PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
ASK {
  wd:Q1067089 wdt:P1552 wd:Q60733114
}

True

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

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

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

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

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

[User] Is throat singing a tradition?

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

[Tool] No examples found

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

[Tool]
Top 10 entity alternatives:
1. overtone singing, also known as overtone chanting, throat singing, harmonic singing (wd:Q691771): singing technique
2. throat singing, also known as throat song, guttural singing (wd:Q1067089): vocal practices found in different cultures of the world and tha... involve a wide range of techniques for producing voice and song
3. Inuit throat singing, also known as Iirngaaq, Inuit vocal games, Katadjak, katajjaq, Nipaquhiit (wd:Q2956224): form of musical performance uniquely found among the Inuit
4. undertone singing, also known as throat singing (wd:Q109584422): vocal technique
5. Tuvan throat singing, also known as Tuvan overtone singing, xɵɵmej, xöömэi, xöömey, xöömej (wd:Q108757333): music genre
6. Mongolian throat singing, also known as hôômij, khoomei, khöömii, khöömiĭ, Mongolian overtone singing (wd:Q16801007): music genre
7. Throat Singing in Kangirsuk (wd:Q85809428): 2019 film
8. Central Asian throat singing (wd:Q110606359): music genre
9. Overtone focusing in biphonic tuvan throat singing (wd:Q89673823): scientific article published on 17 February 2020
10. 'With singing hearts and throaty roarings...' (wd:Q119399998): painting by Jock McFadyen (b.1950), IWM (Imperial War Museums)

[Assistant] From the search results, it seems that "throat singing" (wd:Q1067089) is the most relevant entity. Let's check the properties of this entity to see if it is described as a tradition.

[Tool]
Got 202 rows and 3 columns, showing the first 10 rows below:
| subject                      | property                              | object                                                                                |
| ---------------------------- | ------------------------------------- | -------------------------------------------------------------------------------------------------------------- |
| throat singing (wd:Q1067089) | instance of (wdt:P31)                 | music genre (wd:Q188451)                                                                                |
| throat singing (wd:Q1067089) | Commons category (wdt:P373)           | Throat singing                                                                                |
| throat singing (wd:Q1067089) | image (wdt:P18)                       | <http://commons.wikimedia.org/wiki/Special:FilePath/Charlotte%20-%20Alexia%20-%20Inuit%20throat%20singing.jpg> |
| throat singing (wd:Q1067089) | subclass of (wdt:P279)                | singing (wd:Q27939)                                                                                |
| throat singing (wd:Q1067089) | different from (wdt:P1889)            | overtone singing (wd:Q691771)                                                                                |
| throat singing (wd:Q1067089) | topic's main category (wdt:P910)      | wd:Q8852930                                                                                |
| throat singing (wd:Q1067089) | has characteristic (wdt:P1552)        | music tradition (wd:Q60733114)                                                                                |
| throat singing (wd:Q1067089) | facet of (wdt:P1269)                  | overtone singing (wd:Q691771)                                                                                |
| throat singing (wd:Q1067089) | permanent duplicated item (wdt:P2959) | wd:Q27148690                                                                                |
| throat singing (wd:Q1067089) | audio (wdt:P51)                       | <http://commons.wikimedia.org/wiki/Special:FilePath/Overtone%20singing.ogg>                                    |
| ...                          | ...                                   | ...                                                                                |

[Assistant] Based on the properties of the "throat singing" entity, it is classified as a music genre and has the characteristic of being a music tradition. This suggests that throat singing is indeed a tradition. To confirm this, we can run an ASK query to check if the "has characteristic" property points to "music tradition".

[Tool] True

[Assistant] The ASK query confirms that throat singing has the characteristic of being a music tradition. Therefore, the answer to the question "Is throat singing a tradition?" is True.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly checks if throat singing (wd:Q1067089) has the characteristic of being a music tradition (wd:Q60733114). The result is True, which means that throat singing is indeed considered a tradition. The query follows all the rules and is appropriate for the question.


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?causeOfDeath ?mannerOfDeath WHERE {
  wd:Q1048 wdt:P509 ?causeOfDeath .
  wd:Q1048 wdt:P1196 ?mannerOfDeath
}

Julius Caesar, the author of the phrase 'Alea iacta est', died from bleeding due to homicide (assassination).

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

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

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

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

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

[User] what did the author of the phrase: "Alea iacta est" die of?

[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 Dracula's creator die?

SPARQL:
SELECT ?date WHERE {
  wd:Q3266236 wdt:P170 ?author .
  ?author wdt:P570 ?date
}

Using entities:
Count Dracula, also known as Dracula (wd:Q3266236): title character of Bram Stoker's 1897 gothic horror novel Dracula, statesperson, necromancer, alchemist, magician, soldier

Using properties:
creator, also known as created by, visual artist, sculptor, sculpted by, realized by (wd:P170 as wdt): maker of this creative work or other object (where no more specific property exists)
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:
Where did Abraham Lincoln die?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q91 wdt:P20 ?uri
}

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

Using properties:
place of death, also known as POD, location of death, killed in, deathplace, died in (wd:P20 as wdt): most specific known (e.g. city instead of country, or hospital i... city) death location of a person, animal or fictional character, location

[Assistant] Based on these examples, we need to identify the author of the phrase "Alea iacta est" and then find out the cause of their death. Let's first search for the entity representing the phrase "Alea iacta est".

[Tool]
Top 10 entity alternatives:
1. Alea iacta est, also known as Alea iacta sunt (wd:Q271723): phrase
2. President's message. Alea iacta est (wd:Q84096493): scientific article published on 01 January 2012
3. The ‘Anthropocene’: alea iacta est (wd:Q129631880): scholarly article published on 22 January 2024
4. Strategic initiatives: Alea iacta est! (wd:Q85732933): scientific article published on 01 September 2010
5. Clinical-grade Computational Pathology: Alea Iacta Est (wd:Q92540890): scientific article published on 11 December 2019
6. Particle Therapy in Australia: iacta alea est! (wd:Q99715552): scientific article published on 25 September 2020
7. Alea Jacta Est (wd:Q4714058): album by WarCry
8. Alea Jacta Est (wd:Q25409653): 2012 video game
9. Alea iacta est: a new approach to cardiac output monitoring? (wd:Q45391246): scientific article published in August 2013
10. Alea Jacta Est Tour (wd:Q4714056)

[Assistant] The top result is the phrase "Alea iacta est" itself. Now let's find the author of this phrase.

[Tool]
Top 10 property alternatives:
1. author, also known as writer, written by, poet, playwright, maker (wd:P50 as wdt/p): main creator(s) of a written work (use on works, not humans); us...hor name string) when Wikidata item is unknown or does not exist, creator
2. published in, also known as song on album, tome of the book, tome of, track of, track on (wd:P1433 as wdt/p): larger work that a given work was published in, like a book, journal or music album, part of
3. described by source, also known as mentioned in, source of item, reviewed in, mentioned in news article, talked about in (wd:P1343 as wdt/p): work where this item is described, attested in
4. inception, also known as initial release date, founded in, founded on, founded on date, founding date (wd:P571 as wdt/p): time when an entity begins to exist; for date of official opening use P1619, start time
5. language of work or name, also known as language of the reference, language of URL, language of website, language of work, named in language (wd:P407 as wdt/p): language associated with this creative work (such as books, show...guage" (P103) and "languages spoken, written or signed" (P1412))
6. instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt/p): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
7. Freebase ID, also known as Freebase, MID, Freebase identifier, FID (wd:P646 as wdt/p): identifier for a page in the Freebase database. Format: "/m/0" f...r IDs starting with "/g/", use Google Knowledge Graph ID (P2671)

No other items found

[Assistant] The property `author` (P50) seems to be the correct one to find the author of the phrase "Alea iacta est". Let's use this property to find the author.

[Tool]
Got 1 row and 1 column:
| author                   |
| ------------------------ |
| Julius Caesar (wd:Q1048) |

[Assistant] The author of the phrase "Alea iacta est" is Julius Caesar. Now, let's find out the cause of his death.

[Tool]
Top 10 property alternatives:
1. cause of death, also known as death cause, murder method, method of murder, die of, die from (wd:P509 as wdt/p): underlying or immediate cause of death.  Underlying cause (e.g. ...adest category, e.g. natural causes, accident, homicide, suicide
2. manner of death, also known as type of death, nature of death, death manner, death type, circumstance of death (wd:P1196 as wdt/p): general circumstances of a person's death; e.g. natural causes, ...ysiological mechanism, e.g. heart attack, trauma, pneumonia etc.
3. place of death, also known as POD, location of death, killed in, deathplace, died in (wd:P20 as wdt/p): most specific known (e.g. city instead of country, or hospital i... city) death location of a person, animal or fictional character, location
4. date of death, also known as DOD, deathdate, died, died on, year of death (wd:P570 as wdt/p): dissolved, abolished or demolished date, date on which the subject died, end time
5. killed by, also known as slaughtered by, slain by, shot dead by, murderer, murdered by (wd:P157 as wdt/p): person or organization who killed the subject, significant person
6. medical condition, also known as disability, suffers from, suffer from, sickness, paralympic disability (wd:P1050 as wdt/p): any state relevant to the health of an organism, including diseases and positive conditions
7. place of burial, also known as interment, interment location, interment place, interred at, location of burial (wd:P119 as wdt/p): location of grave, resting place, place of ash-scattering, etc. ...veral places: e.g., re-burials, parts of body buried separately., location
8. described by source, also known as mentioned in, source of item, reviewed in, mentioned in news article, talked about in (wd:P1343 as wdt/p): work where this item is described, attested in
9. posthumous name, also known as Honor Death (wd:P1786 as wdt/p): name given to a person after death (East Asia)

No other items found

[Assistant] The property `cause of death` (P509) is the one we need to find out how Julius Caesar died. Let's execute a SPARQL query to get this information.

[Tool]
Got 1 row and 1 column:
| causeOfDeath          |
| --------------------- |
| bleeding (wd:Q166019) |

[Assistant]
Julius Caesar died from bleeding. This suggests he was likely assassinated, as bleeding is often a result of violent injury.

To provide a more complete answer, let's also check if there is a specific mention of assassination as the manner of death.

[Tool]
Got 1 row and 1 column:
| mannerOfDeath         |
| --------------------- |
| homicide (wd:Q149086) |

[Assistant]
Julius Caesar's manner of death is listed as homicide, which confirms that he was assassinated.

Now, let's combine this information into a final SPARQL query to provide a clear and concise answer to the user's question.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query successfully retrieves the cause of death (bleeding) and the manner of death (homicide) for Julius Caesar, who is indeed the author of the phrase 'Alea iacta est'.


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 ?work ?workLabel ?movement ?movementLabel WHERE {
  wd:Q5577 wdt:P800 ?work .
  OPTIONAL {
    ?work rdfs:label ?workLabel .
    FILTER ( LANG( ?workLabel ) = "en" )
  }
  wd:Q5577 wdt:P135 ?movement .
  OPTIONAL {
    ?movement rdfs:label ?movementLabel .
    FILTER ( LANG( ?movementLabel ) = "en" )
  }
}

Got 6 rows and 4 columns:
| work                                            | workLabel                                | movement               | movementLabel   |
| ----------------------------------------------- | ---------------------------------------- | ---------------------- | --------------- |
| The Great Masturbator (wd:Q167536)              | The Great Masturbator (en)               | surrealism (wd:Q39427) | surrealism (en) |
| The Persistence of Memory (wd:Q25729)           | The Persistence of Memory (en)           | surrealism (wd:Q39427) | surrealism (en) |
| Christ of Saint John of the Cross (wd:Q25749)   | Christ of Saint John of the Cross (en)   | surrealism (wd:Q39427) | surrealism (en) |
| The Face of War (wd:Q2663568)                   | The Face of War (en)                     | surrealism (wd:Q39427) | surrealism (en) |
| The Sacrament of the Last Supper (wd:Q30521)    | The Sacrament of the Last Supper (en)    | surrealism (wd:Q39427) | surrealism (en) |
| Soft Construction with Boiled Beans (wd:Q63844) | Soft Construction with Boiled Beans (en) | surrealism (wd:Q39427) | surrealism (en) |

The key events related to Salvador Dalí include his association with the Surrealist movement and the creation of his famous works such as 'The Persistence of Memory'.

[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 key event related to Salvador Dalí?

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

[Tool]
Example 1:
Question:
To which artistic movement did the painter of The Three Dancers belong?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q3235867 wdt:P170 ?painter .
  ?painter wdt:P135 ?uri .
}

Using entities:
The Three Dancers (wd:Q3235867): painting by Pablo Picasso

Using properties:
movement, also known as subculture, trend, scientific movement, school, philosophical movement (wd:P135 as wdt): literary, artistic, scientific or philosophical movement or scen...ed with this person or work. For political ideologies use P1142.
creator, also known as created by, visual artist, sculptor, sculpted by, realized by (wd:P170 as wdt): maker of this creative work or other object (where no more specific property exists)

[Assistant] Based on the example, we need to identify the key event related to Salvador Dalí. Let's first search for Salvador Dalí in the knowledge graph.

[Tool]
Top 10 entity alternatives:
1. Salvador Dalí (wd:Q5577): Spanish artist (1904–1989), jewelry designer, graphic artist, film director, screenwriter, scenographer, photographer, illustrator, holographer, film actor, sculptor, painter, writer, actor
2. Salvador Dalí, also known as Salvador Dali (wd:Q3945890): 1966 short film by Andy Warhol
3. Salvador Dalí, also known as Salvador Dali, Salvador Dalì (wd:Q28669534): 2016 song by Marracash and Guè Pequeno
4. Salvador Dalí (wd:Q3945892): 1970 film
5. Salvador Dalí (wd:Q76627906): photograph by Man Ray
6. Salvador Dalí (wd:Q110890669): 1941 exhibition at Museum of Modern Art
7. Salvador Dalí (wd:Q104633152): exhibition in 1970
8. Salvador Dali (wd:Q76456803): print in the National Gallery of Art (NGA 147625)
9. Salvador Dali (wd:Q103927804): painting by Otto Duecker
10. Salvador Dalí Desert, also known as Dali Desert, Dalí Desert, Desierto Dali, Salvador Dali Desert (wd:Q1382079): desert in Bolivia

[Assistant] The top result is the correct entity for Salvador Dalí. Now let's look at the properties associated with Salvador Dalí to find the key event.

[Tool]
Top 10 property alternatives:
1. significant event, also known as significant occurrence, significant incident, significant achievment, sign. event, outcome (wd:P793 as wdt/p): significant or notable events associated with the subject
2. participant in, also known as participated in, present at, takes part, took part, took part in (wd:P1344 as wdt/p): event in which a person or organization was/is a participant; inverse of P710 or P1923, significant event, part of
3. educated at, also known as graduated from, graduate of, place of education, school attended, schooled at (wd:P69 as wdt/p): educational institution attended by subject, affiliation
4. field of work, also known as scientific discipline, scientific area, responsible for, research on, research fields (wd:P101 as wdt/p): specialization of a person or organization; see P106 for the occupation, interested in
5. notable work, also known as artwork, wrote, writer of, works, work (wd:P800 as wdt/p): notable scientific, artistic or literary work, or other work of significance among subject's works
6. work location, also known as work place, working from, working at, worked from, worked at (wd:P937 as wdt/p): location where persons or organisations were actively participating in employment, business or other work, significant place, location
7. described by source, also known as mentioned in, source of item, reviewed in, mentioned in news article, talked about in (wd:P1343 as wdt/p): work where this item is described, attested in
8. date of death, also known as DOD, deathdate, died, died on, year of death (wd:P570 as wdt/p): dissolved, abolished or demolished date, date on which the subject died, end time
9. movement, also known as subculture, trend, scientific movement, school, philosophical movement (wd:P135 as wdt/p): literary, artistic, scientific or philosophical movement or scen...ed with this person or work. For political ideologies use P1142.
10. image, also known as still capture, still, screenshot, screen capture, portrait (wd:P18 as wdt/p): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)

No other items found

[Assistant]
The property `significant event` (P793) seems to be the most relevant for finding the key event related to Salvador Dalí. Let's construct a SPARQL query to retrieve the significant events associated with Salvador Dalí.

```sparql
SELECT ?event WHERE {
  wd:Q5577 wdt:P793 ?event .
}
```

Now, let's execute this query.

[Tool]
Got 1 row and 1 column:
| event       |
| ----------- |
| wd:Q1898510 |

[Assistant]
The query returned one significant event related to Salvador Dalí, which is identified by the IRI `wd:Q1898510`. To provide more context, let's fetch the label for this event.

```sparql
SELECT ?event ?eventLabel WHERE {
  wd:Q5577 wdt:P793 ?event .
  OPTIONAL { ?event rdfs:label ?eventLabel . FILTER(LANG(?eventLabel) = "en") }
}
```

Let's execute this updated query.

[Tool]
Got 1 row and 2 columns:
| event       | eventLabel      |
| ----------- | --------------- |
| wd:Q1898510 | exhumation (en) |

[Assistant]
The key event related to Salvador Dalí is his exhumation.

Here is the final SPARQL query and the answer:

```sparql
SELECT ?event ?eventLabel WHERE {
  wd:Q5577 wdt:P793 ?event .
  OPTIONAL { ?event rdfs:label ?eventLabel . FILTER(LANG(?eventLabel) = "en") }
}
```

Answer: The key event related to Salvador Dalí is his exhumation.

Now, let's finalize the answer.

[Tool] Stopped generation process

[User]
Feedback (status=refine):
While the exhumation of Salvador Dalí is indeed a significant event, it might not be considered the most key or defining moment in his life or career. Consider looking for events that are more closely related to his artistic achievements or personal milestones. For example, his first solo exhibition or joining the Surrealist movement could be more relevant.

[Assistant]
Thank you for the feedback. Let's refine our search to focus on more significant artistic or personal milestones in Salvador Dalí's life. We can look for notable works, exhibitions, or movements he was involved in.

First, let's search for properties related to notable works and exhibitions.

### Step 1: Search for Properties Related to Notable Works and Exhibitions
We'll use the `search_property_of_entity` function to find relevant properties.

[Tool]
Top 10 property alternatives:
1. notable work, also known as artwork, wrote, writer of, works, work (wd:P800 as wdt/p): notable scientific, artistic or literary work, or other work of significance among subject's works
2. has works in the collection, also known as has items in the collection, works in collection, work in collection, museum housing this person's work, items in the collection (wd:P6379 as wdt/p): collection that has works of this person or organisation (use archives at [P485] for archives)
3. significant event, also known as significant occurrence, significant incident, significant achievment, sign. event, outcome (wd:P793 as wdt/p): significant or notable events associated with the subject
4. Commons gallery, also known as gallery (wd:P935 as wdt/p): name of the Wikimedia Commons gallery page(s) related to this item (is suitable to allow multiple links to more gallery pages)
5. catalogue raisonné, also known as reasoned catalogue, artist catalogue, artist catalog, Art Catalog (wd:P9969 as wdt/p): comprehensive, annotated listing of all the known artworks by an artist either in a particular medium or all media., catalog
6. Museum of Modern Art artist ID, also known as MoMA artist ID (wd:P2174 as wdt/p): identifier assigned to an artist by the Museum of Modern Art
7. genre, also known as genre of music, type of music, type of film, music genre, literary genre (wd:P136 as wdt/p): creative work's genre or an artist's field of work (P101). Use main subject (P921) to relate creative works to their topic
8. movement, also known as subculture, trend, scientific movement, school, philosophical movement (wd:P135 as wdt/p): literary, artistic, scientific or philosophical movement or scen...ed with this person or work. For political ideologies use P1142.
9. Berlinische Galerie artist ID (wd:P4580 as wdt/p): artist identifier for the Berlinische Galerie
10. Art Gallery of South Australia creator ID, also known as AGSA creator ID (wd:P6804 as wdt/p): identifier for artists in the Art Gallery of South Australia

No other items found

[Assistant]
The properties `notable work` (P800) and `movement` (P135) seem relevant for finding significant artistic achievements and milestones in Salvador Dalí's career. Let's start with `notable work` to find some of his significant works.

### Step 2: Query for Notable Works
We'll construct a SPARQL query to retrieve notable works by Salvador Dalí.

```sparql
SELECT ?work ?workLabel WHERE {
  wd:Q5577 wdt:P800 ?work .
  OPTIONAL { ?work rdfs:label ?workLabel . FILTER(LANG(?workLabel) = "en") }
}
```

Let's execute this query.

[Tool]
Got 6 rows and 2 columns:
| work                                            | workLabel                                |
| ----------------------------------------------- | ---------------------------------------- |
| The Great Masturbator (wd:Q167536)              | The Great Masturbator (en)               |
| The Persistence of Memory (wd:Q25729)           | The Persistence of Memory (en)           |
| Christ of Saint John of the Cross (wd:Q25749)   | Christ of Saint John of the Cross (en)   |
| The Face of War (wd:Q2663568)                   | The Face of War (en)                     |
| The Sacrament of the Last Supper (wd:Q30521)    | The Sacrament of the Last Supper (en)    |
| Soft Construction with Boiled Beans (wd:Q63844) | Soft Construction with Boiled Beans (en) |

[Assistant]
The notable works by Salvador Dalí include several famous paintings such as "The Persistence of Memory," "The Great Masturbator," and "Christ of Saint John of the Cross." These works are significant milestones in his artistic career.

Next, let's look at the movements he was associated with to find more context about his key events.

### Step 3: Query for Movements
We'll construct a SPARQL query to retrieve the movements Salvador Dalí was associated with.

```sparql
SELECT ?movement ?movementLabel WHERE {
  wd:Q5577 wdt:P135 ?movement .
  OPTIONAL { ?movement rdfs:label ?movementLabel . FILTER(LANG(?movementLabel) = "en") }
}
```

Let's execute this query.

[Tool]
Got 1 row and 2 columns:
| movement               | movementLabel   |
| ---------------------- | --------------- |
| surrealism (wd:Q39427) | surrealism (en) |

[Assistant]
Salvador Dalí was associated with the Surrealist movement. This is a significant milestone in his career, as it defines his artistic style and contributions to modern art.

### Final Answer
Combining the notable works and the movement, we can conclude that the key events related to Salvador Dalí include his association with the Surrealist movement and the creation of his famous works such as "The Persistence of Memory."

Here is the final SPARQL query and the answer:

```sparql
SELECT ?work ?workLabel ?movement ?movementLabel WHERE {
  wd:Q5577 wdt:P800 ?work .
  OPTIONAL { ?work rdfs:label ?workLabel . FILTER(LANG(?workLabel) = "en") }
  wd:Q5577 wdt:P135 ?movement .
  OPTIONAL { ?movement rdfs:label ?movementLabel . FILTER(LANG(?movementLabel) = "en") }
}
```

Answer: The key events related to Salvador Dalí include his association with the Surrealist movement and the creation of his famous works such as "The Persistence of Memory."

Now, let's finalize the answer.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query is well-constructed and returns the relevant information about Salvador Dalí's key events, including his notable works and his association with the Surrealist movement. The query follows all the rules and provides a comprehensive answer to the user's question.


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
ASK WHERE {
  wd:Q3616 wdt:P2046 ?tehranArea .
  wd:Q64 wdt:P2046 ?berlinArea .
  FILTER ( ?tehranArea > ?berlinArea )
}

No, the capital of Iran is not bigger than the capital of Germany.

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

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

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

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

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

[User] Is the capital of Iran bigger than that of Germany?

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

[Tool]
Example 1:
Question:
how much is the population densty rank of germany?

SPARQL:
SELECT ( COUNT( DISTINCT ?country ) as ?cnt ) {
  ?country wdt:P31 wd:Q6256 ; wdt:P1082 ?population ; wdt:P2046 ?area .
  wd:Q183 wdt:P1082 ?germanPopulation .
  wd:Q183 wdt:P2046 ?germanArea .
  BIND( ( ?population / ?area ) AS ?density ) BIND( ( ?germanPopulation / ?germanArea ) AS ?germanDensity ) FILTER ( ?density > ?germanDensity )
}

Using entities:
Germany, also known as BR Deutschland, Bundesrepublik Deutschland, Deutschland, Federal Republic of Germany (wd:Q183): country in Central Europe
country, also known as countries (wd:Q6256): distinct territorial body or political entity

Using properties:
population, also known as inhabitants, human population (wd:P1082 as wdt): number of people inhabiting the place; number of people of subject
area, also known as total area, surface area, surface, size, resolution (wd:P2046 as wdt): area occupied by an object
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)

Example 2:
Question:
What is the smallest city by area in Germany?

SPARQL:
SELECT ?res WHERE {
  ?res wdt:P17 wd:Q183 .
  ?res wdt:P31 / wdt:P279 * wd:Q515 .
  ?res wdt:P1082 ?inhabitants .
}
ORDER BY ?inhabitants
LIMIt 1

Using entities:
Germany, also known as BR Deutschland, Bundesrepublik Deutschland, Deutschland, Federal Republic of Germany (wd:Q183): country in Central Europe
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)

Example 3:
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.

[Assistant] From these examples, we can see that we need to find the capitals of Iran and Germany, and then compare their areas. Let's first find the capitals of Iran and Germany.

[Tool]
Top 10 property alternatives:
1. capital of, also known as national capital of, parish seat of, provincial capital of, seat of, state capital (wd:P1376 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): country, state, department, canton or other administrative division of which the municipality is the governmental seat, located in the administrative territorial entity
2. capital, also known as administrative seat, seat of government, seat, principal place, location of capital (wd:P36 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): seat of government of a country, province, state or other type of administrative territorial entity
3. capital cost, also known as budget, cost, unit cost (wd:P2130 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): amount of money spent on building or producing an object
4. authorised capital, also known as authorized capital (wd:P12651 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): maximum amount of share capital that the company is authorised to issue to shareholders
5. number of matches played/races/starts, also known as gp, starts, races, number of games played, mp (wd:P1350 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): matches or games a player or a team played during an event. Also...matches a player officially appeared in during the whole career.
6. uses capitalization for, also known as capitalises, capitalizes, uses capitalisation for (wd:P6106 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): lexical classes and subclasses of words as well as other uses in the language where the first letter is written as a capital letter
7. market capitalization, also known as market cap, market capitalisation (wd:P2226 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): total value of issued shares by a publicly traded company
8. location, also known as neighbourhood, whereabouts, vilage, venue, town (wd:P276 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): location of the object, structure or event. In the case of an ad...ity use P706. Use P7153 for locations associated with the object
9. located in the administrative territorial entity, also known as is in the settlement of, is in the voivodeship of, is in the shire of, is in the state of, is in the territory of (wd:P131 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): the item is located on the territory of the following administra...if the item falls only partially into the administrative entity., location, part of
10. nominal share capital (wd:P8247 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): total of the nominal values of the issued ordinary and preference shares of the entity

[Assistant] The property 'capital' (P36) seems to be the correct one to find the capitals of countries. Now let's find the capitals of Iran and Germany using this property.

[Tool]
Got 1 row and 2 columns:
| iranCapital       | germanyCapital  |
| ----------------- | --------------- |
| Tehran (wd:Q3616) | Berlin (wd:Q64) |

[Assistant] We have found that the capital of Iran is Tehran (wd:Q3616) and the capital of Germany is Berlin (wd:Q64). Next, we need to find the areas of these cities and compare them. Let's search for the property 'area' to proceed.

[Tool]
Top 10 property alternatives:
1. area, also known as total area, surface area, surface, size, resolution (wd:P2046 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): area occupied by an object
2. field of work, also known as scientific discipline, scientific area, responsible for, research on, research fields (wd:P101 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): specialization of a person or organization; see P106 for the occupation, interested in
3. applies to part, also known as when viewed as, valid for part, section, region, per (wd:P518 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): part, aspect, or form of the item to which the claim applies
4. applies to jurisdiction, also known as belongs to jurisdiction, applies to territorial jurisdiction, applies to region, applies to place, applies to geographic place (wd:P1001 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): the item (institution, law, public office, public register...) o...a territorial jurisdiction: a country, state, municipality, ...), significant place, characteristic of, operating area
5. operating area, also known as province served, serves jurisdiction, serves geographic region, serves geographic area, serves area (wd:P2541 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): geographic area or jurisdiction an organisation or industry operates in, serves, or has responsibility for, location
6. water area (wd:P8887 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): area/surface of a geographical entity that is covered by water, area
7. interested in, also known as area of interest, area of research, field of interest, field of research, field of study (wd:P2650 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): item of special or vested interest to this person or organisation
8. location, also known as neighbourhood, whereabouts, vilage, venue, town (wd:P276 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): location of the object, structure or event. In the case of an ad...ity use P706. Use P7153 for locations associated with the object
9. located in the administrative territorial entity, also known as is in the settlement of, is in the voivodeship of, is in the shire of, is in the state of, is in the territory of (wd:P131 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): the item is located on the territory of the following administra...if the item falls only partially into the administrative entity., location, part of
10. electoral district, also known as riding, seat, ward, electoral area, electorate (wd:P768 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): electoral district this person is representing, or of the office... or "office contested" (P541) or "candidacy in election" (P3602)

[Assistant] The property 'area' (P2046) is the correct one to find the areas of the cities. Now, let's retrieve the areas of Tehran and Berlin and compare them.

[Tool]
Got 1 row and 2 columns:
| tehranArea        | berlinArea           |
| ----------------- | -------------------- |
| 686 (xsd:decimal) | 891.12 (xsd:decimal) |

[Assistant]
We have retrieved the areas of Tehran and Berlin:
- Tehran: 686 square kilometers
- Berlin: 891.12 square kilometers

Now, we need to compare these areas to determine if the capital of Iran (Tehran) is bigger than the capital of Germany (Berlin).

Let's write an ASK query to check if the area of Tehran is greater than the area of Berlin.

[Tool] False

[Assistant]
The ASK query returned False, which means that the area of Tehran (686 square kilometers) is not greater than the area of Berlin (891.12 square kilometers).

Therefore, the capital of Iran is not bigger than the capital of Germany.

Here is the final SPARQL query and answer:

```sparql
ASK WHERE {
  wd:Q3616 wdt:P2046 ?tehranArea .
  wd:Q64 wdt:P2046 ?berlinArea .
  FILTER(?tehranArea > ?berlinArea)
}
```

Answer: No, the capital of Iran is not bigger than the capital of Germany.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query is correctly formulated as an ASK query and accurately compares the areas of Tehran and Berlin. The answer provided is correct based on the execution result.


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
ASK WHERE {
  FILTER NOT EXISTS {
    wd:Q30 wdt:P37 ?language .
  }
}

While there is an official language listed in the data, the United States does not have a federally recognized official language.

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

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

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

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

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

[User] how many official languages does the united states of america have?

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

[Tool]
Example 1:
Question:
Which countries have more than two official languages?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P31 wd:Q6256 .
  ?uri wdt:P37 ?language .
}
GROUP BY ?uri
HAVING ( COUNT( ?language ) > 2 )

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

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

Example 2:
Question:
Which country has the most official languages?

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

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

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

Example 3:
Question:
How many programming languages are there?

SPARQL:
SELECT ( COUNT( DISTINCT ?sub ) AS ?count ) WHERE {
  ?sub wdt:P31 / wdt:P279 * wd:Q9143 .
}

Using entities:
programming language, also known as coding language, computer language, computer programming, computer programming language (wd:Q9143): language for communicating instructions to a machine

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)

[Assistant] Based on the examples, we need to identify the United States of America and the property for official language. Let's start by searching for the entity "United States of America".

[Tool]
Top 10 entity alternatives:
1. United States, also known as the U.S. of A, United States of America, the US of America, United States of North America, U. S. (wd:Q30): country primarily located in North America
2. Istadus Unidus the America, also known as United States of America (wd:Q29045433): Wikimedia permanent duplicate item
3. United States of America (wd:Q100270731): scholarly article
4. United States of America (wd:Q103971355): encyclopedic article; Grove Art Online
5. United States of America (wd:Q105870683): vocal track by Kikki Danielsson; 1980 studio recording
6. United States of America (wd:Q105870692): song written and composed by Donna Fargo, originally recorded by Donna Fargo and released in 1974
7. United States of America (wd:Q106315054): vocal track by Donna Fargo; 1974 studio recording
8. United States of America, also known as Star Trek USA, USA (wd:Q99281400): country of the United States of America as depicted in Star Trek
9. United States of America (wd:L350484-S1)
10. United States (of America) (wd:L399000-S1)

[Assistant] The correct entity for the United States of America is wd:Q30. Now let's search for the property "official language".

[Tool]
Top 10 property alternatives:
1. official language, also known as language spoken, spoken in, speaking, language official (wd:P37 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): language designated as official by this item, language used
2. native label, also known as official title, original label, original language label, original language name, original language title (wd:P1705 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): label for the items in their official language  or their original language, name
3. Ethnologue language status, also known as EGIDS, language status (wd:P3823 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): language status identifier by Ethnologue.com using EGIDS scale
4. language regulatory body, also known as language authority, regulatory body (wd:P1018 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): regulatory body of a language
5. languages spoken, written or signed, also known as language of expression, language, wrote language, writes language, uses language (wd:P1412 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): language(s) that a person or a people speaks, writes or signs, including the native language(s)
6. native language, also known as first language, L1 speaker of, language native, mother tongue (wd:P103 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): language or languages a person has learned from early childhood, languages spoken, written or signed
7. language used, also known as languages used, medium of instruction, used language, uses language, working language (wd:P2936 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): language widely used (spoken or written) in this place or at this event or organisation
8. 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 pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): language associated with this creative work (such as books, show...guage" (P103) and "languages spoken, written or signed" (P1412))
9. programmed in, also known as language, programming language, written in (wd:P277 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): the programming language(s) in which the software is developed
10. writing language, also known as languages of writings, languages written, language written, writing languages, language of works (wd:P6886 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): language in which the writer has written their work, languages spoken, written or signed

[Assistant]
The correct property for "official language" is wd:P37. Now we can construct a SPARQL query to count the number of official languages for the United States of America (wd:Q30).

Let's write the SPARQL query:

```sparql
SELECT (COUNT(?language) AS ?count) WHERE {
  wd:Q30 wdt:P37 ?language .
}
```

This query will count the number of official languages for the United States of America.

Let's execute this query.

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

[Assistant]
The SPARQL query returned a count of 1, indicating that the United States of America has 1 official language according to the data in Wikidata.

Therefore, the answer to the question "How many official languages does the United States of America have?" is 1.

Let's provide the final answer.

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The answer provided is not entirely accurate. While the SPARQL query correctly counts the number of official languages associated with the United States according to the property `P37` (official language), the United States does not have a federally recognized official language at the national level. The result might be misleading because it suggests that there is one official language, which is not the case. The query should be refined to reflect this fact or to clarify that no federal official language exists.

[Assistant]
Thank you for the feedback. The United States does not have a federally recognized official language, which means the initial query might be misleading. Let's refine the query to check if there is any official language at the federal level for the United States.

We can do this by checking if the United States (wd:Q30) has any value for the property `P37` (official language) and then verify if there are any qualifiers or additional information that might indicate the scope of the official language (e.g., at the state level).

Let's start by checking if there are any official languages for the United States and then look for any qualifiers that might specify the scope.

First, let's run a query to see if there are any official languages for the United States and what additional information is available.

```sparql
SELECT ?language ?qualifier ?value WHERE {
  wd:Q30 wdt:P37 ?language .
  OPTIONAL { ?statement pq:P37 ?language ; pq:P580 ?value . }
  OPTIONAL { ?statement pq:P37 ?language ; pq:P582 ?value . }
  OPTIONAL { ?statement pq:P37 ?language ; pq:P131 ?qualifier . }
}
```

This query will list any official languages and any qualifiers that might specify the scope or time period.

Let's execute this query.

[Tool]
Got 1 row and 3 columns:
| language           | qualifier | value                               |
| ------------------ | --------- | ----------------------------------- |
| English (wd:Q1860) |           | 2017-02-20T00:00:00Z (xsd:dateTime) |

[Assistant]
The query results show that the United States (wd:Q30) has English (wd:Q1860) listed as an official language, but there are no qualifiers specifying the scope (e.g., at the state level). However, the presence of a date (2017-02-20T00:00:00Z) suggests that this might be a specific context or a point in time.

Given that the United States does not have a federally recognized official language, the result might be misleading. To provide a more accurate answer, we can use an ASK query to check if there is any official language at the federal level.

Let's write an ASK query to check if the United States has any official language without any qualifiers that might indicate a different scope.

```sparql
ASK WHERE {
  wd:Q30 wdt:P37 ?language .
  FILTER NOT EXISTS { ?statement pq:P37 ?language ; pq:P131 ?qualifier . }
}
```

This query will return `false` if there are no official languages at the federal level, which is the case for the United States.

Let's execute this query.

[Tool] True

[Assistant]
The ASK query returned `true`, which indicates that there is an official language for the United States without any qualifiers specifying a different scope. However, this result is still potentially misleading because it does not reflect the fact that the United States does not have a federally recognized official language.

To provide a more accurate and clear answer, we should explicitly state that the United States does not have a federally recognized official language. We can do this by modifying our SPARQL query to check for the absence of a federally recognized official language.

Let's write a final ASK query to confirm that the United States does not have a federally recognized official language.

```sparql
ASK WHERE {
  wd:Q30 wdt:P37 ?language .
  FILTER NOT EXISTS { ?statement pq:P37 ?language ; pq:P131 wd:Q30 . }
}
```

This query will return `false` if there is no federally recognized official language for the United States.

Let's execute this query.

[Tool] True

[Assistant]
The ASK query returned `true`, which indicates that there is an official language for the United States without any qualifiers specifying a different scope. However, this result is still potentially misleading because it does not reflect the fact that the United States does not have a federally recognized official language.

Given the feedback and the nature of the data, the most accurate answer is that the United States does not have a federally recognized official language.

Let's provide the final answer to reflect this fact.

[Tool] Stopped generation process

[User]
Feedback (status=retry):
The SPARQL query is not correctly formulated to answer the question. The query is checking for the existence of a statement that the United States has an official language, which is not the right approach. Instead, we need to check if there is any official language associated with the United States. The correct query should be an ASK query that checks if there is no official language (P37) for the United States (Q30).

[Assistant]
Thank you for the feedback. I understand that the previous query was not correctly formulated. We need to use an ASK query to check if there is no official language (P37) for the United States (Q30).

Let's write the correct ASK query:

```sparql
ASK WHERE {
  FILTER NOT EXISTS { wd:Q30 wdt:P37 ?language . }
}
```

This query will return `true` if there is no official language associated with the United States, and `false` otherwise.

Let's execute this query.

[Tool] False

[Assistant]
The ASK query returned `false`, which indicates that there is at least one official language associated with the United States according to the data in Wikidata. However, as we know from the context, the United States does not have a federally recognized official language.

To provide a more accurate and clear answer, we should explicitly state that while there is an official language listed in the data, the United States does not have a federally recognized official language.

Let's provide the final answer to reflect this fact.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
ASK {
  wd:Q183 wdt:P2046 ?germanyArea .
  wd:Q36 wdt:P2046 ?polandArea .
  FILTER ( ?germanyArea > ?polandArea )
}

True

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

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

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

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

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

[User] Is Germany bigger than Poland?

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

[Tool]
Example 1:
Question:
how much is the population densty rank of germany?

SPARQL:
SELECT ( COUNT( DISTINCT ?country ) as ?cnt ) {
  ?country wdt:P31 wd:Q6256 ; wdt:P1082 ?population ; wdt:P2046 ?area .
  wd:Q183 wdt:P1082 ?germanPopulation .
  wd:Q183 wdt:P2046 ?germanArea .
  BIND( ( ?population / ?area ) AS ?density ) BIND( ( ?germanPopulation / ?germanArea ) AS ?germanDensity ) FILTER ( ?density > ?germanDensity )
}

Using entities:
Germany, also known as BR Deutschland, Bundesrepublik Deutschland, Deutschland, Federal Republic of Germany (wd:Q183): country in Central Europe
country, also known as countries (wd:Q6256): distinct territorial body or political entity

Using properties:
population, also known as inhabitants, human population (wd:P1082 as wdt): number of people inhabiting the place; number of people of subject
area, also known as total area, surface area, surface, size, resolution (wd:P2046 as wdt): area occupied by an object
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)

Example 2:
Question:
How many people live in Poland?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q36 wdt:P1082 ?uri
}

Using entities:
Poland, also known as Republic of Poland (wd:Q36): country in Central Europe

Using properties:
population, also known as inhabitants, human population (wd:P1082 as wdt): number of people inhabiting the place; number of people of subject

Example 3:
Question:
What is the smallest city by area in Germany?

SPARQL:
SELECT ?res WHERE {
  ?res wdt:P17 wd:Q183 .
  ?res wdt:P31 / wdt:P279 * wd:Q515 .
  ?res wdt:P1082 ?inhabitants .
}
ORDER BY ?inhabitants
LIMIt 1

Using entities:
Germany, also known as BR Deutschland, Bundesrepublik Deutschland, Deutschland, Federal Republic of Germany (wd:Q183): country in Central Europe
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]
From the examples, we can see that the area property (wdt:P2046) is used to compare the sizes of countries. We can use this property to compare the areas of Germany and Poland.
Let's first find the area of Germany.

[Tool]
Got 1 row and 1 column:
| area                 |
| -------------------- |
| 357588 (xsd:decimal) |

[Assistant]
The area of Germany is 357,588 square kilometers.
Now let's find the area of Poland.

[Tool]
Got 1 row and 1 column:
| area                 |
| -------------------- |
| 312683 (xsd:decimal) |

[Assistant]
The area of Poland is 312,683 square kilometers.
Since 357,588 is greater than 312,683, Germany is indeed bigger than Poland.
Let's confirm this with an ASK query.

[Tool] True

[Assistant]
The ASK query confirms that Germany is indeed bigger than Poland.
Therefore, the answer to the question "Is Germany bigger than Poland?" is True.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query is correct and follows all the rules. It returns a boolean value which directly answers the question. The entities and properties used are appropriate and the execution result is accurate.


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ( COUNT( DISTINCT ?occupation ) AS ?count ) WHERE {
  wd:Q7245 wdt:P106 ?occupation .
}

Got 1 row and 1 column:
| count        |
| ------------ |
| 12 (xsd:int) |

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

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

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

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

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

[User] How many occupations did Mark Twain have?

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

[Tool]
Example 1:
Question:
How many companies were founded by the founder of Facebook?

SPARQL:
SELECT ( COUNT( DISTINCT ?uri ) AS ?c ) WHERE {
  ?uri wdt:P112 wd:Q36215 .
  ?uri wdt:P31 / wdt:P279 * wd:Q783794 .
}

Using entities:
Mark Zuckerberg, also known as The Zuck, Zuck, Zuckerberg, Mark Elliott Zuckerberg, The Zucc (wd:Q36215): American Internet entrepreneur, chief executive officer, patron of the arts, computer scientist, philanthropist, entrepreneur, programmer
company, also known as co., firm (wd:Q783794): legal entity representing an association of people, whether natural, legal or a mixture of both, with a specific objective

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

Example 2:
Question:
How many films did Hal Roach produce?

SPARQL:
SELECT ( COUNT( ?uri ) as ?c ) WHERE {
  ?uri wdt:P162 wd:Q72792 .
}

Using entities:
Hal Roach (wd:Q72792): American filmmaker (1892–1992), television producer, film director, film producer, screenwriter, director, actor

Using properties:
producer, also known as music producer, record producer, produced by, film producer, creative producer (wd:P162 as wdt): person(s) who produced the film, musical work, theatrical produc...etc.) [for production company, use P272, video games - use P178]

Example 3:
Question:
Where is the birthplace of Goethe?

SPARQL:
SELECT ?res WHERE {
  wd:Q5879 wdt:P19 ?res .
}

Using entities:
Johann Wolfgang von Goethe, also known as Johann Wolfgang Von Goethe, Goethe (wd:Q5879): German writer, artist, natural scientist and politician (1749–1832), theatre manager, autobiographer, art historian, travel writer, visual artist, statesperson, mineralogist, art theorist, music critic, philosopher, playwright, theologian, art critic, politician, librettist, librarian, physicist, zoologist, diplomat, aphorist, polymath, botanist, composer, novelist, literary, diarist, painter, writer, jurist, lawyer, poet

Using properties:
place of birth, also known as POB, location of birth, location born, born in, birthplace (wd:P19 as wdt): most specific known birth location of a person, animal or fictional character, location

[Assistant] From the examples, we can see that the pattern for counting the number of something associated with a person is to use a SELECT COUNT query with the relevant property. In this case, we need to find the property for occupation and the entity for Mark Twain. Let's start by searching for the entity for Mark Twain.

[Tool]
Top 10 entity alternatives:
1. Mark Twain, also known as Samuel L. Clemens, Samuel Langhorne Clemens, Quintus Curtius Snodgrass, Samuel Clemens (wd:Q7245): American author and humorist (1835–1910), science fiction writer, opinion journalist, children's writer, autobiographer, travel writer, prose writer, journalist, humorist, novelist, aphorist, teacher, writer
2. 2362 Mark Twain, also known as Mark Twain (wd:Q959983): asteroid
3. Mark Twain (wd:Q6770075): crater on Mercury
4. Mark Twain (wd:Q6770076): 2001 documentary film on the life of Mark Twain directed by Ken Burns
5. Mark Twain (wd:Q66716403): 1873 article in Cartoon portraits and biographical sketches of men of the day
6. Mark Twain, also known as Mark Twain, St. Louis (wd:Q6770071): neighborhood of St. Louis, Missouri, United States
7. Rosa 'Mark Twain', also known as Mark Twain (wd:Q83668756): rose cultivar
8. Mark Twain (wd:Q116958637): bronze sculpture by unidentified artist
9. Mark Twain (wd:Q20174185): painting by Charles Noel Flagg
10. Mark Twain (wd:Q79493007): print by Samuel Johnson Woolf (American, 1880-1948) (1920.11)

[Assistant] The first result is the correct entity for Mark Twain, the American author and humorist. Now let's search for the property for occupation.

[Tool]
Top 10 property alternatives:
1. occupation, also known as job, work, vocation, profession, employment (wd:P106 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): occupation of a person; see also "field of work" (Property:P101), "position held" (Property:P39)
2. field of this occupation, also known as practice, works in, profession's field, practices, field of this profession (wd:P425 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): field corresponding to this occupation or profession (use only f...fessions - for people use Property:P101, for companies use P452)
3. members have occupation, also known as profession of members, organization of, organization for, organisation of, organisation for (wd:P3989 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): some or all members of this group share the occupation
4. practiced by, also known as proponent, studied by, practitioners, practioner, occupation of this field (wd:P3095 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): type of agents that study this subject or work in this field
5. position held, also known as function, held position, holds position, job, office held (wd:P39 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): subject currently or formerly holds the object position or public office
6. employer, also known as working at, working for, working place, works at, works for (wd:P108 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): person or organization for which the subject works or worked, affiliation
7. country of citizenship, also known as subject of (country), nation of citizenship, national of, nationality, nation (wd:P27 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): the object is a country that recognizes the subject as its citizen, country
8. occupant, also known as tenant, resident, renter, occupiers, lessee (wd:P466 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): person or organization occupying property, used by
9. field of work, also known as scientific discipline, scientific area, responsible for, research on, research fields (wd:P101 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): specialization of a person or organization; see P106 for the occupation, interested in
10. work location, also known as work place, working from, working at, worked from, worked at (wd:P937 as pr/wdtn/p/pq/pqv/wdt/psn/prv/ps/psv/prn/pqn): location where persons or organisations were actively participating in employment, business or other work, significant place, location

[Assistant]
The first result is the correct property for occupation. Now we can construct our SPARQL query to count the number of distinct occupations that Mark Twain had. We will use the entity `wd:Q7245` for Mark Twain and the property `wdt:P106` for occupation.

Let's run the query:

```sparql
SELECT (COUNT(DISTINCT ?occupation) AS ?count) WHERE {
  wd:Q7245 wdt:P106 ?occupation .
}
```

This query will count the number of distinct occupations that Mark Twain had.

[Tool]
Got 1 row and 1 column:
| count        |
| ------------ |
| 12 (xsd:int) |

[Assistant]
The SPARQL query returned a count of 12 distinct occupations for Mark Twain.

Therefore, the answer to the question "How many occupations did Mark Twain have?" is 12.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query is correct and returns the expected result. It counts the distinct occupations of Mark Twain, which is 12. No further refinements are necessary.