PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?deathDate WHERE {
  wd:Q43347 wdt:P570 ?deathDate .
}

Rumi died on December 17, 1273.

[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 Rumi die?

[Assistant] To answer the question "When did Rumi die?", we need to identify the entity for Rumi and the property for the date of death. Let's start by searching for Rumi in the knowledge graph.

[Tool]
Falling back to an unconstrained search on the full precomputed search indices:
SPARQL query contains no IRIs to constrain with

Top 10 entity alternatives:
1. Rumi, also known as Mawlana, Mawlana Rumi, Mawlawi, Jallal ed-Din Muhammad Balkhy, Jelaluddin Rumi (wd:Q43347): Sufi and Persian poet, philosopher, literary, writer, mystic, ʿālim, poet
2. Rum, also known as Arwām, Rūm, Rūmī (wd:Q3943294): autonym
3. Rūmī (wd:Q25908371): crater on planet Mercury
4. POGLUT1, also known as MDSRP, Rumi, protein O-glucosyltransferase 1, MDS010, LGMDR21 (wd:Q18042749): protein-coding gene in the species Homo sapiens
5. Lanmewko, also known as Rumi (wd:Q109342876): Taiwanese Virtual YouTuber, Twitch streamer, VTuber
6. Rumi Rumiyuq (wd:Q25104391): mountain in Bolivia
7. Rumi, also known as るみ (wd:Q97655647): Japanese female given name (るみ)
8. Rumi (wd:Q11241326): Japanese master of ceremonies, rapper
9. Rumi (wd:Q11578550): Japanese actress, actor
10. Rumi (wd:Q19949377)

No other items found

[Assistant] The top result for "Rumi" is the Sufi and Persian poet, philosopher, literary, writer, mystic, ʿālim, poet, which matches our context. The corresponding IRI is `wd:Q43347`. Next, we need to find the property for the date of death. We can search for this property using the IRI of Rumi.

[Tool]
Top 10 property alternatives:
1. date of death, also known as DOD, deathdate, died, died on, year of death (wd:P570 as p/wdt): 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 p/wdt): most specific known (e.g. city instead of country, or hospital i... city) death location of a person, animal or fictional character, location
3. date of birth, also known as year of birth, birthdate, DOB, born on, born (wd:P569 as p/wdt): date on which the subject was born, inception
4. place of birth, also known as POB, location of birth, location born, born in, birthplace (wd:P19 as p/wdt): most specific known birth location of a person, animal or fictional character, location
5. place of burial, also known as interment, interment location, interment place, interred at, location of burial (wd:P119 as p/wdt): location of grave, resting place, place of ash-scattering, etc. ...veral places: e.g., re-burials, parts of body buried separately., location
6. described by source, also known as mentioned in, source of item, reviewed in, mentioned in news article, talked about in (wd:P1343 as p/wdt): work where this item is described, attested in
7. given name, also known as name, proper name, personal name, middle name, forename (wd:P735 as p/wdt): first name or another given name of this person; values used with the property should not link disambiguations nor family names, name
8. pseudonym, also known as nom de plume, nom-de-plume, nome de guerre, pen name, ring name (wd:P742 as p/wdt): alias used by someone (for nickname use P1449), name
9. significant person, also known as significant people, sigper, sign. person, relevant person, relevant people (wd:P3342 as p/wdt): person linked to the item in any possible way
10. Oxford Reference overview ID, also known as OR ID, ORO ID, Overview Oxford Reference ID, Overview Reference Oxford ID, Oxford Reference ID (wd:P12582 as p/wdt): identifier for an overview page for a topic in the Oxford Reference

No other items found

[Assistant]
The top result for the property "date of death" is `wd:P570`. Now we can construct a simple SPARQL query to retrieve the date of death for Rumi.

Let's execute the SPARQL query:

```sparql
SELECT ?deathDate WHERE {
  wd:Q43347 wdt:P570 ?deathDate .
}
```

This query will return the date of death for Rumi.

[Tool]
Got 1 row and 1 column:
| ?deathDate           |
| -------------------- |
| 1273-12-17T00:00:00Z |

[Assistant]
The date of death for Rumi is December 17, 1273.

Here is the final SPARQL query and the answer:

```sparql
SELECT ?deathDate WHERE {
  wd:Q43347 wdt:P570 ?deathDate .
}
```

Answer: Rumi died on December 17, 1273.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT * WHERE {
  ?animal wdt:P31 wd:Q729 ; wdt:P108 ?organization .
}

Despite multiple attempts to search for animals involved in military operations or associated with the Australian Defence Force, no relevant results were found in Wikidata. The specific information about an animal participating in a military operation with the Australian Defence Force might not be well-documented or available in the knowledge graph.

[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]
To answer this question, we need to identify the animal that participated in a military operation with the Australian Defence Force. We'll start by searching for animals that have some connection to military operations or the Australian Defence Force.

First, let's search for animals that might be associated with military operations.

[Tool] Invalid SPARQL query: Variable ?search must occurr in the WHERE clause at least once

[Assistant]
It looks like I made a mistake in the SPARQL query. The `?search` variable must occur in the WHERE clause. Let me correct that and try again.

I will search for animals that are involved in military operations, specifically those that might be associated with the Australian Defence Force.

Let's try the search again with the correct SPARQL query.

[Tool]
No entity items found

No other items found

[Assistant]
It seems that there are no direct results for animals involved in military operations. Let's try a different approach. We can search for animals that are associated with the Australian Defence Force directly.

I will search for animals that are connected to the Australian Defence Force.

Let's try this search:

[Tool]
No entity items found

No other items found

[Assistant]
It appears that there are no direct results for animals associated with the Australian Defence Force. Let's try a more general approach by looking for animals that have been involved in any military service or operation, and then we can filter those results to see if any are specifically linked to the Australian Defence Force.

First, let's find animals that have been involved in military service or operations.

I will search for animals that have participated in military operations.

[Tool]
No entity items found

No other items found

[Assistant]
It seems that there are no direct results for animals involved in military operations in the way I've searched. Let's try a different approach by looking for specific animals that have been documented as participating in military activities, and then we can check if any of these are associated with the Australian Defence Force.

I will search for specific animals that have been involved in military activities and then check their connections to the Australian Defence Force.

Let's start by searching for specific animals that have been involved in military activities.

[Tool]
No entity items found

No other items found

[Assistant]
It seems that the search for animals involved in military operations is not yielding any results. Let's take a step back and try a broader search for animals that have any kind of military or service-related involvement, and then we can narrow it down to those associated with the Australian Defence Force.

I will search for animals that have any military or service-related involvement, and then we can check if any of these are associated with the Australian Defence Force.

Let's start by searching for animals that have any military or service-related involvement.

[Tool]
No entity items found

No other items found

[Assistant]
It seems that even a broader search for animals involved in military or service-related organizations is not yielding any results. Given this, it might be that the specific information about an animal participating in a military operation with the Australian Defence Force is not well-documented in Wikidata.

However, let's try one more approach by searching for specific animals that are known to have served in the military, and then we can check if any of these are associated with the Australian Defence Force.

I will search for specific animals that have served in the military and then check their connections to the Australian Defence Force.

Let's start by searching for specific animals that have served in the military.

[Tool]
No entity items found

No other items found

[Assistant]
It appears that even a broad search for animals that have served in the military is not yielding any results in Wikidata. This suggests that the specific information about an animal participating in a military operation with the Australian Defence Force might not be well-documented or available in the knowledge graph.

Given the lack of results, I will have to conclude that I cannot find a satisfactory SPARQL query to answer the question. Here is my best attempt at a SPARQL query, but it did not return any relevant results.

I will provide a detailed explanation and my best attempt at a SPARQL query.

[Tool] Stopped generation process


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

Got 1 row and 2 columns:
| ?mainSubject                   | ?mainSubjectLabel |
| ------------------------------ | ----------------- |
| Montreux Casino (wd:Q81085137) | Montreux Casino   |

The main subject addressed in the Deep Purple song 'Smoke on the Water' 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] To answer this question, we need to identify the song "Smoke on the Water" by Deep Purple and then find information about its main subject. Let's start by searching for the song in Wikidata.

[Tool]
No entity items found

No other items found

[Assistant]
It seems that the search did not return any results for "Smoke on the Water" by Deep Purple. Let's try a different approach by directly looking up the song using its title and the band's name.

I will use the `list` function to find the entity for "Smoke on the Water" by Deep Purple.

[Tool] Got no rows and 3 columns

[Assistant]
The list function did not return any results, which suggests that there might be an issue with the specific property or object I used. Let's try another approach by searching for the song using a broader query and then filtering the results.

I will use the `search` function again, but this time I will look for songs by Deep Purple and then filter by the title "Smoke on the Water".

[Tool]
No entity items found

No other items found

[Assistant]
It appears that even the broader search did not yield any results for "Smoke on the Water" by Deep Purple. This could be due to the song not being well-represented in Wikidata or the specific properties and labels not matching our search criteria.

Let's try a more direct approach by searching for the song using its title and the band's name in a single search query.

I will use the `search` function to look for the song "Smoke on the Water" by Deep Purple.

[Tool]
No entity items found

No other items found

[Assistant]
It seems that even with a more specific search query, we are not finding the song "Smoke on the Water" by Deep Purple in Wikidata. This could indicate that the song is not well-represented in the knowledge graph, or there might be issues with the data or the properties we are using.

Given the lack of results, I will try one more approach by searching for the song using a simpler query and then manually checking the results to see if the song is present.

I will use the `search` function to look for the song "Smoke on the Water" without specifying the band, and then check the results.

[Tool]
Falling back to an unconstrained search on the full precomputed search indices:
Got more than the maximum supported number of subject candidates (131,072)

Top 10 entity alternatives:
1. Smoke on the Water (wd:Q636399): original song written and composed by Deep Purple; first recorded by Deep Purple
2. Smoke on the Water (wd:Q3435148): compilation album by Deep Purple
3. Smoke on the Water (wd:Q24206648): 2014 television film directed by Dominik Graf
4. Smoke on the Water (wd:Q7545910): 1943 song by Earl Nunn and Zeke Clements
5. 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
6. Smoke on the Water (wd:Q114495787): episode of seaQuest DSV (S3 E3)
7. Smoke on the Water (wd:Q94008225): vocal track by Deep Purple; 1972 studio recording
8. Smoke on the Water (wd:Q94003891): 1973 single by Deep Purple
9. Smoke on the Water (wd:Q93971258): vocal track by Pat Boone; 1996 studio recording; cover version
10. Smoke on the Water (wd:Q114510292): episode of Deadliest Catch (S2 E7)

No other items found

[Assistant]
The search has returned several results, and the first one, `wd:Q636399`, appears to be the correct song "Smoke on the Water" by Deep Purple. Now, let's find the main subject addressed in the song.

We will use the `list` function to explore the properties and values associated with `wd:Q636399` to find information about the song's subject.

[Tool]
Got 195 rows and 3 columns, showing the first 10 rows below:
| subject                         | property                            | object                                   |
| ------------------------------- | ----------------------------------- | ---------------------------------------- |
| Smoke on the Water (wd:Q636399) | instance of (wdt:P31)               | musical work/composition (wd:Q105543609) |
| Smoke on the Water (wd:Q636399) | title (wdt:P1476)                   | Smoke on the Water                       |
| Smoke on the Water (wd:Q636399) | publication date (wdt:P577)         | 1973-01-01T00:00:00Z                     |
| Smoke on the Water (wd:Q636399) | main subject (wdt:P921)             | Montreux Casino (wd:Q81085137)           |
| Smoke on the Water (wd:Q636399) | language of work or name (wdt:P407) | English (wd:Q1860)                       |
| Smoke on the Water (wd:Q636399) | part of (wdt:P361)                  | Machine Head (wd:Q211742)                |
| Smoke on the Water (wd:Q636399) | genre (wdt:P136)                    | hard rock (wd:Q83270)                    |
| Smoke on the Water (wd:Q636399) | performer (wdt:P175)                | Deep Purple (wd:Q101505)                 |
| Smoke on the Water (wd:Q636399) | form of creative work (wdt:P7937)   | song (wd:Q7366)                          |
| Smoke on the Water (wd:Q636399) | composer (wdt:P86)                  | Jon Lord (wd:Q192114)                    |
| ...                             | ...                                 | ...                                      |

[Assistant]
From the list of properties and values associated with "Smoke on the Water" (wd:Q636399), we can see that the main subject of the song is the Montreux Casino (wd:Q81085137).

Now, let's construct a SPARQL query to confirm this and return the main subject of the song.

```sparql
SELECT ?mainSubject ?mainSubjectLabel
WHERE {
  wd:Q636399 wdt:P921 ?mainSubject .
  OPTIONAL { ?mainSubject rdfs:label ?mainSubjectLabel . FILTER(LANG(?mainSubjectLabel) = 'en') }
}
```

This query will return the main subject of the song and its label in English.

Let's execute this query.

[Tool]
Got 1 row and 2 columns:
| ?mainSubject                   | ?mainSubjectLabel |
| ------------------------------ | ----------------- |
| Montreux Casino (wd:Q81085137) | Montreux Casino   |

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

Here is the final SPARQL query and answer:

```sparql
SELECT ?mainSubject ?mainSubjectLabel
WHERE {
  wd:Q636399 wdt:P921 ?mainSubject .
  OPTIONAL { ?mainSubject rdfs:label ?mainSubjectLabel . FILTER(LANG(?mainSubjectLabel) = 'en') }
}
```

Answer: The main subject addressed in the Deep Purple song "Smoke on the Water" is the Montreux Casino.

[Tool] Stopped generation process


Failed to generate SPARQL query

[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]
To find the titles of the Star Wars series movies, we need to identify the entity for the Star Wars franchise and then find the movies associated with it. We can start by searching for the Star Wars franchise in Wikidata.

Let's search for "Star Wars" in the subject position to find the relevant entity.

[Tool]
Falling back to an unconstrained search on the full precomputed search indices:
SPARQL query contains no IRIs to constrain with

Top 10 entity alternatives:
1. Star Wars, also known as 1977 Star Wars (wd:Q462): epic space opera multimedia franchise created by George Lucas
2. 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
3. Star Wars, also known as Star Wars: The Arcade Game (wd:Q54317): 1983 video game
4. Star Wars, also known as Star Wars (Original Motion Picture Soundtrack), Star Wars soundtrack (wd:Q2715371): 1977 soundtrack album
5. Star Wars, also known as 1991 Star Wars, Game Boy Star Wars, NES Star Wars (wd:Q54320): 1991 video game
6. Star Wars (wd:Q30597407): 1977 composition by John Williams
7. Star Wars: Starfighter (wd:Q55225): 2001 video game
8. Star Wars, also known as 1987 Star Wars (wd:Q54319): 1987 video game
9. Star Wars (wd:Q20720789): album by Wilco
10. Star Wars (wd:Q3180475): Japanese manga series

No other items found

[Assistant]
From the search results, the most relevant entity for the Star Wars franchise is "Star Wars" (wd:Q462). Now, we need to find the movies associated with this franchise. We can look for properties that connect the franchise to its movies, such as "part of" or "instance of."

Let's list the triples where "Star Wars" (wd:Q462) is the subject to see what properties and objects are associated with it.

[Tool]
Got 922 rows and 3 columns, showing the first 10 rows below:
| subject             | property                                       | object                                                                                |
| ------------------- | ---------------------------------------------- | --------------------------------------------------------------------------------------------------------- |
| Star Wars (wd:Q462) | instance of (wdt:P31)                          | brand (wd:Q431289)                                                                                |
| Star Wars (wd:Q462) | title (wdt:P1476)                              | Star Wars                                                                                |
| Star Wars (wd:Q462) | Commons category (wdt:P373)                    | Star Wars                                                                                |
| Star Wars (wd:Q462) | image (wdt:P18)                                | <http://commons.wikimedia.org/wiki/Special:FilePath/Star%20Wars%20characters%20at%20Madame%20Tussaud.jpg> |
| Star Wars (wd:Q462) | short name (wdt:P1813)                         | SW                                                                                |
| Star Wars (wd:Q462) | has part(s) (wdt:P527)                         | Star Wars (wd:Q22092344)                                                                                |
| Star Wars (wd:Q462) | country of origin (wdt:P495)                   | United States (wd:Q30)                                                                                |
| Star Wars (wd:Q462) | official website (wdt:P856)                    | <https://www.starwars.com/>                                                                               |
| Star Wars (wd:Q462) | creator (wdt:P170)                             | George Lucas (wd:Q38222)                                                                                |
| Star Wars (wd:Q462) | on focus list of Wikimedia project (wdt:P5008) | wd:Q6173448                                                                                |
| ...                 | ...                                            | ...                                                                                |

[Assistant]
From the list of triples, we can see that the property "has part(s)" (wdt:P527) is used to connect the Star Wars franchise to its parts. We can use this property to find the movies that are part of the Star Wars franchise.

Next, let's execute a SPARQL query to find the movies that are part of the Star Wars franchise and their titles.

```sparql
SELECT ?movie ?title
WHERE {
  wd:Q462 wdt:P527 ?movie .
  ?movie wdt:P1476 ?title .
}
```

This query will return the IRIs of the movies and their titles.

Let's execute this query.

[Tool] Got no rows and 2 columns

[Assistant]
It seems that the query did not return any results. This might be because the "has part(s)" property (wdt:P527) is not directly linking the franchise to the individual movies. Instead, we might need to look for a different property or a more specific way to find the movies.

Let's try another approach. We can search for the individual Star Wars movies and then check if they are part of the Star Wars franchise.

First, let's search for "Star Wars movie" to find the individual movies.


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
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] To find out when the film "Red Notice" was released, I need to identify the entity for the film "Red Notice" and the property for the release date. Let's start by searching for the entity "Red Notice" in Wikidata.

[Tool]
Falling back to an unconstrained search on the full precomputed search indices:
SPARQL query contains no IRIs to constrain with

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. One Piece Film: Red (wd:Q109638221): 2022 anime film
6. Red Hour Productions, also known as Red Hour Films (wd:Q7304367): American film production and distribution company
7. Daylight redirecting film (wd:Q48791781): plastic applied to window to reflect light
8. Red Rock Film Festival (wd:Q7304910): film festival
9. Red Nation Film Festival (wd:Q109229136): film festival focused on films about indigenous people
10. Red Sands (1968 film) (wd:Q124328063): 1968 Soviet film directed by Ali Hamroyev

No other items found

[Assistant] The top result for "Red Notice" is the 2021 film directed by Rawson Marshall Thurber, which seems to be the correct entity. Now, I need to find the property for the release date. I will search for the property related to the release date of this film.

[Tool]
Top 10 property alternatives:
1. publication date, also known as launched, published, publication time, publication, pubdate (wd:P577 as p/wdt): date or point in time when a work was first published or released, point in time
2. distribution format, also known as book format, type of media, publication format, method of distribution, medium (wd:P437 as p/wdt): method (or type) of distribution for the subject
3. genre, also known as genre of music, type of music, type of film, music genre, literary genre (wd:P136 as p/wdt): creative work's genre or an artist's field of work (P101). Use main subject (P921) to relate creative works to their topic
4. title, also known as name, titled, page name, original title, known as (wd:P1476 as p/wdt): published name of a work, such as a newspaper article, a literary work, piece of music, a website, or a performance work, name
5. country of origin, also known as origin country, place of origin, originates from, country, CoO (wd:P495 as p/wdt): country of origin of this item (creative work, food, phrase, product, etc.), significant place, country
6. Decine21 film ID (wd:P7978 as p/wdt): identifier for a film or TV series on the website decine21.com
7. producer, also known as music producer, record producer, produced by, film producer, creative producer (wd:P162 as p/wdt): person(s) who produced the film, musical work, theatrical produc...etc.) [for production company, use P272, video games - use P178]
8. duration, also known as time length, timespan, time span, time interval, runtime (wd:P2047 as p/wdt): length of time of an event or process
9. distributed by, also known as distributor (wd:P750 as p/wdt): distributor of a creative work; distributor for a record label; news agency; film distributor
10. production company, also known as broadcasting company, theatrical troupe, theatre company, theater company, studio (wd:P272 as p/wdt): company that produced this film, audio or performing arts work

No other items found

[Assistant]
The top result for the property related to the release date is `publication date` (P577). Now, I will construct a SPARQL query to retrieve the release date of the film "Red Notice" using the identified entity and property.

Let's execute the SPARQL query:

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

This query will return the release date of the film "Red Notice".

[Tool]
Got 1 row and 1 column:
| ?releaseDate         |
| -------------------- |
| 2021-11-12T00:00:00Z |

[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


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

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] To answer the question "When did Ella Fitzgerald die?", I need to identify the entity for Ella Fitzgerald and the property for the date of death. I will start by searching for Ella Fitzgerald in the knowledge graph.

[Tool]
Falling back to an unconstrained search on the full precomputed search indices:
SPARQL query contains no IRIs to constrain with

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

No other items found

[Assistant] The top result for "Ella Fitzgerald" is the American jazz singer (wd:Q1768). Now I will look for the property that represents the date of death. I will use the list function to find examples of how the property "date of death" is used in the knowledge graph.

[Tool]
Got more than 131,072 rows and 3 columns, showing the first 10 rows below:
| subject                             | property                 | object               |
| ----------------------------------- | ------------------------ | -------------------- |
| Julius Caesar (wd:Q1048)            | date of death (wdt:P570) | -043-03-13T00:00:00Z |
| Mahatma Gandhi (wd:Q1001)           | date of death (wdt:P570) | 1948-01-30T00:00:00Z |
| Charles Darwin (wd:Q1035)           | date of death (wdt:P570) | 1882-04-19T00:00:00Z |
| Carl Linnaeus (wd:Q1043)            | date of death (wdt:P570) | 1778-01-10T00:00:00Z |
| Pythagoras (wd:Q10261)              | date of death (wdt:P570) | -490-01-01T00:00:00Z |
| Jawaharlal Nehru (wd:Q1047)         | date of death (wdt:P570) | 1964-05-27T00:00:00Z |
| John McCain (wd:Q10390)             | date of death (wdt:P570) | 2018-08-25T00:00:00Z |
| Mary Wollstonecraft (wd:Q101638)    | date of death (wdt:P570) | 1797-09-10T00:00:00Z |
| Harriet Beecher Stowe (wd:Q102513)  | date of death (wdt:P570) | 1896-07-01T00:00:00Z |
| Johann Gottlieb Fichte (wd:Q102585) | date of death (wdt:P570) | 1814-01-27T00:00:00Z |
| ...                                 | ...                      | ...                  |

[Assistant] The property for the date of death is `wdt:P570`. Now I will construct a SPARQL query to retrieve the date of death for Ella Fitzgerald (wd:Q1768).

[Tool]
Got 1 row and 1 column:
| ?dateOfDeath         |
| -------------------- |
| 1996-06-15T00:00:00Z |

[Assistant] The date of death for Ella Fitzgerald is June 15, 1996. I will now provide the final SPARQL query and the answer to the user's question.

[Tool] Stopped generation process


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

World War II officially ended on September 2, 1945.

[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] To answer the question about when World War II officially ended, I need to identify the entity for World War II and the property that indicates its end date. Let's start by searching for the entity representing World War II.

[Tool]
Falling back to an unconstrained search on the full precomputed search indices:
SPARQL query contains no IRIs to constrain with

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

No other items found

[Assistant] The top result for "World War II" is `wd:Q362`, which represents the 1939–1945 global conflict. Now, I will look for the property that indicates the end date of World War II.

[Tool]
Top 10 property alternatives:
1. end time, also known as ends, stop time, endtime, fall date, ineffective (wd:P582 as p/wdt): moment when an entity ceases to exist and a statement stops being entirely valid or no longer be true, point in time
2. start time, also known as start date, since, valid, join time, joined (wd:P580 as p/wdt): time an entity begins to exist or a statement starts being valid, point in time
3. point in time, also known as at time, as of, year, when, time of event (wd:P585 as p/wdt): date something took place, existed or a statement was true; for providing time use the "refine date" property (P4241)
4. has effect, also known as has symptom, implies, leads to, led to, outcome (wd:P1542 as p/wdt): effect of this item, followed by
5. follows, also known as prev, successor to, succeeds to, succeeds, split from (wd:P155 as p/wdt): immediately prior item in a series of which the subject is a par... preceding item, e.g. political offices, use "replaces" (P1365)]
6. has cause, also known as implied by, originated due to, initial cause, outcome of, reason (wd:P828 as p/wdt): underlying cause, entity that ultimately resulted in this effect, follows
7. followed by, also known as continued as, then, successor, succeeded by, sequel is (wd:P156 as p/wdt): immediately following item in a series of which the subject is a...een replaced, e.g. political offices, use "replaced by" (P1366)]
8. GND ID, also known as Deutschen Nationalbibliothek ID, Universal Authority File, Integrated Authority File, IndividualisedGnd, GND-IDN (wd:P227 as p/wdtn/wdt): identifier from an international authority file of names, subjects, and organizations
9. location, also known as neighbourhood, whereabouts, vilage, venue, town (wd:P276 as p/wdt): location of the object, structure or event. In the case of an ad...ity use P706. Use P7153 for locations associated with the object
10. history of topic, also known as history of the topic, timeline of subject, timeline of the subject, timeline of the topic, timeline of topic (wd:P2184 as p/wdt): item about the historical development of a subject's topic, exam... To list key events of the topic, use "significant event" (P793), studied in

No other items found

[Assistant] The property `wdt:P582` (end time) seems to be the most relevant for indicating the end date of World War II. Let's construct a SPARQL query to retrieve this information.

[Tool]
Got 1 row and 1 column:
| ?endDate             |
| -------------------- |
| 1945-09-02T00:00:00Z |

[Assistant]
The end date of World War II is September 2, 1945.

Here is the final SPARQL query and answer:

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?material ?label WHERE {
  wd:Q39231 wdt:P186 ?material .
  OPTIONAL {
    ?material rdfs:label ?label FILTER ( LANG( ?label ) = '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]
To answer the question "What is the Fujiyama made of?", we need to identify the entity for Mount Fuji (Fujiyama) and then find the property that describes what it is made of, such as its composition or material.

First, let's search for the entity representing Mount Fuji in Wikidata.

[Tool]
Falling back to an unconstrained search on the full precomputed search indices:
SPARQL query contains no IRIs to constrain with

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. Mount Fuji (wd:Q11455978)
3. Fujisan, also known as Fuji-san, Mount Fuji (wd:Q17212214)
4. 🗻, also known as U+1F5FB, MOUNT FUJI, mountain emoji, Mount Fuji emoji (wd:Q87581497): Unicode character
5. Mount Fuji, also known as Category:IMO 9260079, IMO 9260079 (wd:Q83608290): ship built in 2003
6. Mount Fuji (wd:Q78620185): painting by Yokoi Kinkoku
7. Mount Fuji (wd:Q78754492): painting by Kawanabe Kyōsai
8. Mount Fuji (wd:Q79412063): painting by Kano Tan'yu
9. Mount Fuji, also known as IMO 9509011 (wd:Q105770291): ship built in 2010
10. Mount Fuji (wd:Q49392407): painting by Minol Araki

No other items found

[Assistant]
The top result for "Mount Fuji" is `wd:Q39231`, which is described as a volcano in Yamanashi and Shizuoka Prefectures, Japan. This is likely the correct entity for Mount Fuji (Fujiyama).

Next, we need to find the property that describes what Mount Fuji is made of. We can list the properties of `wd:Q39231` to see if there is a relevant property.

Let's list the properties of `wd:Q39231`.

[Tool]
Got 897 rows and 3 columns, showing the first 10 rows below:
| subject                | property                                                    | object                                                                                |
| ---------------------- | ----------------------------------------------------------- | ------------------------------------------------------------------------------------------------ |
| Mount Fuji (wd:Q39231) | instance of (wdt:P31)                                       | mountain (wd:Q8502)                                                                              |
| Mount Fuji (wd:Q39231) | country (wdt:P17)                                           | Japan (wd:Q17)                                                                                |
| Mount Fuji (wd:Q39231) | located in the administrative territorial entity (wdt:P131) | Shizuoka Prefecture (wd:Q131320)                                                                 |
| Mount Fuji (wd:Q39231) | coordinate location (wdt:P625)                              | POINT(138.727500 35.360555)                                                                      |
| Mount Fuji (wd:Q39231) | Commons category (wdt:P373)                                 | Mount Fuji                                                                                |
| Mount Fuji (wd:Q39231) | image (wdt:P18)                                             | <http://commons.wikimedia.org/wiki/Special:FilePath/Kodaki%20fuji%20frm%20shojinko%20refurb.jpg> |
| Mount Fuji (wd:Q39231) | part of (wdt:P361)                                          | 100 Famous Japanese Mountains (wd:Q1156761)                                                      |
| Mount Fuji (wd:Q39231) | heritage designation (wdt:P1435)                            | Special Place of Scenic Beauty (wd:Q94987823)                                                    |
| Mount Fuji (wd:Q39231) | has part(s) (wdt:P527)                                      | Mount Hōei (wd:Q19900675)                                                                        |
| Mount Fuji (wd:Q39231) | described by source (wdt:P1343)                             | Brockhaus and Efron Encyclopedic Dictionary (wd:Q602358)                                         |
| ...                    | ...                                                         | ...                                                                                |

[Assistant]
From the list of properties, we can see that `wd:Q39231` (Mount Fuji) is an instance of a mountain (`wd:Q8502`). However, we don't see a direct property that describes what it is made of.

We can try searching for properties related to the composition or material of mountains. Let's search for properties that might describe the composition of Mount Fuji.

Let's search for properties related to "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 p/wdt): 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 p/wdt): 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 p/wdt): 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 p/wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
5. described by source, also known as mentioned in, source of item, reviewed in, mentioned in news article, talked about in (wd:P1343 as p/wdt): work where this item is described, attested in
6. topic's main category, also known as article's main category, category, category for this topic, catmain, has category (wd:P910 as p/wdt): main Wikimedia category
7. image, also known as still capture, still, screenshot, screen capture, portrait (wd:P18 as p/wdt): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)
8. area, also known as total area, surface area, surface, size, resolution (wd:P2046 as p/wdt): area occupied by an object

No other items found

[Assistant]
From the search results, the property `wdt:P186` (made from material) seems to be the most relevant for describing what Mount Fuji is made of. Let's check if this property is used for Mount Fuji and what values it has.

Let's execute a SPARQL query to find the materials that Mount Fuji is made of using the `wdt:P186` property.

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

[Assistant]
The SPARQL query returned that Mount Fuji is made of basalt (`wd:Q43338`).

To provide a more complete answer, let's also include the label for `wd:Q43338` (basalt) in the final SPARQL query.

Here is the final SPARQL query and answer:

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

Answer: Mount Fuji is made of basalt.

Now, let's finalize the answer.

[Tool] Stopped generation process