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, we can see that to answer when something happened, we often need to look for a specific date property associated with the event. In this case, we are looking for when HIV was first identified. Let's start by searching for the entity representing HIV in the knowledge graph.

[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 first result, `wd:Q15787`, represents the human immunodeficiency virus (HIV). Now, let's search for properties related to the identification or discovery of HIV.

[Tool]
Top 10 property alternatives:
1. identifies, also known as uniquely determines, is unique key for, is primary key for, is identifier of, identifier of (wd:P10476 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): this item uniquely and purposefully distinguishes instances of this class from each other
2. identified in image by, also known as depicted with identifier, legend for number, lettered as, letter in image, marked as (wd:P7380 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): qualifier for the identification of a value/object with a number, letter or sign inscribed in the image
3. Oxford Reference overview ID, also known as OR ID, ORO ID, Overview Oxford Reference ID, Overview Reference Oxford ID, Oxford Reference ID (wd:P12582 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): identifier for an overview page for a topic in the Oxford Reference
4. Historical Encyclopedia of Siberia ID, also known as HES ID (wd:P13029 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): identifier for an entity in the Historical Encyclopedia of Siberia
5. Historic Environment Scotland ID, also known as HB number, HES ID, HESID, HES number, HS ID (wd:P709 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): identifier for a building in the Historic Environment Scotland database
6. self-identified name, also known as internal name, own name, endonym (wd:P12089 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): name by which a group of humans refers to itself (endonym)
7. Clavis Apocryphorum Novi Testamenti ID, also known as CANT ID (wd:P10345 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): identifier for biblical apocrypha in Clavis Apocryphorum Novi Testamenti
8. Arcade Hub ID, also known as AH ID (wd:P12238 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): identifier of a video game in the Arcade Hub database
9. statement supported by, also known as supported by, statement is supported by, stated by, recognized by, per (wd:P3680 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): entity that supports a given statement
10. Occupational Outlook Handbook ID, also known as OOH ID (wd:P8629 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): identifier for occupations in the Occupational Outlook Handbook

[Assistant] None of these properties seem to directly relate to the identification or discovery of a virus. Let's try a different approach by searching for properties related to the discovery or first appearance of HIV specifically.

[Tool]
Top 10 property alternatives:
1. time of discovery or invention, also known as invented in, time of invention, time of discovery, invention time, invented on (wd:P575 as p/wdt): date or point in time when the item was discovered or invented
2. 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
3. has part(s), also known as has component, incorporates, has ingredient, has ingredients, has member (wd:P527 as p/wdt): part of this subject; inverse property of "part of" (P361). See also "has parts of the class" (P2670).
4. has effect, also known as has symptom, implies, leads to, led to, outcome (wd:P1542 as p/wdt): effect of this item, followed by
5. ScienceDirect topic ID, also known as Science Direct topic ID, Science Direct topic identifier, ScienceDirect topic identifier (wd:P10376 as p/wdt): identifier for a topic on the research publication website ScienceDirect
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. external data available at URL, also known as data, external data URL, URL with external data (wd:P1325 as p/wdt): URL where external data on this item can be found
8. 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)
9. pronunciation audio, also known as listen, audio pronunciation (wd:P443 as p/wdt): audio file with pronunciation, audio
10. prevalence (wd:P1193 as p/wdt): portion in percent of a population with a given disease or disorder

No other items found

[Assistant]
The property `time of discovery or invention` (wd:P575) seems to be the most relevant for our query. Let's construct a SPARQL query to find the date when HIV was first identified using this property.

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

I will now execute this query to get the result.

[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`, which indicates that HIV was first identified in 1983.

Here is the final SPARQL query and the answer:

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

Answer: HIV was first identified in 1983.

[Tool] Stopped generation process


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) is listed as the developer of WordNet in Wikidata. However, it is important to note that WordNet was primarily developed by a team at Princeton University, led by George Miller.

[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. However, we can use the same structure to form our query. We need to find the entity for WordNet and then look for the property that indicates who developed it. 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 top result, "WordNet" (wd:Q533822), seems to be the correct entity for the computational lexicon of English. Now, let's find out what property might indicate who developed WordNet. A likely candidate would be something like "developer" or "creator". Let's search for such properties.

[Tool]
Top 10 property alternatives:
1. commissioned by, also known as client, orderer, made for, commissioner, built for (wd:P88 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): person or organization that commissioned this work
2. developer, also known as written by, video game developer, video game dev, game developer, game dev (wd:P178 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): organization or person that developed the item, creator
3. discoverer or inventor, also known as inventor or discoverer, inventor, invented by, invented, introduced by (wd:P61 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): subject who discovered, first described, invented, or developed this discovery or invention or scientific hypothesis or theory
4. programmer, also known as programmed by, software developer (wd:P943 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): the programmer that wrote the piece of software, developer
5. real estate developer, also known as developer of the property (wd:P6237 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): person or organization responsible for building this item
6. development of anatomical structure, also known as development (wd:P4843 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): biological development of this anatomical structure
7. Databáze her developer ID, also known as Databaze her developer ID, Dh developer ID (wd:P11993 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): identifier of a video game developer in the Databáze her database
8. RAWG developer ID (wd:P11792 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): identifier of a video game developer in the RAWG database
9. Games Database developer ID (wd:P7794 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): identifier of a video game developer in the Games Database
10. collection creator, also known as creator, archive creator, collector, archives of, archive of (wd:P6241 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): entity (person, organization, etc.) that caused a record or collection to be produced or gathered

[Assistant]
The property "developer" (wd:P178) seems to be the most relevant for our purpose. Let's use this property to find out who developed WordNet. We'll construct a simple SPARQL query to retrieve the developer(s) of WordNet.

Here is the initial SPARQL query:

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

Let's execute this query.

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

[Assistant]
The query returned one result: Christiane Fellbaum (wd:Q4558744). However, it is known that WordNet was primarily developed by a team at Princeton University, led by George Miller. Let's verify if there are other developers associated with WordNet by checking for additional properties or statements.

We can try using the `p:P178` property to see if there are any qualifiers or additional statements.

Here is the updated SPARQL query:

```sparql
SELECT ?developer WHERE {
  wd:Q533822 p:P178 ?statement .
  ?statement ps:P178 ?developer .
}
```

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

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

[Assistant]
The query still returns only one result: Christiane Fellbaum (wd:Q4558744). Given that WordNet is known to have been developed by a team at Princeton University, led by George Miller, it seems that the information in Wikidata might be incomplete or not fully reflective of the historical context.

To ensure we are thorough, let's check if there are any other properties or statements that might provide more context about the development of WordNet. We can look for properties like "creator" or "inventor" to see if they provide additional information.

Let's search for the properties "creator" and "inventor" and see if they are used for WordNet.

### Search for "creator" property

[Tool]
Top 10 property alternatives:
1. collection creator, also known as creator, archive creator, collector, archives of, archive of (wd:P6241 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): entity (person, organization, etc.) that caused a record or collection to be produced or gathered
2. founded by, also known as starter, co-founded by, started by, institutor, instituted by (wd:P112 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): founder or co-founder of this organization, religion, place or entity, creator
3. creator, also known as created by, visual artist, sculptor, sculpted by, realized by (wd:P170 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): maker of this creative work or other object (where no more specific property exists)
4. author, also known as writer, written by, poet, playwright, maker (wd:P50 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): main creator(s) of a written work (use on works, not humans); us...hor name string) when Wikidata item is unknown or does not exist, creator
5. author name string, also known as short author name, writer named as, writer name, writer indicated as, songwriting credits string (wd:P2093 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): stores unspecified author or editor name for publications; use i...or editor (P98) does not exist or is not known. Do not use both.
6. notable work, also known as artwork, wrote, writer of, works, work (wd:P800 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): notable scientific, artistic or literary work, or other work of significance among subject's works
7. possible creator, also known as creator (possible), attributed to (wd:P1779 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): for a creative work with considerable uncertainty about the author
8. developer, also known as written by, video game developer, video game dev, game developer, game dev (wd:P178 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): organization or person that developed the item, creator
9. fabrication method, also known as process used, method of cooking, preparation method, obtained by, method used (wd:P2079 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): method, process or technique used to grow, cook, weave, build, assemble, manufacture the item, uses
10. foundational text, also known as main text, treaty, organic law, founding document, establishing document (wd:P457 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): text through which an institution or object has been created or established

[Assistant]
The property "creator" (wd:P170) seems to be a good candidate. Let's check if this property is used for WordNet.

### Check for "creator" property

Here is the SPARQL query to check for the "creator" property:

```sparql
SELECT ?creator WHERE {
  wd:Q533822 wdt:P170 ?creator .
}
```

Let's execute this query.

[Tool] Got no rows and 1 columns

[Assistant]
The query for the "creator" property (wd:P170) did not return any results. This suggests that the information about the creators of WordNet is not stored under this property in Wikidata.

Given that the "developer" property (wd:P178) only returned Christiane Fellbaum, and the "creator" property did not return any results, it seems that the information in Wikidata might be incomplete. However, based on the known historical context, WordNet was primarily developed by a team at Princeton University, led by George Miller.

Since the information in Wikidata is limited, we can conclude that the primary developer listed in Wikidata is Christiane Fellbaum, but it is important to note that the development of WordNet involved a broader team, including George Miller.

### Final Answer

Here is the final SPARQL query and answer:

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

Answer: Christiane Fellbaum (wd:Q4558744) is listed as the developer of WordNet in Wikidata. However, it is important to note that WordNet was primarily developed by a team at Princeton University, led by George Miller.

Let's finalize this with the answer function.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?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 the examples, we need to identify the entity for Norway and the property for the highest point. Let's start by searching for the entity "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 is the correct entity for Norway, which is `wd:Q20`. Now let's search for the property "highest point" to see what options we have.

[Tool]
Top 10 property alternatives:
1. highest point, also known as tallest point, zenith, summit, pinnacle, highest peak (wd:P610 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): 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 prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): 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 prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): 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 prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): 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 prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): 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 prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): 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 prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): 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 prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): maximum sustained wind evaluated by agency
9. ranking, also known as peak position, place, placing, position, rank (wd:P1352 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): 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 prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): easternmost point of a location

[Assistant]
The property "highest point" corresponds to `wd:P610`. We can now construct a simple SPARQL query to find the highest point of Norway. Let's execute this query:

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

This query will return the IRI of the highest point of Norway. If necessary, we can add an optional clause to fetch the label of the highest point for better readability.

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

[Assistant]
The highest point of Norway is Galdhøpiggen, which is represented by the IRI `wd:Q203942`.

To provide a more complete answer, let's add an optional clause to fetch the label of the highest point:

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

This query will return both the IRI and the English label of the highest point of Norway.

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

[Assistant]
The highest point of Norway is Galdhøpiggen, represented by the IRI `wd:Q203942` and the English label "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


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 can see that the SPARQL queries typically involve selecting a specific entity and then querying for a property or relationship associated with that entity. In our case, we need to find the start date of the Weimar Republic. Let's first search for the entity "Weimar Republic" in the knowledge graph.

[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 top result for "Weimar Republic" is `wd:Q41304`. Now, let's find the property that represents the start date of the Weimar Republic. We can search for properties related to "start" or "beginning" for this entity.

[Tool]
Top 10 property alternatives:
1. 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
2. inception, also known as initial release date, founded in, founded on, founded on date, founding date (wd:P571 as p/wdt): time when an entity begins to exist; for date of official opening use P1619, start time
3. native label, also known as official title, original label, original language label, original language name, original language title (wd:P1705 as p/wdt): label for the items in their official language  or their original language, name
4. 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)
5. facet of, also known as subitem of, topic of, subtopic of, subject in, section in (wd:P1269 as p/wdt): topic of which this item is an aspect; item that offers a broader perspective on the same topic
6. official name, also known as long title, name, official title, registered name, full title (wd:P1448 as p/wdt): official name of the subject in its official language(s), name
7. topic's main Wikimedia portal, also known as main portal, portal (wd:P1151 as p/wdt): Wikimedia portal associated with this topic
8. replaces, also known as supersedes, succeeds, replaced, previous job holder, preceded by (wd:P1365 as p/wdt): person, state or item replaced. Use "structure replaces" (P1398)...item was not replaced or predecessor and successor are identical, follows
9. anthem, also known as national anthem, team anthem, official song, official hymn, club anthem (wd:P85 as p/wdt): subject's official anthem
10. pronunciation audio, also known as listen, audio pronunciation (wd:P443 as p/wdt): audio file with pronunciation, audio

No other items found

[Assistant]
The property `wd:P571` (inception) seems to be the most relevant for the start date of the Weimar Republic. Let's construct a simple SPARQL query to retrieve this information.

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

I will now execute this 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 the answer:

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

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

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?port ?numEmployees WHERE {
  wd:Q1027807 wdt:P1128 ?numEmployees .
  OPTIONAL {
    wd:Q1027807 rdfs:label ?portLabel .
    FILTER ( LANG( ?portLabel ) = 'en' )
  }
  BIND( wd:Q1027807 AS ?port )
}

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] Now that we have seen some examples, let's start by searching for the entity "Port of Rotterdam" in the knowledge graph.

[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] From the search results, it seems that the entity "Port of Rotterdam" (wd:Q1027807) is the most relevant one. Next, I will look for the property that represents the number of employees or workforce size.

[Tool]
Top 10 property alternatives:
1. employees, also known as workers, staff, personnel, number of employees, employee count (wd:P1128 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): 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 prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): 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 prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): 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 prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): 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 prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): 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 prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): 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 prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): location where persons or organisations were actively participating in employment, business or other work, significant place, location
8. number of works, also known as works, works posted, work count, posts posted, posts (wd:P3740 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): 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 prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): employment divided by sector
10. in work (wd:P2553 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): 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 appropriate for representing the number of employees. Now, I will check if this property is used for the "Port of Rotterdam" (wd:Q1027807).

[Tool]
Got 1 row and 3 columns:
| subject                         | property              | object               |
| ------------------------------- | --------------------- | -------------------- |
| Port of Rotterdam (wd:Q1027807) | employees (wdt:P1128) | 385000 (xsd:decimal) |

[Assistant]
The "Port of Rotterdam" (wd:Q1027807) has the property "employees" (wdt:P1128) with the value 385,000.

I will now construct the final SPARQL query to retrieve this information.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ( COUNT( ?film ) AS ?count ) WHERE {
  ?film wdt:P31 wd:Q11424 ; wdt:P136 wd:Q842256 ; wdt:P577 ?date .
  BIND( YEAR( ?date ) AS ?year ) FILTER ( ?year = 1978 ) FILTER ( ?film != wd:Q267721 )
}

Got 1 row and 1 column:
| count        |
| ------------ |
| 84 (xsd:int) |

84 other musical films were launched the same year as Grease.

[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 other musical films were launched the same year as Grease?

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

[Tool]
Example 1:
Question:
Who are the developers of DBpedia?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q465 wdt:P112 ?uri .
}

Using entities:
DBpedia, also known as dbpedia.org (wd:Q465): online database project of structured data extracted from Wikipedia

Using properties:
founded by, also known as starter, co-founded by, started by, institutor, instituted by (wd:P112 as wdt): founder or co-founder of this organization, religion, place or entity, creator

Example 2:
Question:
Which U.S. states are in the same timezone as Utah?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q829 wdt:P421 ?timezone .
  ?uri wdt:P31 wd:Q35657 ; wdt:P421 ?timezone .
  FILTER ( ?uri != wd:Q829 )
}

Using entities:
U.S. state, also known as U.S. states, US state, USA state, U.S.A. state, United States states (wd:Q35657): constituent political entity of the United States of America
Utah, also known as Beehive State, State of Utah, Utah, United States, US-UT, UT (wd:Q829): state of the United States of America

Using properties:
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
located in time zone, also known as TZ, time zone, timezone (wd:P421 as wdt): time zone for this item

Example 3:
Question:
How many languages are spoken in Turkmenistan?

SPARQL:
SELECT ( COUNT( DISTINCT ?uri ) as ?c ) WHERE {
  wd:Q874 wdt:P2936 ?uri .
}

Using entities:
Turkmenistan, also known as TKM, tm, Turkmenia (wd:Q874): country in Central Asia

Using properties:
language used, also known as languages used, medium of instruction, used language, uses language, working language (wd:P2936 as wdt): language widely used (spoken or written) in this place or at this event or organisation

[Assistant] Based on the examples, we need to identify the film "Grease" and the year it was released. Then we can look for other musical films released in the same year. Let's start by searching for the entity "Grease" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. fat, also known as grease, fats and oils, fat or oil, dietary fats, dietary fat (wd:Q127980): esters of three fatty acid chains and the alcohol glycerol, one of the three main macronutrients, also known as triglycerides
2. Grease (wd:Q267721): 1978 film by Randal Kleiser
3. grease, also known as lubricating grease, grease lubricant (wd:Q629823): semi-solid, oily lubricant
4. Grease (wd:Q611626): 1972 musical
5. Grease (wd:Q728791): original song written and composed by Barry Gibb; from the 1978 film "Grease", performed by Frankie Valli
6. Grease (wd:Q3776108): 2010 video game
7. Grease (wd:Q104861673): American media franchise
8. Grease (wd:Q114518793): episode of Cheers (S9 E6)
9. Grease (wd:Q116013684): sardana by Martí de Joan
10. Grease (wd:Q126836847): vocal track by Mina; 1986 studio recording; cover version

[Assistant] The correct entity for the film "Grease" is wd:Q267721. Now let's find out the release year of the film "Grease".

[Tool]
Got 2 rows and 1 column:
| year           |
| -------------- |
| 1978 (xsd:int) |
| 1978 (xsd:int) |

[Assistant] The film "Grease" was released in the year 1978. Next, we need to find other musical films that were also released in 1978. We'll first search for the property that indicates a film is a musical.

[Tool]
Top 10 property alternatives:
1. sheet music, also known as musical score (wd:P3030 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): media file containing the musical score (sheet music) for this item
2. composer, also known as music by, written by, songwriter, musical score by, film score by (wd:P86 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): person(s) who wrote the music [for lyricist, use "lyrics by" (P676)], creator
3. featured track(s), also known as songs, soundtrack, OST, music tracks, music track (wd:P10664 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): soundtrack of this audiovisual work
4. soundtrack release, also known as soundtrack single, soundtrack extended play, soundtrack album, has soundtrack album, OST (wd:P406 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): music release that incorporates music directly recorded from the soundtrack of an audiovisual work
5. genre, also known as genre of music, type of music, type of film, music genre, literary genre (wd:P136 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): creative work's genre or an artist's field of work (P101). Use main subject (P921) to relate creative works to their topic
6. All Musicals lyrics ID, also known as All Musicals ID (wd:P12941 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): identifier for lyrics for musicals and songs from musicals at All Musicals
7. cast member, also known as feat., starring (film/play), film starring, starring, actor (wd:P161 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): actor in the subject production [use "character role" (P453) and...use "recorded participant" (P11108) for non-fiction productions], significant person, performer
8. music created for, also known as music for, music for work, music made for, music made for work, music written for (wd:P9899 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): a work, event, etc. for which a musical composition was created ... was written; a film for which motion picture music was created), created for
9. Rate Your Music film genre ID, also known as RYM genre of cinema ID, RYM film genre identifier, RYM film genre ID, RateYourMusic.com film genre ID, Cinemos film genre (wd:P11588 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): identifier for a film genre on Rate Your Music
10. musical quotation or excerpt, also known as musical excerpt, musical quotation (wd:P6670 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): quotation or excerpt from this musical work, in LilyPond syntax

[Assistant]
The property `genre` (wd:P136) seems to be the most relevant for identifying musical films. Now, let's construct a SPARQL query to find other musical films released in 1978.

We will:
1. Find films that have the genre "musical".
2. Ensure these films were released in 1978.
3. Exclude the film "Grease" itself.

Let's execute this query.

[Tool] Got no rows and 1 columns

[Assistant]
It appears that there are no other musical films released in 1978 according to the current query. However, let's double-check by ensuring that the genre "musical" (wd:Q200050) is correctly identified and that the query is structured properly.

First, let's verify the genre "musical" and ensure it is indeed used for films.

[Tool] Got no rows and 3 columns

[Assistant]
It seems that the genre "musical" (wd:Q200050) is not being used in the way we expected. Let's try another approach by searching for the genre "musical" and finding the correct entity for it.

I will search for the genre "musical" again to ensure we have the right entity.

[Tool]
Top 10 entity alternatives:
1. music genre, also known as genre of music, musical genre, musical style, music style, style of music (wd:Q188451): category that identifies pieces of music as belonging to a shared tradition or set of conventions
2. musical genre (wd:L10429-S3)
3. musical genre (wd:L317224-S1)
4. Americana (musical genre) (wd:L985538-S1)
5. Americana (musical genre) (wd:L985537-S1)
6. Americana (musical genre) (wd:L985539-S1)
7. Americana (musical genre) (wd:L985540-S1)
8. musical genre and theory (wd:L677678-S1)
9. musical genre and theory (wd:L624422-S1)
10. musical genre and theory (wd:L624421-S1)

[Assistant]
The correct entity for the genre "musical" is likely wd:Q188451, which is the general category for musical genres. However, we need to specifically find the genre "musical" as it pertains to films. Let's search for the specific genre "musical film" to ensure we have the right entity.

I will search for "musical film" again to find the correct genre entity.

[Tool]
Top 10 entity alternatives:
1. music genre, also known as genre of music, musical genre, musical style, music style, style of music (wd:Q188451): category that identifies pieces of music as belonging to a shared tradition or set of conventions
2. film genre, also known as movie genre, video genre, genre of a movie (wd:Q201658): classification of films based on similarities in narrative elements
3. musical film, also known as film musical, musical movie (wd:Q842256): film genre
4. film genre (wd:L463663-S1)
5. film genre (wd:L463662-S1)
6. film genre (wd:L463657-S1)
7. film genre (wd:L33481-S1)
8. film genre (wd:L31881-S1)
9. film genre (wd:L16853-S1)
10. genre, also known as literary genre, type of music, type of film, music genre, kind of music (wd:P136): creative work's genre or an artist's field of work (P101). Use main subject (P921) to relate creative works to their topic

[Assistant]
The correct entity for the genre "musical film" is wd:Q842256. Now, let's update our SPARQL query to use this entity and find other musical films released in 1978.

Here is the updated query:

```sparql
SELECT ?film WHERE {
  ?film wdt:P31 wd:Q11424 ;  # ?film is an instance of film
        wdt:P136 wd:Q842256 ;  # ?film has the genre "musical film"
        wdt:P577 ?date .  # ?film has a publication date
  BIND(YEAR(?date) AS ?year)  # Extract the year from the date
  FILTER(?year = 1978)  # Filter for films released in 1978
  FILTER(?film != wd:Q267721)  # Exclude the film "Grease"
}
```

Let's execute this query.

[Tool]
Got 84 rows and 1 column, showing the first 5 and last 5 rows below:
| film                                       |
| ------------------------------------------ |
| An Ordinary Miracle (wd:Q1082153)          |
| Mic Maputo Man (wd:Q120043041)             |
| The Buddy Holly Story (wd:Q1211819)        |
| Junior High School (wd:Q12125297)          |
| Legend of the Maiden Tower (wd:Q12844664)  |
| ...                                        |
| New York, New York (wd:Q743367)            |
| Slow Dancing in the Big City (wd:Q7541987) |
| Stony Island (wd:Q7619570)                 |
| The Last Waltz (wd:Q847599)                |
| The Woman who Sings (wd:Q925962)           |

[Assistant]
The query has returned 84 other musical films that were released in 1978, excluding "Grease".

To provide the final answer, we can count the number of results returned by the query.

Here is the final SPARQL query to count the number of other musical films released in 1978:

```sparql
SELECT (COUNT(?film) AS ?count) WHERE {
  ?film wdt:P31 wd:Q11424 ;  # ?film is an instance of film
        wdt:P136 wd:Q842256 ;  # ?film has the genre "musical film"
        wdt:P577 ?date .  # ?film has a publication date
  BIND(YEAR(?date) AS ?year)  # Extract the year from the date
  FILTER(?year = 1978)  # Filter for films released in 1978
  FILTER(?film != wd:Q267721)  # Exclude the film "Grease"
}
```

Let's execute this query to get the count.

[Tool]
Got 1 row and 1 column:
| count        |
| ------------ |
| 84 (xsd:int) |

[Assistant]
The final answer is that 84 other musical films were launched the same year as "Grease" (1978).

Here is the final SPARQL query and the answer:

```sparql
SELECT (COUNT(?film) AS ?count) WHERE {
  ?film wdt:P31 wd:Q11424 ;  # ?film is an instance of film
        wdt:P136 wd:Q842256 ;  # ?film has the genre "musical film"
        wdt:P577 ?date .  # ?film has a publication date
  BIND(YEAR(?date) AS ?year)  # Extract the year from the date
  FILTER(?year = 1978)  # Filter for films released in 1978
  FILTER(?film != wd:Q267721)  # Exclude the film "Grease"
}
```

Answer: 84 other musical films were launched the same year as "Grease".

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
ASK WHERE {
  wd:Q510 wdt:P1589 wd:Q459173 .
  wd:Q459173 wdt:P2044 ?challengerDepth .
  wd:Q513 wdt:P2044 ?everestHeight .
  FILTER ( ?challengerDepth < - ?everestHeight )
}

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] Is the Mariana Trench deeper than Mount Everest is high (above sea level)?

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

[Tool]
Example 1:
Question:
In which time zone is Rome?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q220 wdt:P421 ?uri
}

Using entities:
Rome, also known as Roma, City of Seven Hills, Rome, Italy, The Eternal City (wd:Q220): capital and largest city of Italy

Using properties:
located in time zone, also known as TZ, time zone, timezone (wd:P421 as wdt): time zone for this item

Example 2:
Question:
Which professional surfers were born in Australia?

SPARQL:
SELECT ( COUNT( DISTINCT ?uri ) AS ?c ) WHERE {
  ?uri wdt:P19 ?birtplace .
  ?birtplace wdt:P17 wd:Q408 .
  ?uri wdt:P106 wd:Q13561328 .
}

Using entities:
surfer, also known as surfboarder (wd:Q13561328): sportsperson, practitioner of surfing
Australia, also known as AU, The Commonwealth of Australia, Stria, Straya, Stralia (wd:Q408): country in Oceania

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

Example 3:
Question:
When did princess Diana die?

SPARQL:
SELECT DISTINCT ?date WHERE {
  wd:Q9685 wdt:P570 ?date .
}

Using entities:
Diana, Princess of Wales, also known as Lady Diana Spencer, Princess Diana, Princess Di, Lady Diana Frances Spencer, The People's Princess (wd:Q9685): member of the British royal family and Princess of Wales (1961–1997), mental health advocate, kindergarten teacher, HIV/AIDS activist, environmentalist, peace activist, philanthropist, humanitarian, aristocrat, benefactor, socialite

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

[Assistant]
To answer the question "Is the Mariana Trench deeper than Mount Everest is high (above sea level)?", we need to find the depth of the Mariana Trench and the height of Mount Everest above sea level. We can then compare these values to determine the answer.

First, let's identify the entities for the Mariana Trench and Mount Everest.

[Tool]
Top 10 entity alternatives:
1. Mariana Trench (wd:Q510): deepest oceanic trench on Earth
2. Marianas Trench (wd:Q3291259): Canadian pop rock band
3. "Mariana Trench" T-waves (wd:Q87292168): scientific article published on 01 April 2015
4. Marianas Trench Marine National Monument (wd:Q6612760): US national monument in the Mariana Archipelago
5. Document Recovered From The Marianas Trench (wd:Q60032271): a tale from the SCP web-based collaborative writing project
6. Obligately barophilic bacterium from the Mariana trench (wd:Q35470633): scientific article
7. Self-powered soft robot in the Mariana Trench (wd:Q118298491)
8. So, How Deep Is the Mariana Trench? (wd:Q63230174)
9. oceanic trench, also known as deep sea trench, trench, submarine trench (wd:Q119253): depressions of the sea floor
10. Silybum marianum, also known as milkthistle, variegated thistle, St. Mary's thistle, Scotch thistle, Saint Mary's thistle (wd:Q193798): species of plant of genus Silybum in family Asteraceae

[Assistant]
The correct entity for the Mariana Trench is `wd:Q510`.

Next, let's identify the entity for Mount Everest.

[Tool]
Top 10 entity alternatives:
1. Mount Everest, also known as Chomolungma, Chomolangma, Zhumulangma, World highest peak, Sagarmāthā (wd:Q513): Earth's highest mountain above sea level, located in the Mahalangur Himal sub-range of the Himalayas
2. The Climbers, also known as Chomolungma, Mount Everest, Qomolangma (wd:Q61882061): 2019 film directed by Daniel Lee
3. Mount Everest (wd:Q19977041): band
4. Mount Everest, also known as IMO 9470985 (wd:Q84260471): ship built in 2010
5. Mount Everest, also known as Mount Everest, Australia (wd:Q21939846): mountain in New South Wales, Australia
6. Mount Everest, also known as Mount Everest, South Africa (wd:Q31505139): mountain in the Free State Province, South Africa
7. Mount Everest (wd:Q119187184): painting by G. Hemwell, Hampshire Cultural Trust Headquarters, Winchester (Hampshire County Council’s Fine Art Collection)
8. Mount Everest (wd:Q59014625): article published May 1955
9. Mount Everest (wd:Q60102972): article published March 1921
10. Mount Everest (wd:Q60106019): article published November 1921

[Assistant]
The correct entity for Mount Everest is `wd:Q513`.

Now, let's find the properties for the depth of the Mariana Trench and the height of Mount Everest above sea level.

We'll start by searching for the property related to depth for the Mariana Trench.

[Tool]
Top 10 property alternatives:
1. lowest point, also known as extreme point deepest, extreme point lowest, deepest point (wd:P1589 as p/wdt): point with lowest elevation in a region or path
2. bathymetry image, also known as file bathymetry, bathymetric map, bathymetric chart (wd:P207 as p/wdt): image showing bathymetric chart, bathymetric map
3. elevation above sea level, also known as MAMSL, MASL, height, elevation above mean sea level, elevation (wd:P2044 as p/wdt): height of the item (geographical object) as measured relative to sea level
4. located in or next to body of water, also known as located on body of water, ocean, on bay, next to lake, on coast of (wd:P206 as p/wdt): body of water on or next to which a place is located, location
5. coordinate location, also known as point on a map, gps coordinates, gps location, latitude, location (wd:P625 as p/wdt): geocoordinates of the subject. For Earth, please note that only WGS84 coordinating system is supported at the moment
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. 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
8. country, also known as sovereign state, state, land, host country (wd:P17 as p/wdt): sovereign state that this item is in (not to be used for human beings), located in the administrative territorial entity
9. 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)
10. 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

No other items found

[Assistant]
The property for the depth of the Mariana Trench is `wd:P1589` (lowest point).

Next, let's find the property for the height of Mount Everest above sea level.

[Tool]
Top 10 property alternatives:
1. elevation above sea level, also known as MAMSL, MASL, height, elevation above mean sea level, elevation (wd:P2044 as p/wdt): height of the item (geographical object) as measured relative to sea level
2. height, also known as heighth, size, depth over terrain (wd:P2048 as p): vertical length of an entity, length
3. aerial view, also known as overhead view, sky view, orthophotograph, orthophoto, aerial image (wd:P8592 as p/wdt): image of the subject taken from the air, video, image
4. located in/on physical feature, also known as located on the terrain feature, location (terrain feature), loc (terr), on, on geographical feature (wd:P706 as p/wdt): located on the specified (geo)physical feature. Should not be us...nly political/administrative (P131) or a mountain range (P4552)., location, part of
5. coordinate location, also known as point on a map, gps coordinates, gps location, latitude, location (wd:P625 as p/wdt): geocoordinates of the subject. For Earth, please note that only WGS84 coordinating system is supported at the moment
6. mountain range, also known as system of mountains, hill range, range, parent range, mountain system (wd:P4552 as p/wdt): range or subrange to which the geographical item belongs, geomorphological unit
7. country, also known as sovereign state, state, land, host country (wd:P17 as p/wdt): sovereign state that this item is in (not to be used for human beings), located in the administrative territorial entity
8. panoramic view, also known as panoramic image, panorama view, image panoramic (wd:P4291 as p/wdt): panoramic view of the object, view
9. continent (wd:P30 as p/wdt): continent of which the subject is a part, located in/on physical feature
10. significant place, also known as sign. place, significant location, significant landmark, place associated with the subject, notable place (wd:P7153 as p/wdt): significant or notable places associated with the subject, location

No other items found

[Assistant]
The property for the height of Mount Everest above sea level is `wd:P2044` (elevation above sea level).

Now, let's construct a SPARQL query to retrieve the depth of the Mariana Trench and the height of Mount Everest above sea level, and then compare these values to determine if the Mariana Trench is deeper than Mount Everest is high.

### Step 1: Retrieve the depth of the Mariana Trench and the height of Mount Everest
```sparql
SELECT ?marianaDepth ?everestHeight WHERE {
  wd:Q510 wdt:P1589 ?marianaDepth .
  wd:Q513 wdt:P2044 ?everestHeight .
}
```

Let's execute this query to get the values.

[Tool]
Got 1 row and 2 columns:
| marianaDepth                 | everestHeight         |
| ---------------------------- | --------------------- |
| Challenger Deep (wd:Q459173) | 8848.86 (xsd:decimal) |

[Assistant]
The depth of the Mariana Trench is represented by the entity `Challenger Deep (wd:Q459173)`. To get the exact depth value, we need to query the depth of the Challenger Deep itself.

### Step 2: Retrieve the depth value of the Challenger Deep
```sparql
SELECT ?challengerDepth WHERE {
  wd:Q459173 wdt:P2044 ?challengerDepth .
}
```

Let's execute this query to get the depth value of the Challenger Deep.

[Tool]
Got 1 row and 1 column:
| challengerDepth      |
| -------------------- |
| -10984 (xsd:decimal) |

[Assistant]
The depth of the Challenger Deep, which is the deepest part of the Mariana Trench, is -10984 meters below sea level. The height of Mount Everest above sea level is 8848.86 meters.

Now, let's compare these values to determine if the Mariana Trench is deeper than Mount Everest is high.

### Step 3: Compare the depth of the Mariana Trench and the height of Mount Everest
```sparql
ASK WHERE {
  wd:Q510 wdt:P1589 wd:Q459173 .
  wd:Q459173 wdt:P2044 ?challengerDepth .
  wd:Q513 wdt:P2044 ?everestHeight .
  FILTER(?challengerDepth < -?everestHeight)
}
```

Let's execute this query to get the answer.

[Tool] True

[Assistant]
The SPARQL query confirms that the Mariana Trench is indeed deeper than Mount Everest is high above sea level.

Therefore, the answer to the question "Is the Mariana Trench deeper than Mount Everest is high (above sea level)?" is **True**.

Here is the final SPARQL query:

```sparql
ASK WHERE {
  wd:Q510 wdt:P1589 wd:Q459173 .
  wd:Q459173 wdt:P2044 ?challengerDepth .
  wd:Q513 wdt:P2044 ?everestHeight .
  FILTER(?challengerDepth < -?everestHeight)
}
```

And the answer is **True**.

[Tool] Stopped generation process