PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?parent WHERE {
  wd:Q913 ?parentOf ?parent .
  VALUES ?parentOf {
    wdt:P22 wdt:P25
  }
}

The parents of Socrates are Sophroniscus and Phaenarete.

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

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

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

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

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

[User] Who were the parents of Socrates ?

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

[Tool]
Example 1:
Question:
Who was influenced by Socrates?

SPARQL:
SELECT DISTINCT * WHERE {
  ?uri wdt:P31 wd:Q5 .
  ?uri wdt:P737 wd:Q913 .
}

Using entities:
human, also known as human being, person, people, nonfictional human, non-fictional human (wd:Q5): any member of Homo sapiens, unique extant species of the genus Homo
Socrates (wd:Q913): 5th-century BCE Greek philosopher, philosopher, ethicist, teacher, writer

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

Example 2:
Question:
Who were the parents of Queen Victoria?

SPARQL:
SELECT ?uri WHERE {
  wd:Q9439 ?childof ?uri .
  VALUES ?childof {
    wdt:P22 wdt:P25
  }
}

Using entities:
Victoria, also known as Victoria, Queen of Great Britain, Victoria of the United Kingdom, Victoria Hanover, Alexandrina Victoria von Hannover, Victoria Alexandrina (wd:Q9439): Queen of the United Kingdom from 1837 to 1901, monarch

Using properties:
father, also known as son of, child of, parent, is son of, is daughter of (wd:P22 as wdt): male parent of the subject. For stepfather, use "stepparent" (P3448), parent
mother, also known as mom, is child of, mommy, mum, mummy (wd:P25 as wdt): female parent of the subject. For stepmother, use "stepparent" (P3448), parent

Example 3:
Question:
Who are the parents of the wife of Juan Carlos I?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q19943 wdt:P26 ?spouse .
  ?uri wdt:P40 ?spouse .
}

Using entities:
Juan Carlos I of Spain, also known as Juan Carlos I, Juan Carlos Alfonso Víctor María de Borbón y Borbón-Dos Sicilias (wd:Q19943): King of Spain from 1975 to 2014, military officer, aristocrat, royalty, monarch

Using properties:
spouse, also known as married partner, married to, marry, partner, spouses (wd:P26 as wdt): the subject has the object as their spouse (husband, wife, partn...etc.). Use "unmarried partner" (P451) for non-married companions, significant person, relative
child, also known as has son, kids, mother of, kid, issue (wd:P40 as wdt): subject has object as child. Do not use for stepchildren—use "relative" (P1038), qualified with "type of kinship" (P1039), significant person, relative

[Assistant] From the examples, we can see that the properties `father` (P22) and `mother` (P25) are used to represent the parents of a person. Let's first identify the entity for Socrates.

[Tool]
Top 10 entity alternatives:
1. Socrates (wd:Q913): 5th-century BCE Greek philosopher, philosopher, ethicist, teacher, writer
2. Sócrates, also known as Socrates, Sócrates Brasileiro Sampaio de Souza Vieira de Oliveira (wd:Q102331): Brazilian association football player (1954-2011), association football player, physician
3. Socrates programme, also known as SOCRATES, Socrates II, Socrates I (wd:Q474650): (1994-2006)
4. Socrates, also known as Socrate (wd:Q3963888): 1971 film directed by Roberto Rossellini
5. SOCRATES (wd:Q7553104): mnemonic acronym used by emergency medical services
6. Sócrates, also known as Socrates (wd:Q65171832): 2018 film
7. Socrates Drank the Conium, also known as Socrates (wd:Q933405): Greek rock band
8. Socrates of Rhodes, also known as Socrates, Socrates Rhodius (wd:Q11950679): Greek historian 1st century BCE, author of a history of the Roman civil war, historian
9. SOCRATES, also known as Space Optical Communications Research Advanced Technology Satellite (wd:Q17054612): Japanese micro-satellite launched in 2014
10. Socrates (wd:Q7553101): play by Voltaire

[Assistant]
The correct entity for Socrates, the 5th-century BCE Greek philosopher, is `wd:Q913`. Now, let's construct a SPARQL query to find his parents using the properties `father` (P22) and `mother` (P25).

Here is the initial SPARQL query:

```sparql
SELECT ?parent WHERE {
  wd:Q913 ?parentOf ?parent .
  VALUES ?parentOf { wdt:P22 wdt:P25 }
}
```

Let's execute this query.

[Tool]
Got 2 rows and 1 column:
| parent                     |
| -------------------------- |
| Sophroniscus (wd:Q2303035) |
| Phaenarete (wd:Q2263186)   |

[Assistant]
The query successfully returned the parents of Socrates. The parents are:

- Sophroniscus (wd:Q2303035)
- Phaenarete (wd:Q2263186)

Now, let's provide the final answer.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
ASK WHERE {
  wd:Q567 wdt:P569 ?merkelBirthYear .
  wd:Q9545 wdt:P569 ?blairBirthYear .
  FILTER ( YEAR( ?merkelBirthYear ) = YEAR( ?blairBirthYear ) )
}

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] Were Angela Merkel and Tony Blair born in the same year?

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

[Tool]
Example 1:
Question:
What is the birth name of Angela Merkel?

SPARQL:
SELECT DISTINCT ?name WHERE {
  wd:Q567 wdt:P1477 ?name .
}

Using entities:
Angela Merkel, also known as Angela Dorothea Merkel, leader of the free world, Angela Kasner (wd:Q567): chancellor of Germany from 2005 to 2021, politician, physicist

Using properties:
birth name, also known as née, name at birth, name before marriage, ne, né (wd:P1477 as wdt): full name of a person at birth, if different from their current, generally used name, name

Example 2:
Question:
What is Angela Merkel’s birth name?

SPARQL:
SELECT ?o1 WHERE {
  wd:Q567 wdt:P1477 ?o1 .
}

Using entities:
Angela Merkel, also known as Angela Dorothea Merkel, leader of the free world, Angela Kasner (wd:Q567): chancellor of Germany from 2005 to 2021, politician, physicist

Using properties:
birth name, also known as née, name at birth, name before marriage, ne, né (wd:P1477 as wdt): full name of a person at birth, if different from their current, generally used name, name

Example 3:
Question:
What is the alma mater of the chancellor of Germany Angela Merkel?

SPARQL:
SELECT ?res WHERE {
  wd:Q567 wdt:P69 ?res .
  ?res wdt:P31 / wdt:P279 * wd:Q3918 .
}

Using entities:
university, also known as uni, universities (wd:Q3918): academic institution for further education
Angela Merkel, also known as Angela Dorothea Merkel, leader of the free world, Angela Kasner (wd:Q567): chancellor of Germany from 2005 to 2021, politician, physicist

Using properties:
subclass of, also known as rdfs:subClassOf, sort of, subcategory of, subgenre of, subtype of (wd:P279 as wdt): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
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)
educated at, also known as graduated from, graduate of, place of education, school attended, schooled at (wd:P69 as wdt): educational institution attended by subject, affiliation

[Assistant] None of these examples directly address the question about the birth years of Angela Merkel and Tony Blair, but they do provide useful information about the entities involved. Let's first identify the entities for Angela Merkel and Tony Blair.

[Tool]
Top 10 entity alternatives:
1. Angela Merkel, also known as Angela Dorothea Merkel, leader of the free world, Angela Kasner (wd:Q567): chancellor of Germany from 2005 to 2021, politician, physicist
2. Angela Merkel (wd:Q94746073)
3. Angela Merkel (wd:Q41495976): Polish Biography of Angela Merkel
4. Angela Merkel (wd:Q41495489): German biography of Angela Merkel
5. Angela Merkel (wd:Q125701744)
6. family of Angela Merkel (wd:Q27973987): family of Angela Merkel, the Chancellor of Germany
7. Lentapedia / Angela Merkel (wd:Q17316816): encyclopedia article
8. Angela Merkel and Vladimir Putin (wd:Q127786484)
9. public image of Angela Merkel (wd:Q106041150)
10. Angela Merkel - The Unexpected (wd:Q28464539): 2016 documentary film by Torsten Körner and Matthias Schmidt

[Assistant] The correct entity for Angela Merkel is `wd:Q567`. Now let's find the entity for Tony Blair.

[Tool]
Top 10 entity alternatives:
1. Tony Blair, also known as Rt. Hon. Tony Blair, Sir Anthony Charles Lynton Blair, Sir Tony Blair, The Rt. Hon. Sir Anthony Charles Lynton Blair PC KC, Anthony Blair (wd:Q9545): Prime Minister of the United Kingdom from 1997 to 2007, autobiographer, politician, diplomat, lawyer
2. Tony Blair (wd:Q7821910): 1999 single by Chumbawamba
3. Tony Blair (wd:Q16429010)
4. Tony Blair (wd:Q28052457): painting by Alastair Adams
5. Tony Blair (wd:Q63892269): musical character in the musical TONY! The Blair Musical; the Prime Minister of the United Kingdom and lead figure of the musical
6. Tony Blair Institute (wd:Q30592102): non-profit organization
7. Tony Blair Associates (wd:Q17513270): organization
8. Tony Blair, MP (wd:Q119583376): painting by Philip Oliver Hale (b.1963), Parliamentary Art Collection
9. premiership of Tony Blair, also known as Blair administration (wd:Q7240404): period of the Government of the United Kingdom
10. The Trial of Tony Blair, also known as Trial of Tony Blair (wd:Q1741389): 2007 film

[Assistant] The correct entity for Tony Blair is `wd:Q9545`. Next, we need to find the property for the birth year. We can search for this property using the term "birth year".

[Tool]
Top 10 property alternatives:
1. date of birth, also known as year of birth, birthdate, DOB, born on, born (wd:P569 as pqn/pqv/psn/psv/prn/prv/p/pr/ps/pq/wdt/wdtn): date on which the subject was born, inception
2. birthday, also known as b-day, bday, birth day (wd:P3150 as pqn/pqv/psn/psv/prn/prv/p/pr/ps/pq/wdt/wdtn): item for day and month on which the subject was born. Used when full "date of birth" (P569) isn't known., date of birth
3. place of birth, also known as POB, location of birth, location born, born in, birthplace (wd:P19 as pqn/pqv/psn/psv/prn/prv/p/pr/ps/pq/wdt/wdtn): most specific known birth location of a person, animal or fictional character, location
4. birth name, also known as née, name at birth, name before marriage, ne, né (wd:P1477 as pqn/pqv/psn/psv/prn/prv/p/pr/ps/pq/wdt/wdtn): full name of a person at birth, if different from their current, generally used name, name
5. commemorates, also known as monument to, memorial to, memorializes, marks, in memory of (wd:P547 as pqn/pqv/psn/psv/prn/prv/p/pr/ps/pq/wdt/wdtn): what the place, monument, memorial, or holiday, commemorates, main subject
6. country of citizenship, also known as subject of (country), nation of citizenship, national of, nationality, nation (wd:P27 as pqn/pqv/psn/psv/prn/prv/p/pr/ps/pq/wdt/wdtn): the object is a country that recognizes the subject as its citizen, country
7. inception, also known as initial release date, founded in, founded on, founded on date, founding date (wd:P571 as pqn/pqv/psn/psv/prn/prv/p/pr/ps/pq/wdt/wdtn): time when an entity begins to exist; for date of official opening use P1619, start time
8. date of death, also known as DOD, deathdate, died, died on, year of death (wd:P570 as pqn/pqv/psn/psv/prn/prv/p/pr/ps/pq/wdt/wdtn): dissolved, abolished or demolished date, date on which the subject died, end time
9. Famous Birthdays ID, also known as famousbirthdays.com ID (wd:P11194 as pqn/pqv/psn/psv/prn/prv/p/pr/ps/pq/wdt/wdtn): identifier for entities listed on the Famous Birthdays website
10. category for people born here, also known as category for place of birth, category of people born here (wd:P1464 as pqn/pqv/psn/psv/prn/prv/p/pr/ps/pq/wdt/wdtn): category item that groups people born in this place, related category

[Assistant]
The property for the birth year is `wd:P569`. Now, let's construct a SPARQL query to check if Angela Merkel and Tony Blair were born in the same year.

We will first retrieve the birth years of both individuals and then compare them.

[Tool]
Got 1 row and 2 columns:
| merkelBirthYear                     | blairBirthYear                      |
| ----------------------------------- | ----------------------------------- |
| 1954-07-17T00:00:00Z (xsd:dateTime) | 1953-05-06T00:00:00Z (xsd:dateTime) |

[Assistant]
The birth years of Angela Merkel and Tony Blair are different. Angela Merkel was born on July 17, 1954, and Tony Blair was born on May 6, 1953.

To confirm this, we can use an ASK query to check if their birth years are the same.

Let's construct the ASK query.

[Tool] False

[Assistant]
The ASK query confirms that Angela Merkel and Tony Blair were not born in the same year.

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

Here is the final SPARQL query and answer:

```sparql
ASK WHERE {
  wd:Q567 wdt:P569 ?merkelBirthYear .
  wd:Q9545 wdt:P569 ?blairBirthYear .
  FILTER (YEAR(?merkelBirthYear) = YEAR(?blairBirthYear))
}
```

Answer: **False**

[Tool] Stopped generation process


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 ?gene ?geneLabel ?protein ?proteinLabel WHERE {
  wd:Q18032040 wdt:P31 wd:Q7187 ; wdt:P688 ?protein .
  BIND( wd:Q18032040 AS ?gene ) OPTIONAL {
    ?gene rdfs:label ?geneLabel FILTER ( LANG( ?geneLabel ) = "en" )
  }
  OPTIONAL {
    ?protein rdfs:label ?proteinLabel FILTER ( LANG( ?proteinLabel ) = "en" )
  }
}

TNFAIP1 (wd:Q18032040) is a gene in Homo sapiens that encodes the protein TNF alpha induced protein 1 (wd:Q21116711).

[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 TNFAIP1 ?

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

[Tool] No examples found

[Assistant] Since no similar examples were found, let's search for the entity TNFAIP1 directly.

[Tool]
Top 10 entity alternatives:
1. TNFAIP1, also known as B61, B12, TNF alpha induced protein 1, hBACURD2, BTBD34 (wd:Q18032040): protein-coding gene in the species Homo sapiens
2. TNF alpha induced protein 1, also known as Protein B12, Tumor necrosis factor, alpha-induced protein 1, endothelial, tumor necrosis factor, alpha-induced protein 1 (endothelial), tumor necrosis factor, alpha induced protein 1, TNFAIP1 (wd:Q21116711): mammalian protein found in Homo sapiens
3. TNF alpha induced protein 1, also known as BTB/POZ domain-containing adapte...iated RhoA degradation protein 2, BTB/POZ domain-containing protein TNFAIP1, Tnfaip1, tumor necrosis factor, alpha induced protein 1, tumor necrosis factor, alpha-induced protein 1 (endothelial) (wd:Q28561340): mammalian protein found in Rattus norvegicus
4. Tnfaip1, also known as Bacurd2, Edp-1, Edp1, Tnfip1, tumor necrosis factor, alpha-induced protein 1 (endothelial) (wd:Q18255043): protein-coding gene in the species Mus musculus
5. tnfaip1, also known as zgc:55302, wu:fi06b02, tumor necrosis factor, alpha-induced protein 1 (endothelial) (wd:Q29747183): protein-coding gene in the species Danio rerio
6. TNFAIP1 (wd:Q24590424): gene of the species Macaca nemestrina
7. Tnfaip1, also known as TNF alpha induced protein 1, Edp1 (wd:Q24361388): protein-coding gene in the species Rattus norvegicus
8. Tumor necrosis factor, alpha-induced protein 1 (endothelial), also known as Tnfaip1, tumor necrosis factor induced protein 1, Tumor necrosis factor, alpha-induced protein 1, endothelial, BTB/POZ domain-containing protein TNFAIP1, BTB/POZ domain-containing adapte...iated RhoA degradation protein 2 (wd:Q21496129): mammalian protein found in Mus musculus
9. Potassium channel tetramerization domain containing 13, also known as BTB/POZ domain-containing adapte...iated RhoA degradation protein 1, BTB/POZ domain-containing protein KCTD13, CTD-2574D22.4, KCTD13, polymerase delta-interacting protein 1 (wd:Q21112778): mammalian protein found in Homo sapiens
10. Estrogen is a novel regulator of Tnfaip1 in mouse hippocampus (wd:Q45741790): scientific article published on 11 April 2014

[Assistant] From the search results, it looks like the first option is the most relevant one for TNFAIP1 in humans. Let's confirm this by listing the triples associated with this entity.

[Tool]
Got 164 rows and 3 columns, showing the first 10 rows below:
| subject                | property                       | object                                     |
| ---------------------- | ------------------------------ | ------------------------------------------ |
| TNFAIP1 (wd:Q18032040) | instance of (wdt:P31)          | gene (wd:Q7187)                            |
| TNFAIP1 (wd:Q18032040) | subclass of (wdt:P279)         | protein-coding gene (wd:Q20747295)         |
| TNFAIP1 (wd:Q18032040) | ortholog (wdt:P684)            | Tnfaip1 (wd:Q18255043)                     |
| TNFAIP1 (wd:Q18032040) | found in taxon (wdt:P703)      | Homo sapiens (wd:Q15978631)                |
| TNFAIP1 (wd:Q18032040) | genomic start (wdt:P644)       | 26662628                                   |
| TNFAIP1 (wd:Q18032040) | genomic end (wdt:P645)         | 26674035                                   |
| TNFAIP1 (wd:Q18032040) | strand orientation (wdt:P2548) | forward strand (wd:Q22809680)              |
| TNFAIP1 (wd:Q18032040) | expressed in (wdt:P5572)       | spleen (wd:Q9371)                          |
| TNFAIP1 (wd:Q18032040) | encodes (wdt:P688)             | TNF alpha induced protein 1 (wd:Q21116711) |
| TNFAIP1 (wd:Q18032040) | chromosome (wdt:P1057)         | human chromosome 17 (wd:Q220677)           |
| ...                    | ...                            | ...                                        |

[Assistant]
Based on the triples, we can see that TNFAIP1 (wd:Q18032040) is a gene in Homo sapiens. It is a protein-coding gene located on human chromosome 17. It encodes the protein TNF alpha induced protein 1 (wd:Q21116711).

Let's now construct a SPARQL query to retrieve this information.

[Tool]
SPARQL execution failed:
Tried to allocate 5.7 GB, but only 2.8 GB were available. Clear the cache or allow more memory for QLever during startup

[Assistant]
It seems the query execution failed due to memory limitations. Let's simplify the query to only retrieve the gene and the encoded protein without the labels, and then we can add the labels later if necessary.

Let's try again:

[Tool]
Got 1 row and 2 columns:
| gene | protein                                    |
| ---- | ------------------------------------------ |
|      | TNF alpha induced protein 1 (wd:Q21116711) |

[Assistant]
The query returned the protein encoded by the TNFAIP1 gene, which is TNF alpha induced protein 1 (wd:Q21116711). However, the gene itself was not returned, likely because it was not explicitly bound in the query. Let's modify the query to ensure the gene is also included.

Let's try again:

[Tool]
Got 1 row and 2 columns:
| gene                   | protein                                    |
| ---------------------- | ------------------------------------------ |
| TNFAIP1 (wd:Q18032040) | TNF alpha induced protein 1 (wd:Q21116711) |

[Assistant]
The query successfully returned the gene TNFAIP1 (wd:Q18032040) and the protein it encodes, TNF alpha induced protein 1 (wd:Q21116711).

To provide a complete answer, let's include the labels for better readability:

```sparql
SELECT ?gene ?geneLabel ?protein ?proteinLabel
WHERE {
  wd:Q18032040 wdt:P31 wd:Q7187 ;
               wdt:P688 ?protein .
  BIND(wd:Q18032040 AS ?gene)
  OPTIONAL { ?gene rdfs:label ?geneLabel }
  OPTIONAL { ?protein rdfs:label ?proteinLabel }
}
```

Let's execute this final query:

[Tool]
Got 32 rows and 4 columns, showing the first 5 and last 5 rows below:
| gene                   | geneLabel           | protein                                    | proteinLabel                     |
| ---------------------- | ------------------- | ------------------------------------------ | -------------------------------- |
| TNFAIP1 (wd:Q18032040) | TNFAIP1 (ar)        | TNF alpha induced protein 1 (wd:Q21116711) | TNF alpha induced protein 1 (en) |
| TNFAIP1 (wd:Q18032040) | TNFAIP1 (ar)        | TNF alpha induced protein 1 (wd:Q21116711) | TNF alpha induced protein 1 (nl) |
| TNFAIP1 (wd:Q18032040) | TNFAIP1 (arz)       | TNF alpha induced protein 1 (wd:Q21116711) | TNF alpha induced protein 1 (en) |
| TNFAIP1 (wd:Q18032040) | TNFAIP1 (arz)       | TNF alpha induced protein 1 (wd:Q21116711) | TNF alpha induced protein 1 (nl) |
| TNFAIP1 (wd:Q18032040) | TNFAIP1 (de)        | TNF alpha induced protein 1 (wd:Q21116711) | TNF alpha induced protein 1 (en) |
| ...                    | ...                 | ...                                        | ...                              |
| TNFAIP1 (wd:Q18032040) | TNFAIP1 (tt)        | TNF alpha induced protein 1 (wd:Q21116711) | TNF alpha induced protein 1 (nl) |
| TNFAIP1 (wd:Q18032040) | TNFAIP1 (uk)        | TNF alpha induced protein 1 (wd:Q21116711) | TNF alpha induced protein 1 (en) |
| TNFAIP1 (wd:Q18032040) | TNFAIP1 (uk)        | TNF alpha induced protein 1 (wd:Q21116711) | TNF alpha induced protein 1 (nl) |
| TNFAIP1 (wd:Q18032040) | ടിഎന്‍എഫ്എഐപി1 (ml) | TNF alpha induced protein 1 (wd:Q21116711) | TNF alpha induced protein 1 (en) |
| TNFAIP1 (wd:Q18032040) | ടിഎന്‍എഫ്എഐപി1 (ml) | TNF alpha induced protein 1 (wd:Q21116711) | TNF alpha induced protein 1 (nl) |

[Assistant]
The query returned multiple rows with different language labels for both the gene and the protein. To provide a clear and concise answer, we can filter the results to show only the English labels.

Let's modify the query to filter for English labels:

```sparql
SELECT ?gene ?geneLabel ?protein ?proteinLabel
WHERE {
  wd:Q18032040 wdt:P31 wd:Q7187 ;
               wdt:P688 ?protein .
  BIND(wd:Q18032040 AS ?gene)
  OPTIONAL { ?gene rdfs:label ?geneLabel FILTER(LANG(?geneLabel) = "en") }
  OPTIONAL { ?protein rdfs:label ?proteinLabel FILTER(LANG(?proteinLabel) = "en") }
}
```

Let's execute this final query:

[Tool]
Got 1 row and 4 columns:
| gene                   | geneLabel    | protein                                    | proteinLabel                     |
| ---------------------- | ------------ | ------------------------------------------ | -------------------------------- |
| TNFAIP1 (wd:Q18032040) | TNFAIP1 (en) | TNF alpha induced protein 1 (wd:Q21116711) | TNF alpha induced protein 1 (en) |

[Assistant]
The query successfully returned the gene TNFAIP1 (wd:Q18032040) and the protein it encodes, TNF alpha induced protein 1 (wd:Q21116711), with their respective English labels.

Here is the final answer:

**TNFAIP1 (wd:Q18032040) is a gene in Homo sapiens that encodes the protein TNF alpha induced protein 1 (wd:Q21116711).**

Let's finalize this with the answer function:

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ( COUNT( ?borderingCountry ) AS ?count ) WHERE {
  wd:Q408 wdt:P47 ?borderingCountry .
}

Got 1 row and 1 column:
| count       |
| ----------- |
| 7 (xsd:int) |

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

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

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

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

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

[User] With how many countries does Australia share a border?

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

[Tool]
Example 1:
Question:
With how many countries Iran has borders?

SPARQL:
SELECT ?uri WHERE {
  wd:Q794 wdt:P47 ?uri .
}

Using entities:
Iran, also known as ir, IRI, IRN, Islamic Rep. Iran, Islamic Republic of Iran (wd:Q794): country in Western Asia

Using properties:
shares border with, also known as shares borders with, next to, bordered by, border, borders clockwise (wd:P47 as wdt): countries or administrative subdivisions, of equal level, that t...rders, either by land or water. A single common point is enough.

Example 2:
Question:
How many people live in the capital of Australia?

SPARQL:
SELECT DISTINCT ?number WHERE {
  wd:Q408 wdt:P36 ?uri .
  ?uri wdt:P1082 ?number .
}

Using entities:
Australia, also known as AU, The Commonwealth of Australia, Stria, Straya, Stralia (wd:Q408): country in Oceania

Using properties:
population, also known as inhabitants, human population (wd:P1082 as wdt): number of people inhabiting the place; number of people of subject
capital, also known as administrative seat, seat of government, seat, principal place, location of capital (wd:P36 as wdt): seat of government of a country, province, state or other type of administrative territorial entity

Example 3:
Question:
What is the largest city in Australia?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P31 / wdt:P279 * wd:Q515 ; wdt:P17 wd:Q408 ; wdt:P1082 ?population .
}
ORDER BY DESC ( ?population )
LIMIT 1

Using entities:
Australia, also known as AU, The Commonwealth of Australia, Stria, Straya, Stralia (wd:Q408): country in Oceania
city, also known as cities (wd:Q515): large human settlement

Using properties:
population, also known as inhabitants, human population (wd:P1082 as wdt): number of people inhabiting the place; number of people of subject
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
subclass of, also known as rdfs:subClassOf, sort of, subcategory of, subgenre of, subtype of (wd:P279 as wdt): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)

[Assistant] Based on the examples, we need to identify the entity for Australia and the property for sharing borders. Let's first search for the entity representing Australia.

[Tool]
Top 10 entity alternatives:
1. Australia, also known as AU, The Commonwealth of Australia, Stria, Straya, Stralia (wd:Q408): country in Oceania
2. Australian continent, also known as New Holland, Sahul, Nova Hollandia, Meganesia, Australinea (wd:Q3960): continent in the Earth's Southern Hemisphere
3. Australia (wd:Q275180): 2008 film by Baz Luhrmann
4. Australia (wd:Q205546): 1989 film by Jean-Jacques Andrien
5. Australia (wd:Q4823546): 1996 single by Manic Street Preachers
6. Australia (wd:Q781244): board game
7. Australia (wd:Q16835533): Racehorse
8. Australia, also known as Australia (KA–5) (wd:Q4823541): yacht that raced in the Americas Cup
9. Australia (wd:Q4823543): 2000 album by Howie Day
10. Australia (wd:Q25907685): albedo feature on the planet Mercury

[Assistant]
The correct entity for Australia is `wd:Q408`. Next, let's confirm the property for sharing borders, which is `wdt:P47` based on the example.

Now, we can construct a simple SPARQL query to count the number of countries that share a border with Australia.

[Tool]
Got 1 row and 1 column:
| count       |
| ----------- |
| 7 (xsd:int) |

[Assistant]
The SPARQL query returned a count of 7 countries that share a border with Australia. However, this seems incorrect because Australia is an island nation and does not share land borders with any other countries. Let's verify the results by listing the specific countries that are supposed to share a border with Australia.

We'll modify the query to return the IRIs of the bordering countries.

[Tool]
Got 7 rows and 1 column:
| borderingCountry           |
| -------------------------- |
| Indonesia (wd:Q252)        |
| New Caledonia (wd:Q33788)  |
| Timor-Leste (wd:Q574)      |
| New Zealand (wd:Q664)      |
| Solomon Islands (wd:Q685)  |
| Vanuatu (wd:Q686)          |
| Papua New Guinea (wd:Q691) |

[Assistant]
The query returned the following countries as bordering Australia:
- Indonesia (wd:Q252)
- New Caledonia (wd:Q33788)
- Timor-Leste (wd:Q574)
- New Zealand (wd:Q664)
- Solomon Islands (wd:Q685)
- Vanuatu (wd:Q686)
- Papua New Guinea (wd:Q691)

However, these countries do not actually share a land border with Australia. They are nearby countries, but they do not share a physical border. This suggests that the property `wdt:P47` might be incorrectly used in this context for Australia.

Given that Australia is an island nation, it does not share any land borders with other countries. Therefore, the correct answer to the question is that Australia shares borders with 0 countries.

Let's confirm this by using an ASK query to check if there are any countries that share a border with Australia.

[Tool] True

[Assistant]
The ASK query returned `True`, which indicates that there are indeed countries listed as sharing a border with Australia according to the property `wdt:P47`. However, this is incorrect based on geographical facts.

Since the data in the knowledge graph is not accurate for this specific case, we need to rely on the known fact that Australia is an island nation and does not share any land borders with other countries.

Therefore, the correct answer to the question "With how many countries does Australia share a border?" is 0.

I will provide the final SPARQL query and answer.

[Tool] Stopped generation process


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

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

[Tool]
Example 1:
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

Example 2:
Question:
When did Muhammad die?

SPARQL:
SELECT ?date WHERE {
  wd:Q9458 wdt:P570 ?date .
}

Using entities:
Muhammad, also known as Mohammed, Muhammad ibn Abdullah, Mohammad, Mahomet, Prophet Muhammad (wd:Q9458): founder and main prophet of Islam (c. 570–632), military leader, politician, consignor, merchant, preacher, prophet, herder

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:
Who was buried in the Great Pyramid of Giza?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P119 wd:Q3411252 .
}

Using entities:
Pyramid G1-a (wd:Q3411252): building in Egypt

Using properties:
place of burial, also known as interment, interment location, interment place, interred at, location of burial (wd:P119 as wdt): location of grave, resting place, place of ash-scattering, etc. ...veral places: e.g., re-burials, parts of body buried separately., location

[Assistant] Based on the examples, we need to find the entity for Rumi and the property for the date of death. Let's start by searching for the entity Rumi.

[Tool]
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)

[Assistant] The first result seems to be the correct entity for Rumi. Now let's confirm the property for the date of death.

[Tool]
Top 10 property alternatives:
1. date of death, also known as DOD, deathdate, died, died on, year of death (wd:P570 as pqn/pqv/psn/psv/prn/prv/p/pr/ps/pq/wdt/wdtn): 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 pqn/pqv/psn/psv/prn/prv/p/pr/ps/pq/wdt/wdtn): 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 pqn/pqv/psn/psv/prn/prv/p/pr/ps/pq/wdt/wdtn): date on which the subject was born, inception
4. date of burial or cremation, also known as burial date, cremation date, date of cremation, funeral date (wd:P4602 as pqn/pqv/psn/psv/prn/prv/p/pr/ps/pq/wdt/wdtn): date on which the person was buried or cremated soon after death, point in time
5. number of deaths, also known as dead, qty fatalities, qty deaths, number of fatalities, fatalities (wd:P1120 as pqn/pqv/psn/psv/prn/prv/p/pr/ps/pq/wdt/wdtn): total (cumulative) number of people who died since start as a direct result of an event or cause
6. birthday, also known as b-day, bday, birth day (wd:P3150 as pqn/pqv/psn/psv/prn/prv/p/pr/ps/pq/wdt/wdtn): item for day and month on which the subject was born. Used when full "date of birth" (P569) isn't known., date of birth
7. commemorates, also known as monument to, memorial to, memorializes, marks, in memory of (wd:P547 as pqn/pqv/psn/psv/prn/prv/p/pr/ps/pq/wdt/wdtn): what the place, monument, memorial, or holiday, commemorates, main subject
8. cause of death, also known as death cause, murder method, method of murder, die of, die from (wd:P509 as pqn/pqv/psn/psv/prn/prv/p/pr/ps/pq/wdt/wdtn): underlying or immediate cause of death.  Underlying cause (e.g. ...adest category, e.g. natural causes, accident, homicide, suicide
9. inception, also known as initial release date, founded in, founded on, founded on date, founding date (wd:P571 as pqn/pqv/psn/psv/prn/prv/p/pr/ps/pq/wdt/wdtn): time when an entity begins to exist; for date of official opening use P1619, start time
10. dissolved, abolished or demolished date, also known as ended, publication end date, final issue, date repealed, final year (wd:P576 as pqn/pqv/psn/psv/prn/prv/p/pr/ps/pq/wdt/wdtn): point in time at which the subject (organisation, building) ceas...g equipment,  "discontinued date" (P2669) for stopping a product, end time

[Assistant] The first result is the correct property for the date of death. Now we can construct a simple SPARQL query to find out when Rumi died.

[Tool]
Got 1 row and 1 column:
| date                                |
| ----------------------------------- |
| 1273-12-17T00:00:00Z (xsd:dateTime) |

[Assistant] The result shows that Rumi died on December 17, 1273. We can now provide the final 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 ?animal ?animalLabel WHERE {
  ?animal wdt:P1344 ?operation .
  ?operation wdt:P241 wd:Q625657 .
  OPTIONAL {
    ?animal rdfs:label ?animalLabel .
    FILTER ( LANG( ?animalLabel ) = "en" )
  }
}
LIMIT 10

From the search results, the properties "conflict" (wd:P607), "participant in" (wd:P1344), and "military branch" (wd:P241) seem relevant to our query. We can use these properties to find animals that participated in a military operation with the Australian Defence Force.

Let's start with the "participant in" property (wd:P1344) and see if we can find any animals that participated in a military operation with the Australian Defence Force.

We'll write a SPARQL query to find animals that participated in a military operation with the Australian Defence Force (wd:Q625657).

```sparql
SELECT ?animal ?animalLabel
WHERE {
  ?animal wdt:P1344 ?operation .
  ?operation wdt:P241 wd:Q625657 .
  OPTIONAL { ?animal rdfs:label ?animalLabel . FILTER(LANG(?animalLabel) = "en") }
}
LIMIT 10
```

This query selects the animals that participated in a military operation where the military branch is the Australian Defence Force. It also optionally retrieves the English label of the animal for better readability.

Let's execute this query.
<tool_call>
{"name": "execute", "arguments": {"kg": "wikidata", "sparql": "SELECT ?animal ?animalLabel WHERE { ?animal wdt:P1344 ?operation . ?operation wdt:P241 wd:Q625657 . OPTIONAL { ?animal rdfs:label ?animalLabel . FILTER(LANG(?animalLabel) = \\"en\\") } } LIMIT 10"}}
</tool_call>

[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]
Since we couldn't find any similar examples, let's break down the question into parts and search for relevant entities and properties. We need to identify the animal, the military operation, and the Australian Defence Force.
First, let's search 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). Let's use this entity and search for animals that might have participated in a military operation with the Australian Defence Force.
We can look for animals that have a relationship with the Australian Defence Force through a property such as "military branch" or "participated in". Let's search for properties related to military participation.

[Tool]
Top 10 property alternatives:
1. conflict, also known as war, theatre (military), theater (military), participated in conflict, military conflict (wd:P607 as pqn/pqv/psn/psv/prn/prv/p/pr/ps/pq/wdt/wdtn): battles, wars or other military engagements in which the person or item participated, participant in
2. military unit, also known as member of military unit, unit (wd:P7779 as pqn/pqv/psn/psv/prn/prv/p/pr/ps/pq/wdt/wdtn): smallest military unit that a person is/was in, member of
3. military branch, also known as unit branch, service branch, formation, branch (wd:P241 as pqn/pqv/psn/psv/prn/prv/p/pr/ps/pq/wdt/wdtn): branch to which this military unit, award, office, or person belongs, e.g. Royal Navy, part of
4. 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 pqn/pqv/psn/psv/prn/prv/p/pr/ps/pq/wdt/wdtn): officially assigned designation for a vehicle in military service
5. participant in, also known as participated in, present at, takes part, took part, took part in (wd:P1344 as pqn/pqv/psn/psv/prn/prv/p/pr/ps/pq/wdt/wdtn): event in which a person or organization was/is a participant; inverse of P710 or P1923, significant event, part of
6. target, also known as attack target, military target, target of attack (wd:P533 as pqn/pqv/psn/psv/prn/prv/p/pr/ps/pq/wdt/wdtn): target of an attack or military operation, object of occurrence
7. military or police rank, also known as military rank, naval rank, police rank, rank (wd:P410 as pqn/pqv/psn/psv/prn/prv/p/pr/ps/pq/wdt/wdtn): military or police rank achieved by a person (should usually hav...ualifier), or military or police rank associated with a position
8. participant, also known as suspect, player, party, participants, has participant (wd:P710 as pqn/pqv/psn/psv/prn/prv/p/pr/ps/pq/wdt/wdtn): person, group of people or organization (object) that actively t...ct has role" (P3831). Use P1923 for participants that are teams., significant person
9. size designation, also known as military unit size designation, unit size (wd:P8030 as pqn/pqv/psn/psv/prn/prv/p/pr/ps/pq/wdt/wdtn): organizational size classification or designation used to indica...of an entity within an organization (property under development)
10. service entry, also known as commissioned, time of service entry, service introduction, launch date, in service (wd:P729 as pqn/pqv/psn/psv/prn/prv/p/pr/ps/pq/wdt/wdtn): date or point in time on which a piece or class of equipment entered operational service, start time

[Assistant]
From the search results, the properties "conflict" (wd:P607), "participant in" (wd:P1344), and "military branch" (wd:P241) seem relevant to our query. We can use these properties to find animals that participated in a military operation with the Australian Defence Force.

Let's start with the "participant in" property (wd:P1344) and see if we can find any animals that participated in a military operation with the Australian Defence Force.

We'll write a SPARQL query to find animals that participated in a military operation with the Australian Defence Force (wd:Q625657).

```sparql
SELECT ?animal ?animalLabel
WHERE {
  ?animal wdt:P1344 ?operation .
  ?operation wdt:P241 wd:Q625657 .
  OPTIONAL { ?animal rdfs:label ?animalLabel . FILTER(LANG(?animalLabel) = "en") }
}
LIMIT 10
```

This query selects the animals that participated in a military operation where the military branch is the Australian Defence Force. It also optionally retrieves the English label of the animal for better readability.

Let's execute this query.
<tool_call>
{"name": "execute", "arguments": {"kg": "wikidata", "sparql": "SELECT ?animal ?animalLabel WHERE { ?animal wdt:P1344 ?operation . ?operation wdt:P241 wd:Q625657 . OPTIONAL { ?animal rdfs:label ?animalLabel . FILTER(LANG(?animalLabel) = \\"en\\") } } LIMIT 10"}}
</tool_call>


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?subject WHERE {
  wd:Q636399 wdt:P921 ?subject .
}

The main subject of 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] Let's start by looking at some similar examples.

[Tool] No examples found

[Assistant] Since no examples were found, let's start by searching for the song "Smoke on the Water" by Deep Purple.

[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] The second option seems to be the correct one, which is the original song "Smoke on the Water" by Deep Purple (wd:Q636399). Now, let's look for the main subject or theme of this song. We can search for properties related to the song's content or lyrics.

[Tool]
Top 10 property alternatives:
1. lyricist, also known as text poet, writer (of song lyrics), words by, text writer, text by (wd:P676 as p/wdt): author of song lyrics, author
2. performer, also known as actor, sung by, singer, recording by, recorded by (wd:P175 as p/wdt): actor, musician, band or other performer associated with this role or musical work, contributor to the creative work or subject
3. form of creative work, also known as work type, work form, type of work, artform, type of reference work (wd:P7937 as p/wdt): structure of a creative work
4. composer, also known as music by, written by, songwriter, musical score by, film score by (wd:P86 as p/wdt): person(s) who wrote the music [for lyricist, use "lyrics by" (P676)], creator
5. MusicBrainz work ID, also known as composition ID (MusicBrainz), MBID work, MBW, MB work ID, MusicBrainz composition ID (wd:P435 as wdtn/p/wdt): identifier for a work per the MusicBrainz open music encyclopedia
6. 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
7. 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
8. main subject, also known as primary topic, primary subject, primary keyword, plot keyword, of (wd:P921 as p/wdt): primary topic of a work (see also P180: depicts), depicts
9. 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 p/wdt): language associated with this creative work (such as books, show...guage" (P103) and "languages spoken, written or signed" (P1412))
10. SecondHandSongs work ID, also known as SecondHandSongs song ID, SHS work ID (wd:P2908 as p/wdt): identifier for a creative work, in the SecondHandSongs database about covers and samplings

No other items found

[Assistant] The property "main subject" (wd:P921) seems to be the most relevant for finding out what the song is about. Let's use this property to find the main subject of "Smoke on the Water" (wd:Q636399).

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

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

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

[Tool] Stopped generation process