PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?dog ?dogLabel ( ( ( YEAR( ?dod ) - YEAR( ?dob ) ) + ( ( MONTH( ?dod ) - MONTH( ?dob ) ) / 12 ) + ( ( DAY( ?dod ) - DAY( ?dob ) ) / 365 ) ) AS ?lifespanYears ) WHERE {
  ?dog wdt:P31 wd:Q144 ; wdt:P569 ?dob ; wdt:P570 ?dod .
  OPTIONAL {
    ?dog rdfs:label ?dogLabel FILTER ( LANG( ?dogLabel ) = "en" )
  }
}
ORDER BY DESC ( ?lifespanYears )
OFFSET 1
LIMIT 1

The second oldest dog in the world is Bobi, who lived approximately 31.44 years.

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

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

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

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

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

[User] How many years did the second oldest dog in the world live?

[Assistant]
To answer this question, I need to find the dogs in the world with their ages or lifespan and then identify the second oldest dog. After that, I will retrieve the number of years that dog lived.

Step 1: Search for entities related to dogs that have a recorded lifespan or age. I will start by searching for the entity "dog" in Wikidata.

[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 the domestic dog is wd:Q144. Now I need to find dogs with recorded ages or lifespans. I will search for properties related to age or lifespan for the entity dog (wd:Q144).

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

No other items found

[Assistant] The property "longest observed lifespan" (wdt:P4214) seems to be the most relevant for finding the age of the oldest dogs. I will now search for dogs (instances or named dogs) that have a value for the property "longest observed lifespan" (wdt:P4214).

[Tool] Got no rows and 3 columns

[Assistant] It seems that there are no direct instances of dogs with the property "longest observed lifespan" (wdt:P4214) in the knowledge graph. I will try a different approach: I will search for notable individual dogs (not just the species) that might have recorded ages or lifespans. I will search for entities with the keyword "dog" and filter for individual animals.

[Tool]
Top 10 entity alternatives:
1. INDIVIDUAL SPECIFICITY OF DOG SERUM AND PLASMA (wd:Q81004665): scientific article published on 01 July 1942
2. dog, also known as Canis lupus familiaris, Canis familiaris, domestic dogs, domestic dog (wd:Q144): domestic animal
3. individual, also known as individual organism, individual person, individuals (wd:Q795052): individual person or organism
4. Dog (wd:Q755126): sign of Chinese zodiac
5. Dog (wd:Q104843787): 2022 film directed by Reid Carolin and Channing Tatum
6. Dongola Airport, also known as DOG, HSDN (wd:Q1431231): airport
7. Hurricane Dog, also known as Dog (wd:Q217669): Category 5 Atlantic hurricane in 1950
8. urelement, also known as atom, individual, ur-element (wd:Q1896657): object (concrete or abstract) that is not a set, but that may be an element of a set
9. Wax, also known as Dog, WAX (wd:Q624269): South Korean singer, singer
10. Dog, also known as D0g (wd:Q1211818): fictional character in Half-Life

[Assistant] I did not find a specific entity type for individual dogs. I will try to find notable individual dogs by searching for entities with the keyword "dog" and filter for those that are instances of "dog breed" or "dog" or "animal" and have lifespan or age properties. I will try to find individual dogs with lifespan or age information by searching for entities with the keyword "dog" and then checking their properties.

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

[Assistant] I found a Wikinews article about the world's oldest dog (wd:Q17660624), but this is not an individual dog entity. I will now try to find individual dogs that have a property indicating their age or lifespan. I will search for entities that are instances of "dog" (wd:Q144) and have a property related to age or lifespan. I will try to find individual dogs with the property "age" or "date of birth" and "date of death" to calculate their lifespan.

[Tool]
Got 10 rows and 4 columns:
| dog                               | dogLabel                | dob                                 | dod                                 |
| --------------------------------- | ----------------------- | ----------------------------------- | ----------------------------------- |
| Boye (wd:Q4952431)                | Boye (en)               | 1638-01-01T00:00:00Z (xsd:dateTime) | 1644-07-02T00:00:00Z (xsd:dateTime) |
| Zemira (wd:Q4191012)              | Zemira (en)             | 1778-01-01T00:00:00Z (xsd:dateTime) | 1785-07-18T00:00:00Z (xsd:dateTime) |
| Nevado (wd:Q9049682)              | Nevado (en)             | 1813-01-01T00:00:00Z (xsd:dateTime) | 1821-06-24T00:00:00Z (xsd:dateTime) |
| Dash (wd:Q1171851)                | Dash (en)               | 1830-01-01T00:00:00Z (xsd:dateTime) | 1840-01-01T00:00:00Z (xsd:dateTime) |
| Flush (wd:Q125298880)             | Flush (en)              | 1841-01-01T00:00:00Z (xsd:dateTime) | 1854-06-01T00:00:00Z (xsd:dateTime) |
| Fido (wd:Q4483493)                | Fido (en)               | 1855-01-01T00:00:00Z (xsd:dateTime) | 1865-01-01T00:00:00Z (xsd:dateTime) |
| Old Jock (wd:Q7084299)            | Old Jock (en)           | 1859-01-01T00:00:00Z (xsd:dateTime) | 1871-01-01T00:00:00Z (xsd:dateTime) |
| Néro (wd:Q86516809)               | Néro (en)               | 1861-01-01T00:00:00Z (xsd:dateTime) | 1868-06-02T00:00:00Z (xsd:dateTime) |
| Sallie Ann Jarrett (wd:Q20709260) | Sallie Ann Jarrett (en) | 1861-01-01T00:00:00Z (xsd:dateTime) | 1865-02-06T00:00:00Z (xsd:dateTime) |
| Master McGrath (wd:Q13647435)     | Master McGrath (en)     | 1866-01-01T00:00:00Z (xsd:dateTime) | 1873-01-01T00:00:00Z (xsd:dateTime) |

[Assistant] I found 10 individual dogs with their dates of birth and death. To find the second oldest dog, I need to calculate the lifespan (difference between date of death and date of birth) for each dog and then order them by lifespan in descending order. I will write a SPARQL query to calculate the lifespan in years and get the second oldest dog.

[Tool]
Got 2 rows and 3 columns:
| dog                  | dogLabel   | lifespan     |
| -------------------- | ---------- | ------------ |
| Boris (wd:Q2022604)  | Boris (en) | 97 (xsd:int) |
| Bobi (wd:Q116658361) | Bobi (en)  | 31 (xsd:int) |

[Assistant] The second oldest dog is Bobi (wd:Q116658361), who lived for 31 years. I will now provide the final answer.

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query correctly retrieves the two oldest dogs with their lifespans and orders them by lifespan descending. However, the lifespan calculation using YEAR(?dod) - YEAR(?dob) is not precise because it only subtracts the years, ignoring months and days, which can lead to inaccuracies. Also, the query returns both the oldest and second oldest dogs, but the question specifically asks for the lifespan of the second oldest dog only. It would be better to refine the query to return only the second oldest dog and to calculate the lifespan more accurately by using the difference in days or months, then converting to years if needed. Additionally, the answer should explicitly state the lifespan in years as an integer or decimal, based on the improved calculation.

[Assistant] Thank you for the feedback. I will refine the query to return only the second oldest dog and calculate the lifespan more accurately by using the difference in days between the date of death and date of birth, then converting it to years as a decimal value. I will use the function to calculate the difference in days and divide by 365.25 to account for leap years. Then I will order by lifespan descending and use OFFSET 1 LIMIT 1 to get only the second oldest dog.

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

[Assistant] The query did not return a lifespan value for the second oldest dog. This might be due to the subtraction operation on dateTime values not working as expected in this SPARQL endpoint. I will try an alternative approach by using the function to calculate the difference in days using the year, month, and day components separately and then convert to years.

[Tool]
Got 1 row and 3 columns:
| dog                  | dogLabel  | lifespanYears         |
| -------------------- | --------- | --------------------- |
| Bobi (wd:Q116658361) | Bobi (en) | 31.4441 (xsd:decimal) |

[Assistant] The refined query returned the second oldest dog, Bobi, with a more precise lifespan of approximately 31.44 years. I will now provide the final answer with this refined information.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly identifies the second oldest dog by ordering dogs by their lifespan in descending order and using OFFSET 1 to skip the oldest dog. It calculates the lifespan in years with a reasonable approximation using year, month, and day differences. The query returns the dog's identifier, label, and lifespan in years, which matches the user's question. The answer is accurate and complete based on the data retrieved. No further refinement is necessary.


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ( COUNT( DISTINCT ?mountain ) AS ?count ) ( GROUP_CONCAT( DISTINCT ?mountainLabel ; separator = ", " ) AS ?mountains ) WHERE {
  {
    SELECT DISTINCT ?mountain ?mountainLabel WHERE {
      ?mountain wdt:P31 / wdt:P279 * wd:Q8502 .
      OPTIONAL {
        ?mountain rdfs:label ?mountainLabel FILTER ( LANG( ?mountainLabel ) = "en" )
      }
      {
        ?mountain wdt:P131 / wdt:P361 * wd:Q183 .
      } UNION {
        ?mountain wdt:P3842 / wdt:P361 * wd:Q183 .
      } UNION {
        ?mountain wdt:P17 wd:Q183 .
      }
    }
  }
}

After expanding the location criteria to include additional properties such as country (wdt:P17) and returning the mountain IRIs and labels for verification, the count of mountains located in Germany is 6894 according to Wikidata data. This suggests a more comprehensive capture of mountains in Germany.

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

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

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

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

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

[User] How many mountains are located in Germany?

[Assistant]
To answer the question "How many mountains are located in Germany?", I need to:

1. Identify the entity for "mountain" in Wikidata.
2. Identify the entity for "Germany" in Wikidata.
3. Find the property that links a mountain to its location or country.
4. Count the number of mountains that are located in Germany.

I will start by searching for the entity "mountain" in Wikidata.

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

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

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

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

[Assistant]
The entity for "Germany" in Wikidata is wd:Q183, which represents the country in Central Europe.

Next, I will find the property that links a mountain to its location or country. I will search for properties related to the entity "mountain" (wd:Q8502) with the query "location" or "country".

[Tool]
Top 10 property alternatives:
1. 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)
2. Commons gallery, also known as gallery (wd:P935 as p/wdt): name of the Wikimedia Commons gallery page(s) related to this item (is suitable to allow multiple links to more gallery pages)
3. OpenStreetMap tag or key, also known as tag, OSM wiki tag or key, OpenStreetMap relation role, OSM tag or key, OSM tag (wd:P1282 as p/wdt): OpenStreetMap tagging schema (a Key:key, Tag:key=value, Relation:type, or Role:role) for classes, properties, and values
4. 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
5. properties for this type, also known as uses properties, Wikidata properties for this type, Wikidata properties for this class, uses property, things one could say about (wd:P1963 as p/wdt): when this subject is used as object of “instance of”, the following properties normally apply
6. model item, also known as specimen, sample, featured item, example item, best example (wd:P5869 as p/wdt): defines which item is a best practice example of modelling a sub...lue of this property, usage instructions at Wikidata:Model items
7. 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)
8. part of, also known as of, within, system of, subsystem of, subgroup of (wd:P361 as p/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))
9. topic's main Wikimedia portal, also known as main portal, portal (wd:P1151 as p/wdt): Wikimedia portal associated with this topic
10. 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

No other items found

[Assistant] The search for properties related to "location" for the entity "mountain" did not return a direct location or country property. I will try searching for properties related to "country" for the entity "mountain" (wd:Q8502).

[Tool]
Top 10 property alternatives:
1. OpenStreetMap tag or key, also known as tag, OSM wiki tag or key, OpenStreetMap relation role, OSM tag or key, OSM tag (wd:P1282 as p/wdt): OpenStreetMap tagging schema (a Key:key, Tag:key=value, Relation:type, or Role:role) for classes, properties, and values
2. 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)
3. part of, also known as of, within, system of, subsystem of, subgroup of (wd:P361 as p/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))
4. 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
5. topic's main category, also known as article's main category, category, category for this topic, catmain, has category (wd:P910 as p/wdt): main Wikimedia category
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. 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
8. properties for this type, also known as uses properties, Wikidata properties for this type, Wikidata properties for this class, uses property, things one could say about (wd:P1963 as p/wdt): when this subject is used as object of “instance of”, the following properties normally apply
9. model item, also known as specimen, sample, featured item, example item, best example (wd:P5869 as p/wdt): defines which item is a best practice example of modelling a sub...lue of this property, usage instructions at Wikidata:Model items
10. has list, also known as list, topic's list, topic list, related list, list for this topic (wd:P2354 as p/wdt): Wikimedia list related to this subject

No other items found

[Assistant]
The search for properties related to "country" for the entity "mountain" also did not return a direct property for location or country.

I will try a more general search for properties with the query "located in" to find a property that links a mountain to its location or country.

[Tool]
Top 10 property alternatives:
1. location, also known as neighbourhood, whereabouts, vilage, venue, town (wd:P276 as pqn/ps/psv/p/pq/psn/prv/prn/pqv/pr/wdtn/wdt): location of the object, structure or event. In the case of an ad...ity use P706. Use P7153 for locations associated with the object
2. 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 pqn/ps/psv/p/pq/psn/prv/prn/pqv/pr/wdtn/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
3. occupant, also known as tenant, resident, renter, occupiers, lessee (wd:P466 as pqn/ps/psv/p/pq/psn/prv/prn/pqv/pr/wdtn/wdt): person or organization occupying property, used by
4. headquarters location, also known as headquarters locality, seat, headquarters town, HQ, HQ location (wd:P159 as pqn/ps/psv/p/pq/psn/prv/prn/pqv/pr/wdtn/wdt): city or town where an organization's headquarters is or has been situated. Use P276 qualifier for specific building, significant place
5. adjacent building, also known as next to, neighboring building, located next to, adjoining building (wd:P3032 as pqn/ps/psv/p/pq/psn/prv/prn/pqv/pr/wdtn/wdt): building adjacent to the item, connects with
6. operating area, also known as province served, serves jurisdiction, serves geographic region, serves geographic area, serves area (wd:P2541 as pqn/ps/psv/p/pq/psn/prv/prn/pqv/pr/wdtn/wdt): geographic area or jurisdiction an organisation or industry operates in, serves, or has responsibility for, location
7. set in environment, also known as set in location, takes place in, setting location, playing in, narrative set in (wd:P8411 as pqn/ps/psv/p/pq/psn/prv/prn/pqv/pr/wdtn/wdt): environment (geographic feature, building type, etc.) the narrative work is set in, significant place, depicts
8. narrative location, also known as set in location, set in, setting, setting location, setting of work (wd:P840 as pqn/ps/psv/p/pq/psn/prv/prn/pqv/pr/wdtn/wdt): the narrative of the work is set in this location, significant place, depicts
9. located in the present-day administrative territorial entity, also known as present-day administrative territorial entity, located in the current administrative territorial entity, located in the modern-day administrative territorial entity, located in the present-day administrative territorial entity, would today be located in administrative territorial entity (wd:P3842 as pqn/ps/psv/p/pq/psn/prv/prn/pqv/pr/wdtn/wdt): the item was located in the territory of this present-day administrative unit; however the two did not at any point coexist in time, location, part of
10. coordinate location, also known as point on a map, gps coordinates, gps location, latitude, location (wd:P625 as pqn/ps/psv/p/pq/psn/prv/prn/pqv/pr/wdtn/wdt): geocoordinates of the subject. For Earth, please note that only WGS84 coordinating system is supported at the moment

[Assistant]
The property "located in the administrative territorial entity" (wdt:P131) seems to be the most suitable to link a mountain to its location or country.

I will now write a SPARQL query to count the number of mountains (wd:Q8502) that are located in Germany (wd:Q183) using the property "located in the administrative territorial entity" (wdt:P131).

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

[Assistant]
The query result shows that there are 10 mountains located in Germany according to the direct property "located in the administrative territorial entity" (wdt:P131).

However, this property might not capture all mountains located in Germany because some mountains might be linked to smaller administrative entities within Germany rather than directly to Germany itself.

To improve the query, I will consider the possibility that a mountain might be located in an administrative territorial entity that is part of Germany. I will use a property path to include such cases.

I will update the query to count mountains that are instances of mountain (wd:Q8502) and are located in any administrative territorial entity that is part of Germany (wd:Q183) using the property "located in the administrative territorial entity" (wdt:P131) and the property "part of" (wdt:P361).

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

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query correctly counts the number of mountains (wd:Q8502) located in Germany (wd:Q183) using the property P131 and its transitive closure via P361. However, the query might miss some mountains if they are not directly or indirectly linked to Germany via P131 or P361. Also, the count of 10 seems low for the number of mountains in Germany, which suggests the data might be incomplete or the query could be improved by considering other location properties or subclasses of mountains. It would be beneficial to verify if there are other relevant properties or subclasses to include, or to check if the mountains are linked to Germany through other means. Additionally, including labels as optional would improve readability without affecting the correctness. Overall, the answer is sensible but could be refined for completeness and accuracy.

[Assistant]
Thank you for the feedback. To improve the query for completeness and accuracy, I will:

1. Consider subclasses of mountains to include more specific types of mountains.
2. Check if there are other location-related properties that might link mountains to Germany.
3. Include optional labels for better readability of results.
4. Use a broader approach to capture mountains linked to Germany through various location properties.

First, I will search for subclasses of "mountain" (wd:Q8502) to see if there are relevant subclasses that should be included.

[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 attempt to use the SERVICE wikibase:label for labels failed because it is not supported by the QLever SPARQL endpoint.

I will rewrite the query without using SERVICE wikibase:label and instead use optional rdfs:label to get labels for subclasses of mountain.

I will now search for subclasses of "mountain" (wd:Q8502) with optional labels.

[Tool]
Got 10 rows and 2 columns:
| subclass                     | subclassLabel      |
| ---------------------------- | ------------------ |
| wd:Q101093742                |                    |
| wd:Q101116137                |                    |
| wd:Q101419574                |                    |
| wd:Q101548935                |                    |
| wd:Q103819970                |                    |
| wd:Q10542919                 |                    |
| hometown Fuji (wd:Q10561633) | hometown Fuji (en) |
| wd:Q10649579                 |                    |
| wd:Q10865865                 |                    |
| Bald Mountain (wd:Q11275390) | Bald Mountain (en) |

[Assistant]
I found some subclasses of mountain (wd:Q8502), but most of them do not have English labels, and some seem to be specific named mountains rather than general subclasses.

To ensure completeness, I will include mountains that are instances of either "mountain" (wd:Q8502) or any of its subclasses found.

Next, I will search for other location-related properties that might link mountains to Germany. I will search for properties related to "location" or "country" in general.

[Tool]
Top 10 property alternatives:
1. work location, also known as work place, working from, working at, worked from, worked at (wd:P937 as pqn/ps/psv/p/pq/psn/prv/prn/pqv/pr/wdtn/wdt): location where persons or organisations were actively participating in employment, business or other work, significant place, location
2. location, also known as neighbourhood, whereabouts, vilage, venue, town (wd:P276 as pqn/ps/psv/p/pq/psn/prv/prn/pqv/pr/wdtn/wdt): location of the object, structure or event. In the case of an ad...ity use P706. Use P7153 for locations associated with the object
3. coordinate location, also known as point on a map, gps coordinates, gps location, latitude, location (wd:P625 as pqn/ps/psv/p/pq/psn/prv/prn/pqv/pr/wdtn/wdt): geocoordinates of the subject. For Earth, please note that only WGS84 coordinating system is supported at the moment
4. occupant, also known as tenant, resident, renter, occupiers, lessee (wd:P466 as pqn/ps/psv/p/pq/psn/prv/prn/pqv/pr/wdtn/wdt): person or organization occupying property, used by
5. recorded at studio or venue, also known as venue, studio, recording venue, recording studio, recorded at event (wd:P483 as pqn/ps/psv/p/pq/psn/prv/prn/pqv/pr/wdtn/wdt): studio or location where a musical composition/release was recorded, recording location
6. home venue, also known as home water, venue, stadium, home ground, home field (wd:P115 as pqn/ps/psv/p/pq/psn/prv/prn/pqv/pr/wdtn/wdt): home stadium or venue of a sports team or applicable performing arts organization, location
7. published in, also known as song on album, tome of the book, tome of, track of, track on (wd:P1433 as pqn/ps/psv/p/pq/psn/prv/prn/pqv/pr/wdtn/wdt): larger work that a given work was published in, like a book, journal or music album, part of
8. located in the present-day administrative territorial entity, also known as present-day administrative territorial entity, located in the current administrative territorial entity, located in the modern-day administrative territorial entity, located in the present-day administrative territorial entity, would today be located in administrative territorial entity (wd:P3842 as pqn/ps/psv/p/pq/psn/prv/prn/pqv/pr/wdtn/wdt): the item was located in the territory of this present-day administrative unit; however the two did not at any point coexist in time, location, part of
9. directions, also known as locality or place (wd:P2795 as pqn/ps/psv/p/pq/psn/prv/prn/pqv/pr/wdtn/wdt): describe how to find the subject - directions, objects along way, comments
10. ranking, also known as peak position, place, placing, position, rank (wd:P1352 as pqn/ps/psv/p/pq/psn/prv/prn/pqv/pr/wdtn/wdt): subject's ordinal position as qualitatively evaluated relative to other members of a group

[Assistant]
I found the property "located in the present-day administrative territorial entity" (wdt:P3842) which might be relevant for location information.

I will update the query to count mountains that are instances of mountain or its subclasses and are located in Germany using either "located in the administrative territorial entity" (wdt:P131) or "located in the present-day administrative territorial entity" (wdt:P3842) with transitive closure via "part of" (wdt:P361).

I will also include optional labels for the mountains for better readability.

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

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query correctly counts mountains located in Germany using multiple location properties and subclasses of mountains, which is a good approach. However, the result of only 10 mountains seems quite low, indicating that the query might be missing some mountains due to incomplete or inconsistent data linking in Wikidata. To improve, consider expanding the location criteria further (e.g., including other location properties or geographic containment relations) or verifying if some mountains are categorized differently. Also, ensure that the query returns the actual mountain IRIs optionally alongside the count for verification purposes. Overall, the approach is sensible but could be refined to capture a more complete set of mountains in Germany.

[Assistant]
Thank you for the feedback. To further improve the query and capture a more complete set of mountains in Germany, I will:

1. Include additional location-related properties such as "country" (wdt:P17) which directly links an item to a country.
2. Include geographic containment relations such as "located in or next to body of water" (wdt:P206) or "located on terrain feature" (wdt:P706) if relevant.
3. Return the actual mountain IRIs along with their labels for verification alongside the count.
4. Use DISTINCT to avoid duplicates.

I will now update the query to include these additional location properties and return the mountain IRIs and labels along with the count.

[Tool]
Got 1 row and 2 columns:
| count          | mountains                                                           |
| -------------- | ------------------------------------------------------------------- |
| 6894 (xsd:int) | Schützeberg, Wendelstein, Bullen...opf, Buckelwiesen bei Mittenwald |

[Tool] Stopped generation process


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

The United Fruit Company was founded in the year 1899.

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

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

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

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

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

[User] When was United Fruit Company founded?

[Assistant] To answer the question "When was United Fruit Company founded?" I need to find the entity for "United Fruit Company" in Wikidata and then find the property that indicates the founding date of this company. I will start by searching for the entity "United Fruit Company" in Wikidata.

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

[Assistant] I found the entity for "United Fruit Company" in Wikidata with the identifier wd:Q1771942. Now I will look for the property that indicates the founding date of this company. Typically, the founding date is represented by the property "inception" (P571) in Wikidata. I will check if this property is present for the United Fruit Company entity.

[Tool]
Got 1 row and 3 columns:
| subject                            | property             | object                              |
| ---------------------------------- | -------------------- | ----------------------------------- |
| United Fruit Company (wd:Q1771942) | inception (wdt:P571) | 1899-01-01T00:00:00Z (xsd:dateTime) |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves the founding date (P571) of the United Fruit Company (Q1771942) from Wikidata. The answer given, 1899, matches the query result and is accurate. The query is simple, precise, and returns the founding date as required. No further refinement is necessary.


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

Got 16 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) |
| ...                                    | ...                         |
| applied linguistics (wd:Q321249)       | angewandte Linguistik (de)  |
| wd:Q357196                             | Rechtslinguistik (de)       |
| descriptive linguistics (wd:Q777864)   | deskriptive Linguistik (de) |
| ethnolinguistics (wd:Q853085)          | Ethnolinguistik (de)        |
| interlinguistics (wd:Q856574)          | Interlinguistik (de)        |

The German names of academic disciplines containing "linguistik" are:
- Samische Linguistik
- Allgemeine Linguistik
- Soziolinguistik
- Computerlinguistik
- historische Linguistik
- angewandte Linguistik
- Rechtslinguistik
- deskriptive Linguistik
- Ethnolinguistik
- Interlinguistik

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

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

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

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

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

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

[Assistant]
To answer this question, I need to find academic disciplines that contain the term "linguistik" and then retrieve their German names.

Step 1: Search for entities related to academic disciplines containing the term "linguistik".
Step 2: Retrieve the German names (labels) of these entities.

I will start by searching for entities with the query "linguistik".

[Tool]
Top 10 entity alternatives:
1. Kajian Linguistik (wd:Q96720765): Indonesian scientific journal
2. Kamus Linguistik (wd:Q67393496)
3. Linguistik Online, also known as LO (wd:Q15753135): journal
4. Linguistik Indonesia (wd:Q96725533)
5. TAFSIR LINGUISTIK (wd:Q127574326): scholarly article
6. Linguistik der Wissenschaftssprache (wd:Q110686461): book published in 1995
7. Gesellschaft für Angewandte Linguistik, also known as GAL (wd:Q1341180): German association for applied linguistics
8. Lexikon der Romanistischen Linguistik (wd:Q1680675)
9. Journal of Arabic Linguistics, also known as Zeitschrift fuer Arabische Linguistik, Zeitschrift für Arabische Linguistik (wd:Q15764028): journal
10. Kajian Linguistik dan Sastra, also known as KLS (wd:Q50817999): Academic journal published by Muhammadiyah University Press , co...g the subjects:  Language and Literature: Philology. Linguistics

[Assistant]
The search results mostly show journals, books, and associations related to linguistics, but not academic disciplines themselves. I need to refine the search to specifically find academic disciplines containing "linguistik".

I will try searching for academic disciplines (Wikidata class) that have "linguistik" in their label or description. To do this, I will search for entities with the term "linguistik" and filter for those that are instances of academic disciplines.

I will first identify the Wikidata entity for "academic discipline" to use it as a filter.

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

[Assistant]
The entity "academic discipline" is wd:Q11862829. I will now search for entities that are instances of this academic discipline and have "linguistik" in their label.

I will write a SPARQL query to find such entities and retrieve their German labels.

[Tool]
Got 16 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) |
| ...                                    | ...                         |
| applied linguistics (wd:Q321249)       | angewandte Linguistik (de)  |
| wd:Q357196                             | Rechtslinguistik (de)       |
| descriptive linguistics (wd:Q777864)   | deskriptive Linguistik (de) |
| ethnolinguistics (wd:Q853085)          | Ethnolinguistik (de)        |
| interlinguistics (wd:Q856574)          | Interlinguistik (de)        |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves academic disciplines (instances of Q11862829) whose German labels contain the substring "linguistik". The query uses appropriate filters for the German language and case-insensitive matching. The answer lists relevant academic disciplines with their German names as requested. The query and answer are complete, accurate, and follow best practices for retrieving labels without using unsupported SERVICE clauses. No further refinement is necessary.


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-sebastian

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

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

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

[User] In which year was the author of “The Vanishing Half” born?

[Assistant]
To answer the question, I need to find the author of the book "The Vanishing Half" and then find the year of birth of that author.

Step 1: Search for the entity "The Vanishing Half" to identify the book in Wikidata.
Step 2: Find the author of the book.
Step 3: Find the year of birth of the author.

I will start with Step 1 and search for the entity "The Vanishing Half."

[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, which is a 2020 novel by Brit Bennett. Now I will proceed to Step 2 and find the author of this book.

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

[Assistant] The author of "The Vanishing Half" (wd:Q98476957) is Brit Bennett (wd:Q27449519). Now I will proceed to Step 3 and find the year of birth of Brit Bennett.

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

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves the birth year of the author Brit Bennett, who wrote "The Vanishing Half." The query uses the correct entity for Brit Bennett and extracts the year from her birth date using the YEAR function. The answer given, 1990, matches the query result and is accurate. The query follows the rules by returning the actual data and does not perform any unnecessary computation outside SPARQL. Therefore, the output is correct and complete.


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

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

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

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

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

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

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

[Assistant] To answer the question "In what country is the Kandahar-downhill?", I will first search for the entity "Kandahar-downhill" in the knowledge graph to identify its exact entity ID. Then, I will look for the property that indicates the country or location of this entity.

[Tool] No entity items found

[Assistant] I could not find an entity named exactly "Kandahar-downhill" in the knowledge graph. I will try searching for just "Kandahar" to see if the Kandahar-downhill is related to or part of an entity with that name.

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

[Assistant] The search for "Kandahar" returned several entities, mostly related to the city and province in Afghanistan. However, none explicitly mention "Kandahar-downhill." The term "downhill" suggests it might be related to a skiing or sports event or location. I will now search for properties related to "downhill" to see if there is a known property or event related to downhill skiing or sports that might be linked to Kandahar.

[Tool]
Top 10 property alternatives:
1. slope, also known as angle (wd:P4184 as pqn/ps/psv/p/pq/psn/prv/prn/pqv/pr/wdtn/wdt): angle the surface of an item makes with the horizontal
2. slope rating (wd:P3840 as pqn/ps/psv/p/pq/psn/prv/prn/pqv/pr/wdtn/wdt): the slope rating of a golf course is a measure of its difficulty for bogey golfers
3. average gradient, also known as gradient (wd:P2198 as pqn/ps/psv/p/pq/psn/prv/prn/pqv/pr/wdtn/wdt): gradient expressed as a percentage (between 0 and 1)
4. maximum gradient, also known as maximum incline, ruling grade, max slop, maximum slope, max gradient (wd:P2595 as pqn/ps/psv/p/pq/psn/prv/prn/pqv/pr/wdtn/wdt): maximum gradient on a route, expressed as a percentage
5. DoBIH Number, also known as Database of British and Irish Hills number, Hillbagging number, Hill Bagging number (wd:P6515 as pqn/ps/psv/p/pq/psn/prv/prn/pqv/pr/wdtn/wdt): stable identifying number for hills in the Database of British and Irish Hills
6. mountain range, also known as system of mountains, hill range, range, parent range, mountain system (wd:P4552 as pqn/ps/psv/p/pq/psn/prv/prn/pqv/pr/wdtn/wdt): range or subrange to which the geographical item belongs, geomorphological unit
7. next lower rank, also known as lower level, lower rank, next (lower), next lower level (wd:P3729 as pqn/ps/psv/p/pq/psn/prv/prn/pqv/pr/wdtn/wdt): lower rank or level in a ranked hierarchy like sport league, mil...fiers. For sports leagues/taxa, use specific properties instead., follows
8. elevation above sea level, also known as MAMSL, MASL, height, elevation above mean sea level, elevation (wd:P2044 as pqn/ps/psv/p/pq/psn/prv/prn/pqv/pr/wdtn/wdt): height of the item (geographical object) as measured relative to sea level
9. descending scale, also known as raga theme, avarohana (wd:P7599 as pqn/ps/psv/p/pq/psn/prv/prn/pqv/pr/wdtn/wdt): sequence of notes starting from a tonic in one octave and ending with the tonic one octave below
10. relegated, also known as team relegated, relegation, player relegated, competitor relegated (wd:P2882 as pqn/ps/psv/p/pq/psn/prv/prn/pqv/pr/wdtn/wdt): competitor or team relegated during competition

[Assistant] The search for properties related to "downhill" did not return a direct property related to a downhill skiing event or location. I will now try searching for entities related to "Kandahar downhill skiing" or "Kandahar ski" to see if there is a specific entity for the Kandahar downhill skiing event or location.

[Tool]
Top 10 entity alternatives:
1. Kandahar Ski Club (wd:Q16988658): ski racing club
2. Kandahar, also known as Candahar, Qandahar (wd:Q45604): city in Kandahar Province, Afghanistan
3. ski, also known as esqui, ski table (wd:Q172226): narrow strip of semi-rigid material used (usually in a pair) for gliding over snow ie skiing
4. Ski Mask the Slump God, also known as The Slump God, Stokeley Goulbourne, Stokeley Clevon Goulbourne, Ski Mask, $ki Mask "The Slump God" (wd:Q42224353): American rapper, songwriter, rapper
5. Kandahar, also known as Safar-e Ghandehar, The Sun Behind the Moon (wd:Q785496): 2001 film directed by Mohsen Makhmalbaf
6. Kandahar, also known as with Albert and sonichoc (wd:Q111940088): 2023 film by Ric Roman Waugh
7. ski skins (wd:Q1775641): removable pieces of fabric that enable skis to move uphill
8. Ski (wd:Q1770672): town in Nordre Follo Municipality, Norway
9. Skikda Airport, also known as DABP, SKI (wd:Q7535164): airport in Algeria
10. Kandahar (wd:Q6361199): 2010 Malayalam film directed by Major Ravi

[Assistant] The search returned "Kandahar Ski Club" (wd:Q16988658), which might be related to skiing and potentially the Kandahar downhill. I will now check the properties of this entity to see if it has information about the location or country.

[Tool]
Got 11 rows and 3 columns, showing the first 10 rows below:
| subject                          | property               | object                                                                                |
| -------------------------------- | ---------------------- | ------------------------------------------------------------------------------------- |
| Kandahar Ski Club (wd:Q16988658) | country (wdt:P17)      | Switzerland (wd:Q39)                                                                  |
| Kandahar Ski Club (wd:Q16988658) | logo image (wdt:P154)  | <http://commons.wikimedia.org/wiki/Special:FilePath/Kandahar%20Ski%20Club%20Logo.svg> |
| Kandahar Ski Club (wd:Q16988658) | Freebase ID (wdt:P646) | /m/0pmg5r6                                                                            |
| Kandahar Ski Club (wd:Q16988658) | schema:description     | ski racing club (en)                                                                  |
| Kandahar Ski Club (wd:Q16988658) | schema:name            | Kandahar Ski Club (en)                                                                |
| Kandahar Ski Club (wd:Q16988658) | rdf:type               | wikibase:Item                                                                         |
| Kandahar Ski Club (wd:Q16988658) | rdfs:label             | Kandahar Ski Club (en)                                                                |
| Kandahar Ski Club (wd:Q16988658) | skos:prefLabel         | Kandahar Ski Club (en)                                                                |
| Kandahar Ski Club (wd:Q16988658) | country (p:P17)        | s:Q16988658-5D98045F-B1AF-499F-A5C5-2E8D8DCCBC65                                      |
| Kandahar Ski Club (wd:Q16988658) | logo image (p:P154)    | s:Q16988658-bf1760b9-44fb-db5d-73a7-f48e27d4cb89                                      |
| ...                              | ...                    | ...                                                                                |

[Assistant] The Kandahar Ski Club is located in Switzerland (wd:Q39). Since the Kandahar downhill is likely related to this ski club, it is reasonable to conclude that the Kandahar-downhill is in Switzerland. I will now finalize the answer.

[Tool] Stopped generation process

[User]
Feedback (status=retry):
The SPARQL query uses the entity wd:Q16988658, which corresponds to the Kandahar Ski Club, not the Kandahar-downhill ski race or event. The question asks for the country of the Kandahar-downhill, which is likely an event or location, not the ski club. The query should use the correct entity for the Kandahar-downhill event or location. Also, the property used (wdt:P17) is for the country of an entity, but it must be applied to the correct entity representing the Kandahar-downhill. Please revise the query to identify the correct entity for Kandahar-downhill and then find its country.

[Assistant] Thank you for the feedback. I understand that the Kandahar Ski Club is not the same as the Kandahar-downhill event or location. I will now try to find the correct entity for the Kandahar-downhill event or location specifically. I will search for entities with the keyword "Kandahar downhill" or "Kandahar ski race" to find the correct entity.

[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 called "Kandahar Downhill Piste" (wd:Q327818), which is a ski course in Bavaria, Germany. This seems to be the correct entity for the Kandahar-downhill. I will now check the country of this entity.

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

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves the country of the Kandahar-downhill (wd:Q327818) using the property 'country' (wdt:P17). The query also optionally retrieves the English label of the country. The execution result shows that the Kandahar-downhill is in Germany, which matches the answer provided. The query and answer are accurate and complete for the user's question.


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

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

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

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

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

[Assistant]
To answer the question about the second game developed by Piranha Bytes, I need to identify the entity for Piranha Bytes and then find the games developed by this entity. After that, I will order the games by their release date and select the second one.

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

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

[Assistant] I found the entity for Piranha Bytes as a German video game development company with the identifier wd:Q677273. Next, I will find the games developed by Piranha Bytes (wd:Q677273) and order them by their release date to identify the second game developed by them.

[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 unsupported by the endpoint. I will rewrite the query without using SERVICE wikibase:label and instead use optional clauses to get the labels.

[Tool]
Got 10 rows and 3 columns:
| game                                        | gameLabel                          | releaseDate                         |
| ------------------------------------------- | ---------------------------------- | ----------------------------------- |
| wd:Q767456                                  |                                    | 2001-03-15T00:00:00Z (xsd:dateTime) |
| Gothic II (wd:Q1456987)                     | Gothic II (en)                     | 2002-11-29T00:00:00Z (xsd:dateTime) |
| Gothic II (wd:Q1456987)                     | Gothic II (en)                     | 2003-04-16T00:00:00Z (xsd:dateTime) |
| Gothic II: Night of the Raven (wd:Q2662055) | Gothic II: Night of the Raven (en) | 2003-08-22T00:00:00Z (xsd:dateTime) |
| Gothic 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) |
| Gothic 3 (wd:Q738909)                       | Gothic 3 (en)                      | 2006-11-03T00:00:00Z (xsd:dateTime) |
| Gothic 3 (wd:Q738909)                       | Gothic 3 (en)                      | 2006-11-20T00:00:00Z (xsd:dateTime) |
| Gothic 3 (wd:Q738909)                       | Gothic 3 (en)                      | 2006-11-23T00:00:00Z (xsd:dateTime) |
| Risen (wd:Q1513013)                         | Risen (en)                         | 2009-10-02T00:00:00Z (xsd:dateTime) |

[Assistant] The results show multiple entries for some games with different release dates, but the earliest game developed by Piranha Bytes is wd:Q767456 released on 2001-03-15, and the second game is Gothic II (wd:Q1456987) released on 2002-11-29. I will finalize the answer with the second game developed by Piranha Bytes, which is Gothic II.

[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 release date and using OFFSET 1 to get the second entry. It 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 does not perform extra computation outside SPARQL. Overall, the output is correct and complete.