PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ( COUNT( DISTINCT ?work ) AS ?count ) WHERE {
  wd:Q1621261 wdt:P1441 ?work .
  ?work ?p wd:Q79037 .
}

Got 1 row and 1 column:
| count       |
| ----------- |
| 5 (xsd:int) |

Venom has been a part of 5 works that are related to Spider-Man.

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

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

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

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

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

[User] How many works that are based on Spider-Man has Venom been a part of?

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

[Tool]
Example 1:
Question:
How many James Bond movies do exist?

SPARQL:
SELECT ( COUNT( ?s1 ) as ?c ) WHERE {
  ?s1 wdt:P31 wd:Q11424 .
  ?s1 ?p wd:Q2009573 .
  FILTER ( ?p = wdt:P144 || ?p = wdt:P674 )
}

Using entities:
film, also known as cinematic work, moving picture, movie, motion pictures, motion picture (wd:Q11424): sequence of images that give the impression of movement, stored on film stock
James Bond, also known as Agent 007, double o seven, 007 (wd:Q2009573): secret agent, invented by novellist Ian Fleming, 00 Agent, spy

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

[Assistant]
The example found is about counting the number of James Bond movies, which are instances of films and related to James Bond. This is similar to our question about counting works based on Spider-Man that Venom has been a part of.

Step 1: Identify the entities "Spider-Man" and "Venom" in Wikidata.
Step 2: Identify the property that relates works to "Spider-Man" (e.g., "based on" or "adaptation of").
Step 3: Identify the property that relates "Venom" to these works (e.g., "character in" or "cast member").
Step 4: Count the number of such works.

I will start by searching for the entity "Spider-Man" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Spider-Man, also known as Wallcrawler, The Amazing Spider-Man, The Human Spider, The Fantastic Spider-Man, Web-slinger (wd:Q79037): fictional character in Marvel Comics, photographer, scientist, superhero, inventor, teacher
2. Spider-Man (wd:Q484442): 2002 American film directed by Sam Raimi
3. Spider-Man, also known as Marvel's Spider-Man (wd:Q25340029): 2018 video game developed by Insomniac Games
4. Spider-Man, also known as Spider-Man: The Animated Series (wd:Q569934): American animated television series from 1994–1998
5. Miles Morales, also known as Ultimate Spider-Man, Spider-Man, Miles Gonzalo Morales (wd:Q59826): fictional character in Marvel Comics, superhero, student
6. Spider-Man, also known as Spider-Man 2000 (wd:Q288296): 2000 video game
7. Peter Parker, also known as Peter #1, Peter 1, Peter One, Peter-One, Spider-Man (wd:Q23991129): fictional character from the Marvel Cinematic Universe
8. Marvel's Spider-Man, also known as Spider-Man (wd:Q28207200): American animated television series
9. Spider-Man, also known as Supaidāman (wd:Q2087538): Japanese-US late 1970's television series
10. Spider-Man (wd:Q2333456): US late 1960s animated television series

[Assistant]
The entity "Spider-Man" as a fictional character in Marvel Comics is wd:Q79037. This is the most relevant entity for the question about works based on Spider-Man.

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

[Tool]
Top 10 entity alternatives:
1. Venom (wd:Q23006268): 2018 American film directed by Ruben Fleischer
2. Venom (wd:Q192861): British heavy metal band
3. Venom (wd:Q1621261): fictional character in Marvel Comics
4. venom, also known as animal venom, venoms, zootoxin (wd:Q3386847): mixture of toxins and other substances secreted by an animal for the purpose of causing harm to another
5. UH-1Y Venom, also known as Venom, UH1Y Venom, UH-1Y Super Huey, UH-1 Venom, Super Huey (wd:Q2529305): military utility helicopter model by Bell
6. DH.112 Venom, also known as Venom, DH112, de Havilland Venom, de Havilland DH 112, de Havilland DH.112 Venom (wd:Q284767): 1949 fighter aircraft family by de Havilland
7. Michael Page, also known as MVP, the venenous cactus, Venom, Michael Jerome Reece-Page (wd:Q6833334): Brazilian jiu-jitsu practitioner, English mixed martial artist, mixed martial arts fighter, martial artist, kickboxer, karateka, boxer
8. Eddie Brock, also known as King in Black, Toxin, Venom, Edward Charles Brock, Anti-Venom (wd:Q2604715): fictional character in Marvel Comics, government agent, supervillain, superhero, vigilante, reporter
9. Venom, also known as Venom (Music from the Motion Picture) (wd:Q57326696): 2018 song by Eminem
10. Venom (wd:Q1757437): 1981 film by Tobe Hooper

[Assistant]
The entity "Venom" as a fictional character in Marvel Comics is wd:Q1621261. This is the most relevant entity for the question about works that Venom has been a part of.

Now, I will search for properties that relate works to "Spider-Man" (wd:Q79037) to find how works are based on Spider-Man. I will search for properties related to "based on" or "adaptation of" for the entity Spider-Man.

[Tool]
Top 10 property alternatives:
1. 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
2. creator, also known as created by, visual artist, sculptor, sculpted by, realized by (wd:P170 as p/wdt): maker of this creative work or other object (where no more specific property exists)
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. 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)
5. member of, also known as band member of, membership, member of musical group, in musical group, be member of (wd:P463 as p/wdt): organization, club or musical group to which the subject belongs...ical position, such as a member of parliament (use P39 for that), part of
6. relative, also known as relatives, grandparent, relation, non-binary parent, niece (wd:P1038 as p/wdt): family member (qualify with "kinship to subject", P1039; for direct family member please use specific property), significant person
7. from narrative universe, also known as from narrative, in continuity, from universe, in cycle, in world of (wd:P1080 as p/wdt): subject's fictional entity is in the object narrative. See also P1441 (present in work) and P1445 (fictional universe described in), part of
8. present in work, also known as is shown in, in work, in narrative, character in, from work (wd:P1441 as p/wdt): this (fictional or fictionalized) entity, place, or person appea...r works, P1343 for entities described in non-fictional accounts)
9. educated at, also known as graduated from, graduate of, place of education, school attended, schooled at (wd:P69 as p/wdt): educational institution attended by subject, affiliation
10. image, also known as still capture, still, screenshot, screen capture, portrait (wd:P18 as p/wdt): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)

No other items found

[Assistant]
The property "derivative work" (wdt:P4969) seems to be the most relevant for works based on Spider-Man. It indicates a new work derived from a major part of the original work.

Next, I will search for properties that relate "Venom" (wd:Q1621261) to works. I will search for properties related to "character in" or "cast member" for the entity Venom.

[Tool]
Top 10 property alternatives:
1. present in work, also known as is shown in, in work, in narrative, character in, from work (wd:P1441 as p/wdt): this (fictional or fictionalized) entity, place, or person appea...r works, P1343 for entities described in non-fictional accounts)
2. character type, also known as -dere, stock character, stereotype, dere, character stereotype (wd:P9071 as p/wdt): generic character type that this fictional character represents, manifestation of
3. performer, also known as actor, sung by, singer, recording by, recorded by (wd:P175 as p/wdt): actor, musician, band or other performer associated with this role or musical work, contributor to the creative work or subject
4. from narrative universe, also known as from narrative, in continuity, from universe, in cycle, in world of (wd:P1080 as p/wdt): subject's fictional entity is in the object narrative. See also P1441 (present in work) and P1445 (fictional universe described in), part of
5. Behind The Voice Actors character ID, also known as Behind The Voice Actors character identifier, BTVA character ID, BTVA character identifier (wd:P5107 as p/wdt): identifier of a character from the Behind The Voice Actors website
6. instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as p/wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
7. languages spoken, written or signed, also known as language of expression, language, wrote language, writes language, uses language (wd:P1412 as p/wdt): language(s) that a person or a people speaks, writes or signs, including the native language(s)
8. alternate universe counterpart, also known as parallel universe counterpart, counterpart in parallel universe, counterpart in a parallel universe, counterpart in an AU, counterpart in an alternate universe (wd:P11799 as p/wdt): this fictional character or entity is an alternate universe counterpart of the subject, significant person
9. image, also known as still capture, still, screenshot, screen capture, portrait (wd:P18 as p/wdt): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)
10. sex or gender, also known as gender or sex, sex, gender identity, gender expression, gender (wd:P21 as p/wdt): sex or gender identity of human or animal. For human: male, fema...m, female organism. Groups of same gender use subclass of (P279)

No other items found

[Assistant]
The property "present in work" (wdt:P1441) is the most relevant for indicating that Venom is a character in a work.

Now, I will construct a SPARQL query to count the number of works that are derivative works of Spider-Man (wd:Q79037) and in which Venom (wd:Q1621261) is present.

[Tool]
Got 1 row and 1 column:
| count       |
| ----------- |
| 0 (xsd:int) |

[Assistant]
The query returned 0, which means there are no works that are derivative works of Spider-Man and in which Venom is present according to the current data.

I will now try a slightly different approach. Instead of looking for derivative works of Spider-Man, I will look for works that are based on Spider-Man using the property "based on" (wdt:P144) and check if Venom is present in those works.

[Tool]
Got 1 row and 1 column:
| count       |
| ----------- |
| 0 (xsd:int) |

[Assistant]
The query again returned 0, indicating no works based on Spider-Man have Venom present according to the data.

I will now try to broaden the search by looking for works that are instances of films, TV series, or video games related to Spider-Man and check if Venom is present in those works. This might capture more works where Venom has been a part of.

[Tool]
Got 1 row and 1 column:
| count       |
| ----------- |
| 0 (xsd:int) |

[Assistant]
The query still returned 0, indicating no films, TV series, or video games based on Spider-Man have Venom present according to the data.

I will now try to find works where Venom is present and check if those works are related to Spider-Man by any property. This might help find works indirectly related to Spider-Man.

[Tool]
Got 1 row and 1 column:
| count       |
| ----------- |
| 5 (xsd:int) |

[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 ?precedingKingdom ?precedingKingdomLabel WHERE {
  wd:Q1115291 wdt:P1365 ?precedingKingdom .
  OPTIONAL {
    ?precedingKingdom rdfs:label ?precedingKingdomLabel FILTER ( LANG( ?precedingKingdomLabel ) = "en" )
  }
}

Got 1 row and 2 columns:
| precedingKingdom               | precedingKingdomLabel  |
| ------------------------------ | ---------------------- |
| Ayutthaya Kingdom (wd:Q841364) | Ayutthaya Kingdom (en) |

The kingdom that preceded the Thonburi Kingdom is the Ayutthaya Kingdom.

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

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

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

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

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

[User] Which kingdom preceded the Thonburi Kingdom ?

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

[Tool] No examples found

[Assistant] There are no similar examples found for this question. Next, I will search for the entity "Thonburi Kingdom" in Wikidata to identify its identifier.

[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 entity "Thonburi Kingdom" corresponds to wd:Q1115291 in Wikidata. Now, I will search for properties related to "preceded by" or "predecessor" for this entity to find the kingdom that preceded the Thonburi Kingdom.

[Tool]
Top 10 property alternatives:
1. replaces, also known as supersedes, succeeds, replaced, previous job holder, preceded by (wd:P1365 as p/wdt): person, state or item replaced. Use "structure replaces" (P1398)...item was not replaced or predecessor and successor are identical, follows
2. followed by, also known as continued as, then, successor, succeeded by, sequel is (wd:P156 as p/wdt): immediately following item in a series of which the subject is a...een replaced, e.g. political offices, use "replaced by" (P1366)]
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. 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
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. religion or worldview, also known as believes, worldview, religious affiliation, religion or world view, religion (wd:P140 as p/wdt): religion of a person, organization or religious building, or associated with this subject

No other items found

[Assistant] The property "replaces" (wdt:P1365) seems to be the most relevant for indicating the predecessor or the kingdom that preceded the Thonburi Kingdom. I will now query the kingdom that the Thonburi Kingdom replaced using this property.

[Tool]
Got 1 row and 2 columns:
| precedingKingdom               | precedingKingdomLabel  |
| ------------------------------ | ---------------------- |
| Ayutthaya Kingdom (wd:Q841364) | Ayutthaya Kingdom (en) |

[Tool] Stopped generation process


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

The Hamburg S-Bahn was officially opened in the year 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 https://qlever.cs.uni-freiburg.de/api/wikidata-sebastian

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

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

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

[User] When was the Hamburg S-Bahn officially opened?

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

[Tool]
Example 1:
Question:
Which city has the oldest running metro?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?metro wdt:P31 wd:Q5503 ; wdt:P571 ?inception ; wdt:P131 ?uri .
}
ORDER BY ?inception
LIMIT 1

Using entities:
rapid transit, also known as rail rapid transit, underground, subway, metro (wd:Q5503): high-capacity public transport generally used in urban areas

Using properties:
located in the administrative territorial entity, also known as is in the settlement of, is in the voivodeship of, is in the shire of, is in the state of, is in the territory of (wd:P131 as wdt): the item is located on the territory of the following administra...if the item falls only partially into the administrative entity., location, part of
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
inception, also known as initial release date, founded in, founded on, founded on date, founding date (wd:P571 as wdt): time when an entity begins to exist; for date of official opening use P1619, start time

[Assistant]
The example found is about the oldest running metro and uses the property "inception" (wdt:P571) for the date when an entity begins to exist. However, the user specifically asked for the official opening date of the Hamburg S-Bahn, which might be more precisely represented by the property "date of official opening" (wdt:P1619).

Next, I will search for the entity "Hamburg S-Bahn" in Wikidata to get its identifier. Then I will look for the property related to the official opening date for this entity.

[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 entity "Hamburg S-Bahn" is identified as wd:Q693956 in Wikidata. Now I will search for properties related to the official opening date of this entity. I will specifically look for properties related to "official opening" or "opening date" 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. 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

No other items found

[Assistant]
The property "date of official opening" (wdt:P1619) is the most relevant property for the official opening date of the Hamburg S-Bahn (wd:Q693956).

I will now construct a SPARQL query to retrieve the official opening date of the Hamburg S-Bahn using this property.

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

[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 ?productionCompany ?productionCompanyLabel ?dissolvedDate WHERE {
  wd:Q143276 wdt:P272 ?productionCompany .
  OPTIONAL {
    ?productionCompany rdfs:label ?productionCompanyLabel FILTER ( LANG( ?productionCompanyLabel ) = "en" )
  }
  OPTIONAL {
    ?productionCompany wdt:P576 ?dissolvedDate
  }
}

The production company of Samurai Champloo is Manglobe. According to Wikidata, Manglobe was dissolved on September 30, 2015, so it is no longer existing.

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

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

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

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

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

[User] Is the production company of samurai champloo still existing?

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

[Tool] No examples found

[Assistant] There are no similar examples available, so I will proceed by identifying the production company of "Samurai Champloo" first. Then I will check if that production company still exists. I will start by searching for the entity "Samurai Champloo" in Wikidata.

[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 entity "Samurai Champloo" as a Japanese anime television series is wd:Q143276. Now I will look for the production company of this anime series.

[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 property "production company" (wdt:P272) is the relevant property to find the production company of the anime series. I will now query the production company of Samurai Champloo (wd:Q143276).

[Tool]
Got 1 row and 2 columns:
| productionCompany     | productionCompanyLabel |
| --------------------- | ---------------------- |
| Manglobe (wd:Q645476) | Manglobe (en)          |

[Assistant] The production company of Samurai Champloo is Manglobe (wd:Q645476). Now I will check if Manglobe still exists. I will look for a property that indicates if a company is dissolved or still active.

[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

[Assistant] The property "dissolved, abolished or demolished date" (wdt:P576) indicates the point in time at which the organization was dissolved. I will check if Manglobe (wd:Q645476) has a value for this property. If it has a dissolved date, it means it is no longer existing; if it does not have this property, it might still exist.

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

[Tool] Stopped generation process


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

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

The Mongols tried to invade Japan twice, corresponding to the two parts of the Mongol invasions of Japan: 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 https://qlever.cs.uni-freiburg.de/api/wikidata-sebastian

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

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

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

[User] How often did the Mongols try to invade Japan?

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

[Tool]
Example 1:
Question:
Give me all islands that belong to Japan.

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P31 wd:Q23442 ; wdt:P17 wd:Q17 .
}

Using entities:
Japan, also known as State of Japan, Nippon-koku, Nippon, Nihon-koku, Nihon (wd:Q17): island country in East Asia
island, also known as islands, isle (wd:Q23442): piece of sub-continental land completely surrounded by water

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

[Assistant] The example found is not directly related to the question about the number of Mongol invasions of Japan. I will now search for entities related to "Mongol invasions of Japan" to identify the relevant historical events or concepts.

[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. This entity likely contains information about the number of attempts or invasions. Next, I will check the properties of this entity to find relevant information about the number or frequency of invasions.

[Tool]
Top 10 property alternatives:
1. 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
2. located in the administrative territorial entity, also known as is in the settlement of, is in the voivodeship of, is in the shire of, is in the state of, is in the territory of (wd:P131 as 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
3. has part(s), also known as has component, incorporates, has ingredient, has ingredients, has member (wd:P527 as p/wdt): part of this subject; inverse property of "part of" (P361). See also "has parts of the class" (P2670).
4. 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)
5. 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)
6. location, also known as neighbourhood, whereabouts, vilage, venue, town (wd:P276 as p/wdt): location of the object, structure or event. In the case of an ad...ity use P706. Use P7153 for locations associated with the object

No other items found

[Assistant] The search for properties related to the number of invasions did not yield a direct property indicating the count or frequency of invasions. However, the property "has part(s)" (wdt:P527) might be useful to check if the Mongol invasions of Japan event has parts that correspond to individual invasion attempts. I will now query the parts of the Mongol invasions of Japan to see if they correspond to separate invasion attempts.

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

[Assistant] The Mongol invasions of Japan (wd:Q208156) have two parts: the Battle of Bun'ei (wd:Q2237386) and the Battle of Kōan (wd:Q2237622). These likely correspond to two separate invasion attempts. I will now construct a SPARQL query to count these parts to answer how often the Mongols tried to invade Japan.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ( COUNT( DISTINCT ?war ) AS ?count ) WHERE {
  ?war wdt:P31 wd:Q198 .
  ?war wdt:P710 wd:Q188712 .
}

Got 1 row and 1 column:
| count       |
| ----------- |
| 8 (xsd:int) |

The Empire of Japan participated in 8 distinct 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 https://qlever.cs.uni-freiburg.de/api/wikidata-sebastian

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

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

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

[User] How many wars did the Empire of Japan participate in?

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

[Tool]
Example 1:
Question:
How many emperors did China have?

SPARQL:
SELECT ( COUNT( DISTINCT ?uri ) as ?count ) WHERE {
  ?uri wdt:P39 wd:Q268218
}

Using entities:
Emperor of China, also known as Huang-ti, Huang Ti, Son of Heaven, thearch, Ti (wd:Q268218): sovereign of Imperial China

Using properties:
position held, also known as function, held position, holds position, job, office held (wd:P39 as wdt): subject currently or formerly holds the object position or public office

Example 2:
Question:
In which countries do people speak Japanese?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P31 wd:Q6256 .
  ?uri wdt:P37 wd:Q5287 .
}

Using entities:
Japanese, also known as Japanese language, Japanesic (wd:Q5287): language spoken in East Asia
country, also known as countries (wd:Q6256): distinct territorial body or political entity

Using properties:
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
official language, also known as language spoken, spoken in, speaking, language official (wd:P37 as wdt): language designated as official by this item, language used

Example 3:
Question:
Give me all islands that belong to Japan.

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P31 wd:Q23442 ; wdt:P17 wd:Q17 .
}

Using entities:
Japan, also known as State of Japan, Nippon-koku, Nippon, Nihon-koku, Nihon (wd:Q17): island country in East Asia
island, also known as islands, isle (wd:Q23442): piece of sub-continental land completely surrounded by water

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

[Assistant] The examples found are not directly related to wars or the Empire of Japan. I will now search for the entity "Empire of Japan" in Wikidata to find its identifier. Then I will search for properties related to wars or conflicts in which this entity participated.

[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 entity "Empire of Japan" corresponds to wd:Q188712. Now I will search for properties related to wars or conflicts in which the Empire of Japan participated. I will search for properties of the entity wd:Q188712 with a query related to "war" or "conflict".

[Tool]
Top 10 property alternatives:
1. religion or worldview, also known as believes, worldview, religious affiliation, religion or world view, religion (wd:P140 as p/wdt): religion of a person, organization or religious building, or associated with this subject
2. shares border with, also known as shares borders with, next to, bordered by, border, borders clockwise (wd:P47 as p/wdt): countries or administrative subdivisions, of equal level, that t...rders, either by land or water. A single common point is enough.
3. 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
4. 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
5. flag, also known as flag description (wd:P163 as p/wdt): subject's flag
6. basic form of government, also known as form of government, government, type of government (wd:P122 as p/wdt): subject's government
7. 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
8. 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)
9. population, also known as inhabitants, human population (wd:P1082 as p/wdt): number of people inhabiting the place; number of people of subject
10. described by source, also known as mentioned in, source of item, reviewed in, mentioned in news article, talked about in (wd:P1343 as p/wdt): work where this item is described, attested in

No other items found

[Assistant] The search for properties related to "war" for the Empire of Japan entity did not return relevant properties. I will now try a different approach: I will search for entities that are instances of "war" or "conflict" and check if the Empire of Japan participated in them. I will first find the entity for "war" and then query for wars involving the Empire of Japan.

[Tool]
Top 10 entity alternatives:
1. war, also known as hostilities, military conflict, wars (wd:Q198): organized and prolonged violent conflict between states
2. Waray, also known as Lineyte-Samarnon, war, Waray language, Waray (Philippines), Waray-Waray (wd:Q34279): Visayan language primarily spoken in the islands of Samar and Eastern Leyte
3. Axl Rose, also known as William Axl Rose, William Bruce Bailey, William "Bill" Bailey, W. Axl Rose, William Rose (wd:Q11885): singer-songwriter, American singer, vocalist, musician, activist, pianist
4. War (wd:Q331811): 1983 studio album by U2
5. War, also known as Rogue, Rogue Assassin (wd:Q375512): 2007 film directed by Philip Atwell
6. War, also known as War, West Virginia, War, WV (wd:Q2058013): city in West Virginia, United States
7. A War, also known as Krigen, War (wd:Q19590783): 2015 film by Tobias Lindholm
8. War (wd:Q64390886): 2019 film directed by Siddharth Anand
9. The War, also known as War (wd:Q1167231): 1994 film by Jon Avnet
10. War, also known as The War (wd:Q866673): 2002 film by Aleksei Balabanov

[Assistant] The entity for "war" is wd:Q198. I will now query for wars (instances of wd:Q198) in which the Empire of Japan (wd:Q188712) participated. I will look for conflicts or wars where the Empire of Japan is a participant or involved party.

[Tool]
Got 8 rows and 1 column:
| war                                             |
| ----------------------------------------------- |
| Sino-Japanese Wars of 1893-1945 (wd:Q106640290) |
| Russo-Japanese War (wd:Q159950)                 |
| Second Sino-Japanese War (wd:Q170314)           |
| First Sino-Japanese War (wd:Q178687)            |
| Pacific War (wd:Q184425)                        |
| Soviet–Japanese War (wd:Q220602)                |
| Soviet–Japanese border conflicts (wd:Q696389)   |
| Japanese invasion of Taiwan (wd:Q717373)        |

[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 ?disease ?diseaseLabel WHERE {
  ?disease wdt:P5642 wd:Q662860 .
  ?disease wdt:P31 wd:Q12136 .
  OPTIONAL {
    ?disease rdfs:label ?diseaseLabel FILTER ( LANG( ?diseaseLabel ) = "en" )
  }
}

Got 15 rows and 2 columns, showing the first 5 and last 5 rows below:
| disease                                         | diseaseLabel                          |
| ----------------------------------------------- | ------------------------------------- |
| toothache (wd:Q143925)                          | toothache (en)                        |
| arteriosclerotic heart disease (wd:Q1444550)    | arteriosclerotic heart disease (en)   |
| lung neoplasm (wd:Q15124212)                    | lung neoplasm (en)                    |
| clavicle fracture (wd:Q1746068)                 | clavicle fracture (en)                |
| sinus arrhythmia (wd:Q2583597)                  | sinus arrhythmia (en)                 |
| ...                                             | ...                                   |
| airway obstruction (wd:Q4330029)                | airway obstruction (en)               |
| gingival squamous cell carcinoma (wd:Q53678376) | gingival squamous cell carcinoma (en) |
| multiple pulmonary nodules (wd:Q54972627)       | multiple pulmonary nodules (en)       |
| no reflow phenomenon (wd:Q7045267)              | no reflow phenomenon (en)             |
| tennis elbow (wd:Q736776)                       | tennis elbow (en)                     |

Diseases that can be caused by smoking (smoking is a risk factor) include lung neoplasm, arteriosclerotic heart disease, airway obstruction, gingival squamous cell carcinoma, toothache, and others.

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

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

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

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

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

[User] Which diseases can be caused by smoking ?

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

[Tool]
Example 1:
Question:
What is the most frequent cause of death?

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

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

[Assistant]
From the example, the property "cause of death" (wdt:P509) is relevant to causes of diseases or death. We want to find diseases that can be caused by smoking. So we need to find diseases where smoking is listed as a cause or risk factor.

Next, I will search for the entity "smoking" in Wikidata to get its identifier. Then I will look for properties related to diseases caused by 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 entity "smoking" as breathing in smoke after burning a substance and inhaling is wd:Q662860. Now I will search for properties related to diseases caused by smoking or caused by this entity. I will try searching for properties of the entity smoking with the query "cause" or "cause disease".

[Tool]
Top 10 property alternatives:
1. has effect, also known as has symptom, implies, leads to, led to, outcome (wd:P1542 as p/wdt): effect of this item, followed by
2. described by source, also known as mentioned in, source of item, reviewed in, mentioned in news article, talked about in (wd:P1343 as p/wdt): work where this item is described, attested in
3. risk factor (wd:P5642 as p/wdt): factor associated with a high prevalence of a particular gene, d...ountry of citizenship, race, gender, occupation, anamnesis, etc.
4. 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
5. 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).
6. has characteristic, also known as has quality, inherent property, object has characteristic, parameter, quality (wd:P1552 as p/wdt): inherent or distinguishing quality or feature of the entity. Use a more specific property when possible
7. 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
8. different from, also known as not to be confused with, to be distinguished from, similar to, see also, rejected match (wd:P1889 as p/wdt): item that is different from another item, with which it may be confused
9. page banner, also known as article banner, WV banner, Women in Red banner, WIR banner, Wikivoyage banner (wd:P948 as p/wdt): image at top of an article about the topic, mainly used by Wikivoyage and Women in Red, image
10. image, also known as still capture, still, screenshot, screen capture, portrait (wd:P18 as p/wdt): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)

No other items found

[Assistant] The property "risk factor" (wdt:P5642) seems relevant as smoking is a risk factor for many diseases. I will now try to find diseases for which smoking (wd:Q662860) is a risk factor.

[Tool]
Got 15 rows and 2 columns, showing the first 5 and last 5 rows below:
| disease                                         | diseaseLabel                          |
| ----------------------------------------------- | ------------------------------------- |
| toothache (wd:Q143925)                          | toothache (en)                        |
| arteriosclerotic heart disease (wd:Q1444550)    | arteriosclerotic heart disease (en)   |
| lung neoplasm (wd:Q15124212)                    | lung neoplasm (en)                    |
| clavicle fracture (wd:Q1746068)                 | clavicle fracture (en)                |
| sinus arrhythmia (wd:Q2583597)                  | sinus arrhythmia (en)                 |
| ...                                             | ...                                   |
| airway obstruction (wd:Q4330029)                | airway obstruction (en)               |
| gingival squamous cell carcinoma (wd:Q53678376) | gingival squamous cell carcinoma (en) |
| multiple pulmonary nodules (wd:Q54972627)       | multiple pulmonary nodules (en)       |
| no reflow phenomenon (wd:Q7045267)              | no reflow phenomenon (en)             |
| tennis elbow (wd:Q736776)                       | tennis elbow (en)                     |

[Assistant] I found diseases for which smoking is a risk factor. Some of them are lung neoplasm, arteriosclerotic heart disease, airway obstruction, gingival squamous cell carcinoma, and toothache. I will now finalize the answer with a SPARQL query that returns these diseases and their labels.

[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 ?articleLabel ?publicationDate WHERE {
  VALUES ?article {
    wd:Q112358987 wd:Q112359200 wd:Q40254259 wd:Q47267043 wd:Q47427755 wd:Q47842029 wd:Q57961191 wd:Q64236848
  }
  ?article wdt:P577 ?publicationDate .
  OPTIONAL {
    ?article rdfs:label ?articleLabel FILTER ( LANG( ?articleLabel ) = "en" )
  }
}
ORDER BY ?publicationDate
LIMIT 1

The first article about Liquid State Machines found in Wikidata is "An Application of Spike-Timing-Dependent Plasticity Circuit for Liquid State Machine" (wd:Q57961191), published on August 1, 2007.

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

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

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

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

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

[User] When was the first article about Liquid State Machines published?

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

[Tool]
Example 1:
Question:
Which computer scientist won an oscar?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P106 wd:Q82594 .
  ?uri wdt:P166 ?award .
  ?award wdt:P31 wd:Q19020 .
}

Using entities:
Academy Awards, also known as Oscars, The Oscars (wd:Q19020): annual awards for cinematic achievements
computer scientist, also known as computer professional, computer specialist (wd:Q82594): one who studies or practises computer science

Using properties:
occupation, also known as job, work, vocation, profession, employment (wd:P106 as wdt): occupation of a person; see also "field of work" (Property:P101), "position held" (Property:P39)
award received, also known as win, winner of, recognition title, received award, prize received (wd:P166 as wdt): award or recognition received by a person, organization or creative work
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)

Example 2:
Question:
Which awards did Douglas Hofstadter win?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q319308 wdt:P166 ?uri .
}

Using entities:
Douglas Hofstadter, also known as Douglas R. Hofstadter, Douglas Richard Hofstadter (wd:Q319308): American physicist, computer scientist and  professor of cognitive science, university teacher, computer scientist, philosopher, physicist, writer

Using properties:
award received, also known as win, winner of, recognition title, received award, prize received (wd:P166 as wdt): award or recognition received by a person, organization or creative work

[Assistant] The examples found are not directly related to the question about the first article on Liquid State Machines. I will now search for the entity "Liquid State Machine" in Wikidata to identify its identifier and related information. Then I will look for the earliest publication or article related to it.

[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 entity "liquid state machine" (wd:Q6557482) is identified as a type of artificial neural network. There are also several scientific articles related to liquid state machines, but we need to find the first article about liquid state machines, which means the earliest publication date.

Next, I will search for scientific articles related to the liquid state machine (wd:Q6557482) to find the earliest publication date. I will look for publications that have this entity as a topic or are linked to it.

[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) |

[Assistant] I found 8 scientific articles that have "liquid state machine" as their main subject. To find the first article, I will now check the publication dates of these articles. I will query the publication date (property P577) for these articles.

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

[Assistant]
The earliest article about Liquid State Machines among the ones found is "An Application of Spike-Timing-Dependent Plasticity Circuit for Liquid State Machine" (wd:Q57961191), published on August 1, 2007.

I will now provide this as the final answer.

[Tool] Stopped generation process