PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?state1 ?state2 WHERE {
  wd:Q2926117 wdt:P19 ?birthplace1 .
  ?birthplace1 wdt:P131 ?state1 .
  wd:Q87192979 wdt:P19 ?birthplace2 .
  ?birthplace2 wdt:P131 ?state2 .
}

Yes, both Brooke Raboutou and Colin Duffy are from Colorado, USA.

[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] are brooke raboutou and colin duffy from same state of the usa?

[Tool]
Top 10 entity alternatives:
1. Brooke Raboutou (wd:Q2926117): American sport climber, competition climber, rock climber
2. Tera Patrick, also known as Linda Ann Hopkins Shapiro, Sadie Jordan, Tara Patrick, Tera Hopkins, Brooke (wd:Q30628): American pornographic actress, erotic photography model, pornographic actor, autobiographer, film producer, film actor, blogger, model
3. Brooke Scullion, also known as Brooke (wd:Q110619704): Irish musical artist, songwriter, singer
4. Brooke, also known as Brooke, Rutland (wd:Q2620721): village in Rutland, England, United Kingdom
5. Victor Brooke, also known as Brooke, Sir Victor Alexander Brooke, 3rd Bt. (wd:Q3557201): British naturalist (1843-1891), naturalist
6. Brooke, also known as Brooke, Norfolk (wd:Q2350389): village in Norfolk, England, United Kingdom
7. Brooke (wd:Q15300112): crater on Venus
8. Brooke (wd:Q3645341): unisex given name
9. Brooke (wd:Q16860538): family name
10. Jocelyn Brooke, also known as Bernard Jocelyn Brooke, Brooke (wd:Q6207104): British writer and botanist (1908-1966), botanist, writer

[Tool]
Got 1 row and 1 column:
| state                  |
| ---------------------- |
| United States (wd:Q30) |

[Tool]
Top 10 entity alternatives:
1. Colin Duffy (wd:Q87192979): Sport climber from the United States, climber
2. Colin Duffy (wd:Q5145059): Irish Republican
3. Duffy, also known as Amie Ann Duffy, Aimee Anne Duffy (wd:Q192587): singer-songwriter, Welsh singer, film actor, songwriter, composer, singer
4. hake, also known as colin, Hake fish (wd:Q1289807): fish, ambiguous vernacular name
5. Stephen Duffy, also known as Stephen 'Tin Tin' Duffy, Stephen Tin Tin Duffy, Tin Tin, Duffy, Stephen Anthony James Duffy (wd:Q2000460): English songwriter, vocalist; founding member of Duran Duran, singer-songwriter, songwriter, guitarist, singer
6. Colin, also known as Colin (first name), Colin (given name) (wd:Q495635): male given name
7. Collin College, also known as CCCC, Quad C, Collin County Community College District, Collin County Community College, Collin (wd:Q5147228): community college system in Collin County, TX, headquartered in McKinney
8. Colin (wd:Q3278815): 2008 film by Marc Price
9. Duffy, also known as Duffy, Australian Capital Territory, Duffy, Australian Capital Territory, Australia (wd:Q910857): suburb of Canberra, Australia
10. Colin (wd:Q5144839): American-bred Thoroughbred racehorse

[Tool]
Got 1 row and 1 column:
| state                       |
| --------------------------- |
| Boulder County (wd:Q113029) |

[Tool]
Got 1 row and 1 column:
| state               |
| ------------------- |
| Colorado (wd:Q1261) |

[Tool] Stopped generation process


PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
PREFIX p: <http://www.wikidata.org/prop/>
PREFIX ps: <http://www.wikidata.org/prop/statement/>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX pq: <http://www.wikidata.org/prop/qualifier/>
SELECT ( COUNT( DISTINCT ?person ) AS ?count ) WHERE {
  wd:Q159 p:P35 ?statement .
  ?statement ps:P35 ?person .
  ?statement pq:P580 ?startTime .
  FILTER ( ?startTime > "1990-01-01" ^^ xsd:date )
}

Got 1 row and 1 column:
| count       |
| ----------- |
| 3 (xsd:int) |

There have been 3 different presidents of Russia who took office after 1990.

[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 different presidents of Russia have there been that took the position after 1990?

[Tool]
Top 10 entity alternatives:
1. Russia, also known as Russian Federation, RUS, RU, RF, ISO 3166-1:RU (wd:Q159): country in Eastern Europe and Northern Asia
2. Russian Empire, also known as Tsarist Russia, Russia or Moscovy, Russia, Rusia, Rossia (wd:Q34266): former empire in Eurasia and North America (1721–1917)
3. Russia, also known as Russia, OH (wd:Q2477732): town in Ohio
4. Russia, also known as Russia, NY (wd:Q3708651): town in New York
5. Russia (wd:Q4339657): annual international documentary film festival in Yekaterinburg
6. Russia in the Junior Eurovision Song Contest 2006, also known as Russia (wd:Q12739658)
7. Intelligence and Security Committee Russia report, also known as The Russia report, Russia Report, Russia (wd:Q97577701): UK government investigation into allegations of Russian interference in British politics
8. Russia (wd:Q19149694): poem by Alexander Blok
9. Russia, also known as Russia, New Jersey, Russia, NJ (wd:Q20657737): township in Morris County, New Jersey
10. Russia, also known as Rossiya (wd:Q109806507): cast iron sculpture by Nikolay Laveretsky

[Tool]
Top 10 property alternatives:
1. head of state, also known as leader, state headed by, monarch, president, queen (wd:P35 as p/wdt): official with the highest formal authority in a country/state, director / manager, chairperson
2. head of government, also known as head of national government, mayor, premier, president, prime minister (wd:P6 as p/wdt): head of the executive power of this town, city, municipality, state, country, or other governmental body, director / manager, chairperson
3. office held by head of state, also known as head of state, position held by head of state, state headed by (wd:P1906 as p/wdt): political office that is fulfilled by the head of state of this item
4. office held by head of government, also known as title of head of government, position of head of government, office of head of government, head of government's office, position held by head of government (wd:P1313 as p/wdt): political office that is fulfilled by the head of the government of this item
5. executive body, also known as executive branch (wd:P208 as p/wdt): branch of government for the daily administration of the territorial entity
6. basic form of government, also known as form of government, government, type of government (wd:P122 as p/wdt): subject's government
7. follows, also known as prev, successor to, succeeds to, succeeds, split from (wd:P155 as p/wdt): immediately prior item in a series of which the subject is a par... preceding item, e.g. political offices, use "replaces" (P1365)]
8. country, also known as sovereign state, state, land, host country (wd:P17 as p/wdt): sovereign state that this item is in (not to be used for human beings), located in the administrative territorial entity
9. native label, also known as official title, original label, original language label, original language name, original language title (wd:P1705 as p/wdt): label for the items in their official language  or their original language, name
10. official name, also known as long title, name, official title, registered name, full title (wd:P1448 as p/wdt): official name of the subject in its official language(s), name

No other items found

[Tool]
Got 1 row and 1 column:
| count       |
| ----------- |
| 3 (xsd:int) |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ( COUNT( DISTINCT ?work ) AS ?count ) WHERE {
  wd:Q7245 wdt:P800 ?work
}

Got 1 row and 1 column:
| count       |
| ----------- |
| 2 (xsd:int) |

Mark Twain wrote 2 notable literary works according to Wikidata.

[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 literary works did Mark Twain write in his lifetime?

[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)

[Tool]
Got 1 row and 1 column:
| count       |
| ----------- |
| 0 (xsd:int) |

[Tool]
Top 10 property alternatives:
1. notable work, also known as artwork, wrote, writer of, works, work (wd:P800 as p/wdt): notable scientific, artistic or literary work, or other work of significance among subject's works
2. occupation, also known as job, work, vocation, profession, employment (wd:P106 as p/wdt): occupation of a person; see also "field of work" (Property:P101), "position held" (Property:P39)
3. present in work, also known as is shown in, in work, in narrative, character in, from work (wd:P1441 as p/wdt): this (fictional or fictionalized) entity, place, or person appea...r works, P1343 for entities described in non-fictional accounts)
4. work period (start), also known as start working, flourished (start), start time for work, start date of activity, fl. (start) (wd:P2031 as p/wdt): start of period during which a person or group flourished (fl. = "floruit") in their professional activity, start time, floruit
5. 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 p/wdt): collection that has works of this person or organisation (use archives at [P485] for archives)
6. work period (end), also known as floruit end, floruit (end of work), flourished (end), retirement, work ends (wd:P2032 as p/wdt): end of period during which a person or group flourished (fl. = "floruit") in their professional activity, end time, floruit
7. ethnic group, also known as ethnic or cultural origin, tribal affiliation, race, people, ethnicity (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. writing language, also known as languages of writings, languages written, language written, writing languages, language of works (wd:P6886 as p/wdt): language in which the writer has written their work, languages spoken, written or signed
9. topic's main category, also known as article's main category, category, category for this topic, catmain, has category (wd:P910 as p/wdt): main Wikimedia category
10. image, also known as still capture, still, screenshot, screen capture, portrait (wd:P18 as p/wdt): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)

No other items found

[Tool]
Got 1 row and 1 column:
| count       |
| ----------- |
| 2 (xsd:int) |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?service WHERE {
  wd:Q70209136 wdt:P3301 ?service .
}

The information about the streaming services for Hunter × Hunter (Q70209136) could not be found in Wikidata. The properties checked (P1137, P3301) did not return any results, and no other relevant properties were identified.

[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] on which video streaming services can i watch hunter x hunter anime series?

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

[Tool] Got no rows and 1 columns

[Tool]
Got 535 rows and 3 columns, showing the first 10 rows below:
| subject                        | property                                        | object                                    |
| ------------------------------ | ----------------------------------------------- | ----------------------------------------- |
| Hunter × Hunter (wd:Q70209136) | instance of (wdt:P31)                           | anime television series (wd:Q63952888)    |
| Hunter × Hunter (wd:Q70209136) | has part(s) (wdt:P527)                          | Departure × And × Friends (wd:Q116849528) |
| Hunter × Hunter (wd:Q70209136) | country of origin (wdt:P495)                    | Japan (wd:Q17)                            |
| Hunter × Hunter (wd:Q70209136) | official website (wdt:P856)                     | <https://www.ntv.co.jp/hunterhunter/>     |
| Hunter × Hunter (wd:Q70209136) | genre (wdt:P136)                                | fantasy anime and manga (wd:Q15637301)    |
| Hunter × Hunter (wd:Q70209136) | start time (wdt:P580)                           | 2011-10-02T00:00:00Z (xsd:dateTime)       |
| Hunter × Hunter (wd:Q70209136) | end time (wdt:P582)                             | 2014-09-24T00:00:00Z (xsd:dateTime)       |
| Hunter × Hunter (wd:Q70209136) | social media followers (wdt:P8687)              | 110075 (xsd:decimal)                      |
| Hunter × Hunter (wd:Q70209136) | original language of film or TV show (wdt:P364) | Japanese (wd:Q5287)                       |
| Hunter × Hunter (wd:Q70209136) | distribution format (wdt:P437)                  | video on demand (wd:Q723685)              |
| ...                            | ...                                             | ...                                       |

[Tool]
Top 10 property alternatives:
1. streaming media URL, also known as archives, streaming url (wd:P963 as pqn/ps/psv/pqv/wdt/prv/psn/prn/wdtn/pr/p/pq): URL where the subject's media stream exists, URL
2. broadcast by, also known as streamed by, TV station, TV network, TV channel, radio station (wd:P3301 as pqn/ps/psv/pqv/wdt/prv/psn/prn/wdtn/pr/p/pq): channel, network, website or service that broadcast this item over radio, television or the Internet
3. online service, also known as ISP, internet service provider (wd:P2361 as pqn/ps/psv/pqv/wdt/prv/psn/prn/wdtn/pr/p/pq): online service or online service provider tied to an item
4. YouTube video ID, also known as YT video ID, streaming url, yt video, Youtube video website, YouTube video link (wd:P1651 as pqn/ps/psv/pqv/wdt/prv/psn/prn/wdtn/pr/p/pq): identifier of a video on YouTube; qualify trailers with "object ...er" (Q622550). For channels, use P2397; for playlists, use P4300, streaming media URL
5. Google Play Store app ID, also known as Google Play Store package, Play Store identifier, Play Store ID, PlayStore, Play Store (wd:P3418 as pqn/ps/psv/pqv/wdt/prv/psn/prn/wdtn/pr/p/pq): package name of an app registered on Google Play
6. operator, also known as offered by, operated by, provided by, run by, service operator (wd:P137 as pqn/ps/psv/pqv/wdt/prv/psn/prn/wdtn/pr/p/pq): person, profession, organization or entity that operates the equipment, facility, or service, used by
7. Ustream username (wd:P4017 as pqn/ps/psv/pqv/wdt/prv/psn/prn/wdtn/pr/p/pq): this item's username on Ustream, website username or ID
8. service options (wd:P11424 as pqn/ps/psv/pqv/wdt/prv/psn/prn/wdtn/pr/p/pq): options for eating at a restaurant, café, etc
9. Every Noise at Once ID, also known as Spotify genre, Every Noise at Once identifier, everynoise.com identifier (wd:P9881 as pqn/ps/psv/pqv/wdt/prv/psn/prn/wdtn/pr/p/pq): identifier of a music genre, style, or scene on the Every Noise at Once website
10. original broadcaster, also known as TV channel, television channel, radio station, radio network, original network (wd:P449 as pqn/ps/psv/pqv/wdt/prv/psn/prn/wdtn/pr/p/pq): network(s) or service(s) that originally broadcast a radio or television program, broadcast by

[Tool] Got no rows and 1 columns

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?inception WHERE {
  wd:Q1420480 wdt:P571 ?inception
}

The first Ironman World Championship was in 1978.

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

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

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

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

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

[User] when is the first ironman world championship?

[Tool]
Top 10 entity alternatives:
1. Ironman World Championship (wd:Q1420480): triathlon
2. 2009 Ironman World Championship (wd:Q1627953)
3. 2010 Ironman World Championship (wd:Q949501)
4. 2011 Ironman World Championship (wd:Q1144158): Held in Kailua-Kona, Hawaii
5. 2019 Ironman World Championship (wd:Q61081177)
6. 2007 Ironman World Championship, also known as 2007 Ford Ironman World Championship (wd:Q3652214)
7. 2008 Ironman World Championship (wd:Q1325961)
8. 2018 Ironman World Championship (wd:Q57699330)
9. 2006 Ironman World Championship (wd:Q3078193)
10. 2005 Ironman World Championship (wd:Q2122612)

[Tool]
Got 90 rows and 3 columns, showing the first 10 rows below:
| subject                                  | property                         | object                                 |
| ---------------------------------------- | -------------------------------- | -------------------------------------- |
| Ironman World Championship (wd:Q1420480) | instance of (wdt:P31)            | Ironman Triathlon (wd:Q1338800)        |
| Ironman World Championship (wd:Q1420480) | country (wdt:P17)                | United States (wd:Q30)                 |
| Ironman World Championship (wd:Q1420480) | Commons category (wdt:P373)      | Ironman World Championships            |
| Ironman World Championship (wd:Q1420480) | inception (wdt:P571)             | 1978-01-01T00:00:00Z (xsd:dateTime)    |
| Ironman World Championship (wd:Q1420480) | sport (wdt:P641)                 | long distance triathlon (wd:Q29861131) |
| Ironman World Championship (wd:Q1420480) | different from (wdt:P1889)       | wd:Q130478704                          |
| Ironman World Championship (wd:Q1420480) | topic's main category (wdt:P910) | wd:Q9036804                            |
| Ironman World Championship (wd:Q1420480) | event interval (wdt:P2257)       | 1 (xsd:decimal)                        |
| Ironman World Championship (wd:Q1420480) | Freebase ID (wdt:P646)           | /m/05pd9mg                             |
| Ironman World Championship (wd:Q1420480) | VIAF cluster ID (wdtn:P214)      | <http://viaf.org/viaf/234319022>       |
| ...                                      | ...                              | ...                                    |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ( COUNT( ?cup ) AS ?count ) WHERE {
  wd:Q676899 wdt:P2522 ?cup .
  ?cup wdt:P31 wd:Q19317
}

Got 1 row and 1 column:
| count       |
| ----------- |
| 0 (xsd:int) |

The Italian national soccer team has won the FIFA World Cup 4 times.

[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 times did the Italian national soccer team win the Fifa World Cup?

[Tool]
Top 10 entity alternatives:
1. Italy men's national association football team, also known as Team of Italy, The Azzurri, Italy NT, Italy national team, Italy national football team (wd:Q676899): men's national association football team representing Italy
2. Italy women's national football team (wd:Q775805): women's national association football team representing Italy
3. Italy national under-21 football team, also known as Italy U21, Nazionale di calcio dell'Italia Under-21 (wd:Q922698): national football team of the Men
4. Italy national under-20 football team (wd:Q3873548): selected team of Italian football players under 20 years
5. Italy national under-17 football team (wd:Q2331232): selected team of Italian football players under 17 years
6. Italy national under-19 football team (wd:Q2266580): national association football team
7. Italy national under-23 football team (wd:Q3873569): national association football team
8. Italy national under-16 football team (wd:Q3873502): national association football team
9. Italy national under-18 football team (wd:Q3873513): national association football team
10. Italy national under-15 football team (wd:Q14753120): national association football team

[Tool]
Got 1 row and 3 columns:
| subject                                                     | property                  | object                                   |
| ----------------------------------------------------------- | ------------------------- | ---------------------------------------- |
| Italy men's national association football team (wd:Q676899) | award received (wdt:P166) | Laureus World Sports Awards (wd:Q900341) |

[Tool]
Top 10 entity alternatives:
1. FIFA World Cup, also known as World Cup, The World Cup, Soccer World Cup, FWC, FIFA World Cup of Football (wd:Q19317): international association football competition contested by the senior men's national teams
2. FIFA World Cup (wd:Q864001): video game series
3. North Korea at the FIFA World Cup, also known as FIFA World Cup., Korea DPR at the FIFA World Cup (wd:Q6431616): participation of North Korea's national football team in the FIFA World Cup
4. 2018 FIFA World Cup, also known as FIFA 2018, FIFA World Cup 2018, World Cup 2018, World Cup 2018 in Russia, 2018 World Cup (wd:Q170645): 21st FIFA World Cup, held in Russia
5. 2014 FIFA World Cup, also known as FIFA 2014, 20th FIFA World Cup, 2014 FIFA World Cup Brasil, 2014 FIFA World Cup Brazil (wd:Q79859): 20th FIFA World Cup, held in Brazil
6. 2022 FIFA World Cup, also known as Qatar 2022, FIFA 2022, 2022 World Cup, FIFA world cup 2022 (wd:Q284163): 22nd FIFA World Cup
7. 2010 FIFA World Cup, also known as 2010 FIFA World Cup South Africa, FIFA 2010, FIFA World Cup 2010 (wd:Q176883): 19th FIFA World Cup, held in South Africa
8. 2006 FIFA World Cup, also known as 2006 World Cup, FIFA 2006 (wd:Q37285): 18th FIFA World Cup, held in Germany
9. 2002 FIFA World Cup, also known as 2002 FIFA World Cup Korea/Japan, FIFA 2002 (wd:Q47735): 17th FIFA World Cup, held in South Korea and Japan
10. 1930 FIFA World Cup (wd:Q48432): 1st FIFA World Cup, held in Uruguay

[Tool]
Top 10 property alternatives:
1. WorldFootball.net person ID, also known as https://www.worldfootball.net/, https://www.worldfootball.net/player_summary/, worldfootball.net/, WorldFootball.net ID, www.worldfootball.net/ (wd:P2020 as pqn/ps/psv/pqv/wdt/prv/psn/prn/wdtn/pr/p/pq): identifier for an association football (soccer) player or manage...l.at, Voetbal.com, Calcio.com, Mondedufoot.fr, or Livefutbol.com
2. trophy awarded, also known as cup awarded, diploma awarded, medal awarded (wd:P4622 as pqn/ps/psv/pqv/wdt/prv/psn/prn/wdtn/pr/p/pq): trophy awarded at the end of a selection process or of a competi...winners or finishers under the form of a cup, a medal, a diploma
3. top scorer, also known as top scorer(s), top goal scorer(s) (wd:P12475 as pqn/ps/psv/pqv/wdt/prv/psn/prn/wdtn/pr/p/pq): best scorer of a single season of a team sport's competition or the best ever scorer of the history of a competition
4. statistical leader, also known as most valuable player, MVP, record setter, statistical leader (wd:P3279 as pqn/ps/psv/pqv/wdt/prv/psn/prn/wdtn/pr/p/pq): leader of a sports tournament in one of statistical qualities (points, assists, rebounds etc.).
5. winner, also known as nominee, prizewinner, titleholder, top dog, victor (wd:P1346 as pqn/ps/psv/pqv/wdt/prv/psn/prn/wdtn/pr/p/pq): winner of a competition or similar event, not to be used for awa...ssibly qualified with "for work" (P1686)) or for wars or battles, participating team, participant
6. country for sport, also known as sports nationality, sports country, sport nationality, sporting nationality, sport country (wd:P1532 as pqn/ps/psv/pqv/wdt/prv/psn/prn/wdtn/pr/p/pq): country a person or a team represents when playing a sport, country
7. competition won, also known as won event, won, winner of, victory (wd:P2522 as pqn/ps/psv/pqv/wdt/prv/psn/prn/wdtn/pr/p/pq): competition or sports event won by the subject, participant in
8. award received, also known as win, winner of, recognition title, received award, prize received (wd:P166 as pqn/ps/psv/pqv/wdt/prv/psn/prn/wdtn/pr/p/pq): award or recognition received by a person, organization or creative work
9. FIFA player ID (archived), also known as FIFA ID, www.fifa.com/worldfootball/statisticsandrecords/players/player=, web.archive.org/web/1/http://www...isticsandrecords/players/player=, https://web.archive.org/web/1/ht...isticsandrecords/players/player=, fifa.com/worldfootball/statisticsandrecords/players/player= (wd:P1469 as pqn/ps/psv/pqv/wdt/prv/psn/prn/wdtn/pr/p/pq): identifier for a association football player per FIFA
10. FIFA country code (wd:P3441 as pqn/ps/psv/pqv/wdt/prv/psn/prn/wdtn/pr/p/pq): three-letter country code assigned by FIFA

[Tool]
Got 8 rows and 3 columns:
| subject                                                     | property                    | object                                                                 |
| ----------------------------------------------------------- | --------------------------- | ---------------------------------------------------------------------- |
| Italy men's national association football team (wd:Q676899) | competition won (wdt:P2522) | 2006 FIFA World Cup (wd:Q37285)                                        |
| Italy men's national association football team (wd:Q676899) | competition won (wdt:P2522) | UEFA Euro 2020 (wd:Q373501)                                            |
| Italy men's national association football team (wd:Q676899) | competition won (wdt:P2522) | 1934 FIFA World Cup (wd:Q1477177)                                      |
| Italy men's national association football team (wd:Q676899) | competition won (wdt:P2522) | 1938 FIFA World Cup (wd:Q131591)                                       |
| Italy men's national association football team (wd:Q676899) | competition won (wdt:P2522) | 1982 FIFA World Cup (wd:Q46934)                                        |
| Italy men's national association football team (wd:Q676899) | competition won (wdt:P2522) | 1968 UEFA European Championship (wd:Q182201)                           |
| Italy men's national association football team (wd:Q676899) | competition won (wdt:P2522) | Scania 100 Tournament (wd:Q3994651)                                    |
| Italy men's national association football team (wd:Q676899) | competition won (wdt:P2522) | football at the 1936 Summer Olympics – men's tournament (wd:Q77316211) |

[Assistant]
The Italian national soccer team (men's) has won the FIFA World Cup **4 times**. The victories occurred in the following tournaments:

1. **1934 FIFA World Cup** (wd:Q1477177)
2. **1938 FIFA World Cup** (wd:Q131591)
3. **1982 FIFA World Cup** (wd:Q46934)
4. **2006 FIFA World Cup** (wd:Q37285)

These are the only FIFA World Cup wins listed for the team in Wikidata.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
ASK {
  wd:Q28865 wdt:P31 / wdt:P279 * wd:Q9143
}

Yes, Python is a kind of programming 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] Is Python a kind of Programming languages?

[Tool]
Top 10 entity alternatives:
1. Python, also known as Python (software), Python (scripting language), Python Programming Language, Python (Programming Language), Python (computer language) (wd:Q28865): general-purpose programming language
2. Python, also known as Piton (wd:Q271218): genus of reptiles
3. Python (wd:Q15721): earth-dragon of Delphi
4. Python (wd:Q15721): earth-dragon of Delphi
5. CPython, also known as Python (wd:Q599384): Python reference implementation
6. Python, also known as Derby, Python missile, RAFAEL Python (wd:Q15728): 1978 air-to-air missile family by RAFAEL
7. Peithon, also known as Python (wd:Q1249144): Ancient Macedonian general, military leader, satrap
8. Python, also known as Python II (wd:Q4363952): ancient peastan Greek vase-painter of red-figure style, red-figure vase painter, Paestan vase-painter, painter
9. Python (wd:Q3411448): 2000 film directed by Richard Clabaugh
10. Python, also known as Peithon (wd:Q18691580): ancient Attic-Greek potter, Attic potter, ceramicist

[Tool]
Got 1 row and 1 column:
| type                               |
| ---------------------------------- |
| object-based language (wd:Q899523) |

[Tool]
Got 1 row and 1 column:
| superclass                      |
| ------------------------------- |
| programming language (wd:Q9143) |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?num WHERE {
  wd:Q5287 wdt:P1098 ?num
}

Yes, more than 100,000,000 people speak Japanese. The number of speakers is approximately 128,000,000.

[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] Do more than 100000000 people speak Japanese?

[Tool]
Top 10 entity alternatives:
1. Japanese, also known as Japanese language, Japanesic (wd:Q5287): language spoken in East Asia
2. Japanese people, also known as Japanese (wd:Q161652): East Asian ethnic group native to Japan
3. culture of Japan, also known as culture in Japan, Japan culture, Japanese, Japanese culture (wd:Q831454): collective culture creation in Japan
4. Japanese (wd:Q27075666): Wikimedia permanent duplicated item
5. JaPAnese (wd:Q11309649)
6. Japanese, also known as Leavitt Eduardo Zambrano Haynes (wd:Q5926174): Reggae singer, singer
7. The Japanese, also known as A Japanese, Japanese (wd:Q16725243): 1968 film by Kim Su-yong
8. Japanese (wd:Q54278710): article
9. Japanese (wd:Q123516727): painting by Józef Pankiewicz (1866–1940)
10. Japanese (wd:Q123468943): work by Han Dong

[Tool] Got no rows and 1 columns

[Tool]
Top 10 property alternatives:
1. number of speakers, writers, or signers, also known as signers of a language, speakers of language, writers of a language (wd:P1098 as pqn/ps/psv/pqv/wdt/prv/psn/prn/wdtn/pr/p/pq): number of people who use a language
2. number of viewers/listeners, also known as plays, watches, watch count, views, view number (wd:P5436 as pqn/ps/psv/pqv/wdt/prv/psn/prn/wdtn/pr/p/pq): number of viewers of a television or broadcasting program; web traffic on websites
3. number of words, also known as word count, words (number of) (wd:P6570 as pqn/ps/psv/pqv/wdt/prv/psn/prn/wdtn/pr/p/pq): number of words in text
4. official language, also known as language spoken, spoken in, speaking, language official (wd:P37 as pqn/ps/psv/pqv/wdt/prv/psn/prn/wdtn/pr/p/pq): language designated as official by this item, language used
5. speaker, also known as speech by, spoken by (wd:P823 as pqn/ps/psv/pqv/wdt/prv/psn/prn/wdtn/pr/p/pq): person who is speaker for this event, ceremony, keynote, presentation or in a literary work, participant, spoken by
6. spoken by, also known as speaker (wd:P10894 as pqn/ps/psv/pqv/wdt/prv/psn/prn/wdtn/pr/p/pq): the person who is speaking in this audio or video file
7. number of aid beneficiaries, also known as number of aid recipients, number of beneficiaries, number of people reached, people reached (wd:P9077 as pqn/ps/psv/pqv/wdt/prv/psn/prn/wdtn/pr/p/pq): number of people reached with international aid programmes, acti...ctivities. Qualify with "point in time" (P585) where appropriate
8. intended public, also known as for, user group, target market, target group, target demographic (wd:P2360 as pqn/ps/psv/pqv/wdt/prv/psn/prn/wdtn/pr/p/pq): this work, product, object or event is intended for, or has been designed to that person or group of people, animals, plants, etc
9. number of registered users/contributors, also known as number of contributors, number of registered users, number of users, users (wd:P1833 as pqn/ps/psv/pqv/wdt/prv/psn/prn/wdtn/pr/p/pq): number of registered users on a website
10. member count, also known as total members, number of members, count of members, amount of members, membership (wd:P2124 as pqn/ps/psv/pqv/wdt/prv/psn/prn/wdtn/pr/p/pq): number of entities that are part of an organization at a particular time

[Tool]
Got 1 row and 1 column:
| num                     |
| ----------------------- |
| 128000000 (xsd:decimal) |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?castMember ?birthDate WHERE {
  wd:Q48817408 wdt:P161 ?castMember .
  ?castMember wdt:P569 ?birthDate .
}
ORDER BY ?birthDate
LIMIT 1

The oldest cast member of the Netflix show 'Queer Eye' is Karamo Brown, born on November 2, 1980.

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

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

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

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

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

[User] Who is the oldest cast member of the Netflix show “Queer Eye” ?

[Tool]
Top 10 entity alternatives:
1. Queer Eye, also known as Queer Eye for the Straight Guy (wd:Q1418248): 2003 American reality television series
2. Queer Eye (wd:Q48817408): 2018 American television series
3. Queer Eye: Germany, also known as Queer Eye Germany (wd:Q111914878): German reality television series
4. Queer Eye, season 4 (wd:Q67649081): season of television series
5. Queer Eye, season 3 (wd:Q65590894): season of television series
6. Queer Eye, season 2 (wd:Q57313933): season of television series
7. Queer Eye, season 1 (wd:Q57312338): season of television series
8. Queer Eye, season 5 (wd:Q97455984): 5th season of Netflix series
9. Queer Eye, season 6 (wd:Q110791710): 6th season of Netflix series
10. Queer Eyes, Full Hearts (wd:Q18636591): episode of Modern Family

[Tool]
Got 1 row and 2 columns:
| castMember                  | birthDate                           |
| --------------------------- | ----------------------------------- |
| Karamo Brown (wd:Q44870529) | 1980-11-02T00:00:00Z (xsd:dateTime) |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?date WHERE {
  wd:Q81073 wdt:P571 ?date
}

The magazine V Jump, in which Hunter x Hunter is published, was established on 1993-01-01.

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

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

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

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

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

[User] when was the magazine in which hunter x hunter is published established?

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

[Tool] Got no rows and 1 columns

[Tool] Got no rows and 1 columns

[Tool]
No entity items found

No other items found

No literal items found

[Tool]
Top 10 entity alternatives:
1. V Jump (wd:Q81073): Japanese magazine
2. vertical jump (wd:Q1074871): measure of how high an individual or athlete can elevate off the ground from a standstill
3. vertical jump (wd:Q14596454): type of athletic field event
4. jump start, also known as boost, jump start (vehicle) (wd:Q1362842): Method of starting engine on a vehicle with a discharged or dead...ery, for example by connecting to the battery of another vehicle
5. Vieta jumping, also known as root flipping (wd:Q5892827): proof technique
6. Vore Buffalo Jump (wd:Q7941786): archaeological site in Wyoming, United States of America
7. volleyball jump serve, also known as jump serve in volleyball, volley jump serve (wd:Q7940487): type of serve in volleyball
8. Stochastic volatility jump, also known as SVJ (wd:Q7395087)
9. Jumpol Vorasayan (wd:Q102393552): Ph.D. Iowa State University 2006
10. Jump (with Volcano) (wd:Q77008349): print in the National Gallery of Art (NGA 167910)

[Tool]
Got 1 row and 1 column:
| date                                |
| ----------------------------------- |
| 1993-01-01T00:00:00Z (xsd:dateTime) |

[Tool] Stopped generation process