PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?dog ?dogLabel ( ( ( ( YEAR( ?dod ) - YEAR( ?dob ) ) * 365 ) + ( ( MONTH( ?dod ) - MONTH( ?dob ) ) * 30 ) + ( DAY( ?dod ) - DAY( ?dob ) ) ) / 365.25 AS ?lifespan ) WHERE {
  ?dog wdt:P31 wd:Q144 .
  ?dog wdt:P569 ?dob .
  ?dog wdt:P570 ?dod .
  ?dog rdfs:label ?dogLabel
  FILTER ( LANG( ?dogLabel ) = "en" )
}
ORDER BY DESC ( ?lifespan )
LIMIT 1
OFFSET 1

The second oldest dog in the world is Bobi, who lived for approximately 31.42 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

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 similar examples.

[Tool]
Example 1:
Question:
What is the second highest mountain on Earth?

SPARQL:
SELECT ?res WHERE {
  ?res wdt:P31 / wdt:P279 * wd:Q8502 .
  ?res wdt:P2044 ?elevation .
  ?res wdt:P30 / wdt:P361 wd:Q2 .
}
ORDER BY DESC ( ?elevation )
LIMIT 1
OFFSET 1

Using entities:
Earth, also known as World, The Blue Planet, Planet Earth, Mother Earth (wd:Q2): third planet from the Sun in the Solar System
mountain, also known as mt., mtn., mountains, mount (wd:Q8502): large natural elevation of the Earth's surface

Using properties:
elevation above sea level, also known as MAMSL, MASL, height, elevation above mean sea level, elevation (wd:P2044 as wdt): height of the item (geographical object) as measured relative to sea level
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
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)
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 example found is about the second highest mountain on Earth, which uses the OFFSET 1 clause to get the second highest. We can use a similar approach to find the second oldest dog by ordering dogs by their lifespan and then using OFFSET 1 to get the second oldest.

Next, I will search for the entity "dog" in Wikidata to identify the class for dogs.

[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] The entity for dog as a domestic animal is wd:Q144. Now I will search for properties related to lifespan or age for this entity.

[Tool]
Top 10 property alternatives:
1. life expectancy, also known as life span, lifespan (wd:P2250 as p/wdt): average life expectancy for this group or species, duration
2. longest observed lifespan, also known as maximum age, oldest recorded individual, oldest observed, maximum life span, highest observed lifespan (wd:P4214 as p/wdt): age of the oldest specimen of a taxon for which the age can be verified
3. has characteristic, also known as has quality, inherent property, object has characteristic, parameter, quality (wd:P1552 as p/wdt): inherent or distinguishing quality or feature of the entity. Use a more specific property when possible
4. has use, also known as utility, used in, used for, used as, use (wd:P366 as p/wdt): main use of the subject (includes current and former usage)
5. sequenced genome URL, also known as genome, genome URL (wd:P6800 as p/wdt): sequenced and assembled genome for this taxon, URL
6. instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as p/wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
7. main food source, also known as diet, eats, ecology (Fossilworks field  name), food plant, food source (wd:P1034 as p/wdt): species, genus or family that an organism depends on for nutrition
8. uses, also known as uses trope, used, using, wielded, wields (wd:P2283 as p/wdt): item or concept used by the subject or in the operation (see also instrument [P1303] and armament [P520])
9. Guardian topic ID, also known as Guardian, The Guardian topic, The Guardian topic ID (wd:P3106 as p/wdt): identifier for a topic at the Guardian newspaper website
10. heart rate, also known as heart pulse (wd:P3395 as p/wdt): speed of the heartbeat measured by the number of contractions of the heart per minute (bpm)

No other items found

[Assistant] The property "longest observed lifespan" (wdt:P4214) seems to be the most suitable to find the age of the oldest dog. Now I will write a query to find the second oldest dog by ordering dogs by their longest observed lifespan and using OFFSET 1 to get the second oldest.

[Tool] Got no rows and 3 columns

[Assistant]
The query returned no results, which means there might not be individual dogs (instances of dog) with the property "longest observed lifespan" recorded in Wikidata. Instead, the property might be used for species or breeds.

I will try to find dogs that are instances of "dog" (wd:Q144) and have a recorded age or lifespan using other properties like "age at death" (wdt:P570 for date of death and wdt:P569 for date of birth) or "age" (wdt:P2250). I will first check if there are any dogs with "age at death" or "date of birth" and "date of death" properties.

[Tool]
Top 10 property alternatives:
1. life expectancy, also known as life span, lifespan (wd:P2250 as p/wdt): average life expectancy for this group or species, duration
2. longest observed lifespan, also known as maximum age, oldest recorded individual, oldest observed, maximum life span, highest observed lifespan (wd:P4214 as p/wdt): age of the oldest specimen of a taxon for which the age can be verified
3. described by source, also known as mentioned in, source of item, reviewed in, mentioned in news article, talked about in (wd:P1343 as p/wdt): work where this item is described, attested in
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 p/wdt): earliest date at which an event could have happened. Use as qualifier for other date properties
5. Freebase ID, also known as Freebase, MID, Freebase identifier, FID (wd:P646 as p/wdt): identifier for a page in the Freebase database. Format: "/m/0" f...r IDs starting with "/g/", use Google Knowledge Graph ID (P2671)
6. 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)
7. has use, also known as utility, used in, used for, used as, use (wd:P366 as p/wdt): main use of the subject (includes current and former usage)
8. depicted by, also known as acted by, shown in, seen in, portrayed by, played by (wd:P1299 as p/wdt): object depicting this subject (object being creative works such as books, films, paintings)
9. heart rate, also known as heart pulse (wd:P3395 as p/wdt): speed of the heartbeat measured by the number of contractions of the heart per minute (bpm)
10. studied in, also known as is studied by, studied by, science, that studies, researched in, researched by (wd:P2579 as p/wdt): subject is studied by this science or domain, field of work

No other items found

[Assistant] There is no direct property for "age at death" for dogs, but there is "life expectancy" (wdt:P2250) and "longest observed lifespan" (wdt:P4214). Since the previous query for longest observed lifespan for individual dogs returned no results, I will try to find individual dogs with date of birth (wdt:P569) and date of death (wdt:P570) and calculate their lifespan from these dates. I will first check if there are any dogs with date of birth and date of death recorded.

[Tool]
Got 10 rows and 4 columns:
| dog                                    | dogLabel                    | dob                                 | dod                                 |
| -------------------------------------- | --------------------------- | ----------------------------------- | ----------------------------------- |
| Buddy (wd:Q1060353)                    | Buddy (en)                  | 1997-08-07T00:00:00Z (xsd:dateTime) | 2002-01-02T00:00:00Z (xsd:dateTime) |
| Leuk (wd:Q106613105)                   | Leuk (en)                   | 2013-09-20T00:00:00Z (xsd:dateTime) | 2019-05-02T00:00:00Z (xsd:dateTime) |
| Honey Tree Evil Eye (wd:Q106910672)    | Honey Tree Evil Eye (en)    | 1983-01-01T00:00:00Z (xsd:dateTime) | 1993-05-31T00:00:00Z (xsd:dateTime) |
| wd:Q10807010                           |                             | 1803-01-01T00:00:00Z (xsd:dateTime) | 1861-01-01T00:00:00Z (xsd:dateTime) |
| Finnegan Horowitz Shea (wd:Q110979315) | Finnegan Horowitz Shea (en) | 1998-01-01T00:00:00Z (xsd:dateTime) | 2022-01-21T00:00:00Z (xsd:dateTime) |
| Hurricane (wd:Q111936613)              | Hurricane (en)              | 2009-04-26T00:00:00Z (xsd:dateTime) | 2025-02-18T00:00:00Z (xsd:dateTime) |
| Masao (wd:Q11278222)                   | Masao (en)                  | 1999-10-20T00:00:00Z (xsd:dateTime) | 2006-12-09T00:00:00Z (xsd:dateTime) |
| Kai-kun (wd:Q11293772)                 | Kai-kun (en)                | 2003-10-10T00:00:00Z (xsd:dateTime) | 2018-06-28T00:00:00Z (xsd:dateTime) |
| wd:Q11315153                           |                             | 1997-09-06T00:00:00Z (xsd:dateTime) | 2013-09-16T00:00:00Z (xsd:dateTime) |
| Champy (wd:Q11318246)                  | Champy (en)                 | 1955-04-08T00:00:00Z (xsd:dateTime) | 1967-08-22T00:00:00Z (xsd:dateTime) |

[Assistant] There are dogs with date of birth and date of death recorded. I will now write a query to calculate the lifespan in years for each dog and order them by lifespan to find the second oldest dog.

[Tool]
Got 1 row and 3 columns:
| dog          | dogLabel | lifespan     |
| ------------ | -------- | ------------ |
| wd:Q10807010 |          | 58 (xsd:int) |

[Assistant] The second oldest dog lived for 58 years. I will now provide the final answer with the SPARQL query.

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query correctly identifies the second oldest dog by ordering dogs by lifespan and using OFFSET 1. However, the query does not return the dog's label, which would improve the answer's clarity. Also, the lifespan calculation using YEAR(?dod) - YEAR(?dob) is a rough estimate and may be inaccurate if the dog's birth and death dates are not exact or if the dog lived partial years. A more precise calculation could be done using the difference in days or months divided by 365. Additionally, the answer should mention the dog's name or label if available. Please refine the query to include the dog's label and consider a more precise lifespan calculation.

[Assistant] Thank you for the feedback. I will refine the query to include the dog's label in the result and calculate the lifespan more precisely by using the difference in days between the date of death and date of birth divided by 365 to get the lifespan in years. I will also include the dog's label in the answer for clarity.

[Tool]
Got 1 row and 3 columns:
| dog                                | dogLabel                 | lifespan |
| ---------------------------------- | ------------------------ | -------- |
| Don the Talking Dog (wd:Q55609839) | Don the Talking Dog (en) |          |

[Assistant]
The query returned the dog "Don the Talking Dog" as the second oldest dog, but the lifespan value is missing. This might be due to the subtraction of dates not working as expected in this SPARQL endpoint.

I will try a different approach to calculate the lifespan in days using the function xsd:dateTime and then divide by 365.25 for a more precise year calculation.

[Tool]
Got 1 row and 3 columns:
| dog                                | dogLabel                 | lifespan |
| ---------------------------------- | ------------------------ | -------- |
| Don the Talking Dog (wd:Q55609839) | Don the Talking Dog (en) |          |

[Assistant]
The calculation using xsd:dateTime subtraction also did not return a lifespan value. It seems the endpoint does not support direct date subtraction or the calculation is not working as expected.

I will revert to the previous approach using YEAR(?dod) - YEAR(?dob) for the lifespan calculation and include the dog's label in the answer for clarity.

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query correctly identifies the second oldest dog by ordering dogs by lifespan and using OFFSET 1. However, the query calculates lifespan by subtracting the birth year from the death year, which can be inaccurate if the dog's birth and death dates are not on the same month and day. A more precise calculation would consider the full date difference. Also, the label for the dog is missing in the result, which should be included for better readability. The answer states the dog's name as 'Don the Talking Dog,' but this label is not retrieved in the query result. It is recommended to improve the lifespan calculation and ensure the dog's label is retrieved and shown in the answer.

[Assistant]
Thank you for the feedback. I will improve the lifespan calculation by using the full date difference in days and then converting it to years with decimal precision. I will also ensure the dog's label is retrieved and included in the answer for better readability.

I will use the function to calculate the difference in days between the date of death and date of birth, then divide by 365.25 to get the lifespan in years with decimals. I will also retrieve the dog's label properly.

[Tool]
Got 1 row and 3 columns:
| dog                                | dogLabel                 | lifespan |
| ---------------------------------- | ------------------------ | -------- |
| Don the Talking Dog (wd:Q55609839) | Don the Talking Dog (en) |          |

[Assistant]
The calculation using xsd:dateTime subtraction still does not return a lifespan value. It seems this SPARQL endpoint does not support direct subtraction of dateTime values.

As an alternative, I will calculate the lifespan in days by extracting the year, month, and day components separately and then converting the total difference to years. This approach is more complex but should work around the limitation.

[Tool]
Got 1 row and 3 columns:
| dog          | dogLabel | lifespan              |
| ------------ | -------- | --------------------- |
| wd:Q10807010 |          | 57.9603 (xsd:decimal) |

[Assistant] The query returned the second oldest dog with a lifespan of approximately 57.96 years, but the dog's label is missing. I will add a clause to ensure the dog's English label is retrieved and included in the answer.

[Tool]
Got 1 row and 3 columns:
| dog                  | dogLabel  | lifespan              |
| -------------------- | --------- | --------------------- |
| Bobi (wd:Q116658361) | Bobi (en) | 31.4168 (xsd:decimal) |

[Assistant] The refined query returned the second oldest dog named "Bobi" with a lifespan of approximately 31.42 years. I will provide this as the final answer with the refined SPARQL query.

[Tool] Stopped generation process


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

There are 5296 mountains located in Germany according to Wikidata.

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

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

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 similar examples.

[Tool]
Example 1:
Question:
What is the highest mountain in Germany?

SPARQL:
SELECT ?uri WHERE {
  ?uri wdt:P31 wd:Q8502 ; wdt:P2044 ?elevation ; wdt:P17 wd:Q183 .
}
ORDER BY DESC ( ?elevation )
LIMIT 1

Using entities:
Germany, also known as BR Deutschland, Bundesrepublik Deutschland, Deutschland, Federal Republic of Germany (wd:Q183): country in Central Europe
mountain, also known as mt., mtn., mountains, mount (wd:Q8502): large natural elevation of the Earth's surface

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
elevation above sea level, also known as MAMSL, MASL, height, elevation above mean sea level, elevation (wd:P2044 as wdt): height of the item (geographical object) as measured relative to sea level
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)

Example 2:
Question:
What is the highest mountain in Saxony Germany?

SPARQL:
SELECT ?res WHERE {
  ?res wdt:P31 wd:Q8502 .
  ?res wdt:P131 + wd:Q1202 .
  ?res wdt:P2044 ?height .
}
ORDER BY DESC ( ?height )
LIMIT 1

Using entities:
Saxony, also known as DE-SN, Free State of Saxony, Swobodny stat Sakska, Freistaat Sachsen, Sachsen (wd:Q1202): state in Germany
mountain, also known as mt., mtn., mountains, mount (wd:Q8502): large natural elevation of the Earth's surface

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
elevation above sea level, also known as MAMSL, MASL, height, elevation above mean sea level, elevation (wd:P2044 as wdt): height of the item (geographical object) as measured relative to sea level
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:
how much is the population densty rank of germany?

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

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

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

[Assistant]
From the examples, I see that the entity for Germany is wd:Q183 and the entity for mountain is wd:Q8502. The property for "located in the administrative territorial entity" is wdt:P131. To find how many mountains are located in Germany, I can count the instances of mountains (wd:Q8502) that are located in Germany (wd:Q183) using the property wdt:P131.

I will now construct a SPARQL query to count the number of mountains located in Germany.

[Tool]
Got 1 row and 1 column:
| count          |
| -------------- |
| 5296 (xsd:int) |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly counts the number of distinct mountains (wd:Q8502) located in Germany (wd:Q183) using the property 'located in the administrative territorial entity' (wdt:P131*). The query returns a single integer value representing the count, which matches the answer provided. The query follows the rules by returning the actual identifiers and does not perform any additional computation outside SPARQL. Therefore, the output is correct and complete.


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

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

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 similar examples.

[Tool]
Example 1:
Question:
When was the De Beers company founded?

SPARQL:
SELECT ?date WHERE {
  wd:Q471891 wdt:P571 ?date .
}

Using entities:
De Beers, also known as De Beers Group, The De Beers Group of Companies (wd:Q471891): International corporation that specialises in diamond exploratio...retail, rough diamond sales and industrial diamond manufacturing

Using properties:
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

Example 2:
Question:
When were the Hells Angels founded?

SPARQL:
SELECT ?date WHERE {
  wd:Q622188 wdt:P571 ?date .
}

Using entities:
Hells Angels, also known as H. A., Red & White, Hells Angels Motorcycle Club, HAMC, H.A. (wd:Q622188): outlaw motorcycle club

Using properties:
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 property for the founding date or inception of an entity is wdt:P571. Next, I will search for the entity "United Fruit Company" in Wikidata to get its identifier. Then I will query the founding date using the property wdt:P571.

[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" corresponds to wd:Q1771942. Now I will query the founding date (property wdt:P571) of this entity.

[Tool]
Got 1 row and 1 column:
| date                                |
| ----------------------------------- |
| 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 result is the founding year 1899, which matches the provided answer. The query is simple, precise, and returns the correct identifier and date value. No further refinement is necessary.


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?discipline ?disciplineLabelGerman WHERE {
  ?discipline wdt:P31 / wdt:P279 * wd:Q11862829 .
  ?discipline rdfs:label ?disciplineLabelGerman .
  FILTER ( CONTAINS( LCASE( ?disciplineLabelGerman ) , "linguistik" ) ) FILTER ( LANG( ?disciplineLabelGerman ) = "de" )
}

Got 18 rows and 2 columns, showing the first 5 and last 5 rows below:
| discipline                             | disciplineLabelGerman       |
| -------------------------------------- | --------------------------- |
| Saami linguistics (wd:Q12718141)       | Samische Linguistik (de)    |
| general linguistics (wd:Q14357850)     | Allgemeine Linguistik (de)  |
| sociolinguistics (wd:Q160845)          | Soziolinguistik (de)        |
| computational linguistics (wd:Q182557) | Computerlinguistik (de)     |
| historical linguistics (wd:Q190375)    | historische Linguistik (de) |
| ...                                    | ...                         |
| wd:Q357196                             | Rechtslinguistik (de)       |
| descriptive linguistics (wd:Q777864)   | deskriptive Linguistik (de) |
| ethnolinguistics (wd:Q853085)          | Ethnolinguistik (de)        |
| interlinguistics (wd:Q856574)          | Interlinguistik (de)        |
| interlinguistics (wd:Q856574)          | Interlinguistik (de)        |

The German names of academic disciplines containing "linguistik" include Samische Linguistik, Allgemeine Linguistik, Soziolinguistik, Computerlinguistik, historische Linguistik, Rechtslinguistik, deskriptive Linguistik, Ethnolinguistik, and Interlinguistik, 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

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 similar examples.

[Tool]
Example 1:
Question:
What is the alma mater of the chancellor of Germany Angela Merkel?

SPARQL:
SELECT ?res WHERE {
  wd:Q567 wdt:P69 ?res .
  ?res wdt:P31 / wdt:P279 * wd:Q3918 .
}

Using entities:
university, also known as uni, universities (wd:Q3918): academic institution for further education
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

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)
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:
Which university did Angela Merkel attend?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  VALUES ?types {
    wd:Q3918 wd:Q875538
  }
  wd:Q567 wdt:P69 ?uri .
  ?uri wdt:P31 ?types .
}

Using entities:
university, also known as uni, universities (wd:Q3918): academic institution for further education
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
public university (wd:Q875538): university predominantly funded by public means

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)
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] The examples did not directly address academic disciplines or filtering by German names containing "linguistik." Next, I will search for academic disciplines in Wikidata to identify the relevant entity for academic disciplines. Then I will look for academic disciplines that have German labels containing "linguistik."

[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" (wd:Q11862829) seems to be the relevant one for academic fields of study or professions. Next, I will query for entities that are instances or subclasses of academic discipline and have German labels containing "linguistik."

[Tool]
Got 18 rows and 2 columns, showing the first 5 and last 5 rows below:
| discipline                             | disciplineLabelGerman       |
| -------------------------------------- | --------------------------- |
| Saami linguistics (wd:Q12718141)       | Samische Linguistik (de)    |
| general linguistics (wd:Q14357850)     | Allgemeine Linguistik (de)  |
| sociolinguistics (wd:Q160845)          | Soziolinguistik (de)        |
| computational linguistics (wd:Q182557) | Computerlinguistik (de)     |
| historical linguistics (wd:Q190375)    | historische Linguistik (de) |
| ...                                    | ...                         |
| wd:Q357196                             | Rechtslinguistik (de)       |
| descriptive linguistics (wd:Q777864)   | deskriptive Linguistik (de) |
| ethnolinguistics (wd:Q853085)          | Ethnolinguistik (de)        |
| interlinguistics (wd:Q856574)          | Interlinguistik (de)        |
| interlinguistics (wd:Q856574)          | Interlinguistik (de)        |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves academic disciplines (instances or subclasses of Q11862829) with German labels containing the substring "linguistik". The query filters for German labels and returns both the discipline IRIs and their German labels, which matches the user's request for German names. The answer lists relevant disciplines as expected. The query and answer are complete and accurate for the question asked.


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
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

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 similar examples.

[Tool]
Example 1:
Question:
Who is the novelist of the work a song of ice and fire?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q45875 ?p ?uri .
  ?uri wdt:P106 wd:Q6625963 .
}

Using entities:
A Song of Ice and Fire, also known as A Song of Ice and Fire Series, Game of Thrones, Song of Ice and Fire, ASOIAF (wd:Q45875): series of epic fantasy novels by George R. R. Martin
novelist, also known as novel author, novel writer (wd:Q6625963): writer of novels

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)

Example 2:
Question:
Who wrote the book The Pillars of the Earth?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q1163227 wdt:P50 ?uri .
}

Using entities:
The Pillars of the Earth, also known as The Pillars of the Earth (Kingsbridge, #1) (wd:Q1163227): 1989 novel by Ken Follett

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:
Who wrote the book The pillars of the Earth?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q1163227 wdt:P50 ?uri .
}

Using entities:
The Pillars of the Earth, also known as The Pillars of the Earth (Kingsbridge, #1) (wd:Q1163227): 1989 novel by Ken Follett

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

[Assistant] From the examples, I see that the property for the author of a book is wdt:P50. I will first search for the entity "The Vanishing Half" to get its Wikidata ID. Then I will query the author of this book and finally get the birth year of that author.

[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 "The Vanishing Half" is identified as wd:Q98476957. Now I will query the author of this book using the property wdt:P50.

[Tool]
Got 1 row and 1 column:
| author                      |
| --------------------------- |
| Brit Bennett (wd:Q27449519) |

[Assistant] The author of "The Vanishing Half" is Brit Bennett (wd:Q27449519). Now I will query the birth year of Brit Bennett.

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

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves the birth year of Brit Bennett, the author of "The Vanishing Half". The query uses the correct entity identifier for Brit Bennett and extracts the year from her birth date using the YEAR function. The answer provided is accurate and complete based on the query result. No further refinement is necessary.


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
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 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

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 similar examples.

[Tool]
Example 1:
Question:
What country is Mount Everest in?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q513 wdt:P17 ?uri .
}

Using entities:
Mount Everest, also known as Chomolungma, Chomolangma, Zhumulangma, World highest peak, Sagarmāthā (wd:Q513): Earth's highest mountain above sea level, located in the Mahalangur Himal sub-range of the Himalayas

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

Example 2:
Question:
To which countries does the Himalayan mountain system extend?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q5451 wdt:P17 ?uri .
}

Using entities:
Himalayas, also known as Himalaya, Himálaya, Himalaya Mountains, Himálaya Mountains, Himalayan Mountains (wd:Q5451): mountain range in Asia

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

Example 3:
Question:
What is the highest place of Karakoram?

SPARQL:
SELECT ?uri WHERE {
  wd:Q5469 wdt:P610 ?uri
}

Using entities:
Karakoram, also known as Karakoram Mountains, Karakoram Range, Karakoroum, Karakoroum Mountains, Karakorum (wd:Q5469): major mountain range spanning the borders between India, Pakistan, and China

Using properties:
highest point, also known as tallest point, zenith, summit, pinnacle, highest peak (wd:P610 as wdt): point with highest elevation in a region or path, has part(s)

[Assistant] From the examples, it is clear that the property "country" (wdt:P17) is used to find the country in which a geographical entity is located. Next, I will search for the entity "Kandahar-downhill" in Wikidata to find its identifier.

[Tool] No entity items found

[Assistant] I could not find an entity named "Kandahar-downhill" in Wikidata. It might be known under a slightly different name or spelling. I will try searching for "Kandahar" alone to see if it is related to a downhill or ski event.

[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 mostly entities related to the city and province in Afghanistan, as well as some films and other unrelated entities. There is no direct match for "Kandahar-downhill."

I will try searching for "Kandahar downhill" as a phrase to see if there is a related entity or event, possibly related to skiing or sports.

[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 an entity "Kandahar Downhill Piste" (wd:Q327818) which is a ski course in Bavaria, Germany. This seems to be the entity related to "Kandahar-downhill." Now I will query the country in which this ski course is located using the property "country" (wdt:P17).

[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 is located by using the property 'country' (wdt:P17) on the entity Kandahar Downhill Piste (wd:Q327818). The query also optionally retrieves the English label of the country. The execution result shows that the country is Germany, which matches the answer provided. The query follows the rules by returning the actual identifier (wd:Q183) and the label optionally. The answer is correct and complete.


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?game ?gameLabel WHERE {
  ?game wdt:P178 wd:Q677273 .
  ?game wdt:P577 ?publicationDate .
  OPTIONAL {
    ?game rdfs:label ?gameLabel FILTER ( LANG( ?gameLabel ) = "en" )
  }
}
ORDER BY ?publicationDate
OFFSET 1
LIMIT 1

The second game developed by Piranha Bytes 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

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 similar examples.

[Tool]
Example 1:
Question:
Who developed Minecraft?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q49740 wdt:P178 ?uri .
}

Using entities:
Minecraft, also known as MC, Mine Craft, MineCraft (wd:Q49740): 2011 video game

Using properties:
developer, also known as written by, video game developer, video game dev, game developer, game dev (wd:P178 as wdt): organization or person that developed the item, creator

[Assistant]
The example shows how to find the developer of a game using the property "developer" (wdt:P178). In our case, we want to find the games developed by Piranha Bytes and then determine the second game in the order of development.

Step 1: Identify the entity for Piranha Bytes.
Step 2: Find the games developed by Piranha Bytes using the developer property.
Step 3: Order the games by their publication or release date to determine the second game.

Let's start by searching for the entity "Piranha Bytes".

[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" corresponds to wd:Q677273, which is a German video game development company. Next, I will find the games developed by Piranha Bytes using the developer property (wdt:P178). Then, I will order the games by their publication or release date to identify the second game developed by Piranha Bytes.

[Tool]
SPARQL execution failed:
Error while executing a SERVICE request to <http://wikiba.se:80/ontology#label>: SERVICE responded with HTTP status code: 301, Moved Permanently. First 100 bytes of the response: '<html>
<head><title>301 Moved Permanently</title></head>
<body>
<center><h1>301 Moved Permanently'

[Assistant] The query failed due to the use of SERVICE wikibase:label, which is not supported by the endpoint. I will rewrite the query without using SERVICE wikibase:label and instead use optional rdfs:label for the game labels.

[Tool]
Got 10 rows and 3 columns:
| game                                        | gameLabel                          | publicationDate                     |
| ------------------------------------------- | ---------------------------------- | ----------------------------------- |
| Gothic (wd:Q1088631)                        | Gothic (en)                        |                                     |
| ZenGin (wd:Q71850046)                       | ZenGin (en)                        |                                     |
| Genome (wd:Q71848663)                       | Genome (en)                        |                                     |
| Risen (wd:Q3433133)                         | Risen (en)                         |                                     |
| 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 II: Gold Edition (wd:Q118905969)     | Gothic II: Gold Edition (en)       | 2005-11-29T00:00:00Z (xsd:dateTime) |
| Gothic 3 (wd:Q738909)                       | Gothic 3 (en)                      | 2006-10-13T00:00:00Z (xsd:dateTime) |

[Assistant]
The query results show several games developed by Piranha Bytes, but some entries lack publication dates, and there are duplicates for Gothic II with different dates. To determine the second game developed by Piranha Bytes, I need to filter out entries without publication dates and order by the earliest publication date.

I will refine the query to only include games with publication dates and order them ascending by publication date to find the second game.

[Tool]
Got 2 rows and 3 columns:
| game                    | gameLabel      | publicationDate                     |
| ----------------------- | -------------- | ----------------------------------- |
| wd:Q767456              |                | 2001-03-15T00:00:00Z (xsd:dateTime) |
| Gothic II (wd:Q1456987) | Gothic II (en) | 2002-11-29T00:00:00Z (xsd:dateTime) |

[Assistant] The query results show the first two games developed by Piranha Bytes with publication dates. The first game has no label, but the second game is "Gothic II" with a publication date of 2002-11-29. To confirm the second game, I will try to get the label of the first game (wd:Q767456) to identify it.

[Tool]
Got 1 row and 2 columns:
| game       | gameLabel |
| ---------- | --------- |
| wd:Q767456 |           |

[Assistant] The first game (wd:Q767456) does not have an English label in the data. However, based on the publication dates, the second game developed by Piranha Bytes is "Gothic II" (wd:Q1456987). I will now provide the final answer.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves the second game developed by Piranha Bytes by ordering the games by their publication date and using OFFSET 1 to get the second entry. The query returns the game identifier and its English label, which is appropriate. The answer Gothic II matches the query result. The query follows the rules by returning identifiers and labels optionally, and the approach is sound. No further refinement is necessary.


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ( COUNT( DISTINCT ?album ) AS ?albumCount ) WHERE {
  ?album wdt:P31 wd:Q482994 ; wdt:P175 wd:Q41076 .
}

Got 1 row and 1 column:
| albumCount   |
| ------------ |
| 37 (xsd:int) |

Mariah Carey performs on 37 albums according to Wikidata.

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

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

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

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

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

[User] On how many albums does Mariah Carey perform?

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

[Tool] No examples found

[Assistant] No similar examples found. Next, I will search for the entity "Mariah Carey" in Wikidata to get her identifier.

[Tool]
Top 10 entity alternatives:
1. Mariah Carey, also known as Queen of Christmas, Mirage, Songbird Supreme, Reina de la navidad, Carey (wd:Q41076): American singer and songwriter (born 1969), music video director, singer-songwriter, recording artist, philanthropist, businessperson, film director, film producer, DJ producer, voice actor, entertainer, composer, singer, model, actor
2. Mariah Carey (wd:Q732845): 1990 debut studio album by Mariah Carey
3. WikiProject Mariah Carey (wd:Q21830328): Wikimedia subject-area collaboration
4. Here Is Mariah Carey (wd:Q836551): VHS and DVD by Mariah Carey
5. The Essential Mariah Carey (wd:Q3235035): 2011 compilation album by Mariah Carey
6. M, also known as M By Mariah Carey (wd:Q690434): women's fragrance from Elizabeth Arden
7. Mariah Carey: DVD Collection (wd:Q3282698): album by Mariah Carey
8. Mariah Carey: Her Story (wd:Q131320674): book by the British writer Chris Nickson
9. Mariah Carey Collector's Set (wd:Q18277464): album by Mariah Carey
10. Why Mariah Carey Matters (wd:Q131160633): book written by Andrew Chan

[Assistant] The entity for Mariah Carey as an American singer and songwriter is wd:Q41076. Now I want to find the property that relates Mariah Carey to albums she performs on. I will search for properties related to "perform" for the entity Mariah Carey.

[Tool]
Top 10 property alternatives:
1. 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
2. occupation, also known as job, work, vocation, profession, employment (wd:P106 as p/wdt): occupation of a person; see also "field of work" (Property:P101), "position held" (Property:P39)
3. work period (start), also known as start working, flourished (start), start time for work, start date of activity, fl. (start) (wd:P2031 as p/wdt): start of period during which a person or group flourished (fl. = "floruit") in their professional activity, start time, floruit
4. instrument, also known as uses this instrument, teaches instrument, instruments, plays this instrument, plays instrument (wd:P1303 as p/wdt): musical instrument that a person plays or teaches or used in a music occupation, uses
5. spouse, also known as married partner, married to, marry, partner, spouses (wd:P26 as p/wdt): the subject has the object as their spouse (husband, wife, partn...etc.). Use "unmarried partner" (P451) for non-married companions, significant person, relative
6. voice type, also known as range of voice, register, tessitura, type of voice, vocal type (wd:P412 as p/wdt): person's voice type. expected values: soprano, mezzo-soprano, contralto, countertenor, tenor, baritone, bass (and derivatives)
7. member of, also known as band member of, membership, member of musical group, in musical group, be member of (wd:P463 as p/wdt): organization, club or musical group to which the subject belongs...ical position, such as a member of parliament (use P39 for that), part of
8. instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as p/wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
9. X (Twitter) username, also known as twitter.com/, X username, X handle, x.com/, X account (wd:P2002 as p/wdt): username on X (formerly Twitter); do not include the '@' symbol, website username or ID
10. pseudonym, also known as nom de plume, nom-de-plume, nome de guerre, pen name, ring name (wd:P742 as p/wdt): alias used by someone (for nickname use P1449), name

No other items found

[Assistant] The search for properties related to "perform" for Mariah Carey did not return a direct property linking her to albums she performs on. I will try searching for properties related to "album" for Mariah Carey to find a property that might link her to albums.

[Tool]
Top 10 property alternatives:
1. AllMusic artist ID, also known as AllMusic, AllMusic ID, amg artist (wd:P1728 as p/wdt): identifier for an artist, musical group, or record label in the AllMusic database (record labels are listed as "artists")
2. genre, also known as genre of music, type of music, type of film, music genre, literary genre (wd:P136 as p/wdt): creative work's genre or an artist's field of work (P101). Use main subject (P921) to relate creative works to their topic
3. discography, also known as discography link, recording catalog (wd:P358 as p/wdt): item for list pages with discography of artist or band, list of works
4. 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)
5. country of citizenship, also known as subject of (country), nation of citizenship, national of, nationality, nation (wd:P27 as p/wdt): the object is a country that recognizes the subject as its citizen, country
6. record label, also known as label, labels (wd:P264 as p/wdt): brand and trademark associated with the marketing of subject music recordings and music videos
7. Apple Music artist ID (U.S. version), also known as iTunes author ID, music.apple.com/artist/, iTunes artist ID (American version), https://music.apple.com/artist/, Apple Music artist ID (American version) (wd:P2850 as p/wdt): identifier for a musical artist or author in the U.S. version of Apple Music
8. work period (start), also known as start working, flourished (start), start time for work, start date of activity, fl. (start) (wd:P2031 as p/wdt): start of period during which a person or group flourished (fl. = "floruit") in their professional activity, start time, floruit
9. 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
10. Official Charts artist ID, also known as Official Charts ID (wd:P6559 as p/wdt): identifier for musical acts at Official Charts Company's website

No other items found

[Assistant] No direct property linking Mariah Carey to albums she performs on was found in the property search. I will try to find albums where Mariah Carey is listed as a performer by searching for albums with Mariah Carey as a performer or performer of the album. I will search for properties related to "performer" or "performer of" in general.

[Tool]
Top 10 property alternatives:
1. performer, also known as actor, sung by, singer, recording by, recorded by (wd:P175 as ps/wdt/prn/wdtn/p/psv/pr/prv/pq/pqn/psn/pqv): actor, musician, band or other performer associated with this role or musical work, contributor to the creative work or subject
2. character role, also known as role, plays, performer of, as, acting role (wd:P453 as ps/wdt/prn/wdtn/p/psv/pr/prv/pq/pqn/psn/pqv): specific role played or filled by subject -- use only as qualifier of "cast member" (P161), "voice actor" (P725)
3. cover art by, also known as jacket art by, sleeve art by, designer, cover design by, cover artist (wd:P736 as ps/wdt/prn/wdtn/p/psv/pr/prv/pq/pqn/psn/pqv): name of person or team creating cover artwork for book, record album, single record etc.
4. setlist, also known as set list, works performed, songs performed, performances, performance list (wd:P9793 as ps/wdt/prn/wdtn/p/psv/pr/prv/pq/pqn/psn/pqv): performances of songs or other music at an event; concert program, presented works
5. number of representations, also known as shows, performances, number of shows, number of concerts, no. of shows (wd:P5027 as ps/wdt/prn/wdtn/p/psv/pr/prv/pq/pqn/psn/pqv): number of shows (including the premiere) of a concert tour or a performing arts production
6. used by, also known as armiger, user, played by, of (wd:P1535 as ps/wdt/prn/wdtn/p/psv/pr/prv/pq/pqn/psn/pqv): item or concept that makes use of the subject (use sub-properties when appropriate)
7. depicted by, also known as acted by, shown in, seen in, portrayed by, played by (wd:P1299 as ps/wdt/prn/wdtn/p/psv/pr/prv/pq/pqn/psn/pqv): object depicting this subject (object being creative works such as books, films, paintings)
8. exhibited creator, also known as artist exhibited, exhibited artist (wd:P10661 as ps/wdt/prn/wdtn/p/psv/pr/prv/pq/pqn/psn/pqv): intellectual or creative entity (person or organization) responsible for the elements exhibited, creator
9. Muziekweb performer ID, also known as Muziekweb artist ID, Muziekweb band ID (wd:P5882 as ps/wdt/prn/wdtn/p/psv/pr/prv/pq/pqn/psn/pqv): identifier for a performer, composer or band at Muziekweb
10. talk show guest, also known as show guest, podcast guest, participant in talk show, participant in podcast interview, interviewed person (wd:P5030 as ps/wdt/prn/wdtn/p/psv/pr/prv/pq/pqn/psn/pqv): person invited in a talk show, recorded participant

[Assistant] The property "performer" (wdt:P175) is used to indicate an actor, musician, band, or other performer associated with a creative work such as an album. I will now write a query to count the number of albums where Mariah Carey (wd:Q41076) is listed as a performer.

[Tool]
Got 1 row and 1 column:
| albumCount   |
| ------------ |
| 37 (xsd:int) |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly counts the number of distinct albums on which Mariah Carey performs by filtering for items that are instances of albums and have Mariah Carey as a performer. The query returns the count as 37, which is a reasonable and precise answer to the user's question. The query follows best practices by returning the count directly and uses appropriate properties and entities. No further refinement is necessary.