PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?casualties WHERE {
  wd:Q815436 wdt:P1120 ?casualties .
}

Got 1 row and 1 column:
| casualties         |
| ------------------ |
| 3532 (xsd:decimal) |

[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 casualties were a result of the Troubles?

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

[Tool]
Example 1:
Question:
How many programming languages are there?

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

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

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

Example 2:
Question:
Who is the author of WikiLeaks?

SPARQL:
SELECT ?o1 WHERE {
  wd:Q359 wdt:P50 ?o1 .
}

Using entities:
WikiLeaks, also known as wikileaks.org (wd:Q359): NGO owned by Icelandic company Sunshine Press Productions ehf that has published news leaks provided by anonymous sources

Using properties:
author, also known as writer, written by, poet, playwright, maker (wd:P50 as wdt): main creator(s) of a written work (use on works, not humans); us...hor name string) when Wikidata item is unknown or does not exist, creator

Example 3:
Question:
What is the name of the university where Obama's wife studied?

SPARQL:
SELECT DISTINCT ?uri ?institute WHERE {
  wd:Q76 wdt:P26 / wdt:P69 / wdt:P361 * ?uri .
  ?uri wdt:P31 * / wdt:P279 * wd:Q3918 .
}

Using entities:
university, also known as uni, universities (wd:Q3918): academic institution for further education
Barack Obama, also known as Honorable Barack Obama, BHO, Obama, Barry O Bomber, President Obama (wd:Q76): president of the United States from 2009 to 2017, community organizer, political writer, statesperson, politician, podcaster, memoirist, academic, lawyer, jurist

Using properties:
spouse, also known as married partner, married to, marry, partner, spouses (wd:P26 as wdt): the subject has the object as their spouse (husband, wife, partn...etc.). Use "unmarried partner" (P451) for non-married companions, significant person, relative
subclass of, also known as rdfs:subClassOf, sort of, subcategory of, subgenre of, subtype of (wd:P279 as wdt): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
part of, also known as of, within, system of, subsystem of, subgroup of (wd:P361 as wdt): object of which the subject is a part (if this subject is alread... of "has part" (P527, see also "has parts of the class" (P2670))
educated at, also known as graduated from, graduate of, place of education, school attended, schooled at (wd:P69 as wdt): educational institution attended by subject, affiliation

[Assistant]
None of these examples directly relate to the Troubles or casualties, but they provide a good starting point for understanding how to structure a SPARQL query.

To answer the question about the number of casualties resulting from the Troubles, we need to identify the entity representing the Troubles and the property that links it to the number of casualties. Let's start by searching for the entity "Troubles" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. The Troubles, also known as Troubles, Northern Irish Troubles, Northern Ireland Troubles, Northern Ireland conflict, Conflict in Northern Ireland (wd:Q815436): 1960s–1990s conflict in Northern Ireland
2. Troubles (wd:Q4000425): novel by James Gordon Farrell
3. Troubles (wd:Q7846501): album by Steve Lacy
4. Troubles (wd:Q7846503): UK musical group
5. Troubles (wd:Q109812273): Movie directed by Érik Papatie
6. Troubles (wd:Q129262318): 2022 track by Denzel Curry
7. Troubles (wd:Q131565207): hardback edition (en)
8. Troubles (wd:Q42196874)
9. Troubles (wd:Q7846502): album by Troubles
10. troubles (wd:L1330100-S1)

[Assistant]
The top result, `wd:Q815436`, represents the Troubles, which is the 1960s–1990s conflict in Northern Ireland. This is the entity we need to focus on.

Next, we need to find the property that links the Troubles to the number of casualties. We can search for properties related to "casualties" or "deaths" for this entity.

[Tool]
Top 10 property alternatives:
1. number of deaths, also known as dead, qty fatalities, qty deaths, number of fatalities, fatalities (wd:P1120 as p/wdt): total (cumulative) number of people who died since start as a direct result of an event or cause
2. number of injured, also known as injured, injuries, injury toll, non-fatal injuries, number injured (wd:P1339 as p/wdt): number of people with non-fatal injuries in an event
3. point in time, also known as at time, as of, year, when, time of event (wd:P585 as p/wdt): date something took place, existed or a statement was true; for providing time use the "refine date" property (P4241)
4. end time, also known as ends, stop time, endtime, fall date, ineffective (wd:P582 as p/wdt): moment when an entity ceases to exist and a statement stops being entirely valid or no longer be true, point in time
5. location, also known as neighbourhood, whereabouts, vilage, venue, town (wd:P276 as p/wdt): location of the object, structure or event. In the case of an ad...ity use P706. Use P7153 for locations associated with the object
6. 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
7. image, also known as still capture, still, screenshot, screen capture, portrait (wd:P18 as p/wdt): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)
8. 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
9. start time, also known as start date, since, valid, join time, joined (wd:P580 as p/wdt): time an entity begins to exist or a statement starts being valid, point in time

No other items found

[Assistant]
The property `wd:P1120` (number of deaths) seems to be the most relevant for our query. This property represents the total number of people who died as a direct result of an event or cause.

Now, let's construct a SPARQL query to retrieve the number of casualties resulting from the Troubles.

```sparql
SELECT ?casualties WHERE {
  wd:Q815436 wdt:P1120 ?casualties .
}
```

I will execute this query to get the result.

[Tool]
Got 1 row and 1 column:
| casualties         |
| ------------------ |
| 3532 (xsd:decimal) |

[Assistant]
The SPARQL query successfully returned the number of casualties resulting from the Troubles, which is 3,532.

Here is the final SPARQL query and the answer:

```sparql
SELECT ?casualties WHERE {
  wd:Q815436 wdt:P1120 ?casualties .
}
```

The number of casualties resulting from the Troubles is 3,532.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ( COUNT( ?brother ) AS ?livingBrothers ) WHERE {
  wd:Q189307 wdt:P3373 ?brother .
  FILTER NOT EXISTS {
    ?brother wdt:P570 ?deathDate
  }
}

Got 1 row and 1 column:
| livingBrothers |
| -------------- |
| 6 (xsd:int)    |

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

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

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

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

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

[User] How many of Reinhold Messner's brothers still live?

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

[Tool]
Example 1:
Question:
How often did Jane Fonda marry?

SPARQL:
SELECT ( COUNT( DISTINCT ?uri ) as ?c ) WHERE {
  wd:Q41142 wdt:P26 ?uri .
}

Using entities:
Jane Fonda, also known as Jane Seymour Fonda, Jayne Seymour Fonda, Hanoi Jane (wd:Q41142): American actress and activist, television actor, autobiographer, film producer, voice actor, stage actor, film actor, writer, model, actor

Using properties:
spouse, also known as married partner, married to, marry, partner, spouses (wd:P26 as wdt): the subject has the object as their spouse (husband, wife, partn...etc.). Use "unmarried partner" (P451) for non-married companions, significant person, relative

Example 2:
Question:
Give me all professional skateboarders from Sweden.

SPARQL:
SELECT ?s1 WHERE {
  ?s1 wdt:P641 wd:Q842284 .
  ?s1 wdt:P19 ?o1 .
  ?o1 wdt:P131 * wd:Q34 .
}

Using entities:
Sweden, also known as Swedish Kingdom, Konungariket Sverige, SWE, se, Kingdom of Sweden (wd:Q34): country in Northern Europe
skateboarding, also known as skateboard sport (wd:Q842284): action sport on skateboards

Using properties:
located in the administrative territorial entity, also known as is in the settlement of, is in the voivodeship of, is in the shire of, is in the state of, is in the territory of (wd:P131 as wdt): the item is located on the territory of the following administra...if the item falls only partially into the administrative entity., location, part of
place of birth, also known as POB, location of birth, location born, born in, birthplace (wd:P19 as wdt): most specific known birth location of a person, animal or fictional character, location
sport, also known as play, plays, sport played, sports (wd:P641 as wdt): sport that the subject participates or participated in or is associated with

Example 3:
Question:
What is the last work of Dan Brown?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P50 wd:Q7345 ; wdt:P577 ?date .
}
ORDER BY DESC ( ?date )
LIMIT 1

Using entities:
Dan Brown, also known as Daniel Gerhard Brown, Dan Gerhard Brown (wd:Q7345): American novelist, prose writer, researcher, journalist, musician, novelist, teacher, writer

Using properties:
author, also known as writer, written by, poet, playwright, maker (wd:P50 as wdt): main creator(s) of a written work (use on works, not humans); us...hor name string) when Wikidata item is unknown or does not exist, creator
publication date, also known as launched, published, publication time, publication, pubdate (wd:P577 as wdt): date or point in time when a work was first published or released, point in time

[Assistant] Based on the examples, we need to identify the entity for Reinhold Messner and then find out about his brothers. Let's start by searching for Reinhold Messner.

[Tool]
Top 10 entity alternatives:
1. Reinhold Messner (wd:Q189307): Italian mountaineer, adventurer and explorer, film director, mountaineer, politician, explorer, writer
2. Reinhold Messner, also known as Reinhold Meßner, Reinhold Oswald Messner (wd:Q121337911): Austrian Franciscan priest and philosopher, regular priest, philosopher
3. Markus Lanz und Reinhold Messner (wd:Q55978741): episode of Reinhold Beckmann trifft … (S1 E2)
4. The Unauthorized Biography of Reinhold Messner (wd:Q3989773): album by Ben Folds Five
5. 18092 Reinhold, also known as Reinhold (wd:Q2618622): asteroid
6. Reinhold (wd:Q2141320): lunar impact crater
7. Messner (wd:Q28812178): family name
8. Reinhold, also known as Reinhold (first name), Reinhold (given name) (wd:Q18091397): male given name
9. Bernhard Reinhold, also known as B. Reinhold, Reinhold (wd:Q16761534): German painter, painter
10. Messner (wd:Q1777589): 2012 film directed by Andreas Nickel

[Assistant] The correct entity for Reinhold Messner is the first one, wd:Q189307. Now let's find out about his brothers. We'll look for the property that indicates siblings or brothers.

[Tool]
Top 10 property alternatives:
1. sibling, also known as is sister of, is the brother of, is the sibling of, is the sister of, sib (wd:P3373 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): the subject and the object have at least one common parent (brot...law, etc.) and step-siblings (step-brothers, step-sisters, etc.), relative
2. relative, also known as relatives, grandparent, relation, non-binary parent, niece (wd:P1038 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): family member (qualify with "kinship to subject", P1039; for direct family member please use specific property), significant person
3. family, also known as royal house, noble family, member of family, house, dynasty (wd:P53 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): family, including dynasty and nobility houses. Not family name (use P734 for family name)., member of
4. child, also known as has son, kids, mother of, kid, issue (wd:P40 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): subject has object as child. Do not use for stepchildren—use "relative" (P1038), qualified with "type of kinship" (P1039), significant person, relative
5. significant person, also known as significant people, sigper, sign. person, relevant person, relevant people (wd:P3342 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): person linked to the item in any possible way
6. unmarried partner, also known as romantic partner, partner of, partners, posslq, relationship (wd:P451 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): someone with whom the person is in a relationship without being married. Use "spouse" (P26) for married couples, significant person
7. spouse, also known as married partner, married to, marry, partner, spouses (wd:P26 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): the subject has the object as their spouse (husband, wife, partn...etc.). Use "unmarried partner" (P451) for non-married companions, significant person, relative
8. father, also known as son of, child of, parent, is son of, is daughter of (wd:P22 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): male parent of the subject. For stepfather, use "stepparent" (P3448), parent
9. mother, also known as mom, is child of, mommy, mum, mummy (wd:P25 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): female parent of the subject. For stepmother, use "stepparent" (P3448), parent
10. has basionym, also known as basionym (wd:P566 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): the legitimate, previously published name on which a new combination or name at new rank is based

[Assistant] The property for siblings is wd:P3373. Now let's check if Reinhold Messner has any brothers listed in Wikidata.

[Tool]
Got 8 rows and 1 column:
| brother                            |
| ---------------------------------- |
| Günther Messner (wd:Q195351)       |
| Hubert Messner (wd:Q28032172)      |
| Helmut Messner (wd:Q28053539)      |
| Siegfried Messner (wd:Q28053544)   |
| Erich Messner (wd:Q28053548)       |
| Waltraud Kastlunger (wd:Q28053561) |
| Hansjörg Messner (wd:Q28053579)    |
| Werner Messner (wd:Q28053586)      |

[Assistant] Reinhold Messner has eight siblings listed in Wikidata. To determine how many of them are still alive, we need to check if there is a property indicating death or life status. Let's search for such a property.

[Tool]
Top 10 property alternatives:
1. floruit, also known as work period, living, known alive in, flourished, fl. (wd:P1317 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): date when the person was known to be active or alive, when birth or death not documented
2. number of deaths, also known as dead, qty fatalities, qty deaths, number of fatalities, fatalities (wd:P1120 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): total (cumulative) number of people who died since start as a direct result of an event or cause
3. date of death, also known as DOD, deathdate, died, died on, year of death (wd:P570 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): dissolved, abolished or demolished date, date on which the subject died, end time
4. work location, also known as work place, working from, working at, worked from, worked at (wd:P937 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): location where persons or organisations were actively participating in employment, business or other work, significant place, location
5. located in the present-day administrative territorial entity, also known as present-day administrative territorial entity, located in the current administrative territorial entity, located in the modern-day administrative territorial entity, located in the present-day administrative territorial entity, would today be located in administrative territorial entity (wd:P3842 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): the item was located in the territory of this present-day administrative unit; however the two did not at any point coexist in time, location, part of
6. conflict, also known as war, theatre (military), theater (military), participated in conflict, military conflict (wd:P607 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): battles, wars or other military engagements in which the person or item participated, participant in
7. official residence, also known as resides at, lives in, palace, presidential mansion, presidential palace (wd:P263 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): the residence at which heads of government and other senior figures officially reside, residence
8. residence, also known as living in, place of residence, resided at, resided in, resident in (wd:P551 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): the place where the person is or has been, resident, location
9. image, also known as still capture, still, screenshot, screen capture, portrait (wd:P18 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)
10. start time, also known as start date, since, valid, join time, joined (wd:P580 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): time an entity begins to exist or a statement starts being valid, point in time

[Assistant] The property for indicating whether someone is alive or dead is wd:P570 (date of death). If a person has this property, it means they are deceased. Let's check which of Reinhold Messner's brothers have a date of death listed.

[Tool]
Got 8 rows and 2 columns:
| brother                            | deathDate                           |
| ---------------------------------- | ----------------------------------- |
| Günther Messner (wd:Q195351)       | 1970-06-29T00:00:00Z (xsd:dateTime) |
| Hubert Messner (wd:Q28032172)      |                                     |
| Helmut Messner (wd:Q28053539)      |                                     |
| Siegfried Messner (wd:Q28053544)   | 1985-07-20T00:00:00Z (xsd:dateTime) |
| Erich Messner (wd:Q28053548)       |                                     |
| Waltraud Kastlunger (wd:Q28053561) |                                     |
| Hansjörg Messner (wd:Q28053579)    |                                     |
| Werner Messner (wd:Q28053586)      |                                     |

[Assistant]
From the results, we can see that Günther Messner and Siegfried Messner have a date of death listed, meaning they are deceased. The other six siblings do not have a date of death listed, which suggests they are still alive.

To provide a clear answer, we can count the number of brothers who do not have a date of death listed.

Here is the final SPARQL query:

```sparql
SELECT (COUNT(?brother) AS ?livingBrothers) WHERE {
  wd:Q189307 wdt:P3373 ?brother .
  FILTER NOT EXISTS { ?brother wdt:P570 ?deathDate }
}
```

Let's execute this query to get the count of living brothers.

[Tool]
Got 1 row and 1 column:
| livingBrothers |
| -------------- |
| 6 (xsd:int)    |

[Assistant]
The SPARQL query has returned that 6 of Reinhold Messner's brothers are still alive.

Therefore, the answer to the question "How many of Reinhold Messner's brothers still live?" is 6.

[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 ( COUNT( ?olympicGame ) AS ?count ) WHERE {
  wd:Q25369 wdt:P1344 ?olympicGame .
  ?olympicGame rdfs:label ?label .
  FILTER ( LANG( ?label ) = 'en' ) .
  FILTER ( CONTAINS( ?label , 'Summer Olympics' ) || CONTAINS( ?label , 'Winter Olympics' ) )
}

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

Kobe Bryant participated in 2 Olympic Games.

[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 Olympic Games has Kobe Bryant taken part in?

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

[Tool]
Example 1:
Question:
How many scientists graduated from an Ivy League university?

SPARQL:
SELECT ( COUNT( DISTINCT ?uri ) AS ?c ) WHERE {
  ?uri wdt:P106 wd:Q901 .
  ?uri wdt:P69 ?university .
  wd:Q49113 wdt:P527 ?university .
}

Using entities:
Ivy League (wd:Q49113): athletic conference of 8 American universities
scientist, also known as natural philosopher (wd:Q901): person who use scientific methods to study in an area of interest

Using properties:
occupation, also known as job, work, vocation, profession, employment (wd:P106 as wdt): occupation of a person; see also "field of work" (Property:P101), "position held" (Property:P39)
has part(s), also known as has component, incorporates, has ingredient, has ingredients, has member (wd:P527 as wdt): part of this subject; inverse property of "part of" (P361). See also "has parts of the class" (P2670).
educated at, also known as graduated from, graduate of, place of education, school attended, schooled at (wd:P69 as wdt): educational institution attended by subject, affiliation

Example 2:
Question:
Who is the mayor of New York City?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q60 wdt:P6 ?uri .
}

Using entities:
New York City, also known as Capital of the World, Big Apple, the five boroughs, The City So Nice They Named It Twice, NY, NY (wd:Q60): most populous city in the United States

Using properties:
head of government, also known as head of national government, mayor, premier, president, prime minister (wd:P6 as wdt): head of the executive power of this town, city, municipality, state, country, or other governmental body, director / manager, chairperson

Example 3:
Question:
Which actors play in Big Bang Theory?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q8539 wdt:P161 ?uri
}

Using entities:
The Big Bang Theory, also known as Big Bang Theory, TBBT (wd:Q8539): American television sitcom (2007–2019)

Using properties:
cast member, also known as feat., starring (film/play), film starring, starring, actor (wd:P161 as wdt): actor in the subject production [use "character role" (P453) and...use "recorded participant" (P11108) for non-fiction productions], significant person, performer

[Assistant]
From the examples, we can see how to structure a SPARQL query to count the number of times an entity participates in an event. We need to identify the entity for Kobe Bryant and the property that links him to the Olympic Games he participated in.

First, let's search for the entity representing Kobe Bryant.

[Tool]
Top 10 entity alternatives:
1. Kobe Bryant, also known as Kobe Bean Bryant, Kobe B. Bryant, Kobe, KB24, Black Mamba (wd:Q25369): American basketball player (1978–2020), professional athlete, basketball player, screenwriter
2. Kobe Bryant Day (wd:Q98969272): proposed Federal Holiday
3. death of Kobe Bryant, also known as Kobe Bryant death, Kobe Bryant's death (wd:Q83737550): 26 January 2020 death of Kobe Bryant in helicopter crash
4. Kobe Bryant Boulevard (wd:Q107299206): proposed street renaming in Los Angeles
5. NBA All-Star Game Kobe Bryant Most Valuable Player Award, also known as Kobe Bryant Most Valuable Player Award, Kobe Bryant MVP Award, NBA All-Star Game Kobe Bryant MVP Award (wd:Q31391): basketball award of the National Basketball Association (NBA)
6. Kobe Bryant China Fund (wd:Q6424297): for education, sports, and culture programs for children from China and the United States
7. Statue of Kobe Bryant (wd:Q125101336): sculpture in Los Angeles, California, U.S
8. Kobe Bryant/Ice Cube (wd:Q125446155): episode of All That (S4 E19)
9. Kobe Bryant sexual assault case (wd:Q6424298): legal case
10. Kobe Bryant in NBA Courtside (wd:Q3198120): 1998 video game

[Assistant] The correct entity for Kobe Bryant is `wd:Q25369`. Next, we need to find the property that links Kobe Bryant to the Olympic Games he participated in. Let's search for properties related to "Olympic Games" for Kobe Bryant.

[Tool]
Top 10 property alternatives:
1. sport, also known as play, plays, sport played, sports (wd:P641 as p/wdt): sport that the subject participates or participated in or is associated with
2. Sports-Reference.com Olympic athlete ID (archived), also known as Olympic Reference ID, Sports-Reference.com Olympic athlete identifier (archived), Sports Reference ID, SRID, SR/Olympics athlete ID (wd:P1447 as p/wdt): identifier for an Olympic athlete (sportsperson) at sports-reference.com/olympics/athletes/
3. award received, also known as win, winner of, recognition title, received award, prize received (wd:P166 as p/wdt): award or recognition received by a person, organization or creative work
4. country for sport, also known as sports nationality, sports country, sport nationality, sporting nationality, sport country (wd:P1532 as p/wdt): country a person or a team represents when playing a sport, country
5. Olympic.org athlete ID (archived), also known as Olympic.org athlete identifier (archived), Olympic.org ID (wd:P3171 as p/wdt): identifier for an athlete (sportsperson) at Olympic.org, a former website of the International Olympic Committee
6. participant in, also known as participated in, present at, takes part, took part, took part in (wd:P1344 as p/wdt): event in which a person or organization was/is a participant; inverse of P710 or P1923, significant event, part of
7. member of sports team, also known as plays for, played for, sport team, team, team played for (wd:P54 as p/wdt): sports teams or clubs that the subject represents or represented, member of
8. Olympics.com athlete ID, also known as OC ID (archived), OCID (archived), OlympicChannel athlete ID (archived), OlympicChannel.com athlete ID (archived) (wd:P5815 as p/wdt): identifier for an athlete (sportsperson) at Olympics.com, a webs...c Committee (IOC) - same ID as former website OlympicChannel.com
9. significant event, also known as significant occurrence, significant incident, significant achievment, sign. event, outcome (wd:P793 as p/wdt): significant or notable events associated with the subject
10. league or competition, also known as tournament, sports league, division, cup, competition (wd:P118 as p/wdt): league or competition in which team or player has played, or in which an event occurs, participant in

No other items found

[Assistant]
The property `wdt:P1344` (participant in) seems to be the most relevant for linking Kobe Bryant to the Olympic Games he participated in. Let's use this property to find the Olympic Games events that Kobe Bryant participated in.

We'll start by listing the Olympic Games events that Kobe Bryant participated in.

[Tool]
Got 3 rows and 1 column:
| olympicGame                                  |
| -------------------------------------------- |
| 2007 FIBA Americas Championship (wd:Q178595) |
| 2008 Summer Olympics (wd:Q8567)              |
| 2012 Summer Olympics (wd:Q8577)              |

[Assistant]
The results show that Kobe Bryant participated in three events, including the 2008 Summer Olympics and the 2012 Summer Olympics. However, the 2007 FIBA Americas Championship is not an Olympic Games event. To ensure we only count the Olympic Games, we need to filter out non-Olympic events.

Let's refine the query to count only the Olympic Games events.

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

[Assistant]
It appears that the initial filter did not return any results, which suggests that the property `wdt:P31` (instance of) with the value `wd:Q11302` (Olympic Games) might not be the correct way to filter the events. Let's try another approach by checking the labels of the events to ensure they are indeed Olympic Games.

We'll list the events again and include their labels to verify.

[Tool]
Got 3 rows and 2 columns:
| olympicGame                                  | label                                |
| -------------------------------------------- | ------------------------------------ |
| 2007 FIBA Americas Championship (wd:Q178595) | 2007 FIBA Americas Championship (en) |
| 2008 Summer Olympics (wd:Q8567)              | 2008 Summer Olympics (en)            |
| 2012 Summer Olympics (wd:Q8577)              | 2012 Summer Olympics (en)            |

[Assistant]
The labels confirm that the 2008 Summer Olympics and the 2012 Summer Olympics are indeed Olympic Games events. The 2007 FIBA Americas Championship is not an Olympic Games event, so we need to filter it out.

To filter out non-Olympic Games events, we can use a pattern that checks if the event is labeled as "Summer Olympics" or "Winter Olympics". Let's refine the query to count only the Olympic Games events based on their labels.

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

[Assistant]
The refined query correctly counts the number of Olympic Games that Kobe Bryant participated in, which is 2.

Here is the final SPARQL query and the answer:

```sparql
SELECT (COUNT(?olympicGame) AS ?count) WHERE {
  wd:Q25369 wdt:P1344 ?olympicGame .
  ?olympicGame rdfs:label ?label .
  FILTER(LANG(?label) = 'en') .
  FILTER(CONTAINS(?label, 'Summer Olympics') || CONTAINS(?label, 'Winter Olympics'))
}
```

Kobe Bryant participated in 2 Olympic Games.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?state WHERE {
  wd:Q732858 wdt:P131 ?state .
  ?state wdt:P31 wd:Q35657 .
}

New Jersey (wd:Q1408)

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

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

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

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

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

[User] which state inside USA is batman living in?

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

[Tool]
Example 1:
Question:
Give me all soccer clubs in Spain.

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P31 wd:Q476028 .
  ?uri wdt:P17 wd:Q29 .
}

Using entities:
Spain, also known as Kingdom of Spain (wd:Q29): country in southwestern Europe with territories in Africa
association football club, also known as association soccer club, F.C., FC, football club, soccer club (wd:Q476028): sports club devoted to association football (soccer)

Using properties:
country, also known as sovereign state, state, land, host country (wd:P17 as wdt): sovereign state that this item is in (not to be used for human beings), located in the administrative territorial entity
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)

Example 2:
Question:
In which state Penn State University is located?

SPARQL:
SELECT ?res WHERE {
  wd:Q739627 wdt:P131 ?res .
  ?res wdt:P31 / wdt:P279 * wd:Q106458883 .
}

Using entities:
state (wd:Q106458883): title used for first-level administrative division in several countries
Pennsylvania State University, also known as Penn State, PennState, Penn State University, Pennsylvania agricultural college, Pennsylvania State University, State College (wd:Q739627): public university in Pennsylvania, United States

Using properties:
located in the administrative territorial entity, also known as is in the settlement of, is in the voivodeship of, is in the shire of, is in the state of, is in the territory of (wd:P131 as wdt): the item is located on the territory of the following administra...if the item falls only partially into the administrative entity., location, part of
subclass of, also known as rdfs:subClassOf, sort of, subcategory of, subgenre of, subtype of (wd:P279 as wdt): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)

Example 3:
Question:
Which companies work in the aerospace industry as well as in medicine?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?type wdt:P279 * wd:Q4830453 .
  ?aerospace wdt:P279 * wd:Q3477363 .
  ?healthcare wdt:P279 * wd:Q15067276 .
  ?pharma wdt:P279 * wd:Q507443 .
  ?uri wdt:P452 ?aerospace ; wdt:P452 ?healthcare .
}

Using entities:
health care industry, also known as healthcare industry, health industry, medical care industry, medical industry (wd:Q15067276): industry dedicated to providing health care services and products
aerospace industry, also known as aircraft industry (wd:Q3477363): industrial sector
business (wd:Q4830453): organization undertaking commercial, industrial, or professional activity
pharmaceutical industry, also known as drug industry, pharma, pharmaceutical wholesaler, pharma industry (wd:Q507443): develops, produces, and markets drugs or pharmaceuticals licensed for use as medications

Using properties:
subclass of, also known as rdfs:subClassOf, sort of, subcategory of, subgenre of, subtype of (wd:P279 as wdt): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
industry, also known as branch, economic branch, field of action, field of exercise, sector (wd:P452 as wdt): specific industry of company or organization, part of

[Assistant] Based on the examples, we need to identify the entity for Batman and then find out the state he is associated with. Let's start by searching for Batman.

[Tool]
Top 10 entity alternatives:
1. Batman, also known as the Dark Knight, Mordecai Wayne, Bruce Wayne, Wayne, Bruce, the Insider (wd:Q2695156): fictional character in DC Comics, chief executive officer, philanthropist, businessperson, martial artist, vigilante, detective, superhero
2. Batman, also known as the Bat-Man, the Batman, the Caped Crusader, the Bat, Sir Hemingford Grey (wd:Q2695156): fictional character in DC Comics, chief executive officer, businessperson, martial artist, philanthropist, vigilante, detective, superhero
3. Batman (wd:Q159835): central district and city in Turkey
4. Batman Province, also known as Batman (wd:Q80370): province of Turkey
5. Batman, also known as Tim Burton's Batman (wd:Q116852): 1989 film directed by Tim Burton
6. The Batman, also known as Batman (wd:Q61117344): 2022 superhero film by Matt Reeves
7. Batman: The Animated Series, also known as The Adventures of Batman & Robin., BTAS, Batman The Animated Series, Batman TAS, Batman: TAS (wd:Q673517): American animated television series
8. Batman, also known as Batman: The Movie (wd:Q810857): 1966 film directed by Leslie H. Martinson
9. Batman, also known as Batman 1966, Batman 66, Batman '66 (wd:Q1247201): 1960s American television series
10. The Batman, also known as Batman (wd:Q635933): American animated television series (2004–2008)

[Assistant]
The top result for "Batman" is the fictional character from DC Comics. We will use this entity to find out the state he is associated with. In the context of Batman, he is often associated with Gotham City. Let's find the entity for Gotham City and then determine the state it is located in.

First, let's search for Gotham City.

[Tool]
Top 10 entity alternatives:
1. Gotham City, also known as Gotham (wd:Q732858): fictional city in the DC Universe, best known as the home of Batman
2. Gotham City (wd:Q654286): song by R. Kelly
3. Gotham City (wd:Q104152700): Swiss media
4. Gotham City (wd:Q96379743): location in several theme parks
5. Gotham City (wd:Q96379742): 1981 studio album by Dexter Gordon
6. Gotham City (wd:Q65167993): album
7. Gotham City (wd:Q131379567): themed area at Warner Bros. World Abu Dhabi
8. Gotham City Impostors (wd:Q615990): 2012 video game
9. Gotham City Sirens (wd:Q5587804): American comic book series
10. Babarsari, also known as Babarsari Gotham City, Babarsari Sleman, Babarsari Yogyakarta (wd:Q112918949): settlement and road section in Indonesia

[Assistant]
The top result for "Gotham City" is the fictional city in the DC Universe, which is the home of Batman. Now, let's find out the state where Gotham City is located. We will use the `wdt:P131` property, which indicates the administrative territorial entity where something is located.

Let's execute a SPARQL query to find the state where Gotham City is located.

[Tool]
Got 3 rows and 1 column:
| state                                      |
| ------------------------------------------ |
| New Jersey (wd:Q1408)                      |
| United States (wd:Q30)                     |
| East Coast of the United States (wd:Q4268) |

[Assistant]
The results show that Gotham City is associated with multiple locations, including New Jersey, the United States, and the East Coast of the United States. However, since the question specifically asks for the state inside the USA where Batman lives, we need to filter out the broader regions and focus on the state.

Let's refine the query to ensure we get the correct state. We will use the `wdt:P31` property to filter for states, which are instances of the state class.

Let's execute the refined SPARQL query.

[Tool]
Got 1 row and 1 column:
| state                 |
| --------------------- |
| New Jersey (wd:Q1408) |

[Assistant]
The refined query confirms that Gotham City is located in New Jersey.

Therefore, the state inside the USA where Batman lives is New Jersey.

Here is the final SPARQL query and answer:

```sparql
SELECT ?state WHERE {
  wd:Q732858 wdt:P131 ?state .
  ?state wdt:P31 wd:Q35657 .
}
```

Answer: New Jersey (wd:Q1408)

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
ASK WHERE {
  wd:Q152778 wdt:P112 ?member .
  ?member wdt:P21 wd:Q6581070 .
}

[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 women were amongst the founding members of “The Bridge” (“Die Brücke”)?

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

[Tool]
Example 1:
Question:
In which films did Julia Roberts as well as Richard Gere play?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P31 wd:Q11424 .
  ?uri wdt:P161 wd:Q48410 .
  ?uri wdt:P161 wd:Q40523 .
}

Using entities:
film, also known as cinematic work, moving picture, movie, motion pictures, motion picture (wd:Q11424): sequence of images that give the impression of movement, stored on film stock
Julia Roberts, also known as Julia Fiona Roberts (wd:Q40523): American actress (born 1967), television actor, film producer, voice actor, stage actor, film actor, actor, model
Richard Gere, also known as Richard Tiffany Gere (wd:Q48410): American actor (born 1949), human rights activist, television actor, film director, film producer, photographer, voice actor, stage actor, film actor, actor

Using properties:
cast member, also known as feat., starring (film/play), film starring, starring, actor (wd:P161 as wdt): actor in the subject production [use "character role" (P453) and...use "recorded participant" (P11108) for non-fiction productions], significant person, performer
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)

Example 2:
Question:
When did Muhammad die?

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

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

Using properties:
date of death, also known as DOD, deathdate, died, died on, year of death (wd:P570 as wdt): dissolved, abolished or demolished date, date on which the subject died, end time

Example 3:
Question:
Who is the youngest Darts player?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P106 wd:Q18574233 ; wdt:P569 ?dateOfBirth .
}
ORDER BY DESC ( ?dateOfBirth )
LIMIT 1

Using entities:
darts player, also known as dartsman, dartsperson, dartswoman (wd:Q18574233): person who plays darts

Using properties:
occupation, also known as job, work, vocation, profession, employment (wd:P106 as wdt): occupation of a person; see also "field of work" (Property:P101), "position held" (Property:P39)
date of birth, also known as year of birth, birthdate, DOB, born on, born (wd:P569 as wdt): date on which the subject was born, inception

[Assistant] Based on the examples, we need to identify the entity "The Bridge" ("Die Brücke") and then look for its founding members, specifically those who are women. Let's start by searching for the entity "The Bridge" ("Die Brücke").

[Tool]
Top 10 entity alternatives:
1. Die Brücke, also known as The Bridge (wd:Q152778): German expressionist artist group
2. The Bridge, also known as Die Brücke, Freedom to Die (wd:Q509003): 1959 film directed by Bernhard Wicki
3. The Bridge, also known as Die Brücke (wd:Q1211772): short story by Franz Kafka
4. art group, also known as art circle, artist group, artistic circle, artistic school, group of artists (wd:Q4502119): club or group of artists who share a common ideal
5. Die Brücke (wd:Q1211761): 2008 film directed by Wolfgang Panzer
6. Die Brücke (wd:Q895658): voluntary association
7. Die Brücke (wd:Q1211771): 1958 novel by Gregor Dorfmeister
8. Die Art (wd:Q1211099): German band
9. Art Group, also known as Art Groupart (wd:Q11285786)
10. Group of artists, also known as An Artists’ Group (The Painters of the Brücke) (wd:Q27533790): painting by Ernst Ludwig Kirchner

[Assistant] The correct entity for "The Bridge" ("Die Brücke") is `wd:Q152778`. Now, let's find out the founding members of this art group. We will search for the property that indicates founding members.

[Tool]
Top 10 property alternatives:
1. inception, also known as initial release date, founded in, founded on, founded on date, founding date (wd:P571 as p/wdt): time when an entity begins to exist; for date of official opening use P1619, start time
2. founded by, also known as starter, co-founded by, started by, institutor, instituted by (wd:P112 as p/wdt): founder or co-founder of this organization, religion, place or entity, creator
3. influenced by, also known as favorite player, has influence, informed by, role model (wd:P737 as p/wdt): this person, idea, etc. is informed by that other person, idea, etc., e.g. “Heidegger was influenced by Aristotle”

No other items found

[Assistant]
The property `founded by` (`wdt:P112`) seems to be the most relevant for identifying the founding members of "The Bridge" ("Die Brücke"). Let's use this property to find the founding members and then filter out the women among them.

First, let's list the founding members of "The Bridge" ("Die Brücke").

[Tool]
Got 4 rows and 1 column:
| member                             |
| ---------------------------------- |
| Erich Heckel (wd:Q156700)          |
| Karl Schmidt-Rottluff (wd:Q161143) |
| Fritz Bleyl (wd:Q164124)           |
| Ernst Ludwig Kirchner (wd:Q229272) |

[Assistant]
The founding members of "The Bridge" ("Die Brücke") are Erich Heckel, Karl Schmidt-Rottluff, Fritz Bleyl, and Ernst Ludwig Kirchner. Now, let's check if any of these members are women. We will use the property `sex or gender` (`wdt:P21`) to determine their genders.

Let's write a SPARQL query to check the gender of each founding member.

[Tool]
Got 4 rows and 2 columns:
| member                             | gender             |
| ---------------------------------- | ------------------ |
| Erich Heckel (wd:Q156700)          | male (wd:Q6581097) |
| Karl Schmidt-Rottluff (wd:Q161143) | male (wd:Q6581097) |
| Fritz Bleyl (wd:Q164124)           | male (wd:Q6581097) |
| Ernst Ludwig Kirchner (wd:Q229272) | male (wd:Q6581097) |

[Assistant]
All the founding members of "The Bridge" ("Die Brücke") are male. Therefore, there are no women among the founding members.

To finalize this, we can use an ASK query to confirm that there are no female founding members.

Here is the final SPARQL query:

```sparql
ASK WHERE {
  wd:Q152778 wdt:P112 ?member .
  ?member wdt:P21 wd:Q6581070 .
}
```

This query checks if there is any founding member of "The Bridge" who is female (where `wd:Q6581070` represents the female gender).

Let's execute this query.

[Tool] False

[Assistant]
The ASK query returned `False`, confirming that there are no female founding members of "The Bridge" ("Die Brücke").

Therefore, the answer to the question "How many women were amongst the founding members of ‘The Bridge’ ('Die Brücke')?" is 0.

Final SPARQL query and answer:

```sparql
ASK WHERE {
  wd:Q152778 wdt:P112 ?member .
  ?member wdt:P21 wd:Q6581070 .
}
```

Answer: 0

Let's provide the final answer using the `answer` function.

[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 ?parent ?parentLabel WHERE {
  VALUES ?parentProperty {
    wdt:P22 wdt:P25
  }
  wd:Q913 ?parentProperty ?parent .
  OPTIONAL {
    ?parent rdfs:label ?parentLabel .
    FILTER ( LANG( ?parentLabel ) = "en" )
  }
}

Got 2 rows and 2 columns:
| parent                     | parentLabel       |
| -------------------------- | ----------------- |
| Phaenarete (wd:Q2263186)   | Phaenarete (en)   |
| Sophroniscus (wd:Q2303035) | Sophroniscus (en) |

The parents of Socrates are Phaenarete (mother) and Sophroniscus (father).

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

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

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

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

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

[User] Who were the parents of Socrates ?

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

[Tool]
Example 1:
Question:
Which countries adopted the Euro?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P31 wd:Q6256 .
  ?uri wdt:P38 wd:Q4916 .
}

Using entities:
Euro, also known as euros (wd:Q4916): currency of most countries in the European Union
country, also known as countries (wd:Q6256): distinct territorial body or political entity

Using properties:
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
currency, also known as base currency, used money (wd:P38 as wdt): currency used by item, uses

Example 2:
Question:
Give me all movies with Tom Cruise.

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P161 wd:Q37079 .
}

Using entities:
Tom Cruise, also known as Thomas Cruise Mapother IV (wd:Q37079): American actor and producer (born 1962), stunt performer, aircraft pilot, film producer, film director, screenwriter, film actor, producer, director, writer, actor

Using properties:
cast member, also known as feat., starring (film/play), film starring, starring, actor (wd:P161 as wdt): actor in the subject production [use "character role" (P453) and...use "recorded participant" (P11108) for non-fiction productions], significant person, performer

Example 3:
Question:
Give me all Danish films.

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?type wdt:P279 * wd:Q11424 .
  ?uri wdt:P31 ?type ; wdt:P495 wd:Q35 .
}

Using entities:
film, also known as cinematic work, moving picture, movie, motion pictures, motion picture (wd:Q11424): sequence of images that give the impression of movement, stored on film stock
Denmark, also known as 🇩🇰, Danmark, DEK, TAN, DEN (wd:Q35): country in Northern Europe

Using properties:
subclass of, also known as rdfs:subClassOf, sort of, subcategory of, subgenre of, subtype of (wd:P279 as wdt): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
country of origin, also known as origin country, place of origin, originates from, country, CoO (wd:P495 as wdt): country of origin of this item (creative work, food, phrase, product, etc.), significant place, country

[Assistant] Based on the examples, we need to identify the entity for Socrates and the property for parent or parents. Let's first search for the entity Socrates.

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

[Assistant] The correct entity for Socrates, the ancient Greek philosopher, is `wd:Q913`. Next, let's find the property for parents or parent.

[Tool]
Top 10 property alternatives:
1. parent, also known as guardian, parents (wd:P8810 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): parent of the subject. Only use if neither father (P22) nor mother (P25) nor stepparent (P3448) is applicable., relative
2. mother, also known as mom, is child of, mommy, mum, mummy (wd:P25 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): female parent of the subject. For stepmother, use "stepparent" (P3448), parent
3. father, also known as son of, child of, parent, is son of, is daughter of (wd:P22 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): male parent of the subject. For stepfather, use "stepparent" (P3448), parent
4. child, also known as has son, kids, mother of, kid, issue (wd:P40 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): subject has object as child. Do not use for stepchildren—use "relative" (P1038), qualified with "type of kinship" (P1039), significant person, relative
5. broader concept, also known as parent concept, wider term, wider concept, nearest matched item above this one in external hierarchy, is a child concept of (wd:P4900 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): qualifier to indicate a broader concept that the present item is... source. The statement being qualified should be an exact match.
6. parent club, also known as affiliate of, major league affiliate, parent team (wd:P831 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): parent club of this team
7. parent organization, also known as parent company, parent institution, parent organisation, parent unit, part of (wd:P749 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): parent organization of an organization, opposite of subsidiaries (P355), owned by, part of
8. parent astronomical body, also known as star, primary, parent body, orbits, planetary system (wd:P397 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): major astronomical body the item belongs to
9. subproperty of, also known as subclass of property, superproperty, subPropertyOf, rdfs:subPropertyOf, parent property (wd:P1647 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): all resources related by this property are also related by that property, related property
10. hybrid of, also known as cultivar from, parent breed, parent cultivar, parent of this hybrid, parent of this hybrid, breed, or cultivar (wd:P1531 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): parent of this hybrid breed or cultivar

[Assistant]
The properties for parent are `wd:P22` for father and `wd:P25` for mother. We will use these properties to find the parents of Socrates. Let's construct a SPARQL query to retrieve this information.

Here is the initial SPARQL query:

```sparql
SELECT ?parent ?parentLabel WHERE {
  wd:Q913 wdt:P22 ?father .
  wd:Q913 wdt:P25 ?mother .
  OPTIONAL { ?father rdfs:label ?fatherLabel . FILTER(LANG(?fatherLabel) = "en") }
  OPTIONAL { ?mother rdfs:label ?motherLabel . FILTER(LANG(?motherLabel) = "en") }
  BIND(COALESCE(?fatherLabel, ?motherLabel) AS ?parentLabel)
}
```

This query will retrieve both the father and mother of Socrates, along with their labels in English. Let's execute it.

[Tool]
Got 1 row and 2 columns:
| parent | parentLabel       |
| ------ | ----------------- |
|        | Sophroniscus (en) |

[Assistant]
The query returned only the mother of Socrates, Sophroniscus. However, it seems that the father is not linked directly with the `wdt:P22` property. Let's check if there are any other properties or statements that might provide the father's information.

We can try searching for properties related to Socrates that might include his father.

Let's list the properties of Socrates to see if there are any other relevant properties.

[Tool]
Got 1,374 rows and 3 columns, showing the first 10 rows below:
| subject            | property                         | object                                                                     |
| ------------------ | -------------------------------- | -------------------------------------------------------------------------- |
| Socrates (wd:Q913) | instance of (wdt:P31)            | human (wd:Q5)                                                              |
| Socrates (wd:Q913) | occupation (wdt:P106)            | writer (wd:Q36180)                                                         |
| Socrates (wd:Q913) | sex or gender (wdt:P21)          | male (wd:Q6581097)                                                         |
| Socrates (wd:Q913) | given name (wdt:P735)            | Sokratis (wd:Q61793426)                                                    |
| Socrates (wd:Q913) | date of birth (wdt:P569)         | -468-01-01T00:00:00Z (xsd:dateTime)                                        |
| Socrates (wd:Q913) | Commons category (wdt:P373)      | Socrates                                                                   |
| Socrates (wd:Q913) | image (wdt:P18)                  | <http://commons.wikimedia.org/wiki/Special:FilePath/Socrates%20Louvre.jpg> |
| Socrates (wd:Q913) | country of citizenship (wdt:P27) | Classical Athens (wd:Q844930)                                              |
| Socrates (wd:Q913) | described at URL (wdt:P973)      | <http://www.digiporta.net/index.php?id=144805129>                          |
| Socrates (wd:Q913) | place of birth (wdt:P19)         | Athens (wd:Q1524)                                                          |
| ...                | ...                              | ...                                                                        |

[Assistant]
From the list of properties, we can see that there is no direct property linking Socrates to his father. However, we can try to search for the father using the `rdfs:label` property to see if there is any mention of his father in the labels.

Let's modify the SPARQL query to include a broader search for the father by checking if there are any statements that might indirectly link to his father.

Here is the updated SPARQL query:

```sparql
SELECT ?parent ?parentLabel WHERE {
  VALUES ?parentProperty { wdt:P22 wdt:P25 }
  wd:Q913 ?parentProperty ?parent .
  OPTIONAL { ?parent rdfs:label ?parentLabel . FILTER(LANG(?parentLabel) = "en") }
}
```

This query will check both the `wdt:P22` (father) and `wdt:P25` (mother) properties and retrieve the labels for both parents. Let's execute this query.

[Tool]
Got 2 rows and 2 columns:
| parent                     | parentLabel       |
| -------------------------- | ----------------- |
| Phaenarete (wd:Q2263186)   | Phaenarete (en)   |
| Sophroniscus (wd:Q2303035) | Sophroniscus (en) |

[Assistant]
The query returned both the mother and father of Socrates:

- Mother: Phaenarete
- Father: Sophroniscus

Now we can finalize the SPARQL query and provide the answer to the user.

Here is the final SPARQL query:

```sparql
SELECT ?parent ?parentLabel WHERE {
  VALUES ?parentProperty { wdt:P22 wdt:P25 }
  wd:Q913 ?parentProperty ?parent .
  OPTIONAL { ?parent rdfs:label ?parentLabel . FILTER(LANG(?parentLabel) = "en") }
}
```

And the answer to the user's question is:

The parents of Socrates are Phaenarete (mother) and Sophroniscus (father).

Let's finalize this with the `answer` function.

[Tool] Stopped generation process


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

False

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

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

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

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

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

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

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

[Tool]
Example 1:
Question:
Who is the governor of Texas?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q1439 wdt:P1313 wd:Q5589725 .
  wd:Q5589725 wdt:P1308 ?uri .
}

Using entities:
Texas, also known as Lone Star State, US-TX, TX, Texas, United States, Tex. (wd:Q1439): state of the United States of America
Governor of Texas, also known as Governor of the State of Texas, Texas Governor (wd:Q5589725): head of state and of government of the U.S. state of Texas

Using properties:
position holder, also known as incumbent, noble title holder, office holder, position held by (wd:P1308 as wdt): persons who hold and/or held the position
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 wdt): political office that is fulfilled by the head of the government of this item

Example 2:
Question:
Who is the editor of Forbes?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q25161472 wdt:P98 ?uri .
}

Using entities:
Forbes, also known as forbes.com, Forbes magazine (wd:Q25161472): American business magazine

Using properties:
editor, also known as associate editor, compiled by, ed., edited by, editorial director (wd:P98 as wdt): person who checks and corrects a work (such as a book, newspaper...o establishes the text of an ancient written work or manuscript.

Example 3:
Question:
In what city is the Heineken brewery?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q854383 wdt:P176 / wdt:P159 ?uri .
}

Using entities:
Heineken, also known as Heineken Lager Beer, Heineken Pilsener (wd:Q854383): pale lager beer by the Dutch brewing company Heineken International

Using properties:
headquarters location, also known as headquarters locality, seat, headquarters town, HQ, HQ location (wd:P159 as wdt): city or town where an organization's headquarters is or has been situated. Use P276 qualifier for specific building, significant place
manufacturer, also known as manufactured by, provider, provided by, product of, producer (wd:P176 as wdt): (main or final) manufacturer or producer of this product

[Assistant] Based on the examples, we need to find the birth years of Angela Merkel and Tony Blair. We can use the property "date of birth" (P569) to get this information. Let's first identify the entities for Angela Merkel and Tony Blair.

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

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

[Assistant]
We have identified the correct entities for Angela Merkel (wd:Q567) and Tony Blair (wd:Q9545). Now let's check their birth years using the "date of birth" property (P569).

I will construct a SPARQL query to retrieve the birth years of both individuals.

[Tool]
Got 1 row and 2 columns:
| merkelBirthYear | blairBirthYear |
| --------------- | -------------- |
| 1954 (xsd:int)  | 1953 (xsd:int) |

[Assistant]
The query results show that Angela Merkel was born in 1954 and Tony Blair was born in 1953. Therefore, they were not born in the same year.

To provide a clear answer, I will use an ASK query to confirm this.

[Tool] Stopped generation process