwriter, musical score by, film score by (wd:P86 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): person(s) who wrote the music [for lyricist, use "lyrics by" (P676)], creator
6. performer, also known as actor, sung by, singer, recording by, recorded by (wd:P175 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): actor, musician, band or other performer associated with this role or musical work, contributor to the creative work or subject
7. featured track(s), also known as songs, soundtrack, OST, music tracks, music track (wd:P10664 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): soundtrack of this audiovisual work
8. genre, also known as genre of music, type of music, type of film, music genre, literary genre (wd:P136 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): creative work's genre or an artist's field of work (P101). Use main subject (P921) to relate creative works to their topic
9. beats per minute, also known as BPM, tempo, musical tempo, metronome mark, bpm (wd:P1725 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): tempo of a musical work, indicated numerically (use P1558 for qualitative tempo markings), frequency
10. tempo marking, also known as tempo, musical tempo (wd:P1558 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): qualitative indication about the speed or pace of a given musical piece (use P1725 for numerical tempo markings)

[Assistant]
The property "genre" (wd:P136) seems to be the most relevant for identifying musical films. We will use this property to filter films that are classified as musicals. Now, let's construct a query to find other musical films released in 1978.

We will use the following steps:
1. Find films released in 1978.
2. Filter those films to only include musicals.

Here is the initial query:

```sparql
SELECT ?film ?filmLabel WHERE {
  ?film wdt:P31 wd:Q11424 ;  # Instance of film
         wdt:P577 ?date .    # Release date
  FILTER(YEAR(?date) = 1978)
  ?film wdt:P136 wd:Q208651 .  # Genre is musical
  OPTIONAL { ?film rdfs:label ?filmLabel . FILTER(LANG(?filmLabel) = "en") }
}
```

Let's execute this query to see the results.

[Tool] Got no rows and 2 columns

[Assistant]
It appears that the query did not return any results. This could be due to several reasons, such as the data not being present in the knowledge graph or the query needing refinement. Let's try a different approach to ensure we are capturing all musical films from 1978.

We will break down the query into smaller parts to debug and ensure each part is working correctly. First, let's verify that we can find films released in 1978.

```sparql
SELECT ?film ?filmLabel WHERE {
  ?film wdt:P31 wd:Q11424 ;  # Instance of film
         wdt:P577 ?date .    # Release date
  FILTER(YEAR(?date) = 1978)
  OPTIONAL { ?film rdfs:label ?filmLabel . FILTER(LANG(?filmLabel) = "en") }
}
```

Let's execute this query to see if we can find any films released in 1978.

[Tool]
Got 4,108 rows and 2 columns, showing the first 5 and last 5 rows below:
| film                                                                         | filmLabel                                                           |
| ---------------------------------------------------------------------------- | ------------------------------------------------------------------- |
| Buffalo Bill and the Indians, or Sitting Bull's History Lesson (wd:Q1001943) | Buffalo Bill and the Indians, or Sitting Bull's History Lesson (en) |
| wd:Q100221213                                                                |                                                                     |
| Chaman Khan (wd:Q100256372)                                                  | Chaman Khan (en)                                                    |
| wd:Q100266719                                                                |                                                                     |
| wd:Q100329482                                                                |                                                                     |
| ...                                                                          | ...                                                                 |
| The Wing or the Thigh (wd:Q994421)                                           | The Wing or the Thigh (en)                                          |
| The Wing or the Thigh (wd:Q994421)                                           | The Wing or the Thigh (en)                                          |
| Joshua and the Battle of Jericho (wd:Q99646620)                              | Joshua and the Battle of Jericho (en)                               |
| Shout at the Devil (wd:Q996995)                                              | Shout at the Devil (en)                                             |
| Daniel in the Lion's Den (wd:Q99929843)                                      | Daniel in the Lion's Den (en)                                       |

[Assistant]
The query successfully returned films released in 1978. Now, let's filter these films to only include those that are classified as musicals. We will add the genre filter to the existing query.

Here is the updated query:

```sparql
SELECT ?film ?filmLabel WHERE {
  ?film wdt:P31 wd:Q11424 ;  # Instance of film
         wdt:P577 ?date .    # Release date
  FILTER(YEAR(?date) = 1978)
  ?film wdt:P136 wd:Q208651 .  # Genre is musical
  OPTIONAL { ?film rdfs:label ?filmLabel . FILTER(LANG(?filmLabel) = "en") }
}
```

Let's execute this query again to see if we can find the musical films released in 1978.

[Tool] Got no rows and 2 columns

[Assistant]
It seems that the query is still not returning any results. This could be due to the specific genre classification not being applied consistently or the data not being present in the knowledge graph. Let's try a different approach by using a broader search to find films that are likely to be musicals and then filter them manually.

We can use the property `wdt:P136` to find films that have any genre and then check if "musical" is one of the genres. Here is the updated query:

```sparql
SELECT ?film ?filmLabel WHERE {
  ?film wdt:P31 wd:Q11424 ;  # Instance of film
         wdt:P577 ?date .    # Release date
  FILTER(YEAR(?date) = 1978)
  ?film wdt:P136 ?genre .    # Genre
  ?genre rdfs:label ?genreLabel .
  FILTER(LANG(?genreLabel) = "en")
  FILTER(CONTAINS(LCASE(?genreLabel), "musical"))
  OPTIONAL { ?film rdfs:label ?filmLabel . FILTER(LANG(?filmLabel) = "en") }
}
```

This query will return films released in 1978 and check if any of their genres contain the word "musical". Let's execute this query.

[Tool]
Got 71 rows and 2 columns, showing the first 5 and last 5 rows below:
| film                                       | filmLabel                         |
| ------------------------------------------ | --------------------------------- |
| An Ordinary Miracle (wd:Q1082153)          | An Ordinary Miracle (en)          |
| Mic Maputo Man (wd:Q120043041)             | Mic Maputo Man (en)               |
| The Buddy Holly Story (wd:Q1211819)        | The Buddy Holly Story (en)        |
| Junior High School (wd:Q12125297)          | Junior High School (en)           |
| Legend of the Maiden Tower (wd:Q12844664)  | Legend of the Maiden Tower (en)   |
| ...                                        | ...                               |
| New York, New York (wd:Q743367)            | New York, New York (en)           |
| New York, New York (wd:Q743367)            | New York, New York (en)           |
| Slow Dancing in the Big City (wd:Q7541987) | Slow Dancing in the Big City (en) |
| Stony Island (wd:Q7619570)                 | Stony Island (en)                 |
| The Woman who Sings (wd:Q925962)           | The Woman who Sings (en)          |

[Assistant]
The query has returned a list of films released in 1978 that are classified as musicals. Now, we need to count the number of these films to answer the question.

To count the number of musical films released in 1978, we can modify the query to use the `COUNT` function:

```sparql
SELECT (COUNT(?film) AS ?count) WHERE {
  ?film wdt:P31 wd:Q11424 ;  # Instance of film
         wdt:P577 ?date .    # Release date
  FILTER(YEAR(?date) = 1978)
  ?film wdt:P136 ?genre .    # Genre
  ?genre rdfs:label ?genreLabel .
  FILTER(LANG(?genreLabel) = "en")
  FILTER(CONTAINS(LCASE(?genreLabel), "musical"))
}
```

Let's execute this query to get the count of musical films released in 1978.

[Tool]
Got 1 row and 1 column:
| count        |
| ------------ |
| 71 (xsd:int) |

[Assistant]
The query has returned that there were 71 musical films released in 1978. However, this count includes "Grease" itself. To find the number of other musical films, we need to exclude "Grease" from the count.

Let's modify the query to exclude "Grease" (wd:Q267721):

```sparql
SELECT (COUNT(?film) AS ?count) WHERE {
  ?film wdt:P31 wd:Q11424 ;  # Instance of film
         wdt:P577 ?date .    # Release date
  FILTER(YEAR(?date) = 1978)
  ?film wdt:P136 ?genre .    # Genre
  ?genre rdfs:label ?genreLabel .
  FILTER(LANG(?genreLabel) = "en")
  FILTER(CONTAINS(LCASE(?genreLabel), "musical"))
  FILTER(?film != wd:Q267721)  # Exclude Grease
}
```

Let's execute this query to get the final count of other musical films released in 1978.

[Tool]
Got 1 row and 1 column:
| count        |
| ------------ |
| 69 (xsd:int) |

[Assistant]
The query has returned that there were 69 other musical films released in 1978, excluding "Grease".

Therefore, the answer to the question "How many other musical films were launched the same year as Grease?" is 69.

Here is the final SPARQL query:

```sparql
SELECT (COUNT(?film) AS ?count) WHERE {
  ?film wdt:P31 wd:Q11424 ;  # Instance of film
         wdt:P577 ?date .    # Release date
  FILTER(YEAR(?date) = 1978)
  ?film wdt:P136 ?genre .    # Genre
  ?genre rdfs:label ?genreLabel .
  FILTER(LANG(?genreLabel) = "en")
  FILTER(CONTAINS(LCASE(?genreLabel), "musical"))
  FILTER(?film != wd:Q267721)  # Exclude Grease
}
```

And the answer is 69.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query is correct and returns the expected result. It counts the number of musical films released in the same year as Grease, excluding Grease itself. The query filters the results based on the genre label being in English and containing the word 'musical'. The result is accurate and no further refinements are needed.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
ASK WHERE {
  wd:Q510 wdt:P1589 / wdt:P2044 ?depth .
  wd:Q513 wdt:P2044 ?height .
  FILTER ( ?depth < - ?height )
}

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] Is the Mariana Trench deeper than Mount Everest is high (above sea level)?

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

[Tool]
Example 1:
Question:
In which time zone is Rome?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q220 wdt:P421 ?uri
}

Using entities:
Rome, also known as Roma, City of Seven Hills, Rome, Italy, The Eternal City (wd:Q220): capital and largest city of Italy

Using properties:
located in time zone, also known as TZ, time zone, timezone (wd:P421 as wdt): time zone for this item

Example 2:
Question:
Which professional surfers were born in Australia?

SPARQL:
SELECT ( COUNT( DISTINCT ?uri ) AS ?c ) WHERE {
  ?uri wdt:P19 ?birtplace .
  ?birtplace wdt:P17 wd:Q408 .
  ?uri wdt:P106 wd:Q13561328 .
}

Using entities:
surfer, also known as surfboarder (wd:Q13561328): sportsperson, practitioner of surfing
Australia, also known as AU, The Commonwealth of Australia, Stria, Straya, Stralia (wd:Q408): country in Oceania

Using properties:
occupation, also known as job, work, vocation, profession, employment (wd:P106 as wdt): occupation of a person; see also "field of work" (Property:P101), "position held" (Property:P39)
country, also known as sovereign state, state, land, host country (wd:P17 as wdt): sovereign state that this item is in (not to be used for human beings), located in the administrative territorial entity
place of birth, also known as POB, location of birth, location born, born in, birthplace (wd:P19 as wdt): most specific known birth location of a person, animal or fictional character, location

Example 3:
Question:
When did princess Diana die?

SPARQL:
SELECT DISTINCT ?date WHERE {
  wd:Q9685 wdt:P570 ?date .
}

Using entities:
Diana, Princess of Wales, also known as Lady Diana Spencer, Princess Diana, Princess Di, Lady Diana Frances Spencer, The People's Princess (wd:Q9685): member of the British royal family and Princess of Wales (1961–1997), mental health advocate, kindergarten teacher, HIV/AIDS activist, environmentalist, peace activist, philanthropist, humanitarian, aristocrat, benefactor, socialite

Using properties:
date of death, also known as DOD, deathdate, died, died on, year of death (wd:P570 as wdt): dissolved, abolished or demolished date, date on which the subject died, end time

[Assistant] To answer the question, we need to find the depth of the Mariana Trench and the height of Mount Everest above sea level. We can then compare these values to determine if the Mariana Trench is deeper than Mount Everest is high.

[Tool]
Top 10 entity alternatives:
1. Mariana Trench (wd:Q510): deepest oceanic trench on Earth
2. Marianas Trench (wd:Q3291259): Canadian pop rock band
3. "Mariana Trench" T-waves (wd:Q87292168): scientific article published on 01 April 2015
4. Marianas Trench Marine National Monument (wd:Q6612760): US national monument in the Mariana Archipelago
5. Document Recovered From The Marianas Trench (wd:Q60032271): a tale from the SCP web-based collaborative writing project
6. Obligately barophilic bacterium from the Mariana trench (wd:Q35470633): scientific article
7. Self-powered soft robot in the Mariana Trench (wd:Q118298491)
8. So, How Deep Is the Mariana Trench? (wd:Q63230174)
9. oceanic trench, also known as deep sea trench, trench, submarine trench (wd:Q119253): depressions of the sea floor
10. Silybum marianum, also known as milkthistle, variegated thistle, St. Mary's thistle, Scotch thistle, Saint Mary's thistle (wd:Q193798): species of plant of genus Silybum in family Asteraceae

[Assistant] The first result, "Mariana Trench" (wd:Q510), seems to be the correct entity for our query. Let's now find the depth of the Mariana Trench.

[Tool]
Top 10 property alternatives:
1. lowest point, also known as extreme point deepest, extreme point lowest, deepest point (wd:P1589 as wdt/p): point with lowest elevation in a region or path
2. bathymetry image, also known as file bathymetry, bathymetric map, bathymetric chart (wd:P207 as wdt/p): image showing bathymetric chart, bathymetric map
3. elevation above sea level, also known as MAMSL, MASL, height, elevation above mean sea level, elevation (wd:P2044 as wdt/p): height of the item (geographical object) as measured relative to sea level
4. located in or next to body of water, also known as located on body of water, ocean, on bay, next to lake, on coast of (wd:P206 as wdt/p): body of water on or next to which a place is located, location
5. coordinate location, also known as point on a map, gps coordinates, gps location, latitude, location (wd:P625 as wdt/p): geocoordinates of the subject. For Earth, please note that only WGS84 coordinating system is supported at the moment
6. instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt/p): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
7. described by source, also known as mentioned in, source of item, reviewed in, mentioned in news article, talked about in (wd:P1343 as wdt/p): work where this item is described, attested in
8. country, also known as sovereign state, state, land, host country (wd:P17 as wdt/p): sovereign state that this item is in (not to be used for human beings), located in the administrative territorial entity
9. image, also known as still capture, still, screenshot, screen capture, portrait (wd:P18 as wdt/p): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)
10. GND ID, also known as Deutschen Nationalbibliothek ID, Universal Authority File, Integrated Authority File, IndividualisedGnd, GND-IDN (wd:P227 as p/wdt/wdtn): identifier from an international authority file of names, subjects, and organizations

No other items found

[Assistant] The property "lowest point" (wd:P1589) seems to be the most appropriate for finding the depth of the Mariana Trench. Let's retrieve the value of this property for the Mariana Trench.

[Tool]
Got 1 row and 1 column:
| depth                        |
| ---------------------------- |
| Challenger Deep (wd:Q459173) |

[Assistant] The "lowest point" of the Mariana Trench is the "Challenger Deep" (wd:Q459173). Now, let's find the depth of the Challenger Deep.

[Tool]
Top 10 property alternatives:
1. elevation above sea level, also known as MAMSL, MASL, height, elevation above mean sea level, elevation (wd:P2044 as wdt/p): height of the item (geographical object) as measured relative to sea level
2. located in or next to body of water, also known as located on body of water, ocean, on bay, next to lake, on coast of (wd:P206 as wdt/p): body of water on or next to which a place is located, location
3. coordinate location, also known as point on a map, gps coordinates, gps location, latitude, location (wd:P625 as wdt/p): geocoordinates of the subject. For Earth, please note that only WGS84 coordinating system is supported at the moment
4. part of, also known as of, within, system of, subsystem of, subgroup of (wd:P361 as wdt/p): object of which the subject is a part (if this subject is alread... of "has part" (P527, see also "has parts of the class" (P2670))
5. instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt/p): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
6. country, also known as sovereign state, state, land, host country (wd:P17 as p): sovereign state that this item is in (not to be used for human beings), located in the administrative territorial entity
7. image, also known as still capture, still, screenshot, screen capture, portrait (wd:P18 as wdt/p): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)
8. Freebase ID, also known as Freebase, MID, Freebase identifier, FID (wd:P646 as wdt/p): identifier for a page in the Freebase database. Format: "/m/0" f...r IDs starting with "/g/", use Google Knowledge Graph ID (P2671)
9. Microsoft Academic ID, also known as MS Academic ID, Microsoft ID, Microsoft Academic Work ID, Microsoft Academic topic ID, Microsoft Academic Source ID (wd:P6366 as p/wdt/wdtn): identifier for an object or topic in the Microsoft Academic Graph (until 31 December 2021)
10. locator map image, also known as locator map, map image, map (wd:P242 as wdt/p): geographic map image which highlights the location of the subject within some larger entity, image

No other items found

[Assistant] The property "elevation above sea level" (wd:P2044) seems to be the most appropriate for finding the depth of the Challenger Deep. Let's retrieve the value of this property for the Challenger Deep.

[Tool]
Got 1 row and 1 column:
| depth                |
| -------------------- |
| -10984 (xsd:decimal) |

[Assistant]
The depth of the Challenger Deep, which is the lowest point of the Mariana Trench, is -10,984 meters below sea level. Now, let's find the height of Mount Everest above sea level.

First, we need to identify the entity for Mount Everest.

[Tool]
Top 10 entity alternatives:
1. Mount Everest, also known as Chomolungma, Chomolangma, Zhumulangma, World highest peak, Sagarmāthā (wd:Q513): Earth's highest mountain above sea level, located in the Mahalangur Himal sub-range of the Himalayas
2. The Climbers, also known as Chomolungma, Mount Everest, Qomolangma (wd:Q61882061): 2019 film directed by Daniel Lee
3. Mount Everest (wd:Q19977041): band
4. Mount Everest, also known as IMO 9470985 (wd:Q84260471): ship built in 2010
5. Mount Everest, also known as Mount Everest, Australia (wd:Q21939846): mountain in New South Wales, Australia
6. Mount Everest, also known as Mount Everest, South Africa (wd:Q31505139): mountain in the Free State Province, South Africa
7. Mount Everest (wd:Q119187184): painting by G. Hemwell, Hampshire Cultural Trust Headquarters, Winchester (Hampshire County Council’s Fine Art Collection)
8. Mount Everest (wd:Q59014625): article published May 1955
9. Mount Everest (wd:Q60102972): article published March 1921
10. Mount Everest (wd:Q60106019): article published November 1921

[Assistant]
The first result, "Mount Everest" (wd:Q513), seems to be the correct entity for our query. Let's now find the height of Mount Everest above sea level.

We will use the same property "elevation above sea level" (wd:P2044) to find the height of Mount Everest.

[Tool]
Got 1 row and 1 column:
| height                |
| --------------------- |
| 8848.86 (xsd:decimal) |

[Assistant]
The height of Mount Everest above sea level is 8,848.86 meters.

Now, we have the depth of the Mariana Trench (-10,984 meters) and the height of Mount Everest (8,848.86 meters). To determine if the Mariana Trench is deeper than Mount Everest is high, we need to compare these values.

Since the depth of the Mariana Trench is a negative number, we can directly compare the absolute values:

- Depth of Mariana Trench: 10,984 meters
- Height of Mount Everest: 8,848.86 meters

Clearly, 10,984 meters is greater than 8,848.86 meters. Therefore, the Mariana Trench is deeper than Mount Everest is high.

Let's confirm this with an ASK query in SPARQL.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query is correctly formulated as an ASK query and accurately compares the depth of the Mariana Trench with the height of Mount Everest. The result is true, which means the Mariana Trench is indeed deeper than Mount Everest is high above sea level. No further refinements are necessary.


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?goal ?goalLabel WHERE {
  wd:Q322170 wdt:P3712 ?goal .
  OPTIONAL {
    ?goal rdfs:label ?goalLabel .
    FILTER ( LANG( ?goalLabel ) = "en" )
  }
}

The suffragettes stood for women's suffrage.

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

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

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

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

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

[User] What did the suffragettes stand for?

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

[Tool]
Example 1:
Question:
How heavy is Jupiter's lightest moon?

SPARQL:
SELECT * WHERE {
  ?res wdt:P31 wd:Q61702557 .
  ?res wdt:P2067 ?mass .
}
ORDER BY ?mass
LIMIT 1

Using entities:
moon of Jupiter, also known as Jovial moon, Jovian satellite, moons of Jupiter, natural satellite of Jupiter (wd:Q61702557): natural satellite orbiting Jupiter

Using properties:
mass, also known as displacement, molecular mass, molecular weight, weight (wd:P2067 as wdt): mass (in colloquial usage also known as weight) of the item
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)

Example 2:
Question:
Give me all cars that are produced in Germany.

SPARQL:
SELECT DISTINCT * WHERE {
  ?s1 wdt:P31 wd:Q59773381 .
  ?s1 wdt:P176 ?o1 .
  ?o1 wdt:P17 wd:Q183 .
  ?o1 wdt:P31 wd:Q786820 .
}

Using entities:
Germany, also known as BR Deutschland, Bundesrepublik Deutschland, Deutschland, Federal Republic of Germany (wd:Q183): country in Central Europe
automobile model series, also known as car model line, car series, car model series, car line, automobile series (wd:Q59773381): group of related automobile models
automobile manufacturer, also known as auto manufacturer, car manufacturer, car maker, car builder, automotive company (wd:Q786820): company that makes automobiles

Using properties:
manufacturer, also known as manufactured by, provider, provided by, product of, producer (wd:P176 as wdt): (main or final) manufacturer or producer of this product
country, also known as sovereign state, state, land, host country (wd:P17 as wdt): sovereign state that this item is in (not to be used for human beings), located in the administrative territorial entity
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)

Example 3:
Question:
Which country has the most official languages?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P31 wd:Q6256 .
  ?uri wdt:P37 ?language .
}
GROUP BY ?uri
ORDER BY DESC ( COUNT( ?language ) )
LIMIT 1

Using entities:
country, also known as countries (wd:Q6256): distinct territorial body or political entity

Using properties:
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
official language, also known as language spoken, spoken in, speaking, language official (wd:P37 as wdt): language designated as official by this item, language used

[Assistant]
Based on the examples, we can see that the SPARQL queries typically involve identifying specific entities and properties relevant to the question. For the question "What did the suffragettes stand for?", we need to identify the entity representing the suffragettes and the properties that describe what they stood for.

First, let's search for the entity "suffragettes" in the knowledge graph.

[Tool]
Top 10 entity alternatives:
1. suffragette, also known as suffragettes (wd:Q322170): member of the Woman's Social and Political Union who advocated for women's right to vote
2. suffragettes (wd:L328761-F2)
3. Coontown Suffragettes (wd:Q20004604): 1914 silent short film
4. Song Suffragettes (wd:Q28401911): All Female Songwriter Round
5. The Suffragettes (wd:Q20477383): painting by Victor Pasmore
6. The Clam-Shell Suffragettes (wd:Q20802220): 1915 short film
7. The medical suffragettes (wd:Q50179887): scientific article
8. A Cure for Suffragettes (wd:Q24238887): 1913 film by Edward Dillon
9. Kate Sheppard National Memorial, also known as Tribute to the Suffragettes (wd:Q23073296): memorial to the women's suffrage campaign, located in Christchurch, New Zealand
10. Historiography of the Suffragettes (wd:Q64876675)

[Assistant]
The top result for "suffragettes" is `wd:Q322170`, which represents a member of the Woman's Social and Political Union who advocated for women's right to vote. This seems to be the most relevant entity for our query.

Next, we need to find out what the suffragettes stood for. We can look for properties that describe their goals or beliefs. Let's search for properties related to "goals" or "beliefs" for the entity `wd:Q322170`.

[Tool]
Top 10 property alternatives:
1. has goal, also known as intention, mission, objective, objective of project or action, project of (wd:P3712 as wdt/p): desired result or outcome
2. field of work, also known as scientific discipline, scientific area, responsible for, research on, research fields (wd:P101 as wdt/p): specialization of a person or organization; see P106 for the occupation, interested in
3. 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
4. on focus list of Wikimedia project, also known as WikiProject, Wikimedia project focus list, part of Wikimedia project, item created or edited for Wikimedia project, item created or edited as part of Wikimedia project (wd:P5008 as wdt/p): property to indicate that an item is of particular interest for ... they are not notable for Wikidata. See also P6104, P972, P2354.
5. image, also known as still capture, still, screenshot, screen capture, portrait (wd:P18 as wdt/p): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)
6. subclass of, also known as rdfs:subClassOf, sort of, subcategory of, subgenre of, subtype of (wd:P279 as wdt/p): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
7. instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt/p): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
8. indigenous to, also known as native to, originating from, spoken by (wd:P2341 as wdt/p): place or ethnic group where a language, art genre, cultural trad...biological species or variety is found (or was originally found)
9. member of, also known as band member of, membership, member of musical group, in musical group, be member of (wd:P463 as wdt/p): organization, club or musical group to which the subject belongs...ical position, such as a member of parliament (use P39 for that), part of
10. GND ID, also known as Deutschen Nationalbibliothek ID, Universal Authority File, Integrated Authority File, IndividualisedGnd, GND-IDN (wd:P227 as p/wdt/wdtn): identifier from an international authority file of names, subjects, and organizations

No other items found

[Assistant]
The top result for properties related to "goals" for the entity `wd:Q322170` is `wdt:P3712`, which stands for "has goal" or "intention." This property seems to be the most relevant for understanding what the suffragettes stood for.

Now, let's construct a SPARQL query to find the goals of the suffragettes.

```sparql
SELECT ?goal WHERE {
  wd:Q322170 wdt:P3712 ?goal .
}
```

This query will retrieve the goals associated with the suffragettes. Let's execute it.

[Tool]
Got 1 row and 1 column:
| goal                          |
| ----------------------------- |
| women's suffrage (wd:Q205204) |

[Assistant]
The SPARQL query returned that the suffragettes stood for "women's suffrage" (wd:Q205204).

To provide a more complete answer, we can also include the label of the goal in the query. Here is the refined SPARQL query:

```sparql
SELECT ?goal ?goalLabel WHERE {
  wd:Q322170 wdt:P3712 ?goal .
  OPTIONAL { ?goal rdfs:label ?goalLabel . FILTER(LANG(?goalLabel) = "en") }
}
```

Let's execute this refined query.

[Tool]
Got 1 row and 2 columns:
| goal                          | goalLabel             |
| ----------------------------- | --------------------- |
| women's suffrage (wd:Q205204) | women's suffrage (en) |

[Assistant]
The refined SPARQL query confirms that the suffragettes stood for "women's suffrage."

Here is the final SPARQL query and the answer to the user's question:

```sparql
SELECT ?goal ?goalLabel WHERE {
  wd:Q322170 wdt:P3712 ?goal .
  OPTIONAL { ?goal rdfs:label ?goalLabel . FILTER(LANG(?goalLabel) = "en") }
}
```

The suffragettes stood for women's suffrage.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query effectively retrieves the goal of the suffragettes from Wikidata, which is women's suffrage. The query follows all the rules and returns the IRI of the item along with its label.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?height WHERE {
  wd:Q130736 wdt:P2044 ?height .
}

The height of the mountain on which Günther Messner died is 8,126 meters.

[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 high is the mountain on which Günther Messner died?

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

[Tool]
Example 1:
Question:
Which country has the most official languages?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P31 wd:Q6256 .
  ?uri wdt:P37 ?language .
}
GROUP BY ?uri
ORDER BY DESC ( COUNT( ?language ) )
LIMIT 1

Using entities:
country, also known as countries (wd:Q6256): distinct territorial body or political entity

Using properties:
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
official language, also known as language spoken, spoken in, speaking, language official (wd:P37 as wdt): language designated as official by this item, language used

Example 2:
Question:
Where is Sungkyunkwan University?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  VALUES ?p {
    wdt:P17 wdt:P276 wdt:P131
  }
  wd:Q41085 ?p ?uri .
}

Using entities:
Sungkyunkwan University, also known as 성균관대학교 (wd:Q41085): private university in Seoul, South Korea

Using properties:
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 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
country, also known as sovereign state, state, land, host country (wd:P17 as wdt): sovereign state that this item is in (not to be used for human beings), located in the administrative territorial entity
location, also known as neighbourhood, whereabouts, vilage, venue, town (wd:P276 as 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

Example 3:
Question:
Who is the author of WikiLeaks?

SPARQL:
SELECT ?o1 WHERE {
  wd:Q359 wdt:P50 ?o1 .
}

Using entities:
WikiLeaks, also known as wikileaks.org (wd:Q359): NGO owned by Icelandic company Sunshine Press Productions ehf that has published news leaks provided by anonymous sources

Using properties:
author, also known as writer, written by, poet, playwright, maker (wd:P50 as wdt): main creator(s) of a written work (use on works, not humans); us...hor name string) when Wikidata item is unknown or does not exist, creator

[Assistant] Based on the examples, we need to identify the mountain where Günther Messner died and then find its height. Let's first search for Günther Messner in the knowledge graph.

[Tool]
Top 10 entity alternatives:
1. Günther Messner (wd:Q195351): Italian mountain climber (1946-1970), mountaineer, explorer
2. Günther Messner Biwak (wd:Q65236135)
3. Alta Via Günther Messner (wd:Q3955425)
4. Albert Günther, also known as Albert Charles Lewis Gotthilf Günther, Günther, Albert Karl Ludwig Gotthilf Günther, Albert C. L. G. Gunther (wd:Q57514): Anglo-German zoologist (1830–1914), zoological collector, ichthyologist, herpetologist, biologist, zoologist, curator
5. Günther, also known as Gunther & The Sunshine Girls, Mats Söderlund (wd:Q465670): Swedish singer, singer, model
6. Gunther, also known as Gyntiarios, Guntiarius, Gundichar, Guntiar, Gunthahar (wd:Q339094): semi-legendary king of Burgundy of the early 5th century, politician
7. Gunther, also known as Big Van Walter, Walter Hahn, Walter (wd:Q11330762): Austrian professional wrestler, professional wrestler
8. Gunther (wd:Q897146): Archbishop of Cologne in Germany, Catholic priest
9. Matthäus Günther, also known as Günther, m. gunther, Matthüs Günther, Matthaus Gunther, Matthaus Gundter (wd:Q463504): German artist (1705-1788), university teacher, painter
10. Frauenarzt, also known as Vicente de Teba, Gynikologe, Räuber Rob, MC Digital F, Vicente de Teba Költerhoff (wd:Q487008): German rapper from Berlin, recording artist, rapper, singer

[Assistant] The correct entity for Günther Messner is `wd:Q195351`. Now, let's find out the mountain where he died. We can look for properties related to his death, such as the place of death.

[Tool]
Top 10 property alternatives:
1. date of death, also known as DOD, deathdate, died, died on, year of death (wd:P570 as wdt/p): dissolved, abolished or demolished date, date on which the subject died, end time
2. place of death, also known as POD, location of death, killed in, deathplace, died in (wd:P20 as wdt/p): most specific known (e.g. city instead of country, or hospital i... city) death location of a person, animal or fictional character, location
3. sex or gender, also known as gender or sex, sex, gender identity, gender expression, gender (wd:P21 as wdt/p): sex or gender identity of human or animal. For human: male, fema...m, female organism. Groups of same gender use subclass of (P279)
4. given name, also known as name, proper name, personal name, middle name, forename (wd:P735 as wdt/p): first name or another given name of this person; values used with the property should not link disambiguations nor family names, name
5. languages spoken, written or signed, also known as language of expression, language, wrote language, writes language, uses language (wd:P1412 as wdt/p): language(s) that a person or a people speaks, writes or signs, including the native language(s)
6. sibling, also known as is sister of, is the brother of, is the sibling of, is the sister of, sib (wd:P3373 as wdt/p): the subject and the object have at least one common parent (brot...law, etc.) and step-siblings (step-brothers, step-sisters, etc.), relative
7. date of birth, also known as year of birth, birthdate, DOB, born on, born (wd:P569 as wdt/p): date on which the subject was born, inception
8. place of birth, also known as POB, location of birth, location born, born in, birthplace (wd:P19 as wdt/p): most specific known birth location of a person, animal or fictional character, location
9. country of citizenship, also known as subject of (country), nation of citizenship, national of, nationality, nation (wd:P27 as wdt/p): the object is a country that recognizes the subject as its citizen, country
10. sport, also known as play, plays, sport played, sports (wd:P641 as wdt/p): sport that the subject participates or participated in or is associated with

No other items found

[Assistant] The property `place of death` (`wdt:P20`) seems relevant. Let's use it to find the mountain where Günther Messner died.

[Tool]
Got 1 row and 1 column:
| mountain                  |
| ------------------------- |
| Nanga Parbat (wd:Q130736) |

[Assistant] The mountain where Günther Messner died is Nanga Parbat (`wd:Q130736`). Now, let's find the height of Nanga Parbat.

[Tool]
Top 10 property alternatives:
1. elevation above sea level, also known as MAMSL, MASL, height, elevation above mean sea level, elevation (wd:P2044 as wdt/p): height of the item (geographical object) as measured relative to sea level
2. coordinate location, also known as point on a map, gps coordinates, gps location, latitude, location (wd:P625 as wdt/p): geocoordinates of the subject. For Earth, please note that only WGS84 coordinating system is supported at the moment
3. instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt/p): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
4. mountain range, also known as system of mountains, hill range, range, parent range, mountain system (wd:P4552 as wdt/p): range or subrange to which the geographical item belongs, geomorphological unit
5. topographic prominence, also known as prominence (wd:P2660 as wdt/p): height of a mountain or hill relative to the lowest contour line encircling it (on Earth, maximum 8,848 m), height
6. image, also known as still capture, still, screenshot, screen capture, portrait (wd:P18 as wdt/p): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)
7. country, also known as sovereign state, state, land, host country (wd:P17 as wdt/p): sovereign state that this item is in (not to be used for human beings), located in the administrative territorial entity
8. aerial view, also known as overhead view, sky view, orthophotograph, orthophoto, aerial image (wd:P8592 as wdt/p): image of the subject taken from the air, video, image
9. Freebase ID, also known as Freebase, MID, Freebase identifier, FID (wd:P646 as wdt/p): identifier for a page in the Freebase database. Format: "/m/0" f...r IDs starting with "/g/", use Google Knowledge Graph ID (P2671)
10. continent (wd:P30 as wdt/p): continent of which the subject is a part, located in/on physical feature

No other items found

[Assistant] The property `elevation above sea level` (`wdt:P2044`) is the one we need to find the height of Nanga Parbat. Let's execute the SPARQL query to get the height.

[Tool]
Got 1 row and 1 column:
| height             |
| ------------------ |
| 8126 (xsd:decimal) |

[Assistant]
The height of Nanga Parbat, the mountain where Günther Messner died, is 8,126 meters.

Here is the final SPARQL query and answer:

```sparql
SELECT ?height WHERE {
  wd:Q130736 wdt:P2044 ?height .
}
```

Answer: The height of the mountain on which Günther Messner died is 8,126 meters.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query correctly identifies the height of Nanga Parbat, the mountain where Günther Messner died, using the appropriate entity and property from Wikidata.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
ASK WHERE {
  wd:Q66432 wdt:P1344 wd:Q33881
}

No, Udo Bölts did not participate in the Tour de France.

[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] Did Udo Bölts participate in the Tour de France?

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

[Tool]
Example 1:
Question:
When did the Boston Tea Party take place?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q19024 wdt:P585 ?uri
}

Using entities:
Boston Tea Party, also known as Boston Tea-party, Destruction of the Tea (wd:Q19024): 1773 American protest against British taxation

Using properties:
point in time, also known as at time, as of, year, when, time of event (wd:P585 as wdt): date something took place, existed or a statement was true; for providing time use the "refine date" property (P4241)

Example 2:
Question:
How many films did Leonardo DiCaprio star in?

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

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

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

Example 3:
Question:
Who was the wife of President Lincoln?

SPARQL:
SELECT ?o1 WHERE {
  wd:Q91 wdt:P26 ?o1 .
}

Using entities:
Abraham Lincoln, also known as President Lincoln, Uncle Abe, Honest Abe, Abe Lincoln (wd:Q91): president of the United States from 1861 to 1865, military officer, statesperson, postmaster, politician, farmer, writer, lawyer

Using properties:
spouse, also known as married partner, married to, marry, partner, spouses (wd:P26 as wdt): the subject has the object as their spouse (husband, wife, partn...etc.). Use "unmarried partner" (P451) for non-married companions, significant person, relative

[Assistant] Based on the examples, we need to identify the entity for Udo Bölts and the property for participating in the Tour de France. Let's start by searching for the entity Udo Bölts.

[Tool]
Top 10 entity alternatives:
1. Udo Bölts (wd:Q66432): German racing cyclist, sport cyclist
2. Bolt, also known as American Dog (wd:Q212792): 2008 animated film by Chris Williams and Byron Howard
3. Wadjet, also known as Buto, Udṓ, Weʾd’ōjet (wd:Q146007): Ancient Egyptian snake-headed goddess, symbolizing Lower Egypt
4. bolt, also known as Bolt (fastener), screw (controversial) (wd:Q125183448): small metal rods used as fasteners having a head and often helical threading and being usually secured by a nut or by riveting
5. latch, also known as sneck, bolt, catch (wd:Q2477852): mechanical fastener that can be disengaged to allow motion, e.g. for a door
6. Bolt, also known as Bolt Technologies, Bolt Technologies OÜ, mTakso, Taxify (wd:Q20529164): Estonian company in field of peer-to-peer ridesharing and food delivery
7. Udo (wd:Q496912): island
8. Bolt, also known as Bolt, West Virginia, Bolt, WV (wd:Q4406898): census-designated place in Raleigh County, West Virginia
9. quarrel, also known as crossbow bolt, bolt (wd:Q891691): projectile or arrow used in a crossbow
10. bolt (wd:Q15107531): mechanical part of a firearm

[Assistant] The correct entity for Udo Bölts is `wd:Q66432`. Now let's find the property for participating in the Tour de France. I'll search for the property "participate" and then check if it is used with the Tour de France.

[Tool]
Top 10 property alternatives:
1. participant in, also known as participated in, present at, takes part, took part, took part in (wd:P1344 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): event in which a person or organization was/is a participant; inverse of P710 or P1923, significant event, part of
2. participant, also known as suspect, player, party, participants, has participant (wd:P710 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): person, group of people or organization (object) that actively t...ct has role" (P3831). Use P1923 for participants that are teams., significant person
3. member of, also known as band member of, membership, member of musical group, in musical group, be member of (wd:P463 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): organization, club or musical group to which the subject belongs...ical position, such as a member of parliament (use P39 for that), part of
4. physically interacts with, also known as interact, interact with (wd:P129 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): physical entity that the subject interacts with
5. number of participants, also known as number of competitors, number of teams, participant count, participants, qty participants (wd:P1132 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): number of participants of an event, e.g. people or groups of people that take part in the event (NO units), quantity
6. start time, also known as start date, since, valid, join time, joined (wd:P580 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): time an entity begins to exist or a statement starts being valid, point in time
7. proportion, also known as %, share, ratio, part, percent (wd:P1107 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): to be used as a qualifier, value must be between 0 and 1
8. language style, also known as manner, term register, obsolete, register, sense register (wd:P6191 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): to denote of the way a sense of a word is used, e.g., slang, vulgarism, babytalk, colloquialism, etc.
9. aircraft registration, also known as reg, registration, tail number (wd:P426 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): identifier assigned to an individual aircraft by civil aircraft registry, fleet or registration number
10. registration page URL, also known as signup URL, sign-up URL, sign up URL, signup page, sign-up page (wd:P11904 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): URL of a webpage used to sign up for a service, URL

[Assistant]
The property "participant in" (`wd:P1344`) seems to be the most relevant for indicating participation in events. Next, I will verify if this property is used with the Tour de France.

I will first search for the entity "Tour de France".

[Tool]
Top 10 entity alternatives:
1. Tour de France, also known as Souvenir Henri Desgrange, TdF, Le Tour de France, Le Tour, La Grande Boucle (wd:Q33881): multiple-stage bicycle race primarily held in France
2. Tour de France (wd:Q2425911): 1983 Kraftwerk song
3. Pro Cycling Manager 2011, also known as Pro Cycling Manager: Season 2011, Tour de France (wd:Q3406163): 2011 video game
4. Tour de France (wd:Q23925083): 2016 film by Rachid Djaïdani
5. Tour de France, also known as Le Tour de France (wd:Q16964929): video game series
6. Tour de France (wd:Q3533332)
7. Tour de France (wd:Q70142694): 1985 video game
8. 2014 Tour de France, also known as Tour de France 2014, TdF 2014, Le Tour de France 2014 (wd:Q895853): 2014 edition of a multiple-stage bicycle race primarily held in France
9. 2012 Tour de France (wd:Q219414): cycling competition
10. 2010 Tour de France (wd:Q217287): cycling race

[Assistant]
The correct entity for the Tour de France is `wd:Q33881`. Now, I will check if Udo Bölts (`wd:Q66432`) has the property "participant in" (`wd:P1344`) with the object being the Tour de France (`wd:Q33881`).

Let's run a SPARQL query to check this.

[Tool] False

[Assistant]
The SPARQL query returned `False`, which means that Udo Bölts did not participate in the Tour de France.

Therefore, the answer to the question is:

**No, Udo Bölts did not participate in the Tour de France.**

I will now provide the final SPARQL query and answer.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query is correctly formulated as an ASK query and returns the correct boolean result. The answer provided is accurate based on the query result.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?child ?profession WHERE {
  ?child wdt:P106 ?profession .
  VALUES ?child {
    wd:Q124306663 wd:Q124306740 wd:Q311238 wd:Q357974
  }
}

Got 16 rows and 2 columns, showing the first 5 and last 5 rows below:
| child                      | profession                      |
| -------------------------- | ------------------------------- |
| Sean Lennon (wd:Q311238)   | singer (wd:Q177220)             |
| Sean Lennon (wd:Q311238)   | screenwriter (wd:Q28389)        |
| Sean Lennon (wd:Q311238)   | actor (wd:Q33999)               |
| Sean Lennon (wd:Q311238)   | composer (wd:Q36834)            |
| Sean Lennon (wd:Q311238)   | pianist (wd:Q486748)            |
| ...                        | ...                             |
| Julian Lennon (wd:Q357974) | composer (wd:Q36834)            |
| Julian Lennon (wd:Q357974) | singer-songwriter (wd:Q488205)  |
| Julian Lennon (wd:Q357974) | poet (wd:Q49757)                |
| Julian Lennon (wd:Q357974) | recording artist (wd:Q55960555) |
| Julian Lennon (wd:Q357974) | guitarist (wd:Q855091)          |

The professions of John Lennon's sons are as follows:
- **Sean Lennon**: Singer, Screenwriter, Actor, Composer, Pianist
- **Julian Lennon**: Composer, Singer-songwriter, Poet, Recording artist, Guitarist

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

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

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

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

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

[User] What are the professions of John Lennon’s sons?

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

[Tool]
Example 1:
Question:
Which programming languages influenced Javascript?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q2005 wdt:P737 ?uri .
}

Using entities:
JavaScript, also known as JS, js, Java Script, Javascript, javascript (wd:Q2005): high-level programming language

Using properties:
influenced by, also known as favorite player, has influence, informed by, role model (wd:P737 as wdt): this person, idea, etc. is informed by that other person, idea, etc., e.g. “Heidegger was influenced by Aristotle”

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

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

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

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

Example 3:
Question:
When did the Boston Tea Party take place?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q19024 wdt:P585 ?uri
}

Using entities:
Boston Tea Party, also known as Boston Tea-party, Destruction of the Tea (wd:Q19024): 1773 American protest against British taxation

Using properties:
point in time, also known as at time, as of, year, when, time of event (wd:P585 as wdt): date something took place, existed or a statement was true; for providing time use the "refine date" property (P4241)

[Assistant] Based on the examples, we need to identify the entity for John Lennon and the property for his children. Then, we can find the professions of those children.

[Tool]
Top 10 entity alternatives:
1. John Lennon, also known as John Winston Ono Lennon, John Ono Lennon (wd:Q1203): British musician (1940–1980), political activist, singer-songwriter, recording artist, record producer, peace activist, public figure, film director, film producer, prose writer, film actor, guitarist, composer, painter, pianist, singer, writer, actor, poet
2. Liverpool John Lennon Airport, also known as John Lennon, John Lennon airport, Liverpool Airport, Liverpool (John Lennon), LPL (wd:Q8727): airport in Liverpool, England
3. John Lennon Silva Santos, also known as John Lennon (wd:Q6244563): association football player, Brazilian footballer
4. John Brown Lennon, also known as John B. Lennon, John Lennon (wd:Q6223555): American labor leader (1850–1923), trade unionist
5. John Lennon, also known as J. John Lennon, Justin John Lennon (wd:Q11979004): Scottish tourism researcher, university teacher, tourism scholar
6. John Lennon (wd:Q65548791): Irish hurler born 1996
7. John Lennon (wd:Q6244553): British naval captain, military personnel
8. John Lennon (wd:Q12871620): 1855 - 3 Aug 1921
9. John Lennon (wd:Q125643070): 2005 edition of written work by Jordi Sierra i Fabra
10. John Lennon (wd:Q106781630): 2015 song by SCH

[Assistant] The correct entity for John Lennon is `wd:Q1203`. Now let's find out who his children are using the property `P40` (child).

[Tool]
Got 4 rows and 1 column:
| child                           |
| ------------------------------- |
| John Ono Lennon (wd:Q124306663) |
| John Lennon III (wd:Q124306740) |
| Sean Lennon (wd:Q311238)        |
| Julian Lennon (wd:Q357974)      |

[Assistant] We have identified John Lennon's children. Now, we need to find their professions. We will use the property `P106` (occupation) to find their professions.

[Tool]
Got 16 rows and 2 columns, showing the first 5 and last 5 rows below:
| child                      | profession                      |
| -------------------------- | ------------------------------- |
| Sean Lennon (wd:Q311238)   | singer (wd:Q177220)             |
| Sean Lennon (wd:Q311238)   | screenwriter (wd:Q28389)        |
| Sean Lennon (wd:Q311238)   | actor (wd:Q33999)               |
| Sean Lennon (wd:Q311238)   | composer (wd:Q36834)            |
| Sean Lennon (wd:Q311238)   | pianist (wd:Q486748)            |
| ...                        | ...                             |
| Julian Lennon (wd:Q357974) | composer (wd:Q36834)            |
| Julian Lennon (wd:Q357974) | singer-songwriter (wd:Q488205)  |
| Julian Lennon (wd:Q357974) | poet (wd:Q49757)                |
| Julian Lennon (wd:Q357974) | recording artist (wd:Q55960555) |
| Julian Lennon (wd:Q357974) | guitarist (wd:Q855091)          |

[Assistant]
We have found the professions of John Lennon's sons. Let's summarize the results:

- **Sean Lennon**:
  - Singer
  - Screenwriter
  - Actor
  - Composer
  - Pianist

- **Julian Lennon**:
  - Composer
  - Singer-songwriter
  - Poet
  - Recording artist
  - Guitarist

Now, let's finalize the SPARQL query and provide the answer.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query is correct and returns the expected results. It identifies the correct children of John Lennon and their respective professions. The query follows all the rules and guidelines provided. Well done!


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?stadium WHERE {
  wd:Q213417 wdt:P115 ?stadium .
}

The New York Yankees play in Yankee Stadium.

[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 stadium do the New York Yankees play?

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

[Tool]
Example 1:
Question:
Which classes does the Millepede belong to?

SPARQL:
SELECT ?uri WHERE {
  wd:Q11185669 wdt:P279 ?uri .
}

Using entities:
millipede (wd:Q11185669): common name of myriapods

Using properties:
subclass of, also known as rdfs:subClassOf, sort of, subcategory of, subgenre of, subtype of (wd:P279 as wdt): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain

Example 2:
Question:
Which films did Stanley Kubrick direct?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P57 wd:Q2001
}

Using entities:
Stanley Kubrick, also known as Kubrick (wd:Q2001): American filmmaker (1928–1999), executive producer, camera operator, cinematographer, film producer, film director, screenwriter, photographer, scenographer, film editor, director

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

Example 3:
Question:
Give me all chemical elements.

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P31 wd:Q11344 .
}

Using entities:
chemical element, also known as atomic element, chemical elements, element (wd:Q11344): species of atoms having the same number of protons in the atomic...s, or the same stability (or half-lifetime if they are unstable)

Using properties:
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)

[Assistant] Based on the examples, we need to identify the entity for the New York Yankees and the property that links them to their home stadium. Let's first search for the entity representing the New York Yankees.

[Tool]
Top 10 entity alternatives:
1. New York Yankees, also known as New York Highlanders, Yankees, NY Yankees, NYY, Highlanders (wd:Q213417): baseball team and Major League Baseball franchise in the Bronx, New York, United States
2. New York Yankees (wd:Q979646): defunct American football team
3. New York Yankees (wd:Q1275088): American football team from 1946 to 1949
4. New York Yankees (wd:Q7014865): American football team in 1936
5. New York Yankees (wd:Q16845944): association football (soccer) team
6. New York Yankees (wd:Q7014868): American football team in 1940
7. New York Yankees Museum (wd:Q7014872): baseball museum in The Bronx
8. New York Black Yankees, also known as Black Yankees (wd:Q7013072): Negro League Baseball team active from 1931-1948; record: 243-462-19 (.345)
9. history of the New York Yankees, also known as New York Yankees history, the New York Yankees history (wd:Q5870090): Sports team history
10. Saratoga Harlem Yankees, also known as Glens Falls-Saratoga, New York Harlem Yankees, Schenectady Yankees (wd:Q7423363): defunct American basketball team

[Assistant]
The correct entity for the New York Yankees is `wd:Q213417`. Now, let's find the property that links the New York Yankees to their home stadium. A likely candidate would be something like "home ground" or "stadium".

Let's search for the property related to the home stadium.

[Tool]
Top 10 property alternatives:
1. home venue, also known as home water, venue, stadium, home ground, home field (wd:P115 as wdt/p): home stadium or venue of a sports team or applicable performing arts organization, location
2. sport, also known as play, plays, sport played, sports (wd:P641 as wdt/p): sport that the subject participates or participated in or is associated with
3. league or competition, also known as tournament, sports league, division, cup, competition (wd:P118 as wdt/p): league or competition in which team or player has played, or in which an event occurs, participant in
4. country, also known as sovereign state, state, land, host country (wd:P17 as wdt/p): sovereign state that this item is in (not to be used for human beings), located in the administrative territorial entity
5. inception, also known as initial release date, founded in, founded on, founded on date, founding date (wd:P571 as wdt/p): time when an entity begins to exist; for date of official opening use P1619, start time
6. head coach, also known as team manager, trainer, senior coach, manager, coached by (wd:P286 as wdt/p): on-field manager or head coach of a sports club (not to be confu...eneral manager P505, which is not a coaching position) or person
7. headquarters location, also known as headquarters locality, seat, headquarters town, HQ, HQ location (wd:P159 as wdt/p): city or town where an organization's headquarters is or has been situated. Use P276 qualifier for specific building, significant place
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. official website, also known as site (official), home page, site official, URL, URL (official) (wd:P856 as wdt/p): URL of the official page of an item (current or former). Usage: ...but see the "Hijacked or dead websites" section of the Talk page, URL
10. SportsLogos.net team ID, also known as SportsLogos.net ID (wd:P9122 as wdt/p): identifier for a sports organization on the website Sports Logos

No other items found

[Assistant]
The property `wdt:P115` (home venue) seems to be the correct one to link the New York Yankees to their home stadium. Let's construct a SPARQL query to find the home stadium of the New York Yankees.

Here is the SPARQL query:

```sparql
SELECT ?stadium WHERE {
  wd:Q213417 wdt:P115 ?stadium .
}
```

Let's execute this query to get the result.

[Tool]
Got 1 row and 1 column:
| stadium                     |
| --------------------------- |
| Yankee Stadium (wd:Q753529) |

[Assistant]
The SPARQL query successfully returned the home stadium of the New York Yankees, which is Yankee Stadium.

Here is the final SPARQL query and the answer:

```sparql
SELECT ?stadium WHERE {
  wd:Q213417 wdt:P115 ?stadium .
}
```

Answer: The New York Yankees play in Yankee Stadium.

Now, let's provide the final answer using the `answer` function.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query correctly identifies the home stadium of the New York Yankees, and the answer provided is accurate.