PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?animal ?animalLabel WHERE {
  ?animal wdt:P31 wd:Q729 .
  ?animal wdt:P607 ?operation .
  ?operation wdt:P241 wd:Q625657 .
  OPTIONAL {
    ?animal rdfs:label ?animalLabel .
    FILTER ( LANG( ?animalLabel ) = "en" )
  }
}

There is no animal recorded in the knowledge graph that has participated in a military operation with the Australian Defence Force.

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

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

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

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

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

[User] Which animal participated in a military operation with the Australian Defence Force?

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

[Tool] No examples found

[Assistant] There were no similar examples found. Let's start by searching for the Australian Defence Force.

[Tool]
Top 10 entity alternatives:
1. Australian Defence Force, also known as ADF, military of Australia, mil Australia, armed forces of Australia, Australian Armed Forces (wd:Q625657): combined military forces of Australia
2. Australian Defence Force Academy, also known as ADFA (wd:Q371370): staff college
3. Australian Defence Force ranks (wd:Q3113099): ranks of officers and enlisted personnel
4. Australian Defence Force Ensign (wd:Q12409908): flag of the Australian Defence Force
5. Australian Defence Force Cadets, also known as ADFC, Australian Service Cadet Scheme (wd:Q4824077): youth organization operated by the Australian military
6. Australian Defence Force Journal...of Australian profession of arms, also known as Australian defence force journal (wd:Q15746503): journal
7. Australian Defence Force Parachuting School, also known as Parachute Training School, PTS (wd:Q7133934): unit of the Australian Army
8. Australian Defence Force Investigative Service (wd:Q4824075): investigative arm of Australian Defence Force Police
9. Australian Defence Force Helicopter School (wd:Q48817728): military unit
10. Flags of the Australian Defence Force (wd:Q55611758)

[Assistant] The top result is the Australian Defence Force (wd:Q625657). Now let's look for properties related to military operations involving animals.

[Tool]
Top 10 property alternatives:
1. military unit, also known as member of military unit, unit (wd:P7779 as ps/pq/pqv/prn/psn/prv/wdtn/psv/wdt/pqn/pr/p): smallest military unit that a person is/was in, member of
2. target, also known as attack target, military target, target of attack (wd:P533 as ps/pq/pqv/prn/psn/prv/wdtn/psv/wdt/pqn/pr/p): target of an attack or military operation, object of occurrence
3. taxon especially protected in area, also known as animal protected, plant protected, protected animal, protected plant, protected species (wd:P6569 as ps/pq/pqv/prn/psn/prv/wdtn/psv/wdt/pqn/pr/p): species and other taxa especially protected in a area, whether s...ountries/subnational entities: use red lists properties instead.
4. animal breed, also known as breed, sheep breed, horse breed, goat breed, duck breed (wd:P4743 as ps/pq/pqv/prn/psn/prv/wdtn/psv/wdt/pqn/pr/p): subject item belongs to a specific group of domestic animals, generally given by association
5. conflict, also known as war, theatre (military), theater (military), participated in conflict, military conflict (wd:P607 as ps/pq/pqv/prn/psn/prv/wdtn/psv/wdt/pqn/pr/p): battles, wars or other military engagements in which the person or item participated, participant in
6. military branch, also known as unit branch, service branch, formation, branch (wd:P241 as ps/pq/pqv/prn/psn/prv/wdtn/psv/wdt/pqn/pr/p): branch to which this military unit, award, office, or person belongs, e.g. Royal Navy, part of
7. military designation, also known as designation, 1962 US military aerospace vehicle designation, USDOD aircraft designation, Tri-Service aerospace vehicle designation, mission design series designation (wd:P798 as ps/pq/pqv/prn/psn/prv/wdtn/psv/wdt/pqn/pr/p): officially assigned designation for a vehicle in military service
8. species kept, also known as plant taxon in botanical garden, resident animals, plant taxon in collection, resident animal species, rose varity in rose garden (wd:P1990 as ps/pq/pqv/prn/psn/prv/wdtn/psv/wdt/pqn/pr/p): taxa, preferably species, present at a zoo, botanical garden, co...stitution. NOT specific animals, not for any geographic location, has part(s) of the class
9. item operated, also known as fleet, item used, operated, operates, operator of (wd:P121 as ps/pq/pqv/prn/psn/prv/wdtn/psv/wdt/pqn/pr/p): equipment, installation or service operated by the subject, uses
10. has pet, also known as pet, keeps pet, owns pet (wd:P1429 as ps/pq/pqv/prn/psn/prv/wdtn/psv/wdt/pqn/pr/p): pet that a person owns

[Assistant]
None of these properties directly describe an animal participating in a military operation. However, we can try to combine the 'military operation' property (P607) with the 'animal' type to see if we can find any relevant entities.

Let's first check the 'military operation' property (P607) to understand how it is used.

[Tool]
Got 65,536 rows and 3 columns, showing the first 10 rows below:
| subject                                                | property            | object                                     |
| ------------------------------------------------------ | ------------------- | ------------------------------------------ |
| Richard I. Merchant (wd:Q111678651)                    | conflict (wdt:P607) | Vietnam (wd:Q881)                          |
| Serge Kurschat (wd:Q103847780)                         | conflict (wdt:P607) | Democratic Republic of the Congo (wd:Q974) |
| Vladimir Lenin (wd:Q1394)                              | conflict (wdt:P607) | October Revolution (wd:Q42976)             |
| Luis Gerardo Afán de Ribera y Nestares (wd:Q100082630) | conflict (wdt:P607) | World War II (wd:Q362)                     |
| JW GROM (wd:Q1438930)                                  | conflict (wdt:P607) | Haiti (wd:Q790)                            |
| Julius Caesar (wd:Q1048)                               | conflict (wdt:P607) | Caesar's Civil War (wd:Q1238338)           |
| Mahatma Gandhi (wd:Q1001)                              | conflict (wdt:P607) | Salt March (wd:Q239344)                    |
| Dante Alighieri (wd:Q1067)                             | conflict (wdt:P607) | Battle of Campaldino (wd:Q2888241)         |
| Hans Leopold (wd:Q1001260)                             | conflict (wdt:P607) | World War I (wd:Q361)                      |
| Fidel Castro (wd:Q11256)                               | conflict (wdt:P607) | Cuban Revolution (wd:Q11264)               |
| ...                                                    | ...                 | ...                                        |

[Assistant]
The 'conflict' property (P607) is used to indicate participation in various conflicts and military operations. However, it seems to be primarily used for human participants. We need to find a way to filter for animals specifically.

Let's try to find entities that are both animals and have participated in a military operation. We can use the 'instance of' property (P31) to filter for animals and then check if they have the 'conflict' property (P607).

First, let's identify the 'instance of' property (P31) and the 'animal' class (Q729).

[Tool]
Top 10 entity alternatives:
1. animal, also known as Animalia, the animal kingdom, animals, Metazoa (wd:Q729): kingdom of multicellular eukaryotic organisms
2. Animal (wd:Q547234): 2010 debut studio album by Kesha
3. The Animal, also known as Animal (wd:Q547517): 2001 film by Luke Greenfield
4. Animal, also known as Stuntwoman, L'Animal (wd:Q475286): 1977 film directed by Claude Zidi
5. creature, also known as animal (wd:Q1274979): created entity, often referring to an animal
6. Animal (wd:Q3617501): 1987 song by Def Leppard
7. Animal (wd:Q115947372): 2023 film by Sandeep Reddy Vanga
8. Animal (wd:Q17317998): 2014 film by Brett Simmons
9. Animal (wd:Q2531316): Pearl Jam song
10. Animal (wd:Q1846505): Muppet character, drummer

[Assistant]
The top result is the 'animal' class (wd:Q729). Now, let's construct a SPARQL query to find animals that have participated in a military operation with the Australian Defence Force (wd:Q625657).

We will use the 'instance of' property (P31) to filter for animals and the 'conflict' property (P607) to check for participation in a military operation. Additionally, we will use the 'military branch' property (P241) to ensure the operation involved the Australian Defence Force.

Let's execute this query:

```sparql
SELECT ?animal ?animalLabel
WHERE {
  ?animal wdt:P31 wd:Q729 .
  ?animal wdt:P607 ?operation .
  ?operation wdt:P241 wd:Q625657 .
  OPTIONAL { ?animal rdfs:label ?animalLabel . FILTER(LANG(?animalLabel) = "en") }
}
```

This query will find animals that are instances of the 'animal' class, have participated in a military operation, and the operation involves the Australian Defence Force.

Let's execute this query.

[Tool] Got no rows and 2 columns

[Assistant]
The query did not return any results, which suggests that there might not be any animals in the knowledge graph that are recorded as having participated in a military operation with the Australian Defence Force.

To further verify this, let's try a simpler query to see if there are any animals that have participated in any military operations at all, regardless of the military branch.

```sparql
SELECT ?animal ?animalLabel
WHERE {
  ?animal wdt:P31 wd:Q729 .
  ?animal wdt:P607 ?operation .
  OPTIONAL { ?animal rdfs:label ?animalLabel . FILTER(LANG(?animalLabel) = "en") }
}
LIMIT 10
```

This query will find animals that are instances of the 'animal' class and have participated in any military operation. If this query returns results, we can then further refine it to include the Australian Defence Force.

Let's execute this query.

[Tool] Got no rows and 2 columns

[Assistant]
The simpler query also did not return any results, which suggests that there are no animals in the knowledge graph that are recorded as having participated in any military operations, including those involving the Australian Defence Force.

Given this, it appears that the knowledge graph does not contain information about animals participating in military operations with the Australian Defence Force. Therefore, the answer to the question is that there is no such animal recorded in the knowledge graph.

I will provide this as the final answer.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The system correctly identified that there is no animal recorded in the knowledge graph that has participated in a military operation with the Australian Defence Force. The SPARQL query is well-formed and appropriate for the task, and the execution result confirms the absence of such records.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?subject ?subjectLabel WHERE {
  wd:Q636399 wdt:P921 ?subject .
  OPTIONAL {
    ?subject rdfs:label ?subjectLabel .
    FILTER ( LANG( ?subjectLabel ) = "en" )
  }
}

The main subject of the song 'Smoke on the Water' by Deep Purple is the Montreux Casino.

[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 was the main subject addressed in the Deep Purple song “Smoke on the water”?

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

[Tool] No examples found

[Assistant] Since we didn't find any similar examples, let's start by searching for the song "Smoke on the Water" by Deep Purple in the knowledge graph.

[Tool]
Top 10 entity alternatives:
1. Smoke on the Water: a Tribute to Deep Purple (wd:Q3963036): album by various artists
2. Smoke on the Water (wd:Q636399): original song written and composed by Deep Purple; first recorded by Deep Purple
3. Smoke on the Water (wd:Q3435148): compilation album by Deep Purple
4. Smoke on the Water (wd:Q24206648): 2014 television film directed by Dominik Graf
5. Smoke on the Water (wd:Q7545910): 1943 song by Earl Nunn and Zeke Clements
6. Smoke on the Water / Child in Time / Woman from Tokyo, also known as Child in Time, Woman from Tokyo, Smoke on the Water (wd:Q15931285): 1977 3-track single by Deep Purple
7. Smoke on the Water (wd:Q114495787): episode of seaQuest DSV (S3 E3)
8. Smoke on the Water (wd:Q94008225): vocal track by Deep Purple; 1972 studio recording
9. Smoke on the Water (wd:Q94003891): 1973 single by Deep Purple
10. Smoke on the Water (wd:Q93971258): vocal track by Pat Boone; 1996 studio recording; cover version

[Assistant] From the search results, it seems that the correct entity for the song "Smoke on the Water" by Deep Purple is wd:Q636399. Now, let's look for properties related to the subject or theme of the song.

[Tool]
Top 10 property alternatives:
1. main subject, also known as primary topic, primary subject, primary keyword, plot keyword, of (wd:P921 as wdt/p): primary topic of a work (see also P180: depicts), depicts
2. title, also known as name, titled, page name, original title, known as (wd:P1476 as wdt/p): published name of a work, such as a newspaper article, a literary work, piece of music, a website, or a performance work, name
3. lyricist, also known as text poet, writer (of song lyrics), words by, text writer, text by (wd:P676 as wdt/p): author of song lyrics, author
4. 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)
5. language of work or name, also known as language of the reference, language of URL, language of website, language of work, named in language (wd:P407 as wdt/p): language associated with this creative work (such as books, show...guage" (P103) and "languages spoken, written or signed" (P1412))
6. composer, also known as music by, written by, songwriter, musical score by, film score by (wd:P86 as wdt/p): person(s) who wrote the music [for lyricist, use "lyrics by" (P676)], creator
7. genre, also known as genre of music, type of music, type of film, music genre, literary genre (wd:P136 as wdt/p): creative work's genre or an artist's field of work (P101). Use main subject (P921) to relate creative works to their topic
8. publication date, also known as launched, published, publication time, publication, pubdate (wd:P577 as wdt/p): date or point in time when a work was first published or released, point in time
9. 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))
10. form of creative work, also known as work type, work form, type of work, artform, type of reference work (wd:P7937 as wdt/p): structure of a creative work

No other items found

[Assistant]
The property "main subject" (wd:P921) looks like it could provide us with the information we need about the subject of the song. Let's construct a SPARQL query to find out what the main subject of the song "Smoke on the Water" (wd:Q636399) is.

Here's the initial SPARQL query:

```sparql
SELECT ?subject
WHERE {
  wd:Q636399 wdt:P921 ?subject .
}
```

Let's execute this query.

[Tool]
Got 1 row and 1 column:
| subject                        |
| ------------------------------ |
| Montreux Casino (wd:Q81085137) |

[Assistant]
The main subject of the song "Smoke on the Water" is the Montreux Casino (wd:Q81085137).

To provide a more complete answer, let's also include the label of the Montreux Casino in the query. We'll use an OPTIONAL clause to fetch the label if it exists.

Here's the updated SPARQL query:

```sparql
SELECT ?subject ?subjectLabel
WHERE {
  wd:Q636399 wdt:P921 ?subject .
  OPTIONAL { ?subject rdfs:label ?subjectLabel . FILTER(LANG(?subjectLabel) = "en") }
}
```

Let's execute this query.

[Tool]
Got 1 row and 2 columns:
| subject                        | subjectLabel         |
| ------------------------------ | -------------------- |
| Montreux Casino (wd:Q81085137) | Montreux Casino (en) |

[Assistant]
The main subject of the song "Smoke on the Water" by Deep Purple is the Montreux Casino.

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

```sparql
SELECT ?subject ?subjectLabel
WHERE {
  wd:Q636399 wdt:P921 ?subject .
  OPTIONAL { ?subject rdfs:label ?subjectLabel . FILTER(LANG(?subjectLabel) = "en") }
}
```

The main subject of the song "Smoke on the Water" by Deep Purple is the Montreux Casino.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query correctly identifies the main subject of the song 'Smoke on the Water' by Deep Purple, which is the Montreux Casino. The query follows all the rules and returns the IRI along with the label in English.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT DISTINCT ?movie ?title WHERE {
  ?movie wdt:P31 wd:Q11424 ; rdfs:label ?title .
  FILTER ( CONTAINS( LCASE( ?title ) , 'star wars' ) ) FILTER ( LANG( ?title ) = 'en' ) FILTER ( REGEX( ?title , 'Episode [IVX]+' ) )
}

Got 9 rows and 2 columns:
| movie                                                        | title                                               |
| ------------------------------------------------------------ | --------------------------------------------------- |
| Star Wars: Episode I – The Phantom Menace (wd:Q165713)       | Star Wars: Episode I – The Phantom Menace (en)      |
| Star Wars: Episode IV – A New Hope (wd:Q17738)               | Star Wars: Episode IV – A New Hope (en)             |
| Star Wars: Episode II – Attack of the Clones (wd:Q181069)    | Star Wars: Episode II – Attack of the Clones (en)   |
| Star Wars: Episode V – The Empire Strikes Back (wd:Q181795)  | Star Wars: Episode V – The Empire Strikes Back (en) |
| Star Wars: Episode VI – Return of the Jedi (wd:Q181803)      | Star Wars: Episode VI – Return of the Jedi (en)     |
| Star Wars: Episode VIII – The Last Jedi (wd:Q18486021)       | Star Wars: Episode VIII – The Last Jedi (en)        |
| Star Wars: Episode IX – The Rise of Skywalker (wd:Q20977110) | Star Wars: Episode IX – The Rise of Skywalker (en)  |
| Star Wars: Episode III – Revenge of the Sith (wd:Q42051)     | Star Wars: Episode III – Revenge of the Sith (en)   |
| Star Wars: Episode VII – The Force Awakens (wd:Q6074)        | Star Wars: Episode VII – The Force Awakens (en)     |

The titles of the Star Wars series movies are: Star Wars: Episode I – The Phantom Menace, Star Wars: Episode II – Attack of the Clones, Star Wars: Episode III – Revenge of the Sith, Star Wars: Episode IV – A New Hope, Star Wars: Episode V – The Empire Strikes Back, Star Wars: Episode VI – Return of the Jedi, Star Wars: Episode VII – The Force Awakens, Star Wars: Episode VIII – The Last Jedi, and Star Wars: Episode IX – The Rise of Skywalker.

[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 titles of the Star Wars series movies?

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

[Tool]
Example 1:
Question:
Which movies did Kurosawa direct?

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

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

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

Example 2:
Question:
Show me all books in Asimov's Foundation series.

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q1564644 wdt:P527 ?uri
}

Using entities:
Foundation series, also known as The Foundation Series (wd:Q1564644): series of science fiction books by Isaac Asimov

Using properties:
has part(s), also known as has component, incorporates, has ingredient, has ingredients, has member (wd:P527 as wdt): part of this subject; inverse property of "part of" (P361). See also "has parts of the class" (P2670).

Example 3:
Question:
Give me all movies with Tom Cruise.

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P161 wd:Q37079 .
}

Using entities:
Tom Cruise, also known as Thomas Cruise Mapother IV (wd:Q37079): American actor and producer (born 1962), stunt performer, aircraft pilot, film producer, film director, screenwriter, film actor, producer, director, writer, 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

[Assistant] Based on these examples, we can see that we need to identify the entity for the Star Wars series and then find the movies that are part of it. Let's first search for the Star Wars series entity.

[Tool]
Top 10 entity alternatives:
1. Star Wars live-action TV series (wd:Q13365728): television series
2. Star Wars: The Black Series, also known as TBS (wd:Q29017943): ongoing Star Wars toy line produced by the Hasbro company
3. Lego Star Wars Ultimate Collector Series, also known as LEGO UCS (wd:Q56320655): Line of Lego Star Wars sets for expert builders
4. Star Wars: X-Wing - Collector Series (wd:Q29649749): video game compilation
5. Vader Immortal: A Star Wars VR Series, also known as Vader Immortal (wd:Q63228502): 2019 video game
6. 'Star Wars' headed to TV as weekly series (wd:Q17654148): Wikinews article
7. Star Wars, also known as 1977 Star Wars (wd:Q462): epic space opera multimedia franchise created by George Lucas
8. Star Wars: Episode IV – A New Hope, also known as 1977 Star Wars, A New Hope, Star Wars, Star Wars: A New Hope, Star Wars: Episode IV (wd:Q17738): 1977 film directed by George Lucas
9. Star Wars, also known as Star Wars: The Arcade Game (wd:Q54317): 1983 video game
10. Star Wars, also known as Star Wars (Original Motion Picture Soundtrack), Star Wars soundtrack (wd:Q2715371): 1977 soundtrack album

[Assistant]
The correct entity for the Star Wars series is likely "Star Wars" (wd:Q462), which is described as the epic space opera multimedia franchise created by George Lucas. Next, we need to find the movies that are part of this series. We can use the `has part` property (wdt:P527) to find the movies that are part of the Star Wars series.

Let's construct a SPARQL query to find the movies that are part of the Star Wars series.

[Tool]
Got 1 row and 1 column:
| movie                    |
| ------------------------ |
| Star Wars (wd:Q22092344) |

[Assistant]
It seems that the query returned only one result, which is the Star Wars multimedia franchise itself. This suggests that the `has part` property might not be the right choice for finding individual movies in the series. Instead, let's try to find the specific movies that are part of the Star Wars film series by searching for the individual movies directly.

We can use the `instance of` property (wdt:P31) to filter for films and then check if they are part of the Star Wars series using the `part of` property (wdt:P361).

Let's construct a new SPARQL query to find the titles of the Star Wars series movies.

[Tool] Got no rows and 2 columns

[Assistant]
It appears that the query did not return any results, which suggests that the `part of` property (wdt:P361) might not be the correct way to link the individual movies to the Star Wars series. Let's try another approach by searching for the individual movies that are part of the Star Wars film series and then verify their titles.

We can use the `part of` property (wdt:P361) in a different way, or we can look for specific properties that link the movies to the Star Wars franchise. Let's try using the `main subject` property (wdt:P921) to see if it links the movies to the Star Wars series.

Let's construct a new SPARQL query to find the titles of the Star Wars series movies using the `main subject` property.

[Tool]
Got 1 row and 2 columns:
| movie                                                      | title                                            |
| ---------------------------------------------------------- | ------------------------------------------------ |
| Star Wars: Evolution of the Lightsaber Duel (wd:Q24905130) | Star Wars: Evolution of the Lightsaber Duel (en) |

[Assistant]
The query returned a single result, which is a documentary about the evolution of lightsaber duels in Star Wars. This suggests that the `main subject` property (wdt:P921) might not be the best choice either.

Let's try another approach by searching for the individual movies that are part of the Star Wars film series using the `part of` property (wdt:P361) and the `instance of` property (wdt:P31) again, but this time let's also include the `series` property (wdt:P179) to see if it provides the correct linkage.

Let's construct a new SPARQL query to find the titles of the Star Wars series movies using the `series` property (wdt:P179).

[Tool] Got no rows and 2 columns

[Assistant]
It seems that the `series` property (wdt:P179) also did not yield the expected results. Given that the previous attempts have not been successful, let's take a different approach by directly searching for the individual Star Wars movies and then verifying their titles.

We can search for entities with "Star Wars" in their labels and filter for those that are instances of films (wd:Q11424).

Let's construct a new SPARQL query to find the titles of the Star Wars series movies by searching for entities with "Star Wars" in their labels and filtering for films.

[Tool]
Got 38 rows and 2 columns, showing the first 5 and last 5 rows below:
| movie                                                                           | title                                                                 |
| ------------------------------------------------------------------------------- | --------------------------------------------------------------------- |
| Star Wars: SC 38 – Reimagined (wd:Q108730566)                                   | Star Wars: SC 38 – Reimagined (en)                                    |
| Star Wars: Origins (wd:Q109422005)                                              | Star Wars: Origins (en)                                               |
| Lego Star Wars: Summer Vacation (wd:Q115818998)                                 | Lego Star Wars: Summer Vacation (en)                                  |
| LEGO Star Wars: The Padawan Menace (wd:Q11752288)                               | LEGO Star Wars: The Padawan Menace (en)                               |
| Lego Star Wars: The Empire Strikes Out (wd:Q11753786)                           | Lego Star Wars: The Empire Strikes Out (en)                           |
| ...                                                                             | ...                                                                   |
| Lego Star Wars: The New Yoda Chronicles: Clash of the Skywalkers (wd:Q75502074) | Lego Star Wars: The New Yoda Chronicles: Clash of the Skywalkers (en) |
| Star Wars: Revelations (wd:Q7601101)                                            | Star Wars: Revelations (en)                                           |
| Star Wars Gangsta Rap (wd:Q7601132)                                             | Star Wars Gangsta Rap (en)                                            |
| Star Wars Uncut (wd:Q7601171)                                                   | Star Wars Uncut (en)                                                  |
| Battle Star Wars (wd:Q99932549)                                                 | Battle Star Wars (en)                                                 |

[Assistant]
The query returned a list of entities with "Star Wars" in their titles, but many of these are not the main Star Wars film series. To refine the results, we need to filter out the main Star Wars films, which are typically referred to as "Episode" followed by a number.

Let's add a filter to the query to include only the main Star Wars films by checking if the title contains "Episode" and a number.

Here is the refined SPARQL query:

[Tool]
Got 9 rows and 2 columns:
| movie                                                        | title                                               |
| ------------------------------------------------------------ | --------------------------------------------------- |
| Star Wars: Episode I – The Phantom Menace (wd:Q165713)       | Star Wars: Episode I – The Phantom Menace (en)      |
| Star Wars: Episode IV – A New Hope (wd:Q17738)               | Star Wars: Episode IV – A New Hope (en)             |
| Star Wars: Episode II – Attack of the Clones (wd:Q181069)    | Star Wars: Episode II – Attack of the Clones (en)   |
| Star Wars: Episode V – The Empire Strikes Back (wd:Q181795)  | Star Wars: Episode V – The Empire Strikes Back (en) |
| Star Wars: Episode VI – Return of the Jedi (wd:Q181803)      | Star Wars: Episode VI – Return of the Jedi (en)     |
| Star Wars: Episode VIII – The Last Jedi (wd:Q18486021)       | Star Wars: Episode VIII – The Last Jedi (en)        |
| Star Wars: Episode IX – The Rise of Skywalker (wd:Q20977110) | Star Wars: Episode IX – The Rise of Skywalker (en)  |
| Star Wars: Episode III – Revenge of the Sith (wd:Q42051)     | Star Wars: Episode III – Revenge of the Sith (en)   |
| Star Wars: Episode VII – The Force Awakens (wd:Q6074)        | Star Wars: Episode VII – The Force Awakens (en)     |

[Assistant]
The refined query has successfully returned the titles of the main Star Wars series movies. Here are the titles of the Star Wars series movies:

1. **Star Wars: Episode I – The Phantom Menace**
2. **Star Wars: Episode II – Attack of the Clones**
3. **Star Wars: Episode III – Revenge of the Sith**
4. **Star Wars: Episode IV – A New Hope**
5. **Star Wars: Episode V – The Empire Strikes Back**
6. **Star Wars: Episode VI – Return of the Jedi**
7. **Star Wars: Episode VII – The Force Awakens**
8. **Star Wars: Episode VIII – The Last Jedi**
9. **Star Wars: Episode IX – The Rise of Skywalker**

I will now provide the final SPARQL query and the answer to the user's question.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query is correct and returns all the titles of the Star Wars series movies. The query filters for movies that have 'Star Wars' in their title and contain the word 'Episode' followed by a Roman numeral, ensuring that only the main series movies are returned. The results match the expected list of Star Wars movies.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?releaseDate WHERE {
  wd:Q58815001 wdt:P577 ?releaseDate .
}

The film "Red Notice" was released on November 12, 2021.

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

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

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

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

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

[User] When the film Red Notice was released?

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

[Tool]
Example 1:
Question:
When is the movie Worst Case Scenario going to be in cinemas in the Netherlands?

SPARQL:
SELECT DISTINCT ?date WHERE {
  wd:Q6667101 wdt:P577 ?date
}

Using entities:
Worst Case Scenario (wd:Q6667101): 2008 unfinished film

Using properties:
publication date, also known as launched, published, publication time, publication, pubdate (wd:P577 as wdt): date or point in time when a work was first published or released, point in time

[Assistant] Based on the example, we need to find the release date of the film "Red Notice". Let's first search for the entity "Red Notice" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Red Notice (wd:Q58815001): 2021 film directed by Rawson Marshall Thurber
2. Interpol Red Notice, also known as Red Notice (wd:Q47612352)
3. Red Notice (wd:Q119843350): action comedy film series
4. SAS: Red Notice (wd:Q65119432): 2021 film directed by Magnus Martens
5. Bill Browder, Red Notice (wd:Q102079684): scientific article published on 31 December 1969
6. Bill Browder’s ‘Red Notice’ (wd:Q102080235)
7. Notice of redundancy (wd:Q57829991)
8. Critical notice: Darwinian reductionism (wd:Q107095554): journalArticle
9. Notice of redundant publications (wd:Q80587358): scientific article published on 17 September 2004
10. Notice of redundant publications (wd:Q82576463): scientific article published on 01 November 2010

[Assistant] The top result is the 2021 film "Red Notice" directed by Rawson Marshall Thurber. We will use this entity to find the release date. Now let's look for the property "release date" or "publication date".

[Tool]
Top 10 property alternatives:
1. date of commercialization, also known as availability date, commercialization date, product release date, release date (wd:P5204 as ps/pq/pqv/prn/psn/prv/wdtn/psv/wdt/pqn/pr/p): date when a product was first commercialized
2. publication date, also known as launched, published, publication time, publication, pubdate (wd:P577 as ps/pq/pqv/prn/psn/prv/wdtn/psv/wdt/pqn/pr/p): date or point in time when a work was first published or released, point in time
3. announcement date, also known as released, date teased, made public on, public announcement date, published (wd:P6949 as ps/pq/pqv/prn/psn/prv/wdtn/psv/wdt/pqn/pr/p): time of the first public presentation of a subject by the creator, of information by the media, point in time
4. inception, also known as initial release date, founded in, founded on, founded on date, founding date (wd:P571 as ps/pq/pqv/prn/psn/prv/wdtn/psv/wdt/pqn/pr/p): time when an entity begins to exist; for date of official opening use P1619, start time
5. day of regular release (wd:P6437 as ps/pq/pqv/prn/psn/prv/wdtn/psv/wdt/pqn/pr/p): day(s) when parts of this creative work are released, aired, published, etc
6. service entry, also known as commissioned, time of service entry, service introduction, launch date, in service (wd:P729 as ps/pq/pqv/prn/psn/prv/wdtn/psv/wdt/pqn/pr/p): date or point in time on which a piece or class of equipment entered operational service, start time
7. date of official opening, also known as launch date, launched, officially opened on, official opening, official opening time (wd:P1619 as ps/pq/pqv/prn/psn/prv/wdtn/psv/wdt/pqn/pr/p): date or point in time a place, organization, or event officially opened, start time, inception
8. UTC date of spacecraft launch, also known as time of spacecraft launch, spacecraft launch time, spacecraft launch date, launch time, launch date (wd:P619 as ps/pq/pqv/prn/psn/prv/wdtn/psv/wdt/pqn/pr/p): date of spacecraft launch in UTC
9. production date, also known as date of production, filming date, filming time, production date, year of production (wd:P2754 as ps/pq/pqv/prn/psn/prv/wdtn/psv/wdt/pqn/pr/p): period of active production of (creative) work; the only date st... publication date (P577); in general cases, use inception (P571)
10. place of publication, also known as published in (place), where published, release region, released in, published from (wd:P291 as ps/pq/pqv/prn/psn/prv/wdtn/psv/wdt/pqn/pr/p): geographical place of publication of the edition (use 1st edition when referring to works), significant place

[Assistant]
The property "publication date" (wd:P577) seems to be the most appropriate for finding the release date of a film. Now, let's construct a SPARQL query to get the release date of the film "Red Notice".

Here is the SPARQL query:

```sparql
SELECT ?releaseDate WHERE {
  wd:Q58815001 wdt:P577 ?releaseDate .
}
```

Let's execute this query.

[Tool]
Got 1 row and 1 column:
| releaseDate                         |
| ----------------------------------- |
| 2021-11-12T00:00:00Z (xsd:dateTime) |

[Assistant]
The film "Red Notice" was released on November 12, 2021.

Here is the final SPARQL query and answer:

```sparql
SELECT ?releaseDate WHERE {
  wd:Q58815001 wdt:P577 ?releaseDate .
}
```

Answer: The film "Red Notice" was released on November 12, 2021.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query correctly retrieves the release date of the film 'Red Notice' from Wikidata, and the human-readable answer accurately reflects this information.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?date WHERE {
  wd:Q1768 wdt:P570 ?date .
}

Ella Fitzgerald died on June 15, 1996.

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

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

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

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

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

[User] When did Ella Fitzgerald die?

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

[Tool]
Example 1:
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

Example 2:
Question:
When did Michael Jackson die?

SPARQL:
SELECT ?o1 WHERE {
  wd:Q2831 wdt:P570 ?o1 .
}

Using entities:
Michael Jackson, also known as M.J., MJ, M. J., The King of Pop, King of Pop (wd:Q2831): American singer, songwriter, and dancer (1958–2009), singer-songwriter, recording artist, record producer, businessperson, philanthropist, music arranger, autobiographer, film director, choreographer, art collector, entrepreneur, screenwriter, voice actor, songwriter, film actor, composer, dancer, singer, actor, model, poet

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

Example 3:
Question:
When was the death of Shakespeare?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q692 wdt:P570 ?uri .
}

Using entities:
William Shakespeare, also known as Bard of Avon, The Bard of Avon, The Bard, Swan of Avon (wd:Q692): English playwright and poet (1564–1616), playwright

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] Based on the examples, it seems that we need to find the entity for Ella Fitzgerald and then use the property `wdt:P570` to get her date of death. Let's first search for the entity representing Ella Fitzgerald.

[Tool]
Top 10 entity alternatives:
1. Ella Fitzgerald, also known as The First Lady of Jazz, The First Lady of Song, Lady Ella, Queen of Jazz (wd:Q1768): American jazz singer (1917–1996), singer-songwriter, recording artist, jazz musician, bandleader, film actor, conductor, composer, singer
2. Ella Fitzgerald, also known as Donnell Robinson (wd:Q100786004): American drag queen, drag queen
3. Ella Fitzgerald (wd:Q28732978): tram stop in the 19th arrondissement of Paris, France
4. Ella Fitzgerald 1937-1938, also known as 1937-1938 (wd:Q30880995): 1990 compilation album by Ella Fitzgerald
5. Ella Fitzgerald 1935-1937, also known as 1935-1937 (wd:Q30880987): 1990 compilation album by Ella Fitzgerald
6. Ella Fitzgerald 1938-1939, also known as 1938-1939 (wd:Q48833657): 1990 compilation album by Ella Fitzgerald
7. Ella Fitzgerald 1939, also known as 1939 (wd:Q48833865): 1990 compilation album by Ella Fitzgerald
8. Ella Fitzgeraldstraat (wd:Q52321478): street in Amsterdam, the Netherlands
9. Ella Fitzgeraldstraat (wd:Q19116594): street in Arnhem, the Netherlands
10. Ella Fitzgeraldplein (wd:Q19116593): street in Utrecht, the Netherlands

[Assistant]
The top result, `wd:Q1768`, represents Ella Fitzgerald, the American jazz singer. Now, let's construct a SPARQL query to find out when she died using the `wdt:P570` property.

Here is the SPARQL query:

```sparql
SELECT ?date WHERE {
  wd:Q1768 wdt:P570 ?date .
}
```

I will now execute this query.

[Tool]
Got 1 row and 1 column:
| date                                |
| ----------------------------------- |
| 1996-06-15T00:00:00Z (xsd:dateTime) |

[Assistant]
The SPARQL query has returned the date of death for Ella Fitzgerald, which is June 15, 1996.

Here is the final answer:

Ella Fitzgerald died on June 15, 1996.

And here is the final SPARQL query:

```sparql
SELECT ?date WHERE {
  wd:Q1768 wdt:P570 ?date .
}
```

I will now 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 retrieves the date of death for Ella Fitzgerald from Wikidata, and the human-readable answer is accurate.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?endDate WHERE {
  wd:Q362 wdt:P582 ?endDate .
}

World War II officially ended on 1945-09-02.

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

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

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

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

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

[User] When did World War II officially end?

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

[Tool]
Example 1:
Question:
When did Operation Overlord commence?

SPARQL:
SELECT DISTINCT ?date WHERE {
  wd:Q8641370 wdt:P580 ?date .
}

Using entities:
Operation Overlord, also known as Battle of Normandy, Normandy landings (wd:Q8641370): 1944 successful Allied invasion of Nazi-held western Europe in World War II

Using properties:
start time, also known as start date, since, valid, join time, joined (wd:P580 as wdt): time an entity begins to exist or a statement starts being valid, point in time

[Assistant] Based on the example, we need to find the end date of World War II. Let's first identify the entity for World War II.

[Tool]
Top 10 entity alternatives:
1. World War II, also known as 2nd World War, WWII, WW II, WW2, WW 2 (wd:Q362): 1939–1945 global conflict
2. Theatre of War, also known as World War II, World War II RTS: Theatre of War (wd:Q2631997): 2006 video game developed by 1C Company
3. Tyneside Scottish, also known as World War II (wd:Q7860541): military unit
4. World War II (wd:Q104869378): series of books written by American author Chris Lynch
5. World War II (wd:Q131576266): online exhibition from Indiana State Library
6. World War II (wd:Q110874375): US magazine
7. World War II, also known as mbcwwii, The Military Book Club's World War II (wd:Q15053459): 1995 edition by the Military Book Club
8. World War II, also known as Second World War (wd:Q105750294): scenario in the video game Civilization II
9. World War II, also known as The Military Book Club's World War II (wd:Q15053456): book by John Ellis, 1993 edition
10. Theatre of War, also known as World War II (wd:Q108371810): video game series

[Assistant] The correct entity for World War II is `wd:Q362`. Now let's find the property for the end date of an event.

[Tool]
Top 10 property alternatives:
1. dissolved, abolished or demolished date, also known as ended, publication end date, final issue, date repealed, final year (wd:P576 as ps/pq/pqv/prn/psn/prv/wdtn/psv/wdt/pqn/pr/p): point in time at which the subject (organisation, building) ceas...g equipment,  "discontinued date" (P2669) for stopping a product, end time
2. end time, also known as ends, stop time, endtime, fall date, ineffective (wd:P582 as ps/pq/pqv/prn/psn/prv/wdtn/psv/wdt/pqn/pr/p): moment when an entity ceases to exist and a statement stops being entirely valid or no longer be true, point in time
3. date of death, also known as DOD, deathdate, died, died on, year of death (wd:P570 as ps/pq/pqv/prn/psn/prv/wdtn/psv/wdt/pqn/pr/p): dissolved, abolished or demolished date, date on which the subject died, end time
4. latest date, also known as terminus ante quem, prior to, on or before this date, no later than, max date (wd:P1326 as ps/pq/pqv/prn/psn/prv/wdtn/psv/wdt/pqn/pr/p): latest possible time that something could have occurred. Use as qualifier for other date properties
5. earliest end date, also known as end date (earliest) (wd:P8554 as ps/pq/pqv/prn/psn/prv/wdtn/psv/wdt/pqn/pr/p): (qualifier) earliest date on which the statement could have begun to no longer be true, earliest date, end time
6. work period (end), also known as floruit end, floruit (end of work), flourished (end), retirement, work ends (wd:P2032 as ps/pq/pqv/prn/psn/prv/wdtn/psv/wdt/pqn/pr/p): end of period during which a person or group flourished (fl. = "floruit") in their professional activity, end time, floruit
7. date of official closure, also known as closed, official closure, date of closure, closure date, closing date (wd:P3999 as ps/pq/pqv/prn/psn/prv/wdtn/psv/wdt/pqn/pr/p): date of official closure of a building or event, end time
8. end of covered period, also known as coverage end date, covered period end (wd:P7104 as ps/pq/pqv/prn/psn/prv/wdtn/psv/wdt/pqn/pr/p): end of period covered by an archive set, a historical book, or a collection
9. end period, also known as until period (wd:P3416 as ps/pq/pqv/prn/psn/prv/wdtn/psv/wdt/pqn/pr/p): last period in which the statement is still valid (including the period-value)
10. earliest date, also known as no earlier than, terminus post quem, start date (earliest), on or after this date, minimum date (wd:P1319 as ps/pq/pqv/prn/psn/prv/wdtn/psv/wdt/pqn/pr/p): earliest date at which an event could have happened. Use as qualifier for other date properties

[Assistant] The property for the end date of an event is `wdt:P582`. Now we can construct a simple SPARQL query to find the end date of World War II.

[Tool]
Got 1 row and 1 column:
| endDate                             |
| ----------------------------------- |
| 1945-09-02T00:00:00Z (xsd:dateTime) |

[Assistant] The end date of World War II is 1945-09-02. Let's format the answer properly.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query correctly retrieves the end date of World War II from Wikidata, and the human-readable answer is accurate.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?material ?materialLabel WHERE {
  wd:Q39231 wdt:P186 ?material .
  OPTIONAL {
    ?material rdfs:label ?materialLabel .
    FILTER ( LANG( ?materialLabel ) = "en" )
  }
}

Got 1 row and 2 columns:
| material           | materialLabel |
| ------------------ | ------------- |
| basalt (wd:Q43338) | basalt (en)   |

Mount Fuji is made of basalt.

[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 is the Fujiyama made of?

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

[Tool] No examples found

[Assistant] Since we couldn't find any similar examples, let's start by searching for the entity "Fujiyama".

[Tool]
Top 10 entity alternatives:
1. Mount Fuji, also known as Mt. Fuji, Mt Fuji, Fujiyama, Fuji-yama, Fuji-san (wd:Q39231): volcano in Yamanashi and Shizuoka Prefectures, Japan
2. Fujiyama, also known as Fujiyama (roller coaster) (wd:Q788077): roller coaster in Fujiyoshida, Japan
3. Fujiyama, also known as 藤山 (wd:Q19364120): Japanese family name (藤山)
4. Fujiyama (wd:Q17174578): dissolved municipality in Chiisagata district, Nagano prefecture, Japan
5. Fujiyama (wd:Q11623847): dissolved municipality in Asa district, Yamaguchi prefecture, Japan
6. Fujiyama (wd:Q24876637): Japanese mangaka, illustrator
7. Fujiyama (wd:Q46945234): painting by John Henry Twachtman
8. Kazuhito Fujiyama, also known as Fujiyama (wd:Q88822958): botanist
9. Ryuji Fujiyama (wd:Q1092137): Japanese association football player, association football player
10. Tomofumi Fujiyama, also known as Fujiyama Tomofumi (wd:Q38277863): Japanese association football player (1994-), association football player

[Assistant] The first result, Mount Fuji (wd:Q39231), seems to be the correct entity for our query. Now, let's look for properties related to the composition or material of Mount Fuji.

[Tool]
Top 10 property alternatives:
1. has part(s), also known as has component, incorporates, has ingredient, has ingredients, has member (wd:P527 as wdt/p): part of this subject; inverse property of "part of" (P361). See also "has parts of the class" (P2670).
2. made from material, also known as material used, manufactured out of, manufactured from, made of material, made of (wd:P186 as wdt/p): material the subject or the object is made of or derived from (do not confuse with P10672 which is used for processes), uses
3. 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))
4. 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)
5. 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
6. 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
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. area, also known as total area, surface area, surface, size, resolution (wd:P2046 as wdt/p): area occupied by an object

No other items found

[Assistant]
The property "made from material" (wdt:P186) seems to be the most relevant for finding out what Mount Fuji is made of. Let's construct a SPARQL query to retrieve the materials that Mount Fuji is made from.

```sparql
SELECT ?material WHERE {
  wd:Q39231 wdt:P186 ?material .
}
```

Now, let's execute this query.

[Tool]
Got 1 row and 1 column:
| material           |
| ------------------ |
| basalt (wd:Q43338) |

[Assistant]
The query returned that Mount Fuji is made of basalt (wd:Q43338). To provide a more complete answer, let's also include the label of the material in the query.

```sparql
SELECT ?material ?materialLabel WHERE {
  wd:Q39231 wdt:P186 ?material .
  OPTIONAL { ?material rdfs:label ?materialLabel . FILTER(LANG(?materialLabel) = "en") }
}
```

Let's execute this updated query.

[Tool]
Got 1 row and 2 columns:
| material           | materialLabel |
| ------------------ | ------------- |
| basalt (wd:Q43338) | basalt (en)   |

[Assistant]
The query confirms that Mount Fuji is made of basalt.

Here is the final SPARQL query and the answer:

```sparql
SELECT ?material ?materialLabel WHERE {
  wd:Q39231 wdt:P186 ?material .
  OPTIONAL { ?material rdfs:label ?materialLabel . FILTER(LANG(?materialLabel) = "en") }
}
```

Answer: Mount Fuji is made of basalt.

Now, let's finalize the answer.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query correctly identifies the material Mount Fuji is made of, which is basalt, and returns the IRI along with the label.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
ASK WHERE {
  wd:Q1650 wdt:P2043 ?weserLength .
  wd:Q584 wdt:P2043 ?rhineLength .
  FILTER ( ?weserLength > ?rhineLength )
}

False

[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 Weser longer than the Rhine?

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

[Tool]
Example 1:
Question:
Which countries are connected by the Rhine?

SPARQL:
SELECT DISTINCT ?uri ?x ?y WHERE {
  wd:Q584 wdt:P17 ?uri .
  ?uri wdt:P31 wd:Q6256 .
}

Using entities:
Rhine (wd:Q584): river in Western Europe to the North Sea
country, also known as countries (wd:Q6256): distinct territorial body or political entity

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

Example 2:
Question:
how big is the total area of North Rhine- Westphalia?

SPARQL:
SELECT DISTINCT ?val WHERE {
  wd:Q1198 wdt:P2046 ?val .
}

Using entities:
North Rhine-Westphalia, also known as Nordrhein-Westfalen, NRW (wd:Q1198): state in Germany

Using properties:
area, also known as total area, surface area, surface, size, resolution (wd:P2046 as wdt): area occupied by an object

Example 3:
Question:
Which rivers flow into the North Sea?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P31 wd:Q4022 .
  ?uri wdt:P403 wd:Q1693 .
}

Using entities:
North Sea, also known as German Ocean, the North Sea, the German Sea, the British Ocean, German Sea (wd:Q1693): marginal sea of the Atlantic Ocean
river, also known as rivers (wd:Q4022): larger natural watercourse

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)
mouth of the watercourse, also known as flows into, watercourse outflow, watercourse ends in, watercourse drain, tributary of (wd:P403 as wdt): the body of water to which the watercourse drains

[Assistant] From the examples, we can see that the Rhine is represented by the entity `wd:Q584`. We need to find the entities for the rivers Weser and Rhine, and the property for their length. Let's start by searching for the entity "Weser".

[Tool]
Top 10 entity alternatives:
1. Weser, also known as Werser (wd:Q1650): river in north-western Germany
2. Seydlitz, also known as German cruiser Seydlitz, Weser (wd:Q662855): 1939 Admiral Hipper-class cruiser
3. Weser (wd:Q2563829): German pilot vessel
4. Weser (wd:Q28657280): nature preserve in North Rhine-Westphalia, Germany
5. Pola I, also known as Arngast, Baltica, Beta, Drochtersen, IMO 5387740 (wd:Q83582689): ship built in 1958
6. Weser (wd:Q56515182): ship built by Schichau Unterweser
7. Weser (wd:Q37225304): family name
8. Weser (wd:Q34857185): part of a river in Germany
9. Weser (wd:Q2563832): 1931 German ship
10. Weser, also known as IMO 6620266, Wabda (wd:Q106677892): bulk carrier launched in 1966

[Assistant] The correct entity for the river Weser is `wd:Q1650`. Now let's search for the entity "Rhine".

[Tool]
Top 10 entity alternatives:
1. Rhine (wd:Q584): river in Western Europe to the North Sea
2. Rhine, also known as Rhine, GA, Rhine, Georgia (wd:Q2156232): town in Dodge County, Georgia, USA
3. Rhine (wd:Q1886951): civil town in Sheboygan County, Wisconsin
4. Seerhein, also known as Rhine (wd:Q481339): river
5. Rhine (wd:Q7320550): ship
6. rhyne, also known as reen, rhine (wd:Q7321737): term for a drainage ditch or canal in parts of England and Wales
7. Rhine Center, also known as Rhine, Rhine Center, WI, Rhine Center, Wisconsin (wd:Q7320551): unincorporated community in Sheboygan County, Wisconsin
8. Rhine (wd:Q37575224): family name
9. Rhine (wd:Q110815477): given name
10. Rhine (wd:Q104267295): townland in Cloonee, County Longford, Ireland

[Assistant] The correct entity for the river Rhine is `wd:Q584`. Next, we need to find the property for the length of a river. Let's search for the property "length".

[Tool]
Top 10 property alternatives:
1. duration, also known as time length, timespan, time span, time interval, runtime (wd:P2047 as ps/pq/pqv/prn/psn/prv/wdtn/psv/wdt/pqn/pr/p): length of time of an event or process
2. length, also known as size, long, LOA, distance, dimension (wd:P2043 as ps/pq/pqv/prn/psn/prv/wdtn/psv/wdt/pqn/pr/p): measured dimension of an object
3. block size, also known as block length (wd:P2599 as ps/pq/pqv/prn/psn/prv/wdtn/psv/wdt/pqn/pr/p): size of block for this block cipher or hash function
4. perimeter, also known as boundary length, circumference, coastline (wd:P2547 as ps/pq/pqv/prn/psn/prv/wdtn/psv/wdt/pqn/pr/p): linear distance around the outside of an object
5. term length of position, also known as term length, time in position, term of position, position term, length of term (wd:P2097 as ps/pq/pqv/prn/psn/prv/wdtn/psv/wdt/pqn/pr/p): length of time in years (unit: Q577) a person (usually a politician) is to serve in a particular position. Do not add bounds., duration
6. area, also known as total area, surface area, surface, size, resolution (wd:P2046 as ps/pq/pqv/prn/psn/prv/wdtn/psv/wdt/pqn/pr/p): area occupied by an object
7. width, also known as breadth, bus width, size (wd:P2049 as ps/pq/pqv/prn/psn/prv/wdtn/psv/wdt/pqn/pr/p): width of an object, length
8. height, also known as heighth, size, depth over terrain (wd:P2048 as ps/pq/pqv/prn/psn/prv/wdtn/psv/wdt/pqn/pr/p): vertical length of an entity, length
9. beam, also known as width, breadth, BOA (wd:P2261 as ps/pq/pqv/prn/psn/prv/wdtn/psv/wdt/pqn/pr/p): width of a ship at its widest point measured at its nominal waterline
10. elevation above sea level, also known as MAMSL, MASL, height, elevation above mean sea level, elevation (wd:P2044 as ps/pq/pqv/prn/psn/prv/wdtn/psv/wdt/pqn/pr/p): height of the item (geographical object) as measured relative to sea level

[Assistant]
The correct property for the length of a river is `wd:P2043`. Now we can construct a SPARQL query to compare the lengths of the Weser and the Rhine.

We will first retrieve the lengths of both rivers and then compare them using an ASK query.

[Tool]
Got 1 row and 2 columns:
| weserLength       | rhineLength          |
| ----------------- | -------------------- |
| 451 (xsd:decimal) | 1232.7 (xsd:decimal) |

[Assistant]
We have retrieved the lengths of the Weser and the Rhine. The Weser is 451 kilometers long, and the Rhine is 1232.7 kilometers long.

Now, let's construct an ASK query to determine if the Weser is longer than the Rhine.

[Tool] False

[Assistant]
The ASK query has returned `False`, indicating that the Weser is not longer than the Rhine.

Therefore, the answer to the question "Is the Weser longer than the Rhine?" is **False**.

Here is the final SPARQL query:

```sparql
ASK WHERE {
  wd:Q1650 wdt:P2043 ?weserLength .
  wd:Q584 wdt:P2043 ?rhineLength .
  FILTER(?weserLength > ?rhineLength)
}
```

And the answer is **False**.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query is correctly formulated as an ASK query and accurately compares the lengths of the Weser and Rhine rivers. The result is logically consistent with the fact that the Rhine is indeed longer than the Weser. No further refinements are necessary.