PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?show ?showLabel WHERE {
  ?show wdt:P31 wd:Q5398426 ; wdt:P580 ?startTime .
  FILTER ( YEAR( ?startTime ) = 1975 ) OPTIONAL {
    ?show rdfs:label ?showLabel FILTER ( LANG( ?showLabel ) = "en" )
  }
}

Got 249 rows and 2 columns, showing the first 5 and last 5 rows below:
| show                                               | showLabel                                  |
| -------------------------------------------------- | ------------------------------------------ |
| Gabriela (wd:Q10287918)                            | Gabriela (en)                              |
| On the Mat (wd:Q10340891)                          | On the Mat (en)                            |
| Cher (wd:Q10381261)                                | Cher (en)                                  |
| Carry On Laughing (wd:Q1045360)                    | Carry On Laughing (en)                     |
| Space: 1999 (wd:Q1045743)                          | Space: 1999 (en)                           |
| ...                                                | ...                                        |
| Beschlossen und verkündet (wd:Q829585)             | Beschlossen und verkündet (en)             |
| wd:Q86210547                                       |                                            |
| Krempoli - Ein Platz für wilde Kinder (wd:Q872205) | Krempoli - Ein Platz für wilde Kinder (en) |
| Krempoli - Ein Platz für wilde Kinder (wd:Q872205) | Krempoli - Ein Platz für wilde Kinder (en) |
| Miś Uszatek (wd:Q929924)                           | Miś Uszatek (en)                           |

Saturday Night Live first aired in 1975. Other shows that also first aired in 1975 include Gabriela, On the Mat, Cher, Carry On Laughing, and Space: 1999, among others.

Which shows aired for the first time the same year as Saturday Night Live ?

[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 with notes:
1. When direct properties are missing, chain related properties (e.g., child→child for grandchildren) or search for indirect connections to answer complex relationship queries.
2. For set/class queries (e.g., all cities, all holidays), use instance/subclass chains (P31/P279) for completeness and relevant filters. Be aware of data incompleteness and schema inconsistencies in Wikidata.
3. If queries return too many irrelevant or too few results, try varying properties (country, location, observed in, etc.) and subclass chains, but be ready to report data/model limitations if results remain incomplete or noisy.
4. When a direct property or class is missing or inconsistently used, try related or broader properties/classes (e.g., use inception for independence date, or musical work/composition for musicals) to increase recall.
5. For calculated properties (e.g., population density), combine relevant direct properties (e.g., population, area) in SPARQL queries, as Wikidata may not store the derived value directly.
6. When entity search returns multiple candidates, use context (description, instance type, location, etc.) to disambiguate and select the most relevant entity for the query.

No general notes for all knowledge graphs available

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/
qlss: <https://qlever.cs.uni-freiburg.de/spatialSearch/
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. Search for the 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. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
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 just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. 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 shows aired for the first time the same year as Saturday Night Live ?

[Assistant]
Step 1: Identify the entity "Saturday Night Live" in Wikidata and find the property that indicates the year it first aired (premiere date or inception).

I will search for the entity "Saturday Night Live" first.

[Tool]
Top 10 entity alternatives:
1. Saturday Night Live, also known as Saturday Night, NBC's Saturday Night (wd:Q13979): American late-night live television sketch comedy show, instance of television series
2. Saturday Night Live (wd:Q6122059): instance of television series, television series
3. Saturday Night Live (wd:Q28452163): album by Trouble Funk, instance of album
4. Saturday Night Live (wd:Q23039888): instance of television series, tv show aired in 2016
5. Saturday Night Live Korea, also known as SNL Korea, SNL Korea 6, SNL.KOREA (wd:Q7426718): 2011 South Korean television series, instance of television program
6. Le Saturday Night Live (wd:Q28194825): French late-night live television comedy show, instance of television program
7. Saturday Night Live Japan (wd:Q11304712): instance of television program, Japanese TV series or program
8. history of Saturday Night Live, also known as Saturday Night Live history (wd:Q5866387): instance of aspect of history, List article
9. Katy Perry/Robyn, also known as Saturday Night Live S37E9, Saturday Night Live, season 37 episode 9, Saturday Night Live Season 37 Episode 9 (wd:Q5483492): episode of Saturday Night Live (S37 E9), instance of television series episode
10. Saturday Night Live Samurai (wd:Q7426709): instance of recurring Saturday Night Live sketch, instance of television character, instance of fictional human, recurring SNL character, samurai

[Assistant]
The entity "Saturday Night Live" is identified as wd:Q13979, which is an American late-night live television sketch comedy show and an instance of a television series.

Step 2: Find the property that indicates the year it first aired for this entity. Usually, the property for the first air date or inception is P571 (inception).

I will check the inception date of Saturday Night Live (wd:Q13979).

[Tool] Got no rows and 3 columns

[Assistant]
No direct inception (P571) property found for Saturday Night Live (wd:Q13979).

Step 3: I will try to find other properties related to the start date or first air date for this entity. I will search for properties related to "first air" or "premiere" for this entity.

[Tool]
Top 10 property alternatives:
1. original broadcaster, also known as original channel, original network, radio network, radio station, television channel (wd:P449 as wdt/p): network(s) or service(s) that originally broadcast a radio or television program, subproperty of broadcast by
2. start time, also known as start date, since, join time, joined, join date (wd:P580 as wdt/p): time an entity begins to exist or a statement starts being valid, subproperty of point in time
3. country of origin, also known as comes from, place of origin, origin country, originates from, country (wd:P495 as wdt/p): country of origin of this item (creative work, food, phrase, product, etc.), subproperty of significant place, subproperty of country
4. Freebase ID, also known as FID, Freebase, Freebase identifier, MID (wd:P646 as wdt/p): identifier for a page in the Freebase database. Format: "/m/0" f...r IDs starting with "/g/", use Google Knowledge Graph ID (P2671)
5. original language of film or TV show, also known as original language, language of the original work, media language, created on language (wd:P364 as wdt/p): language in which a film or a performance work was originally cr... works and songs; use P407 ("language of work or name") instead., subproperty of language of work or name
6. presenter, also known as TV host, TV presenter, presented by, hostess, hosted by (wd:P371 as wdt/p): main role in presenting a radio or television program or a performing arts show, subproperty of significant person, subproperty of cast member
7. title, also known as page name, titled, original title, name, known as (wd:P1476 as wdt/p): published name of a work, such as a newspaper article, a literary work, piece of music, a website, or a performance work, subproperty of name
8. distribution format, also known as media format, media type, medium, method of distribution, publication format (wd:P437 as wdt/p): method (or type) of distribution for the subject
9. award received, also known as winner of, win, recognition title, received award, award won (wd:P166 as wdt/p): award or recognition received by a person, organization or creative work
10. instance of, also known as example of, has type, is a, is an, is of type (wd:P31 as wdt/p): type to which this subject corresponds/belongs. Different from P...tains, and the volcano is a type (instance) of volcanic landform

No other items found

[Assistant]
The property "start time" (P580) seems relevant for the start date of the show.

Step 4: I will check the start time (P580) for Saturday Night Live (wd:Q13979).

[Tool]
Got 1 row and 3 columns:
| subject                         | property              | object                              |
| ------------------------------- | --------------------- | ----------------------------------- |
| Saturday Night Live (wd:Q13979) | start time (wdt:P580) | 1975-10-11T00:00:00Z (xsd:dateTime) |

[Assistant]
Saturday Night Live first aired on 1975-10-11.

Step 5: Now I will find other shows (television series) that first aired in the same year, 1975.

I will query for television series (instance of television series) with start time (P580) in the year 1975.

[Tool]
Got 249 rows and 2 columns, showing the first 5 and last 5 rows below:
| show                                               | showLabel                                  |
| -------------------------------------------------- | ------------------------------------------ |
| Gabriela (wd:Q10287918)                            | Gabriela (en)                              |
| On the Mat (wd:Q10340891)                          | On the Mat (en)                            |
| Cher (wd:Q10381261)                                | Cher (en)                                  |
| Carry On Laughing (wd:Q1045360)                    | Carry On Laughing (en)                     |
| Space: 1999 (wd:Q1045743)                          | Space: 1999 (en)                           |
| ...                                                | ...                                        |
| Beschlossen und verkündet (wd:Q829585)             | Beschlossen und verkündet (en)             |
| wd:Q86210547                                       |                                            |
| Krempoli - Ein Platz für wilde Kinder (wd:Q872205) | Krempoli - Ein Platz für wilde Kinder (en) |
| Krempoli - Ein Platz für wilde Kinder (wd:Q872205) | Krempoli - Ein Platz für wilde Kinder (en) |
| Miś Uszatek (wd:Q929924)                           | Miś Uszatek (en)                           |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?team ?teamLabel ( COUNT( ?final ) AS ?championshipCount ) WHERE {
  ?final wdt:P31 wd:Q842375 ; wdt:P1346 ?team .
  ?team wdt:P118 wd:Q155223 .
  OPTIONAL {
    ?team rdfs:label ?teamLabel FILTER ( LANG( ?teamLabel ) = "en" )
  }
}
GROUP BY ?team ?teamLabel
ORDER BY DESC ( ?championshipCount )
LIMIT 10

Got 10 rows and 3 columns:
| team                               | teamLabel                  | championshipCount |
| ---------------------------------- | -------------------------- | ----------------- |
| Boston Celtics (wd:Q131371)        | Boston Celtics (en)        | 17 (xsd:int)      |
| Los Angeles Lakers (wd:Q121783)    | Los Angeles Lakers (en)    | 16 (xsd:int)      |
| Chicago Bulls (wd:Q128109)         | Chicago Bulls (en)         | 6 (xsd:int)       |
| San Antonio Spurs (wd:Q159729)     | San Antonio Spurs (en)     | 5 (xsd:int)       |
| Golden State Warriors (wd:Q157376) | Golden State Warriors (en) | 4 (xsd:int)       |
| Philadelphia 76ers (wd:Q138089)    | Philadelphia 76ers (en)    | 3 (xsd:int)       |
| Detroit Pistons (wd:Q169661)       | Detroit Pistons (en)       | 3 (xsd:int)       |
| New York Knicks (wd:Q131364)       | New York Knicks (en)       | 2 (xsd:int)       |
| Houston Rockets (wd:Q161345)       | Houston Rockets (en)       | 2 (xsd:int)       |
| Milwaukee Bucks (wd:Q169637)       | Milwaukee Bucks (en)       | 2 (xsd:int)       |

The NBA teams that have won the most seasons (NBA championships) are:
1. Boston Celtics with 17 championships
2. Los Angeles Lakers with 16 championships
3. Chicago Bulls with 6 championships
4. San Antonio Spurs with 5 championships
5. Golden State Warriors with 4 championships
6. Philadelphia 76ers with 3 championships
7. Detroit Pistons with 3 championships
8. New York Knicks with 2 championships
9. Houston Rockets with 2 championships
10. Milwaukee Bucks with 2 championships

Which NBA teams have won the most seasons?

[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 with notes:
1. When direct properties are missing, chain related properties (e.g., child→child for grandchildren) or search for indirect connections to answer complex relationship queries.
2. For set/class queries (e.g., all cities, all holidays), use instance/subclass chains (P31/P279) for completeness and relevant filters. Be aware of data incompleteness and schema inconsistencies in Wikidata.
3. If queries return too many irrelevant or too few results, try varying properties (country, location, observed in, etc.) and subclass chains, but be ready to report data/model limitations if results remain incomplete or noisy.
4. When a direct property or class is missing or inconsistently used, try related or broader properties/classes (e.g., use inception for independence date, or musical work/composition for musicals) to increase recall.
5. For calculated properties (e.g., population density), combine relevant direct properties (e.g., population, area) in SPARQL queries, as Wikidata may not store the derived value directly.
6. When entity search returns multiple candidates, use context (description, instance type, location, etc.) to disambiguate and select the most relevant entity for the query.

No general notes for all knowledge graphs available

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/
qlss: <https://qlever.cs.uni-freiburg.de/spatialSearch/
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. Search for the 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. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
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 just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. 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 NBA teams have won the most seasons?

[Assistant]
To answer the question "Which NBA teams have won the most seasons?" we need to clarify the meaning of "won the most seasons." Typically, this refers to NBA teams that have won the most NBA championships (titles) across seasons.

Step 1: Identify the entity "NBA" and the concept of "NBA championship" or "NBA title."
Step 2: Identify properties related to NBA teams and their championship wins.
Step 3: Query for NBA teams and count their championship wins.
Step 4: Sort the teams by the number of championships won in descending order to find those with the most wins.

I will start by searching for the entity representing the NBA league and the property or entity representing NBA championships. Then I will find NBA teams and their championship counts.

[Tool]
Top 10 entity alternatives:
1. National Basketball Association, also known as Basketball Association of America, National Basketball Assn., Nat'l Basketball Assn., NBA, BAA (wd:Q155223): North American professional men's basketball league, instance of professional sports league, instance of basketball association, instance of basketball league, instance of online database
2. National Book Award, also known as American National Book Award, NBA, ANBA, American NBA (wd:Q572316)
3. New Barnet railway station, also known as NBA, New Barnet Station (wd:Q1217434): railway station in New Barnet, London Borough of Barnet, England, UK, instance of railway station
4. Niger Basin Authority, also known as ABN, Autorite du Bassin du Niger, Autorité du Bassin du Niger, NBA (wd:Q1162546): intergovernmental organisation in West Africa, instance of intergovernmental organization
5. Sybil, also known as NBA (wd:Q2915054): 1973 book Sybil. The True Story of A Woman Possessed By 16 Separaste Personalities., instance of written work
6. National Biodiversity Authority, also known as NBA (wd:Q5054185): instance of government agency, government agency in India
7. Nba Saghru, also known as NBA (wd:Q3874050): Moroccan singer-songwriter, poet and painter (1982-2011), instance of human, singer-songwriter, painter, poet
8. .nba (wd:Q26980848): instance of sponsored top-level domain
9. Namibia Sports Commission, also known as NBA, Namibia Sports Commission*, Namibia Sport Commission, Namibia Sport Commision (wd:Q1490962)
10. NBA (wd:Q17062168): instance of musical work/composition, song by American rapper Joe Budden

[Assistant]
The National Basketball Association (NBA) is represented by wd:Q155223. Now I will search for NBA teams and their championship wins. To do this, I need to find the property or entity that represents NBA championships or titles and then find the teams that have won them.

I will search for the property related to "NBA championship" or "NBA title" to find the relevant data.

[Tool]
Top 10 property alternatives:
1. Hoopla artist ID, also known as hoopladigital.com (wd:P6868 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): identifier for an individual on Hoopla
2. sports discipline competed in, also known as discipline of sport, weight class competed in, sports event, sports disciplines competed in, sports discipline (wd:P2416 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): discipline an athlete competed in within a sport, subproperty of field of work
3. NBA.com player ID (wd:P3647 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): identifier for a basketball player on the National Basketball Association's website
4. statistical leader, also known as most valuable player, MVP, record setter, statistical leader (wd:P3279 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): leader of a sports tournament in one of statistical qualities (points, assists, rebounds etc.).
5. winner, also known as nominee, prizewinner, titleholder, top dog, victor (wd:P1346 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): winner of a competition or similar event, not to be used for awa...ssibly qualified with "for work" (P1686)) or for wars or battles, subproperty of participating team, subproperty of participant
6. Eurobasket.com coach ID, also known as eurobasket.com coach identifier, eurobasket.com ID, eurobasket.com identifier, eurobasket ID (wd:P6865 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): identifier for a basketball coach at Eurobasket.com
7. Eurobasket.com player ID, also known as Eurobasket.com ID, eurobasket.com identifier (wd:P3527 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): ID for a basketball player at Eurobasket.com
8. Basketball Reference NBA player ID, also known as Basketball-Reference.com NBA player ID, BRCNBA (wd:P2685 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): identifier for a NBA player on the Basketball Reference website
9. LNB Élite player ID, also known as French basketball championship ID identifier (new schema), French basketball championship ID (new schema) (wd:P11344 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): new schema for the identifiers of the French league of basketball
10. competition won, also known as victory, won event, won, winner of (wd:P2522 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): competition or sports event won by the subject, subproperty of participant in

[Assistant]
The property "competition won" (wdt:P2522) seems to be the most relevant for finding which competitions or championships a team has won.

Next, I will find NBA teams and count how many times they have won the NBA championship. The NBA championship is an event, so I need to find the entity for the NBA Finals or NBA championship event and then count the wins for each team.

I will search for the entity representing the NBA Finals or NBA championship event.

[Tool]
Top 10 entity alternatives:
1. NBA Finals, also known as Basketball Association of Americ...ld Championship Series [1947-49], NBA World Championship Series [until 1987], NBA title, National Basketball Association ...Championship Series [until 1987], BAA World Championship Series [1947-49] (wd:Q842375)
2. Bill Russell NBA Finals Most Valuable Player Award, also known as NBA Finals MVP Award, NBA Finals MVP, NBA Finals Most Valuable Player Award, NBA Finals Most Valuable Player, Bill Russell NBA Finals MVP Award (wd:Q739499): instance of most valuable player award, award
3. 2022 NBA Finals (wd:Q112083544): 2022 edition of the NBA Finals, instance of NBA Finals
4. 2019 NBA Finals (wd:Q63940565): 2019 edition of the NBA Finals, between the Golden State Warriors and the Toronto Raptors, instance of NBA Finals
5. NBA conference finals, also known as National Basketball Association conference finals (wd:Q2628047): Eastern and Western championship series of the NBA
6. 2008 NBA Finals (wd:Q2036161): 2008 basketball championship series, instance of NBA Finals
7. 2023 NBA Finals (wd:Q118126592): 2023 edition of the NBA Finals, instance of NBA Finals
8. 2021 NBA Finals (wd:Q107377340): 2021 edition of the NBA Finals between the Phoenix Suns and Milwaukee Bucks, instance of NBA Finals
9. 2011 NBA Finals (wd:Q1320332): 2011 basketball championship series, instance of NBA Finals
10. 2012 NBA Finals (wd:Q2628013): 2012 basketball championship series, instance of NBA Finals

[Assistant]
The NBA Finals is represented by wd:Q842375. This entity represents the NBA championship series.

Now, I will query for NBA teams and count how many times each team has won the NBA Finals (wd:Q842375) using the property "winner" (wdt:P1346) for the NBA Finals events. Then I will sort the teams by the number of wins in descending order to find the teams with the most championships.

[Tool]
SPARQL execution failed:
Error while executing a SERVICE request to <http://wikiba.se:80/ontology#label>: SERVICE responded with HTTP status code: 301, Moved Permanently. First 100 bytes of the response: '<html>
<head><title>301 Moved Permanently</title></head>
<body>
<center><h1>301 Moved Permanently'

[Assistant]
The query failed due to the use of the SERVICE wikibase:label, which is not supported by the QLever SPARQL endpoint. I will rewrite the query without using this service and instead use optional clauses to get the team labels.

I will now retry the query without the SERVICE wikibase:label clause.

[Tool]
Got 10 rows and 3 columns:
| team                               | teamLabel                  | championshipCount |
| ---------------------------------- | -------------------------- | ----------------- |
| Boston Celtics (wd:Q131371)        | Boston Celtics (en)        | 17 (xsd:int)      |
| Los Angeles Lakers (wd:Q121783)    | Los Angeles Lakers (en)    | 16 (xsd:int)      |
| Chicago Bulls (wd:Q128109)         | Chicago Bulls (en)         | 6 (xsd:int)       |
| San Antonio Spurs (wd:Q159729)     | San Antonio Spurs (en)     | 5 (xsd:int)       |
| Golden State Warriors (wd:Q157376) | Golden State Warriors (en) | 4 (xsd:int)       |
| Philadelphia 76ers (wd:Q138089)    | Philadelphia 76ers (en)    | 3 (xsd:int)       |
| Detroit Pistons (wd:Q169661)       | Detroit Pistons (en)       | 3 (xsd:int)       |
| New York Knicks (wd:Q131364)       | New York Knicks (en)       | 2 (xsd:int)       |
| Houston Rockets (wd:Q161345)       | Houston Rockets (en)       | 2 (xsd:int)       |
| Milwaukee Bucks (wd:Q169637)       | Milwaukee Bucks (en)       | 2 (xsd:int)       |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?height WHERE {
  wd:Q4176 wdt:P2048 ?height .
}

The height of the Cologne Cathedral is 157 meters.

[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 with notes:
1. When direct properties are missing, chain related properties (e.g., child→child for grandchildren) or search for indirect connections to answer complex relationship queries.
2. For set/class queries (e.g., all cities, all holidays), use instance/subclass chains (P31/P279) for completeness and relevant filters. Be aware of data incompleteness and schema inconsistencies in Wikidata.
3. If queries return too many irrelevant or too few results, try varying properties (country, location, observed in, etc.) and subclass chains, but be ready to report data/model limitations if results remain incomplete or noisy.
4. When a direct property or class is missing or inconsistently used, try related or broader properties/classes (e.g., use inception for independence date, or musical work/composition for musicals) to increase recall.
5. For calculated properties (e.g., population density), combine relevant direct properties (e.g., population, area) in SPARQL queries, as Wikidata may not store the derived value directly.
6. When entity search returns multiple candidates, use context (description, instance type, location, etc.) to disambiguate and select the most relevant entity for the query.

No general notes for all knowledge graphs available

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/
qlss: <https://qlever.cs.uni-freiburg.de/spatialSearch/
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. Search for the 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. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
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 just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. 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 high is the Cologne Cathedral ?

[Assistant] To answer the question "How high is the Cologne Cathedral?" I need to find the entity for "Cologne Cathedral" in Wikidata and then find the property that represents its height. Typically, the height of buildings or structures is represented by the property "height" (P2048) in Wikidata. I will first search for the entity "Cologne Cathedral" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Cologne Cathedral, also known as High Cathedral of St. Peter (wd:Q4176): Roman-Catholic cathedral in Cologne, Germany, instance of tourist attraction, instance of Catholic cathedral, instance of church building
2. Cologne Cathedral (wd:Q20891184): painting by Oskar Kokoschka, instance of painting
3. Cologne Cathedral Seal (wd:Q1795817): part of Cologne Cathedral
4. Baptistry Cologne Cathedral (wd:Q106498805): instance of church building
5. Cologne Cathedral, 1869 (wd:Q119111134): painting by James Webb (1835–1895), Bristol Museum & Art Gallery, instance of painting
6. Bells of Cologne Cathedral (wd:Q1795814): instance of church bell
7. Finials of Cologne Cathedral (wd:Q1408999): instance of work of art, cologne cathedral
8. Organs of Cologne Cathedral, also known as Organs in Cologne Cathedral (wd:Q7102106): instance of set
9. Surroundings of Cologne Cathedral (wd:Q65233962): cologne Cathedral surroundings, instance of pedestrian zone
10. The Beggar from Cologne Cathedral, also known as Beggar from Cologne Cathedral (wd:Q15241065): 1927 film by Rolf Randolf, instance of film

[Assistant] The entity for "Cologne Cathedral" is wd:Q4176. Now I will check the property for the height of this entity, which is likely to be property P2048 (height). I will query the height of Cologne Cathedral using this property.

[Tool]
Got 1 row and 1 column:
| height            |
| ----------------- |
| 157 (xsd:decimal) |

[Tool] Stopped generation process


PREFIX ps: <http://www.wikidata.org/prop/statement/>
PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX pq: <http://www.wikidata.org/prop/qualifier/>
PREFIX p: <http://www.wikidata.org/prop/>
SELECT ( COUNT( ?person ) AS ?countBeforeBrezhnev ) WHERE {
  ?person p:P39 ?statement .
  ?statement ps:P39 wd:Q1048744 .
  ?statement pq:P580 ?startDate .
  FILTER ( ?startDate < "1964-10-14T00:00:00Z" ^^ xsd:dateTime )
}

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

There were 2 General Secretaries of the CPSU before Leonid Brezhnev: Joseph Stalin and Nikita Khrushchev.

How many General Secretaries of the CPSU were there before Leonid Brezhnev?

[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 with notes:
1. When direct properties are missing, chain related properties (e.g., child→child for grandchildren) or search for indirect connections to answer complex relationship queries.
2. For set/class queries (e.g., all cities, all holidays), use instance/subclass chains (P31/P279) for completeness and relevant filters. Be aware of data incompleteness and schema inconsistencies in Wikidata.
3. If queries return too many irrelevant or too few results, try varying properties (country, location, observed in, etc.) and subclass chains, but be ready to report data/model limitations if results remain incomplete or noisy.
4. When a direct property or class is missing or inconsistently used, try related or broader properties/classes (e.g., use inception for independence date, or musical work/composition for musicals) to increase recall.
5. For calculated properties (e.g., population density), combine relevant direct properties (e.g., population, area) in SPARQL queries, as Wikidata may not store the derived value directly.
6. When entity search returns multiple candidates, use context (description, instance type, location, etc.) to disambiguate and select the most relevant entity for the query.

No general notes for all knowledge graphs available

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/
qlss: <https://qlever.cs.uni-freiburg.de/spatialSearch/
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. Search for the 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. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
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 just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. 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 General Secretaries of the CPSU were there before Leonid Brezhnev?

[Assistant]
To answer the question "How many General Secretaries of the CPSU were there before Leonid Brezhnev?", I need to:

1. Identify the entity for the Communist Party of the Soviet Union (CPSU).
2. Identify the position "General Secretary of the CPSU".
3. Find the list of people who held the position "General Secretary of the CPSU".
4. Identify Leonid Brezhnev in that list and count how many held the position before him.

I will start by searching for the entity of the Communist Party of the Soviet Union (CPSU).

[Tool]
Top 10 entity alternatives:
1. Communist Party of the Soviet Union, also known as RCP(b), Russian Communist Party (Bolsheviks), CPSU, Bolshevik Party, All-Union Communist Party (Bolsheviks) (wd:Q79854): Marxist–Leninist founding and ruling party of the Soviet Union, instance of communist party
2. Communist Party of the Soviet Union (wd:Q4229911): Political party founded in 2001, instance of political party
3. Communist Party of the Soviet Union (wd:Q3701765): Political party founded in 1992, instance of political party
4. Congress of the Communist Party of the Soviet Union (wd:Q2415599)
5. The History of the Communist Party of the Soviet Union, also known as A Short History of the Communist Party of the Soviet Union (wd:Q2657367): history book commissioned by Joseph Stalin, instance of creative work, instance of written work
6. ideology of the Communist Party of the Soviet Union (wd:Q13574353): soviet Union Communist Russian party government, instance of ideology
7. Program of the Communist Party of the Soviet Union (wd:Q7248428)
8. Charter of the Communist Party of the Soviet Union (wd:Q4478159)
9. Organisation of the Communist Party of the Soviet Union (wd:Q11315556): instance of Wikimedia page
10. Purges of the Communist Party of the Soviet Union (wd:Q6591430): series of political events

[Assistant]
The entity for the Communist Party of the Soviet Union (CPSU) is wd:Q79854.

Next, I will search for the position "General Secretary of the CPSU" to find its entity.

[Tool]
Top 10 entity alternatives:
1. General Secretary of the Communist Party of the Soviet Union, also known as General Secretary of the Soviet Union, Gensek of the CPSU, First Secretary of the Communist Party of the Soviet Union (wd:Q1048744)
2. Secretary of the Communist Party of the Soviet Union (wd:Q115147301)
3. General Department of the Communist Party of the Soviet Union (wd:Q16682297): instance of Department of the Central Committee of the Communist Party of the Soviet Union, organization
4. General Secretary Xi Jin-ping’s ...and Their Practical Significance (wd:Q120857259): scholarly article in Studies on Mao Zedong and Deng Xiaoping Theories, no. 9, 2019, instance of scholarly article
5. Communist Party of the Soviet Union, also known as RCP(b), Russian Communist Party (Bolsheviks), CPSU, Bolshevik Party, All-Union Communist Party (Bolsheviks) (wd:Q79854): Marxist–Leninist founding and ruling party of the Soviet Union, instance of communist party
6. Communist Party of the Soviet Union (wd:Q4229911): Political party founded in 2001, instance of political party
7. Communist Party of the Soviet Union (wd:Q3701765): Political party founded in 1992, instance of political party
8. Appeal to the General Secretary ...States of America, Ronald Reagan (wd:Q43929775): scientific article published on July 13, 1985, instance of scholarly article
9. General Secretary of the Chinese Communist Party, also known as General Secretary of the Central...e of the Chinese Communist Party, General Secretary of the CCP, General Secretary of the Communist Party of China, CCP General Secretary, General Secretary (wd:Q849418)
10. Congress of the Communist Party of the Soviet Union (wd:Q2415599)

[Assistant]
The entity for the position "General Secretary of the Communist Party of the Soviet Union" is wd:Q1048744.

Now, I will find the people who held the position "General Secretary of the CPSU" (wd:Q1048744) and identify Leonid Brezhnev among them. Then I will count how many held the position before him. I will start by searching for Leonid Brezhnev's entity.

[Tool]
Top 10 entity alternatives:
1. Leonid Brezhnev, also known as Brezhnev, Leonid Ilyich Brezhnev (wd:Q765165): General Secretary of the Communist Party of the Soviet Union (1906–1982), military personnel, instance of human, politician, engineer, surveyor
2. Admiral Kuznetsov, also known as aircraft carrier Admiral Kuznetsov, Tbilisi, Riga, Leonid Brezhnev, Admiral Flota Sovetskovo Soyuza Kuznetsov (wd:Q274924): 1985 Kuznetsov-class aircraft carrier, instance of aircraft carrier, instance of aircraft cruiser
3. Starry Metropolis, also known as Olivia, Neptune, IMO 7359498, Leonid Brezhnev, Kareliya (wd:Q22968553): instance of cruise ship, ship built in 1976
4. Leonid Brezhnev (wd:Q47510378): painting by Burton Philip Silverman, instance of painting
5. Leonid Brezhnev, also known as Whiskey on the Rocks Leonid Brezhnev (wd:Q131583282): Leonid Brezhnev as depicted in television series Whiskey on the Rocks, instance of historical character, instance of television character, instance of fictional human, politician
6. Leonid Brezhnev (wd:Q58345911): Scientific article (April 2014), instance of scholarly article
7. Leonid Brezhnev (wd:Q47511657): painting by Harvey Simpson, instance of painting
8. Leonid Brezhnev (wd:Q47510509): painting by Boris Chaliapin, instance of painting
9. Leonid Brezhnev (wd:Q47509621): painting by Boris Artzybasheff, instance of painting
10. Yuri Brezhnev, also known as Yuri Leonidovich Brezhnev (wd:Q8061482): Soviet politician (1933-2013), instance of human, politician

[Assistant]
The entity for Leonid Brezhnev is wd:Q765165.

Next, I will find the list of people who held the position "General Secretary of the Communist Party of the Soviet Union" (wd:Q1048744) and order them by the start date of their term to identify those before Leonid Brezhnev. I will also check the start date of Leonid Brezhnev's term as General Secretary to filter the list accordingly.

[Tool]
SPARQL execution failed:
Error while executing a SERVICE request to <http://wikiba.se:80/ontology#label>: SERVICE responded with HTTP status code: 301, Moved Permanently. First 100 bytes of the response: '<html>
<head><title>301 Moved Permanently</title></head>
<body>
<center><h1>301 Moved Permanently'

[Assistant]
The query failed because the SERVICE wikibase:label is not supported by the QLever SPARQL endpoint. I will rewrite the query without using SERVICE wikibase:label and instead use rdfs:label with an OPTIONAL clause to get the labels.

I will now re-execute the query to get the list of people who held the position "General Secretary of the Communist Party of the Soviet Union" (wd:Q1048744) with their start dates, ordered by start date.

[Tool]
Got 7 rows and 3 columns:
| person                             | personLabel                | startDate                           |
| ---------------------------------- | -------------------------- | ----------------------------------- |
| Joseph Stalin (wd:Q855)            | Joseph Stalin (en)         | 1922-04-03T00:00:00Z (xsd:dateTime) |
| Nikita Khrushchev (wd:Q35314)      | Nikita Khrushchev (en)     | 1953-09-14T00:00:00Z (xsd:dateTime) |
| Leonid Brezhnev (wd:Q765165)       | Leonid Brezhnev (en)       | 1964-10-14T00:00:00Z (xsd:dateTime) |
| Yuri Andropov (wd:Q44071)          | Yuri Andropov (en)         | 1982-11-12T00:00:00Z (xsd:dateTime) |
| Constantine Chernenko (wd:Q127442) | Constantine Chernenko (en) | 1984-02-13T00:00:00Z (xsd:dateTime) |
| Mikhail Gorbachev (wd:Q30487)      | Mikhail Gorbachev (en)     | 1985-03-11T00:00:00Z (xsd:dateTime) |
| Volodymyr Ivashko (wd:Q1065213)    | Volodymyr Ivashko (en)     | 1991-08-24T00:00:00Z (xsd:dateTime) |

[Assistant]
There are 7 people who held the position of General Secretary of the Communist Party of the Soviet Union with known start dates. Among them, Leonid Brezhnev started his term on 1964-10-14.

The people who held the position before Leonid Brezhnev are:
- Joseph Stalin
- Nikita Khrushchev

So, there were 2 General Secretaries of the CPSU before Leonid Brezhnev.

I will now provide the final answer.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?dateOfDeath WHERE {
  wd:Q6711 wdt:P570 ?dateOfDeath .
}

Allen Ginsberg died on April 5, 1997.

[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 with notes:
1. When direct properties are missing, chain related properties (e.g., child→child for grandchildren) or search for indirect connections to answer complex relationship queries.
2. For set/class queries (e.g., all cities, all holidays), use instance/subclass chains (P31/P279) for completeness and relevant filters. Be aware of data incompleteness and schema inconsistencies in Wikidata.
3. If queries return too many irrelevant or too few results, try varying properties (country, location, observed in, etc.) and subclass chains, but be ready to report data/model limitations if results remain incomplete or noisy.
4. When a direct property or class is missing or inconsistently used, try related or broader properties/classes (e.g., use inception for independence date, or musical work/composition for musicals) to increase recall.
5. For calculated properties (e.g., population density), combine relevant direct properties (e.g., population, area) in SPARQL queries, as Wikidata may not store the derived value directly.
6. When entity search returns multiple candidates, use context (description, instance type, location, etc.) to disambiguate and select the most relevant entity for the query.

No general notes for all knowledge graphs available

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/
qlss: <https://qlever.cs.uni-freiburg.de/spatialSearch/
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. Search for the 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. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
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 just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. Do not use 'SERVICE wikibase:label { bd:serviceParam wikibase:language ...' in SPARQL queries. It is not SPARQL standard and unsupported by the used QLever SPARQL endpoints. Use rdfs:label or similar properties to get labels instead.

[User] When did Allen Ginsberg die?

[Assistant] To answer the question "When did Allen Ginsberg die?" I need to find the entity for Allen Ginsberg in Wikidata and then find the property that corresponds to the date of death. I will start by searching for the entity Allen Ginsberg in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Allen Ginsberg, also known as Irwin Allen Ginsberg (wd:Q6711): American poet and writer (1926–1997), instance of human, autobiographer, photographer, screenwriter, playwright, musician, diarist, teacher, writer, poet
2. Allen Ginsberg (wd:Q102249265): Ph.D. Rutgers University, New Brunswick 1986, instance of human
3. Allen Ginsberg Library (wd:Q117833470): this entity is a library in the state of colorado. it was identified and verified by the chr task force, instance of library
4. Untitled (Allen Ginsberg) (wd:Q76558858): print in the National Gallery of Art (NGA 155538), instance of work of art
5. Allen Ginsberg no number (wd:Q64163104): photograph in the National Gallery of Art (NGA 88333), instance of photograph
6. Allen Ginsberg no number (wd:Q64163625): photograph in the National Gallery of Art (NGA 88331), instance of photograph
7. Allen Ginsberg no number (wd:Q64163102): photograph in the National Gallery of Art (NGA 88336), instance of photograph
8. Allen Ginsberg no number (wd:Q64163079): photograph in the National Gallery of Art (NGA 88334), instance of photograph
9. Allen Ginsberg no number (wd:Q64162725): photograph in the National Gallery of Art (NGA 88330), instance of photograph
10. Allen Ginsberg no number (wd:Q64162264): photograph in the National Gallery of Art (NGA 88332), instance of photograph

[Assistant] The entity for Allen Ginsberg as the American poet and writer is wd:Q6711. Now I will look for the property that corresponds to the date of death for this entity.

[Tool]
Got 1 row and 3 columns:
| subject                   | property                 | object                              |
| ------------------------- | ------------------------ | ----------------------------------- |
| Allen Ginsberg (wd:Q6711) | date of death (wdt:P570) | 1997-04-05T00:00:00Z (xsd:dateTime) |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ( COUNT( ?award ) AS ?totalOscars ) WHERE {
  ?movie wdt:P57 wd:Q55400 ; wdt:P166 ?award .
  ?award wdt:P31 / wdt:P279 * wd:Q19020 .
}

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

All movies directed by Hayao Miyazaki combined have won 2 Oscars.

How many Oscars did all the movies directed by Hayao Miyazaki combined win?

[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 with notes:
1. When direct properties are missing, chain related properties (e.g., child→child for grandchildren) or search for indirect connections to answer complex relationship queries.
2. For set/class queries (e.g., all cities, all holidays), use instance/subclass chains (P31/P279) for completeness and relevant filters. Be aware of data incompleteness and schema inconsistencies in Wikidata.
3. If queries return too many irrelevant or too few results, try varying properties (country, location, observed in, etc.) and subclass chains, but be ready to report data/model limitations if results remain incomplete or noisy.
4. When a direct property or class is missing or inconsistently used, try related or broader properties/classes (e.g., use inception for independence date, or musical work/composition for musicals) to increase recall.
5. For calculated properties (e.g., population density), combine relevant direct properties (e.g., population, area) in SPARQL queries, as Wikidata may not store the derived value directly.
6. When entity search returns multiple candidates, use context (description, instance type, location, etc.) to disambiguate and select the most relevant entity for the query.

No general notes for all knowledge graphs available

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/
qlss: <https://qlever.cs.uni-freiburg.de/spatialSearch/
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. Search for the 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. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
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 just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. 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 Oscars did all the movies directed by Hayao Miyazaki combined win?

[Assistant]
Step 1: Identify the entity for Hayao Miyazaki in Wikidata.
Step 2: Identify the property for "director" and find all movies directed by Hayao Miyazaki.
Step 3: Identify the property for "Oscar awards won" or similar for movies.
Step 4: Sum the number of Oscars won by all movies directed by Hayao Miyazaki.

I will start by searching for the entity "Hayao Miyazaki" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Hayao Miyazaki, also known as Tsutomu Teruki, Miyazaki Hayao (wd:Q55400): Japanese animator, film director, and mangaka (born 1941), mechanical designer, character designer, animation director, instance of human, environmentalist, film producer, film director, comics artist, screenwriter, illustrator, film editor, director, animator, lyricist, mangaka
2. Hayao Miyazaki (wd:Q11454632): Japanese baseball player, instance of human, baseball player
3. Hayao Miyazaki bibliography (wd:Q130265400): instance of bibliography
4. Never-Ending Man: Hayao Miyazaki (wd:Q42859983): instance of film, 2016 film
5. Hayao Miyazaki's Daydream Data Notes, also known as Miyazaki Hayao no Zassō Nōto (wd:Q888819): collection of manga and illustrated writings by Hayao Miyazaki, instance of manga series
6. Hayao Miyazaki and the Heron (wd:Q130287351): 2024 documentary film, instance of film
7. Miyazaki Prefecture, also known as Miyazaki (wd:Q130300): instance of prefecture of Japan, prefecture of Japan
8. Miyazaki (wd:Q204308): instance of prefectural capital of Japan, city in Miyazaki prefecture, Japan, instance of core city of Japan, instance of city of Japan, instance of big city
9. 6905 Miyazaki, also known as Miyazaki (wd:Q717728): instance of asteroid, asteroid
10. Hayao (wd:Q60786238): instance of undifferentiated Japanese kana given name, Japanese male given name (はやお), instance of male given name

[Assistant] I found the entity for Hayao Miyazaki as wd:Q55400. Next, I will find all movies directed by Hayao Miyazaki using the property "director" (wdt:P57) and then find the number of Oscars won by these movies. The property for awards received by a film is "award received" (wdt:P166), and I will filter for Oscars (Academy Awards). Then I will sum the number of Oscars won by all these movies.

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

[Assistant] The query returned 0 Oscars, which seems unlikely given Hayao Miyazaki's reputation. I will refine the query to count the number of Oscar awards won by each movie directed by Hayao Miyazaki more accurately. I will check for awards that are instances of Academy Award (wd:Q19020) or its subclasses and count them properly. I will also check if the awards have qualifiers indicating the number of awards won.

[Tool]
Got 1 row and 1 column:
| totalOscars |
| ----------- |
| 2 (xsd:int) |

[Assistant] The refined query shows that all movies directed by Hayao Miyazaki combined have won 2 Oscars. I will now finalize the answer.

[Tool] Stopped generation process


SELECT ( 2024 - 1945 AS ?minAge ) ( 2024 - 1928 AS ?maxAge ) WHERE {
}

People of the Silent Generation were born between 1928 and 1945. As of 2024, they belong to the age range of 79 to 96 years old.

People of what age belong to the Silent Generation ?

[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 with notes:
1. When direct properties are missing, chain related properties (e.g., child→child for grandchildren) or search for indirect connections to answer complex relationship queries.
2. For set/class queries (e.g., all cities, all holidays), use instance/subclass chains (P31/P279) for completeness and relevant filters. Be aware of data incompleteness and schema inconsistencies in Wikidata.
3. If queries return too many irrelevant or too few results, try varying properties (country, location, observed in, etc.) and subclass chains, but be ready to report data/model limitations if results remain incomplete or noisy.
4. When a direct property or class is missing or inconsistently used, try related or broader properties/classes (e.g., use inception for independence date, or musical work/composition for musicals) to increase recall.
5. For calculated properties (e.g., population density), combine relevant direct properties (e.g., population, area) in SPARQL queries, as Wikidata may not store the derived value directly.
6. When entity search returns multiple candidates, use context (description, instance type, location, etc.) to disambiguate and select the most relevant entity for the query.

No general notes for all knowledge graphs available

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/
qlss: <https://qlever.cs.uni-freiburg.de/spatialSearch/
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. Search for the 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. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
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 just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. 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] People of what age belong to the Silent Generation ?

[Assistant]
To answer the question "People of what age belong to the Silent Generation?", I need to find the birth years or birth date range that define the Silent Generation. This will allow me to determine the age range of people who belong to this generation.

Step 1: Identify the entity "Silent Generation" in Wikidata.
Step 2: Find properties related to the birth date range or birth years associated with the Silent Generation.
Step 3: Use the birth date range to calculate the current age range of people belonging to the Silent Generation.

I will start by searching for the entity "Silent Generation" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Silent Generation, also known as Lucky Few Generation (wd:Q3044693)
2. The Silent Generation (wd:Q7764217): album by The Dimes, instance of album
3. The Silent Generation (wd:Q112669391): instance of film, 2020 film
4. Silent zone generation (wd:Q124647370): instance of United States patent, US patent 11495205
5. Winter’s Children - The Silent Generation (wd:Q85671231): 2005 documentary film by Jens Schanze, instance of film
6. China Witness, also known as China Witness: Voices from a Silent Generation. (wd:Q113681807): History of China in the 20th century, instance of book
7. Comparison of Silent Navigator Waveform Generation Methods (wd:Q92381831): scientific article published on 30 May 2019, instance of scholarly article
8. AI-assisted sound effect generation for silent video (wd:Q122763863): instance of United States patent, US patent 11381888
9. From the silent generation to the involved generation (wd:Q40372593): scientific article published on April 1, 1978, instance of scholarly article
10. Retirement income decisions: the silent generation speaks. (wd:Q40344803): scientific article published on January 2005, instance of scholarly article

[Assistant] The entity "Silent Generation" in Wikidata is identified as wd:Q3044693. Now, I will look for properties related to the birth date range or birth years associated with this entity to determine the age range of people belonging to the Silent Generation.

[Tool]
Got 124 rows and 3 columns, showing the first 10 rows below:
| subject                         | property                                              | object                                                                                |
| ------------------------------- | ----------------------------------------------------- | -------------------------------------------------------------------------------------- |
| Silent Generation (wd:Q3044693) | instance of (wdt:P31)                                 | cultural generation of western society (wd:Q15925549)                                  |
| Silent Generation (wd:Q3044693) | image (wdt:P18)                                       | <http://commons.wikimedia.org/wiki/Special:FilePath/Girl%20listening%20to%20radio.gif> |
| Silent Generation (wd:Q3044693) | subclass of (wdt:P279)                                | Great Power Saeculum (wd:Q113207359)                                                   |
| Silent Generation (wd:Q3044693) | location (wdt:P276)                                   | worldwide (wd:Q13780930)                                                               |
| Silent Generation (wd:Q3044693) | follows (wdt:P155)                                    | Greatest Generation (wd:Q27995878)                                                     |
| Silent Generation (wd:Q3044693) | followed by (wdt:P156)                                | Baby Boomers (wd:Q797819)                                                              |
| Silent Generation (wd:Q3044693) | Freebase ID (wdt:P646)                                | /m/02zksw                                                                              |
| Silent Generation (wd:Q3044693) | Quora topic ID (wdt:P3417)                            | Silent-Generation                                                                      |
| Silent Generation (wd:Q3044693) | Encyclopædia Britannica Online ID (wdt:P1417)         | topic/Silent-Generation                                                                |
| Silent Generation (wd:Q3044693) | Encyclopedia of China (Third Edition) ID (wdt:P10565) | 125938                                                                                |
| ...                             | ...                                                   | ...                                                                                |

[Assistant]
The entity Silent Generation (wd:Q3044693) is a cultural generation of western society and is located worldwide. It follows the Greatest Generation (wd:Q27995878) and is followed by the Baby Boomers (wd:Q797819). However, there is no direct property indicating the birth date range or birth years in the first 10 properties.

I will now look for statements or qualifiers that might indicate the birth date range or birth years for the Silent Generation. I will check for properties related to "start time" (P580) and "end time" (P582) or any other relevant properties that might indicate the birth years.

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Assistant] There are no direct properties for start time (P580) or end time (P582) for the Silent Generation entity. I will try to find other properties that might indicate the birth years or birth date range by searching for properties related to "birth" or "date" for this entity.

[Tool]
Top 10 property alternatives:
1. location, also known as neighborhood, locale, neighbourhood, place, place held (wd:P276 as wdt/p): location of the object, structure or event. In the case of an ad...ity use P706. Use P7153 for locations associated with the object
2. follows, also known as prev, successor to, succeeds to, succeeds, split from (wd:P155 as wdt/p): immediately prior item in a series of which the subject is a par... preceding item, e.g. political offices, use "replaces" (P1365)]
3. image, also known as img, infobox image, photo, photograph, picture (wd:P18 as wdt/p): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)
4. followed by, also known as prequel of, sequel is, succeeded by, then, successor (wd:P156 as wdt/p): immediately following item in a series of which the subject is a...een replaced, e.g. political offices, use "replaced by" (P1366)]
5. Freebase ID, also known as FID, Freebase, Freebase identifier, MID (wd:P646 as wdt/p): identifier for a page in the Freebase database. Format: "/m/0" f...r IDs starting with "/g/", use Google Knowledge Graph ID (P2671)
6. instance of, also known as example of, has type, is a, is an, is of type (wd:P31 as wdt/p): type to which this subject corresponds/belongs. Different from P...tains, and the volcano is a type (instance) of volcanic landform
7. subclass of, also known as subcategory of, subgenre of, sort of, subtype of, type of (wd:P279 as wdt/p): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain

No other items found

[Assistant]
There are no direct properties related to birth or date for the Silent Generation entity. I will try a different approach by searching for the birth date range of the generations that precede and follow the Silent Generation, namely the Greatest Generation (wd:Q27995878) and the Baby Boomers (wd:Q797819), to infer the birth years of the Silent Generation.

I will start by checking the birth date range or birth years for the Greatest Generation.

[Tool]
Got 125 rows and 3 columns, showing the first 10 rows below:
| subject                            | property                                      | object                                                                         |
| ---------------------------------- | --------------------------------------------- | ------------------------------------------------------------------------------ |
| Greatest Generation (wd:Q27995878) | instance of (wdt:P31)                         | cultural generation of western society (wd:Q15925549)                          |
| Greatest Generation (wd:Q27995878) | image (wdt:P18)                               | <http://commons.wikimedia.org/wiki/Special:FilePath/Lange-MigrantMother02.jpg> |
| Greatest Generation (wd:Q27995878) | subclass of (wdt:P279)                        | Great Power Saeculum (wd:Q113207359)                                           |
| Greatest Generation (wd:Q27995878) | has part(s) (wdt:P527)                        | Interbellum Generation (wd:Q6045552)                                           |
| Greatest Generation (wd:Q27995878) | follows (wdt:P155)                            | Lost Generation (wd:Q234105)                                                   |
| Greatest Generation (wd:Q27995878) | followed by (wdt:P156)                        | Silent Generation (wd:Q3044693)                                                |
| Greatest Generation (wd:Q27995878) | Google Knowledge Graph ID (wdtn:P2671)        | <http://g.co/kg/g/11csqv5l_j>                                                  |
| Greatest Generation (wd:Q27995878) | Quora topic ID (wdt:P3417)                    | Greatest-Generation                                                            |
| Greatest Generation (wd:Q27995878) | Encyclopædia Britannica Online ID (wdt:P1417) | topic/Greatest-Generation                                                      |
| Greatest Generation (wd:Q27995878) | schema:description                            | 1900年代から1920年代に生まれた世代 (ja)                                                     |
| ...                                | ...                                           | ...                                                                            |

[Assistant]
The entity Greatest Generation (wd:Q27995878) is also a cultural generation of western society. The description property (schema:description) mentions "1900年代から1920年代に生まれた世代" which translates from Japanese as "generation born from the 1900s to the 1920s." This gives a birth year range for the Greatest Generation.

Now, I will check the birth date range or birth years for the Baby Boomers (wd:Q797819) to infer the birth years of the Silent Generation.

[Tool]
Got 324 rows and 3 columns, showing the first 10 rows below:
| subject                   | property                         | object                                                                           |
| ------------------------- | -------------------------------- | -------------------------------------------------------------------------------- |
| Baby Boomers (wd:Q797819) | instance of (wdt:P31)            | cultural generation of western society (wd:Q15925549)                            |
| Baby Boomers (wd:Q797819) | Commons category (wdt:P373)      | Baby boomers                                                                     |
| Baby Boomers (wd:Q797819) | image (wdt:P18)                  | <http://commons.wikimedia.org/wiki/Special:FilePath/U.S.BirthRate.1909.2003.png> |
| Baby Boomers (wd:Q797819) | subclass of (wdt:P279)           | Millennial Saeculum (wd:Q113207194)                                              |
| Baby Boomers (wd:Q797819) | described by source (wdt:P1343)  | St. James Encyclopedia of Popular Culture (wd:Q7588466)                          |
| Baby Boomers (wd:Q797819) | follows (wdt:P155)               | Silent Generation (wd:Q3044693)                                                  |
| Baby Boomers (wd:Q797819) | followed by (wdt:P156)           | Generation X (wd:Q379689)                                                        |
| Baby Boomers (wd:Q797819) | start time (wdt:P580)            | 1946-01-01T00:00:00Z (xsd:dateTime)                                              |
| Baby Boomers (wd:Q797819) | topic's main category (wdt:P910) | wd:Q86777387                                                                     |
| Baby Boomers (wd:Q797819) | end time (wdt:P582)              | 1964-01-01T00:00:00Z (xsd:dateTime)                                              |
| ...                       | ...                              | ...                                                                              |

[Assistant]
The entity Baby Boomers (wd:Q797819) is a cultural generation of western society. It has a start time (wdt:P580) of 1946-01-01 and an end time (wdt:P582) of 1964-01-01, indicating the birth years for this generation.

From the information gathered:
- Greatest Generation birth years: 1900s to 1920s
- Baby Boomers birth years: 1946 to 1964

Since the Silent Generation follows the Greatest Generation and precedes the Baby Boomers, I will now check if the Silent Generation entity has any statements or qualifiers indicating its birth years or birth date range, possibly between the 1920s and 1946.

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Assistant] There are no direct start time or end time properties for the Silent Generation entity. I will now try to find any statements or descriptions that mention the birth years or birth date range for the Silent Generation by querying for the rdfs:label and schema:description properties.

[Tool]
Got 27 rows and 3 columns, showing the first 10 rows below:
| subject                         | property   | object                       |
| ------------------------------- | ---------- | ---------------------------- |
| Silent Generation (wd:Q3044693) | rdfs:label | Den stille generasjonen (nb) |
| Silent Generation (wd:Q3044693) | rdfs:label | Generación silenciosa (es)   |
| Silent Generation (wd:Q3044693) | rdfs:label | Generació silenciosa (ca)    |
| Silent Generation (wd:Q3044693) | rdfs:label | Generasi Bisu (id)           |
| Silent Generation (wd:Q3044693) | rdfs:label | génération silencieuse (fr)  |
| Silent Generation (wd:Q3044693) | rdfs:label | Generazione silenziosa (it)  |
| Silent Generation (wd:Q3044693) | rdfs:label | Geração Silenciosa (pt)      |
| Silent Generation (wd:Q3044693) | rdfs:label | Sessiz Kuşak (tr)            |
| Silent Generation (wd:Q3044693) | rdfs:label | Silensyong Henerasyon (bcl)  |
| Silent Generation (wd:Q3044693) | rdfs:label | Silent Generation (cs)       |
| ...                             | ...        | ...                          |

[Tool]
Got 13 rows and 3 columns, showing the first 10 rows below:
| subject                         | property           | object                                                                   |
| ------------------------------- | ------------------ | ------------------------------------------------------------------------ |
| Silent Generation (wd:Q3044693) | schema:description | 1928–1945-yillar oralig‘ida tug‘ilgan shaxslar guruhi (uz)               |
| Silent Generation (wd:Q3044693) | schema:description | 1928 ile 1945 yılları arasında doğan kohort (tr)                         |
| Silent Generation (wd:Q3044693) | schema:description | 1930年代から1940年代前半に生まれた世代 (ja)                                             |
| Silent Generation (wd:Q3044693) | schema:description | cohorte demográfica que sigue a ...osa y precede a los baby boomers (es) |
| Silent Generation (wd:Q3044693) | schema:description | cohort succeeding the Greatest Generation, born from 1928 to 1945 (en)   |
| Silent Generation (wd:Q3044693) | schema:description | generació de gent nascuda generalment entre 1928 a i 1945 (ca)           |
| Silent Generation (wd:Q3044693) | schema:description | generasjonen med mennesker som er født cirka 1928 til 1945 (nb)          |
| Silent Generation (wd:Q3044693) | schema:description | generatie geboren tussen 1928-1945 (nl)                                  |
| Silent Generation (wd:Q3044693) | schema:description | generazione composta dai nati tra il 1928 e il 1945 (it)                 |
| Silent Generation (wd:Q3044693) | schema:description | lidé narození v letech 1928–1945 (cs)                                    |
| ...                             | ...                | ...                                                                      |

[Assistant]
The schema:description property for the Silent Generation (wd:Q3044693) indicates that this generation consists of people born from 1928 to 1945.

Now, I will calculate the current age range of people belonging to the Silent Generation based on the birth years 1928 to 1945 and the current year 2024. The age range is from (2024 - 1945) to (2024 - 1928).

[Tool] Stopped generation process