PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
ASK {
  {
    wd:Q1739008 wdt:P69 wd:Q1945756 .
    wd:Q15934341 wdt:P69 wd:Q1945756 .
  }
}

True

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

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

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

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

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

[User] Were at least two of the founders of tencent educated at the same university?

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

[Tool]
Example 1:
Question:
How many moons does Mars have?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q111 wdt:P398 ?uri .
}

Using entities:
Mars, also known as Red Planet, Sol IV, Sol 4, Planet Mars, ♂ (wd:Q111): fourth planet in the Solar System from the Sun

Using properties:
child astronomical body, also known as child body, secondary, satellite, orbited by, has planet (wd:P398 as wdt): minor body that belongs to the item

Example 2:
Question:
Which programming languages were influenced by Perl?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P31 wd:Q9143 ; wdt:P737 wd:Q42478 .
}

Using entities:
Perl, also known as Perl/CGI, Practical Extraction and Reporting Language, perl, Pearl (wd:Q42478): interpreted programming language first released in 1987
programming language, also known as coding language, computer language, computer programming, computer programming language (wd:Q9143): language for communicating instructions to a machine

Using properties:
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
influenced by, also known as favorite player, has influence, informed by, role model (wd:P737 as wdt): this person, idea, etc. is informed by that other person, idea, etc., e.g. “Heidegger was influenced by Aristotle”

Example 3:
Question:
How heavy is Jupiter's lightest moon?

SPARQL:
SELECT * WHERE {
  ?res wdt:P31 wd:Q61702557 .
  ?res wdt:P2067 ?mass .
}
ORDER BY ?mass
LIMIT 1

Using entities:
moon of Jupiter, also known as Jovial moon, Jovian satellite, moons of Jupiter, natural satellite of Jupiter (wd:Q61702557): natural satellite orbiting Jupiter

Using properties:
mass, also known as displacement, molecular mass, molecular weight, weight (wd:P2067 as wdt): mass (in colloquial usage also known as weight) of the item
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)

[Assistant] Based on the examples, we need to identify the founders of Tencent and their educational institutions. We'll start by searching for the entity representing Tencent.

[Tool]
Top 10 entity alternatives:
1. Tencent, also known as Téngxùn Kònggǔ Yǒuxiàn Gōngsī, Teng xun (firma), Tencent Group, Téngxùn, Tengxun (wd:Q860580): Chinese multinational investment holding conglomerate
2. Tencent QQ, also known as QQ, Qq, QICQ, OPEN-ICQ, OpenICQ (wd:Q857640): instant messaging software service
3. Tencent Video, also known as v.qq.com, TencentVideo, WeTV (wd:Q28136991): video streaming service from China
4. Tencent Pictures (wd:Q22080095): Chinese film production company
5. Tencent Games, also known as Tencent Game (wd:Q39057776): division of Tencent Holdings
6. Tencent Music, also known as tencentmusic.com, Tencent Music Entertainment, Tencent Music Entertainment Group, TME (wd:Q56011958): Chinese music distribution company
7. Tencent Weibo (wd:Q3983400): Chinese microblogging website launched by Tencent in April 2010
8. WeGame, also known as Tencent Game Platform, Tencent Games Platform, Tencent Gaming Platform, Tencent WeGame, TGP (wd:Q10852223): video gaming platform of Tencent
9. QQ browser, also known as tbs, TBS, Tencent Browsing Service, Tencent Explorer, Tencent Traveler (wd:Q6862273): web browser
10. Tencent Comic (wd:Q22101513): Chinese animation and comics network

[Assistant] The correct entity for Tencent is `wd:Q860580`. Next, we need to find the founders of Tencent. We can use the property "founded by" (`P112`) to find the founders.

[Tool]
Got 5 rows and 1 column:
| founder                      |
| ---------------------------- |
| Zhang Zhidong (wd:Q15934341) |
| Ma Huateng (wd:Q1739008)     |
| Zeng Liqing (wd:Q18653685)   |
| Daniel Xu (wd:Q18659347)     |
| Chen Yidan (wd:Q20689208)    |

[Assistant]
We have identified the founders of Tencent:
- Zhang Zhidong (wd:Q15934341)
- Ma Huateng (wd:Q1739008)
- Zeng Liqing (wd:Q18653685)
- Daniel Xu (wd:Q18659347)
- Chen Yidan (wd:Q20689208)

Next, we need to find out where these founders were educated. We can use the property "educated at" (`P69`) to find their educational institutions.

[Tool] Got no rows and 2 columns

[Assistant]
It seems that the direct approach of querying for the educational institutions of the founders did not yield any results. Let's try another approach by querying each founder individually to see if we can find their educational institutions.

We will start with Ma Huateng (wd:Q1739008).

[Tool]
Got 2 rows and 1 column:
| university                           |
| ------------------------------------ |
| Shenzhen University (wd:Q1945756)    |
| Shenzhen Middle School (wd:Q7494378) |

[Assistant]
Ma Huateng (wd:Q1739008) was educated at:
- Shenzhen University (wd:Q1945756)
- Shenzhen Middle School (wd:Q7494378)

Next, let's check the educational institutions for the other founders.

We will now query for Zhang Zhidong (wd:Q15934341).

[Tool]
Got 2 rows and 1 column:
| university                                        |
| ------------------------------------------------- |
| Shenzhen University (wd:Q1945756)                 |
| South China University of Technology (wd:Q835366) |

[Assistant]
Zhang Zhidong (wd:Q15934341) was educated at:
- Shenzhen University (wd:Q1945756)
- South China University of Technology (wd:Q835366)

We have found that both Ma Huateng and Zhang Zhidong were educated at Shenzhen University (wd:Q1945756). Therefore, at least two of the founders of Tencent were educated at the same university.

To confirm this, we can use an ASK query to check if there are at least two founders who were educated at the same university.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query is correctly formulated as an ASK query and it verifies that at least two of the founders of Tencent were educated at the same university, which is Shenzhen University. The result is true, indicating that the statement is correct.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ( COUNT( ?airport ) AS ?count ) WHERE {
  ?airport wdt:P31 wd:Q644371 ; wdt:P131 ?location ; wdt:P238 ?iataCode .
  ?location wdt:P131 * wd:Q1055 .
}

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

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

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

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

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

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

[User] How many international airports are located within the city of Hamburg ?

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

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

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

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

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

Example 2:
Question:
Where did the architect of the Eiffel Tower study?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q243 wdt:P84 ?x .
  ?x wdt:P69 ?uri
}

Using entities:
Eiffel Tower (wd:Q243): tower located on the Champ de Mars in Paris, France

Using properties:
educated at, also known as graduated from, graduate of, place of education, school attended, schooled at (wd:P69 as wdt): educational institution attended by subject, affiliation
architect, also known as architecture firm (wd:P84 as wdt): person or architectural firm responsible for designing this building, designed by

Example 3:
Question:
What is the most frequent cause of death?

SPARQL:
SELECT ?uri WHERE {
  ?s wdt:P509 ?uri .
}
GROUP BY ?uri
ORDER BY DESC ( COUNT( DISTINCT ?s ) )
LIMIT 1

Using properties:
cause of death, also known as death cause, murder method, method of murder, die of, die from (wd:P509 as wdt): underlying or immediate cause of death.  Underlying cause (e.g. ...adest category, e.g. natural causes, accident, homicide, suicide

[Assistant] Based on the examples, we need to identify the relevant entities and properties for our question. We are looking for international airports in Hamburg. Let's start by searching for the entity representing Hamburg.

[Tool]
Top 10 entity alternatives:
1. Hamburg, also known as Free and Hanseatic City of Hamburg, Hamburg, Germany, Hamburg, Freie und Hansestadt (wd:Q1055): city and state in the North of Germany
2. Hamburger SV, also known as Hamburg SV, HSV, H.S.V., Hamburg Sport Club, HamburgerSV (wd:Q51974): German sports club based in Hamburg, with its largest branch being its football department
3. Hamburg, also known as Hamburg, IA, Hamburg, Iowa (wd:Q998718): city in Fremont County, Iowa, United States
4. Hamburg, also known as Hamburg, AR, Hamburg, Arkansas (wd:Q79657): city in Ashley County, Arkansas, United States
5. Hamburg, also known as Hamburg, Minnesota, Hamburg, MN (wd:Q1005682): city in Carver County, Minnesota, United States
6. Hamburg, also known as Hamburg, New Jersey, Hamburg, NJ (wd:Q1082931): borough in Sussex County, New Jersey, United States
7. Hamburg, also known as Hamburg, New York, Hamburg, NY (wd:Q742785): town in Erie County, New York, United States
8. Hamburg, also known as Hamburg, IL (wd:Q2672139): village in Calhoun County, Illinois, United States
9. Hamburg, also known as Hamburg, PA, Hamburg, Pennsylvania (wd:Q1181264): borough in Berks County, Pennsylvania, United States
10. Hamburg, also known as Hamburgh (wd:Q939795): breed of chicken developed in Germany and Holland prior to 1700

[Assistant] The correct entity for Hamburg, Germany is `wd:Q1055`. Next, let's find the property that represents "international airport". We can search for this property to ensure we have the right one.

[Tool]
Top 10 property alternatives:
1. airline hub, also known as hub airport, hub (wd:P113 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): airport that serves as a hub for an airline
2. IATA airport code, also known as International Air Transport Association airport code, uio, IATA station code, IATA location identifier (wd:P238 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): three-letter identifier for designating airports, railway stations or cities (for airlines, see P229)
3. aerodrome reference point, also known as airfield reference point, airport reference point, ARP (wd:P2786 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): official coordinates for airport. Aeronautical publications use format with degrees, minutes and seconds concatenated, coordinate location
4. image of design plans, also known as plan view image, plan view, plan image, floor plan, design plan (wd:P3311 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): image representing the plan of a building or place, detail map
5. ICAO airport code, also known as ICAO aerodrome code, ICAO airfield code, International Civil Aviation Organization airport code (wd:P239 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): four-character alphanumeric identifier for designating airports (for airlines, see P230)
6. FAA airport code, also known as FAA LID (wd:P240 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): three-letter or four-letter alphanumeric code identifying United States airports
7. Ex-USSR internal airport code, also known as russian airport codes (wd:P5851 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): airport code similar to IATA codes but only for Russian and CEI airports
8. UTC date of spacecraft landing, also known as landing time, time of spacecraft landing, spacecraft landing time, spacecraft landing date, landing date (wd:P620 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): date of spacecraft landing in UTC
9. location of landing, also known as landing site, landing location (wd:P1158 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): location where the craft landed, significant place, location
10. Tierstimmenarchiv ID, also known as TSA, TSA ID (wd:P5397 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): identifier for a species in the Animal Sound Archive Berlin

[Assistant] None of the properties listed directly represent "international airport." However, we can use the property `P31` (instance of) to find entities that are instances of "international airport." First, let's search for the entity representing "international airport."

[Tool]
Top 10 entity alternatives:
1. international airport (wd:Q644371): airport serving international destinations
2. International Airport (wd:Q55614529): 1985 film directed by Don Chaffey
3. Domodedovo International Airport, also known as Moscow-Domodedovo Airport, UUDD, Moscow International Airport, Moscow Domodedovo Airport, Moscow Airport (wd:Q186614): international airport serving Moscow, Russia
4. John F. Kennedy International Airport, also known as Kennedy International Airport, New York International Airport, KJFK, JFK International Airport, Kennedy International (wd:Q8685): international airport in New York, United States
5. John F. Kennedy International Airport, also known as Idlewild Airport, Kennedy International, JFK International Airport, KJFK, JFK Airport (wd:Q8685): international airport in New York, United States
6. Sheremetyevo International Airport, also known as Moscow Airport, Moscow International Airport, Sheremetyevo Airport, SVO, UUEE (wd:Q192733): international airport serving Moscow, Russia
7. Istanbul-Atatürk, also known as LTBA, Istanbul Atatürk Airport, Istanbul Ataturk Airport, ISL, Atatürk International Airport (wd:Q86473): international airport in Istanbul, Turkey
8. Dubai International Airport, also known as DXB, OMDB (wd:Q193439): international airport in Dubai, United Arab Emirates
9. Singapore Changi Airport, also known as WSSS, Changi Airport Singapore, SIN, Changi Airport, Changi International Airport (wd:Q32159): international airport in Singapore
10. Tokyo International Airport, also known as Haneda Airport, HND, RJTT (wd:Q204853): international airport serving Tokyo, Japan

[Assistant]
The correct entity for "international airport" is `wd:Q644371`. Now, we need to find the airports that are instances of "international airport" and are located within Hamburg. We can use the property `P131` (located in the administrative territorial entity) to find the location of the airports.

Let's construct a SPARQL query to find the international airports located in Hamburg.

```sparql
SELECT ?airport WHERE {
  ?airport wdt:P31 wd:Q644371 ;
           wdt:P131 wd:Q1055 .
}
```

This query will return the IRIs of the international airports located in Hamburg. Let's execute it.

[Tool] Got no rows and 1 columns

[Assistant]
It seems there are no results returned, which suggests that there might be no international airports directly classified as such within the city of Hamburg. However, it's possible that the airports are classified differently or are located in nearby areas but still serve Hamburg.

Let's try a different approach. We can look for airports in Hamburg and then check if they are international airports. We can use the property `P238` (IATA airport code) to identify airports, as international airports typically have IATA codes.

Let's construct a new SPARQL query to find airports in Hamburg and check if they have IATA codes.

```sparql
SELECT ?airport WHERE {
  ?airport wdt:P131 wd:Q1055 ;
           wdt:P238 ?iataCode .
}
```

This query will return the IRIs of airports in Hamburg that have IATA codes. Let's execute it.

[Tool] Got no rows and 1 columns

[Assistant]
It appears that there are no airports in Hamburg with IATA codes according to the current data in Wikidata. This suggests that either the data is incomplete or the airports serving Hamburg are located outside the city limits but still serve the city.

Let's try another approach by looking for airports that serve Hamburg, regardless of their exact location. We can use the property `P131` to find airports that are located in the state of Hamburg, which includes the city and surrounding areas.

Let's construct a SPARQL query to find airports in the state of Hamburg and check if they have IATA codes.

```sparql
SELECT ?airport WHERE {
  ?airport wdt:P131 wd:Q1055 ;
           wdt:P238 ?iataCode .
}
```

However, since the previous query did not return any results, let's broaden the search to include airports that serve Hamburg, even if they are not directly within the city limits. We can use the property `P131` to find airports in the state of Hamburg and then check if they have IATA codes.

```sparql
SELECT ?airport WHERE {
  ?airport wdt:P131 wd:Q1055 ;
           wdt:P238 ?iataCode .
}
```

Since this is the same query, let's try a different approach by looking for airports that serve Hamburg, even if they are not directly within the city limits. We can use the property `P131` to find airports in the state of Hamburg and then check if they have IATA codes.

```sparql
SELECT ?airport WHERE {
  ?airport wdt:P131 wd:Q1055 ;
           wdt:P238 ?iataCode .
}
```

Let's execute this query again to see if we get any results.

[Tool] Got no rows and 1 columns

[Assistant]
It seems that there are no airports with IATA codes directly within the city or state of Hamburg according to the current data in Wikidata. This suggests that the airports serving Hamburg might be located in nearby areas but are not classified as being within the city or state of Hamburg.

To find the airports that serve Hamburg, we can look for airports that are associated with Hamburg in some way. One approach is to search for airports that have Hamburg as a destination or are known to serve Hamburg.

Let's try a different approach by searching for airports that are known to serve Hamburg. We can use the property `P131` to find airports in the broader region around Hamburg and then check if they have IATA codes.

```sparql
SELECT ?airport WHERE {
  ?airport wdt:P131 ?location ;
           wdt:P238 ?iataCode .
  ?location wdt:P131* wd:Q1055 .
}
```

This query will return the IRIs of airports that are located in the broader region around Hamburg and have IATA codes. Let's execute it.

[Tool]
Got 4 rows and 1 column:
| airport                                   |
| ----------------------------------------- |
| Hamburg Finkenwerder Airport (wd:Q663819) |
| Hamburg Hauptbahnhof (wd:Q6456)           |
| Hamburg Airport (wd:Q27706)               |
| Hamburg Finkenwerder Airport (wd:Q663819) |

[Assistant]
We have found several airports that are associated with Hamburg. However, we need to filter out the ones that are actually international airports. Let's refine the query to include only those airports that are instances of "international airport" (`wd:Q644371`).

Here is the refined SPARQL query:

```sparql
SELECT ?airport WHERE {
  ?airport wdt:P31 wd:Q644371 ;
           wdt:P131 ?location ;
           wdt:P238 ?iataCode .
  ?location wdt:P131* wd:Q1055 .
}
```

This query will return the IRIs of international airports that are located in the broader region around Hamburg and have IATA codes. Let's execute it.

[Tool]
Got 1 row and 1 column:
| airport                     |
| --------------------------- |
| Hamburg Airport (wd:Q27706) |

[Assistant]
The query has returned one international airport that serves Hamburg: Hamburg Airport (`wd:Q27706`).

To answer the original question, there is 1 international airport located within the broader region of Hamburg.

Let's finalize the answer with the SPARQL query and the result.

```sparql
SELECT (COUNT(?airport) AS ?count) WHERE {
  ?airport wdt:P31 wd:Q644371 ;
           wdt:P131 ?location ;
           wdt:P238 ?iataCode .
  ?location wdt:P131* wd:Q1055 .
}
```

This query will count the number of international airports that serve Hamburg. Let's execute it to confirm the count.

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

[Assistant]
The SPARQL query confirms that there is 1 international airport located within the broader region of Hamburg.

Therefore, the answer to the question "How many international airports are located within the city of Hamburg?" is:

There is 1 international airport located within the broader region of Hamburg.

Let's finalize the answer with the SPARQL query and the result.

```sparql
SELECT (COUNT(?airport) AS ?count) WHERE {
  ?airport wdt:P31 wd:Q644371 ;
           wdt:P131 ?location ;
           wdt:P238 ?iataCode .
  ?location wdt:P131* wd:Q1055 .
}
```

The answer is 1.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query is well-constructed and correctly identifies the number of international airports located within the city of Hamburg. The result is accurate and matches the expected outcome.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?date WHERE {
  wd:Q15787 wdt:P575 ?date .
}

HIV was first identified in 1983.

[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 HIV first identified?

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

[Tool]
Example 1:
Question:
In which U.S. state is Area 51 located?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q177397 wdt:P131 ?obj .
  ?obj wdt:P131 ?uri .
}

Using entities:
Area 51, also known as Paradise Ranch, Groom Lake, Area 51 (hawkin I., Nevada Area 51, KXTA (wd:Q177397): classified U.S. Military Base situated in Groom Lake, Nevada

Using properties:
located in the administrative territorial entity, also known as is in the settlement of, is in the voivodeship of, is in the shire of, is in the state of, is in the territory of (wd:P131 as wdt): the item is located on the territory of the following administra...if the item falls only partially into the administrative entity., location, part of

Example 2:
Question:
Which television shows were created by Walt Disney?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P31 / wdt:P279 * wd:Q7725310 ; wdt:P170 wd:Q8704 .
}

Using entities:
series of creative works, also known as serial (wd:Q7725310): ordered set of creative works
Walt Disney (wd:Q8704): American animator and producer (1901–1966), television presenter, film producer, film director, caricaturist, manufacturer, screenwriter, illustrator, voice actor, film actor, producer, director, animator, inventor, painter, writer, actor

Using properties:
creator, also known as created by, visual artist, sculptor, sculpted by, realized by (wd:P170 as wdt): maker of this creative work or other object (where no more specific property exists)
subclass of, also known as rdfs:subClassOf, sort of, subcategory of, subgenre of, subtype of (wd:P279 as wdt): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)

Example 3:
Question:
When did the Ming dynasty dissolve?

SPARQL:
SELECT DISTINCT ?date WHERE {
  wd:Q9903 wdt:P576 ?date .
}

Using entities:
Ming dynasty, also known as Ming period, Ming imperium, Ming China, Ming-era, Ming Empire (wd:Q9903): imperial dynasty of China from 1368 to 1644

Using properties:
dissolved, abolished or demolished date, also known as ended, publication end date, final issue, date repealed, final year (wd:P576 as wdt): point in time at which the subject (organisation, building) ceas...g equipment,  "discontinued date" (P2669) for stopping a product, end time

[Assistant] Based on the examples, it seems like we need to find the entity for HIV and then look for the property that indicates when it was first identified. Let's start by searching for the entity "HIV".

[Tool]
Top 10 entity alternatives:
1. HIV, also known as AIDS virus, human immunodeficiency virus (wd:Q15787): human retrovirus, cause of AIDS
2. Hiv (wd:Q5959052): village in Iran
3. .hiv (wd:Q20573587): top level domain
4. human immunodeficiency virus infectious disease, also known as HIV+, HIV infection, HIV infections, HIV-positive (wd:Q18556697): disease affecting the human immune system
5. HIV/AIDS in Ethiopia, also known as HIV (wd:Q65409016): Ethiopia faces an epidemic among sub-populations and geographic areas
6. HyperChem Input File, also known as HIV, .hin, HyperChem molecule file format, HIV molecule file format (wd:Q28530466): file format
7. HIV (wd:Q87558374): scientific article published on 01 August 2014
8. HIV (wd:Q93531156)
9. HIV. (wd:Q43247940): scientific article published in February 1998
10. HIV. (wd:Q45982420): scientific article published in February 2001

[Assistant] The correct entity for HIV is the first one: "HIV, also known as AIDS virus, human immunodeficiency virus (wd:Q15787)". Now let's search for properties related to "identification" or "discovery" for this entity.

[Tool]
Top 10 property alternatives:
1. FAST ID, also known as Faceted Application of Subject Terminology ID, FAST subject heading, fst, worldcat.org/fast (wd:P2163 as p/wdt/wdtn): authority control identifier in WorldCat's “FAST Linked Data” authority file
2. Library of Congress authority ID, also known as LCSH Authority File ID, LC Name Authority ID, LCSH ID, LCSH LCCN, LC Subject Heading ID (wd:P244 as p/wdt/wdtn): Library of Congress name authority (persons, families, corporate...lowed by 8-10 digits (see regex). For manifestations, use P1144]
3. Australian Educational Vocabulary ID, also known as AEV ID, AEV identifier, Australian Educational Vocabulary identifier, Schools Online Thesaurus ID, Schools Online Thesaurus identifier (wd:P7033 as p/wdt/wdtn): ID for curriculum term in one of the controlled vocabularies at Australian education vocabularies
4. time of discovery or invention, also known as invented in, time of invention, time of discovery, invention time, invented on (wd:P575 as wdt/p): date or point in time when the item was discovered or invented
5. described by source, also known as mentioned in, source of item, reviewed in, mentioned in news article, talked about in (wd:P1343 as wdt/p): work where this item is described, attested in
6. Encyclopædia Britannica Online ID, also known as Encyclopaedia Britannica Online ID, Encyclopedia Britannica Online ID, EBID, EB ID, Britannica online ID (wd:P1417 as wdt/p): identifier for an article in the online version of Encyclopædia Britannica
7. Golden ID (wd:P7502 as wdt/p): ID page in Golden knowledge base
8. National Library of Spain ID, also known as BNE ID, BNE identifier, LEM ID, National Library of Spain ID, National Library of Spain identifier (wd:P950 as p/wdt/wdtn): identifier from the authority file of the National Library of Spain. Format for persons: "XX" followed by 4 to 7 digits
9. GND ID, also known as Deutschen Nationalbibliothek ID, Universal Authority File, Integrated Authority File, IndividualisedGnd, GND-IDN (wd:P227 as p/wdt/wdtn): identifier from an international authority file of names, subjects, and organizations
10. NL CR AUT ID, also known as Czech National Authority Database, NLCR AUT ID (wd:P691 as wdt/p): identifier in the Czech National Authority Database of National Library of the Czech Republic (NL CR)

No other items found

[Assistant]
The property "time of discovery or invention" (wd:P575) seems to be the most relevant for our purpose. Let's construct a SPARQL query to find the value of this property for the entity "HIV" (wd:Q15787).

Here is the SPARQL query:

```sparql
SELECT ?date WHERE {
  wd:Q15787 wdt:P575 ?date .
}
```

Let's execute this query.

[Tool]
Got 1 row and 1 column:
| date                                |
| ----------------------------------- |
| 1983-01-01T00:00:00Z (xsd:dateTime) |

[Assistant]
The SPARQL query returned the date "1983-01-01T00:00:00Z" for when HIV was first identified.

Therefore, HIV was first identified in 1983.

Let's finalize the answer with the SPARQL query and the result.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query correctly identifies the date when HIV was first identified using the appropriate entity and property from Wikidata.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?developer WHERE {
  wd:Q533822 wdt:P178 ?developer .
}

Christiane Fellbaum (wd:Q4558744) developed WordNet.

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

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

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

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

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

[User] Who developed WordNet?

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

[Tool]
Example 1:
Question:
Which airports does Air China serve?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q51747 wdt:P113 ?uri .
}

Using entities:
Air China, also known as CCA, Air China Limited, CA (wd:Q51747): flag carrier of the People's Republic of China

Using properties:
airline hub, also known as hub airport, hub (wd:P113 as wdt): airport that serves as a hub for an airline

Example 2:
Question:
Give me all soccer clubs in Spain.

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

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

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

Example 3:
Question:
What is the location of the Houses of Parliament?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q62408 wdt:P131 ?uri .
}

Using entities:
Palace of Westminster, also known as Houses of Parliament, St. Stephen's, Westminster Palace (wd:Q62408): meeting place of the Parliament of the United Kingdom, located in London, England, UK

Using properties:
located in the administrative territorial entity, also known as is in the settlement of, is in the voivodeship of, is in the shire of, is in the state of, is in the territory of (wd:P131 as wdt): the item is located on the territory of the following administra...if the item falls only partially into the administrative entity., location, part of

[Assistant] None of these examples directly relate to the development of WordNet, but we can see how entities and properties are used in SPARQL queries. Let's start by searching for the entity "WordNet".

[Tool]
Top 10 entity alternatives:
1. WordNet, also known as Princeton WordNet (wd:Q533822): Princeton University's computational lexicon of English, a seman...nodes connected by hypernymy, hyponymy, holonymy, meronymy, etc.
2. word net, also known as wordnet (wd:Q2594143): network of words connected by semantic relationships (synonyms, antonyms, hypernyms, hyponyms, meronyms, holonyms)
3. WordNet (wd:Q125949755)
4. WordNet (wd:Q98000414): chapter in book from 2010
5. WordNet: An Electronic Lexical Database, also known as WordNet, WordNet.  An  Electronic Lexical Database (wd:Q35839029): book edition published in 1998 with Fellbaum as editor
6. Arabic WordNet, also known as AWN (wd:Q28136431)
7. Estonian Wordnet (wd:Q31277898)
8. Malayalam WordNet (wd:Q25112033): WordNet for Malayalam language by CUSAT
9. Chinese WordNet, also known as CWN (wd:Q65924663): Huang et al. wordnet in Chinese
10. GF WordNet (wd:Q112258758): a lexical resource which combines WordNet resources from many languages and makes them compatible within GF

[Assistant] The first result, "WordNet" (wd:Q533822), seems to be the correct entity for the computational lexicon of English. Now let's look at the properties associated with this entity to find out who developed it.

[Tool]
Top 10 property alternatives:
1. developer, also known as written by, video game developer, video game dev, game developer, game dev (wd:P178 as wdt/p): organization or person that developed the item, creator
2. software version identifier, also known as software release number, software version number, software release version, software update identifier, software update number (wd:P348 as wdt/p): numeric or nominal identifier of a version of a software program or file format, current or past, has edition or translation, edition number
3. used by, also known as armiger, user, played by, of (wd:P1535 as wdt/p): item or concept that makes use of the subject (use sub-properties when appropriate)
4. inception, also known as initial release date, founded in, founded on, founded on date, founding date (wd:P571 as wdt/p): time when an entity begins to exist; for date of official opening use P1619, start time
5. copyright license, also known as content licence, content license, copyright licence, licence, license (wd:P275 as wdt/p): license under which this copyrighted work is released
6. language of work or name, also known as language of the reference, language of URL, language of website, language of work, named in language (wd:P407 as wdt/p): language associated with this creative work (such as books, show...guage" (P103) and "languages spoken, written or signed" (P1412))
7. official website, also known as site (official), home page, site official, URL, URL (official) (wd:P856 as wdt/p): URL of the official page of an item (current or former). Usage: ...but see the "Hijacked or dead websites" section of the Talk page, URL
8. instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt/p): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)

No other items found

[Assistant]
The property "developer" (wd:P178) appears to be the most relevant for finding out who developed WordNet. Let's construct a SPARQL query to retrieve the developer(s) of WordNet.

Here is the SPARQL query:

```sparql
SELECT ?developer WHERE {
  wd:Q533822 wdt:P178 ?developer .
}
```

Now, let's execute this query.

[Tool]
Got 1 row and 1 column:
| developer                         |
| --------------------------------- |
| Christiane Fellbaum (wd:Q4558744) |

[Assistant]
The developer of WordNet is Christiane Fellbaum (wd:Q4558744).

To provide a complete answer, I will include the IRI of the developer in the final SPARQL query.

Here is the final SPARQL query:

```sparql
SELECT ?developer WHERE {
  wd:Q533822 wdt:P178 ?developer .
}
```

And the answer to the question is:

Christiane Fellbaum (wd:Q4558744) developed WordNet.

Now, I will provide the final answer using the `answer` function.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query correctly identifies Christiane Fellbaum as the developer of WordNet using the appropriate entity and property from Wikidata.


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?highestPoint ?highestPointLabel WHERE {
  wd:Q20 wdt:P610 ?highestPoint .
  OPTIONAL {
    ?highestPoint rdfs:label ?highestPointLabel .
    FILTER ( LANG( ?highestPointLabel ) = "en" )
  }
}

Got 1 row and 2 columns:
| highestPoint              | highestPointLabel |
| ------------------------- | ----------------- |
| Galdhøpiggen (wd:Q203942) | Galdhøpiggen (en) |

The highest point of Norway is Galdhøpiggen.

[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 highest point of Norway?

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

[Tool]
Example 1:
Question:
Give me all Dutch parties.

SPARQL:
SELECT ?s1 WHERE {
  ?s1 wdt:P17 wd:Q55 .
  ?s1 wdt:P31 wd:Q7278 .
}

Using entities:
Netherlands, also known as 🇳🇱, the Netherlands, NL, nl, NED (wd:Q55): country in Northwestern Europe with territories in the Caribbean
political party, also known as political parties, parties (politics), party (politics), parties in politics, party in politics (wd:Q7278): organization that seeks to influence government policy and actio...nd be elected to directly take part on government or legislation

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

Example 2:
Question:
Who created the comic Captain America?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q190679 wdt:P170 ?uri .
}

Using entities:
Captain America, also known as The Star Spangled Man With A Plan, Steve Rogers, Steven Rogers (wd:Q190679): fictional character in Marvel Comics, military personnel

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

Example 3:
Question:
Who is the editor of Forbes?

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

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

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

[Assistant] Based on these examples, we can see how to structure a SPARQL query to find specific information about entities. To find the highest point of Norway, we need to identify the entity for Norway and the property that indicates the highest point of a country. Let's start by searching for the entity representing Norway.

[Tool]
Top 10 entity alternatives:
1. Norway, also known as Kingdom of Norway (wd:Q20): country in Northern Europe
2. Norway, also known as Norway, Iowa, Norway, IA (wd:Q1913264): city in Iowa, United States
3. Norway, also known as Norway, MI, Norway, Michigan (wd:Q2480177): city in Dickinson County, Michigan, United States
4. Norway, also known as Norway, SC, Norway, South Carolina (wd:Q3243583): town in South Carolina
5. Kingdom of Norway, also known as Norway, Norwegian Empire (wd:Q2196956): former Kingdom from 872 to 1397
6. Norway, also known as Norway, Maine, Norway, ME (wd:Q2734330): town in Maine, USA
7. Norway, also known as Norway, IN, Norway, Indiana (wd:Q2118814): census-designated place in Indiana
8. Kingdom of Norway, also known as Norway, Norwegian Kingdom (wd:Q2597352): short-lived monarchy in Northern Europe (1814)
9. Norway, also known as Norway, New York, Norway, NY (wd:Q944765): town in New York, USA
10. Norway, also known as Norway, WI (wd:Q3046928): town in Wisconsin, USA

[Assistant] The first result, `wd:Q20`, represents the country of Norway. Now, let's find the property that indicates the highest point of a country. We can search for properties related to "highest point" or "elevation" to find the appropriate property.

[Tool]
Top 10 property alternatives:
1. highest point, also known as tallest point, zenith, summit, pinnacle, highest peak (wd:P610 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): point with highest elevation in a region or path, has part(s)
2. maximum operating altitude, also known as ceiling, maximum altitude, service ceiling (wd:P2254 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): ceiling or maximum density altitude at which an aircraft can operate, elevation above sea level
3. maximum temperature record, also known as highest temperature (wd:P6591 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): maximum temperature reached at this location, temperature
4. elevation above sea level, also known as MAMSL, MASL, height, elevation above mean sea level, elevation (wd:P2044 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): height of the item (geographical object) as measured relative to sea level
5. coordinate location, also known as point on a map, gps coordinates, gps location, latitude, location (wd:P625 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): geocoordinates of the subject. For Earth, please note that only WGS84 coordinating system is supported at the moment
6. coordinates of northernmost point, also known as coordinate of northernmost point, extreme point north, farthest north, most northerly, northerly (wd:P1332 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): northernmost point of a location. For an administrative entity this includes offshore islands
7. coordinates of westernmost point, also known as westerly point, wikibase:geoLongitude ?W, westernmost point coordinates, most western, extreme west (wd:P1335 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): westernmost point of a location
8. maximum sustained winds, also known as strongest sustained winds, wind speed, sustained winds, maximum wind speed, highest wind speed (wd:P2895 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): maximum sustained wind evaluated by agency
9. ranking, also known as peak position, place, placing, position, rank (wd:P1352 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): subject's ordinal position as qualitatively evaluated relative to other members of a group
10. coordinates of easternmost point, also known as coordinate of easternmost point, easterly, easternmost point coordinates, extreme point east, farthest east (wd:P1334 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): easternmost point of a location

[Assistant]
The property `wd:P610` (highest point) seems to be the most relevant for our query. Now, let's construct a SPARQL query to find the highest point of Norway using the entity `wd:Q20` and the property `wd:P610`.

Here is the initial SPARQL query:

```sparql
SELECT ?highestPoint WHERE {
  wd:Q20 wdt:P610 ?highestPoint .
}
```

Let's execute this query to see if it returns the expected result.

[Tool]
Got 1 row and 1 column:
| highestPoint              |
| ------------------------- |
| Galdhøpiggen (wd:Q203942) |

[Assistant]
The query returned `Galdhøpiggen` (wd:Q203942) as the highest point of Norway. To provide a more complete answer, we can include the label of the highest point in the result. Let's modify the query to include the label of the highest point.

Here is the updated SPARQL query:

```sparql
SELECT ?highestPoint ?highestPointLabel WHERE {
  wd:Q20 wdt:P610 ?highestPoint .
  OPTIONAL { ?highestPoint rdfs:label ?highestPointLabel . FILTER(LANG(?highestPointLabel) = "en") }
}
```

Let's execute this query to get the label of the highest point.

[Tool]
Got 1 row and 2 columns:
| highestPoint              | highestPointLabel |
| ------------------------- | ----------------- |
| Galdhøpiggen (wd:Q203942) | Galdhøpiggen (en) |

[Assistant]
The query successfully returned `Galdhøpiggen` as the highest point of Norway, along with its English label.

Therefore, the highest point of Norway is Galdhøpiggen.

Here is the final SPARQL query and answer:

```sparql
SELECT ?highestPoint ?highestPointLabel WHERE {
  wd:Q20 wdt:P610 ?highestPoint .
  OPTIONAL { ?highestPoint rdfs:label ?highestPointLabel . FILTER(LANG(?highestPointLabel) = "en") }
}
```

Answer: The highest point of Norway is Galdhøpiggen.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query correctly identifies Galdhøpiggen as the highest point of Norway, and the label is provided in English as requested.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?startDate WHERE {
  wd:Q41304 wdt:P571 ?startDate .
}

The Weimar Republic started on November 9, 1918.

[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 did the Weimar Republic start?

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

[Tool]
Example 1:
Question:
Which programming languages influenced Javascript?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q2005 wdt:P737 ?uri .
}

Using entities:
JavaScript, also known as JS, js, Java Script, Javascript, javascript (wd:Q2005): high-level programming language

Using properties:
influenced by, also known as favorite player, has influence, informed by, role model (wd:P737 as wdt): this person, idea, etc. is informed by that other person, idea, etc., e.g. “Heidegger was influenced by Aristotle”

Example 2:
Question:
Who is the owner of Universal Studios?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q168383 wdt:P127 ?uri .
}

Using entities:
Universal Pictures, also known as Universal City Studios LLC, Universal Film Manufacturing Company, Universal-International Pictures Inc., Universal Studios (wd:Q168383): American film studio

Using properties:
owned by, also known as belongs to, stockholders, stockholder, shareholders, shareholder (wd:P127 as wdt): owner of the subject

Example 3:
Question:
What is the second highest mountain on Earth?

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

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

Using properties:
elevation above sea level, also known as MAMSL, MASL, height, elevation above mean sea level, elevation (wd:P2044 as wdt): height of the item (geographical object) as measured relative to sea level
subclass of, also known as rdfs:subClassOf, sort of, subcategory of, subgenre of, subtype of (wd:P279 as wdt): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
continent (wd:P30 as wdt): continent of which the subject is a part, located in/on physical feature
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
part of, also known as of, within, system of, subsystem of, subgroup of (wd:P361 as wdt): object of which the subject is a part (if this subject is alread... of "has part" (P527, see also "has parts of the class" (P2670))

[Assistant] Based on the examples, we need to identify the entity for the Weimar Republic and the property for the start date. Let's first search for the entity "Weimar Republic".

[Tool]
Top 10 entity alternatives:
1. Weimar Republic, also known as German Republic, Weimar Germany, Republic of Germany, Germany, German Reich (wd:Q41304): Germany in the years 1918/1919–1933
2. flag of the Weimar Republic, also known as the Weimar Republic flag, Weimar Republic flag (wd:Q4485721): flag
3. Landtag (Weimar Republic) (wd:Q1453030): federated state parliament in German Weimar Republic (1919—1934)
4. history of the Weimar Republic, also known as the Weimar Republic history, Weimar Republic history (wd:Q104731094): aspect of German history
5. historian of the Weimar Republic, also known as historian of Weimar Republic, Weimar Republic historian (wd:Q104730830): historian specializing in history of the Weimar Republic
6. hyperinflation in the Weimar Republic (wd:Q695052): occurrence of hyperinflation in early 20th century Germany
7. states of the Weimar Republic (wd:Q3712151): federated states of the Weimar Republic
8. States of the Weimar Republic (wd:Q3712151): federated states of the Weimar Republic
9. Reichsadler of the Weimar Republic, also known as coat of arms of the Weimar Republic (wd:Q25401250)
10. Landtag of Bavaria (Weimar Republic) (wd:Q812353): Parliament of the Free State of Bavaria during the period of the Weimar Republic

[Assistant] The correct entity for the Weimar Republic is `wd:Q41304`. Now let's search for the property that represents the start date of a time period or government.

[Tool]
Top 10 property alternatives:
1. start time, also known as start date, since, valid, join time, joined (wd:P580 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): time an entity begins to exist or a statement starts being valid, point in time
2. inception, also known as initial release date, founded in, founded on, founded on date, founding date (wd:P571 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): time when an entity begins to exist; for date of official opening use P1619, start time
3. earliest date, also known as no earlier than, terminus post quem, start date (earliest), on or after this date, minimum date (wd:P1319 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): earliest date at which an event could have happened. Use as qualifier for other date properties
4. work period (start), also known as start working, flourished (start), start time for work, start date of activity, fl. (start) (wd:P2031 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): start of period during which a person or group flourished (fl. = "floruit") in their professional activity, start time, floruit
5. Gregorian calendar start date, also known as adoption of the Gregorian calendar, Gregorian calendar first used, first date (N.S.) (wd:P7295 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): date the Gregorian calendar was first used
6. date of official opening, also known as launch date, launched, officially opened on, official opening, official opening time (wd:P1619 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): date or point in time a place, organization, or event officially opened, start time, inception
7. start of covered period, also known as coverage start date, covered period start (wd:P7103 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): start of period covered by an archive set, a historical book, or a collection
8. service entry, also known as commissioned, time of service entry, service introduction, launch date, in service (wd:P729 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): date or point in time on which a piece or class of equipment entered operational service, start time
9. UTC date of spacecraft launch, also known as time of spacecraft launch, spacecraft launch time, spacecraft launch date, launch time, launch date (wd:P619 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): date of spacecraft launch in UTC
10. publication date, also known as launched, published, publication time, publication, pubdate (wd:P577 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): date or point in time when a work was first published or released, point in time

[Assistant]
The most appropriate property for the start date of a time period or government is `wd:P580` (start time). Now, let's construct a simple SPARQL query to find the start date of the Weimar Republic.

```sparql
SELECT ?startDate WHERE {
  wd:Q41304 wdt:P580 ?startDate .
}
```

I will now execute this query to get the start date of the Weimar Republic.

[Tool] Got no rows and 1 columns

[Assistant]
It seems that the Weimar Republic does not have a direct `wd:P580` (start time) property. Let's try another approach by searching for properties related to the establishment or foundation of the Weimar Republic.

I will use the `search_property_of_entity` function to find properties related to the establishment or foundation of the Weimar Republic.

[Tool]
Top 10 property alternatives:
1. inception, also known as initial release date, founded in, founded on, founded on date, founding date (wd:P571 as wdt/p): time when an entity begins to exist; for date of official opening use P1619, start time
2. official religion, also known as established religion, state religion (wd:P3075 as wdt/p): official religion in this administrative entity
3. described by source, also known as mentioned in, source of item, reviewed in, mentioned in news article, talked about in (wd:P1343 as wdt/p): work where this item is described, attested in
4. capital, also known as administrative seat, seat of government, seat, principal place, location of capital (wd:P36 as wdt/p): seat of government of a country, province, state or other type of administrative territorial entity
5. native label, also known as official title, original label, original language label, original language name, original language title (wd:P1705 as wdt/p): label for the items in their official language  or their original language, name
6. official name, also known as long title, name, official title, registered name, full title (wd:P1448 as wdt/p): official name of the subject in its official language(s), name
7. replaced by, also known as heir, superseded by, successor, succeeded by, next job holder (wd:P1366 as wdt/p): other person or item which continues the item by replacing it in... nor identical, but adds to the series (e.g. books in a series)., followed by

No other items found

[Assistant]
The property `wd:P571` (inception) seems to be the most relevant for the establishment or foundation of the Weimar Republic. Let's update our SPARQL query to use this property instead.

```sparql
SELECT ?startDate WHERE {
  wd:Q41304 wdt:P571 ?startDate .
}
```

I will now execute this updated query to get the start date of the Weimar Republic.

[Tool]
Got 1 row and 1 column:
| startDate                           |
| ----------------------------------- |
| 1918-11-09T00:00:00Z (xsd:dateTime) |

[Assistant]
The start date of the Weimar Republic is 1918-11-09.

Here is the final SPARQL query and answer:

```sparql
SELECT ?startDate WHERE {
  wd:Q41304 wdt:P571 ?startDate .
}
```

Answer: The Weimar Republic started on November 9, 1918.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query correctly identifies the start date of the Weimar Republic using the inception property (P571). The result matches the historical fact that the Weimar Republic began on November 9, 1918.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?numEmployees WHERE {
  wd:Q1027807 wdt:P1128 ?numEmployees .
}

The Port of Rotterdam employs 385,000 people.

[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 perople do the Port of Rotterdam employ?

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

[Tool]
Example 1:
Question:
Who is the youngest Darts player?

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

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

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

Example 2:
Question:
In which year was Rachel Stevens born?

SPARQL:
SELECT DISTINCT ?year WHERE {
  wd:Q241665 wdt:P569 ?date .
  BIND( YEAR( ?date ) as ?year )
}

Using entities:
Rachel Stevens, also known as Rachel Lauren Stevens (wd:Q241665): British singer, actress and model, singer-songwriter, recording artist, film actor, singer, actor, model

Using properties:
date of birth, also known as year of birth, birthdate, DOB, born on, born (wd:P569 as wdt): date on which the subject was born, inception

Example 3:
Question:
Which movies did Kurosawa direct?

SPARQL:
SELECT DISTINCT * WHERE {
  ?uri wdt:P57 wd:Q8006 .
}

Using entities:
Akira Kurosawa, also known as Kurosawa Akira, Kurosawa (wd:Q8006): Japanese filmmaker (1910–1998), film producer, film director, screenwriter, film editor, producer, director, painter, writer

Using properties:
director, also known as film director, movie director, game director, directed by (wd:P57 as wdt): director(s) of film, TV-series, stageplay, video game or similar, contributor to the creative work or subject, creator

[Assistant] Based on the examples, we need to identify the entity for the Port of Rotterdam and the property for employment or number of employees. Let's start by searching for the entity "Port of Rotterdam".

[Tool]
Top 10 entity alternatives:
1. Port of Rotterdam (wd:Q1027807): largest port in Europe, located in the city of Rotterdam, Netherlands
2. Port of Rotterdam Authority, also known as Directie bedrijfsvoering Havenbedrijf   etc. (wd:Q2187039): manager of Rotterdam's port and industrial area in the Netherlands
3. The Port of Rotterdam (wd:Q59543911): painting by Eugène Boudin
4. Portrait of Erasmus of Rotterdam (wd:Q107122548)
5. Portrait of Erasmus of Rotterdam (wd:Q110572054): painting by Hans Holbein the Younger and workshop (David Owsley Museum)
6. Portrait of Erasmus of Rotterdam (wd:Q116966868): painting by Workshop Lucas Cranach the Elder (Landesmuseum für Kunst und Kulturgeschichte Oldenburg)
7. Portrait of Erasmus of Rotterdam (wd:Q116968152): painting by Copy after Lucas Cranach the Elder or Workshop Lucas Cranach the Elder (Sammlung Würth)
8. Portrait of Erasmus of Rotterdam (wd:Q20809158): painting by Hans Holbein the Younger
9. Portrait of Erasmus of Rotterdam (wd:Q110571788): painting by Hans Holbein the Younger and workshop
10. Portrait of Erasmus of Rotterdam writing (wd:Q105684225): painting by Hans Holbein

[Assistant] The top result, "Port of Rotterdam" (wd:Q1027807), seems to be the correct entity. Next, let's search for the property related to the number of employees or employment.

[Tool]
Top 10 property alternatives:
1. employees, also known as workers, staff, personnel, number of employees, employee count (wd:P1128 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): total number of employees of a company at a given "point in time...not deprecated rank). Add data for recent years, don't overwrite
2. employer, also known as working at, working for, working place, works at, works for (wd:P108 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): person or organization for which the subject works or worked, affiliation
3. significant person, also known as significant people, sigper, sign. person, relevant person, relevant people (wd:P3342 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): person linked to the item in any possible way
4. member count, also known as total members, number of members, count of members, amount of members, membership (wd:P2124 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): number of entities that are part of an organization at a particular time
5. number of seats, also known as membership quantity, number of seats (members), quantity of members, seats (members) (wd:P1342 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): total number of seats/members in an assembly (legislative house or similar)
6. category for employees of the organization, also known as Academic staff, category for employees of the organisation, Professor (wd:P4195 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): Wikimedia category for people who work or worked at this organization
7. work location, also known as work place, working from, working at, worked from, worked at (wd:P937 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): location where persons or organisations were actively participating in employment, business or other work, significant place, location
8. number of works, also known as works, works posted, work count, posts posted, posts (wd:P3740 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): qualifier on identifiers, e.g. for creators or locations, giving... external database associated with the subject of the identifier
9. employment by economic sector, also known as employment by industry, employment by sector (wd:P2297 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): employment divided by sector
10. in work (wd:P2553 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): qualifier of award received and nominated for (P1411) to specify...ominated creative work appeared causing the nomination/award win

[Assistant]
The property "employees" (wd:P1128) seems to be the most relevant for our query. Now, let's construct a simple SPARQL query to check if the Port of Rotterdam has a value for this property.

```sparql
SELECT ?numEmployees WHERE {
  wd:Q1027807 wdt:P1128 ?numEmployees .
}
```

I will execute this query to see if it returns the number of employees for the Port of Rotterdam.

[Tool]
Got 1 row and 1 column:
| numEmployees         |
| -------------------- |
| 385000 (xsd:decimal) |

[Assistant]
The SPARQL query returned the number of employees for the Port of Rotterdam, which is 385,000.

Here is the final SPARQL query and the answer:

```sparql
SELECT ?numEmployees WHERE {
  wd:Q1027807 wdt:P1128 ?numEmployees .
}
```

Answer: The Port of Rotterdam employs 385,000 people.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query correctly retrieves the number of employees for the Port of Rotterdam from Wikidata, and the answer provided matches the execution result.