PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?developer ?developerLabel WHERE {
  wd:Q533822 wdt:P178 ?developer .
  OPTIONAL {
    ?developer rdfs:label ?developerLabel FILTER ( lang( ?developerLabel ) = "en" )
  }
}

According to current Wikidata data, Christiane Fellbaum is listed as the developer of WordNet. However, this result reflects only what is currently encoded in Wikidata. Notably, George A. Miller and Princeton University are widely recognized in external sources as key contributors to the development of WordNet, but they are not presently linked as developers in Wikidata. This highlights a limitation of the data, and Wikidata may need updating to reflect the broader scholarly consensus.

[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 http://amur:7001

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 break down the question:

1. The entity "WordNet" is likely a lexical database or project.
2. The property "developed by" or "developer" is needed to find who created or developed WordNet.

First, I will search for the entity "WordNet" in Wikidata to get its identifier.

[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 most relevant entity for "WordNet" as a lexical database is wd:Q533822 (WordNet, also known as Princeton WordNet).

Next, I need to find the property that links a project or database to its developer or creator. I will search for properties related to "developer" or "developed by".

[Tool]
Top 10 property alternatives:
1. commissioned by, also known as client, orderer, made for, commissioner, built for (wd:P88 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): 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 pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): 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 pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): 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 pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): the programmer that wrote the piece of software, developer
5. real estate developer, also known as developer of the property (wd:P6237 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): person or organization responsible for building this item
6. development of anatomical structure, also known as development (wd:P4843 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): biological development of this anatomical structure
7. Databáze her developer ID, also known as Databaze her developer ID, Dh developer ID (wd:P11993 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): identifier of a video game developer in the Databáze her database
8. RAWG developer ID (wd:P11792 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): identifier of a video game developer in the RAWG database
9. Games Database developer ID (wd:P7794 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): 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 pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): entity (person, organization, etc.) that caused a record or collection to be produced or gathered

[Assistant]
The most relevant property for "developer" is wd:P178 (developer).

Now, I will check if there is a value for wd:P178 (developer) for wd:Q533822 (WordNet).

[Tool]
Got 1 row and 3 columns:
| subject              | property             | object                            |
| -------------------- | -------------------- | --------------------------------- |
| WordNet (wd:Q533822) | developer (wdt:P178) | Christiane Fellbaum (wd:Q4558744) |

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

[Tool]
Got 1 row and 2 columns:
| developer                         | developerLabel           |
| --------------------------------- | ------------------------ |
| Christiane Fellbaum (wd:Q4558744) | Christiane Fellbaum (en) |

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The answer identifies Christiane Fellbaum as the developer of WordNet, which is partially correct. However, WordNet was primarily developed by a team at Princeton University led by George A. Miller, with Christiane Fellbaum as a significant contributor. The current SPARQL query only retrieves one developer (Christiane Fellbaum), possibly due to the use of the 'developer' (wdt:P178) property, which may not fully capture all contributors or the institution involved. To improve the answer, consider also checking for properties such as 'creator' (wdt:P170), 'author' (wdt:P50), or 'publisher' (wdt:P123), and include Princeton University and George A. Miller if present. The query should be refined to ensure all relevant developers and contributors are included.

[Assistant]
Thank you for the feedback. You are correct: the development of WordNet involved a team at Princeton University, led by George A. Miller, with Christiane Fellbaum as a key contributor. The previous query only checked the 'developer' property, which may not capture all relevant contributors or the institution.

Step-by-step, here's how I'll proceed:
1. Identify additional relevant properties: 'creator' (wdt:P170), 'author' (wdt:P50), and 'publisher' (wdt:P123).
2. Query all these properties for WordNet (wd:Q533822) to see if they reference George A. Miller, Princeton University, or other contributors.
3. Combine the results to provide a more comprehensive answer.

First, I will check which of these properties are used for WordNet.

[Tool]
Got 271 rows and 3 columns, showing the first 10 rows below:
| subject              | property                               | object                                                           |
| -------------------- | -------------------------------------- | ---------------------------------------------------------------- |
| WordNet (wd:Q533822) | instance of (wdt:P31)                  | lexical database (wd:Q6537693)                                   |
| WordNet (wd:Q533822) | language of work or name (wdt:P407)    | English (wd:Q1860)                                               |
| WordNet (wd:Q533822) | full work available at URL (wdt:P953)  | <https://wordnet.princeton.edu/download/current-version>         |
| WordNet (wd:Q533822) | image (wdt:P18)                        | <http://commons.wikimedia.org/wiki/Special:FilePath/WordNet.PNG> |
| WordNet (wd:Q533822) | copyright status (wdt:P6216)           | copyrighted (wd:Q50423863)                                       |
| WordNet (wd:Q533822) | inception (wdt:P571)                   | 1998-01-01T00:00:00Z (xsd:dateTime)                              |
| WordNet (wd:Q533822) | described by source (wdt:P1343)        | WordNet: An Electronic Lexical Database (wd:Q35839029)           |
| WordNet (wd:Q533822) | copyright license (wdt:P275)           | BSD licenses (wd:Q191307)                                        |
| WordNet (wd:Q533822) | official website (wdt:P856)            | <https://wordnet.princeton.edu/>                                 |
| WordNet (wd:Q533822) | software version identifier (wdt:P348) | 3.1                                                              |
| ...                  | ...                                    | ...                                                              |

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Assistant]
None of the properties 'creator' (wdt:P170), 'author' (wdt:P50), or 'publisher' (wdt:P123) are directly used for WordNet (wd:Q533822). Only 'developer' (wdt:P178) is present, pointing to Christiane Fellbaum.

However, the feedback suggests that George A. Miller and Princeton University should also be considered. Since these are not directly linked via the above properties, I will:
- Check if there are other properties linking WordNet to George A. Miller or Princeton University.
- Search for George A. Miller and Princeton University as entities, and see if they are linked to WordNet in any way.

First, I will search for George A. Miller and Princeton University in Wikidata to get their entity IDs.

[Tool]
Top 10 entity alternatives:
1. George Armitage Miller, also known as George A. Miller, George Miller (wd:Q670658): American psychologist (1920–2012), university teacher, psychologist
2. George Arthur Miller (wd:Q2495778): British polo player (1867–1935), polo player
3. George Abram Miller, also known as G. A. Miller (wd:Q1506815): American mathematician (1863–1951), historian of mathematics, university teacher, mathematician
4. Alf Miller, also known as Alfred George Abraham Miller (wd:Q23461809): association football player (1917-1999), association football player
5. George Miller Andrews, also known as George Andrews (wd:Q75636713): Peerage person ID=262014
6. George Miller Armstrong, also known as G. M. Armstrong, G.M.Armstr. (wd:Q21505333): American botanist and plant pathologist (1893-1986), botanical collector, phytopathologist, botanist
7. George A. Miller (1920-2012). (wd:Q30353436): scientific article
8. George Alfred Miller (wd:Q118153037): graduated from University of Washington with a Ph. D. in Sociology
9. George Albert Miller (wd:Q94487076)
10. George Augustus Miller Jr. (wd:Q62592140): 1894-1983 , armorer, engineer, armourer, inventor

[Tool]
Top 10 entity alternatives:
1. Princeton University Press, also known as Princeton Univ. Press (wd:Q1708210): independent publisher with close connections to Princeton University
2. Princeton University Library, also known as pulibrary (wd:Q3297681): main library system of Princeton University
3. Princeton University Stadium, also known as Powers Field at Princeton Stadium (wd:Q6587721): stadium in Princeton
4. Princeton University Chapel (wd:Q4378798): university chapel at Princeton University, Princeton, New Jersey, U.S.A.
5. Princeton University Band (wd:Q7245022): marching band and pep band of Princeton University
6. history of Princeton University, also known as Princeton University history (wd:Q5866083): aspect of history
7. Princeton University Orchestra (wd:Q60768772): Flagship symphony orchestra of Princeton University
8. Trustees of Princeton University, also known as The Trustees of Princeton University, Princeton University Trustees (wd:Q7848211): member board responsible for managing Princeton University
9. Princeton University, Fall (wd:Q105535923): painting by Paul Cornoyer
10. Princeton University, 1746–1946 (wd:Q60097185): scientific article published in Nature

[Tool]
Top 10 entity alternatives:
1. Princeton University Press, also known as Princeton Univ. Press (wd:Q1708210): independent publisher with close connections to Princeton University
2. Princeton University Library, also known as pulibrary (wd:Q3297681): main library system of Princeton University
3. Princeton University Stadium, also known as Powers Field at Princeton Stadium (wd:Q6587721): stadium in Princeton
4. Princeton University Chapel (wd:Q4378798): university chapel at Princeton University, Princeton, New Jersey, U.S.A.
5. Princeton University Band (wd:Q7245022): marching band and pep band of Princeton University
6. history of Princeton University, also known as Princeton University history (wd:Q5866083): aspect of history
7. Princeton University Orchestra (wd:Q60768772): Flagship symphony orchestra of Princeton University
8. Trustees of Princeton University, also known as The Trustees of Princeton University, Princeton University Trustees (wd:Q7848211): member board responsible for managing Princeton University
9. Princeton University, Fall (wd:Q105535923): painting by Paul Cornoyer
10. Princeton University, 1746–1946 (wd:Q60097185): scientific article published in Nature

[Tool]
Top 10 entity alternatives:
1. Princeton University Press, also known as Princeton Univ. Press (wd:Q1708210): independent publisher with close connections to Princeton University
2. Princeton University Library, also known as pulibrary (wd:Q3297681): main library system of Princeton University
3. Princeton University Stadium, also known as Powers Field at Princeton Stadium (wd:Q6587721): stadium in Princeton
4. Princeton University Chapel (wd:Q4378798): university chapel at Princeton University, Princeton, New Jersey, U.S.A.
5. Princeton University Band (wd:Q7245022): marching band and pep band of Princeton University
6. history of Princeton University, also known as Princeton University history (wd:Q5866083): aspect of history
7. Princeton University Orchestra (wd:Q60768772): Flagship symphony orchestra of Princeton University
8. Trustees of Princeton University, also known as The Trustees of Princeton University, Princeton University Trustees (wd:Q7848211): member board responsible for managing Princeton University
9. Princeton University, Fall (wd:Q105535923): painting by Paul Cornoyer
10. Princeton University, 1746–1946 (wd:Q60097185): scientific article published in Nature

[Tool]
Top 10 entity alternatives:
1. Princeton University Press, also known as Princeton Univ. Press (wd:Q1708210): independent publisher with close connections to Princeton University
2. Princeton University Library, also known as pulibrary (wd:Q3297681): main library system of Princeton University
3. Princeton University Stadium, also known as Powers Field at Princeton Stadium (wd:Q6587721): stadium in Princeton
4. Princeton University Chapel (wd:Q4378798): university chapel at Princeton University, Princeton, New Jersey, U.S.A.
5. Princeton University Band (wd:Q7245022): marching band and pep band of Princeton University
6. history of Princeton University, also known as Princeton University history (wd:Q5866083): aspect of history
7. Princeton University Orchestra (wd:Q60768772): Flagship symphony orchestra of Princeton University
8. Trustees of Princeton University, also known as The Trustees of Princeton University, Princeton University Trustees (wd:Q7848211): member board responsible for managing Princeton University
9. Princeton University, Fall (wd:Q105535923): painting by Paul Cornoyer
10. Princeton University, 1746–1946 (wd:Q60097185): scientific article published in Nature

[Tool]
Top 10 entity alternatives:
1. Incorporated Educational Institution Kokugakuin University (wd:Q11448396)
2. Kokugakuin University Tochigi Educational Institution (wd:Q11448397): private education institution of Japan
3. [Reconstruct university hospitals as educational institutions] (wd:Q77830905): scientific article published on 01 April 1999
4. educational institution, also known as educational institute, education institute, education institution, school (wd:Q2385804): institution that provides education
5. University and Society: Educatio...dom and Institutional Obligation (wd:Q106926097): scientific article
6. educational institution (wd:L3576-S6)
7. educational institution (wd:L6505-S1)
8. secondary school, also known as secondary educational institution, high school (wd:Q159334): organization where secondary education is provided
9. secondary school, also known as secondary educational institution, high school (wd:Q159334): organization where secondary education is provided
10. Princeton University Press, also known as Princeton Univ. Press (wd:Q1708210): independent publisher with close connections to Princeton University

[Assistant]
It appears that the search is not returning the main Princeton University entity, but based on Wikidata conventions, the correct entity for Princeton University is wd:Q21578.

Now, I will check if there are any properties linking WordNet (wd:Q533822) to George Armitage Miller (wd:Q670658) or Princeton University (wd:Q21578), possibly as "developer", "author", "creator", "publisher", or other relevant properties. I will also check for "affiliation" or "sponsor".

First, let's check if George Armitage Miller (wd:Q670658) is linked to WordNet in any way.

[Tool]
Got 271 rows and 3 columns, showing the first 10 rows below:
| subject              | property                               | object                                                           |
| -------------------- | -------------------------------------- | ---------------------------------------------------------------- |
| WordNet (wd:Q533822) | instance of (wdt:P31)                  | lexical database (wd:Q6537693)                                   |
| WordNet (wd:Q533822) | language of work or name (wdt:P407)    | English (wd:Q1860)                                               |
| WordNet (wd:Q533822) | full work available at URL (wdt:P953)  | <https://wordnet.princeton.edu/download/current-version>         |
| WordNet (wd:Q533822) | image (wdt:P18)                        | <http://commons.wikimedia.org/wiki/Special:FilePath/WordNet.PNG> |
| WordNet (wd:Q533822) | copyright status (wdt:P6216)           | copyrighted (wd:Q50423863)                                       |
| WordNet (wd:Q533822) | inception (wdt:P571)                   | 1998-01-01T00:00:00Z (xsd:dateTime)                              |
| WordNet (wd:Q533822) | described by source (wdt:P1343)        | WordNet: An Electronic Lexical Database (wd:Q35839029)           |
| WordNet (wd:Q533822) | copyright license (wdt:P275)           | BSD licenses (wd:Q191307)                                        |
| WordNet (wd:Q533822) | official website (wdt:P856)            | <https://wordnet.princeton.edu/>                                 |
| WordNet (wd:Q533822) | software version identifier (wdt:P348) | 3.1                                                              |
| ...                  | ...                                    | ...                                                              |

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool]
Got 1 row and 3 columns:
| subject              | property             | object                            |
| -------------------- | -------------------- | --------------------------------- |
| WordNet (wd:Q533822) | developer (wdt:P178) | Christiane Fellbaum (wd:Q4558744) |

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Assistant]
Despite checking a wide range of properties, the only direct developer listed for WordNet (wd:Q533822) is Christiane Fellbaum (wd:Q4558744). There are no direct links to George A. Miller or Princeton University in the main entity's properties.

To be thorough, I will now check the reverse direction: whether George A. Miller (wd:Q670658) or Princeton University (wd:Q21578) are linked to WordNet as a work, project, or affiliation. First, I will confirm the entity ID for Princeton University.

[Tool]
Top 10 entity alternatives:
1. Princeton University Press, also known as Princeton Univ. Press (wd:Q1708210): independent publisher with close connections to Princeton University
2. Princeton University Library, also known as pulibrary (wd:Q3297681): main library system of Princeton University
3. Princeton University Stadium, also known as Powers Field at Princeton Stadium (wd:Q6587721): stadium in Princeton
4. Princeton University Chapel (wd:Q4378798): university chapel at Princeton University, Princeton, New Jersey, U.S.A.
5. Princeton University Band (wd:Q7245022): marching band and pep band of Princeton University
6. history of Princeton University, also known as Princeton University history (wd:Q5866083): aspect of history
7. Princeton University Orchestra (wd:Q60768772): Flagship symphony orchestra of Princeton University
8. Trustees of Princeton University, also known as The Trustees of Princeton University, Princeton University Trustees (wd:Q7848211): member board responsible for managing Princeton University
9. Princeton University, Fall (wd:Q105535923): painting by Paul Cornoyer
10. Princeton University, 1746–1946 (wd:Q60097185): scientific article published in Nature

[Tool]
Top 10 entity alternatives:
1. Princeton, also known as Princeton, New Jersey, Princeton borough, Princeton, NJ, Princeton, N.J. (wd:Q138518): municipality in Mercer County, New Jersey, United States
2. Princeton, also known as Princeton, Kentucky, Princeton, KY (wd:Q2923146): city in Caldwell County, Kentucky, United States
3. Princeton, also known as Princeton, IN, Princeton, Indiana (wd:Q2179848): city and seat of Gibson County, Indiana, United States
4. Princeton, also known as Princeton, IL, Princeton, Illinois (wd:Q574878): city in and the county seat of Bureau County, Illinois, United States
5. Princeton, also known as Princeton, MN, Princeton, Minnesota (wd:Q2228008): city in Minnesota, United States
6. Princeton, also known as Princeton, IA, Princeton, Iowa (wd:Q1928154): city in Iowa, United States of America
7. Princeton, also known as Princeton, WV, Princeton, West Virginia (wd:Q747388): city in Mercer County, West Virginia, United States
8. Princeton, also known as Princeton, Texas, Princeton, TX (wd:Q620301): city in Collin County, Texas, United States
9. Princeton, also known as Princeton, Missouri, Princeton, MO (wd:Q964799): city in Mercer County, Missouri
10. Princeton, also known as Princeton, Kansas, Princeton, KS (wd:Q2343489): city in Franklin County, Kansas, United States

[Tool]
Top 10 entity alternatives:
1. Princeton University Press, also known as Princeton Univ. Press (wd:Q1708210): independent publisher with close connections to Princeton University
2. Princeton University Library, also known as pulibrary (wd:Q3297681): main library system of Princeton University
3. Princeton University Stadium, also known as Powers Field at Princeton Stadium (wd:Q6587721): stadium in Princeton
4. Princeton University Chapel (wd:Q4378798): university chapel at Princeton University, Princeton, New Jersey, U.S.A.
5. University Hall, also known as University Main Building (wd:Q10709729): the main building of Uppsala University in Uppsala, Sweden
6. Princeton University Band (wd:Q7245022): marching band and pep band of Princeton University
7. history of Princeton University, also known as Princeton University history (wd:Q5866083): aspect of history
8. Princeton University Orchestra (wd:Q60768772): Flagship symphony orchestra of Princeton University
9. University Hall-Old Main (wd:Q110974155): university building in Saint Paul, Minnesota
10. Trustees of Princeton University, also known as The Trustees of Princeton University, Princeton University Trustees (wd:Q7848211): member board responsible for managing Princeton University

[Tool]
Top 10 entity alternatives:
1. Princeton University Press, also known as Princeton Univ. Press (wd:Q1708210): independent publisher with close connections to Princeton University
2. Princeton University Library, also known as pulibrary (wd:Q3297681): main library system of Princeton University
3. Princeton University Stadium, also known as Powers Field at Princeton Stadium (wd:Q6587721): stadium in Princeton
4. Princeton University Chapel (wd:Q4378798): university chapel at Princeton University, Princeton, New Jersey, U.S.A.
5. Princeton University Band (wd:Q7245022): marching band and pep band of Princeton University
6. history of Princeton University, also known as Princeton University history (wd:Q5866083): aspect of history
7. Princeton University Orchestra (wd:Q60768772): Flagship symphony orchestra of Princeton University
8. Trustees of Princeton University, also known as The Trustees of Princeton University, Princeton University Trustees (wd:Q7848211): member board responsible for managing Princeton University
9. Princeton University, Fall (wd:Q105535923): painting by Paul Cornoyer
10. Princeton University, 1746–1946 (wd:Q60097185): scientific article published in Nature

[Tool]
Top 10 entity alternatives:
1. Bournemouth University, also known as Bournemouth Municipal College, Bournemouth University Higher Education Corporation (wd:Q3182943): university in Bournemouth, Christchurch and Poole, UK
2. University Higher Education National Authority, also known as SUNEDU (wd:Q24960120): Public organism in Peru
3. university-type higher education institution (wd:Q9364387)
4. Higher Technological and University Education (wd:Q59091844): scientific article published in Nature
5. Higher education: The university experiment (wd:Q58926041): scientific article published in Nature
6. university and higher education teachers (wd:Q108289393)
7. university and higher education teachers (wd:Q108305371)
8. University and Higher Technical Education (wd:Q60133668): scientific article published in Nature
9. university (institution of higher education) (wd:L1358901-S1)
10. Times Higher Education World University Rankings (wd:Q1318318): annual publication of university rankings

[Tool]
Got 406 rows and 3 columns, showing the first 10 rows below:
| subject                             | property                         | object                                    |
| ----------------------------------- | -------------------------------- | ----------------------------------------- |
| George Armitage Miller (wd:Q670658) | instance of (wdt:P31)            | human (wd:Q5)                             |
| George Armitage Miller (wd:Q670658) | occupation (wdt:P106)            | psychologist (wd:Q212980)                 |
| George Armitage Miller (wd:Q670658) | sex or gender (wdt:P21)          | male (wd:Q6581097)                        |
| George Armitage Miller (wd:Q670658) | given name (wdt:P735)            | George (wd:Q15921732)                     |
| George Armitage Miller (wd:Q670658) | date of birth (wdt:P569)         | 1920-02-03T00:00:00Z (xsd:dateTime)       |
| George Armitage Miller (wd:Q670658) | country of citizenship (wdt:P27) | United States (wd:Q30)                    |
| George Armitage Miller (wd:Q670658) | family name (wdt:P734)           | Miller (wd:Q1605060)                      |
| George Armitage Miller (wd:Q670658) | place of birth (wdt:P19)         | Charleston (wd:Q44564)                    |
| George Armitage Miller (wd:Q670658) | date of death (wdt:P570)         | 2012-07-22T00:00:00Z (xsd:dateTime)       |
| George Armitage Miller (wd:Q670658) | educated at (wdt:P69)            | George Washington University (wd:Q432637) |
| ...                                 | ...                              | ...                                       |

[Tool]
Got 1 row and 3 columns:
| subject                             | property                | object                     |
| ----------------------------------- | ----------------------- | -------------------------- |
| George Armitage Miller (wd:Q670658) | notable work (wdt:P800) | Miller's law (wd:Q1542532) |

[Tool]
Got 2 rows and 3 columns:
| subject                             | property                 | object                           |
| ----------------------------------- | ------------------------ | -------------------------------- |
| George Armitage Miller (wd:Q670658) | field of work (wdt:P101) | cognitive science (wd:Q147638)   |
| George Armitage Miller (wd:Q670658) | field of work (wdt:P101) | cognitive psychology (wd:Q23373) |

[Tool]
Got 5 rows and 3 columns:
| subject                             | property            | object                                            |
| ----------------------------------- | ------------------- | ------------------------------------------------- |
| George Armitage Miller (wd:Q670658) | employer (wdt:P108) | University of Oxford (wd:Q34433)                  |
| George Armitage Miller (wd:Q670658) | employer (wdt:P108) | Massachusetts Institute of Technology (wd:Q49108) |
| George Armitage Miller (wd:Q670658) | employer (wdt:P108) | The Rockefeller University (wd:Q270272)           |
| George Armitage Miller (wd:Q670658) | employer (wdt:P108) | wd:Q13371                                         |
| George Armitage Miller (wd:Q670658) | employer (wdt:P108) | wd:Q21578                                         |

[Tool]
Got 895 rows and 3 columns, showing the first 10 rows below:
| subject   | property                                                    | object                                                                                |
| --------- | ----------------------------------------------------------- | ----------------------------------------------------------------------------------------- |
| wd:Q21578 | instance of (wdt:P31)                                       | private university (wd:Q902104)                                                           |
| wd:Q21578 | country (wdt:P17)                                           | United States (wd:Q30)                                                                    |
| wd:Q21578 | located in the administrative territorial entity (wdt:P131) | Princeton (wd:Q138518)                                                                    |
| wd:Q21578 | coordinate location (wdt:P625)                              | POINT(-74.656111 40.345278) (geo:wktLiteral)                                              |
| wd:Q21578 | Commons category (wdt:P373)                                 | Princeton University                                                                      |
| wd:Q21578 | image (wdt:P18)                                             | <http://commons.wikimedia.org/wiki/Special:FilePath/ClevelandTowerWatercolor20060829.jpg> |
| wd:Q21578 | location (wdt:P276)                                         | Princeton (wd:Q138518)                                                                    |
| wd:Q21578 | inception (wdt:P571)                                        | 1746-01-01T00:00:00Z (xsd:dateTime)                                                       |
| wd:Q21578 | described by source (wdt:P1343)                             | Encyclopædia Britannica 11th edition (wd:Q867541)                                         |
| wd:Q21578 | official name (wdt:P1448)                                   | Princeton University (en)                                                                 |
| ...       | ...                                                         | ...                                                                                |

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Assistant]
After an extensive search, the only direct developer listed for WordNet (wd:Q533822) in Wikidata is Christiane Fellbaum (wd:Q4558744). There are no direct links to George A. Miller (wd:Q670658) or Princeton University (wd:Q21578) as developers, creators, authors, or publishers of WordNet in the current Wikidata data.

However, George A. Miller is listed as an employee of Princeton University, and it is well-known from external sources that he led the team that developed WordNet. Wikidata's current structure does not directly encode this information in the properties of the WordNet item.

Given the limitations of the data, the best possible SPARQL query to answer "Who developed WordNet?" is to retrieve all entities linked via the 'developer' property, and optionally their labels.

Here is the refined query, which will return all developers listed in Wikidata for WordNet:

```sparql
SELECT ?developer ?developerLabel WHERE {
  wd:Q533822 wdt:P178 ?developer .
  OPTIONAL { ?developer rdfs:label ?developerLabel FILTER (lang(?developerLabel) = "en") }
}
```

This will return Christiane Fellbaum. Unfortunately, George A. Miller and Princeton University are not currently linked as developers in Wikidata for WordNet.

If you would like to improve Wikidata, you could consider adding these relationships.

Final answer:

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query is correct in that it retrieves the developer(s) of WordNet according to Wikidata using the property 'developer' (wdt:P178). However, the answer should clarify that the result is based solely on Wikidata's current data, which may be incomplete. Notably, George A. Miller and Princeton University are widely recognized as key contributors to WordNet, but they are not currently linked via the 'developer' property in Wikidata. The answer should mention this limitation more explicitly and suggest that Wikidata may need updating to reflect the broader consensus. The query itself is fine, but the answer should be more transparent about the data source's limitations.

[Tool] Stopped generation process


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

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

The highest point of Norway is Galdhøpiggen (wd:Q203942).

[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 http://amur:7001

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 break down the question:

1. The user wants to know the highest point of Norway.
2. We need to identify the entity for Norway and the property for "highest point".
3. Then, we need to find the value for this property for Norway.

First, I'll search for the entity "Norway" in Wikidata.

[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 correct entity for the country Norway is wd:Q20.

Next, I need to find the property that represents the "highest point" of a country. I'll search for the property "highest point" in Wikidata.

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

[Assistant]
The property for "highest point" is wd:P610.

Now, I will construct a SPARQL query to find the highest point (wd:P610) of Norway (wd:Q20). I will also try to get the label of the highest point for better readability, but the main result will be the IRI.

Let's proceed to execute a query that retrieves the highest point of Norway.

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

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves the highest point of Norway using the appropriate property (wdt:P610) and returns the IRI of the item as required. The answer matches the expected result, and the query structure is correct. No further refinement is needed.


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

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 http://amur:7001

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 break down the question:

1. The entity in question is the "Weimar Republic".
2. The property we are interested in is the "start date" or "inception".

First, I will search for the entity "Weimar Republic" in Wikidata to get its identifier.

[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 main entity for the Weimar Republic is wd:Q41304.

Next, I need to find the property that represents the "start date" or "inception" for this entity. I will search for properties related to "start date" for the Weimar Republic.

[Tool]
Top 10 property alternatives:
1. inception, also known as initial release date, founded in, founded on, founded on date, founding date (wd:P571 as p/wdt): time when an entity begins to exist; for date of official opening use P1619, start time
2. dissolved, abolished or demolished date, also known as ended, publication end date, final issue, date repealed, final year (wd:P576 as p/wdt): point in time at which the subject (organisation, building) ceas...g equipment,  "discontinued date" (P2669) for stopping a product, end time
3. anthem, also known as national anthem, team anthem, official song, official hymn, club anthem (wd:P85 as p/wdt): subject's official anthem

No other items found

[Assistant]
The property "inception" (wdt:P571) is the correct one to use for the start date of the Weimar Republic.

Now, I will write a SPARQL query to retrieve the inception date of the Weimar Republic (wd:Q41304).

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

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves the inception date (P571) of the Weimar Republic (wd:Q41304), which matches the expected answer. The query returns the IRI of the item and the date in the correct format. No further refinement is needed.


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

The Port of Rotterdam employs 385,000 people according to Wikidata.

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

You have access to the following knowledge graphs:
wikidata at http://amur:7001

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]
First, I need to identify the correct entity for the "Port of Rotterdam" in Wikidata. Then, I need to find the relevant property that represents the number of employees or people employed by an organization.

Step 1: Search for the entity "Port of Rotterdam".

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

[Assistant]
There are two relevant entities:
- Port of Rotterdam (wd:Q1027807): the physical port
- Port of Rotterdam Authority (wd:Q2187039): the organization managing the port

Since the question is about employment, it is more likely referring to the Port of Rotterdam Authority (wd:Q2187039), as the port itself does not employ people directly.

Next, I need to find the property that represents the number of employees for an organization. I will search for properties related to "number of employees".

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

[Assistant]
The most relevant property is "employees" (wd:P1128), which represents the number of employees of an organization.

Now, I will check if the Port of Rotterdam Authority (wd:Q2187039) has a value for the "employees" (wd:P1128) property.

[Tool] Got no rows and 3 columns

[Assistant]
It appears that there is no direct value for the number of employees (wd:P1128) for the Port of Rotterdam Authority (wd:Q2187039) in Wikidata.

To ensure completeness, I will also check if the general "Port of Rotterdam" entity (wd:Q1027807) has a value for the "employees" property.

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

[Tool]
Got 1 row and 2 columns:
| port                            | employees            |
| ------------------------------- | -------------------- |
| Port of Rotterdam (wd:Q1027807) | 385000 (xsd:decimal) |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves the number of employees (wdt:P1128) for the Port of Rotterdam (wd:Q1027807) from Wikidata. The answer matches the query result, and the query returns the IRI as required. No further refinement is necessary.


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

Got 1 row and 1 column:
| count        |
| ------------ |
| 47 (xsd:int) |

There were 47 other musical films released in the same year as Grease (1978).

[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 http://amur:7001

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 break down the question:

1. We need to identify the entity for "Grease" (the film).
2. We need to find the release year of "Grease".
3. We need to find other musical films released in the same year.
4. We need to count how many such films there are (excluding "Grease" itself).

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 relevant entity for "Grease" (the 1978 film) is wd:Q267721.

Next, let's find the release year of this film.

[Tool]
Got 524 rows and 3 columns, showing the first 10 rows below:
| subject             | property                     | object                              |
| ------------------- | ---------------------------- | ----------------------------------- |
| Grease (wd:Q267721) | instance of (wdt:P31)        | film (wd:Q11424)                    |
| Grease (wd:Q267721) | title (wdt:P1476)            | Grease (en)                         |
| Grease (wd:Q267721) | publication date (wdt:P577)  | 1978-06-13T00:00:00Z (xsd:dateTime) |
| Grease (wd:Q267721) | Commons category (wdt:P373)  | Grease (film)                       |
| Grease (wd:Q267721) | part of (wdt:P361)           | National Film Registry (wd:Q823422) |
| Grease (wd:Q267721) | collection (wdt:P195)        | Museum of Modern Art (wd:Q188740)   |
| Grease (wd:Q267721) | country of origin (wdt:P495) | United States (wd:Q30)              |
| Grease (wd:Q267721) | official website (wdt:P856)  | <http://www.greasemovie.com>        |
| Grease (wd:Q267721) | genre (wdt:P136)             | romantic comedy (wd:Q860626)        |
| Grease (wd:Q267721) | cast member (wdt:P161)       | John Travolta (wd:Q80938)           |
| ...                 | ...                          | ...                                 |

[Assistant]
The release date of "Grease" is 1978-06-13, so the release year is 1978.

Now, we need to find other musical films released in 1978. First, let's identify the Wikidata entity for "musical film" to use as a genre or instance.

[Tool]
Top 10 entity alternatives:
1. musical film, also known as film musical, musical movie (wd:Q842256): film genre
2. musical film (wd:L428534-S1)
3. Musical Films (wd:Q121091316): production company
4. Theorising the film musical (wd:Q57622181): doctoral thesis
5. College Course Film: THE HOLLYWOOD MUSICAL (wd:Q108309950): journal article from 'Journal of the University Film and Video Association' published in 1983
6. The Beatles. Una filmografía musical (wd:Q125618465): 1995 edition of written work by Magí Crusells
7. The Beatles. Una filmografía musical (wd:Q125618437): written work by Magí Crusells
8. High School Musical: El Desafio (Mexican film), also known as High School Musical: el desafío (wd:Q2636649): 2008 film directed by Eduardo Ripari
9. Rock This Town Musical Film Fest Pau (wd:Q125736952)
10. film, also known as cinematic work, moving picture, movie, motion pictures, motion picture (wd:Q11424): sequence of images that give the impression of movement, stored on film stock

[Assistant]
The relevant entity for "musical film" as a genre is wd:Q842256.

Now, let's search for films that:
- Have genre (wdt:P136) "musical film" (wd:Q842256)
- Have publication date (wdt:P577) in 1978

We also need to exclude "Grease" (wd:Q267721) from the count.

Let's construct a SPARQL query to count the number of such films.

[Tool]
Got 1 row and 1 column:
| count        |
| ------------ |
| 47 (xsd:int) |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query is correct and follows all the rules: it counts the number of distinct musical films (wd:Q842256) that are instances of film (wd:Q11424), released in 1978 (using the publication date property), and excludes 'Grease' (wd:Q267721) itself. The answer is clear and matches the user's question. No further refinement is needed.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
ASK WHERE {
  {
    SELECT ( MAX( ?trenchDepthRaw ) AS ?trenchDepth ) ( MAX( ?everestHeightRaw ) AS ?everestHeight ) WHERE {
      wd:Q459173 wdt:P2044 ?trenchDepthRaw .
      wd:Q513 wdt:P2044 ?everestHeightRaw .
    }
  }
  FILTER ( ABS( ?trenchDepth ) > ?everestHeight )
}

Yes, the maximum measured depth of Challenger Deep in the Mariana Trench is greater than the maximum measured height of Mount Everest above sea level.

[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 http://amur:7001

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 break down the question:

1. We need to find the depth of the Mariana Trench.
2. We need to find the height of Mount Everest above sea level.
3. We need to compare the depth (as a positive number) of the Mariana Trench to the height of Mount Everest.
4. The answer should be True if the trench is deeper than Everest is high, otherwise False.

First, let's search for the entities "Mariana Trench" and "Mount Everest" in Wikidata to get their identifiers.

[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

[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

[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 wdt/p/wdtn): identifier from an international authority file of names, subjects, and organizations

No other items found

[Tool]
Got 1 row and 3 columns:
| subject                  | property                 | object                       |
| ------------------------ | ------------------------ | ---------------------------- |
| Mariana Trench (wd:Q510) | lowest point (wdt:P1589) | Challenger Deep (wd:Q459173) |

[Tool]
Got 289 rows and 3 columns, showing the first 10 rows below:
| subject                      | property                                       | object                                                                                |
| ---------------------------- | ---------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Challenger Deep (wd:Q459173) | instance of (wdt:P31)                          | graben (wd:Q192810)                                                                                |
| Challenger Deep (wd:Q459173) | country (p:P17)                                | s:Q459173-9e520206-4979-e429-e670-18bc2cf2c3a7                                                                                |
| Challenger Deep (wd:Q459173) | coordinate location (wdt:P625)                 | POINT(142.591667 11.373333) (geo:wktLiteral)                                                                                |
| Challenger Deep (wd:Q459173) | Commons category (wdt:P373)                    | Challenger Deep                                                                                |
| Challenger Deep (wd:Q459173) | image (wdt:P18)                                | <http://commons.wikimedia.org/wiki/Special:FilePath/Challenger%20Deep%20Dive%20071222%20by%20V%20Vescovo%20in%20DSV%20Limiting%20Factor%20-%20Southern%20Wall%20of%20Western%20Pool.jpg> |
| Challenger Deep (wd:Q459173) | part of (wdt:P361)                             | Mariana Trench (wd:Q510)                                                                                |
| Challenger Deep (wd:Q459173) | elevation above sea level (wdt:P2044)          | -10984 (xsd:decimal)                                                                                |
| Challenger Deep (wd:Q459173) | named after (wdt:P138)                         | HMS Challenger (wd:Q119397)                                                                                |
| Challenger Deep (wd:Q459173) | locator map image (wdt:P242)                   | <http://commons.wikimedia.org/wiki/Special:FilePath/Marianatrenchmap.png>                                                                                |
| Challenger Deep (wd:Q459173) | located in or next to body of water (wdt:P206) | North Pacific Ocean (wd:Q12353254)                                                                                |
| ...                          | ...                                            | ...                                                                                |

[Tool]
Top 10 property alternatives:
1. height, also known as heighth, size, depth over terrain (wd:P2048 as p): vertical length of an entity, length
2. 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
3. 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
4. size comparison diagram (wd:P8512 as p/wdt): property for the diagram file that compares the item size with the size of another known object
5. made from material, also known as material used, manufactured out of, manufactured from, made of material, made of (wd:P186 as p/wdt): material the subject or the object is made of or derived from (do not confuse with P10672 which is used for processes), uses
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. 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
8. topographic prominence, also known as prominence (wd:P2660 as p/wdt): height of a mountain or hill relative to the lowest contour line encircling it (on Earth, maximum 8,848 m), height
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. location, also known as neighbourhood, whereabouts, vilage, venue, town (wd:P276 as p/wdt): location of the object, structure or event. In the case of an ad...ity use P706. Use P7153 for locations associated with the object

No other items found

[Tool]
Got 1 row and 3 columns:
| subject                 | property                              | object                |
| ----------------------- | ------------------------------------- | --------------------- |
| Mount Everest (wd:Q513) | elevation above sea level (wdt:P2044) | 8848.86 (xsd:decimal) |

[Tool] True

[Tool] Stopped generation process

[User]
Feedback (status=retry):
The SPARQL query uses the property P2044 (elevation above sea level) for both Challenger Deep and Mount Everest. However, for Challenger Deep (the deepest point in the Mariana Trench), the depth is typically represented as a negative value (below sea level), while for Mount Everest, the elevation is a positive value (above sea level). The query uses ABS(?trenchDepth) to compare the absolute values, which is correct. However, the property P2044 is not always consistently used for underwater depths in Wikidata; sometimes P4511 (depth) or P2043 (length) is used for underwater features. It would be more robust to check for the correct property for depth for Challenger Deep. Additionally, the query should ensure that it is comparing the maximum depth of Challenger Deep (not just any value of elevation or depth). Please refine the query to use the most appropriate properties for each entity and ensure the comparison is between the maximum depth of Challenger Deep and the elevation of Mount Everest.

[Assistant]
Thank you for the feedback. Let's refine the approach step by step:

1. For Mount Everest, the elevation above sea level (P2044) is appropriate and well-maintained.
2. For Challenger Deep (the deepest point in the Mariana Trench), depth may be represented by:
   - elevation above sea level (P2044) as a negative value,
   - depth (P4511) as a positive value,
   - or possibly other properties.
3. We should check which properties are actually used for Challenger Deep and select the maximum (deepest) value if there are multiple.
4. We will compare the maximum depth (as a positive number) of Challenger Deep to the elevation of Mount Everest.

Let's first check which properties are used for depth/elevation for Challenger Deep by listing all its relevant properties and values.

[Tool]
Got 289 rows and 3 columns, showing the first 10 rows below:
| subject                      | property                                       | object                                                                                |
| ---------------------------- | ---------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Challenger Deep (wd:Q459173) | instance of (wdt:P31)                          | graben (wd:Q192810)                                                                                |
| Challenger Deep (wd:Q459173) | country (p:P17)                                | s:Q459173-9e520206-4979-e429-e670-18bc2cf2c3a7                                                                                |
| Challenger Deep (wd:Q459173) | coordinate location (wdt:P625)                 | POINT(142.591667 11.373333) (geo:wktLiteral)                                                                                |
| Challenger Deep (wd:Q459173) | Commons category (wdt:P373)                    | Challenger Deep                                                                                |
| Challenger Deep (wd:Q459173) | image (wdt:P18)                                | <http://commons.wikimedia.org/wiki/Special:FilePath/Challenger%20Deep%20Dive%20071222%20by%20V%20Vescovo%20in%20DSV%20Limiting%20Factor%20-%20Southern%20Wall%20of%20Western%20Pool.jpg> |
| Challenger Deep (wd:Q459173) | part of (wdt:P361)                             | Mariana Trench (wd:Q510)                                                                                |
| Challenger Deep (wd:Q459173) | elevation above sea level (wdt:P2044)          | -10984 (xsd:decimal)                                                                                |
| Challenger Deep (wd:Q459173) | named after (wdt:P138)                         | HMS Challenger (wd:Q119397)                                                                                |
| Challenger Deep (wd:Q459173) | locator map image (wdt:P242)                   | <http://commons.wikimedia.org/wiki/Special:FilePath/Marianatrenchmap.png>                                                                                |
| Challenger Deep (wd:Q459173) | located in or next to body of water (wdt:P206) | North Pacific Ocean (wd:Q12353254)                                                                                |
| ...                          | ...                                            | ...                                                                                |

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool]
Got 1 row and 3 columns:
| subject                      | property                              | object               |
| ---------------------------- | ------------------------------------- | -------------------- |
| Challenger Deep (wd:Q459173) | elevation above sea level (wdt:P2044) | -10984 (xsd:decimal) |

[Tool]
Got 1 row and 3 columns:
| subject                 | property                              | object                |
| ----------------------- | ------------------------------------- | --------------------- |
| Mount Everest (wd:Q513) | elevation above sea level (wdt:P2044) | 8848.86 (xsd:decimal) |

[Assistant]
Based on the data:
- Challenger Deep (wd:Q459173) only has elevation above sea level (wdt:P2044) with a value of -10984 meters (no depth or length properties are present).
- Mount Everest (wd:Q513) has elevation above sea level (wdt:P2044) with a value of 8848.86 meters.

Therefore, the most robust query will:
- Use the absolute value of Challenger Deep's elevation (as depth).
- Compare it to Mount Everest's elevation.

To ensure robustness, I will use MAX() in case there are multiple values for either property (e.g., due to multiple measurements).

Here is the refined SPARQL query:

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly uses an ASK query to compare the maximum depth of Challenger Deep (Mariana Trench) and the maximum height of Mount Everest above sea level, using the appropriate Wikidata entities and the 'elevation above sea level' property. The query returns a boolean result as required for this type of question. All rules are followed, and the answer is correct and complete.