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 ?predecessor WHERE {
  wd:Q1115291 wdt:P1365 ?predecessor .
  OPTIONAL {
    ?predecessor rdfs:label ?label FILTER ( lang( ?label ) = "en" )
  }
}

The kingdom that preceded the Thonburi Kingdom was the Ayutthaya Kingdom (wd:Q841364).

[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] Which kingdom preceded the Thonburi Kingdom ?

[Assistant]
Let's break down the question:

1. The user is asking for the kingdom that existed before the Thonburi Kingdom.
2. First, I need to identify the entity for "Thonburi Kingdom" in Wikidata.
3. Then, I need to find the property that links a country or kingdom to its predecessor.
4. Finally, I will write a SPARQL query to retrieve the predecessor kingdom.

Step 1: Search for the entity "Thonburi Kingdom".

[Tool]
Top 10 entity alternatives:
1. Thonburi Kingdom (wd:Q1115291): former country
2. kingdom, also known as regna, regnum (wd:Q36732): taxonomic rank
3. realm, also known as kingdom, kingdom (politics) (wd:Q1250464): denomination of a particular political entity, which is theoreti...ts political regime; but not necessarily identified with a state
4. The Kingdom, also known as Kingdom (wd:Q467582): 2007 film directed by Peter Berg
5. Kingdom (wd:Q30947892): 2019 South Korean TV series
6. kingdom, also known as realm (wd:Q417175): state over which a king or queen rules
7. Thonburi, also known as Thonburi Si Mahasamut (wd:Q6580711): area of Bangkok
8. Kingdom, also known as Kingudamu (wd:Q1058984): Japanese manga series
9. Kingdom (wd:Q18328031): 2014 TV series
10. Finnjet, also known as GTS Finnjet, IMO 7359632, Kingdom (wd:Q1418193): ship built in 1977

[Assistant]
The correct entity for "Thonburi Kingdom" is wd:Q1115291.

Next, I need to find the property that links a country or kingdom to its predecessor. Commonly, this is "preceded by" or a similar property. I will search for properties related to "preceded by".

[Tool]
Top 10 property alternatives:
1. follows, also known as prev, successor to, succeeds to, succeeds, split from (wd:P155 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): immediately prior item in a series of which the subject is a par... preceding item, e.g. political offices, use "replaces" (P1365)]
2. replaces, also known as supersedes, succeeds, replaced, previous job holder, preceded by (wd:P1365 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): person, state or item replaced. Use "structure replaces" (P1398)...item was not replaced or predecessor and successor are identical, follows
3. followed by, also known as continued as, then, successor, succeeded by, sequel is (wd:P156 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): immediately following item in a series of which the subject is a...een replaced, e.g. political offices, use "replaced by" (P1366)]
4. appears after lexeme form, also known as follows lexeme form, preceded by lexeme form (wd:P11953 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): this lexeme form appears only when it comes after this other specific lexeme form
5. appears before lexeme form, also known as precedes lexeme form (wd:P11952 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): this lexeme form appears only when this other specific lexeme form comes after it
6. latest date, also known as terminus ante quem, prior to, on or before this date, no later than, max date (wd:P1326 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): latest possible time that something could have occurred. Use as qualifier for other date properties
7. appears after phonological feature, also known as follows phonological feature, preceded by phonological feature (wd:P11951 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): this lexeme form appears only when it comes after another lexeme form with this phonological feature
8. partially coincident with, also known as intersecting with, has overlap with, has common parts with, has common members with, has common instances with (wd:P1382 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): object that partially overlaps with the subject in its instances, parts, or members, different from
9. founded by, also known as starter, co-founded by, started by, institutor, instituted by (wd:P112 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): founder or co-founder of this organization, religion, place or entity, creator
10. identifies, also known as uniquely determines, is unique key for, is primary key for, is identifier of, identifier of (wd:P10476 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): this item uniquely and purposefully distinguishes instances of this class from each other

[Assistant]
The most relevant property for "preceded by" is likely "replaces" (wd:P1365) or "follows" (wd:P155). However, "replaces" (wd:P1365) is described as "person, state or item replaced," which matches the idea of a predecessor.

To be sure, I will check which of these properties is used for the Thonburi Kingdom entity.

[Tool]
Got 157 rows and 3 columns, showing the first 10 rows below:
| subject                        | property                                           | object                                                                                |
| ------------------------------ | -------------------------------------------------- | --------------------------------------------------------------------------------------- |
| Thonburi Kingdom (wd:Q1115291) | instance of (wdt:P31)                              | historical country (wd:Q3024240)                                                        |
| Thonburi Kingdom (wd:Q1115291) | Commons category (wdt:P373)                        | Thonburi Kingdom                                                                        |
| Thonburi Kingdom (wd:Q1115291) | inception (wdt:P571)                               | 1768-01-01T00:00:00Z (xsd:dateTime)                                                     |
| Thonburi Kingdom (wd:Q1115291) | followed by (wdt:P156)                             | Rattanakosin Kingdom (wd:Q1155700)                                                      |
| Thonburi Kingdom (wd:Q1115291) | topic's main category (wdt:P910)                   | wd:Q10210525                                                                            |
| Thonburi Kingdom (wd:Q1115291) | religion or worldview (wdt:P140)                   | Theravāda (wd:Q132265)                                                                  |
| Thonburi Kingdom (wd:Q1115291) | dissolved, abolished or demolished date (wdt:P576) | 1782-01-01T00:00:00Z (xsd:dateTime)                                                     |
| Thonburi Kingdom (wd:Q1115291) | locator map image (wdt:P242)                       | <http://commons.wikimedia.org/wiki/Special:FilePath/Kingdom%20of%20thonburi%201782.png> |
| Thonburi Kingdom (wd:Q1115291) | capital (wdt:P36)                                  | Thonburi (wd:Q6580711)                                                                  |
| Thonburi Kingdom (wd:Q1115291) | replaces (wdt:P1365)                               | Ayutthaya Kingdom (wd:Q841364)                                                          |
| ...                            | ...                                                | ...                                                                                |

[Assistant]
From the results, the property "replaces" (wdt:P1365) is used to indicate the predecessor of the Thonburi Kingdom, and its value is "Ayutthaya Kingdom" (wd:Q841364).

To answer the question, I will write a SPARQL query that retrieves the kingdom replaced by the Thonburi Kingdom, returning its IRI and optionally its label.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves the predecessor of the Thonburi Kingdom using the appropriate property (wdt:P1365, 'replaces'/'preceded by'). The result matches the expected answer, and the query returns the IRI of the predecessor as required. The use of OPTIONAL for the label is appropriate and does not interfere with the main result. No further refinement is necessary.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?dateOfOpening WHERE {
  wd:Q693956 wdt:P1619 ?dateOfOpening
}

The Hamburg S-Bahn was officially opened on January 1, 1906.

[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 was the Hamburg S-Bahn officially opened?

[Assistant]
Let's break down the question:

1. The user wants to know the official opening date of the Hamburg S-Bahn.
2. First, I need to identify the correct entity for "Hamburg S-Bahn" in Wikidata.
3. Then, I need to find the property that represents the "official opening date" or similar (e.g., "inception", "start time", "opening date").
4. Finally, I will construct a SPARQL query to retrieve this information.

First, I will search for the entity "Hamburg S-Bahn" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Hamburg S-Bahn (wd:Q693956): metro railway in Hamburg, Germany
2. Hamburg City S-Bahn, also known as City-S-Bahn, City-Tunnel (wd:Q321980): railway tunnel
3. S-Bahnhof Hamburg-Altona station, also known as Altona (S-Bahn), Hamburg-Altona(S), Hamburg-Altona (S-Bahn) (wd:Q70585725): railway station in Germany
4. S-Bahn Hamburg GmbH (wd:Q99869216): German railway company operating the Hamburg S-Bahn
5. Hamburg Airport S-Bahn line (wd:Q1430696): railway line in Germany
6. Train Simulator: Hamburg S1 S-Bahn Route Add-On (wd:Q108105621): Train Simulator DLC
7. Hamburg, also known as Free and Hanseatic City of Hamburg, Hamburg, Germany, Hamburg, Freie und Hansestadt (wd:Q1055): city and state in the North of Germany
8. Hamburger SV, also known as Hamburg SV, HSV, H.S.V., Hamburg Sport Club, HamburgerSV (wd:Q51974): German sports club based in Hamburg, with its largest branch being its football department
9. Hamburg, also known as Hamburg, IA, Hamburg, Iowa (wd:Q998718): city in Fremont County, Iowa, United States
10. Hamburg, also known as Hamburg, AR, Hamburg, Arkansas (wd:Q79657): city in Ashley County, Arkansas, United States

[Assistant]
The most relevant entity for "Hamburg S-Bahn" is wd:Q693956 (Hamburg S-Bahn: metro railway in Hamburg, Germany).

Next, I need to find the property that represents the "official opening date" or similar for this entity. Common properties for this are "inception", "start time", or "opening date". I will search for properties related to "opening" for this entity.

[Tool]
Top 10 property alternatives:
1. date of official opening, also known as launch date, launched, officially opened on, official opening, official opening time (wd:P1619 as p/wdt): date or point in time a place, organization, or event officially opened, start time, inception
2. inception, also known as initial release date, founded in, founded on, founded on date, founding date (wd:P571 as p/wdt): time when an entity begins to exist; for date of official opening use P1619, start time
3. operator, also known as offered by, operated by, provided by, run by, service operator (wd:P137 as p/wdt): person, profession, organization or entity that operates the equipment, facility, or service, used by
4. image, also known as still capture, still, screenshot, screen capture, portrait (wd:P18 as p/wdt): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)
5. located in the administrative territorial entity, also known as is in the settlement of, is in the voivodeship of, is in the shire of, is in the state of, is in the territory of (wd:P131 as p/wdt): the item is located on the territory of the following administra...if the item falls only partially into the administrative entity., location, part of
6. logo image, also known as masthead, wordmark, trademark, trade mark, sign (wd:P154 as p/wdt): graphic mark or emblem commonly used by commercial enterprises, organizations and products, icon
7. 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)
8. official website, also known as site (official), home page, site official, URL, URL (official) (wd:P856 as p/wdt): URL of the official page of an item (current or former). Usage: ...but see the "Hijacked or dead websites" section of the Talk page, URL
9. part of, also known as of, within, system of, subsystem of, subgroup of (wd:P361 as p/wdt): object of which the subject is a part (if this subject is alread... of "has part" (P527, see also "has parts of the class" (P2670))
10. Freebase ID, also known as Freebase, MID, Freebase identifier, FID (wd:P646 as p/wdt): identifier for a page in the Freebase database. Format: "/m/0" f...r IDs starting with "/g/", use Google Knowledge Graph ID (P2671)

No other items found

[Assistant]
The most relevant properties for the official opening date are:
- "date of official opening" (wdt:P1619)
- "inception" (wdt:P571), but the description suggests to prefer P1619 for official opening.

Therefore, I will query the "date of official opening" (wdt:P1619) for the Hamburg S-Bahn (wd:Q693956). I will also include the label as optional information.

[Tool]
Got 1 row and 1 column:
| dateOfOpening                       |
| ----------------------------------- |
| 1906-01-01T00:00:00Z (xsd:dateTime) |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves the official opening date of the Hamburg S-Bahn using the appropriate entity (wd:Q693956) and property (wdt:P1619). The result matches the answer provided. The query is concise and follows all the required rules. No further refinement is needed.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
ASK {
  wd:Q645476 wdt:P576 ?dissolved
}

No, the production company of Samurai Champloo (Manglobe) is no longer existing. It was dissolved on September 30, 2015.

[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 production company of samurai champloo still existing?

[Assistant]
Let's break down the question:

1. We need to identify the production company of "Samurai Champloo".
2. Then, we need to check if that company still exists (i.e., is not dissolved or defunct).

Step 1: Search for the entity "Samurai Champloo" in Wikidata to get its identifier.

[Tool]
Top 10 entity alternatives:
1. Samurai Champloo (wd:Q143276): Japanese anime television series
2. Samurai Champloo (wd:Q66138325): Japanese manga series
3. Samurai Champloo: Sidetracked, also known as Samurai Champloo HIPHOP Samurai Action (wd:Q3548610): 2006 video game
4. Samurai Champloo soundtrack releases, also known as Music of Samurai Champloo (wd:Q106856784): Wikipedia article covering multiple soundtrack releases
5. Samurai Champloo Music Record: Departure, also known as Departure (wd:Q7413091): 2004 soundtrack album by Nujabes / Fat Jon
6. Samurai Champloo Music Record: Impression, also known as Impression (wd:Q3149468): 2004 soundtrack album by Force of Nature / Nujabes / Fat Jon
7. Samurai Champloo Music Record: Masta, also known as Masta (wd:Q104829750): 2004 soundtrack album by FORCE OF NATURE / tsutchie
8. Samurai Champloo Music Record: Playlist (wd:Q104829743): 2004 soundtrack album by tsutchie
9. Postmodern Is Old Hat: Samurai Champloo (wd:Q56608596): article
10. samurai, also known as buke, bushi (wd:Q38142): military nobility of pre-industrial Japan

[Assistant]
The most relevant entity for the anime series "Samurai Champloo" is wd:Q143276.

Next, I need to find the production company for this anime. I will search for properties related to "production company" for this entity.

[Tool]
Top 10 property alternatives:
1. production company, also known as broadcasting company, theatrical troupe, theatre company, theater company, studio (wd:P272 as p/wdt): company that produced this film, audio or performing arts work
2. director, also known as film director, movie director, game director, directed by (wd:P57 as p/wdt): director(s) of film, TV-series, stageplay, video game or similar, contributor to the creative work or subject, creator
3. genre, also known as genre of music, type of music, type of film, music genre, literary genre (wd:P136 as p/wdt): creative work's genre or an artist's field of work (P101). Use main subject (P921) to relate creative works to their topic
4. derivative work, also known as is source work of, is adapted as, is the basis for, iteration, reboot (wd:P4969 as p/wdt): new work of art (film, book, software, etc.) derived from major part of this work
5. screenwriter, also known as story by, teleplay by, video game writer, writer (of screenplays), writing credits (wd:P58 as p/wdt): person(s) who wrote the script for subject item, film crew member, author
6. country of origin, also known as origin country, place of origin, originates from, country, CoO (wd:P495 as p/wdt): country of origin of this item (creative work, food, phrase, product, etc.), significant place, country
7. original language of film or TV show, also known as media language, original language, language of the original work, created on language (wd:P364 as p/wdt): language in which a film or a performance work was originally cr... works and songs; use P407 ("language of work or name") instead., language of work or name
8. Kinobox film ID, also known as www.kinobox.cz/film/, kinobox.cz/film/, https://www.kinobox.cz/film/ (wd:P12096 as p/wdt): identifier for a film on the Kinobox.cz website
9. distributed by, also known as distributor (wd:P750 as p/wdt): distributor of a creative work; distributor for a record label; news agency; film distributor
10. original broadcaster, also known as TV channel, television channel, radio station, radio network, original network (wd:P449 as p/wdt): network(s) or service(s) that originally broadcast a radio or television program, broadcast by

No other items found

[Assistant]
The relevant property for "production company" is wdt:P272.

Now, I will check which company is listed as the production company for "Samurai Champloo" (wd:Q143276) using this property.

[Tool]
Got 1 row and 1 column:
| company               |
| --------------------- |
| Manglobe (wd:Q645476) |

[Assistant]
The production company for "Samurai Champloo" is Manglobe (wd:Q645476).

Next, I need to check if Manglobe still exists. This typically involves checking for properties like "dissolved", "defunct", or similar. I will search for properties related to "dissolved" or "defunct" for Manglobe.

[Tool]
Top 10 property alternatives:
1. 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
2. replaced by, also known as heir, superseded by, successor, succeeded by, next job holder (wd:P1366 as p/wdt): other person or item which continues the item by replacing it in... nor identical, but adds to the series (e.g. books in a series)., followed by
3. 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
4. location of formation, also known as place of incorporation, originates from, place of establishment, place of formation, place of foundation (wd:P740 as p/wdt): location where a group or organization was formed, location of creation

No other items found

[Tool]
Got 1 row and 1 column:
| dissolved                           |
| ----------------------------------- |
| 2015-09-30T00:00:00Z (xsd:dateTime) |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The ASK query checks if the production company (Manglobe, wd:Q645476) has a dissolved date (wdt:P576), which directly answers whether the company still exists. The answer and explanation are accurate and supported by the SPARQL query and its result.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ( COUNT( ?invasion ) AS ?numInvasions ) WHERE {
  wd:Q208156 wdt:P527 ?invasion
}

Got 1 row and 1 column:
| numInvasions |
| ------------ |
| 2 (xsd:int)  |

The Mongols tried to invade Japan twice, as represented by the Battle of Bun'ei and the Battle of Kōan.

[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 often did the Mongols try to invade Japan?

[Assistant]
Let's break down the question:

1. The user wants to know how many times the Mongols attempted to invade Japan.
2. We need to identify the entities for "Mongols," "Japan," and the concept of "invasion attempts."
3. We should look for historical events (likely "invasions" or "military campaigns") where the Mongols were the attackers and Japan was the target.

First, let's search for the entity "Mongol invasions of Japan" in Wikidata, as this may directly correspond to the historical events in question.

[Tool]
Top 10 entity alternatives:
1. Mongol invasions of Japan, also known as Attempted Mongol Invasions, 1274-1281, Japan--History--Attempted Mongol Invasions, 1274-1281 (wd:Q208156): attempts by the Mongol Empire to conquer Japan
2. Mongol invasions of Korea (wd:Q702266): Mongol Yuán Conquests of the Korean peninsula
3. Mongol invasions of Georgia, also known as Mongol conquests of Kingdom of Georgia (wd:Q2917513): 1220–1236 Mongol invasions of the Kingdom of Georgia
4. Mongol invasions of Vietnam (wd:Q2448644): 13th-century military campaigns of the Mongol Empire and Yuan-dynasty China in Vietnam
5. Mongol invasions of India (wd:Q2079940): history of Mongols with India
6. Mongol invasions of Sakhalin (wd:Q11344677): 1264 Invasions of the island Sakhalin by the Mongolian Yuan dynasty
7. Mongol invasions of Durdzuketi (wd:Q6899563): invasions of the territory of modern Chechnya and Ingushetia
8. Mongol invasions of Lithuania (wd:Q63859061)
9. Mongol invasions of Azerbaijan (wd:Q107354947)
10. Mongol invasions of Alania (wd:Q113130790)

[Assistant]
The entity "Mongol invasions of Japan" (wd:Q208156) directly corresponds to the historical attempts by the Mongol Empire to conquer Japan.

Next, I will check if this entity has properties or linked events that specify the number of invasion attempts or list the individual invasions. If there are sub-events (such as specific invasion attempts), we can count them.

I will search for properties of wd:Q208156 related to "event," "invasion," or "campaign" to find the relevant sub-events.

[Tool]
Top 10 property alternatives:
1. point in time, also known as at time, as of, year, when, time of event (wd:P585 as p/wdt): date something took place, existed or a statement was true; for providing time use the "refine date" property (P4241)
2. Commons gallery, also known as gallery (wd:P935 as p/wdt): name of the Wikimedia Commons gallery page(s) related to this item (is suitable to allow multiple links to more gallery pages)
3. 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
4. participant, also known as suspect, player, party, participants, has participant (wd:P710 as p/wdt): person, group of people or organization (object) that actively t...ct has role" (P3831). Use P1923 for participants that are teams., significant person
5. 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)
6. located in the administrative territorial entity, also known as is in the settlement of, is in the voivodeship of, is in the shire of, is in the state of, is in the territory of (wd:P131 as p/wdt): the item is located on the territory of the following administra...if the item falls only partially into the administrative entity., location, part of
7. country, also known as sovereign state, state, land, host country (wd:P17 as p/wdt): sovereign state that this item is in (not to be used for human beings), located in the administrative territorial entity
8. topic's main category, also known as article's main category, category, category for this topic, catmain, has category (wd:P910 as p/wdt): main Wikimedia category
9. 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)
10. Freebase ID, also known as Freebase, MID, Freebase identifier, FID (wd:P646 as p/wdt): identifier for a page in the Freebase database. Format: "/m/0" f...r IDs starting with "/g/", use Google Knowledge Graph ID (P2671)

No other items found

[Assistant]
The search for properties related to "event" did not directly reveal sub-events or invasion attempts. However, it's common in Wikidata for major historical events to have "has part(s)" or "part of" relationships to their sub-events.

I will now search for properties of wd:Q208156 related to "part" to see if the Mongol invasions of Japan are broken down into individual invasion attempts as parts of the main event.

[Tool]
Top 10 property alternatives:
1. has part(s), also known as has component, incorporates, has ingredient, has ingredients, has member (wd:P527 as p/wdt): part of this subject; inverse property of "part of" (P361). See also "has parts of the class" (P2670).
2. part of, also known as of, within, system of, subsystem of, subgroup of (wd:P361 as p/wdt): object of which the subject is a part (if this subject is alread... of "has part" (P527, see also "has parts of the class" (P2670))
3. 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
4. located in the administrative territorial entity, also known as is in the settlement of, is in the voivodeship of, is in the shire of, is in the state of, is in the territory of (wd:P131 as p/wdt): the item is located on the territory of the following administra...if the item falls only partially into the administrative entity., location, part of
5. 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)
6. topic's main category, also known as article's main category, category, category for this topic, catmain, has category (wd:P910 as p/wdt): main Wikimedia category
7. country, also known as sovereign state, state, land, host country (wd:P17 as p/wdt): sovereign state that this item is in (not to be used for human beings), located in the administrative territorial entity
8. image, also known as still capture, still, screenshot, screen capture, portrait (wd:P18 as p/wdt): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)
9. depicted by, also known as acted by, shown in, seen in, portrayed by, played by (wd:P1299 as p/wdt): object depicting this subject (object being creative works such as books, films, paintings)
10. point in time, also known as at time, as of, year, when, time of event (wd:P585 as p/wdt): date something took place, existed or a statement was true; for providing time use the "refine date" property (P4241)

No other items found

[Tool]
Got 2 rows and 3 columns:
| subject                                | property               | object                         |
| -------------------------------------- | ---------------------- | ------------------------------ |
| Mongol invasions of Japan (wd:Q208156) | has part(s) (wdt:P527) | Battle of Kōan (wd:Q2237622)   |
| Mongol invasions of Japan (wd:Q208156) | has part(s) (wdt:P527) | Battle of Bun'ei (wd:Q2237386) |

[Tool]
Got 2 rows and 2 columns:
| invasion                       | invasionLabel         |
| ------------------------------ | --------------------- |
| Battle of Bun'ei (wd:Q2237386) | Battle of Bun'ei (en) |
| Battle of Kōan (wd:Q2237622)   | Battle of Kōan (en)   |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly counts the number of invasions by using the 'has part(s)' (wdt:P527) property on the 'Mongol invasions of Japan' entity (wd:Q208156). The result (2) matches the historical record (the two invasions: Battle of Bun'ei and Battle of Kōan). The query returns the actual count as requested by the question, and no irrelevant items are included. The answer is correct and complete.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ( COUNT( DISTINCT ?war ) AS ?warCount ) WHERE {
  wd:Q188712 wdt:P1344 ?war
}

Got 1 row and 1 column:
| warCount    |
| ----------- |
| 4 (xsd:int) |

The Empire of Japan participated in 4 wars 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 wars did the Empire of Japan participate in?

[Assistant]
First, let's break down the question:

1. We need to identify the entity for "Empire of Japan" in Wikidata.
2. We need to find the property that links a country/entity to wars it participated in.
3. We need to count the number of wars associated with the Empire of Japan.

Let's start by searching for the entity "Empire of Japan" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Empire of Japan, also known as Great Japanese Empire, Great Empire of Japan, Japanese Empire, Imperial Japan, Japan (wd:Q188712): Empire in the Asia-Pacific region from 1868 to 1947
2. The Imperial Japanese Empire, also known as Japanese Empire, The Empire of Japan, Imperial Japanese Empire, Imperial Japan, Empire of Japan (wd:Q11435655): 1982 film directed by Toshio Masuda
3. Empire of Japan (wd:Q5374199): internal politics of the Empire of Japan
4. education in the Empire of Japan, also known as education of the Empire of Japan, Empire of Japan education (wd:Q3509524): overview of the education system in the Empire of Japan
5. Empire of Japan borders (wd:Q87763038)
6. Constitution of the Empire of Japan, also known as Meiji Constitution (wd:Q122804): constitution of Japan from 1889 to 1947
7. Empire of Japan–Russian Empire relations (wd:Q16835196): bilateral diplomatic relations
8. demographics of the Empire of Japan, also known as demography of Imperial Japan (wd:Q5256018): Japan's demographics (1868 – 1945)
9. economy of the Empire of Japan (wd:Q5374198): economy of the country
10. Map of the Empire of Japan (wd:Q100273566): 1878 map of Japan

[Assistant]
The most relevant entity for "Empire of Japan" is wd:Q188712, which represents the historical state from 1868 to 1947.

Next, I need to find the property that links a country or entity to wars it participated in. I'll search for properties related to "war participant" or "conflict participant".

[Tool]
Top 10 property alternatives:
1. conflict, also known as war, theatre (military), theater (military), participated in conflict, military conflict (wd:P607 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): battles, wars or other military engagements in which the person or item participated, participant in
2. military unit, also known as member of military unit, unit (wd:P7779 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): smallest military unit that a person is/was in, member of
3. participant in, also known as participated in, present at, takes part, took part, took part in (wd:P1344 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): event in which a person or organization was/is a participant; inverse of P710 or P1923, significant event, part of
4. participant, also known as suspect, player, party, participants, has participant (wd:P710 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): person, group of people or organization (object) that actively t...ct has role" (P3831). Use P1923 for participants that are teams., significant person
5. TracesOfWar person ID, also known as TOW person ID, Traces Of War person ID (wd:P8578 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): identifier for historical persons associated with post-18th-century wars
6. victim(s), also known as sexual abuse victim, terrorist victim, shooting victim, torture victim, victims (wd:P8032 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): person(s) who were harmed from an act or event, object of occurrence, participant
7. Lives of WWI ID, also known as LivesOfWWI ID, Lives of World War I ID, Lives of the First World War ID, Lives of the 1st World War ID, IWM Lives ID (wd:P4601 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): identifier for a British or Commonwealth person who served durin...he Imperial War Museums' 'Lives of the First World War' database
8. recorded participant, also known as on-screen participant, onscreen participant, participant recorded (wd:P11108 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): agent taking an active role as a participant in a nonfiction moving image or other recorded work, participant
9. target, also known as attack target, military target, target of attack (wd:P533 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): target of an attack or military operation, object of occurrence
10. military branch, also known as unit branch, service branch, formation, branch (wd:P241 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): branch to which this military unit, award, office, or person belongs, e.g. Royal Navy, part of

[Assistant]
The most relevant properties are:
- "conflict" (wdt:P607): battles, wars, or other military engagements in which the entity participated.
- "participant in" (wdt:P1344): event in which a person or organization was/is a participant.

Since we are interested in the wars the Empire of Japan participated in, "participant in" (wdt:P1344) is likely the most direct property to use.

Next, I will check if the Empire of Japan (wd:Q188712) is linked to wars via wdt:P1344.

[Tool]
Got 4 rows and 3 columns:
| subject                      | property                   | object                                |
| ---------------------------- | -------------------------- | ------------------------------------- |
| Empire of Japan (wd:Q188712) | participant in (wdt:P1344) | Russo-Japanese War (wd:Q159950)       |
| Empire of Japan (wd:Q188712) | participant in (wdt:P1344) | Second Sino-Japanese War (wd:Q170314) |
| Empire of Japan (wd:Q188712) | participant in (wdt:P1344) | First Sino-Japanese War (wd:Q178687)  |
| Empire of Japan (wd:Q188712) | participant in (wdt:P1344) | Pacific War (wd:Q184425)              |

[Assistant]
The property "participant in" (wdt:P1344) links the Empire of Japan to four wars:
1. Russo-Japanese War
2. Second Sino-Japanese War
3. First Sino-Japanese War
4. Pacific War

To ensure completeness, I should also check if the "conflict" property (wdt:P607) links the Empire of Japan to additional wars.

[Tool] Got no rows and 3 columns

[Assistant]
The "conflict" property (wdt:P607) does not link the Empire of Japan to any wars, so "participant in" (wdt:P1344) is the correct property to use.

Now, I will construct a SPARQL query to count the number of wars the Empire of Japan participated in using wdt:P1344.

[Tool]
Got 1 row and 1 column:
| warCount    |
| ----------- |
| 4 (xsd:int) |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly counts the number of distinct wars in which the Empire of Japan (wd:Q188712) participated, using the appropriate property (wdt:P1344). The query returns the actual count as requested by the user's question, and does not include any unnecessary computation or unsupported clauses. The answer is accurate and complete for the question asked.


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 DISTINCT ?disease ?diseaseLabel WHERE {
  {
    wd:Q662860 wdt:P1542 ?disease .
  } UNION {
    ?disease wdt:P828 wd:Q662860 .
  } UNION {
    ?disease wdt:P5642 wd:Q662860 .
  }
  OPTIONAL {
    ?disease rdfs:label ?diseaseLabel .
    FILTER ( LANG( ?diseaseLabel ) = "en" )
  }
}

Got 96 rows and 2 columns, showing the first 5 and last 5 rows below:
| disease                                     | diseaseLabel                      |
| ------------------------------------------- | --------------------------------- |
| insulin resistance (wd:Q1053470)            | insulin resistance (en)           |
| tic disorder (wd:Q1077139)                  | tic disorder (en)                 |
| necrotizing enterocolitis (wd:Q10859678)    | necrotizing enterocolitis (en)    |
| cancer (wd:Q12078)                          | cancer (en)                       |
| appendicitis (wd:Q121041)                   | appendicitis (en)                 |
| ...                                         | ...                               |
| liver disease (wd:Q929737)                  | liver disease (en)                |
| purpura (wd:Q935293)                        | purpura (en)                      |
| dentine hypersensitivity (wd:Q948056)       | dentine hypersensitivity (en)     |
| skin disease (wd:Q949302)                   | skin disease (en)                 |
| smoking-related lung disease (wd:Q98608770) | smoking-related lung disease (en) |

The following diseases are associated with smoking in Wikidata, either as being caused by smoking, having smoking as a risk factor, or as an effect of smoking. This means the list includes diseases that smoking can cause, diseases for which smoking increases the risk, and diseases that are effects of smoking. If you want only diseases strictly caused by smoking, focus on those linked via the 'has cause' property (P828).

[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] Which diseases can be caused by smoking ?

[Assistant]
Let's break down the question:

1. The main entity is "smoking".
2. The question asks for "diseases" that "can be caused by" smoking.

First, I need to identify the Wikidata entity for "smoking" and the property that links causes (smoking) to effects (diseases).

Step 1: Search for the entity "smoking".

[Tool]
Top 10 entity alternatives:
1. smoking, also known as smoke (wd:Q662860): breathing in smoke after burning a substance and inhaling, done in order to be tasted and absorbed into the bloodstream
2. smoking, also known as food smoking (wd:Q193713): exposing food to the smoke to flavour or preserve it
3. Smoking/No Smoking, also known as Smoking / No Smoking (wd:Q674929): 1993 film by Alain Resnais
4. Smoking (wd:Q7546026): brand of rolling papers
5. 🚬, also known as cigarette emoji, smoking emoji, SMOKING SYMBOL, U+1F6AC (wd:Q87581997): Unicode character which may be used to communicate 'cigarette'
6. Smoking (wd:Q55525268)
7. pottery smoking, also known as Smoking (wd:Q123929966): ceramic finishing technique for waterproofing, involving firing plant elements to enhance impermeability
8. [Smoking--passive smoking] (wd:Q69790331): scientific article published on 01 January 1985
9. Smoking (wd:Q68339472): scientific article published on 01 February 1988
10. Smoking (wd:Q75942855): scientific article published on 01 December 1951

[Assistant]
The most relevant entity for "smoking" as a health behavior is wd:Q662860 ("smoking, also known as smoke: breathing in smoke after burning a substance and inhaling, done in order to be tasted and absorbed into the bloodstream").

Next, I need to find the property that links a cause (smoking) to diseases. Likely candidates are properties related to "cause", "risk factor", or "has cause".

I'll search for properties related to "cause" in Wikidata.

[Tool]
Top 10 property alternatives:
1. has cause, also known as implied by, originated due to, initial cause, outcome of, reason (wd:P828 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): underlying cause, entity that ultimately resulted in this effect, follows
2. has effect, also known as has symptom, implies, leads to, led to, outcome (wd:P1542 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): effect of this item, followed by
3. end cause, also known as cause of ending, ended by, end has cause, end reason, reason for end (wd:P1534 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): qualifier used together with the end date qualifier (P582) to specify the reason for the end
4. has immediate cause, also known as immediate result of, is immediately caused by, is proximately caused by, proximate effect of, proximately caused by (wd:P1478 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): nearest, proximate thing that directly resulted in the subject a...ause) and 'has contributing factor'. See 'Help:Modeling causes'., has cause
5. constraint clarification, also known as why this constraint?, reason, rationale, constraint reason, property constraint comment (wd:P6607 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): qualifier to provide details on the function or purpose of a property constraint on a given property, Wikidata usage instructions
6. perpetrator, also known as robber, perpetrators, perpetrated by, perp, offender (wd:P8031 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): person or organization who carried out this harmful, illegal, or immoral act, participant
7. created for, also known as cause of creation, commissioned for, made for, occasion, on the occasion of (wd:P9883 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): a work, event, etc. for which the subject work was created (e.g.... play was created for; a film that a screenplay was written for)
8. cause of destruction, also known as destroyed by, reason of failure (wd:P770 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): item which caused the destruction of the subject item, has cause
9. cause of death, also known as death cause, murder method, method of murder, die of, die from (wd:P509 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): underlying or immediate cause of death.  Underlying cause (e.g. ...adest category, e.g. natural causes, accident, homicide, suicide
10. has contributing factor, also known as contributing factor, significant contributing factor, is/was indirectly caused by, indirectly caused by, indirect cause (wd:P1479 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): thing that significantly influenced, but did not directly cause,...use' and 'has immediate cause'.  See '[[Help:Modeling causes]]'., has cause

[Assistant]
The most relevant properties for linking "smoking" to diseases it causes are:

- has effect (wd:P1542): effect of this item, followed by
- has cause (wd:P828): underlying cause, entity that ultimately resulted in this effect, follows

Since we want to find diseases that can be caused by smoking, "has effect" (wd:P1542) is likely the most direct: it links a cause (smoking) to its effects (diseases).

Next, I will check what kinds of effects are linked to "smoking" (wd:Q662860) using the property "has effect" (wd:P1542).

[Tool]
Got 7 rows and 3 columns:
| subject              | property               | object                                             |
| -------------------- | ---------------------- | -------------------------------------------------- |
| smoking (wd:Q662860) | has effect (wdt:P1542) | tuberculosis (wd:Q12204)                           |
| smoking (wd:Q662860) | has effect (wdt:P1542) | pneumonia (wd:Q12192)                              |
| smoking (wd:Q662860) | has effect (wdt:P1542) | myocardial infarction (wd:Q12152)                  |
| smoking (wd:Q662860) | has effect (wdt:P1542) | lung cancer (wd:Q47912)                            |
| smoking (wd:Q662860) | has effect (wdt:P1542) | chronic obstructive pulmonary disease (wd:Q199804) |
| smoking (wd:Q662860) | has effect (wdt:P1542) | Crohn's disease (wd:Q1472)                         |
| smoking (wd:Q662860) | has effect (wdt:P1542) | pulmonary emphysema (wd:Q188605)                   |

[Tool]
Got 7 rows and 1 column:
| disease                                            |
| -------------------------------------------------- |
| myocardial infarction (wd:Q12152)                  |
| pneumonia (wd:Q12192)                              |
| tuberculosis (wd:Q12204)                           |
| Crohn's disease (wd:Q1472)                         |
| pulmonary emphysema (wd:Q188605)                   |
| chronic obstructive pulmonary disease (wd:Q199804) |
| lung cancer (wd:Q47912)                            |

[Tool]
Got 7 rows and 2 columns:
| disease                                            | diseaseLabel                               |
| -------------------------------------------------- | ------------------------------------------ |
| myocardial infarction (wd:Q12152)                  | myocardial infarction (en)                 |
| pneumonia (wd:Q12192)                              | pneumonia (en)                             |
| tuberculosis (wd:Q12204)                           | tuberculosis (en)                          |
| Crohn's disease (wd:Q1472)                         | Crohn's disease (en)                       |
| pulmonary emphysema (wd:Q188605)                   | pulmonary emphysema (en)                   |
| chronic obstructive pulmonary disease (wd:Q199804) | chronic obstructive pulmonary disease (en) |
| lung cancer (wd:Q47912)                            | lung cancer (en)                           |

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query is mostly correct and returns relevant diseases associated with smoking using the property 'has effect' (wd:P1542). However, it may miss diseases that are linked to smoking via other properties such as 'cause of' (P828) or 'risk factor' (P5642), which are also commonly used in Wikidata to represent causal relationships. To improve completeness, consider expanding the query to include diseases where smoking is listed as a cause or risk factor. Additionally, the query correctly returns IRIs and optionally includes labels, which is good practice.

[Assistant]
Thank you for the feedback. To improve completeness, I will expand the query to include diseases where smoking (wd:Q662860) is linked as:

- an effect via 'has effect' (wd:P1542)
- a cause via 'has cause' (wd:P828)
- a risk factor via 'risk factor' (wd:P5642)

This will ensure we capture all diseases associated with smoking through these properties.

I will use a UNION to combine results from all three properties. The query will return the disease IRIs and optionally their English labels.

Here is my step-by-step plan:
1. For each property, find diseases where smoking is the cause, effect, or risk factor.
2. Use UNION to combine the results.
3. Optionally include disease labels.

Now, I will write and execute the improved SPARQL query.

[Tool]
Got 96 rows and 2 columns, showing the first 5 and last 5 rows below:
| disease                                     | diseaseLabel                      |
| ------------------------------------------- | --------------------------------- |
| insulin resistance (wd:Q1053470)            | insulin resistance (en)           |
| tic disorder (wd:Q1077139)                  | tic disorder (en)                 |
| necrotizing enterocolitis (wd:Q10859678)    | necrotizing enterocolitis (en)    |
| cancer (wd:Q12078)                          | cancer (en)                       |
| appendicitis (wd:Q121041)                   | appendicitis (en)                 |
| ...                                         | ...                               |
| liver disease (wd:Q929737)                  | liver disease (en)                |
| purpura (wd:Q935293)                        | purpura (en)                      |
| dentine hypersensitivity (wd:Q948056)       | dentine hypersensitivity (en)     |
| skin disease (wd:Q949302)                   | skin disease (en)                 |
| smoking-related lung disease (wd:Q98608770) | smoking-related lung disease (en) |

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query is generally well-constructed and covers multiple relevant properties ("has effect"/P1542, "has cause"/P828, and "risk factor"/P5642) to identify diseases related to smoking. It also correctly returns the IRIs of the diseases and optionally their labels. However, the query could be further refined to improve precision:

1. The property P1542 (has effect) is used in the direction wd:Q662860 wdt:P1542 ?disease, which means 'smoking has effect disease'. This is correct. However, the other two properties (P828 and P5642) are used as '?disease wdt:P828 wd:Q662860' and '?disease wdt:P5642 wd:Q662860', which means 'disease has cause smoking' and 'disease has risk factor smoking', respectively. This is also correct.

2. However, the query may include some diseases where the relationship is not strictly 'caused by' but rather 'associated with' or 'risk factor for', which is broader than the user's question. If the user specifically wants only diseases that are caused by smoking, it would be better to focus on the 'has cause' (P828) property. If a comprehensive list is desired, the current approach is acceptable, but the answer should clarify the distinction between 'caused by', 'risk factor for', and 'effect of'.

3. The answer text should clarify that the list includes diseases for which smoking is a cause, risk factor, or effect, as the query does not strictly filter for 'caused by'.

Overall, the query is sensible and mostly correct, but a refinement to clarify the relationships and possibly filter more strictly for 'caused by' (if that is the user's intent) would improve precision.

[Tool] Stopped generation process


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?article ?date ?label WHERE {
  ?article wdt:P921 wd:Q6557482 .
  ?article wdt:P577 ?date .
  OPTIONAL {
    ?article rdfs:label ?label FILTER ( lang( ?label ) = "en" )
  }
}
ORDER BY ASC ( ?date )
LIMIT 1

According to Wikidata, the earliest article about Liquid State Machines (with main subject wd:Q6557482) is 'An Application of Spike-Timing-Dependent Plasticity to a Circuit for Liquid State Machine' (wd:Q57961191), published on 2007-08-01. Please note that Wikidata coverage may be incomplete, so earlier articles may exist that are not represented in the database.

[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 was the first article about Liquid State Machines published?

[Assistant]
Let's break down the question:

1. The user wants to know the publication date of the first article about "Liquid State Machines."
2. I need to identify the entity for "Liquid State Machines" in Wikidata.
3. Then, I need to find articles (likely scholarly articles or papers) about this topic and determine their publication dates.
4. Finally, I need to find the earliest publication date.

First, I will search for the entity "Liquid State Machines" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. liquid state machine, also known as LSM (wd:Q6557482): type of artificial neural network
2. Energy Constraints Improve Liquid State Machine Performance (wd:Q130947914): scientific article published on 28 July 2020
3. The cerebellum as a liquid state machine (wd:Q47287092): scientific article
4. Reinforcement Learning With Low-Complexity Liquid State Machines (wd:Q90049673): scientific article published on 27 August 2019
5. finite-state machine, also known as FA, finite automata, finite automaton, finite-state automaton, FSM (wd:Q176452): mathematical model of computation; abstract machine that can be in exactly one of a finite number of states at any given time
6. Liquid State (wd:Q3242295): song by English rock band Muse
7. Finite-state machine, also known as State Machine (wd:Q6035379)
8. Liquid State, also known as Liquid State Limited (wd:Q112312670): record label
9. Hardware-aware liquid state mach... 2D/3D Network-on-Chip platforms (wd:Q113863454): scientific article published in 2022
10. Effects of synaptic connectivity...liquid state machine performance (wd:Q47267043): scientific article published on November 17, 2012

[Assistant]
The main concept "liquid state machine" is represented by wd:Q6557482, which is a type of artificial neural network. There are also several articles with "liquid state machine" in the title, but the user is asking for the first article about the concept, not a specific article.

Next, I need to find articles (likely scholarly articles) about wd:Q6557482 and determine their publication dates. To do this, I will search for properties linking articles to the concept wd:Q6557482, such as "main subject" or "about".

[Tool]
Top 10 property alternatives:
1. Microsoft Academic ID, also known as MS Academic ID, Microsoft ID, Microsoft Academic Work ID, Microsoft Academic topic ID, Microsoft Academic Source ID (wd:P6366 as wdt/p/wdtn): identifier for an object or topic in the Microsoft Academic Graph (until 31 December 2021)
2. subclass of, also known as rdfs:subClassOf, sort of, subcategory of, subgenre of, subtype of (wd:P279 as p/wdt): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
3. Freebase ID, also known as Freebase, MID, Freebase identifier, FID (wd:P646 as p/wdt): identifier for a page in the Freebase database. Format: "/m/0" f...r IDs starting with "/g/", use Google Knowledge Graph ID (P2671)

No other items found

[Tool]
Top 10 property alternatives:
1. main subject, also known as primary topic, primary subject, primary keyword, plot keyword, of (wd:P921 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): primary topic of a work (see also P180: depicts), depicts
2. facet of, also known as subitem of, topic of, subtopic of, subject in, section in (wd:P1269 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): topic of which this item is an aspect; item that offers a broader perspective on the same topic
3. category's main topic, also known as item for this category, cmt, main article in category, main category topic, main subject of category (wd:P301 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): primary topic of the subject Wikimedia category, main subject
4. academic major, also known as academic subject, college major, field of study, major, studied (wd:P812 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): major someone studied at college/university
5. depicts, also known as portrays, presents, preserves, quotes, represents (wd:P180 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): entity visually depicted in an image, literarily described in a ...o an audiovisual or other medium; see also P921,  'main subject', has part(s)
6. field of work, also known as scientific discipline, scientific area, responsible for, research on, research fields (wd:P101 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): specialization of a person or organization; see P106 for the occupation, interested in
7. curriculum topics, also known as included subject, included topic, subject included, syllabus includes, topic included (wd:P8263 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): topic or subject included in the curriculum
8. Wikidata item of this property, also known as associated item, Wikidata property subject item, Wikidata item for this property, subject of this property, subject of property (wd:P1629 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): item corresponding to the concept represented by the property
9. topic's main category, also known as article's main category, category, category for this topic, catmain, has category (wd:P910 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): main Wikimedia category
10. statement is subject of, also known as statement is the subject of, subject of the statement, subject of the claim, subject of, related item of statement (wd:P805 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): (qualifying) item that describes the relation identified in this statement

[Tool]
Got 8 rows and 3 columns:
| subject                                                                             | property                | object                             |
| ----------------------------------------------------------------------------------- | ----------------------- | ---------------------------------- |
| Novel Integrated Methods of Evol... and Particle Swarm Optimisation (wd:Q112358987) | main subject (wdt:P921) | liquid state machine (wd:Q6557482) |
| Spatio-/spectro-temporal pattern...bilistic spiking neural networks (wd:Q112359200) | main subject (wdt:P921) | liquid state machine (wd:Q6557482) |
| A Digital Liquid State Machine W...pplication to Speech Recognition (wd:Q40254259)  | main subject (wdt:P921) | liquid state machine (wd:Q6557482) |
| Effects of synaptic connectivity...liquid state machine performance (wd:Q47267043)  | main subject (wdt:P921) | liquid state machine (wd:Q6557482) |
| Liquid state machine with dendri...uromorphic VLSI implementations. (wd:Q47427755)  | main subject (wdt:P921) | liquid state machine (wd:Q6557482) |
| Anytime multipurpose emotion rec...d State Machine based framework. (wd:Q47842029)  | main subject (wdt:P921) | liquid state machine (wd:Q6557482) |
| An Application of Spike-Timing-D...Circuit for Liquid State Machine (wd:Q57961191)  | main subject (wdt:P921) | liquid state machine (wd:Q6557482) |
| Information-Theoretic Intrinsic ...rning in Spiking Neural Networks (wd:Q64236848)  | main subject (wdt:P921) | liquid state machine (wd:Q6557482) |

[Tool]
Top 10 property alternatives:
1. publication date, also known as launched, published, publication time, publication, pubdate (wd:P577 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): date or point in time when a work was first published or released, point in time
2. dissolved, abolished or demolished date, also known as ended, publication end date, final issue, date repealed, final year (wd:P576 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): point in time at which the subject (organisation, building) ceas...g equipment,  "discontinued date" (P2669) for stopping a product, end time
3. place of publication, also known as published in (place), where published, release region, released in, published from (wd:P291 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): geographical place of publication of the edition (use 1st edition when referring to works), significant place
4. published in, also known as song on album, tome of the book, tome of, track of, track on (wd:P1433 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): larger work that a given work was published in, like a book, journal or music album, part of
5. announcement date, also known as released, date teased, made public on, public announcement date, published (wd:P6949 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): time of the first public presentation of a subject by the creator, of information by the media, point in time
6. edition number, also known as number of event, software version number, version number (wd:P393 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): number of an edition (first, second, ... as 1, 2, ...) or event, series ordinal
7. publisher, also known as video game publisher, software publisher, comic book publisher, sheet music publisher, publishing house (wd:P123 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): organization or person responsible for publishing books, periodicals, printed music, podcasts, games or software
8. distribution format, also known as book format, type of media, publication format, method of distribution, medium (wd:P437 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): method (or type) of distribution for the subject
9. publication interval, also known as frequency of publication, interval of publication, publication cycle, publication frequency, update frequency (wd:P2896 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): standard publication interval for daily or periodical publications, event interval
10. platform, also known as computing platform, runtime, published on, platforms, hardware (wd:P400 as pqn/pqv/p/pq/ps/wdt/psn/prn/psv/prv/wdtn/pr): platform for which a work was developed or released, or the specific platform version of a software product

[Tool]
Got 8 rows and 2 columns:
| article                                                                             | date                                |
| ----------------------------------------------------------------------------------- | ----------------------------------- |
| An Application of Spike-Timing-D...Circuit for Liquid State Machine (wd:Q57961191)  | 2007-08-01T00:00:00Z (xsd:dateTime) |
| Novel Integrated Methods of Evol... and Particle Swarm Optimisation (wd:Q112358987) | 2012-01-01T00:00:00Z (xsd:dateTime) |
| Effects of synaptic connectivity...liquid state machine performance (wd:Q47267043)  | 2012-11-17T00:00:00Z (xsd:dateTime) |
| Spatio-/spectro-temporal pattern...bilistic spiking neural networks (wd:Q112359200) | 2013-01-01T00:00:00Z (xsd:dateTime) |
| Liquid state machine with dendri...uromorphic VLSI implementations. (wd:Q47427755)  | 2014-10-27T00:00:00Z (xsd:dateTime) |
| A Digital Liquid State Machine W...pplication to Speech Recognition (wd:Q40254259)  | 2015-01-27T00:00:00Z (xsd:dateTime) |
| Anytime multipurpose emotion rec...d State Machine based framework. (wd:Q47842029)  | 2018-02-01T00:00:00Z (xsd:dateTime) |
| Information-Theoretic Intrinsic ...rning in Spiking Neural Networks (wd:Q64236848)  | 2019-01-01T00:00:00Z (xsd:dateTime) |

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The approach is reasonable, but the SPARQL query is limited to a hardcoded set of articles (using VALUES) rather than searching for all articles about Liquid State Machines in Wikidata. This risks missing relevant articles, especially if there are items not included in the VALUES list. The query should be refined to dynamically select all articles about Liquid State Machines, for example by using a property such as 'main subject' (P921) or 'about' (P1703) with the appropriate entity for Liquid State Machine. This will ensure completeness and accuracy.

[Assistant]
Thank you for the feedback. You are correct: the previous query only considered a fixed set of articles, which could miss relevant results. To improve, I will write a SPARQL query that dynamically selects all articles whose main subject (P921) is "liquid state machine" (wd:Q6557482), and retrieves their publication dates. I will then order by publication date to find the earliest one.

Here is my step-by-step plan:
1. Select all items (?article) where ?article wdt:P921 wd:Q6557482.
2. Retrieve their publication date (?date) using wdt:P577.
3. Order the results by date and select the earliest one (LIMIT 1).
4. Optionally, retrieve the article's label for context.

Now, I will write and execute this improved query.

[Tool]
Got 1 row and 3 columns:
| article                                                                            | date                                | label                                                                    |
| ---------------------------------------------------------------------------------- | ----------------------------------- | ------------------------------------------------------------------------ |
| An Application of Spike-Timing-D...Circuit for Liquid State Machine (wd:Q57961191) | 2007-08-01T00:00:00Z (xsd:dateTime) | An Application of Spike-Timing-D...Circuit for Liquid State Machine (en) |

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query is mostly correct and follows the rules, but there is a potential issue: it assumes that the earliest article about Liquid State Machines is the one with the earliest publication date among those with main subject wd:Q6557482. However, it does not guarantee that this is truly the first article ever published about Liquid State Machines, as Wikidata coverage may be incomplete. Additionally, the query should use ORDER BY ASC(?date) to make the ordering explicit (though ascending is default), and LIMIT 1 is correct for getting the earliest. The answer should also mention the possibility of incompleteness in Wikidata. The query returns the IRI as required, and labels are optional and correctly placed. Overall, the output is sensible but could be improved by clarifying the limitations of the data source in the answer.

[Tool] Stopped generation process