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 ?dog ?dogLabel ?dob ?dod ( YEAR( ?dod ) - YEAR( ?dob ) AS ?age ) WHERE {
  ?dog wdt:P31 wd:Q144 .
  ?dog wdt:P569 ?dob .
  ?dog wdt:P570 ?dod .
  OPTIONAL {
    ?dog rdfs:label ?dogLabel FILTER ( LANG( ?dogLabel ) = "en" )
  }
}
ORDER BY DESC ( ?age )
LIMIT 2

The second oldest dog in the world lived 31 years.

[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 years did the second oldest dog in the world live?

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

[Tool]
Example 1:
Question:
When did Michael Jackson die?

SPARQL:
SELECT ?o1 WHERE {
  wd:Q2831 wdt:P570 ?o1 .
}

Using entities:
Michael Jackson, also known as M.J., MJ, M. J., The King of Pop, King of Pop (wd:Q2831): American singer, songwriter, and dancer (1958–2009), singer-songwriter, recording artist, record producer, businessperson, philanthropist, music arranger, autobiographer, film director, choreographer, art collector, entrepreneur, screenwriter, voice actor, songwriter, film actor, composer, dancer, singer, actor, model, poet

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

Example 2:
Question:
Give me the capitals of all countries in Africa.

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?country wdt:P31 wd:Q6256 .
  ?country wdt:P30 wd:Q15 .
  ?country wdt:P36 ?uri .
}

Using entities:
Africa, also known as African continent (wd:Q15): continent
country, also known as countries (wd:Q6256): distinct territorial body or political entity

Using properties:
continent (wd:P30 as wdt): continent of which the subject is a part, located in/on physical feature
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)
capital, also known as administrative seat, seat of government, seat, principal place, location of capital (wd:P36 as wdt): seat of government of a country, province, state or other type of administrative territorial entity

Example 3:
Question:
How high is the Yokohama Marine Tower?

SPARQL:
SELECT DISTINCT ?num WHERE {
  wd:Q1207989 wdt:P2048 ?num .
}

Using entities:
Yokohama Marine Tower (wd:Q1207989): lighthouse in Japan

Using properties:
height, also known as heighth, size, depth over terrain (wd:P2048 as wdt): vertical length of an entity, length

[Assistant]
The question is about the lifespan of the second oldest dog in the world. We need to identify entities related to "dog" and "oldest" and find the lifespan or age at death of the second oldest dog.

Step 1: Search for entities related to "oldest dog" or "oldest dogs" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. World's oldest dog dies at age 20 (wd:Q17660624): Wikinews article
2. dog, also known as Canis lupus familiaris, Canis familiaris, domestic dogs, domestic dog (wd:Q144): domestic animal
3. Dog (wd:Q755126): sign of Chinese zodiac
4. Dog (wd:Q104843787): 2022 film directed by Reid Carolin and Channing Tatum
5. Dongola Airport, also known as DOG, HSDN (wd:Q1431231): airport
6. Hurricane Dog, also known as Dog (wd:Q217669): Category 5 Atlantic hurricane in 1950
7. Wax, also known as Dog, WAX (wd:Q624269): South Korean singer, singer
8. Dog, also known as D0g (wd:Q1211818): fictional character in Half-Life
9. hound, also known as dog (wd:Q1637371): heraldic animal
10. dog, also known as shutter, shutter dog (wd:Q5288153): tool in engineering

[Assistant]
The entity "dog" (wd:Q144) is the general entity for domestic dogs. There is no direct entity for "oldest dog" as an individual dog, but we can look for dogs with lifespan or age at death information.

Step 2: Search for properties related to "age" or "lifespan" for dogs.

[Tool]
Top 10 property alternatives:
1. longest observed lifespan, also known as maximum age, oldest recorded individual, oldest observed, maximum life span, highest observed lifespan (wd:P4214 as wdt/p): age of the oldest specimen of a taxon for which the age can be verified
2. life expectancy, also known as life span, lifespan (wd:P2250 as wdt/p): average life expectancy for this group or species, duration
3. has use, also known as utility, used in, used for, used as, use (wd:P366 as wdt/p): main use of the subject (includes current and former usage)
4. earliest date, also known as no earlier than, terminus post quem, start date (earliest), on or after this date, minimum date (wd:P1319 as wdt/p): earliest date at which an event could have happened. Use as qualifier for other date properties
5. used by, also known as armiger, user, played by, of (wd:P1535 as wdt/p): item or concept that makes use of the subject (use sub-properties when appropriate)
6. instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt/p): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
7. OpenStreetMap tag or key, also known as tag, OSM wiki tag or key, OpenStreetMap relation role, OSM tag or key, OSM tag (wd:P1282 as wdt/p): OpenStreetMap tagging schema (a Key:key, Tag:key=value, Relation:type, or Role:role) for classes, properties, and values
8. taxon rank, also known as taxonomic rank, type of taxon, rank (wd:P105 as wdt/p): level in a taxonomic hierarchy, instance of
9. main food source, also known as diet, eats, ecology (Fossilworks field  name), food plant, food source (wd:P1034 as wdt/p): species, genus or family that an organism depends on for nutrition
10. image, also known as still capture, still, screenshot, screen capture, portrait (wd:P18 as wdt/p): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)

No other items found

[Assistant]
The property "longest observed lifespan" (wdt:P4214) seems relevant for the age of the oldest individual dog. We can use this property to find dogs with their longest observed lifespan.

Step 3: Query for dogs (instances of dog) with their longest observed lifespan, order by lifespan descending, and get the second oldest.

[Tool] Got no rows and 3 columns

[Assistant]
It seems that there are no instances of dogs (wd:Q144) with the property longest observed lifespan (wdt:P4214) directly. Possibly, individual famous dogs are not classified as instances of dog but as individual animals.

Step 4: Let's try to find individual dogs with the property "longest observed lifespan" (wdt:P4214) without restricting to instance of dog.

[Tool]
Got 2 rows and 3 columns:
| dog                                | dogLabel                   | lifespan           |
| ---------------------------------- | -------------------------- | ------------------ |
| Pinus longaeva (wd:Q1116374)       | Pinus longaeva (en)        | 4854 (xsd:decimal) |
| Fitzroya cupressoides (wd:Q847304) | Fitzroya cupressoides (en) | 3622 (xsd:decimal) |

[Assistant]
The results are not dogs but tree species with long lifespans. The property longest observed lifespan (wdt:P4214) is used for species, not individual animals.

Step 5: Let's try to find individual dogs by searching for entities with "dog" in their label and check if they have lifespan or age at death information.

[Tool]
Top 10 entity alternatives:
1. dog, also known as Canis lupus familiaris, Canis familiaris, domestic dogs, domestic dog (wd:Q144): domestic animal
2. Dog (wd:Q755126): sign of Chinese zodiac
3. Dog (wd:Q104843787): 2022 film directed by Reid Carolin and Channing Tatum
4. Dongola Airport, also known as DOG, HSDN (wd:Q1431231): airport
5. Hurricane Dog, also known as Dog (wd:Q217669): Category 5 Atlantic hurricane in 1950
6. Wax, also known as Dog, WAX (wd:Q624269): South Korean singer, singer
7. Dog, also known as D0g (wd:Q1211818): fictional character in Half-Life
8. hound, also known as dog (wd:Q1637371): heraldic animal
9. dog, also known as shutter, shutter dog (wd:Q5288153): tool in engineering
10. Canis familiaris, also known as dog, domestic dog (wd:Q20717272): species of mammal

[Assistant] No individual dogs with lifespan in the top search results. Let's try to find individual dogs by searching for entities with "oldest dog" in their label.

[Tool]
Top 10 entity alternatives:
1. World's oldest dog dies at age 20 (wd:Q17660624): Wikinews article
2. dog, also known as Canis lupus familiaris, Canis familiaris, domestic dogs, domestic dog (wd:Q144): domestic animal
3. Dog (wd:Q755126): sign of Chinese zodiac
4. Dog (wd:Q104843787): 2022 film directed by Reid Carolin and Channing Tatum
5. Dongola Airport, also known as DOG, HSDN (wd:Q1431231): airport
6. Hurricane Dog, also known as Dog (wd:Q217669): Category 5 Atlantic hurricane in 1950
7. Wax, also known as Dog, WAX (wd:Q624269): South Korean singer, singer
8. Dog, also known as D0g (wd:Q1211818): fictional character in Half-Life
9. hound, also known as dog (wd:Q1637371): heraldic animal
10. dog, also known as shutter, shutter dog (wd:Q5288153): tool in engineering

[Assistant]
No individual oldest dogs found directly. Let's try to find individual dogs with date of birth and date of death to calculate lifespan and order by age.

Step 6: Query for individual dogs with date of birth and date of death, order by age (date of death - date of birth) descending, and get the second oldest.

[Tool]
Got 2 rows and 5 columns:
| dog                  | dogLabel   | dob                                 | dod                                 | age          |
| -------------------- | ---------- | ----------------------------------- | ----------------------------------- | ------------ |
| Boris (wd:Q2022604)  | Boris (en) | 1901-01-01T00:00:00Z (xsd:dateTime) | 1998-01-01T00:00:00Z (xsd:dateTime) | 97 (xsd:int) |
| Bobi (wd:Q116658361) | Bobi (en)  | 1992-05-11T00:00:00Z (xsd:dateTime) | 2023-10-21T00:00:00Z (xsd:dateTime) | 31 (xsd:int) |

[Assistant]
We found two dogs with date of birth and date of death information. The oldest dog is Boris who lived 97 years. The second oldest dog is Bobi who lived 31 years.

Now I will provide the answer to the user question: The second oldest dog in the world lived 31 years.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves the two oldest dogs by calculating their ages from their birth and death dates. The query orders the dogs by age in descending order and limits the results to two, which is appropriate for identifying the second oldest dog. The answer "The second oldest dog in the world lived 31 years" is accurate based on the query results. The query also optionally retrieves English labels for better readability, which is good practice. Overall, the output is correct and complete.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ( COUNT( DISTINCT ?mountain ) AS ?count ) WHERE {
  ?mountain wdt:P31 / wdt:P279 * wd:Q8502 ; wdt:P131 * wd:Q183 .
}

There are 5327 mountains located in Germany or its subdivisions according to Wikidata, considering instances of mountains and their subclasses located in Germany or its subdivisions.

[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 mountains are located in Germany?

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

[Tool]
Example 1:
Question:
How many missions does the Soyuz programme have?

SPARQL:
SELECT ( COUNT( DISTINCT ?uri ) AS ?c ) WHERE {
  ?uri wdt:P361 wd:Q207329
}

Using entities:
Soyuz programme (wd:Q207329): crewed spaceflight programme of the Soviet Union

Using properties:
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))

Example 2:
Question:
Which countries have more than two official languages?

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

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

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

Example 3:
Question:
Which city has the most inhabitants?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P31 / wdt:279 * wd:Q515 ; wdt:P1082 ?population .
}
ORDER BY DESC ( ?population )
LIMIT 1

Using entities:
city, also known as cities (wd:Q515): large human settlement

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

[Assistant]
From the examples, we see that to count entities of a certain type located in a specific place, we can use the property "instance of" (wdt:P31) for the type and "located in the administrative territorial entity" (wdt:P131) for the location.

Step 1: Identify the entity for "mountain" in Wikidata.
Step 2: Identify the entity for "Germany" in Wikidata.
Step 3: Construct a query to count mountains located in Germany using these entities.

I will first search for the entity "mountain" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. mountain, also known as mt., mtn., mountains, mount (wd:Q8502): large natural elevation of the Earth's surface
2. Mountain (wd:Q1255792): American band
3. Mountain, also known as Mountain, ND, Mountain, North Dakota (wd:Q2032181): city in North Dakota, USA
4. Mountain, also known as Town of Mountain (wd:Q1885759): civil town in Oconto County, Wisconsin
5. The Mountain, also known as Mountain (wd:Q910112): 1956 film by Edward Dmytryk
6. ☶, also known as U+2636, gèn, TRIGRAM FOR MOUNTAIN, mountain, immobility (wd:Q2068902): trigram for mountain (U+2636), immobility, or stability: gen4, gèn, ken
7. The Mountain, also known as Mountain (wd:Q1192451): 1991 film by Markus Imhoof
8. 4-8-2, also known as 241, 2D1, 4/7, Mohawk, Mountain (wd:Q1473477): locomotive wheel arrangement
9. Mountain (wd:Q13422857): human settlement in Wisconsin, United States of America
10. Mountain (wd:Q29293936): 2014 simulation video game

[Assistant]
The entity for "mountain" is wd:Q8502 which is described as a large natural elevation of the Earth's surface.

Next, I will search for the entity "Germany" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Germany, also known as BR Deutschland, Bundesrepublik Deutschland, Deutschland, Federal Republic of Germany (wd:Q183): country in Central Europe
2. Weimar Republic, also known as German Republic, Weimar Germany, Republic of Germany, Germany, German Reich (wd:Q41304): Germany in the years 1918/1919–1933
3. German Reich, also known as Deutsches Reich, German Empire, German Realm, Germany, Imperial Germany (wd:Q1206012): official name for the German nation state from 1871 to 1945, and name of Germany until 1949
4. Burg Drachenfels, also known as Germany (wd:Q1011486): castle
5. Germany (wd:Q5552078): European Parliament constituency
6. Geilenkirchen-Heinsberg, also known as Germany (wd:Q2268218)
7. Germany, also known as Germany, IN, Germany, Indiana (wd:Q18151573): unincorporated community in Clark County, Indiana
8. Germany (wd:Q19147844): poem by Vladimir Mayakovsky
9. Germany, also known as Ludwig, West (wd:Q3839160): Hetalia character
10. Germany, also known as Germany, Texas, Germany, TX (wd:Q15221573): unincorporated community in Houston County, Texas, United States

[Assistant]
The entity for "Germany" is wd:Q183 which is a country in Central Europe.

Now, I will construct a SPARQL query to count the number of mountains (wd:Q8502) located in Germany (wd:Q183). The property for "instance of" is wdt:P31 and the property for "located in the administrative territorial entity" is wdt:P131. I will use these to filter mountains located in Germany.

[Tool]
Got 1 row and 1 column:
| count        |
| ------------ |
| 10 (xsd:int) |

[Assistant]
The query result shows that there are 10 mountains located in Germany according to the data in Wikidata.

I will now provide the final answer with the SPARQL query.

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query correctly counts the number of mountains (wd:Q8502) located in Germany (wd:Q183) using the property 'located in the administrative territorial entity' (wdt:P131). However, the result of 10 mountains seems surprisingly low given the geography of Germany, which has many more mountains. This might be due to incomplete data or the strictness of the query only counting items explicitly marked as instance of mountain and located directly in Germany. To improve, consider including subclasses of mountains or checking for mountains located in subdivisions of Germany (e.g., states) to get a more comprehensive count. Also, adding labels as optional could help verify the results. Overall, the query is syntactically correct but might be missing relevant items, so refinement is recommended.

[Assistant]
The feedback suggests that the current query might be too strict by only counting mountains that are directly located in Germany (wd:Q183) using wdt:P131. Many mountains might be located in subdivisions (states or regions) of Germany rather than directly in Germany itself. Also, including subclasses of mountains could help capture more relevant items.

To refine the query:
1. Instead of only checking for direct location in Germany, we can check for mountains located in any administrative territorial entity that is part of Germany (using wdt:P131* to traverse the location hierarchy).
2. We can include subclasses of mountain by using wdt:P31/wdt:P279* wd:Q8502 to include instances of subclasses of mountain.
3. Add optional labels for mountains to help verify the results.

I will now construct and execute this refined query.

[Tool]
Got 1 row and 1 column:
| count          |
| -------------- |
| 5327 (xsd:int) |

[Assistant]
The refined query, which counts instances of mountains or their subclasses located in Germany or its subdivisions, returns a count of 5327 mountains. This is a much more reasonable number given the geography of Germany.

I will now provide the final answer with the refined SPARQL query.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly counts the number of mountains (instances of mountain or its subclasses) located in Germany or its subdivisions using the property 'located in the administrative territorial entity' (wdt:P131*). The query returns a single count value, which matches the provided answer. The approach is appropriate and the query follows the rules for returning identifiers (count as a literal) and uses the correct properties and entities. No further refinement is necessary.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?inception WHERE {
  wd:Q1771942 wdt:P571 ?inception .
}

The United Fruit Company was founded in the year 1899.

[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 United Fruit Company founded?

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

[Tool]
Example 1:
Question:
What was the first Queen album?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P31 wd:Q482994 .
  ?uri wdt:P175 wd:Q15862 .
  ?uri wdt:P577 ?date
}
ORDER BY ?date
LIMIT 1

Using entities:
Queen (wd:Q15862): British musical group; rock band
album, also known as full album, record album, music album, full-length album, audio album (wd:Q482994): grouping of album releases by an artist usually released at the ...racks but in different formats for consumption (digital, CD, LP)

Using properties:
performer, also known as actor, sung by, singer, recording by, recorded by (wd:P175 as wdt): actor, musician, band or other performer associated with this role or musical work, contributor to the creative work or subject
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)
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

Example 2:
Question:
Give me the runtime of Toy Story.

SPARQL:
SELECT ?val WHERE {
  wd:Q171048 wdt:P2047 ?val .
}

Using entities:
Toy Story, also known as Toy Story 1 (wd:Q171048): 1995 American animated film

Using properties:
duration, also known as time length, timespan, time span, time interval, runtime (wd:P2047 as wdt): length of time of an event or process

Example 3:
Question:
What are the names of the Teenage Mutant Ninja Turtles?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P361 wd:Q1381762 .
}

Using entities:
Teenage Mutant Ninja Turtles, also known as Ninja Turtles, Teenage Mutant Hero Turtles, TMNT (wd:Q1381762): fictional team of four teenage anthropomorphic turtles

Using properties:
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))

[Assistant]
The question is about the founding date of the United Fruit Company. From the examples, we see that to find the founding date of an organization, we typically look for the entity representing the organization and then query for a property related to the founding date or inception.

Step 1: Search for the entity "United Fruit Company" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. United Fruit Company (wd:Q1771942): American corporation
2. United Fruit Company Building (wd:Q79310992): historic building in Wellington, New Zealand
3. United Fruit Company Infrastructure (wd:Q65591387): Tentative World Heritage Site in Colombia
4. Kossick v. United Fruit Company (wd:Q19089122): United States Supreme Court case
5. United Fruit Company Medal (obverse) (wd:Q122934149): bronze medal-sample by Paul Manship
6. United Fruit Company Medal (reverse) (wd:Q122934151): bronze medal-sample by Paul Manship
7. American Banana Company v. United Fruit Company (wd:Q19037455)
8. United Fruit Company strike of 1913 (wd:Q105962401)
9. United States v. Fruit Growers' Express Company (wd:Q19030798): United States Supreme Court case
10. United Surety Company v. American Fruit Product Company (wd:Q19041555)

[Assistant] The entity "United Fruit Company" is wd:Q1771942. Now, I need to find the property that represents the founding date or inception of this company. I will search for properties related to "founding date" or "inception" for this entity.

[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 wdt/p): 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 wdt/p): founder or co-founder of this organization, religion, place or entity, creator

No other items found

[Assistant] The property "inception" (wdt:P571) corresponds to the founding date of the entity. Now I will write a SPARQL query to get the inception date of the United Fruit Company (wd:Q1771942).

[Tool]
Got 1 row and 1 column:
| inception                           |
| ----------------------------------- |
| 1899-01-01T00:00:00Z (xsd:dateTime) |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves the inception date (property P571) of the United Fruit Company (wd:Q1771942) from Wikidata. The answer provided, 1899, matches the query result and is accurate. The query is simple, direct, and returns the correct founding year as a date. No further refinement is necessary.


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 DISTINCT ?discipline ?disciplineLabel WHERE {
  ?discipline wdt:P279 * wd:Q11862829 .
  ?discipline rdfs:label ?disciplineLabel .
  FILTER ( CONTAINS( LCASE( ?disciplineLabel ) , "linguistik" ) ) FILTER ( LANG( ?disciplineLabel ) = "de" )
}

Got 34 rows and 2 columns, showing the first 5 and last 5 rows below:
| discipline                                 | disciplineLabel                      |
| ------------------------------------------ | ------------------------------------ |
| Saami linguistics in Sweden (wd:Q10660925) | Samische Linguistik in Schweden (de) |
| statistical linguistics (wd:Q112633055)    | statistische Linguistik (de)         |
| textology (wd:Q1206895)                    | Textlinguistik (de)                  |
| Saami linguistics (wd:Q12718141)           | Samische Linguistik (de)             |
| generative linguistics (wd:Q1369684)       | Generative Linguistik (de)           |
| ...                                        | ...                                  |
| ethnolinguistics (wd:Q853085)              | Ethnolinguistik (de)                 |
| interlinguistics (wd:Q856574)              | Interlinguistik (de)                 |
| corpus linguistics (wd:Q865083)            | Korpuslinguistik (de)                |
| forensic linguistics (wd:Q938318)          | forensische Linguistik (de)          |
| variational sociolinguistics (wd:Q999272)  | Varietätenlinguistik (de)            |

The German names of academic disciplines containing "linguistik" include Samische Linguistik in Schweden, statistische Linguistik, Textlinguistik, Samische Linguistik, Generative Linguistik, Ethnolinguistik, Interlinguistik, Korpuslinguistik, forensische Linguistik, and Varietätenlinguistik, among others.

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

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

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

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

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

[User] What are the German names of academic disciplines containing “linguistik”?

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

[Tool]
Example 1:
Question:
In which countries do people speak Japanese?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P31 wd:Q6256 .
  ?uri wdt:P37 wd:Q5287 .
}

Using entities:
Japanese, also known as Japanese language, Japanesic (wd:Q5287): language spoken in East Asia
country, also known as countries (wd:Q6256): distinct territorial body or political entity

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

Example 2:
Question:
Give me the birth place of Frank Sinatra.

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q40912 wdt:P19 ?uri
}

Using entities:
Frank Sinatra, also known as Old Blue Eyes, Ol' Blue Eyes, Sinatra, Francis Albert Sinatra (wd:Q40912): American singer and actor (1915–1998), television actor, film producer, film director, voice actor, film actor, director, producer, singer, actor

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

Example 3:
Question:
Which movies starring Brad Pitt were directed by Guy Ritchie?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?type wdt:P279 * wd:Q11424 .
  ?uri wdt:P31 ?type ; wdt:P161 wd:Q35332 ; wdt:P57 wd:Q192990 .
}

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
Guy Ritchie, also known as Guy Stuart Ritchie, Guy Stewart Ritchie (wd:Q192990): English filmmaker, film producer, film director, screenwriter, entrepreneur, director, judoka
Brad Pitt, also known as William Bradley Pitt, Brad Jolie-Pitt (wd:Q35332): American actor and filmmaker (born 1963), television producer, executive producer, television actor, film producer, film director, voice actor, film actor, producer, director, model, 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
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)
director, also known as film director, movie director, game director, directed by (wd:P57 as wdt): director(s) of film, TV-series, stageplay, video game or similar, contributor to the creative work or subject, creator

[Assistant]
The question asks for the German names of academic disciplines containing the term "linguistik."

Step 1: Identify the entity for "academic discipline" in Wikidata.
Step 2: Find academic disciplines that have "linguistik" in their German label.

I will first search for the entity "academic discipline" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. academic discipline, also known as research field, sub-discipline, field of study, field of research, discipline (wd:Q11862829): academic field of study or profession
2. academic discipline (wd:L24102-S1)
3. field of work, also known as purpose, specialty, research, research field, research fields (wd:P101): specialization of a person or organization; see P106 for the occupation
4. English studies, also known as English philology, English major, English language and literature, English (academic discipline), English language (wd:Q27968): discipline that studies the English language and literature
5. French language studies, also known as French (academic discipline), French philology, French major, French language, French language and literature (wd:Q11333703): discipline that studies the French language and literature
6. fictional academic discipline (wd:Q126090312)
7. legal management (academic discipline), also known as LegMa (wd:Q6517540): academic and professional discipline that is a hybrid between the study of law and management
8. Academic freedom and academic discipline (wd:Q72399041): scientific article published on 01 April 1969
9. academic discipline and profession (wd:L454629-S1)
10. postcolonialism, - academic discipline (wd:L1319105-S1)

[Assistant] The entity "academic discipline" is wd:Q11862829 in Wikidata. Now I will search for academic disciplines (instances or subclasses of wd:Q11862829) that have "linguistik" in their German label.

[Tool]
Got 34 rows and 2 columns, showing the first 5 and last 5 rows below:
| discipline                                 | disciplineLabel                      |
| ------------------------------------------ | ------------------------------------ |
| Saami linguistics in Sweden (wd:Q10660925) | Samische Linguistik in Schweden (de) |
| statistical linguistics (wd:Q112633055)    | statistische Linguistik (de)         |
| textology (wd:Q1206895)                    | Textlinguistik (de)                  |
| Saami linguistics (wd:Q12718141)           | Samische Linguistik (de)             |
| generative linguistics (wd:Q1369684)       | Generative Linguistik (de)           |
| ...                                        | ...                                  |
| ethnolinguistics (wd:Q853085)              | Ethnolinguistik (de)                 |
| interlinguistics (wd:Q856574)              | Interlinguistik (de)                 |
| corpus linguistics (wd:Q865083)            | Korpuslinguistik (de)                |
| forensic linguistics (wd:Q938318)          | forensische Linguistik (de)          |
| variational sociolinguistics (wd:Q999272)  | Varietätenlinguistik (de)            |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves academic disciplines that are subclasses of the academic field of study and whose German labels contain the substring "linguistik". The answer lists relevant German names of such disciplines, matching the query results. The query respects the requirement to return identifiers and labels, and filters for German labels only. The execution result shows a reasonable number of results with appropriate labels. No further refinement is necessary.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?birthYear WHERE {
  wd:Q27449519 wdt:P569 ?birthDate .
  BIND( YEAR( ?birthDate ) AS ?birthYear )
}

Got 1 row and 1 column:
| birthYear      |
| -------------- |
| 1990 (xsd:int) |

The author of "The Vanishing Half," Brit Bennett, was born in the year 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] In which year was the author of “The Vanishing Half” born?

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

[Tool]
Example 1:
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 2:
Question:
Who created Goofy?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q111135 wdt:P170 ?uri .
}

Using entities:
Goofy, also known as Dippy Dawg, George Geef, Goofy Goof (wd:Q111135): Disney cartoon character

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

Example 3:
Question:
Which U.S. state has been admitted latest?

SPARQL:
SELECT ?uri WHERE {
  ?uri wdt:P31 wd:Q35657 .
  ?uri wdt:P571 ?date
}
ORDER BY DESC ( ?date )
LIMIT 1

Using entities:
U.S. state, also known as U.S. states, US state, USA state, U.S.A. state, United States states (wd:Q35657): constituent political entity of the United States of America

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)
inception, also known as initial release date, founded in, founded on, founded on date, founding date (wd:P571 as wdt): time when an entity begins to exist; for date of official opening use P1619, start time

[Assistant]
The question is asking for the birth year of the author of the book "The Vanishing Half."

Step 1: Identify the entity for the book "The Vanishing Half."
Step 2: Find the author of this book.
Step 3: Find the birth year of the author.

I will start by searching for the entity "The Vanishing Half" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. The Vanishing Half (wd:Q98476957): 2020 novel by Brit Bennett
2. The Vanishing, also known as Vanishing (wd:Q616301): 1993 film directed by George Sluizer
3. The Vanishing, also known as L'Homme qui voulait savoir, Vanishing (wd:Q1895379): 1988 film by George Sluizer
4. The Vanishing, also known as Keepers, The Devil to Pay (wd:Q29514859): 2018 film directed by Kristoffer Nyholm
5. The Half (wd:Q63109025)
6. The Vanishing (wd:Q129416681): 2024 film by Karim Moussaoui
7. The Vanishing, also known as Far Cry: The Vanishing, Far Cry 6: The Vanishing (wd:Q114877764): 2022 Far Cry 6 mission that is a crossover with Stranger Things
8. The Vanishing (wd:Q114523535): episode of Scream (S2 E10)
9. The Vanishing (wd:Q114539612): episode of Z Nation (S4 E3)
10. The Vanishing (wd:Q123307638): 1993 film by George Sluizer

[Assistant]
The entity for "The Vanishing Half" is wd:Q98476957, which is a 2020 novel by Brit Bennett.

Next, I will find the author of this book.

[Tool]
Got 1 row and 3 columns:
| subject                           | property         | object                      |
| --------------------------------- | ---------------- | --------------------------- |
| The Vanishing Half (wd:Q98476957) | author (wdt:P50) | Brit Bennett (wd:Q27449519) |

[Assistant]
The author of "The Vanishing Half" is Brit Bennett (wd:Q27449519).

Now, I will find the birth year of Brit Bennett.

[Tool]
Got 1 row and 3 columns:
| subject                     | property                 | object                              |
| --------------------------- | ------------------------ | ----------------------------------- |
| Brit Bennett (wd:Q27449519) | date of birth (wdt:P569) | 1990-01-01T00:00:00Z (xsd:dateTime) |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly identifies the author of 'The Vanishing Half' as Brit Bennett (wd:Q27449519) and retrieves her date of birth (wdt:P569). It then extracts the year from the birth date using the YEAR() function. The query returns the correct birth year 1990, matching the answer provided. The query follows the rules by returning the actual identifier and computing the year within SPARQL. No further refinement is needed.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?country ?countryLabel WHERE {
  wd:Q327818 wdt:P17 ?country .
  OPTIONAL {
    ?country rdfs:label ?countryLabel FILTER ( LANG( ?countryLabel ) = "en" )
  }
}

Got 1 row and 2 columns:
| country           | countryLabel |
| ----------------- | ------------ |
| Germany (wd:Q183) | Germany (en) |

The Kandahar-downhill (Kandahar Downhill Piste) is located in the country Germany.

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

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

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

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

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

[User] In what country is the Kandahar-downhill?

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

[Tool]
Example 1:
Question:
What is Donald Trump's main business?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P488 wd:Q22686 ; wdt:P31 / wdt:P279 * wd:Q4830453 . MINUS {
    ?uri wdt:P576 ?closed
  }
}

Using entities:
Donald Trump, also known as President Donald Trump, President Donald J. Trump, President Trump, President Donald J Trump, Trump (wd:Q22686): president of the United States (2017–2021, 2025–present), real estate entrepreneur, chief executive officer, television producer, business magnate, businessperson, game show host, film producer, entrepreneur, politician, merchant, investor, writer, actor
business (wd:Q4830453): organization undertaking commercial, industrial, or professional activity

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)
chairperson, also known as dean, headed by, leader, president, chairwoman (wd:P488 as wdt): presiding member of an organization, group or body, director / manager, significant person
dissolved, abolished or demolished date, also known as ended, publication end date, final issue, date repealed, final year (wd:P576 as wdt): point in time at which the subject (organisation, building) ceas...g equipment,  "discontinued date" (P2669) for stopping a product, end time

Example 2:
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

Example 3:
Question:
Who has Tom Cruise been married to?

SPARQL:
SELECT DISTINCT ?o1 WHERE {
  wd:Q37079 wdt:P26 ?o1 .
}

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:
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

[Assistant]
The question is asking for the country where the Kandahar-downhill is located. Kandahar-downhill is likely a ski or sports event or location. To answer this, I need to find the entity for "Kandahar-downhill" in Wikidata and then find the property that links it to a country.

Step 1: Search for the entity "Kandahar-downhill" in Wikidata.

[Tool] No entity items found

[Assistant] No direct entity for "Kandahar-downhill" was found in Wikidata. It might be listed under a slightly different name or as part of a larger event or location. I will try searching for "Kandahar" alone to see if there is an entity related to the Kandahar downhill ski race or location.

[Tool]
Top 10 entity alternatives:
1. Kandahar, also known as Candahar, Qandahar (wd:Q45604): city in Kandahar Province, Afghanistan
2. Kandahar, also known as Safar-e Ghandehar, The Sun Behind the Moon (wd:Q785496): 2001 film directed by Mohsen Makhmalbaf
3. Kandahar, also known as with Albert and sonichoc (wd:Q111940088): 2023 film by Ric Roman Waugh
4. Kandahar (wd:Q6361199): 2010 Malayalam film directed by Major Ravi
5. Kandahar, also known as Kandahar, Saskatchewan (wd:Q14874966): organized hamlet in Saskatchewan, Canada
6. Kandahar (wd:Q55956959): episode of The Punisher (S1 E3)
7. Kandahar (wd:Q50902249): painting by Victor Vasarely
8. Kandahar Province, also known as Ghandahar Province, Qandahar Province (wd:Q173808): province of Afghanistan
9. Kandahar International Airport, also known as OAKN, Kandahar Airfield, KDH, KAF, Kandahar Airport (wd:Q33156): international airport serving Kandahar, Afghanistan
10. Kandahar District, also known as Dand District (wd:Q2673970): district in Kandahar Province, Afghanistan

[Assistant]
The search for "Kandahar" returned several entities, but none explicitly mention "Kandahar-downhill." The closest relevant entity might be the city Kandahar (wd:Q45604) or Kandahar Province (wd:Q173808) in Afghanistan. However, "Kandahar-downhill" is likely related to skiing or a sports event, which might not be directly linked to the city or province.

I will try to search for "downhill" to see if there is a skiing event or location related to Kandahar.

[Tool]
Top 10 entity alternatives:
1. downhill, also known as downhill skiing (wd:Q319899): alpine skiing discipline
2. Downhill (wd:Q495820): 1927 film by Alfred Hitchcock
3. Downhill (wd:Q60737609): 2020 film directed by Nat Faxon and Jim Rash
4. Downhill, also known as Downhill, County Londonderry (wd:Q702677): village in County Londonderry, Northern Ireland, UK
5. Downhill (wd:Q17426163): 2014 film by James Rouse
6. The Downhill, also known as Downhill, The Decline, Spoiled Rotten (wd:Q7730794): 1961 film by Giannis Dalianidis
7. Downhill, also known as Wrong Trail (wd:Q20649844): 2016 film by Patricio Valladares
8. Downhill (wd:Q5303164): hamlet in Cornwall, England, United Kingdom
9. Downhill (wd:Q123287845): 1973 film
10. Downhill (wd:Q104356325): townland in Londonderry, Northern Ireland

[Assistant] The search for "downhill" returned the entity "downhill skiing" (wd:Q319899) which is relevant to the context of skiing. Since "Kandahar-downhill" might be a specific downhill skiing event or course, I will now search for properties related to "Kandahar" (wd:Q45604) that might link it to a skiing event or location.

[Tool]
Top 10 property alternatives:
1. country, also known as sovereign state, state, land, host country (wd:P17 as wdt/p): sovereign state that this item is in (not to be used for human beings), located in the administrative territorial entity
2. elevation above sea level, also known as MAMSL, MASL, height, elevation above mean sea level, elevation (wd:P2044 as wdt/p): height of the item (geographical object) as measured relative to sea level
3. instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt/p): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
4. area, also known as total area, surface area, surface, size, resolution (wd:P2046 as wdt/p): area occupied by an object
5. 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/p): the item is located on the territory of the following administra...if the item falls only partially into the administrative entity., location, part of
6. described by source, also known as mentioned in, source of item, reviewed in, mentioned in news article, talked about in (wd:P1343 as wdt/p): work where this item is described, attested in
7. coordinate location, also known as point on a map, gps coordinates, gps location, latitude, location (wd:P625 as wdt/p): geocoordinates of the subject. For Earth, please note that only WGS84 coordinating system is supported at the moment
8. official website, also known as site (official), home page, site official, URL, URL (official) (wd:P856 as wdt/p): URL of the official page of an item (current or former). Usage: ...but see the "Hijacked or dead websites" section of the Talk page, URL
9. image, also known as still capture, still, screenshot, screen capture, portrait (wd:P18 as wdt/p): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)
10. Freebase ID, also known as Freebase, MID, Freebase identifier, FID (wd:P646 as wdt/p): identifier for a page in the Freebase database. Format: "/m/0" f...r IDs starting with "/g/", use Google Knowledge Graph ID (P2671)

No other items found

[Assistant]
The search for properties related to "Kandahar" (wd:Q45604) with the query "ski" did not return any skiing-related properties. The most relevant property is "country" (wdt:P17), which indicates the sovereign state the item is in.

Since "Kandahar-downhill" might be a specific skiing event or location not directly linked to the city of Kandahar, I will try a different approach. I will search for the entity "Kandahar downhill" without the hyphen to see if it exists in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Kandahar Downhill Piste (wd:Q327818): ski course in Bavaria, Germany
2. Kandahar, also known as Candahar, Qandahar (wd:Q45604): city in Kandahar Province, Afghanistan
3. downhill, also known as downhill skiing (wd:Q319899): alpine skiing discipline
4. Kandahar, also known as Safar-e Ghandehar, The Sun Behind the Moon (wd:Q785496): 2001 film directed by Mohsen Makhmalbaf
5. Downhill (wd:Q495820): 1927 film by Alfred Hitchcock
6. Kandahar, also known as with Albert and sonichoc (wd:Q111940088): 2023 film by Ric Roman Waugh
7. Downhill (wd:Q60737609): 2020 film directed by Nat Faxon and Jim Rash
8. Downhill, also known as Downhill, County Londonderry (wd:Q702677): village in County Londonderry, Northern Ireland, UK
9. Kandahar (wd:Q6361199): 2010 Malayalam film directed by Major Ravi
10. Downhill (wd:Q17426163): 2014 film by James Rouse

[Assistant]
I found the entity "Kandahar Downhill Piste" (wd:Q327818), which is a ski course in Bavaria, Germany. This seems to be the correct entity for "Kandahar-downhill."

Next, I will query the country where this ski course is located. I will check the property "country" (wdt:P17) for this entity.

[Tool]
Got 1 row and 2 columns:
| country           | countryLabel |
| ----------------- | ------------ |
| Germany (wd:Q183) | Germany (en) |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves the country where the Kandahar-downhill (Kandahar Downhill Piste) is located using the property 'country' (wdt:P17). The query returns the expected result, Germany, with its English label. The answer is accurate and complete based on the data from Wikidata. No further refinement is necessary.


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 ?game ?gameLabel WHERE {
  ?game wdt:P178 wd:Q677273 .
  ?game wdt:P31 wd:Q7889 .
  ?game wdt:P577 ?releaseDate .
  OPTIONAL {
    ?game rdfs:label ?gameLabel FILTER ( LANG( ?gameLabel ) = "en" )
  }
}
ORDER BY ?releaseDate
LIMIT 1
OFFSET 1

The second game developed by Piranha Bytes, ordered by release date, is Gothic II.

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

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

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

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

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

[User] What is the second game developed by Piranha Bytes?

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

[Tool]
Example 1:
Question:
What is the capital of Canada?

SPARQL:
SELECT DISTINCT ?o1 WHERE {
  wd:Q16 wdt:P36 ?o1 .
}

Using entities:
Canada, also known as CA, CAN, Dominion of Canada, ISO 3166-1:CA (wd:Q16): country in North America

Using properties:
capital, also known as administrative seat, seat of government, seat, principal place, location of capital (wd:P36 as wdt): seat of government of a country, province, state or other type of administrative territorial entity

Example 2:
Question:
Give me the runtime of Toy Story.

SPARQL:
SELECT ?val WHERE {
  wd:Q171048 wdt:P2047 ?val .
}

Using entities:
Toy Story, also known as Toy Story 1 (wd:Q171048): 1995 American animated film

Using properties:
duration, also known as time length, timespan, time span, time interval, runtime (wd:P2047 as wdt): length of time of an event or process

Example 3:
Question:
Who was called Scarface?

SPARQL:
SELECT ?uri {
  ?uri wdt:P1449 ?nickname .
  FILTER REGEX( STR( ?nickname ) , "^Scarface$" )
}

Using properties:
nickname, also known as informal name, moniker, other name, othername, other_names (wd:P1449 as wdt): informal name (for a pseudonym use P742), name

[Assistant]
To answer the question "What is the second game developed by Piranha Bytes?", we need to identify the entity for "Piranha Bytes" and find the games developed by this entity. Then we need to order these games by their development or release date and select the second one.

Step 1: Search for the entity "Piranha Bytes" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Piranha Bytes (wd:Q677273): German video game development company
2. piranha, also known as piraña, Serrasalmus nattereri (wd:Q185215): group of fish
3. Piranha 3D, also known as Piranha (wd:Q733995): 2010 film directed by Alexandre Aja
4. Piranha, also known as Mowag Piranha (wd:Q687702): armored fighting vehicle family by MOWAG
5. Piranha (wd:Q1140216): 1978 film by Joe Dante
6. Piranha (wd:Q1215873): 1995 television film
7. Piranha (wd:Q1551737): 2006 film by Andrey Kavun
8. Bytes (wd:Q5004626): 1993 debut studio album by Black Dog Productions
9. Piranha (wd:Q4394050): single by The Grace
10. Piranha (wd:Q3389104): fictional comic book character

[Assistant] The entity "Piranha Bytes" (wd:Q677273) corresponds to a German video game development company. Next, I will find the games developed by Piranha Bytes. The property for "developer" in Wikidata is usually "wdt:P178". I will query for games where Piranha Bytes is the developer.

[Tool]
Got 17 rows and 3 columns, showing the first 5 and last 5 rows below:
| game                                         | gameLabel                          | releaseDate                         |
| -------------------------------------------- | ---------------------------------- | ----------------------------------- |
| wd:Q767456                                   |                                    | 2001-03-15T00:00:00Z (xsd:dateTime) |
| Gothic II (wd:Q1456987)                      | Gothic II (en)                     | 2002-11-29T00:00:00Z (xsd:dateTime) |
| Gothic II (wd:Q1456987)                      | Gothic II (en)                     | 2003-04-16T00:00:00Z (xsd:dateTime) |
| Gothic II: Night of the Raven (wd:Q2662055)  | Gothic II: Night of the Raven (en) | 2003-08-22T00:00:00Z (xsd:dateTime) |
| Gothic 3 (wd:Q738909)                        | Gothic 3 (en)                      | 2006-10-13T00:00:00Z (xsd:dateTime) |
| ...                                          | ...                                | ...                                 |
| Risen 3: Titan Lords (wd:Q15918760)          | Risen 3: Titan Lords (en)          | 2014-08-15T00:00:00Z (xsd:dateTime) |
| Elex (wd:Q25345979)                          | Elex (en)                          | 2017-10-17T00:00:00Z (xsd:dateTime) |
| Elex II (wd:Q107248040)                      | Elex II (en)                       | 2022-03-01T00:00:00Z (xsd:dateTime) |
| Risen (wd:Q1513013)                          | Risen (en)                         | 2023-01-24T00:00:00Z (xsd:dateTime) |
| Gothic Classic Khorinis Saga (wd:Q125849513) | Gothic Classic Khorinis Saga (en)  | 2024-06-27T00:00:00Z (xsd:dateTime) |

[Assistant] The query results show a list of video games developed by Piranha Bytes ordered by their release date. The first game has no label but a release date of 2001-03-15. The second game is "Gothic II" with a release date of 2002-11-29. To confirm the second game developed by Piranha Bytes, I will select the second game from the ordered list.

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query retrieves two games developed by Piranha Bytes ordered by the game identifier, but the ordering by ?game (which is an IRI) does not guarantee the chronological order of game development. Therefore, the query might not correctly identify the second game developed by Piranha Bytes. To improve, the query should order the games by their release date (property P577) to ensure the second game is chronologically correct. Also, the query should filter for games that have a release date to enable ordering. This refinement will provide a more accurate answer to the question.

[Assistant] Thank you for the feedback. I will refine the query to order the games developed by Piranha Bytes by their release date (property P577) and filter for games that have a release date. Then I will select the second game chronologically.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves the second game developed by Piranha Bytes ordered by release date, using the OFFSET 1 to skip the first game. It filters for video games and orders by release date, which is appropriate for the question. The answer Gothic II is accurate based on the query result. The query also optionally retrieves the English label of the game, which is helpful for readability. Overall, the output is correct and complete.