PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?work ?workLabel WHERE {
  wd:Q550311 wdt:P800 ?work .
  ?work wdt:P921 wd:Q16003532 .
  OPTIONAL {
    ?work rdfs:label ?workLabel FILTER ( LANG( ?workLabel ) = "en" )
  }
}

Among the other representative works of Yoshihiro Togashi, the author of Hunter x Hunter, "Yu Yu Hakusho" is the one that talks about a middle school student.

among the other representative work of the author of hunter x hunter, which one is talking about middle school student?

[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 with notes:
1. When direct properties are missing, chain related properties (e.g., child→child for grandchildren) or search for indirect connections to answer complex relationship queries.
2. For set/class queries (e.g., all cities, all holidays), use instance/subclass chains (P31/P279) for completeness and relevant filters. Be aware of data incompleteness and schema inconsistencies in Wikidata.
3. If queries return too many irrelevant or too few results, try varying properties (country, location, observed in, etc.) and subclass chains, but be ready to report data/model limitations if results remain incomplete or noisy.
4. When a direct property or class is missing or inconsistently used, try related or broader properties/classes (e.g., use inception for independence date, or musical work/composition for musicals) to increase recall.
5. For calculated properties (e.g., population density), combine relevant direct properties (e.g., population, area) in SPARQL queries, as Wikidata may not store the derived value directly.
6. When entity search returns multiple candidates, use context (description, instance type, location, etc.) to disambiguate and select the most relevant entity for the query.

No general notes for all knowledge graphs available

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/
qlss: <https://qlever.cs.uni-freiburg.de/spatialSearch/
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. Search for the 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. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
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 just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. 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] among the other representative work of the author of hunter x hunter, which one is talking about middle school student?

[Assistant]
Step 1: Identify the author of "Hunter x Hunter."
Step 2: Find other representative works of this author.
Step 3: Among these works, find the one(s) that talk about a middle school student.

I will start by searching for the author of "Hunter x Hunter."

[Tool]
Top 10 entity alternatives:
1. Hunter × Hunter, also known as HXH, Hunter x Hunter, HUNTER × HUNTER (wd:Q696071): instance of manga series, Japanese manga series
2. Hunter × Hunter, also known as HxH, Hunter x Hunter, H×H (wd:Q70209136): 2011 Japanese anime television series, instance of anime television series
3. Hunter × Hunter, also known as Hunter x Hunter, HxH (wd:Q21232458): 1999 Japanese anime television series, instance of anime television series
4. Monster Hunter Generations, also known as Monster Hunter X (wd:Q20856979): 2015 Japanese video game, instance of video game
5. Hunter Xcite (wd:Q60767092)
6. Xavier Hunter (wd:Q100806788): basketball player, instance of human
7. Hunter × Hunter: Phantom Rouge, also known as Hunter X Hunter: Phantom Rouge (wd:Q11398237): instance of anime film, 2013 film by Yūzō Satō
8. City Hunter XYZ (wd:Q97170832): fan film by Filip Wong, instance of fan film
9. Monster Hunter Generations Ultimate, also known as Monster Hunter XX (wd:Q30302563): instance of video game, 2017 video game
10. XX: Beautiful Hunter (wd:Q8042573): 1994 film by Masaru Konuma, instance of film

[Assistant] The entity "Hunter × Hunter" as a manga series is wd:Q696071. Next, I will find the author of this manga series.

[Tool]
Got 1 row and 3 columns:
| subject                      | property         | object                         |
| ---------------------------- | ---------------- | ------------------------------ |
| Hunter × Hunter (wd:Q696071) | author (wdt:P50) | Yoshihiro Togashi (wd:Q550311) |

[Assistant] The author of "Hunter × Hunter" is Yoshihiro Togashi (wd:Q550311). Now I will find other representative works of Yoshihiro Togashi.

[Tool]
Got 3 rows and 3 columns:
| subject                        | property                | object                       |
| ------------------------------ | ----------------------- | ---------------------------- |
| Yoshihiro Togashi (wd:Q550311) | notable work (wdt:P800) | Hunter × Hunter (wd:Q696071) |
| Yoshihiro Togashi (wd:Q550311) | notable work (wdt:P800) | Yu Yu Hakusho (wd:Q18225473) |
| Yoshihiro Togashi (wd:Q550311) | notable work (wdt:P800) | Level E (wd:Q483557)         |

[Assistant] Yoshihiro Togashi's other representative works include "Yu Yu Hakusho" (wd:Q18225473) and "Level E" (wd:Q483557). Now I will check which of these works talk about a middle school student. I will start by checking the description or main subject of "Yu Yu Hakusho."

[Tool]
Got 1 row and 3 columns:
| subject                      | property                | object                               |
| ---------------------------- | ----------------------- | ------------------------------------ |
| Yu Yu Hakusho (wd:Q18225473) | main subject (wdt:P921) | middle school student (wd:Q16003532) |

[Assistant] "Yu Yu Hakusho" has the main subject "middle school student" (wd:Q16003532). Now I will check the main subject of "Level E" to see if it also talks about middle school students.

[Tool] Got no rows and 3 columns

[Assistant]
"Level E" does not have a main subject related to middle school students. Therefore, among the other representative works of Yoshihiro Togashi, "Yu Yu Hakusho" is the one that talks about middle school students.

I will now prepare the final answer with this information.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?characteristic ?characteristicLabel WHERE {
  wd:Q1067089 wdt:P1552 ?characteristic .
  OPTIONAL {
    ?characteristic rdfs:label ?characteristicLabel FILTER ( LANG( ?characteristicLabel ) = "en" )
  }
}

Got 1 row and 2 columns:
| characteristic                 | characteristicLabel  |
| ------------------------------ | -------------------- |
| music tradition (wd:Q60733114) | music tradition (en) |

Throat singing (wd:Q1067089) is characterized as a music tradition (wd:Q60733114), indicating that it is indeed a tradition.

[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 with notes:
1. When direct properties are missing, chain related properties (e.g., child→child for grandchildren) or search for indirect connections to answer complex relationship queries.
2. For set/class queries (e.g., all cities, all holidays), use instance/subclass chains (P31/P279) for completeness and relevant filters. Be aware of data incompleteness and schema inconsistencies in Wikidata.
3. If queries return too many irrelevant or too few results, try varying properties (country, location, observed in, etc.) and subclass chains, but be ready to report data/model limitations if results remain incomplete or noisy.
4. When a direct property or class is missing or inconsistently used, try related or broader properties/classes (e.g., use inception for independence date, or musical work/composition for musicals) to increase recall.
5. For calculated properties (e.g., population density), combine relevant direct properties (e.g., population, area) in SPARQL queries, as Wikidata may not store the derived value directly.
6. When entity search returns multiple candidates, use context (description, instance type, location, etc.) to disambiguate and select the most relevant entity for the query.

No general notes for all knowledge graphs available

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/
qlss: <https://qlever.cs.uni-freiburg.de/spatialSearch/
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. Search for the 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. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
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 just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. 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] To answer whether throat singing is a tradition, I need to check if throat singing is classified or described as a tradition in Wikidata. I will first search for the entity "throat singing" in Wikidata to identify its QID (entity identifier). Then, I will check if it is linked to any property or class that indicates it is a tradition.

[Tool]
Top 10 entity alternatives:
1. overtone singing, also known as throat singing, overtone chanting, harmonic singing (wd:Q691771)
2. throat singing, also known as throat song, guttural singing (wd:Q1067089)
3. Inuit throat singing, also known as Nipaquhiit, Piqqusiraarniq, Pirkusirtuk, Qiarvaaqtuq, Inuit vocal games (wd:Q2956224)
4. undertone singing, also known as throat singing (wd:Q109584422)
5. Tuvan throat singing, also known as xöömey, xɵɵmej, xöömэi, xöömej, xóómej (wd:Q108757333)
6. Mongolian throat singing, also known as khoomei, Mongolian overtone singing, khöömiĭ, khöömii, hôômij (wd:Q16801007)
7. Throat Singing in Kangirsuk (wd:Q85809428): instance of film, 2019 film
8. Central Asian throat singing (wd:Q110606359)
9. Overtone focusing in biphonic tuvan throat singing (wd:Q89673823): scientific article published on 17 February 2020, instance of scholarly article
10. 'With singing hearts and throaty roarings...' (wd:Q119399998): painting by Jock McFadyen (b.1950), IWM (Imperial War Museums), instance of painting

[Assistant] There are multiple entities related to throat singing. The most general one seems to be "throat singing" (wd:Q1067089). I will check if this entity is described as a tradition or linked to any property indicating it is 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] The entity "throat singing" (wd:Q1067089) is an instance of "music genre" and has the characteristic "music tradition" (wd:Q60733114). This indicates that throat singing is considered a music tradition. I will now finalize the answer.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?causeOfDeath ?causeOfDeathLabel WHERE {
  wd:Q1048 wdt:P509 ?causeOfDeath .
  OPTIONAL {
    ?causeOfDeath rdfs:label ?causeOfDeathLabel FILTER ( LANG( ?causeOfDeathLabel ) = "en" )
  }
}

Got 1 row and 2 columns:
| causeOfDeath          | causeOfDeathLabel |
| --------------------- | ----------------- |
| bleeding (wd:Q166019) | bleeding (en)     |

The author of the phrase "Alea iacta est" is Julius Caesar. According to Wikidata, Julius Caesar died of bleeding.

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

[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 with notes:
1. When direct properties are missing, chain related properties (e.g., child→child for grandchildren) or search for indirect connections to answer complex relationship queries.
2. For set/class queries (e.g., all cities, all holidays), use instance/subclass chains (P31/P279) for completeness and relevant filters. Be aware of data incompleteness and schema inconsistencies in Wikidata.
3. If queries return too many irrelevant or too few results, try varying properties (country, location, observed in, etc.) and subclass chains, but be ready to report data/model limitations if results remain incomplete or noisy.
4. When a direct property or class is missing or inconsistently used, try related or broader properties/classes (e.g., use inception for independence date, or musical work/composition for musicals) to increase recall.
5. For calculated properties (e.g., population density), combine relevant direct properties (e.g., population, area) in SPARQL queries, as Wikidata may not store the derived value directly.
6. When entity search returns multiple candidates, use context (description, instance type, location, etc.) to disambiguate and select the most relevant entity for the query.

No general notes for all knowledge graphs available

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/
qlss: <https://qlever.cs.uni-freiburg.de/spatialSearch/
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. Search for the 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. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
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 just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. 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]
Step 1: Identify the author of the phrase "Alea iacta est." This phrase is historically attributed to Julius Caesar.

Step 2: Verify the entity for Julius Caesar in Wikidata and find the property related to cause of death.

Step 3: Query the cause of death for Julius Caesar in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Julius Caesar, also known as Divine Julius (wd:Q1048): Roman general and dictator (100 – 44 BC), ancient Roman military personnel, Ancient Roman politician, Ancient Roman historian, Ancient Roman priest, instance of human, military leader, politician, historian, memoirist, writer, orator, ruler, poet
2. Julius Caesar, also known as The Tragedy of Julius Caesar, Tragedy of Julius Caesar (wd:Q215750): play by William Shakespeare, instance of dramatic work
3. Julius Caesar (wd:Q734950): 1953 film by Joseph L. Mankiewicz, instance of film
4. Julius Caesar (wd:Q631476): instance of impact crater, lunar impact crater
5. Julius Caesar, also known as Cajus Julius Caesar (wd:Q3649980): 1914 film directed by Enrico Guazzoni, instance of film
6. Julius Caesar (wd:Q315616): 2002 TV miniseries directed by Uli Edel, instance of television film
7. Julius Caesar (wd:Q1612412): 1970 film by Stuart Burge, instance of film
8. Julius Caesar (wd:Q3534298): English cricketer (1830-1878), instance of human, cricketer
9. Julius Caesar, also known as Sir Julius Cæser, Sir Julius Caesar, Cesare Adelmare, Julius Caesar Aldemare (wd:Q1712531): instance of human, English judge, politician, jurist, judge
10. Julius Caesar, also known as Giulio Cesare (wd:Q3769550): 1909 film by Giovanni Pastrone, instance of short film

[Assistant] The author of the phrase "Alea iacta est" is Julius Caesar (wd:Q1048). Next, I will find the cause of death for Julius Caesar in Wikidata.

[Tool]
Got 1 row and 2 columns:
| causeOfDeath          | causeOfDeathLabel |
| --------------------- | ----------------- |
| bleeding (wd:Q166019) | bleeding (en)     |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?award ?awardLabel WHERE {
  wd:Q5577 wdt:P166 ?award .
  OPTIONAL {
    ?award rdfs:label ?awardLabel FILTER ( LANG( ?awardLabel ) = "en" )
  }
}

Got 5 rows and 2 columns:
| award                                                            | awardLabel                                             |
| ---------------------------------------------------------------- | ------------------------------------------------------ |
| Grand Cross of the Order of Isabella the Catholic (wd:Q17365974) | Grand Cross of the Order of Isabella the Catholic (en) |
| Grand Cross of the Order of Charles III (wd:Q20866649)           | Grand Cross of the Order of Charles III (en)           |
| Gold Medal of the Generalitat of Catalonia (wd:Q211672)          | Gold Medal of the Generalitat of Catalonia (en)        |
| Gold Medal of Merit in the Fine Arts (wd:Q3753203)               | Gold Medal of Merit in the Fine Arts (en)              |
| Gold Medal for Tourism Merit (wd:Q42128907)                      | Gold Medal for Tourism Merit (en)                      |

Key events related to Salvador Dalí include receiving several notable awards such as the Grand Cross of the Order of Isabella the Catholic, the Grand Cross of the Order of Charles III, the Gold Medal of the Generalitat of Catalonia, the Gold Medal of Merit in the Fine Arts, and the Gold Medal for Tourism Merit.

What was the key event related to Salvador Dalí?

[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 with notes:
1. When direct properties are missing, chain related properties (e.g., child→child for grandchildren) or search for indirect connections to answer complex relationship queries.
2. For set/class queries (e.g., all cities, all holidays), use instance/subclass chains (P31/P279) for completeness and relevant filters. Be aware of data incompleteness and schema inconsistencies in Wikidata.
3. If queries return too many irrelevant or too few results, try varying properties (country, location, observed in, etc.) and subclass chains, but be ready to report data/model limitations if results remain incomplete or noisy.
4. When a direct property or class is missing or inconsistently used, try related or broader properties/classes (e.g., use inception for independence date, or musical work/composition for musicals) to increase recall.
5. For calculated properties (e.g., population density), combine relevant direct properties (e.g., population, area) in SPARQL queries, as Wikidata may not store the derived value directly.
6. When entity search returns multiple candidates, use context (description, instance type, location, etc.) to disambiguate and select the most relevant entity for the query.

No general notes for all knowledge graphs available

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/
qlss: <https://qlever.cs.uni-freiburg.de/spatialSearch/
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. Search for the 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. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
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 just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. 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]
To answer the question about the key event related to Salvador Dalí, I need to identify what kind of key events are typically associated with a person like Salvador Dalí. These could be major life events, artistic achievements, exhibitions, awards, or other significant historical moments.

Step 1: Identify the entity Salvador Dalí in Wikidata.
Step 2: Explore properties related to key events or significant moments in his life or career.

I will start by searching for the entity Salvador Dalí in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Salvador Dalí (wd:Q5577): Spanish artist (1904–1989), instance of human, jewelry designer, graphic artist, film director, scenographer, screenwriter, 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, instance of short film
3. Salvador Dalí, also known as Salvador Dali, Salvador Dalì (wd:Q28669534): 2016 song by Marracash and Guè Pequeno, instance of musical work/composition
4. Salvador Dalí (wd:Q3945892): instance of film, 1970 film
5. Salvador Dalí (wd:Q76627906): instance of photograph, photograph by Man Ray
6. Salvador Dalí (wd:Q110890669): 1941 exhibition at Museum of Modern Art, instance of art exhibition, instance of event
7. Salvador Dalí (wd:Q104633152): instance of temporary exhibition, exhibition in 1970
8. Salvador Dali (wd:Q76456803): print in the National Gallery of Art (NGA 147625), instance of print
9. Salvador Dali (wd:Q103927804): painting by Otto Duecker, instance of painting
10. Salvador Dalí Desert, also known as Salvador Dali Desert, Desierto Dali, Dalí Desert, Dali Desert (wd:Q1382079): instance of desert, desert in Bolivia

[Assistant]
The entity for Salvador Dalí as a Spanish artist is wd:Q5577. Now, I will look for key events related to him. Key events could be found under properties like "notable work," "significant event," "award received," "exhibition," or "major life event."

I will search for properties related to significant events or key moments for Salvador Dalí (wd:Q5577).

[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. 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)
3. 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, subproperty of significant event, subproperty of part of
4. image, also known as img, infobox image, photo, photograph, picture (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)
5. educated at, also known as graduate of, place of education, school attended, schooled at, college attended (wd:P69 as wdt/p): educational institution attended by subject, subproperty of affiliation
6. field of work, also known as scientific discipline, purpose, scientific area, responsible for, research on (wd:P101 as wdt/p): specialization of a person or organization; see P106 for the occupation, subproperty of interested in
7. 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., subproperty of catalog
8. official website, also known as site (official), site official, URL, URL (official), URL official (wd:P856 as wdt/p): URL of the official page of an item (current or former). Usage: ...but see the "Hijacked or dead websites" section of the Talk page, subproperty of URL
9. work location, also known as worked from, work place, working from, working at, worked at (wd:P937 as wdt/p): location where persons or organisations were actively participating in employment, business or other work, subproperty of significant place, subproperty of location
10. described by source, also known as mentioned in, mentioned in news article, reviewed in, source of item, subject in (wd:P1343 as wdt/p): work where this item is described, subproperty of attested in

No other items found

[Assistant] The property "significant event" (wdt:P793) is the most relevant for key events related to Salvador Dalí. I will now query for the significant events associated with Salvador Dalí (wd:Q5577).

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

[Assistant]
The only significant event listed for Salvador Dalí is "exhumation." This might be considered a key event related to him. However, this seems to be a posthumous event rather than a key event during his lifetime or career.

I will now check for other notable events or achievements related to Salvador Dalí by looking at other properties such as awards, exhibitions, or major works.

[Tool]
Top 10 property alternatives:
1. award received, also known as winner of, win, recognition title, received award, award won (wd:P166 as wdt/p): award or recognition received by a person, organization or creative work
2. 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
3. country of citizenship, also known as subject of (country), nation of citizenship, national of, nationality, nation (wd:P27 as wdt/p): the object is a country that recognizes the subject as its citizen, subproperty of country
4. 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, subproperty of significant event, subproperty of part of
5. member of, also known as of organization, membership, member of musical group, in musical group, band member of (wd:P463 as wdt/p): organization, club or musical group to which the subject belongs...ical position, such as a member of parliament (use P39 for that), subproperty of part of
6. image, also known as img, infobox image, photo, photograph, picture (wd:P18 as wdt/p): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)
7. described by source, also known as mentioned in, mentioned in news article, reviewed in, source of item, subject in (wd:P1343 as wdt/p): work where this item is described, subproperty of attested in
8. given name, also known as proper name, first name, personal name, name, middle name (wd:P735 as wdt/p): first name or another given name of this person; values used with the property should not link disambiguations nor family names, subproperty of name
9. instance of, also known as example of, has type, is a, is an, is of type (wd:P31 as wdt/p): type to which this subject corresponds/belongs. Different from P...tains, and the volcano is a type (instance) of volcanic landform
10. place of birth, also known as location born, POB, location of birth, born in, born at (wd:P19 as wdt/p): most specific known birth location of a person, animal or fictional character, subproperty of location

No other items found

[Assistant] The property "award received" (wdt:P166) is relevant for notable achievements. I will query for awards received by Salvador Dalí (wd:Q5577) to see if there are any key events related to awards or recognitions.

[Tool]
Got 5 rows and 2 columns:
| award                                                            | awardLabel                                             |
| ---------------------------------------------------------------- | ------------------------------------------------------ |
| Grand Cross of the Order of Isabella the Catholic (wd:Q17365974) | Grand Cross of the Order of Isabella the Catholic (en) |
| Grand Cross of the Order of Charles III (wd:Q20866649)           | Grand Cross of the Order of Charles III (en)           |
| Gold Medal of the Generalitat of Catalonia (wd:Q211672)          | Gold Medal of the Generalitat of Catalonia (en)        |
| Gold Medal of Merit in the Fine Arts (wd:Q3753203)               | Gold Medal of Merit in the Fine Arts (en)              |
| Gold Medal for Tourism Merit (wd:Q42128907)                      | Gold Medal for Tourism Merit (en)                      |

[Assistant]
Salvador Dalí received several notable awards, including the Grand Cross of the Order of Isabella the Catholic, the Grand Cross of the Order of Charles III, the Gold Medal of the Generalitat of Catalonia, the Gold Medal of Merit in the Fine Arts, and the Gold Medal for Tourism Merit. These awards can be considered key events related to him.

I will now finalize the answer mentioning these key awards as key events related to Salvador Dalí.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?city ?cityLabel ?population WHERE {
  VALUES ?city {
    wd:Q3616 wd:Q64
  }
  ?city wdt:P1082 ?population .
  OPTIONAL {
    ?city rdfs:label ?cityLabel FILTER ( LANG( ?cityLabel ) = "en" )
  }
}

The capital of Iran, Tehran, has a population of about 8,693,706, while the capital of Germany, Berlin, has a population of about 3,782,202. Therefore, the capital of Iran is bigger than that of Germany in terms of population.

Is the capital of Iran bigger than that 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 with notes:
1. When direct properties are missing, chain related properties (e.g., child→child for grandchildren) or search for indirect connections to answer complex relationship queries.
2. For set/class queries (e.g., all cities, all holidays), use instance/subclass chains (P31/P279) for completeness and relevant filters. Be aware of data incompleteness and schema inconsistencies in Wikidata.
3. If queries return too many irrelevant or too few results, try varying properties (country, location, observed in, etc.) and subclass chains, but be ready to report data/model limitations if results remain incomplete or noisy.
4. When a direct property or class is missing or inconsistently used, try related or broader properties/classes (e.g., use inception for independence date, or musical work/composition for musicals) to increase recall.
5. For calculated properties (e.g., population density), combine relevant direct properties (e.g., population, area) in SPARQL queries, as Wikidata may not store the derived value directly.
6. When entity search returns multiple candidates, use context (description, instance type, location, etc.) to disambiguate and select the most relevant entity for the query.

No general notes for all knowledge graphs available

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/
qlss: <https://qlever.cs.uni-freiburg.de/spatialSearch/
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. Search for the 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. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
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 just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. 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]
To answer this question, I need to:

1. Identify the capitals of Iran and Germany.
2. Retrieve the population or area (as a measure of size) of these capitals.
3. Compare the sizes to determine which capital is bigger.

I will start by finding the capitals of Iran and Germany.

[Tool]
Got 1 row and 4 columns:
| capitalIran       | capitalIranLabel | capitalGermany  | capitalGermanyLabel |
| ----------------- | ---------------- | --------------- | ------------------- |
| Tehran (wd:Q3616) | Tehran (en)      | Berlin (wd:Q64) | Berlin (en)         |

[Assistant] The capital of Iran is Tehran and the capital of Germany is Berlin. Next, I will retrieve the population of Tehran and Berlin to compare their sizes.

[Tool]
Got 2 rows and 3 columns:
| city              | cityLabel   | population            |
| ----------------- | ----------- | --------------------- |
| Tehran (wd:Q3616) | Tehran (en) | 8693706 (xsd:decimal) |
| Berlin (wd:Q64)   | Berlin (en) | 3782202 (xsd:decimal) |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ( COUNT( ?language ) AS ?count ) WHERE {
  wd:Q30 wdt:P37 ?language .
}

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

The United States of America has 1 official language according to Wikidata.

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

[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 with notes:
1. When direct properties are missing, chain related properties (e.g., child→child for grandchildren) or search for indirect connections to answer complex relationship queries.
2. For set/class queries (e.g., all cities, all holidays), use instance/subclass chains (P31/P279) for completeness and relevant filters. Be aware of data incompleteness and schema inconsistencies in Wikidata.
3. If queries return too many irrelevant or too few results, try varying properties (country, location, observed in, etc.) and subclass chains, but be ready to report data/model limitations if results remain incomplete or noisy.
4. When a direct property or class is missing or inconsistently used, try related or broader properties/classes (e.g., use inception for independence date, or musical work/composition for musicals) to increase recall.
5. For calculated properties (e.g., population density), combine relevant direct properties (e.g., population, area) in SPARQL queries, as Wikidata may not store the derived value directly.
6. When entity search returns multiple candidates, use context (description, instance type, location, etc.) to disambiguate and select the most relevant entity for the query.

No general notes for all knowledge graphs available

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/
qlss: <https://qlever.cs.uni-freiburg.de/spatialSearch/
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. Search for the 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. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
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 just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. 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] To answer the question about how many official languages the United States of America has, I will first identify the entity for the United States of America in Wikidata. Then, I will look for properties related to official languages for this entity. Finally, I will count the number of official languages associated with the United States of America. I will start by searching for the entity "United States of America" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. United States, also known as U.S., United States of North America, U. S., US, the US of America (wd:Q30): country primarily located in North America, instance of constitutional republic, instance of democratic republic, instance of federal republic, instance of sovereign state, instance of superpower, instance of country
2. Istadus Unidus the America, also known as United States of America (wd:Q29045433): instance of Wikimedia permanent duplicate item, Wikimedia permanent duplicate item
3. United States of America (wd:Q100270731): instance of scholarly article, scholarly article
4. United States of America (wd:Q103971355): encyclopedic article; Grove Art Online, instance of encyclopedia article
5. United States of America (wd:Q105870683): vocal track by Kikki Danielsson; 1980 studio recording, instance of music track with vocals
6. United States of America (wd:Q105870692): song written and composed by Donna Fargo, originally recorded by Donna Fargo and released in 1974, instance of musical work/composition
7. United States of America (wd:Q106315054): vocal track by Donna Fargo; 1974 studio recording, instance of music track with vocals
8. United States of America, also known as USA, Star Trek USA (wd:Q99281400): country of the United States of America as depicted in Star Trek, instance of real country in a fiction work, instance of Star Trek location
9. United States of America (wd:L484283-S1)
10. United States of America (wd:L1318310-S1)

[Assistant] The entity for the United States of America in Wikidata is wd:Q30. Next, I will look for properties related to official languages for this entity. I will search for properties of the United States of America (wd:Q30) that might indicate official languages.

[Tool]
Top 10 property alternatives:
1. official language, also known as language official, language spoken, speaking, spoken in (wd:P37 as wdt/p): language designated as official by this item, subproperty of language used
2. native label, also known as official name, original title, official title, original label, original language label (wd:P1705 as wdt/p): label for the items in their official language  or their original language, subproperty of name
3. language used, also known as working languages, working language, used language, uses language, medium of instruction (wd:P2936 as wdt/p): language widely used (spoken or written) in this place or at this event or organisation
4. official name, also known as long name, long title, name, official title, registered name (wd:P1448 as wdt/p): official name of the subject in its official language(s), subproperty of name
5. basic form of government, also known as form of government, government, type of government (wd:P122 as wdt/p): subject's government
6. official symbol, also known as emblem, floral emblem, official emblem, official tartan, state tartan (wd:P2238 as wdt/p): official symbol of an organisation
7. capital, also known as principal place, seat of government, seat, location of capital, has capital (wd:P36 as wdt/p): seat of government of a country, province, state or other type of administrative territorial entity
8. ethnic group, also known as tribal affiliation, race, people, ethnicity, culture (wd:P172 as wdt/p): subject's ethnicity (consensus is that a VERY high standard of p...agreed on by scholars, or 3) is fictional and portrayed as such)
9. main regulatory text, also known as authorizing legislation, statutory authorization, statutory authority, regulated by, legally established by (wd:P92 as wdt/p): text setting the main rules by which the subject is regulated
10. official website, also known as site (official), site official, URL, URL (official), URL official (wd:P856 as wdt/p): URL of the official page of an item (current or former). Usage: ...but see the "Hijacked or dead websites" section of the Talk page, subproperty of URL

No other items found

[Assistant] The property "official language" (wdt:P37) is the relevant property for official languages of the United States of America (wd:Q30). I will now query how many official languages are listed for the United States of America using this property.

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

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?country ?countryLabel ?area WHERE {
  VALUES ?country {
    wd:Q183 wd:Q36
  }
  ?country wdt:P2046 ?area .
  OPTIONAL {
    ?country rdfs:label ?countryLabel FILTER ( LANG( ?countryLabel ) = "en" )
  }
}

Germany has an area of 357,588 square kilometers, and Poland has an area of 312,683 square kilometers. Therefore, Germany is bigger than Poland.

[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 with notes:
1. When direct properties are missing, chain related properties (e.g., child→child for grandchildren) or search for indirect connections to answer complex relationship queries.
2. For set/class queries (e.g., all cities, all holidays), use instance/subclass chains (P31/P279) for completeness and relevant filters. Be aware of data incompleteness and schema inconsistencies in Wikidata.
3. If queries return too many irrelevant or too few results, try varying properties (country, location, observed in, etc.) and subclass chains, but be ready to report data/model limitations if results remain incomplete or noisy.
4. When a direct property or class is missing or inconsistently used, try related or broader properties/classes (e.g., use inception for independence date, or musical work/composition for musicals) to increase recall.
5. For calculated properties (e.g., population density), combine relevant direct properties (e.g., population, area) in SPARQL queries, as Wikidata may not store the derived value directly.
6. When entity search returns multiple candidates, use context (description, instance type, location, etc.) to disambiguate and select the most relevant entity for the query.

No general notes for all knowledge graphs available

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/
qlss: <https://qlever.cs.uni-freiburg.de/spatialSearch/
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. Search for the 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. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
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 just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. 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] To answer whether Germany is bigger than Poland, we need to compare the area of Germany and Poland. I will first find the entities for Germany and Poland in Wikidata and then retrieve their respective areas. After that, I will compare the areas to determine which country is bigger.

[Tool]
Top 10 entity alternatives:
1. Germany, also known as Federal Republic of Germany, Deutschland, Bundesrepublik Deutschland, BR Deutschland (wd:Q183): instance of sovereign state, country in Central Europe, instance of country
2. Weimar Republic, also known as Germany, Weimar Germany, Republic of Germany, German Republic, German Reich (wd:Q41304): Germany in the years 1918/1919–1933, instance of historical country, instance of historical period
3. German Reich, also known as Imperial Germany, Germany, German Realm, German Empire, Deutsches Reich (wd:Q1206012): official name for the German nation state from 1871 to 1945, and name of Germany until 1949, instance of historical country
4. Burg Drachenfels, also known as Germany (wd:Q1011486): instance of castle, castle
5. Germany (wd:Q5552078): instance of constituency of the European Parliament, European Parliament constituency
6. Geilenkirchen-Heinsberg, also known as Germany (wd:Q2268218): instance of district of Germany
7. Germany, also known as Germany, IN, Germany, Indiana (wd:Q18151573): instance of unincorporated community in the United States, unincorporated community in Clark County, Indiana
8. Germany (wd:Q19147844): poem by Vladimir Mayakovsky, instance of literary work
9. Germany (wd:Q22006587): instance of horse, race horse
10. Germany (wd:Q37444958): instance of family name, family name

[Tool]
Top 10 entity alternatives:
1. Poland, also known as Republic of Poland (wd:Q36): instance of country bordering the Baltic Sea, instance of sovereign state, instance of successor state, country in Central Europe, instance of country
2. Polish–Lithuanian Commonwealth, also known as Poland-Lithuania, Republic of Both Nations, Polish-Lithuanian Commonwealth, Poland–Lithuania, Poland (wd:Q172107): historical multinational monarchist state in Central Europe (Crown of the Kingdom of Poland and the Grand Duchy of Lithuania), instance of historical country
3. Crown of the Kingdom of Poland, also known as Kingdom of Poland, Poland (wd:Q171348): 1385–1795 territorial possessions of the King of Poland, instance of historical country, instance of Crown land, instance of realm
4. Poland, also known as Poland, OH, Poland, Ohio (wd:Q2300093): village in Ohio, United States, instance of village of Ohio
5. Poland, also known as Poland, Maine, Poland, ME (wd:Q1572109): town in Androscoggin County, Maine, United States, instance of town in the United States
6. Polish, also known as Poland (wd:Q385059)
7. Poland, also known as Boran (wd:Q5763870): instance of village, village in Kiribati
8. Poland, also known as Poland, NY, Poland, Chautauqua County, New York, Poland, New York (wd:Q7208883): town in Chautauqua County, New York, United States, instance of town in the United States, instance of town of New York
9. Poland, also known as Poland, NY, Poland, Herkimer County, New York (wd:Q7208887): village in Herkimer County, New York, United States, instance of village in the United States, instance of village of New York
10. Poland - The Warsaw Concert, also known as Poland (wd:Q599300): album by Tangerine Dream, instance of album

[Assistant] I found the entities for Germany (wd:Q183) and Poland (wd:Q36) in Wikidata. Now I will retrieve the area of both Germany and Poland to compare their sizes.

[Tool]
Got 2 rows and 3 columns:
| country           | countryLabel | area                 |
| ----------------- | ------------ | -------------------- |
| Germany (wd:Q183) | Germany (en) | 357588 (xsd:decimal) |
| Poland (wd:Q36)   | Poland (en)  | 312683 (xsd:decimal) |

[Tool] Stopped generation process


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

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

Mark Twain had 12 distinct occupations according to Wikidata.

How many occupations did Mark Twain have?

[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 with notes:
1. When direct properties are missing, chain related properties (e.g., child→child for grandchildren) or search for indirect connections to answer complex relationship queries.
2. For set/class queries (e.g., all cities, all holidays), use instance/subclass chains (P31/P279) for completeness and relevant filters. Be aware of data incompleteness and schema inconsistencies in Wikidata.
3. If queries return too many irrelevant or too few results, try varying properties (country, location, observed in, etc.) and subclass chains, but be ready to report data/model limitations if results remain incomplete or noisy.
4. When a direct property or class is missing or inconsistently used, try related or broader properties/classes (e.g., use inception for independence date, or musical work/composition for musicals) to increase recall.
5. For calculated properties (e.g., population density), combine relevant direct properties (e.g., population, area) in SPARQL queries, as Wikidata may not store the derived value directly.
6. When entity search returns multiple candidates, use context (description, instance type, location, etc.) to disambiguate and select the most relevant entity for the query.

No general notes for all knowledge graphs available

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/
qlss: <https://qlever.cs.uni-freiburg.de/spatialSearch/
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. Search for the 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. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
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 just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. 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] Step 1: Identify the entity "Mark Twain" in Wikidata to get his entity ID. I will search for "Mark Twain" in the knowledge graph.

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

[Assistant] Step 2: The entity for Mark Twain is wd:Q7245. Now I will search for the property related to "occupation" for this entity to find how many occupations Mark Twain had.

[Tool]
Top 10 property alternatives:
1. occupation, also known as avocation, career, craft, employ, employment (wd:P106 as wdt/p): occupation of a person; see also "field of work" (Property:P101), "position held" (Property:P39)
2. country of citizenship, also known as subject of (country), nation of citizenship, national of, nationality, nation (wd:P27 as wdt/p): the object is a country that recognizes the subject as its citizen, subproperty of country
3. place of birth, also known as location born, POB, location of birth, born in, born at (wd:P19 as wdt/p): most specific known birth location of a person, animal or fictional character, subproperty of location
4. date of birth, also known as year of birth, DOB, born on, birthyear, born (wd:P569 as wdt/p): date on which the subject was born, subproperty of inception
5. residence, also known as living in, place of residence, resided at, resided in, resident in (wd:P551 as wdt/p): the place where the person is or has been, resident, subproperty of location
6. work period (start), also known as start date of activity, start time for work, start working, years active, fl. (start) (wd:P2031 as wdt/p): start of period during which a person or group flourished (fl. = "floruit") in their professional activity, subproperty of start time, subproperty of floruit
7. ethnic group, also known as tribal affiliation, race, people, ethnicity, culture (wd:P172 as p): subject's ethnicity (consensus is that a VERY high standard of p...agreed on by scholars, or 3) is fictional and portrayed as such)
8. notable work, also known as representative of artwork type, representative work, significance, significant works, they created (wd:P800 as wdt/p): notable scientific, artistic or literary work, or other work of significance among subject's works
9. present in work, also known as appeared in, is shown in, in work, in narrative, from work (wd:P1441 as wdt/p): this (fictional or fictionalized) entity, place, or person appea...r works, P1343 for entities described in non-fictional accounts)
10. spouse, also known as spouses, married partner, married to, marry, partner (wd:P26 as wdt/p): the subject has the object as their spouse (husband, wife, partn...etc.). Use "unmarried partner" (P451) for non-married companions, subproperty of significant person, subproperty of relative

No other items found

[Assistant] Step 3: The property for occupation is wd:P106. I will now query the occupations of Mark Twain (wd:Q7245) using this property and count how many distinct occupations he had.

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

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?placeOfBirth ?placeOfBirthLabel WHERE {
  wd:Q79904 wdt:P19 ?placeOfBirth .
  OPTIONAL {
    ?placeOfBirth rdfs:label ?placeOfBirthLabel FILTER ( LANG( ?placeOfBirthLabel ) = "en" )
  }
}

Got 1 row and 2 columns:
| placeOfBirth           | placeOfBirthLabel  |
| ---------------------- | ------------------ |
| New York City (wd:Q60) | New York City (en) |

The author of The Catcher in the Rye, J. D. Salinger, was born in New York City.

Where was the author of The Catcher in the Rye born ?

[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 with notes:
1. When direct properties are missing, chain related properties (e.g., child→child for grandchildren) or search for indirect connections to answer complex relationship queries.
2. For set/class queries (e.g., all cities, all holidays), use instance/subclass chains (P31/P279) for completeness and relevant filters. Be aware of data incompleteness and schema inconsistencies in Wikidata.
3. If queries return too many irrelevant or too few results, try varying properties (country, location, observed in, etc.) and subclass chains, but be ready to report data/model limitations if results remain incomplete or noisy.
4. When a direct property or class is missing or inconsistently used, try related or broader properties/classes (e.g., use inception for independence date, or musical work/composition for musicals) to increase recall.
5. For calculated properties (e.g., population density), combine relevant direct properties (e.g., population, area) in SPARQL queries, as Wikidata may not store the derived value directly.
6. When entity search returns multiple candidates, use context (description, instance type, location, etc.) to disambiguate and select the most relevant entity for the query.

No general notes for all knowledge graphs available

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/
qlss: <https://qlever.cs.uni-freiburg.de/spatialSearch/
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. Search for the 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. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
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 just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. 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] Where was the author of The Catcher in the Rye born ?

[Assistant]
Step 1: Identify the entity "The Catcher in the Rye" and find its author.
Step 2: Find the place of birth of the identified author.

I will start by searching for the entity "The Catcher in the Rye" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. The Catcher in the Rye, also known as Catcher in the Rye (wd:Q183883): 1951 novel by J. D. Salinger, instance of literary work
2. Catcher in the Rye (wd:Q5051738): instance of musical group, Chinese punk rock band
3. Catcher In the Rye (wd:Q16184712): instance of extended play, 2011 EP by Datarock
4. Catcher in the Rye (wd:Q110065569): 2021 single by Ross Jennings, instance of single
5. Catcher in the Rye (wd:Q10443835): instance of musical work/composition, song by Guns N' Roses
6. The Catcher in the Rye (wd:Q16689546): 2012 debut studio album by Ann, instance of album
7. The Catcher in the Rye (wd:Q125291968): instance of version, edition or translation, 2001 Back Bay edition
8. Catchers in the rye (wd:Q68693473): scientific article published on 01 January 1990, instance of scholarly article
9. The Catcher in the Rye in popular culture (wd:Q7721703): Influence of J.D. Salinger's 1951 novel, instance of cultural depiction
10. Holden’s Characterization in The Catcher in the Rye (wd:Q119978837): instance of scholarly article

[Assistant]
The entity "The Catcher in the Rye" as a 1951 novel by J. D. Salinger is identified as wd:Q183883.

Next, I will find the author of this novel.

[Tool]
Got 1 row and 2 columns:
| author                     | authorLabel         |
| -------------------------- | ------------------- |
| J. D. Salinger (wd:Q79904) | J. D. Salinger (en) |

[Assistant]
The author of "The Catcher in the Rye" is J. D. Salinger (wd:Q79904).

Now, I will find the place of birth of J. D. Salinger.

[Tool]
Got 1 row and 2 columns:
| placeOfBirth           | placeOfBirthLabel  |
| ---------------------- | ------------------ |
| New York City (wd:Q60) | New York City (en) |

[Tool] Stopped generation process