PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX imdb: <https://www.imdb.com/>
SELECT ?series ?label ( COUNT( ?episode ) AS ?numEpisodes ) WHERE {
  ?series imdb:titleType "tvSeries" .
  ?series imdb:numVotes ?votes .
  FILTER ( ?votes > 1000000 ) OPTIONAL {
    ?series rdfs:label ?label
  }
  ?episode imdb:titleType "tvEpisode" .
  ?episode imdb:parentTitle ?series .
}
GROUP BY ?series ?label
ORDER BY DESC ( ?numEpisodes )
LIMIT 5

Got 5 rows and 3 columns:
| series                                  | label            | numEpisodes   |
| --------------------------------------- | ---------------- | ------------- |
| Friends (imdb_title:tt0108778)          | Friends          | 235 (xsd:int) |
| The Walking Dead (imdb_title:tt1520211) | The Walking Dead | 177 (xsd:int) |
| Game of Thrones (imdb_title:tt0944947)  | Game of Thrones  | 74 (xsd:int)  |
| Breaking Bad (imdb_title:tt0903747)     | Breaking Bad     | 62 (xsd:int)  |
| Stranger Things (imdb_title:tt4574334)  | Montauk          | 42 (xsd:int)  |

Among TV series with more than 1 million votes, 'Friends' has the most episodes (235), followed by 'The Walking Dead' (177), 'Game of Thrones' (74), 'Breaking Bad' (62), and 'Stranger Things' (42).

Series with more than 1 M votes with most episodes

[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:
imdb at https://qlever.cs.uni-freiburg.de/api/imdb

You can use the following SPARQL prefixes implicitly in all functions:
imdb: <https://www.imdb.com/
imdb_name: <https://www.imdb.com/name/
imdb_principal: <https://www.imdb.com/principal/
imdb_title: <https://www.imdb.com/title/
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
skos: <http://www.w3.org/2004/02/skos/core#
xsd: <http://www.w3.org/2001/XMLSchema#
owl: <http://www.w3.org/2002/07/owl#
foaf: <http://xmlns.com/foaf/0.1/
dct: <http://purl.org/dc/terms/
dc: <http://purl.org/dc/elements/1.1/
prov: <http://www.w3.org/ns/prov#
schema: <http://schema.org/
geo: <http://www.opengis.net/ont/geosparql#
geosparql: <http://www.opengis.net/ont/geosparql#
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
wikibase: <http://wikiba.se/ontology#
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] Series with more than 1 M votes with most episodes

[Assistant]
Let's break down the question:

- We are looking for TV series.
- The series should have more than 1 million votes.
- Among those, we want the one(s) with the most episodes.

Step 1: Identify how to find TV series in the IMDb KG.
Step 2: Find the property for the number of votes for a series.
Step 3: Find the property for the number of episodes for a series.
Step 4: Filter for series with more than 1 million votes.
Step 5: Sort by the number of episodes, descending, and get the top result(s).

Let's start by searching for the entity representing "series" or "TV series".

[Tool]
Top 10 entity alternatives:
1. TV Series (imdb_title:tt12162648)
2. TV Series (imdb_title:tt36848431)
3. TV Series (imdb_title:tt2011675)
4. TV Series (imdb_title:tt11177420)
5. Enforcer the Movie, also known as Enforcer TV Series, Enforcer the Movie (imdb_title:tt5206646)
6. HTOWN TV Series, also known as MUDDY WATERS (TV SERIES) (imdb_title:tt8571338)
7. Punisher TV Series (imdb_title:tt5691642)
8. Preacher (TV Series) (imdb_title:tt5821682)
9. Pilot TV Series (imdb_title:tt1561886)
10. Nabhadas TV Series (imdb_title:tt33888546)

[Assistant]
The search for "tv series" returned several specific series and some generic entries. However, to find all TV series, we likely need to use a type or genre property rather than a specific entity.

Next, let's search for properties related to "type" or "genre" to see how TV series are classified in the IMDb KG.

[Tool]
Top 10 property alternatives:
1. type (rdf:type)
2. title Type (imdb:titleType)
3. genre (imdb:genre)
4. editor (imdb_principal:editor)
5. label (rdfs:label)
6. profession (imdb:profession)
7. writer (imdb_principal:writer)
8. writer (imdb:writer)
9. self (imdb_principal:self)
10. birth Year (imdb:birthYear)

[Tool]
No entity items found

No other items found

No literal items found

[Tool]
No entity items found

No other items found

No literal items found

[Tool]
No entity items found

No other items found

No literal items found

[Tool]
No entity items found

No other items found

No literal items found

[Tool]
Top 10 property alternatives:
1. num Votes (imdb:numVotes)
2. editor (imdb_principal:editor)
3. episode Number (imdb:episodeNumber)
4. average Rating (imdb:averageRating)
5. original Title (imdb:originalTitle)
6. genre (imdb:genre)
7. birth Year (imdb:birthYear)
8. type (rdf:type)
9. season Number (imdb:seasonNumber)
10. label (rdfs:label)

[Tool]
Top 10 property alternatives:
1. episode Number (imdb:episodeNumber)
2. genre (imdb:genre)
3. season Number (imdb:seasonNumber)
4. original Title (imdb:originalTitle)
5. runtime Minutes (imdb:runtimeMinutes)
6. writer (imdb_principal:writer)
7. writer (imdb:writer)
8. archive footage (imdb_principal:archive_footage)
9. editor (imdb_principal:editor)
10. self (imdb_principal:self)

[Tool]
Top 10 property alternatives:
1. episode Number (imdb:episodeNumber)
2. season Number (imdb:seasonNumber)
3. runtime Minutes (imdb:runtimeMinutes)
4. genre (imdb:genre)
5. num Votes (imdb:numVotes)
6. average Rating (imdb:averageRating)
7. start Year (imdb:startYear)
8. original Title (imdb:originalTitle)
9. end Year (imdb:endYear)
10. birth Year (imdb:birthYear)

[Tool]
Top 10 property alternatives:
1. genre (imdb:genre)
2. episode Number (imdb:episodeNumber)
3. season Number (imdb:seasonNumber)
4. original Title (imdb:originalTitle)
5. writer (imdb_principal:writer)
6. writer (imdb:writer)
7. type (rdf:type)
8. title Type (imdb:titleType)
9. label (rdfs:label)
10. start Year (imdb:startYear)

[Tool]
No entity items found

No other items found

No literal items found

[Tool]
No entity items found

No other items found

No literal items found

[Tool]
No entity items found

No other items found

Top 10 literal alternatives:
1. Drama

[Tool]
Got 65,536 rows and 3 columns, showing the first 10 rows below:
| subject                                                    | property                    | object       |
| ---------------------------------------------------------- | --------------------------- | ------------ |
| Coronation Street (imdb_title:tt0053494)                   | title Type (imdb:titleType) | tvSeries     |
| Il buono (imdb_title:tt0060196)                            | title Type (imdb:titleType) | movie        |
| The Immigrant (imdb_title:tt0008133)                       | title Type (imdb:titleType) | short        |
| Poppies Are Also Flowers (imdb_title:tt0060848)            | title Type (imdb:titleType) | tvMovie      |
| Die Lederstrumpferzählungen (imdb_title:tt0062689)         | title Type (imdb:titleType) | tvMiniSeries |
| You're in Love (imdb_title:tt0062513)                      | title Type (imdb:titleType) | tvShort      |
| 000 Franc Train Robbery (imdb_title:tt0057157)             | title Type (imdb:titleType) | tvEpisode    |
| Take It Out in Trade: The Outtakes (imdb_title:tt0066435)  | title Type (imdb:titleType) | video        |
| The Bob Hope Vietnam Christmas Show (imdb_title:tt0060178) | title Type (imdb:titleType) | tvSpecial    |
| Sesame Street (imdb_title:tt0063951)                       | title Type (imdb:titleType) | tvSeries     |
| ...                                                        | ...                         | ...          |

[Tool]
Got 65,536 rows and 3 columns, showing the first 10 rows below:
| subject                                                        | property                  | object          |
| -------------------------------------------------------------- | ------------------------- | --------------- |
| Coronation Street (imdb_title:tt0053494)                       | num Votes (imdb:numVotes) | 6389 (xsd:int)  |
| Sesame Street (imdb_title:tt0063951)                           | num Votes (imdb:numVotes) | 16447 (xsd:int) |
| Days of Our Lives (imdb_title:tt0058796)                       | num Votes (imdb:numVotes) | 7883 (xsd:int)  |
| Today (imdb_title:tt0044298)                                   | num Votes (imdb:numVotes) | 2604 (xsd:int)  |
| The Tonight Show Starring Johnny Carson (imdb_title:tt0055708) | num Votes (imdb:numVotes) | 4482 (xsd:int)  |
| NFL Monday Night Football (imdb_title:tt0065323)               | num Votes (imdb:numVotes) | 1626 (xsd:int)  |
| The Young and the Restless (imdb_title:tt0069658)              | num Votes (imdb:numVotes) | 7173 (xsd:int)  |
| Wheel of Fortune (imdb_title:tt0072584)                        | num Votes (imdb:numVotes) | 3911 (xsd:int)  |
| Neighbours (imdb_title:tt0088580)                              | num Votes (imdb:numVotes) | 6747 (xsd:int)  |
| Saturday Night Live (imdb_title:tt0072562)                     | num Votes (imdb:numVotes) | 56000 (xsd:int) |
| ...                                                            | ...                       | ...             |

[Tool]
Got 65,536 rows and 3 columns, showing the first 10 rows below:
| subject                                    | property                            | object       |
| ------------------------------------------ | ----------------------------------- | ------------ |
| Nipsey Russell (imdb_title:tt0556441)      | episode Number (imdb:episodeNumber) | 24 (xsd:int) |
| Bob Newhart (imdb_title:tt0556316)         | episode Number (imdb:episodeNumber) | 1 (xsd:int)  |
| Creepers (imdb_title:tt0559001)            | episode Number (imdb:episodeNumber) | 13 (xsd:int) |
| Gisele MacKenzie (imdb_title:tt0556321)    | episode Number (imdb:episodeNumber) | 18 (xsd:int) |
| Guy Marks (imdb_title:tt0556389)           | episode Number (imdb:episodeNumber) | 26 (xsd:int) |
| Ruth Buzzi (imdb_title:tt0556410)          | episode Number (imdb:episodeNumber) | 9 (xsd:int)  |
| Professor Backwards (imdb_title:tt0556383) | episode Number (imdb:episodeNumber) | 20 (xsd:int) |
| Second Childhood (imdb_title:tt0100186)    | episode Number (imdb:episodeNumber) | 5 (xsd:int)  |
| What (imdb_title:tt0510984)                | episode Number (imdb:episodeNumber) | 2 (xsd:int)  |
| Steve Lawrence (imdb_title:tt0556434)      | episode Number (imdb:episodeNumber) | 17 (xsd:int) |
| ...                                        | ...                                 | ...          |

[Tool]
Got 65,536 rows and 3 columns, showing the first 10 rows below:
| subject                                              | property                          | object       |
| ---------------------------------------------------- | --------------------------------- | ------------ |
| Nipsey Russell (imdb_title:tt0556441)                | season Number (imdb:seasonNumber) | 8 (xsd:int)  |
| David & Goliath (imdb_title:tt0556327)               | season Number (imdb:seasonNumber) | 1 (xsd:int)  |
| Creepers (imdb_title:tt0559001)                      | season Number (imdb:seasonNumber) | 3 (xsd:int)  |
| Ruth Buzzi (imdb_title:tt0556410)                    | season Number (imdb:seasonNumber) | 7 (xsd:int)  |
| Jr. (imdb_title:tt0556353)                           | season Number (imdb:seasonNumber) | 2 (xsd:int)  |
| So Long (imdb_title:tt0522917)                       | season Number (imdb:seasonNumber) | 4 (xsd:int)  |
| The Snapper (imdb_title:tt0108170)                   | season Number (imdb:seasonNumber) | 9 (xsd:int)  |
| Egomaniac (imdb_title:tt0559789)                     | season Number (imdb:seasonNumber) | 5 (xsd:int)  |
| Battle of the Sexes: The Good (imdb_title:tt0346381) | season Number (imdb:seasonNumber) | 6 (xsd:int)  |
| Brothers (imdb_title:tt0537275)                      | season Number (imdb:seasonNumber) | 19 (xsd:int) |
| ...                                                  | ...                               | ...          |

[Tool]
Got 65,536 rows and 3 columns, showing the first 10 rows below:
| subject                               | property        | object               |
| ------------------------------------- | --------------- | -------------------- |
| Fred Astaire (imdb_name:nm0000001)    | type (rdf:type) | Person (imdb:Person) |
| Lauren Bacall (imdb_name:nm0000002)   | type (rdf:type) | Person (imdb:Person) |
| Brigitte Bardot (imdb_name:nm0000003) | type (rdf:type) | Person (imdb:Person) |
| John Belushi (imdb_name:nm0000004)    | type (rdf:type) | Person (imdb:Person) |
| Ingmar Bergman (imdb_name:nm0000005)  | type (rdf:type) | Person (imdb:Person) |
| Ingrid Bergman (imdb_name:nm0000006)  | type (rdf:type) | Person (imdb:Person) |
| Humphrey Bogart (imdb_name:nm0000007) | type (rdf:type) | Person (imdb:Person) |
| Marlon Brando (imdb_name:nm0000008)   | type (rdf:type) | Person (imdb:Person) |
| Richard Burton (imdb_name:nm0000009)  | type (rdf:type) | Person (imdb:Person) |
| James Cagney (imdb_name:nm0000010)    | type (rdf:type) | Person (imdb:Person) |
| ...                                   | ...             | ...                  |

[Tool]
Got 65,536 rows and 3 columns, showing the first 10 rows below:
| subject                                       | property           | object                  |
| --------------------------------------------- | ------------------ | ----------------------- |
| William J. Bell (imdb_name:nm0068589)         | label (rdfs:label) | William J. Bell         |
| David Letterman (imdb_name:nm0001468)         | label (rdfs:label) | David Letterman         |
| Ernesto Alonso (imdb_name:nm0022172)          | label (rdfs:label) | Ernesto Alonso          |
| Lee Phillip Bell (imdb_name:nm0068347)        | label (rdfs:label) | Lee Phillip Bell        |
| Hans Christian Andersen (imdb_name:nm0026153) | label (rdfs:label) | Hans Christian Andersen |
| Bradley Bell (imdb_name:nm0068074)            | label (rdfs:label) | Bradley Bell            |
| Alan Bateman (imdb_name:nm0060783)            | label (rdfs:label) | Alan Bateman            |
| Walter Avancini (imdb_name:nm0042771)         | label (rdfs:label) | Walter Avancini         |
| Paul Alter (imdb_name:nm0022750)              | label (rdfs:label) | Paul Alter              |
| Mário Márcio Bandarra (imdb_name:nm0051678)   | label (rdfs:label) | Mário Márcio Bandarra   |
| ...                                           | ...                | ...                     |

[Tool]
Got 7 rows and 3 columns:
| series                                  | votes             | label            |
| --------------------------------------- | ----------------- | ---------------- |
| Game of Thrones (imdb_title:tt0944947)  | 2459626 (xsd:int) | Game of Thrones  |
| Breaking Bad (imdb_title:tt0903747)     | 2366485 (xsd:int) | Breaking Bad     |
| Stranger Things (imdb_title:tt4574334)  | 1467414 (xsd:int) | Montauk          |
| Stranger Things (imdb_title:tt4574334)  | 1467414 (xsd:int) | Stranger Things  |
| Friends (imdb_title:tt0108778)          | 1153806 (xsd:int) | Friends          |
| The Walking Dead (imdb_title:tt1520211) | 1152865 (xsd:int) | The Walking Dead |
| Sherlock (imdb_title:tt1475582)         | 1055671 (xsd:int) | Sherlock         |

[Tool]
Got 5 rows and 3 columns:
| series                                  | label            | numEpisodes   |
| --------------------------------------- | ---------------- | ------------- |
| Friends (imdb_title:tt0108778)          | Friends          | 235 (xsd:int) |
| The Walking Dead (imdb_title:tt1520211) | The Walking Dead | 177 (xsd:int) |
| Game of Thrones (imdb_title:tt0944947)  | Game of Thrones  | 74 (xsd:int)  |
| Breaking Bad (imdb_title:tt0903747)     | Breaking Bad     | 62 (xsd:int)  |
| Stranger Things (imdb_title:tt4574334)  | Montauk          | 42 (xsd:int)  |

[Tool] Stopped generation process


PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
PREFIX imdb: <https://www.imdb.com/>
SELECT ?decade ( AVG( ?rating ) AS ?avgRating ) ( COUNT( ?movie ) AS ?numMovies ) WHERE {
  ?movie imdb:genre "Sci-Fi" .
  ?movie imdb:titleType "movie" .
  ?movie imdb:averageRating ?rating .
  ?movie imdb:numVotes ?votes .
  ?movie imdb:startYear ?year .
  FILTER ( ?votes > 10000 ) FILTER ( xsd:integer ( ?year ) > 1800 ) BIND( FLOOR( xsd:integer ( ?year ) / 10 ) * 10 AS ?decade )
}
GROUP BY ?decade
ORDER BY ?decade

Got 11 rows and 3 columns, showing the first 5 and last 5 rows below:
| decade             | avgRating             | numMovies     |
| ------------------ | --------------------- | ------------- |
| 1920 (xsd:decimal) | 8.3 (xsd:decimal)     | 1 (xsd:int)   |
| 1930 (xsd:decimal) | 7.53333 (xsd:decimal) | 6 (xsd:int)   |
| 1940 (xsd:decimal) | 6.6 (xsd:decimal)     | 2 (xsd:int)   |
| 1950 (xsd:decimal) | 6.855 (xsd:decimal)   | 20 (xsd:int)  |
| 1960 (xsd:decimal) | 6.5 (xsd:decimal)     | 14 (xsd:int)  |
| ...                | ...                   | ...           |
| 1980 (xsd:decimal) | 6.49101 (xsd:decimal) | 89 (xsd:int)  |
| 1990 (xsd:decimal) | 6.06944 (xsd:decimal) | 108 (xsd:int) |
| 2000 (xsd:decimal) | 6.05767 (xsd:decimal) | 163 (xsd:int) |
| 2010 (xsd:decimal) | 6.22395 (xsd:decimal) | 263 (xsd:int) |
| 2020 (xsd:decimal) | 5.76832 (xsd:decimal) | 101 (xsd:int) |

This query returns the average rating of Sci-Fi movies (not series) by decade, for movies with over 10,000 votes. The results include the decade, the average rating, and the number of movies per decade.

Average rating of Sci-Fi movies (not series) by decade, for movies with over 10k votes

[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:
imdb at https://qlever.cs.uni-freiburg.de/api/imdb

You can use the following SPARQL prefixes implicitly in all functions:
imdb: <https://www.imdb.com/
imdb_name: <https://www.imdb.com/name/
imdb_principal: <https://www.imdb.com/principal/
imdb_title: <https://www.imdb.com/title/
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
skos: <http://www.w3.org/2004/02/skos/core#
xsd: <http://www.w3.org/2001/XMLSchema#
owl: <http://www.w3.org/2002/07/owl#
foaf: <http://xmlns.com/foaf/0.1/
dct: <http://purl.org/dc/terms/
dc: <http://purl.org/dc/elements/1.1/
prov: <http://www.w3.org/ns/prov#
schema: <http://schema.org/
geo: <http://www.opengis.net/ont/geosparql#
geosparql: <http://www.opengis.net/ont/geosparql#
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
wikibase: <http://wikiba.se/ontology#
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] Average rating of Sci-Fi movies (not series) by decade, for movies with over 10k votes

[Assistant]
Let's break down the question:

1. We need to find Sci-Fi movies (not series).
2. For each movie, we need its average rating, number of votes, and release year.
3. Only include movies with over 10,000 votes.
4. Group by decade and calculate the average rating per decade.

Step 1: Find how Sci-Fi genre is represented in the knowledge graph.
Step 2: Find how to distinguish movies from series.
Step 3: Find properties for average rating, number of votes, and release year.
Step 4: Build the query to group by decade and filter by votes.

Let's start by searching for the "Sci-Fi" genre entity.

[Tool]
Top 10 entity alternatives:
1. Horror & Hell, also known as Melodrama,  Horror & Hell,  Sci-Fi (imdb_title:tt27052883)
2. Sci-Fi, also known as Horror & Hell, Melodrama,  Sci-Fi (imdb_title:tt27052883)
3. Melodrama, also known as Horror & Hell,  Sci-Fi, Melodrama (imdb_title:tt27052883)
4. Sci-Fi (imdb_title:tt20860308)
5. Sci-Fi (imdb_title:tt7115780)
6. Sci-Fi (imdb_title:tt2922258)
7. Sci-fi (imdb_title:tt13372198)
8. Sci-Fi (imdb_title:tt3857958)
9. Sci-fi (imdb_title:tt34383614)
10. Sci-Fi (imdb_title:tt31922618)

[Tool]
Top 10 property alternatives:
1. genre (imdb:genre)
2. title Type (imdb:titleType)
3. type (rdf:type)
4. original Title (imdb:originalTitle)
5. composer (imdb_principal:composer)
6. editor (imdb_principal:editor)
7. writer (imdb_principal:writer)
8. writer (imdb:writer)
9. alt Label (skos:altLabel)
10. known For Title (imdb:knownForTitle)

[Tool]
No entity items found

No other items found

Top 10 literal alternatives:
1. Sci-Fi

[Tool]
Top 10 property alternatives:
1. title Type (imdb:titleType)
2. parent Title (imdb:parentTitle)
3. primary Title (imdb:primaryTitle)
4. type (rdf:type)
5. known For Title (imdb:knownForTitle)
6. genre (imdb:genre)
7. original Title (imdb:originalTitle)
8. editor (imdb_principal:editor)
9. label (rdfs:label)
10. profession (imdb:profession)

[Tool]
No entity items found

No other items found

Top 10 literal alternatives:
1. movie

[Tool]
No entity items found

No other items found

No literal items found

[Tool]
Top 10 property alternatives:
1. average Rating (imdb:averageRating)
2. birth Year (imdb:birthYear)
3. genre (imdb:genre)
4. num Votes (imdb:numVotes)
5. title Type (imdb:titleType)
6. is Adult (imdb:isAdult)
7. original Title (imdb:originalTitle)
8. season Number (imdb:seasonNumber)
9. type (rdf:type)
10. profession (imdb:profession)

[Tool]
Top 10 property alternatives:
1. num Votes (imdb:numVotes)
2. episode Number (imdb:episodeNumber)
3. season Number (imdb:seasonNumber)
4. average Rating (imdb:averageRating)
5. runtime Minutes (imdb:runtimeMinutes)
6. birth Year (imdb:birthYear)
7. genre (imdb:genre)
8. label (rdfs:label)
9. type (rdf:type)
10. editor (imdb_principal:editor)

[Tool]
Top 10 property alternatives:
1. start Year (imdb:startYear)
2. end Year (imdb:endYear)
3. birth Year (imdb:birthYear)
4. death Year (imdb:deathYear)
5. season Number (imdb:seasonNumber)
6. title Type (imdb:titleType)
7. original Title (imdb:originalTitle)
8. known For Title (imdb:knownForTitle)
9. genre (imdb:genre)
10. parent Title (imdb:parentTitle)

[Tool]
Got 65,536 rows and 3 columns, showing the first 10 rows below:
| subject                                                         | property           | object      |
| --------------------------------------------------------------- | ------------------ | ----------- |
| Snow White and the Seven Dwarfs (imdb_title:tt0029583)          | genre (imdb:genre) | Adventure   |
| King of Crime (imdb_title:tt0013086)                            | genre (imdb:genre) | Crime       |
| Gone with the Wind (imdb_title:tt0031381)                       | genre (imdb:genre) | Drama       |
| Babes in Toyland (imdb_title:tt0024852)                         | genre (imdb:genre) | Comedy      |
| The Adventures of Robin Hood (imdb_title:tt0029843)             | genre (imdb:genre) | Action      |
| The Private Lives of Elizabeth and Essex (imdb_title:tt0031826) | genre (imdb:genre) | Biography   |
| Häxan (imdb_title:tt0013257)                                    | genre (imdb:genre) | Documentary |
| Snow White and the Seven Dwarfs (imdb_title:tt0029583)          | genre (imdb:genre) | Animation   |
| Snow White and the Seven Dwarfs (imdb_title:tt0029583)          | genre (imdb:genre) | Family      |
| Fantasia (imdb_title:tt0032455)                                 | genre (imdb:genre) | Animation   |
| ...                                                             | ...                | ...         |

[Tool]
Got 65,536 rows and 3 columns, showing the first 10 rows below:
| subject                                                   | property           | object |
| --------------------------------------------------------- | ------------------ | ------ |
| American Horror Story (imdb_title:tt1844624)              | genre (imdb:genre) | Sci-Fi |
| Untitled Star Trek 4 Film (imdb_title:tt2660888)          | genre (imdb:genre) | Sci-Fi |
| The Fantastic Four: First Steps (imdb_title:tt10676052)   | genre (imdb:genre) | Sci-Fi |
| War of the Worlds (imdb_title:tt0407304)                  | genre (imdb:genre) | Sci-Fi |
| Captain America: The First Avenger (imdb_title:tt0458339) | genre (imdb:genre) | Sci-Fi |
| Rise of the Planet of the Apes (imdb_title:tt1318514)     | genre (imdb:genre) | Sci-Fi |
| Edge of Tomorrow (imdb_title:tt1631867)                   | genre (imdb:genre) | Sci-Fi |
| Star Trek (imdb_title:tt0796366)                          | genre (imdb:genre) | Sci-Fi |
| Man of Steel (imdb_title:tt0770828)                       | genre (imdb:genre) | Sci-Fi |
| The Avengers (imdb_title:tt0848228)                       | genre (imdb:genre) | Sci-Fi |
| ...                                                       | ...                | ...    |

[Tool]
Got 65,536 rows and 3 columns, showing the first 10 rows below:
| subject                                                    | property                    | object       |
| ---------------------------------------------------------- | --------------------------- | ------------ |
| Coronation Street (imdb_title:tt0053494)                   | title Type (imdb:titleType) | tvSeries     |
| Il buono (imdb_title:tt0060196)                            | title Type (imdb:titleType) | movie        |
| The Immigrant (imdb_title:tt0008133)                       | title Type (imdb:titleType) | short        |
| Poppies Are Also Flowers (imdb_title:tt0060848)            | title Type (imdb:titleType) | tvMovie      |
| Die Lederstrumpferzählungen (imdb_title:tt0062689)         | title Type (imdb:titleType) | tvMiniSeries |
| You're in Love (imdb_title:tt0062513)                      | title Type (imdb:titleType) | tvShort      |
| 000 Franc Train Robbery (imdb_title:tt0057157)             | title Type (imdb:titleType) | tvEpisode    |
| Take It Out in Trade: The Outtakes (imdb_title:tt0066435)  | title Type (imdb:titleType) | video        |
| The Bob Hope Vietnam Christmas Show (imdb_title:tt0060178) | title Type (imdb:titleType) | tvSpecial    |
| Sesame Street (imdb_title:tt0063951)                       | title Type (imdb:titleType) | tvSeries     |
| ...                                                        | ...                         | ...          |

[Tool]
Got 65,536 rows and 3 columns, showing the first 10 rows below:
| subject                                                        | property                            | object            |
| -------------------------------------------------------------- | ----------------------------------- | ----------------- |
| Coronation Street (imdb_title:tt0053494)                       | average Rating (imdb:averageRating) | 5.6 (xsd:decimal) |
| Sesame Street (imdb_title:tt0063951)                           | average Rating (imdb:averageRating) | 8.1 (xsd:decimal) |
| Days of Our Lives (imdb_title:tt0058796)                       | average Rating (imdb:averageRating) | 5.3 (xsd:decimal) |
| Today (imdb_title:tt0044298)                                   | average Rating (imdb:averageRating) | 4.6 (xsd:decimal) |
| The Tonight Show Starring Johnny Carson (imdb_title:tt0055708) | average Rating (imdb:averageRating) | 8.5 (xsd:decimal) |
| NFL Monday Night Football (imdb_title:tt0065323)               | average Rating (imdb:averageRating) | 8.2 (xsd:decimal) |
| Wheel of Fortune (imdb_title:tt0072584)                        | average Rating (imdb:averageRating) | 6.7 (xsd:decimal) |
| Saturday Night Live (imdb_title:tt0072562)                     | average Rating (imdb:averageRating) | 8 (xsd:int)       |
| EastEnders (imdb_title:tt0088512)                              | average Rating (imdb:averageRating) | 4.8 (xsd:decimal) |
| Entertainment Tonight (imdb_title:tt0081857)                   | average Rating (imdb:averageRating) | 3.6 (xsd:decimal) |
| ...                                                            | ...                                 | ...               |

[Tool]
Got 65,536 rows and 3 columns, showing the first 10 rows below:
| subject                                                        | property                  | object          |
| -------------------------------------------------------------- | ------------------------- | --------------- |
| Coronation Street (imdb_title:tt0053494)                       | num Votes (imdb:numVotes) | 6389 (xsd:int)  |
| Sesame Street (imdb_title:tt0063951)                           | num Votes (imdb:numVotes) | 16447 (xsd:int) |
| Days of Our Lives (imdb_title:tt0058796)                       | num Votes (imdb:numVotes) | 7883 (xsd:int)  |
| Today (imdb_title:tt0044298)                                   | num Votes (imdb:numVotes) | 2604 (xsd:int)  |
| The Tonight Show Starring Johnny Carson (imdb_title:tt0055708) | num Votes (imdb:numVotes) | 4482 (xsd:int)  |
| NFL Monday Night Football (imdb_title:tt0065323)               | num Votes (imdb:numVotes) | 1626 (xsd:int)  |
| The Young and the Restless (imdb_title:tt0069658)              | num Votes (imdb:numVotes) | 7173 (xsd:int)  |
| Wheel of Fortune (imdb_title:tt0072584)                        | num Votes (imdb:numVotes) | 3911 (xsd:int)  |
| Neighbours (imdb_title:tt0088580)                              | num Votes (imdb:numVotes) | 6747 (xsd:int)  |
| Saturday Night Live (imdb_title:tt0072562)                     | num Votes (imdb:numVotes) | 56000 (xsd:int) |
| ...                                                            | ...                       | ...             |

[Tool]
Got 65,536 rows and 3 columns, showing the first 10 rows below:
| subject                                                        | property                    | object         |
| -------------------------------------------------------------- | --------------------------- | -------------- |
| Coronation Street (imdb_title:tt0053494)                       | start Year (imdb:startYear) | 1960 (xsd:int) |
| Sesame Street (imdb_title:tt0063951)                           | start Year (imdb:startYear) | 1969 (xsd:int) |
| Days of Our Lives (imdb_title:tt0058796)                       | start Year (imdb:startYear) | 1965 (xsd:int) |
| Today (imdb_title:tt0044298)                                   | start Year (imdb:startYear) | 1952 (xsd:int) |
| The Tonight Show Starring Johnny Carson (imdb_title:tt0055708) | start Year (imdb:startYear) | 1962 (xsd:int) |
| NFL Monday Night Football (imdb_title:tt0065323)               | start Year (imdb:startYear) | 1970 (xsd:int) |
| Crossroads (imdb_title:tt0057741)                              | start Year (imdb:startYear) | 1964 (xsd:int) |
| Disneyland (imdb_title:tt0046593)                              | start Year (imdb:startYear) | 1954 (xsd:int) |
| Search for Tomorrow (imdb_title:tt0043229)                     | start Year (imdb:startYear) | 1951 (xsd:int) |
| General Hospital (imdb_title:tt0056758)                        | start Year (imdb:startYear) | 1963 (xsd:int) |
| ...                                                            | ...                         | ...            |

[Tool]
SPARQL execution failed:
Invalid SPARQL query: The expression "FLOOR ( ?year / 10 ) * 10" does not aggregate ?year. All non-aggregated variables must be part of the GROUP BY clause.

[Tool]
SPARQL execution failed:
Invalid SPARQL query: The expression "FLOOR ( xsd:integer ( ?year ) / 10 ) * 10" does not aggregate ?year. All non-aggregated variables must be part of the GROUP BY clause.

[Tool]
Got 10 rows and 2 columns:
| year           | rating            |
| -------------- | ----------------- |
| 1927 (xsd:int) | 8.3 (xsd:decimal) |
| 1931 (xsd:int) | 7.8 (xsd:decimal) |
| 1931 (xsd:int) | 7.6 (xsd:decimal) |
| 1933 (xsd:int) | 7.6 (xsd:decimal) |
| 1932 (xsd:int) | 7.3 (xsd:decimal) |
| 1935 (xsd:int) | 7.8 (xsd:decimal) |
| 1939 (xsd:int) | 7.1 (xsd:decimal) |
| 1941 (xsd:int) | 6.8 (xsd:decimal) |
| 1943 (xsd:int) | 6.4 (xsd:decimal) |
| 1951 (xsd:int) | 7.7 (xsd:decimal) |

[Tool]
SPARQL execution failed:
Invalid SPARQL query: The target ?decade of an AS clause was already used in the query body.

[Tool]
Got 11 rows and 3 columns, showing the first 5 and last 5 rows below:
| decade             | avgRating             | numMovies     |
| ------------------ | --------------------- | ------------- |
| 1920 (xsd:decimal) | 8.3 (xsd:decimal)     | 1 (xsd:int)   |
| 1930 (xsd:decimal) | 7.53333 (xsd:decimal) | 6 (xsd:int)   |
| 1940 (xsd:decimal) | 6.6 (xsd:decimal)     | 2 (xsd:int)   |
| 1950 (xsd:decimal) | 6.855 (xsd:decimal)   | 20 (xsd:int)  |
| 1960 (xsd:decimal) | 6.5 (xsd:decimal)     | 14 (xsd:int)  |
| ...                | ...                   | ...           |
| 1980 (xsd:decimal) | 6.49101 (xsd:decimal) | 89 (xsd:int)  |
| 1990 (xsd:decimal) | 6.06944 (xsd:decimal) | 108 (xsd:int) |
| 2000 (xsd:decimal) | 6.05767 (xsd:decimal) | 163 (xsd:int) |
| 2010 (xsd:decimal) | 6.22395 (xsd:decimal) | 263 (xsd:int) |
| 2020 (xsd:decimal) | 5.76832 (xsd:decimal) | 101 (xsd:int) |

[Tool] Stopped generation process


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX imdb: <https://www.imdb.com/>
SELECT ?series ?seriesLabel ( AVG( ?rating ) AS ?avgEpisodeRating ) ( COUNT( ?episode ) AS ?numEpisodes ) WHERE {
  ?episode imdb:titleType "tvEpisode" ; imdb:parentTitle ?series ; imdb:averageRating ?rating ; imdb:numVotes ?votes .
  FILTER ( ?votes >= 10000 ) ?series imdb:titleType "tvSeries" .
  OPTIONAL {
    ?series rdfs:label ?seriesLabel
  }
}
GROUP BY ?series ?seriesLabel
HAVING ( COUNT( ?episode ) >= 20 )
ORDER BY DESC ( ?avgEpisodeRating )
LIMIT 10

Got 10 rows and 4 columns:
| series                                     | seriesLabel         | avgEpisodeRating      | numEpisodes  |
| ------------------------------------------ | ------------------- | --------------------- | ------------ |
| One Piece: Wan pîsu (imdb_title:tt0388629) | One Piece: Wan pîsu | 9.46667 (xsd:decimal) | 45 (xsd:int) |
| Attack on Titan (imdb_title:tt2560140)     | Shingeki no Kyojin  | 9.10444 (xsd:decimal) | 90 (xsd:int) |
| Attack on Titan (imdb_title:tt2560140)     | Attack on Titan     | 9.10444 (xsd:decimal) | 90 (xsd:int) |
| Mr. Robot (imdb_title:tt4158110)           | Mr. Robot           | 9.09615 (xsd:decimal) | 26 (xsd:int) |
| Breaking Bad (imdb_title:tt0903747)        | Breaking Bad        | 8.95968 (xsd:decimal) | 62 (xsd:int) |
| The Sopranos (imdb_title:tt0141842)        | The Sopranos        | 8.91304 (xsd:decimal) | 23 (xsd:int) |
| Dark (imdb_title:tt5753856)                | Dark                | 8.88462 (xsd:decimal) | 26 (xsd:int) |
| Daredevil (imdb_title:tt3322312)           | Daredevil           | 8.875 (xsd:decimal)   | 28 (xsd:int) |
| Doctor Who (imdb_title:tt0436992)          | Doctor Who          | 8.8 (xsd:decimal)     | 21 (xsd:int) |
| Better Call Saul (imdb_title:tt3032476)    | Better Call Saul    | 8.79841 (xsd:decimal) | 63 (xsd:int) |

The top 10 series by average episode rating (for series with at least 20 episodes with at least 10,000 votes each) are:
1. One Piece: Wan pîsu (imdb_title:tt0388629) – avg 9.47 (45 episodes)
2. Attack on Titan (imdb_title:tt2560140) – avg 9.10 (90 episodes)
3. Mr. Robot (imdb_title:tt4158110) – avg 9.10 (26 episodes)
4. Breaking Bad (imdb_title:tt0903747) – avg 8.96 (62 episodes)
5. The Sopranos (imdb_title:tt0141842) – avg 8.91 (23 episodes)
6. Dark (imdb_title:tt5753856) – avg 8.88 (26 episodes)
7. Daredevil (imdb_title:tt3322312) – avg 8.88 (28 episodes)
8. Doctor Who (imdb_title:tt0436992) – avg 8.80 (21 episodes)
9. Better Call Saul (imdb_title:tt3032476) – avg 8.80 (63 episodes)
Note: Attack on Titan appears twice with different labels but is the same series.

Top 10 series by average episode rating, for series with at least 20 episodes with min 10k votes each

[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:
imdb at https://qlever.cs.uni-freiburg.de/api/imdb

You can use the following SPARQL prefixes implicitly in all functions:
imdb: <https://www.imdb.com/
imdb_name: <https://www.imdb.com/name/
imdb_principal: <https://www.imdb.com/principal/
imdb_title: <https://www.imdb.com/title/
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
skos: <http://www.w3.org/2004/02/skos/core#
xsd: <http://www.w3.org/2001/XMLSchema#
owl: <http://www.w3.org/2002/07/owl#
foaf: <http://xmlns.com/foaf/0.1/
dct: <http://purl.org/dc/terms/
dc: <http://purl.org/dc/elements/1.1/
prov: <http://www.w3.org/ns/prov#
schema: <http://schema.org/
geo: <http://www.opengis.net/ont/geosparql#
geosparql: <http://www.opengis.net/ont/geosparql#
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
wikibase: <http://wikiba.se/ontology#
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] Top 10 series by average episode rating, for series with at least 20 episodes with min 10k votes each

[Assistant]
Let's break down the question:

1. We need to find TV series.
2. For each series, we need to consider only episodes with at least 10,000 votes.
3. Only include series with at least 20 such episodes.
4. For those series, calculate the average rating of those episodes.
5. Return the top 10 series by this average.

Step 1: Identify how to find TV series and episodes in the IMDb KG.
Step 2: Find properties for episode/series relationship, episode rating, and episode vote count.
Step 3: Build a query to filter episodes by vote count, group by series, count episodes, and average rating.

First, let's search for how TV series and episodes are modeled.

[Tool]
Top 10 entity alternatives:
1. TV Series (imdb_title:tt12162648)
2. TV Series (imdb_title:tt36848431)
3. TV Series (imdb_title:tt2011675)
4. TV Series (imdb_title:tt11177420)
5. Enforcer the Movie, also known as Enforcer TV Series, Enforcer the Movie (imdb_title:tt5206646)
6. HTOWN TV Series, also known as MUDDY WATERS (TV SERIES) (imdb_title:tt8571338)
7. Punisher TV Series (imdb_title:tt5691642)
8. Preacher (TV Series) (imdb_title:tt5821682)
9. Pilot TV Series (imdb_title:tt1561886)
10. Nabhadas TV Series (imdb_title:tt33888546)

[Tool]
Top 10 property alternatives:
1. episode Number (imdb:episodeNumber)
2. season Number (imdb:seasonNumber)
3. genre (imdb:genre)
4. original Title (imdb:originalTitle)
5. writer (imdb_principal:writer)
6. writer (imdb:writer)
7. self (imdb_principal:self)
8. archive footage (imdb_principal:archive_footage)
9. producer (imdb_principal:producer)
10. editor (imdb_principal:editor)

[Tool]
Top 10 property alternatives:
1. parent Title (imdb:parentTitle)
2. primary Title (imdb:primaryTitle)
3. episode Number (imdb:episodeNumber)
4. title Type (imdb:titleType)
5. self (imdb_principal:self)
6. is Adult (imdb:isAdult)
7. genre (imdb:genre)
8. season Number (imdb:seasonNumber)
9. writer (imdb_principal:writer)
10. writer (imdb:writer)

[Tool]
Top 10 property alternatives:
1. average Rating (imdb:averageRating)
2. label (rdfs:label)
3. genre (imdb:genre)
4. season Number (imdb:seasonNumber)
5. episode Number (imdb:episodeNumber)
6. original Title (imdb:originalTitle)
7. title Type (imdb:titleType)
8. num Votes (imdb:numVotes)
9. type (rdf:type)
10. birth Year (imdb:birthYear)

[Tool]
Top 10 property alternatives:
1. num Votes (imdb:numVotes)
2. editor (imdb_principal:editor)
3. episode Number (imdb:episodeNumber)
4. average Rating (imdb:averageRating)
5. original Title (imdb:originalTitle)
6. genre (imdb:genre)
7. birth Year (imdb:birthYear)
8. type (rdf:type)
9. season Number (imdb:seasonNumber)
10. label (rdfs:label)

[Tool]
Top 10 property alternatives:
1. title Type (imdb:titleType)
2. parent Title (imdb:parentTitle)
3. primary Title (imdb:primaryTitle)
4. type (rdf:type)
5. known For Title (imdb:knownForTitle)
6. genre (imdb:genre)
7. original Title (imdb:originalTitle)
8. editor (imdb_principal:editor)
9. label (rdfs:label)
10. profession (imdb:profession)

[Tool]
No entity items found

No other items found

No literal items found

[Tool]
No entity items found

No other items found

No literal items found

[Tool]
No entity items found

No other items found

No literal items found

[Tool]
Got 65,536 rows and 3 columns, showing the first 10 rows below:
| subject                                                    | property                    | object       |
| ---------------------------------------------------------- | --------------------------- | ------------ |
| Coronation Street (imdb_title:tt0053494)                   | title Type (imdb:titleType) | tvSeries     |
| Il buono (imdb_title:tt0060196)                            | title Type (imdb:titleType) | movie        |
| The Immigrant (imdb_title:tt0008133)                       | title Type (imdb:titleType) | short        |
| Poppies Are Also Flowers (imdb_title:tt0060848)            | title Type (imdb:titleType) | tvMovie      |
| Die Lederstrumpferzählungen (imdb_title:tt0062689)         | title Type (imdb:titleType) | tvMiniSeries |
| You're in Love (imdb_title:tt0062513)                      | title Type (imdb:titleType) | tvShort      |
| 000 Franc Train Robbery (imdb_title:tt0057157)             | title Type (imdb:titleType) | tvEpisode    |
| Take It Out in Trade: The Outtakes (imdb_title:tt0066435)  | title Type (imdb:titleType) | video        |
| The Bob Hope Vietnam Christmas Show (imdb_title:tt0060178) | title Type (imdb:titleType) | tvSpecial    |
| Sesame Street (imdb_title:tt0063951)                       | title Type (imdb:titleType) | tvSeries     |
| ...                                                        | ...                         | ...          |

[Tool]
Got 65,536 rows and 3 columns, showing the first 10 rows below:
| subject                                                              | property                        | object                                                           |
| -------------------------------------------------------------------- | ------------------------------- | ---------------------------------------------------------------- |
| Survivor: The Reunion (imdb_title:tt0274914)                         | parent Title (imdb:parentTitle) | Survivor (imdb_title:tt0239195)                                  |
| Episode #1.1 (imdb_title:tt0546152)                                  | parent Title (imdb:parentTitle) | Coronation Street (imdb_title:tt0053494)                         |
| Sesame Street Presents: The Street We Live On (imdb_title:tt0438410) | parent Title (imdb:parentTitle) | Sesame Street (imdb_title:tt0063951)                             |
| Challenge 2000: Stratos-Fear (imdb_title:tt0233099)                  | parent Title (imdb:parentTitle) | Real World/Road Rules Challenge (imdb_title:tt0176095)           |
| Episode #1.1161 (imdb_title:tt0528655)                               | parent Title (imdb:parentTitle) | The Bold and the Beautiful (imdb_title:tt0092325)                |
| Las Vegas Reunion (imdb_title:tt0362074)                             | parent Title (imdb:parentTitle) | The Real World (imdb_title:tt0103520)                            |
| Exklusiv! (imdb_title:tt0064304)                                     | parent Title (imdb:parentTitle) | Tatort (imdb_title:tt0806910)                                    |
| The Last Tonight Show (imdb_title:tt0266213)                         | parent Title (imdb:parentTitle) | The Tonight Show Starring Johnny Carson (imdb_title:tt0055708)   |
| The Entertainers (imdb_title:tt0318948)                              | parent Title (imdb:parentTitle) | 60 Minutes (imdb_title:tt0123338)                                |
| Showdown! (imdb_title:tt0511810)                                     | parent Title (imdb:parentTitle) | American Idol: The Search for a Superstar (imdb_title:tt0319931) |
| ...                                                                  | ...                             | ...                                                              |

[Tool]
Got 65,536 rows and 3 columns, showing the first 10 rows below:
| subject                                                        | property                            | object            |
| -------------------------------------------------------------- | ----------------------------------- | ----------------- |
| Coronation Street (imdb_title:tt0053494)                       | average Rating (imdb:averageRating) | 5.6 (xsd:decimal) |
| Sesame Street (imdb_title:tt0063951)                           | average Rating (imdb:averageRating) | 8.1 (xsd:decimal) |
| Days of Our Lives (imdb_title:tt0058796)                       | average Rating (imdb:averageRating) | 5.3 (xsd:decimal) |
| Today (imdb_title:tt0044298)                                   | average Rating (imdb:averageRating) | 4.6 (xsd:decimal) |
| The Tonight Show Starring Johnny Carson (imdb_title:tt0055708) | average Rating (imdb:averageRating) | 8.5 (xsd:decimal) |
| NFL Monday Night Football (imdb_title:tt0065323)               | average Rating (imdb:averageRating) | 8.2 (xsd:decimal) |
| Wheel of Fortune (imdb_title:tt0072584)                        | average Rating (imdb:averageRating) | 6.7 (xsd:decimal) |
| Saturday Night Live (imdb_title:tt0072562)                     | average Rating (imdb:averageRating) | 8 (xsd:int)       |
| EastEnders (imdb_title:tt0088512)                              | average Rating (imdb:averageRating) | 4.8 (xsd:decimal) |
| Entertainment Tonight (imdb_title:tt0081857)                   | average Rating (imdb:averageRating) | 3.6 (xsd:decimal) |
| ...                                                            | ...                                 | ...               |

[Tool]
Got 65,536 rows and 3 columns, showing the first 10 rows below:
| subject                                                        | property                  | object          |
| -------------------------------------------------------------- | ------------------------- | --------------- |
| Coronation Street (imdb_title:tt0053494)                       | num Votes (imdb:numVotes) | 6389 (xsd:int)  |
| Sesame Street (imdb_title:tt0063951)                           | num Votes (imdb:numVotes) | 16447 (xsd:int) |
| Days of Our Lives (imdb_title:tt0058796)                       | num Votes (imdb:numVotes) | 7883 (xsd:int)  |
| Today (imdb_title:tt0044298)                                   | num Votes (imdb:numVotes) | 2604 (xsd:int)  |
| The Tonight Show Starring Johnny Carson (imdb_title:tt0055708) | num Votes (imdb:numVotes) | 4482 (xsd:int)  |
| NFL Monday Night Football (imdb_title:tt0065323)               | num Votes (imdb:numVotes) | 1626 (xsd:int)  |
| The Young and the Restless (imdb_title:tt0069658)              | num Votes (imdb:numVotes) | 7173 (xsd:int)  |
| Wheel of Fortune (imdb_title:tt0072584)                        | num Votes (imdb:numVotes) | 3911 (xsd:int)  |
| Neighbours (imdb_title:tt0088580)                              | num Votes (imdb:numVotes) | 6747 (xsd:int)  |
| Saturday Night Live (imdb_title:tt0072562)                     | num Votes (imdb:numVotes) | 56000 (xsd:int) |
| ...                                                            | ...                       | ...             |

[Tool]
Got 65,536 rows and 3 columns, showing the first 10 rows below:
| subject                                              | property                          | object       |
| ---------------------------------------------------- | --------------------------------- | ------------ |
| Nipsey Russell (imdb_title:tt0556441)                | season Number (imdb:seasonNumber) | 8 (xsd:int)  |
| David & Goliath (imdb_title:tt0556327)               | season Number (imdb:seasonNumber) | 1 (xsd:int)  |
| Creepers (imdb_title:tt0559001)                      | season Number (imdb:seasonNumber) | 3 (xsd:int)  |
| Ruth Buzzi (imdb_title:tt0556410)                    | season Number (imdb:seasonNumber) | 7 (xsd:int)  |
| Jr. (imdb_title:tt0556353)                           | season Number (imdb:seasonNumber) | 2 (xsd:int)  |
| So Long (imdb_title:tt0522917)                       | season Number (imdb:seasonNumber) | 4 (xsd:int)  |
| The Snapper (imdb_title:tt0108170)                   | season Number (imdb:seasonNumber) | 9 (xsd:int)  |
| Egomaniac (imdb_title:tt0559789)                     | season Number (imdb:seasonNumber) | 5 (xsd:int)  |
| Battle of the Sexes: The Good (imdb_title:tt0346381) | season Number (imdb:seasonNumber) | 6 (xsd:int)  |
| Brothers (imdb_title:tt0537275)                      | season Number (imdb:seasonNumber) | 19 (xsd:int) |
| ...                                                  | ...                               | ...          |

[Tool]
Got 65,536 rows and 3 columns, showing the first 10 rows below:
| subject                                    | property                            | object       |
| ------------------------------------------ | ----------------------------------- | ------------ |
| Nipsey Russell (imdb_title:tt0556441)      | episode Number (imdb:episodeNumber) | 24 (xsd:int) |
| Bob Newhart (imdb_title:tt0556316)         | episode Number (imdb:episodeNumber) | 1 (xsd:int)  |
| Creepers (imdb_title:tt0559001)            | episode Number (imdb:episodeNumber) | 13 (xsd:int) |
| Gisele MacKenzie (imdb_title:tt0556321)    | episode Number (imdb:episodeNumber) | 18 (xsd:int) |
| Guy Marks (imdb_title:tt0556389)           | episode Number (imdb:episodeNumber) | 26 (xsd:int) |
| Ruth Buzzi (imdb_title:tt0556410)          | episode Number (imdb:episodeNumber) | 9 (xsd:int)  |
| Professor Backwards (imdb_title:tt0556383) | episode Number (imdb:episodeNumber) | 20 (xsd:int) |
| Second Childhood (imdb_title:tt0100186)    | episode Number (imdb:episodeNumber) | 5 (xsd:int)  |
| What (imdb_title:tt0510984)                | episode Number (imdb:episodeNumber) | 2 (xsd:int)  |
| Steve Lawrence (imdb_title:tt0556434)      | episode Number (imdb:episodeNumber) | 17 (xsd:int) |
| ...                                        | ...                                 | ...          |

[Tool]
Got 65,536 rows and 3 columns, showing the first 10 rows below:
| subject                                       | property           | object                  |
| --------------------------------------------- | ------------------ | ----------------------- |
| William J. Bell (imdb_name:nm0068589)         | label (rdfs:label) | William J. Bell         |
| David Letterman (imdb_name:nm0001468)         | label (rdfs:label) | David Letterman         |
| Ernesto Alonso (imdb_name:nm0022172)          | label (rdfs:label) | Ernesto Alonso          |
| Lee Phillip Bell (imdb_name:nm0068347)        | label (rdfs:label) | Lee Phillip Bell        |
| Hans Christian Andersen (imdb_name:nm0026153) | label (rdfs:label) | Hans Christian Andersen |
| Bradley Bell (imdb_name:nm0068074)            | label (rdfs:label) | Bradley Bell            |
| Alan Bateman (imdb_name:nm0060783)            | label (rdfs:label) | Alan Bateman            |
| Walter Avancini (imdb_name:nm0042771)         | label (rdfs:label) | Walter Avancini         |
| Paul Alter (imdb_name:nm0022750)              | label (rdfs:label) | Paul Alter              |
| Mário Márcio Bandarra (imdb_name:nm0051678)   | label (rdfs:label) | Mário Márcio Bandarra   |
| ...                                           | ...                | ...                     |

[Tool]
Got 65,536 rows and 3 columns, showing the first 10 rows below:
| subject                                                         | property           | object      |
| --------------------------------------------------------------- | ------------------ | ----------- |
| Snow White and the Seven Dwarfs (imdb_title:tt0029583)          | genre (imdb:genre) | Adventure   |
| King of Crime (imdb_title:tt0013086)                            | genre (imdb:genre) | Crime       |
| Gone with the Wind (imdb_title:tt0031381)                       | genre (imdb:genre) | Drama       |
| Babes in Toyland (imdb_title:tt0024852)                         | genre (imdb:genre) | Comedy      |
| The Adventures of Robin Hood (imdb_title:tt0029843)             | genre (imdb:genre) | Action      |
| The Private Lives of Elizabeth and Essex (imdb_title:tt0031826) | genre (imdb:genre) | Biography   |
| Häxan (imdb_title:tt0013257)                                    | genre (imdb:genre) | Documentary |
| Snow White and the Seven Dwarfs (imdb_title:tt0029583)          | genre (imdb:genre) | Animation   |
| Snow White and the Seven Dwarfs (imdb_title:tt0029583)          | genre (imdb:genre) | Family      |
| Fantasia (imdb_title:tt0032455)                                 | genre (imdb:genre) | Animation   |
| ...                                                             | ...                | ...         |

[Assistant]
We now have the following key information for the query:
- TV series: `imdb:titleType` = "tvSeries"
- Episodes: `imdb:titleType` = "tvEpisode"
- Episode-to-series link: `imdb:parentTitle`
- Episode rating: `imdb:averageRating`
- Episode votes: `imdb:numVotes`
- Series label: `rdfs:label`

Plan:
1. Select all episodes (`imdb:titleType` = "tvEpisode") with at least 10,000 votes.
2. For each episode, get its parent series via `imdb:parentTitle`.
3. Group by series, count episodes per series, filter to series with at least 20 such episodes.
4. For each such series, calculate the average of the episode ratings.
5. Order by average rating descending, limit to top 10.
6. Optionally, get the series label.

Now, let's write the SPARQL query accordingly.

[Tool]
Got 10 rows and 4 columns:
| series                                     | seriesLabel         | avgEpisodeRating      | numEpisodes  |
| ------------------------------------------ | ------------------- | --------------------- | ------------ |
| One Piece: Wan pîsu (imdb_title:tt0388629) | One Piece: Wan pîsu | 9.46667 (xsd:decimal) | 45 (xsd:int) |
| Attack on Titan (imdb_title:tt2560140)     | Shingeki no Kyojin  | 9.10444 (xsd:decimal) | 90 (xsd:int) |
| Attack on Titan (imdb_title:tt2560140)     | Attack on Titan     | 9.10444 (xsd:decimal) | 90 (xsd:int) |
| Mr. Robot (imdb_title:tt4158110)           | Mr. Robot           | 9.09615 (xsd:decimal) | 26 (xsd:int) |
| Breaking Bad (imdb_title:tt0903747)        | Breaking Bad        | 8.95968 (xsd:decimal) | 62 (xsd:int) |
| The Sopranos (imdb_title:tt0141842)        | The Sopranos        | 8.91304 (xsd:decimal) | 23 (xsd:int) |
| Dark (imdb_title:tt5753856)                | Dark                | 8.88462 (xsd:decimal) | 26 (xsd:int) |
| Daredevil (imdb_title:tt3322312)           | Daredevil           | 8.875 (xsd:decimal)   | 28 (xsd:int) |
| Doctor Who (imdb_title:tt0436992)          | Doctor Who          | 8.8 (xsd:decimal)     | 21 (xsd:int) |
| Better Call Saul (imdb_title:tt3032476)    | Better Call Saul    | 8.79841 (xsd:decimal) | 63 (xsd:int) |

[Tool] Stopped generation process


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX imdb: <https://www.imdb.com/>
PREFIX imdb_principal: <https://www.imdb.com/principal/>
SELECT DISTINCT ?person ?personLabel ?title1 ?title1Label ?title2 ?title2Label WHERE {
  ?title1 imdb_principal:actor ?person .
  ?title2 imdb_principal:director ?person .
  FILTER ( ?title1 != ?title2 ) ?title1 imdb:numVotes ?votes1 .
  ?title2 imdb:numVotes ?votes2 .
  FILTER ( ?votes1 > 100000 && ?votes2 > 100000 ) OPTIONAL {
    ?person rdfs:label ?personLabel
  }
  OPTIONAL {
    ?title1 imdb:primaryTitle ?title1Label
  }
  OPTIONAL {
    ?title2 imdb:primaryTitle ?title2Label
  }
}
LIMIT 100

Got 100 rows and 6 columns, showing the first 5 and last 5 rows below:
| person                                 | personLabel      | title1                                | title1Label    | title2                                      | title2Label          |
| -------------------------------------- | ---------------- | ------------------------------------- | -------------- | ------------------------------------------- | -------------------- |
| Alfred Hitchcock (imdb_name:nm0000033) | Alfred Hitchcock | Rope (imdb_title:tt0040746)           | Rope           | Rebecca (imdb_title:tt0032976)              | Rebecca              |
| Alfred Hitchcock (imdb_name:nm0000033) | Alfred Hitchcock | Rope (imdb_title:tt0040746)           | Rope           | Notorious (imdb_title:tt0038787)            | Notorious            |
| Alfred Hitchcock (imdb_name:nm0000033) | Alfred Hitchcock | Rope (imdb_title:tt0040746)           | Rope           | Strangers on a Train (imdb_title:tt0044079) | Strangers on a Train |
| Alfred Hitchcock (imdb_name:nm0000033) | Alfred Hitchcock | Rope (imdb_title:tt0040746)           | Rope           | Dial M for Murder (imdb_title:tt0046912)    | Dial M for Murder    |
| Alfred Hitchcock (imdb_name:nm0000033) | Alfred Hitchcock | Rope (imdb_title:tt0040746)           | Rope           | Rear Window (imdb_title:tt0047396)          | Rear Window          |
| ...                                    | ...              | ...                                   | ...            | ...                                         | ...                  |
| George Clooney (imdb_name:nm0000123)   | George Clooney   | Batman & Robin (imdb_title:tt0118688) | Batman & Robin | The Monuments Men (imdb_title:tt2177771)    | The Monuments Men    |
| George Clooney (imdb_name:nm0000123)   | George Clooney   | Three Kings (imdb_title:tt0120188)    | Three Kings    | Good Night (imdb_title:tt0433383)           |  and Good Luck.      |
| George Clooney (imdb_name:nm0000123)   | George Clooney   | Three Kings (imdb_title:tt0120188)    | Three Kings    | Good Night (imdb_title:tt0433383)           | Good Night           |
| George Clooney (imdb_name:nm0000123)   | George Clooney   | Three Kings (imdb_title:tt0120188)    | Three Kings    | The Ides of March (imdb_title:tt1124035)    | The Ides of March    |
| George Clooney (imdb_name:nm0000123)   | George Clooney   | Three Kings (imdb_title:tt0120188)    | Three Kings    | The Monuments Men (imdb_title:tt2177771)    | The Monuments Men    |

People who were a principal actor in one title and a principal director in a different title, both with over 100,000 votes, include (for example):
- Alfred Hitchcock (actor in 'Rope', director in 'Rebecca', 'Notorious', etc.)
- George Clooney (actor in 'Batman & Robin', 'Three Kings', director in 'The Monuments Men', 'Good Night, and Good Luck.', etc.).
The full result includes the IRIs for the people and the titles.

People who were a principal actor and a principal director in different titles, both with over 100k votes

[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:
imdb at https://qlever.cs.uni-freiburg.de/api/imdb

You can use the following SPARQL prefixes implicitly in all functions:
imdb: <https://www.imdb.com/
imdb_name: <https://www.imdb.com/name/
imdb_principal: <https://www.imdb.com/principal/
imdb_title: <https://www.imdb.com/title/
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
skos: <http://www.w3.org/2004/02/skos/core#
xsd: <http://www.w3.org/2001/XMLSchema#
owl: <http://www.w3.org/2002/07/owl#
foaf: <http://xmlns.com/foaf/0.1/
dct: <http://purl.org/dc/terms/
dc: <http://purl.org/dc/elements/1.1/
prov: <http://www.w3.org/ns/prov#
schema: <http://schema.org/
geo: <http://www.opengis.net/ont/geosparql#
geosparql: <http://www.opengis.net/ont/geosparql#
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
wikibase: <http://wikiba.se/ontology#
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 who were a principal actor and a principal director in different titles, both with over 100k votes

[Assistant]
Let's break down the question:

1. We are looking for people who were a principal actor in one title and a principal director in a different title.
2. Both titles must have over 100,000 votes.

Step 1: Identify properties/entities for "principal actor", "principal director", "votes", and "title".
Step 2: Find how to link people to their principal roles in titles.
Step 3: Find how to get the number of votes for a title.
Step 4: Combine these to find people who satisfy the criteria.

Let's start by searching for properties related to "principal actor" and "principal director".

[Tool]
Top 10 property alternatives:
1. actor (imdb_principal:actor)
2. director (imdb_principal:director)
3. director (imdb:director)
4. actress (imdb_principal:actress)
5. casting director (imdb_principal:casting_director)
6. cinematographer (imdb_principal:cinematographer)
7. primary Title (imdb:primaryTitle)
8. profession (imdb:profession)
9. producer (imdb_principal:producer)
10. birth Year (imdb:birthYear)

[Tool]
Top 10 property alternatives:
1. director (imdb_principal:director)
2. director (imdb:director)
3. casting director (imdb_principal:casting_director)
4. primary Title (imdb:primaryTitle)
5. cinematographer (imdb_principal:cinematographer)
6. production designer (imdb_principal:production_designer)
7. producer (imdb_principal:producer)
8. profession (imdb:profession)
9. parent Title (imdb:parentTitle)
10. known For Title (imdb:knownForTitle)

[Tool]
Top 10 property alternatives:
1. num Votes (imdb:numVotes)
2. editor (imdb_principal:editor)
3. episode Number (imdb:episodeNumber)
4. average Rating (imdb:averageRating)
5. original Title (imdb:originalTitle)
6. genre (imdb:genre)
7. birth Year (imdb:birthYear)
8. type (rdf:type)
9. season Number (imdb:seasonNumber)
10. label (rdfs:label)

[Tool]
Top 10 property alternatives:
1. original Title (imdb:originalTitle)
2. known For Title (imdb:knownForTitle)
3. title Type (imdb:titleType)
4. parent Title (imdb:parentTitle)
5. primary Title (imdb:primaryTitle)
6. label (rdfs:label)
7. genre (imdb:genre)
8. editor (imdb_principal:editor)
9. self (imdb_principal:self)
10. type (rdf:type)

[Tool]
Top 10 property alternatives:
1. director (imdb_principal:director)
2. director (imdb:director)
3. primary Title (imdb:primaryTitle)
4. parent Title (imdb:parentTitle)
5. editor (imdb_principal:editor)
6. profession (imdb:profession)
7. title Type (imdb:titleType)
8. known For Title (imdb:knownForTitle)
9. actor (imdb_principal:actor)
10. producer (imdb_principal:producer)

[Tool]
Got 65,536 rows and 3 columns, showing the first 10 rows below:
| subject                                                     | property                     | object                                                    |
| ----------------------------------------------------------- | ---------------------------- | --------------------------------------------------------- |
| La dama duende (imdb_title:tt0010041)                       | actor (imdb_principal:actor) | Rafael Banquells (imdb_name:nm0052486)                    |
| Lady of the Night (imdb_title:tt0015724)                    | actor (imdb_principal:actor) | Miguel Córcega (imdb_name:nm0194720)                      |
| Uncle Tom's Cabin (imdb_title:tt0000472)                    | actor (imdb_principal:actor) | Siegmund Lubin (imdb_name:nm0002615)                      |
| How It Feels to Be Run Over (imdb_title:tt0000309)          | actor (imdb_principal:actor) | Cecil M. Hepworth (imdb_name:nm0378408)                   |
| After Many Years (imdb_title:tt0000629)                     | actor (imdb_principal:actor) | Mack Sennett (imdb_name:nm0784407)                        |
| The Great Train Robbery (imdb_title:tt0000439)              | actor (imdb_principal:actor) | Gilbert M. 'Broncho Billy' Anderson (imdb_name:nm0001908) |
| El fantasma del castillo (imdb_title:tt0001602)             | actor (imdb_principal:actor) | Fernando Delgado (imdb_name:nm0216941)                    |
| 'Ostler Joe (imdb_title:tt0000627)                          | actor (imdb_principal:actor) | D.W. Griffith (imdb_name:nm0000428)                       |
| Colonel Heeza Liar's Forbidden Fruit (imdb_title:tt0013938) | actor (imdb_principal:actor) | Walter Lantz (imdb_name:nm0487237)                        |
| Dessinateur express (imdb_title:tt0000063)                  | actor (imdb_principal:actor) | Georges Méliès (imdb_name:nm0617588)                      |
| ...                                                         | ...                          | ...                                                       |

[Tool]
Got 65,536 rows and 3 columns, showing the first 10 rows below:
| subject                                        | property                           | object                                   |
| ---------------------------------------------- | ---------------------------------- | ---------------------------------------- |
| Gallopin' Gals (imdb_title:tt0032504)          | director (imdb_principal:director) | Joseph Barbera (imdb_name:nm0053484)     |
| Blue Monday (imdb_title:tt0029928)             | director (imdb_principal:director) | William Hanna (imdb_name:nm0360253)      |
| The Bells of St. Mary's (imdb_title:tt0052616) | director (imdb_principal:director) | Tom Donovan (imdb_name:nm0233071)        |
| Los días duros (imdb_title:tt0065679)          | director (imdb_principal:director) | Julio César Mármol (imdb_name:nm0549373) |
| Cold Turkey (imdb_title:tt0066927)             | director (imdb_principal:director) | Norman Lear (imdb_name:nm0005131)        |
| Two Thousand Weeks (imdb_title:tt0063971)      | director (imdb_principal:director) | Tim Burstall (imdb_name:nm0123364)       |
| Il buono (imdb_title:tt0060196)                | director (imdb_principal:director) | Sergio Leone (imdb_name:nm0001466)       |
| Uncle Tom's Cabin (imdb_title:tt0000472)       | director (imdb_principal:director) | Siegmund Lubin (imdb_name:nm0002615)     |
| El quelite (imdb_title:tt0064864)              | director (imdb_principal:director) | Jorge Fons (imdb_name:nm0284639)         |
| Saiyûki (imdb_title:tt0054265)                 | director (imdb_principal:director) | Osamu Tezuka (imdb_name:nm0856804)       |
| ...                                            | ...                                | ...                                      |

[Tool]
Got 65,536 rows and 3 columns, showing the first 10 rows below:
| subject                                                        | property                  | object          |
| -------------------------------------------------------------- | ------------------------- | --------------- |
| Coronation Street (imdb_title:tt0053494)                       | num Votes (imdb:numVotes) | 6389 (xsd:int)  |
| Sesame Street (imdb_title:tt0063951)                           | num Votes (imdb:numVotes) | 16447 (xsd:int) |
| Days of Our Lives (imdb_title:tt0058796)                       | num Votes (imdb:numVotes) | 7883 (xsd:int)  |
| Today (imdb_title:tt0044298)                                   | num Votes (imdb:numVotes) | 2604 (xsd:int)  |
| The Tonight Show Starring Johnny Carson (imdb_title:tt0055708) | num Votes (imdb:numVotes) | 4482 (xsd:int)  |
| NFL Monday Night Football (imdb_title:tt0065323)               | num Votes (imdb:numVotes) | 1626 (xsd:int)  |
| The Young and the Restless (imdb_title:tt0069658)              | num Votes (imdb:numVotes) | 7173 (xsd:int)  |
| Wheel of Fortune (imdb_title:tt0072584)                        | num Votes (imdb:numVotes) | 3911 (xsd:int)  |
| Neighbours (imdb_title:tt0088580)                              | num Votes (imdb:numVotes) | 6747 (xsd:int)  |
| Saturday Night Live (imdb_title:tt0072562)                     | num Votes (imdb:numVotes) | 56000 (xsd:int) |
| ...                                                            | ...                       | ...             |

[Tool]
Got 65,536 rows and 3 columns, showing the first 10 rows below:
| subject                                                        | property                          | object                                  |
| -------------------------------------------------------------- | --------------------------------- | --------------------------------------- |
| Coronation Street (imdb_title:tt0053494)                       | primary Title (imdb:primaryTitle) | Coronation Street                       |
| Sesame Street (imdb_title:tt0063951)                           | primary Title (imdb:primaryTitle) | Sesame Street                           |
| Days of Our Lives (imdb_title:tt0058796)                       | primary Title (imdb:primaryTitle) | Days of Our Lives                       |
| Today (imdb_title:tt0044298)                                   | primary Title (imdb:primaryTitle) | Today                                   |
| The Tonight Show Starring Johnny Carson (imdb_title:tt0055708) | primary Title (imdb:primaryTitle) | The Tonight Show Starring Johnny Carson |
| Crossroads (imdb_title:tt0057741)                              | primary Title (imdb:primaryTitle) | Crossroads                              |
| The Guiding Light (imdb_title:tt0044265)                       | primary Title (imdb:primaryTitle) | Guiding Light                           |
| Disneyland (imdb_title:tt0046593)                              | primary Title (imdb:primaryTitle) | The Magical World of Disney             |
| Search for Tomorrow (imdb_title:tt0043229)                     | primary Title (imdb:primaryTitle) | Search for Tomorrow                     |
| General Hospital (imdb_title:tt0056758)                        | primary Title (imdb:primaryTitle) | General Hospital                        |
| ...                                                            | ...                               | ...                                     |

[Tool]
Got 65,536 rows and 3 columns, showing the first 10 rows below:
| subject                                       | property           | object                  |
| --------------------------------------------- | ------------------ | ----------------------- |
| William J. Bell (imdb_name:nm0068589)         | label (rdfs:label) | William J. Bell         |
| David Letterman (imdb_name:nm0001468)         | label (rdfs:label) | David Letterman         |
| Ernesto Alonso (imdb_name:nm0022172)          | label (rdfs:label) | Ernesto Alonso          |
| Lee Phillip Bell (imdb_name:nm0068347)        | label (rdfs:label) | Lee Phillip Bell        |
| Hans Christian Andersen (imdb_name:nm0026153) | label (rdfs:label) | Hans Christian Andersen |
| Bradley Bell (imdb_name:nm0068074)            | label (rdfs:label) | Bradley Bell            |
| Alan Bateman (imdb_name:nm0060783)            | label (rdfs:label) | Alan Bateman            |
| Walter Avancini (imdb_name:nm0042771)         | label (rdfs:label) | Walter Avancini         |
| Paul Alter (imdb_name:nm0022750)              | label (rdfs:label) | Paul Alter              |
| Mário Márcio Bandarra (imdb_name:nm0051678)   | label (rdfs:label) | Mário Márcio Bandarra   |
| ...                                           | ...                | ...                     |

[Assistant]
We have identified the following:
- To find principal actors: use imdb_principal:actor
- To find principal directors: use imdb_principal:director
- To get the number of votes for a title: use imdb:numVotes
- To get the title's label: use imdb:primaryTitle or rdfs:label

Now, we need to:
1. Find people who are a principal actor in a title with >100,000 votes.
2. Find people who are a principal director in a different title with >100,000 votes.
3. Return the IRIs of these people, and optionally the titles (with labels) for which they were actor and director.

Let's write a SPARQL query that:
- Finds ?person who is a principal actor in ?title1 (with >100k votes)
- Finds the same ?person as a principal director in ?title2 (with >100k votes)
- Ensures ?title1 != ?title2
- Optionally returns the labels of the person and the titles

Let's proceed to execute this query.

[Tool]
Got 100 rows and 6 columns, showing the first 5 and last 5 rows below:
| person                                 | personLabel      | title1                                | title1Label    | title2                                      | title2Label          |
| -------------------------------------- | ---------------- | ------------------------------------- | -------------- | ------------------------------------------- | -------------------- |
| Alfred Hitchcock (imdb_name:nm0000033) | Alfred Hitchcock | Rope (imdb_title:tt0040746)           | Rope           | Rebecca (imdb_title:tt0032976)              | Rebecca              |
| Alfred Hitchcock (imdb_name:nm0000033) | Alfred Hitchcock | Rope (imdb_title:tt0040746)           | Rope           | Notorious (imdb_title:tt0038787)            | Notorious            |
| Alfred Hitchcock (imdb_name:nm0000033) | Alfred Hitchcock | Rope (imdb_title:tt0040746)           | Rope           | Strangers on a Train (imdb_title:tt0044079) | Strangers on a Train |
| Alfred Hitchcock (imdb_name:nm0000033) | Alfred Hitchcock | Rope (imdb_title:tt0040746)           | Rope           | Dial M for Murder (imdb_title:tt0046912)    | Dial M for Murder    |
| Alfred Hitchcock (imdb_name:nm0000033) | Alfred Hitchcock | Rope (imdb_title:tt0040746)           | Rope           | Rear Window (imdb_title:tt0047396)          | Rear Window          |
| ...                                    | ...              | ...                                   | ...            | ...                                         | ...                  |
| George Clooney (imdb_name:nm0000123)   | George Clooney   | Batman & Robin (imdb_title:tt0118688) | Batman & Robin | The Monuments Men (imdb_title:tt2177771)    | The Monuments Men    |
| George Clooney (imdb_name:nm0000123)   | George Clooney   | Three Kings (imdb_title:tt0120188)    | Three Kings    | Good Night (imdb_title:tt0433383)           |  and Good Luck.      |
| George Clooney (imdb_name:nm0000123)   | George Clooney   | Three Kings (imdb_title:tt0120188)    | Three Kings    | Good Night (imdb_title:tt0433383)           | Good Night           |
| George Clooney (imdb_name:nm0000123)   | George Clooney   | Three Kings (imdb_title:tt0120188)    | Three Kings    | The Ides of March (imdb_title:tt1124035)    | The Ides of March    |
| George Clooney (imdb_name:nm0000123)   | George Clooney   | Three Kings (imdb_title:tt0120188)    | Three Kings    | The Monuments Men (imdb_title:tt2177771)    | The Monuments Men    |

[Tool] Stopped generation process


PREFIX imdb: <https://www.imdb.com/>
SELECT ?person WHERE {
  ?person imdb:profession ?prof .
  FILTER ( ?prof = "actor" || ?prof = "actress" ) ?person imdb:knownForTitle ?title .
  ?title imdb:isAdult ?isAdult .
  OPTIONAL {
    ?title imdb:numVotes ?numVotes .
  }
  FILTER ( ?isAdult = true || ( ?isAdult = false && ?numVotes >= 100000 ) )
}
GROUP BY ?person
HAVING ( SUM( IF( ?isAdult = true , 1 , 0 ) ) >= 3 && SUM( IF( ?isAdult = false && ?numVotes >= 100000 , 1 , 0 ) ) >= 1 )
LIMIT 10

Got 10 rows and 1 column:
| person                                |
| ------------------------------------- |
| Ginger Lynn (imdb_name:nm0000259)     |
| Nina Hartley (imdb_name:nm0000440)    |
| Belladonna (imdb_name:nm0068643)      |
| Matt Bradshaw (imdb_name:nm0103508)   |
| Sheri Burke (imdb_name:nm0121862)     |
| Lil' Cinderella (imdb_name:nm0162417) |
| Billy Dee (imdb_name:nm0214150)       |
| Tony De Sergio (imdb_name:nm0221127)  |
| Frank Gun (imdb_name:nm0272283)       |
| Jeanna Fine (imdb_name:nm0277618)     |

These are actors who are known for at least three adult titles and also for at least one non-adult title with 100,000 or more votes: Ginger Lynn, Nina Hartley, Belladonna, Matt Bradshaw, Sheri Burke, Lil' Cinderella, Billy Dee, Tony De Sergio, Frank Gun, and Jeanna Fine. The query returns their IMDB identifiers.

Actors who are known for at least three adult titles but also for one non-adult title with >= 100k votes

[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:
imdb at https://qlever.cs.uni-freiburg.de/api/imdb

You can use the following SPARQL prefixes implicitly in all functions:
imdb: <https://www.imdb.com/
imdb_name: <https://www.imdb.com/name/
imdb_principal: <https://www.imdb.com/principal/
imdb_title: <https://www.imdb.com/title/
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
skos: <http://www.w3.org/2004/02/skos/core#
xsd: <http://www.w3.org/2001/XMLSchema#
owl: <http://www.w3.org/2002/07/owl#
foaf: <http://xmlns.com/foaf/0.1/
dct: <http://purl.org/dc/terms/
dc: <http://purl.org/dc/elements/1.1/
prov: <http://www.w3.org/ns/prov#
schema: <http://schema.org/
geo: <http://www.opengis.net/ont/geosparql#
geosparql: <http://www.opengis.net/ont/geosparql#
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
wikibase: <http://wikiba.se/ontology#
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] Actors who are known for at least three adult titles but also for one non-adult title with >= 100k votes

[Assistant]
Let's break down the question:

1. We need to find actors.
2. These actors must be known for at least three "adult" titles.
3. They must also be known for at least one "non-adult" title that has at least 100,000 votes.

Step 1: Identify how to find "actors" in the knowledge graph.
Step 2: Identify how to determine if a title is "adult" or "non-adult".
Step 3: Identify how to get the number of votes for a title.
Step 4: Identify how to find which titles an actor is "known for".

Let's start by searching for properties related to "known for", "adult", "votes", and "actor".

First, let's search for the property that links actors to their "known for" titles.

[Tool]
Top 10 property alternatives:
1. known For Title (imdb:knownForTitle)
2. profession (imdb:profession)
3. birth Year (imdb:birthYear)
4. genre (imdb:genre)
5. writer (imdb_principal:writer)
6. writer (imdb:writer)
7. actor (imdb_principal:actor)
8. original Title (imdb:originalTitle)
9. producer (imdb_principal:producer)
10. self (imdb_principal:self)

[Assistant]
The property imdb:knownForTitle links a person to their "known for" titles.

Next, let's check how to determine if a title is "adult" or "non-adult". I'll search for properties related to "adult".

[Tool]
Top 10 property alternatives:
1. is Adult (imdb:isAdult)
2. self (imdb_principal:self)
3. editor (imdb_principal:editor)
4. profession (imdb:profession)
5. genre (imdb:genre)
6. parent Title (imdb:parentTitle)
7. type (rdf:type)
8. birth Year (imdb:birthYear)
9. title Type (imdb:titleType)
10. end Year (imdb:endYear)

[Assistant]
The property imdb:isAdult seems to indicate whether a title is adult or not.

Now, let's find the property for the number of votes a title has. I'll search for "votes".

[Tool]
Top 10 property alternatives:
1. num Votes (imdb:numVotes)
2. editor (imdb_principal:editor)
3. episode Number (imdb:episodeNumber)
4. average Rating (imdb:averageRating)
5. original Title (imdb:originalTitle)
6. genre (imdb:genre)
7. birth Year (imdb:birthYear)
8. type (rdf:type)
9. season Number (imdb:seasonNumber)
10. label (rdfs:label)

[Assistant]
The property imdb:numVotes gives the number of votes for a title.

Now, let's confirm how to identify "actors". I'll search for "actor" to see if there's a property or class for this.

[Tool]
Top 10 property alternatives:
1. actor (imdb_principal:actor)
2. actress (imdb_principal:actress)
3. director (imdb_principal:director)
4. director (imdb:director)
5. birth Year (imdb:birthYear)
6. profession (imdb:profession)
7. cinematographer (imdb_principal:cinematographer)
8. casting director (imdb_principal:casting_director)
9. producer (imdb_principal:producer)
10. writer (imdb_principal:writer)

[Assistant] The property imdb_principal:actor links actors to titles. However, since we want to find people who are actors, it may be more efficient to filter people whose profession includes "actor" or "actress". Let's check if "profession" can be used for this purpose by searching for the property values of imdb:profession.

[Tool]
No entity items found

No other items found

Top 10 literal alternatives:
1. actor

[Tool]
No entity items found

No other items found

Top 10 literal alternatives:
1. actress

[Assistant]
The profession property uses the literals "actor" and "actress". So, we can filter people whose imdb:profession is "actor" or "actress".

Now, let's summarize the plan for the SPARQL query:

- Select people (?person) whose imdb:profession is "actor" or "actress".
- For each person, get their imdb:knownForTitle (?title).
- For each known-for title, check if imdb:isAdult is 1 (adult) or 0 (non-adult).
- Count the number of adult titles (imdb:isAdult = 1) per person; must be at least 3.
- For non-adult titles (imdb:isAdult = 0), check if imdb:numVotes >= 100000; must have at least one such title.

Let's write a SPARQL query that implements this logic.

[Tool] Got no rows and 1 columns

[Assistant]
The query returned no results. This could be due to several reasons:

- The use of FILTER and BIND in combination with GROUP BY and HAVING may not be supported as expected.
- The data may not have enough actors meeting these strict criteria.
- The way optional votes are handled may be causing issues.

Let's try a step-by-step approach:

1. First, let's check if we can get any actors with at least three adult titles in their known-for list.
2. Then, we'll check if any of their non-adult known-for titles have at least 100,000 votes.

Let's start by listing a few actors with at least three adult titles in their known-for list.

[Tool] Got no rows and 2 columns

[Assistant]
Even when searching for actors with at least three adult titles in their known-for list, the query returns no results. This suggests that either:

- The dataset may not have actors with three or more adult titles in their "known for" list.
- The "isAdult" property may not be used as expected, or the values may not be 1/0 as assumed.

Let's verify the possible values for imdb:isAdult by listing some examples.

[Tool]
Got 65,536 rows and 3 columns, showing the first 10 rows below:
| subject                                                        | property                | object              |
| -------------------------------------------------------------- | ----------------------- | ------------------- |
| Coronation Street (imdb_title:tt0053494)                       | is Adult (imdb:isAdult) | false (xsd:boolean) |
| Sangen om den røde rubin (imdb_title:tt0064929)                | is Adult (imdb:isAdult) | true (xsd:boolean)  |
| Sesame Street (imdb_title:tt0063951)                           | is Adult (imdb:isAdult) | false (xsd:boolean) |
| Days of Our Lives (imdb_title:tt0058796)                       | is Adult (imdb:isAdult) | false (xsd:boolean) |
| Today (imdb_title:tt0044298)                                   | is Adult (imdb:isAdult) | false (xsd:boolean) |
| The Tonight Show Starring Johnny Carson (imdb_title:tt0055708) | is Adult (imdb:isAdult) | false (xsd:boolean) |
| NFL Monday Night Football (imdb_title:tt0065323)               | is Adult (imdb:isAdult) | false (xsd:boolean) |
| Crossroads (imdb_title:tt0057741)                              | is Adult (imdb:isAdult) | false (xsd:boolean) |
| The Guiding Light (imdb_title:tt0044265)                       | is Adult (imdb:isAdult) | false (xsd:boolean) |
| All My Children (imdb_title:tt0065272)                         | is Adult (imdb:isAdult) | false (xsd:boolean) |
| ...                                                            | ...                     | ...                 |

[Assistant]
The imdb:isAdult property uses boolean values: true and false, not 1 and 0.

Let's adjust the query to use true/false for imdb:isAdult and try again to find actors with at least three adult titles in their known-for list.

[Tool]
Got 10 rows and 2 columns:
| person                                  | adultCount  |
| --------------------------------------- | ----------- |
| Ginger Lynn (imdb_name:nm0000259)       | 3 (xsd:int) |
| Nina Hartley (imdb_name:nm0000440)      | 3 (xsd:int) |
| Peter North (imdb_name:nm0000561)       | 4 (xsd:int) |
| Juliet Anderson (imdb_name:nm0000753)   | 4 (xsd:int) |
| Tom Byron (imdb_name:nm0001000)         | 4 (xsd:int) |
| Racquel Darrian (imdb_name:nm0001106)   | 4 (xsd:int) |
| Samantha Fox (imdb_name:nm0001237)      | 4 (xsd:int) |
| John Holmes (imdb_name:nm0001360)       | 4 (xsd:int) |
| Little Oral Annie (imdb_name:nm0001913) | 4 (xsd:int) |
| Christy Canyon (imdb_name:nm0001985)    | 4 (xsd:int) |

[Assistant]
Now we have a list of actors with at least three adult titles in their known-for list.

Next, for each of these actors, we need to check if they have at least one non-adult (imdb:isAdult false) known-for title with imdb:numVotes >= 100000.

Let's write a query that, for actors with at least three adult known-for titles, checks if they also have at least one non-adult known-for title with at least 100,000 votes.

[Tool]
Got 10 rows and 1 column:
| person                                |
| ------------------------------------- |
| Ginger Lynn (imdb_name:nm0000259)     |
| Nina Hartley (imdb_name:nm0000440)    |
| Belladonna (imdb_name:nm0068643)      |
| Matt Bradshaw (imdb_name:nm0103508)   |
| Sheri Burke (imdb_name:nm0121862)     |
| Lil' Cinderella (imdb_name:nm0162417) |
| Billy Dee (imdb_name:nm0214150)       |
| Tony De Sergio (imdb_name:nm0221127)  |
| Frank Gun (imdb_name:nm0272283)       |
| Jeanna Fine (imdb_name:nm0277618)     |

[Tool] Stopped generation process