PREFIX imdb_principal: <https://www.imdb.com/principal/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX imdb: <https://www.imdb.com/>
PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
SELECT ?person ?movie ( SAMPLE( ?personLabel_ ) AS ?personLabel ) ( SAMPLE( ?movieLabel_ ) AS ?movieLabel ) ( SAMPLE( ?age_ ) AS ?age ) ( SAMPLE( ?startYear_ ) AS ?startYear ) ( SAMPLE( ?birthYear_ ) AS ?birthYear ) WHERE {
  ?movie imdb:type "movie" .
  ?movie imdb:numVotes ?votes .
  FILTER ( ?votes >= 100000 ) ?movie imdb:startYear ?startYear_ .
  {
    ?movie imdb_principal:actor ?person .
  } UNION {
    ?movie imdb_principal:actress ?person .
  }
  ?person imdb:birthYear ?birthYear_ .
  BIND( ( xsd:int ( ?startYear_ ) - xsd:int ( ?birthYear_ ) ) AS ?age_ ) OPTIONAL {
    ?person rdfs:label ?personLabel_
  }
  OPTIONAL {
    ?movie rdfs:label ?movieLabel_
  }
}
GROUP BY ?person ?movie
ORDER BY DESC ( ?age )
LIMIT 100

Using properties:
- label (rdfs:label)
- birth Year (imdb:birthYear), also known as birthYear
- num Votes (imdb:numVotes), also known as numVotes
- actor (imdb_principal:actor)
- actress (imdb_principal:actress)
- start Year (imdb:startYear), also known as startYear
- type (imdb:type)

Got 100 rows and 7 columns, showing the first 5 and last 5 rows below:
| person                                    | movie                                                    | personLabel         | movieLabel                        | age          | startYear      | birthYear      |
| ----------------------------------------- | -------------------------------------------------------- | ------------------- | --------------------------------- | ------------ | -------------- | -------------- |
| Kamini Kaushal (imdb_name:nm0442470)      | Laal Singh Chaddha (imdb_title:tt10028196)               | Kamini Kaushal      | Laal Singh Chaddha                | 95 (xsd:int) | 2022 (xsd:int) | 1927 (xsd:int) |
| Fernanda Montenegro (imdb_name:nm0005244) | Ainda Estou Aqui (imdb_title:tt14961016)                 | Fernanda Montenegro | Ainda Estou Aqui                  | 95 (xsd:int) | 2024 (xsd:int) | 1929 (xsd:int) |
| Angela Lansbury (imdb_name:nm0001450)     | How the Grinch Stole Christmas (imdb_title:tt2709692)    | Angela Lansbury     | The Grinch                        | 93 (xsd:int) | 2018 (xsd:int) | 1925 (xsd:int) |
| James Hong (imdb_name:nm0393222)          | Everything Everywhere All at Once (imdb_title:tt6710474) | James Hong          | Everything Everywhere All at Once | 93 (xsd:int) | 2022 (xsd:int) | 1929 (xsd:int) |
| Ellen Albertini Dow (imdb_name:nm0016687) | Wedding Crashers (imdb_title:tt0396269)                  | Ellen Albertini Dow | Wedding Crashers                  | 92 (xsd:int) | 2005 (xsd:int) | 1913 (xsd:int) |
| ...                                       | ...                                                      | ...                 | ...                               | ...          | ...            | ...            |
| Harrison Ford (imdb_name:nm0000148)       | Captain America: Brave New World (imdb_title:tt14513804) | Harrison Ford       | Captain America: Brave New World  | 83 (xsd:int) | 2025 (xsd:int) | 1942 (xsd:int) |
| Christopher Lloyd (imdb_name:nm0000502)   | Nobody (imdb_title:tt7888964)                            | Christopher Lloyd   | Nobody                            | 83 (xsd:int) | 2021 (xsd:int) | 1938 (xsd:int) |
| Diana Rigg (imdb_name:nm0001671)          | Last Night in Soho (imdb_title:tt9639470)                | Diana Rigg          | Last Night in Soho                | 83 (xsd:int) | 2021 (xsd:int) | 1938 (xsd:int) |
| Bruce Dern (imdb_name:nm0001136)          | Once Upon a Time in... Hollywood (imdb_title:tt7131622)  | Bruce Dern          | Once Upon a Time in... Hollywood  | 83 (xsd:int) | 2019 (xsd:int) | 1936 (xsd:int) |
| Michael Caine (imdb_name:nm0000323)       | Now You See Me 2 (imdb_title:tt3110958)                  | Michael Caine       | Now You See Me 2                  | 83 (xsd:int) | 2016 (xsd:int) | 1933 (xsd:int) |

Answer: Here are the 100 oldest actors (including actresses) at the time of a movie (not series) with at least 100,000 votes. Due to data limitations, the IMDb knowledge graph does not provide a way to distinguish 'main' actors from supporting actors in the principal cast, so all actors and actresses are included. The results are deduplicated by person and movie to avoid alternate title duplicates. Only movies with imdb:type 'movie' are included, which should exclude TV movies and shorts. The age is calculated as the difference between the movie's start year and the person's birth year, which may not be exact if only years are available.

Here are the 100 oldest actors (including actresses) at the time of a movie (not series) with at least 100,000 votes. Due to data limitations, the IMDb knowledge graph does not provide a way to distinguish 'main' actors from supporting actors in the principal cast, so all actors and actresses are included. The results are deduplicated by person and movie to avoid alternate title duplicates. Only movies with imdb:type 'movie' are included, which should exclude TV movies and shorts. The age is calculated as the difference between the movie's start year and the person's birth year, which may not be exact if only years are available.

SPARQL query over imdb:
PREFIX imdb_principal: <https://www.imdb.com/principal/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX imdb: <https://www.imdb.com/>
PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
SELECT ?person ?movie ( SAMPLE( ?personLabel_ ) AS ?personLabel ) ( SAMPLE( ?movieLabel_ ) AS ?movieLabel ) ( SAMPLE( ?age_ ) AS ?age ) ( SAMPLE( ?startYear_ ) AS ?startYear ) ( SAMPLE( ?birthYear_ ) AS ?birthYear ) WHERE {
  ?movie imdb:type "movie" .
  ?movie imdb:numVotes ?votes .
  FILTER ( ?votes >= 100000 ) ?movie imdb:startYear ?startYear_ .
  {
    ?movie imdb_principal:actor ?person .
  } UNION {
    ?movie imdb_principal:actress ?person .
  }
  ?person imdb:birthYear ?birthYear_ .
  BIND( ( xsd:int ( ?startYear_ ) - xsd:int ( ?birthYear_ ) ) AS ?age_ ) OPTIONAL {
    ?person rdfs:label ?personLabel_
  }
  OPTIONAL {
    ?movie rdfs:label ?movieLabel_
  }
}
GROUP BY ?person ?movie
ORDER BY DESC ( ?age )
LIMIT 100

Using properties:
- label (rdfs:label)
- birth Year (imdb:birthYear), also known as birthYear
- num Votes (imdb:numVotes), also known as numVotes
- actor (imdb_principal:actor)
- actress (imdb_principal:actress)
- start Year (imdb:startYear), also known as startYear
- type (imdb:type)

Execution result:
Got 100 rows and 7 columns, showing the first 5 and last 5 rows below:
| person                                    | movie                                                    | personLabel         | movieLabel                        | age          | startYear      | birthYear      |
| ----------------------------------------- | -------------------------------------------------------- | ------------------- | --------------------------------- | ------------ | -------------- | -------------- |
| Kamini Kaushal (imdb_name:nm0442470)      | Laal Singh Chaddha (imdb_title:tt10028196)               | Kamini Kaushal      | Laal Singh Chaddha                | 95 (xsd:int) | 2022 (xsd:int) | 1927 (xsd:int) |
| Fernanda Montenegro (imdb_name:nm0005244) | Ainda Estou Aqui (imdb_title:tt14961016)                 | Fernanda Montenegro | Ainda Estou Aqui                  | 95 (xsd:int) | 2024 (xsd:int) | 1929 (xsd:int) |
| Angela Lansbury (imdb_name:nm0001450)     | How the Grinch Stole Christmas (imdb_title:tt2709692)    | Angela Lansbury     | The Grinch                        | 93 (xsd:int) | 2018 (xsd:int) | 1925 (xsd:int) |
| James Hong (imdb_name:nm0393222)          | Everything Everywhere All at Once (imdb_title:tt6710474) | James Hong          | Everything Everywhere All at Once | 93 (xsd:int) | 2022 (xsd:int) | 1929 (xsd:int) |
| Ellen Albertini Dow (imdb_name:nm0016687) | Wedding Crashers (imdb_title:tt0396269)                  | Ellen Albertini Dow | Wedding Crashers                  | 92 (xsd:int) | 2005 (xsd:int) | 1913 (xsd:int) |
| ...                                       | ...                                                      | ...                 | ...                               | ...          | ...            | ...            |
| Harrison Ford (imdb_name:nm0000148)       | Captain America: Brave New World (imdb_title:tt14513804) | Harrison Ford       | Captain America: Brave New World  | 83 (xsd:int) | 2025 (xsd:int) | 1942 (xsd:int) |
| Christopher Lloyd (imdb_name:nm0000502)   | Nobody (imdb_title:tt7888964)                            | Christopher Lloyd   | Nobody                            | 83 (xsd:int) | 2021 (xsd:int) | 1938 (xsd:int) |
| Diana Rigg (imdb_name:nm0001671)          | Last Night in Soho (imdb_title:tt9639470)                | Diana Rigg          | Last Night in Soho                | 83 (xsd:int) | 2021 (xsd:int) | 1938 (xsd:int) |
| Bruce Dern (imdb_name:nm0001136)          | Once Upon a Time in... Hollywood (imdb_title:tt7131622)  | Bruce Dern          | Once Upon a Time in... Hollywood  | 83 (xsd:int) | 2019 (xsd:int) | 1936 (xsd:int) |
| Michael Caine (imdb_name:nm0000323)       | Now You See Me 2 (imdb_title:tt3110958)                  | Michael Caine       | Now You See Me 2                  | 83 (xsd:int) | 2016 (xsd:int) | 1933 (xsd:int) |

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

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.

Available knowledge graphs:
- imdb at https://qlever.dev/api/imdb with prefix index for entities and similarity index for properties

Index types used for entities and properties:
- Prefix index: Retrieves items by overlap between query keywords and item words. The query keywords can match item words exactly or as prefixes.
- Similarity index: Retrieves items by cosine similarity between their embeddings and the query embedding.

SPARQL prefixes for use in function calls:
- bd: <http://www.bigdata.com/rdf#
- dc: <http://purl.org/dc/elements/1.1/
- dct: <http://purl.org/dc/terms/
- foaf: <http://xmlns.com/foaf/0.1/
- geo: <http://www.opengis.net/ont/geosparql#
- geosparql: <http://www.opengis.net/ont/geosparql#
- gn: <http://www.geonames.org/ontology#
- hint: <http://www.bigdata.com/queryHints#
- 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/
- owl: <http://www.w3.org/2002/07/owl#
- prov: <http://www.w3.org/ns/prov#
- qb: <http://purl.org/linked-data/cube#
- rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
- rdfs: <http://www.w3.org/2000/01/rdf-schema#
- schema: <http://schema.org/
- skos: <http://www.w3.org/2004/02/skos/core#
- void: <http://rdfs.org/ns/void#
- wikibase: <http://wikiba.se/ontology#
- xsd: <http://www.w3.org/2001/XMLSchema#

Additional rules to follow:
- Explain your thought process before and after each step and function call.
- Do not ask the user for clarification until you have made an initial attempt at completing the task. When the task input is incomplete or ambiguous, proceed based on reasonable assumptions.
- Do not use "SERVICE wikibase:label { bd:serviceParam wikibase:language ..." in SPARQL queries. It is unsupported by the used QLever SPARQL endpoints. Use rdfs:label or similar properties to get labels instead.
- Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
- 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.
- 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.
- 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.
- For questions with a "True" or "False" answer the SPARQL query should be an ASK query.

[User] 100 oldest main actors at the time of a movie (not series) with >= 100k votes

[Assistant]
Let's break down the question:

1. We need to find movies (not series) with at least 100,000 votes.
2. For each such movie, we want the main actors.
3. For each main actor, we want their age at the time of the movie's release.
4. We want the 100 oldest such main actors (i.e., highest age at time of movie).

Step 1: Identify entities and properties.
- "Movie (not series)": We need to filter for movies, not TV series.
- "Votes": There should be a property for the number of votes a movie has.
- "Main actor": There should be a way to get the principal/main actors for a movie.
- "Actor's age at time of movie": We need the actor's birth date and the movie's release date.
- "Order by age, limit 100": Sort and limit.

Step 2: Search for relevant properties.

First, let's search for the property for "votes" in the IMDb KG.

Question: votes

Top 10 property alternatives:
1. num Votes (imdb:numVotes, matched via numVotes), also known as numVotes
2. self (imdb_principal:self)
3. id (imdb:id)
4. label (rdfs:label)
5. actor (imdb_principal:actor)
6. type (imdb:type)
7. type (rdf:type)
8. death Year (imdb:deathYear, matched via deathYear), also known as deathYear
9. editor (imdb_principal:editor)
10. genre (imdb:genre)

[Assistant]
The property for the number of votes is imdb:numVotes.

Next, let's search for the property that distinguishes movies from series. Let's try "type".

Question: type

Top 10 property alternatives:
1. type (imdb:type)
2. type (rdf:type)
3. id (imdb:id)
4. label (rdfs:label)
5. self (imdb_principal:self)
6. actor (imdb_principal:actor)
7. genre (imdb:genre)
8. primary Title (imdb:primaryTitle, matched via primaryTitle), also known as primaryTitle
9. alt Label (skos:altLabel, matched via altLabel), also known as altLabel
10. editor (imdb_principal:editor)

[Assistant]
Question: movie

No entity items found

No other items found

Top 10 literal alternatives:
1. movie ("movie")

[Assistant]
The type property imdb:type can be filtered with the literal "movie" to select movies.

Next, let's find the property for the main actor. Let's search for "main actor" and "actor".

Question: main actor

Top 10 property alternatives:
1. actor (imdb_principal:actor)
2. casting director (imdb_principal:casting_director, matched via casting_director), also known as casting_director
3. director (imdb_principal:director)
4. director (imdb:director)
5. primary Title (imdb:primaryTitle), also known as primaryTitle
6. actress (imdb_principal:actress)
7. producer (imdb_principal:producer)
8. self (imdb_principal:self)
9. known For Title (imdb:knownForTitle, matched via knownForTitle), also known as knownForTitle
10. id (imdb:id)

[Assistant]
Question: principal

Top 10 property alternatives:
1. primary Title (imdb:primaryTitle), also known as primaryTitle
2. self (imdb_principal:self)
3. id (imdb:id)
4. actor (imdb_principal:actor)
5. label (rdfs:label)
6. type (imdb:type)
7. type (rdf:type)
8. profession (imdb:profession)
9. parent Title (imdb:parentTitle, matched via parentTitle), also known as parentTitle
10. editor (imdb_principal:editor)

[Assistant]
Question: cast

Top 10 property alternatives:
1. casting director (imdb_principal:casting_director, matched via casting_director), also known as casting_director
2. actor (imdb_principal:actor)
3. director (imdb_principal:director)
4. director (imdb:director)
5. id (imdb:id)
6. producer (imdb_principal:producer)
7. type (imdb:type)
8. type (rdf:type)
9. genre (imdb:genre)
10. known For Title (imdb:knownForTitle, matched via knownForTitle), also known as knownForTitle

[Assistant]
Question: birth

Top 10 property alternatives:
1. birth Year (imdb:birthYear, matched via birthYear), also known as birthYear
2. death Year (imdb:deathYear, matched via deathYear), also known as deathYear
3. self (imdb_principal:self)
4. id (imdb:id)
5. type (imdb:type)
6. type (rdf:type)
7. label (rdfs:label)
8. actor (imdb_principal:actor)
9. season Number (imdb:seasonNumber, matched via seasonNumber), also known as seasonNumber
10. end Year (imdb:endYear, matched via endYear), also known as endYear

[Assistant]
Question: release

Top 10 property alternatives:
1. self (imdb_principal:self)
2. id (imdb:id)
3. label (rdfs:label)
4. type (imdb:type)
5. type (rdf:type)
6. profession (imdb:profession)
7. actor (imdb_principal:actor)
8. editor (imdb_principal:editor)
9. genre (imdb:genre)
10. composer (imdb_principal:composer)

[Assistant]
Question: year

Top 10 property alternatives:
1. end Year (imdb:endYear, matched via endYear), also known as endYear
2. start Year (imdb:startYear), also known as startYear
3. death Year (imdb:deathYear, matched via deathYear), also known as deathYear
4. birth Year (imdb:birthYear, matched via birthYear), also known as birthYear
5. season Number (imdb:seasonNumber, matched via seasonNumber), also known as seasonNumber
6. id (imdb:id)
7. self (imdb_principal:self)
8. type (imdb:type)
9. type (rdf:type)
10. label (rdfs:label)

[Assistant]
Question: main

Top 10 property alternatives:
1. primary Title (imdb:primaryTitle), also known as primaryTitle
2. self (imdb_principal:self)
3. id (imdb:id)
4. actor (imdb_principal:actor)
5. label (rdfs:label)
6. type (imdb:type)
7. type (rdf:type)
8. editor (imdb_principal:editor)
9. parent Title (imdb:parentTitle, matched via parentTitle), also known as parentTitle
10. profession (imdb:profession)

[Assistant]
Question: order

Top 10 property alternatives:
1. id (imdb:id)
2. type (imdb:type)
3. type (rdf:type)
4. label (rdfs:label)
5. self (imdb_principal:self)
6. editor (imdb_principal:editor)
7. actor (imdb_principal:actor)
8. profession (imdb:profession)
9. primary Title (imdb:primaryTitle, matched via primaryTitle), also known as primaryTitle
10. production designer (imdb_principal:production_designer, matched via production_designer), also known as production_designer

[Assistant]
Question: ordering

Top 10 property alternatives:
1. id (imdb:id)
2. type (imdb:type)
3. type (rdf:type)
4. label (rdfs:label)
5. editor (imdb_principal:editor)
6. self (imdb_principal:self)
7. actor (imdb_principal:actor)
8. profession (imdb:profession)
9. genre (imdb:genre)
10. composer (imdb_principal:composer)

[Assistant]
Question: principal

Top 10 property alternatives:
1. primary Title (imdb:primaryTitle), also known as primaryTitle
2. self (imdb_principal:self)
3. id (imdb:id)
4. actor (imdb_principal:actor)
5. label (rdfs:label)
6. type (imdb:type)
7. type (rdf:type)
8. profession (imdb:profession)
9. parent Title (imdb:parentTitle, matched via parentTitle), also known as parentTitle
10. editor (imdb_principal:editor)

[Assistant]
list

Got more than 131,072 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)  |
| Dama de noche (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)    |
| La familia Dressel (imdb_title:tt0026334)          | actor (imdb_principal:actor) | Fernando Wagner (imdb_name:nm0905884)   |
| The Old House (imdb_title:tt0028055)               | actor (imdb_principal:actor) | Mel Blanc (imdb_name:nm0000305)         |
| Mickey's Elephant (imdb_title:tt0027961)           | actor (imdb_principal:actor) | James MacDonald (imdb_name:nm0531763)   |
| Déjate de amigos (imdb_title:tt0019844)            | actor (imdb_principal:actor) | Antonio Ferrer (imdb_name:nm0274532)    |
| 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)      |
| Brilliant Marriage (imdb_title:tt0027399)          | actor (imdb_principal:actor) | Hugh Marlowe (imdb_name:nm0549280)      |
| ...                                                | ...                          | ...                                     |

[Assistant]
Question: main

Top 10 property alternatives:
1. primary Title (imdb:primaryTitle), also known as primaryTitle
2. id (imdb:id)
3. actor (imdb_principal:actor)
4. label (rdfs:label)
5. type (imdb:type)
6. type (rdf:type)
7. original Title (imdb:originalTitle, matched via originalTitle), also known as originalTitle
8. alt Label (skos:altLabel, matched via altLabel), also known as altLabel
9. is Adult (imdb:isAdult, matched via isAdult), also known as isAdult
10. director (imdb_principal:director)

No other items found

[Assistant]
Question: order

Top 10 property alternatives:
1. id (imdb:id)
2. type (imdb:type)
3. type (rdf:type)
4. label (rdfs:label)
5. actor (imdb_principal:actor)
6. primary Title (imdb:primaryTitle, matched via primaryTitle), also known as primaryTitle
7. alt Label (skos:altLabel, matched via altLabel), also known as altLabel
8. is Adult (imdb:isAdult, matched via isAdult), also known as isAdult
9. original Title (imdb:originalTitle, matched via originalTitle), also known as originalTitle
10. director (imdb_principal:director)

No other items found

[Assistant]
Question: principal

Top 10 property alternatives:
1. primary Title (imdb:primaryTitle), also known as primaryTitle
2. id (imdb:id)
3. actor (imdb_principal:actor)
4. label (rdfs:label)
5. type (imdb:type)
6. type (rdf:type)
7. original Title (imdb:originalTitle, matched via originalTitle), also known as originalTitle
8. alt Label (skos:altLabel, matched via altLabel), also known as altLabel
9. is Adult (imdb:isAdult, matched via isAdult), also known as isAdult
10. writer (imdb_principal:writer)

No other items found

[Assistant]
imdb_title:tt0010041

Got 28 rows and 3 columns, showing the first 10 rows below:
| subject                               | property                            | object                                 |
| ------------------------------------- | ----------------------------------- | -------------------------------------- |
| La dama duende (imdb_title:tt0010041) | alt Label (skos:altLabel)           | La dama duende (lang:es)               |
| La dama duende (imdb_title:tt0010041) | label (rdfs:label)                  | La dama duende                         |
| La dama duende (imdb_title:tt0010041) | type (rdf:type)                     | Movie (imdb:Movie)                     |
| La dama duende (imdb_title:tt0010041) | actor (imdb_principal:actor)        | Rafael Banquells (imdb_name:nm0052486) |
| La dama duende (imdb_title:tt0010041) | actress (imdb_principal:actress)    | Carmen Rodríguez (imdb_name:nm0735761) |
| La dama duende (imdb_title:tt0010041) | writer (imdb:writer)                | José Ardévol (imdb_name:nm0034146)     |
| La dama duende (imdb_title:tt0010041) | primary Title (imdb:primaryTitle)   | The Ghost Lady                         |
| La dama duende (imdb_title:tt0010041) | original Title (imdb:originalTitle) | La dama duende                         |
| La dama duende (imdb_title:tt0010041) | id (imdb:id)                        | tt0010041                              |
| La dama duende (imdb_title:tt0010041) | is Adult (imdb:isAdult)             | false (xsd:boolean)                    |
| ...                                   | ...                                 | ...                                    |

[Assistant]
imdb_title:tt0010041

Got 5 rows and 3 columns:
| subject                               | property                     | object                                     |
| ------------------------------------- | ---------------------------- | ------------------------------------------ |
| La dama duende (imdb_title:tt0010041) | actor (imdb_principal:actor) | Rafael Banquells (imdb_name:nm0052486)     |
| La dama duende (imdb_title:tt0010041) | actor (imdb_principal:actor) | Ramón Quadreny (imdb_name:nm0702738)       |
| La dama duende (imdb_title:tt0010041) | actor (imdb_principal:actor) | Josep Balaguer (imdb_name:nm0049367)       |
| La dama duende (imdb_title:tt0010041) | actor (imdb_principal:actor) | Julián de la Cantera (imdb_name:nm0134528) |
| La dama duende (imdb_title:tt0010041) | actor (imdb_principal:actor) | Leandro Cinca (imdb_name:nm0162403)        |

[Assistant] imdb_title:tt0010041

[Assistant]
imdb_name:nm0052486

Got 11 rows and 3 columns, showing the first 10 rows below:
| subject                                | property                             | object                                              |
| -------------------------------------- | ------------------------------------ | --------------------------------------------------- |
| Rafael Banquells (imdb_name:nm0052486) | label (rdfs:label)                   | Rafael Banquells                                    |
| Rafael Banquells (imdb_name:nm0052486) | type (rdf:type)                      | Person (imdb:Person)                                |
| Rafael Banquells (imdb_name:nm0052486) | known For Title (imdb:knownForTitle) | Los tres mosqueteros y medio (imdb_title:tt0049880) |
| Rafael Banquells (imdb_name:nm0052486) | profession (imdb:profession)         | actor                                               |
| Rafael Banquells (imdb_name:nm0052486) | birth Year (imdb:birthYear)          | 1917 (xsd:int)                                      |
| Rafael Banquells (imdb_name:nm0052486) | death Year (imdb:deathYear)          | 1990 (xsd:int)                                      |
| Rafael Banquells (imdb_name:nm0052486) | known For Title (imdb:knownForTitle) | Vivir un poco (imdb_title:tt0216516)                |
| Rafael Banquells (imdb_name:nm0052486) | known For Title (imdb:knownForTitle) | Abandonada (imdb_title:tt0243054)                   |
| Rafael Banquells (imdb_name:nm0052486) | known For Title (imdb:knownForTitle) | Rocambole (imdb_title:tt0366081)                    |
| Rafael Banquells (imdb_name:nm0052486) | profession (imdb:profession)         | cinematographer                                     |
| ...                                    | ...                                  | ...                                                 |

[Assistant] imdb_title:tt0010041

[Assistant]
We have now identified the following:
- To get movies: filter imdb:type = "movie"
- To get votes: imdb:numVotes
- To get actors: imdb_principal:actor (and imdb_principal:actress for actresses)
- To get actor's birth year: imdb:birthYear
- To get movie's release year: imdb:startYear
- To get main actors: There is no explicit "main" property, but typically the first actor(s) listed are considered main. However, without an ordering property, we may have to include all actors.

Let's check if there is an ordering property for actors in a movie, which would allow us to select only the main actor(s).

imdb_title:tt0010041

Got 28 rows and 3 columns, showing the first 10 rows below:
| subject                               | property                            | object                                 |
| ------------------------------------- | ----------------------------------- | -------------------------------------- |
| La dama duende (imdb_title:tt0010041) | alt Label (skos:altLabel)           | La dama duende (lang:es)               |
| La dama duende (imdb_title:tt0010041) | label (rdfs:label)                  | La dama duende                         |
| La dama duende (imdb_title:tt0010041) | type (rdf:type)                     | Movie (imdb:Movie)                     |
| La dama duende (imdb_title:tt0010041) | actor (imdb_principal:actor)        | Rafael Banquells (imdb_name:nm0052486) |
| La dama duende (imdb_title:tt0010041) | actress (imdb_principal:actress)    | Carmen Rodríguez (imdb_name:nm0735761) |
| La dama duende (imdb_title:tt0010041) | writer (imdb:writer)                | José Ardévol (imdb_name:nm0034146)     |
| La dama duende (imdb_title:tt0010041) | primary Title (imdb:primaryTitle)   | The Ghost Lady                         |
| La dama duende (imdb_title:tt0010041) | original Title (imdb:originalTitle) | La dama duende                         |
| La dama duende (imdb_title:tt0010041) | id (imdb:id)                        | tt0010041                              |
| La dama duende (imdb_title:tt0010041) | is Adult (imdb:isAdult)             | false (xsd:boolean)                    |
| ...                                   | ...                                 | ...                                    |

[Assistant]
There is no explicit ordering property for actors in the movie entity. Therefore, we will include all actors and actresses for each movie, as there is no way to reliably select only the "main" actor(s) from the data available.

Now, let's put together the SPARQL query:
- Select movies (imdb:type = "movie") with imdb:numVotes >= 100000
- Get their imdb:startYear
- Get their actors and actresses (imdb_principal:actor and imdb_principal:actress)
- Get the actor's/actress's imdb:birthYear
- Compute age at release: startYear - birthYear
- Order by age descending, limit 100

Let's write the query.

execute

SELECT ?person ?personLabel ?movie ?movieLabel ?age ?startYear ?birthYear WHERE {
  ?movie imdb:type "movie" .
  ?movie imdb:numVotes ?votes .
  FILTER(?votes >= 100000)
  ?movie imdb:startYear ?startYear .
  {
    ?movie imdb_principal:actor ?person .
  } UNION {
    ?movie imdb_principal:actress ?person .
  }
  ?person imdb:birthYear ?birthYear .
  BIND((xsd:int(?startYear) - xsd:int(?birthYear)) AS ?age)
  OPTIONAL { ?person rdfs:label ?personLabel }
  OPTIONAL { ?movie rdfs:label ?movieLabel }
}
ORDER BY DESC(?age)
LIMIT 100

Got 100 rows and 7 columns, showing the first 5 and last 5 rows below:
| person                                    | personLabel         | movie                                                    | movieLabel                        | age          | startYear      | birthYear      |
| ----------------------------------------- | ------------------- | -------------------------------------------------------- | --------------------------------- | ------------ | -------------- | -------------- |
| Kamini Kaushal (imdb_name:nm0442470)      | Kamini Kaushal      | Laal Singh Chaddha (imdb_title:tt10028196)               | Laal Singh Chaddha                | 95 (xsd:int) | 2022 (xsd:int) | 1927 (xsd:int) |
| Fernanda Montenegro (imdb_name:nm0005244) | Fernanda Montenegro | Ainda Estou Aqui (imdb_title:tt14961016)                 | Ainda Estou Aqui                  | 95 (xsd:int) | 2024 (xsd:int) | 1929 (xsd:int) |
| Angela Lansbury (imdb_name:nm0001450)     | Angela Lansbury     | How the Grinch Stole Christmas (imdb_title:tt2709692)    | How the Grinch Stole Christmas    | 93 (xsd:int) | 2018 (xsd:int) | 1925 (xsd:int) |
| Angela Lansbury (imdb_name:nm0001450)     | Angela Lansbury     | How the Grinch Stole Christmas (imdb_title:tt2709692)    | The Grinch                        | 93 (xsd:int) | 2018 (xsd:int) | 1925 (xsd:int) |
| James Hong (imdb_name:nm0393222)          | James Hong          | Everything Everywhere All at Once (imdb_title:tt6710474) | Everything Everywhere All at Once | 93 (xsd:int) | 2022 (xsd:int) | 1929 (xsd:int) |
| ...                                       | ...                 | ...                                                      | ...                               | ...          | ...            | ...            |
| Yoshi Oida (imdb_name:nm0645224)          | Yoshi Oida          | Silence (imdb_title:tt0490215)                           | Silence                           | 83 (xsd:int) | 2016 (xsd:int) | 1933 (xsd:int) |
| Michael Gough (imdb_name:nm0001284)       | Michael Gough       | Sleepy Hollow (imdb_title:tt0162661)                     | Sleepy Hollow                     | 83 (xsd:int) | 1999 (xsd:int) | 1916 (xsd:int) |
| Dan Birnbaum (imdb_name:nm1250340)        | Dan Birnbaum        | Mulholland Dr. (imdb_title:tt0166924)                    | Mulholland Dr.                    | 83 (xsd:int) | 2001 (xsd:int) | 1918 (xsd:int) |
| John Randolph (imdb_name:nm0709907)       | John Randolph       | You've Got Mail (imdb_title:tt0128853)                   | You've Got Mail                   | 83 (xsd:int) | 1998 (xsd:int) | 1915 (xsd:int) |
| Christopher Lee (imdb_name:nm0000489)     | Christopher Lee     | Charlie and the Chocolate Factory (imdb_title:tt0367594) | Charlie and the Chocolate Factory | 83 (xsd:int) | 2005 (xsd:int) | 1922 (xsd:int) |

[Assistant]
answer

SELECT ?person ?personLabel ?movie ?movieLabel ?age ?startYear ?birthYear WHERE {
  ?movie imdb:type "movie" .
  ?movie imdb:numVotes ?votes .
  FILTER(?votes >= 100000)
  ?movie imdb:startYear ?startYear .
  {
    ?movie imdb_principal:actor ?person .
  } UNION {
    ?movie imdb_principal:actress ?person .
  }
  ?person imdb:birthYear ?birthYear .
  BIND((xsd:int(?startYear) - xsd:int(?birthYear)) AS ?age)
  OPTIONAL { ?person rdfs:label ?personLabel }
  OPTIONAL { ?movie rdfs:label ?movieLabel }
}
ORDER BY DESC(?age)
LIMIT 100

Answer: Here are the 100 oldest main actors (including actresses) at the time of a movie (not series) with at least 100,000 votes. The results include the actor, the movie, their age at the time of release, and the relevant years. Note that due to data limitations, all actors and actresses are included, not just the 'main' ones.

[feedback]
Feedback (status=refine):
The query is mostly correct and returns a sensible list of the oldest actors at the time of a movie with at least 100,000 votes. However, there are two main issues that need refinement:

1. The query does not restrict the actors to only 'main' actors. The user specifically asked for the oldest 'main' actors, but the query includes all actors and actresses listed in the principal cast, which may include supporting roles. If the knowledge graph provides a way to distinguish the order or importance of actors (e.g., an 'ordering' or 'category' property in the principal relation), this should be used to filter for main actors only (typically those with ordering = 1 or similar).

2. There is a duplicate entry for Angela Lansbury in the results, both for 'How the Grinch Stole Christmas' and 'The Grinch' with the same year and age. This suggests that the query may not be handling alternate titles or duplicate movie entries correctly. Consider grouping or deduplicating results by person and movie.

If the knowledge graph does not provide a way to distinguish main actors, this limitation should be clearly stated in the answer, and the query should be as close as possible to the user's intent.

[Assistant]
Thank you for the feedback. Let's address the two main issues:

1. Restricting to 'main' actors:
- In IMDb data, the "principal" cast often includes an ordering or category property (sometimes called "ordering", "category", or "job") that can be used to select only the main actors (usually those with ordering = 1).
- Let's check if such a property exists in the IMDb knowledge graph for the principal cast.

2. Deduplication of results:
- Duplicate entries (e.g., Angela Lansbury for "How the Grinch Stole Christmas" and "The Grinch") may be due to alternate titles or multiple labels for the same movie. We should group by the unique movie identifier and person.

First, let's investigate if there is an ordering or category property for the principal cast. We'll look at the properties for a principal relation between a movie and an actor.

list

imdb_title:tt2709692

Got 72 rows and 3 columns, showing the first 10 rows below:
| subject                                               | property                            | object                                |
| ----------------------------------------------------- | ----------------------------------- | ------------------------------------- |
| How the Grinch Stole Christmas (imdb_title:tt2709692) | alt Label (skos:altLabel)           | A Grincs (lang:hu)                    |
| How the Grinch Stole Christmas (imdb_title:tt2709692) | label (rdfs:label)                  | How the Grinch Stole Christmas        |
| How the Grinch Stole Christmas (imdb_title:tt2709692) | type (rdf:type)                     | Movie (imdb:Movie)                    |
| How the Grinch Stole Christmas (imdb_title:tt2709692) | actor (imdb_principal:actor)        | Kenan Thompson (imdb_name:nm0860380)  |
| How the Grinch Stole Christmas (imdb_title:tt2709692) | genre (imdb:genre)                  | Animation                             |
| How the Grinch Stole Christmas (imdb_title:tt2709692) | actress (imdb_principal:actress)    | Angela Lansbury (imdb_name:nm0001450) |
| How the Grinch Stole Christmas (imdb_title:tt2709692) | writer (imdb:writer)                | Dr. Seuss (imdb_name:nm0317450)       |
| How the Grinch Stole Christmas (imdb_title:tt2709692) | primary Title (imdb:primaryTitle)   | The Grinch                            |
| How the Grinch Stole Christmas (imdb_title:tt2709692) | original Title (imdb:originalTitle) | The Grinch                            |
| How the Grinch Stole Christmas (imdb_title:tt2709692) | id (imdb:id)                        | tt2709692                             |
| ...                                                   | ...                                 | ...                                   |

[Assistant]
Got 152 rows and 3 columns, showing the first 10 rows below:
| subject                                                                                | property                   | object                                |
| -------------------------------------------------------------------------------------------- | -------------------------- | ------------------------------------- |
| Ingrid (imdb_title:tt0089340)                                                                | self (imdb_principal:self) | Angela Lansbury (imdb_name:nm0001450) |
| The Spencer Tracy Legacy: A Tribute by Katharine Hepburn (imdb_title:tt0091994)              | self (imdb_principal:self) | Angela Lansbury (imdb_name:nm0001450) |
| The Wonderful Wizard of Oz: 50 Years of Magic (imdb_title:tt0100950)                         | self (imdb_principal:self) | Angela Lansbury (imdb_name:nm0001450) |
| Fantasia 2000 (imdb_title:tt0120910)                                                         | self (imdb_principal:self) | Angela Lansbury (imdb_name:nm0001450) |
| The Films of John Frankenheimer (imdb_title:tt0142290)                                       | self (imdb_principal:self) | Angela Lansbury (imdb_name:nm0001450) |
| Hollywood Backstage (imdb_title:tt0157230)                                                   | self (imdb_principal:self) | Angela Lansbury (imdb_name:nm0001450) |
| The Best of Disney Music: A Legacy in Song - Part I (imdb_title:tt0229248)                   | self (imdb_principal:self) | Angela Lansbury (imdb_name:nm0001450) |
| The Dream Is Alive: 20th Anniversary Celebration of Walt Disney World (imdb_title:tt0240465) | self (imdb_principal:self) | Angela Lansbury (imdb_name:nm0001450) |
| Circus of the Stars #5 (imdb_title:tt0242373)                                                | self (imdb_principal:self) | Angela Lansbury (imdb_name:nm0001450) |
| The Grand Opening of Euro Disney (imdb_title:tt0243935)                                      | self (imdb_principal:self) | Angela Lansbury (imdb_name:nm0001450) |
| ...                                                                                | ...                        | ...                                   |

[Assistant]
Question: ordering

Top 10 property alternatives:
1. id (imdb:id)
2. type (imdb:type)
3. type (rdf:type)
4. label (rdfs:label)
5. editor (imdb_principal:editor)
6. self (imdb_principal:self)
7. actor (imdb_principal:actor)
8. profession (imdb:profession)
9. genre (imdb:genre)
10. composer (imdb_principal:composer)

[Assistant]
Question: category

Top 10 property alternatives:
1. id (imdb:id)
2. type (imdb:type)
3. type (rdf:type)
4. label (rdfs:label)
5. actor (imdb_principal:actor)
6. self (imdb_principal:self)
7. genre (imdb:genre)
8. editor (imdb_principal:editor)
9. profession (imdb:profession)
10. primary Title (imdb:primaryTitle, matched via primaryTitle), also known as primaryTitle

[Assistant]
Question: job

Top 10 property alternatives:
1. profession (imdb:profession)
2. id (imdb:id)
3. label (rdfs:label)
4. type (imdb:type)
5. type (rdf:type)
6. self (imdb_principal:self)
7. editor (imdb_principal:editor)
8. production designer (imdb_principal:production_designer, matched via production_designer), also known as production_designer
9. actor (imdb_principal:actor)
10. primary Title (imdb:primaryTitle, matched via primaryTitle), also known as primaryTitle

[Assistant]
Question: cast ordering

Top 10 property alternatives:
1. id (imdb:id)
2. type (imdb:type)
3. type (rdf:type)
4. label (rdfs:label)
5. self (imdb_principal:self)
6. actor (imdb_principal:actor)
7. average Rating (imdb:averageRating, matched via averageRating), also known as averageRating
8. primary Title (imdb:primaryTitle, matched via primaryTitle), also known as primaryTitle
9. episode Number (imdb:episodeNumber, matched via episodeNumber), also known as episodeNumber
10. season Number (imdb:seasonNumber), also known as seasonNumber

[Assistant]
imdb_title:tt2709692

Got 6 rows and 3 columns:
| subject                                               | property                     | object                                     |
| ----------------------------------------------------- | ---------------------------- | ------------------------------------------ |
| How the Grinch Stole Christmas (imdb_title:tt2709692) | actor (imdb_principal:actor) | Kenan Thompson (imdb_name:nm0860380)       |
| How the Grinch Stole Christmas (imdb_title:tt2709692) | actor (imdb_principal:actor) | Benedict Cumberbatch (imdb_name:nm1212722) |
| How the Grinch Stole Christmas (imdb_title:tt2709692) | actor (imdb_principal:actor) | Pharrell Williams (imdb_name:nm1214289)    |
| How the Grinch Stole Christmas (imdb_title:tt2709692) | actor (imdb_principal:actor) | Sam Lavagnino (imdb_name:nm5373238)        |
| How the Grinch Stole Christmas (imdb_title:tt2709692) | actor (imdb_principal:actor) | Ramone Hamilton (imdb_name:nm6878893)      |
| How the Grinch Stole Christmas (imdb_title:tt2709692) | actor (imdb_principal:actor) | Tristan O'Hare (imdb_name:nm10232559)      |

[Assistant]
imdb_title:tt2709692

Got 4 rows and 3 columns:
| subject                                               | property                         | object                                 |
| ----------------------------------------------------- | -------------------------------- | -------------------------------------- |
| How the Grinch Stole Christmas (imdb_title:tt2709692) | actress (imdb_principal:actress) | Angela Lansbury (imdb_name:nm0001450)  |
| How the Grinch Stole Christmas (imdb_title:tt2709692) | actress (imdb_principal:actress) | Rashida Jones (imdb_name:nm0429069)    |
| How the Grinch Stole Christmas (imdb_title:tt2709692) | actress (imdb_principal:actress) | Scarlett Estevez (imdb_name:nm5411494) |
| How the Grinch Stole Christmas (imdb_title:tt2709692) | actress (imdb_principal:actress) | Cameron Seely (imdb_name:nm7372981)    |

[Assistant] imdb_title:tt2709692

[Assistant]
imdb_name:nm0001450

Got 11 rows and 3 columns, showing the first 10 rows below:
| subject                               | property                             | object                                          |
| ------------------------------------- | ------------------------------------ | ----------------------------------------------- |
| Angela Lansbury (imdb_name:nm0001450) | label (rdfs:label)                   | Angela Lansbury                                 |
| Angela Lansbury (imdb_name:nm0001450) | type (rdf:type)                      | Person (imdb:Person)                            |
| Angela Lansbury (imdb_name:nm0001450) | known For Title (imdb:knownForTitle) |  She Wrote (imdb_title:tt0086765)               |
| Angela Lansbury (imdb_name:nm0001450) | profession (imdb:profession)         | actress                                         |
| Angela Lansbury (imdb_name:nm0001450) | birth Year (imdb:birthYear)          | 1925 (xsd:int)                                  |
| Angela Lansbury (imdb_name:nm0001450) | death Year (imdb:deathYear)          | 2022 (xsd:int)                                  |
| Angela Lansbury (imdb_name:nm0001450) | known For Title (imdb:knownForTitle) | The Manchurian Candidate (imdb_title:tt0056218) |
| Angela Lansbury (imdb_name:nm0001450) | known For Title (imdb:knownForTitle) | Death on the Nile (imdb_title:tt0077413)        |
| Angela Lansbury (imdb_name:nm0001450) | known For Title (imdb:knownForTitle) | Anastasia (imdb_title:tt0118617)                |
| Angela Lansbury (imdb_name:nm0001450) | profession (imdb:profession)         | executive                                       |
| ...                                   | ...                                  | ...                                             |

[Assistant]
It appears that the IMDb knowledge graph does not expose an explicit "ordering" or "category" property for actors/actresses in the principal cast. The only available properties are "actor" and "actress" for each movie, and there is no way to distinguish which of these are the main actors.

For deduplication, we can group by the unique movie and person identifiers to avoid duplicate entries due to alternate titles.

Given these constraints, I will:
- Clearly state the limitation about main actors in the answer.
- Refine the query to group by unique movie and person to avoid duplicates.

Let's write the refined query.

execute

SELECT ?person ?personLabel ?movie ?movieLabel (MAX(?age) AS ?age) (MAX(?startYear) AS ?startYear) (MAX(?birthYear) AS ?birthYear) WHERE {
  ?movie imdb:type "movie" .
  ?movie imdb:numVotes ?votes .
  FILTER(?votes >= 100000)
  ?movie imdb:startYear ?startYear .
  {
    ?movie imdb_principal:actor ?person .
  } UNION {
    ?movie imdb_principal:actress ?person .
  }
  ?person imdb:birthYear ?birthYear .
  BIND((xsd:int(?startYear) - xsd:int(?birthYear)) AS ?age)
  OPTIONAL { ?person rdfs:label ?personLabel }
  OPTIONAL { ?movie rdfs:label ?movieLabel }
}
GROUP BY ?person ?personLabel ?movie ?movieLabel
ORDER BY DESC(?age)
LIMIT 100

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

[Assistant]
The error is because ?age is already defined in the BIND clause and cannot be used as an aggregate target. Instead, we should use MIN or MAX directly in the BIND expression or restructure the query to avoid this conflict.

Since we want to deduplicate by person and movie, and since each (person, movie) pair should have only one age, we can simply remove the GROUP BY and use SELECT DISTINCT to avoid duplicates.

Let's try this approach.

SELECT DISTINCT ?person ?personLabel ?movie ?movieLabel ?age ?startYear ?birthYear WHERE {
  ?movie imdb:type "movie" .
  ?movie imdb:numVotes ?votes .
  FILTER(?votes >= 100000)
  ?movie imdb:startYear ?startYear .
  {
    ?movie imdb_principal:actor ?person .
  } UNION {
    ?movie imdb_principal:actress ?person .
  }
  ?person imdb:birthYear ?birthYear .
  BIND((xsd:int(?startYear) - xsd:int(?birthYear)) AS ?age)
  OPTIONAL { ?person rdfs:label ?personLabel }
  OPTIONAL { ?movie rdfs:label ?movieLabel }
}
ORDER BY DESC(?age)
LIMIT 100

Got 100 rows and 7 columns, showing the first 5 and last 5 rows below:
| person                                    | personLabel         | movie                                                    | movieLabel                        | age          | startYear      | birthYear      |
| ----------------------------------------- | ------------------- | -------------------------------------------------------- | --------------------------------- | ------------ | -------------- | -------------- |
| Fernanda Montenegro (imdb_name:nm0005244) | Fernanda Montenegro | Ainda Estou Aqui (imdb_title:tt14961016)                 | Ainda Estou Aqui                  | 95 (xsd:int) | 2024 (xsd:int) | 1929 (xsd:int) |
| Kamini Kaushal (imdb_name:nm0442470)      | Kamini Kaushal      | Laal Singh Chaddha (imdb_title:tt10028196)               | Laal Singh Chaddha                | 95 (xsd:int) | 2022 (xsd:int) | 1927 (xsd:int) |
| Angela Lansbury (imdb_name:nm0001450)     | Angela Lansbury     | How the Grinch Stole Christmas (imdb_title:tt2709692)    | The Grinch                        | 93 (xsd:int) | 2018 (xsd:int) | 1925 (xsd:int) |
| Angela Lansbury (imdb_name:nm0001450)     | Angela Lansbury     | How the Grinch Stole Christmas (imdb_title:tt2709692)    | How the Grinch Stole Christmas    | 93 (xsd:int) | 2018 (xsd:int) | 1925 (xsd:int) |
| James Hong (imdb_name:nm0393222)          | James Hong          | Everything Everywhere All at Once (imdb_title:tt6710474) | Everything Everywhere All at Once | 93 (xsd:int) | 2022 (xsd:int) | 1929 (xsd:int) |
| ...                                       | ...                 | ...                                                      | ...                               | ...          | ...            | ...            |
| Christopher Lee (imdb_name:nm0000489)     | Christopher Lee     | Corpse Bride (imdb_title:tt0121164)                      | Corpse Bride                      | 83 (xsd:int) | 2005 (xsd:int) | 1922 (xsd:int) |
| Michael Caine (imdb_name:nm0000323)       | Michael Caine       | Now You See Me 2 (imdb_title:tt3110958)                  | Now You See Me 2                  | 83 (xsd:int) | 2016 (xsd:int) | 1933 (xsd:int) |
| Robert Duvall (imdb_name:nm0000380)       | Robert Duvall       | The Judge (imdb_title:tt1872194)                         | The Judge                         | 83 (xsd:int) | 2014 (xsd:int) | 1931 (xsd:int) |
| Anthony Hopkins (imdb_name:nm0000164)     | Anthony Hopkins     | The Father (imdb_title:tt10272386)                       | The Father                        | 83 (xsd:int) | 2020 (xsd:int) | 1937 (xsd:int) |
| Bruce Dern (imdb_name:nm0001136)          | Bruce Dern          | Once Upon a Time in... Hollywood (imdb_title:tt7131622)  | Once Upon a Time in... Hollywood  | 83 (xsd:int) | 2019 (xsd:int) | 1936 (xsd:int) |

[Assistant]
answer

SELECT DISTINCT ?person ?personLabel ?movie ?movieLabel ?age ?startYear ?birthYear WHERE {
  ?movie imdb:type "movie" .
  ?movie imdb:numVotes ?votes .
  FILTER(?votes >= 100000)
  ?movie imdb:startYear ?startYear .
  {
    ?movie imdb_principal:actor ?person .
  } UNION {
    ?movie imdb_principal:actress ?person .
  }
  ?person imdb:birthYear ?birthYear .
  BIND((xsd:int(?startYear) - xsd:int(?birthYear)) AS ?age)
  OPTIONAL { ?person rdfs:label ?personLabel }
  OPTIONAL { ?movie rdfs:label ?movieLabel }
}
ORDER BY DESC(?age)
LIMIT 100

Answer: Here are the 100 oldest main actors (including actresses) at the time of a movie (not series) with at least 100,000 votes. Due to data limitations, the IMDb knowledge graph does not provide a way to distinguish 'main' actors from supporting actors in the principal cast, so all actors and actresses are included. The results are deduplicated by person and movie to avoid alternate title duplicates.

[feedback]
Feedback (status=refine):
The query is mostly correct and returns relevant results, but there are a few areas for improvement:

1. The query does not distinguish 'main' actors from supporting actors, which is acknowledged in the answer. If the knowledge graph does not support this distinction, this limitation should be clearly stated in both the answer and the query documentation.
2. The query may return duplicate rows for the same person and movie if the movie has multiple titles (as seen with Angela Lansbury and 'How the Grinch Stole Christmas'). To avoid this, consider grouping by person and movie or using DISTINCT more carefully.
3. The query does not explicitly exclude TV series or other non-movie types beyond filtering for imdb:type 'movie'. If there are other types that could be confused with movies (e.g., TV movies, shorts), consider refining the filter.
4. The answer should clarify that the age is calculated as the difference between the movie's start year and the person's birth year, which may not be exact if only years are available.

Overall, the query is close to correct but would benefit from deduplication and clearer documentation of its limitations.

[Assistant]
Thank you for the detailed feedback. Here’s how I’ll address each point:

1. Main Actors Limitation:
- The IMDb knowledge graph does not provide an explicit way to distinguish 'main' actors from supporting actors in the principal cast. This limitation will be clearly stated in both the answer and the query documentation.

2. Deduplication:
- To avoid duplicate rows for the same person and movie (due to multiple titles/labels), I will group by the unique movie and person identifiers and select only one label per group (using GROUP BY and SAMPLE).

3. Movie Type Filtering:
- imdb:type "movie" is used, but there could be other types like "tvMovie", "short", etc. I will explicitly filter for imdb:type "movie" only, which should exclude TV movies and shorts, but I will clarify this in the answer.

4. Age Calculation Clarification:
- The answer will clarify that age is calculated as the difference between the movie's start year and the person's birth year, which may not be exact if only years are available.

Let’s write the improved query and answer.

execute

SELECT ?person ?movie (SAMPLE(?personLabel) AS ?personLabel) (SAMPLE(?movieLabel) AS ?movieLabel) (SAMPLE(?age) AS ?age) (SAMPLE(?startYear) AS ?startYear) (SAMPLE(?birthYear) AS ?birthYear) WHERE {
  ?movie imdb:type "movie" .
  ?movie imdb:numVotes ?votes .
  FILTER(?votes >= 100000)
  ?movie imdb:startYear ?startYear .
  {
    ?movie imdb_principal:actor ?person .
  } UNION {
    ?movie imdb_principal:actress ?person .
  }
  ?person imdb:birthYear ?birthYear .
  BIND((xsd:int(?startYear) - xsd:int(?birthYear)) AS ?age)
  OPTIONAL { ?person rdfs:label ?personLabel }
  OPTIONAL { ?movie rdfs:label ?movieLabel }
}
GROUP BY ?person ?movie
ORDER BY DESC(?age)
LIMIT 100

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

[Assistant]
The error is due to using the same variable name (?personLabel, ?movieLabel, etc.) in both the OPTIONAL and the SAMPLE AS clause. To fix this, I will use different variable names in the SAMPLE AS clause.

Let's update the query accordingly.

SELECT ?person ?movie (SAMPLE(?personLabel_) AS ?personLabel) (SAMPLE(?movieLabel_) AS ?movieLabel) (SAMPLE(?age_) AS ?age) (SAMPLE(?startYear_) AS ?startYear) (SAMPLE(?birthYear_) AS ?birthYear) WHERE {
  ?movie imdb:type "movie" .
  ?movie imdb:numVotes ?votes .
  FILTER(?votes >= 100000)
  ?movie imdb:startYear ?startYear_ .
  {
    ?movie imdb_principal:actor ?person .
  } UNION {
    ?movie imdb_principal:actress ?person .
  }
  ?person imdb:birthYear ?birthYear_ .
  BIND((xsd:int(?startYear_) - xsd:int(?birthYear_)) AS ?age_)
  OPTIONAL { ?person rdfs:label ?personLabel_ }
  OPTIONAL { ?movie rdfs:label ?movieLabel_ }
}
GROUP BY ?person ?movie
ORDER BY DESC(?age)
LIMIT 100

Got 100 rows and 7 columns, showing the first 5 and last 5 rows below:
| person                                    | movie                                                    | personLabel         | movieLabel                        | age          | startYear      | birthYear      |
| ----------------------------------------- | -------------------------------------------------------- | ------------------- | --------------------------------- | ------------ | -------------- | -------------- |
| Kamini Kaushal (imdb_name:nm0442470)      | Laal Singh Chaddha (imdb_title:tt10028196)               | Kamini Kaushal      | Laal Singh Chaddha                | 95 (xsd:int) | 2022 (xsd:int) | 1927 (xsd:int) |
| Fernanda Montenegro (imdb_name:nm0005244) | Ainda Estou Aqui (imdb_title:tt14961016)                 | Fernanda Montenegro | Ainda Estou Aqui                  | 95 (xsd:int) | 2024 (xsd:int) | 1929 (xsd:int) |
| Angela Lansbury (imdb_name:nm0001450)     | How the Grinch Stole Christmas (imdb_title:tt2709692)    | Angela Lansbury     | The Grinch                        | 93 (xsd:int) | 2018 (xsd:int) | 1925 (xsd:int) |
| James Hong (imdb_name:nm0393222)          | Everything Everywhere All at Once (imdb_title:tt6710474) | James Hong          | Everything Everywhere All at Once | 93 (xsd:int) | 2022 (xsd:int) | 1929 (xsd:int) |
| Ellen Albertini Dow (imdb_name:nm0016687) | Wedding Crashers (imdb_title:tt0396269)                  | Ellen Albertini Dow | Wedding Crashers                  | 92 (xsd:int) | 2005 (xsd:int) | 1913 (xsd:int) |
| ...                                       | ...                                                      | ...                 | ...                               | ...          | ...            | ...            |
| Harrison Ford (imdb_name:nm0000148)       | Captain America: Brave New World (imdb_title:tt14513804) | Harrison Ford       | Captain America: Brave New World  | 83 (xsd:int) | 2025 (xsd:int) | 1942 (xsd:int) |
| Christopher Lloyd (imdb_name:nm0000502)   | Nobody (imdb_title:tt7888964)                            | Christopher Lloyd   | Nobody                            | 83 (xsd:int) | 2021 (xsd:int) | 1938 (xsd:int) |
| Diana Rigg (imdb_name:nm0001671)          | Last Night in Soho (imdb_title:tt9639470)                | Diana Rigg          | Last Night in Soho                | 83 (xsd:int) | 2021 (xsd:int) | 1938 (xsd:int) |
| Bruce Dern (imdb_name:nm0001136)          | Once Upon a Time in... Hollywood (imdb_title:tt7131622)  | Bruce Dern          | Once Upon a Time in... Hollywood  | 83 (xsd:int) | 2019 (xsd:int) | 1936 (xsd:int) |
| Michael Caine (imdb_name:nm0000323)       | Now You See Me 2 (imdb_title:tt3110958)                  | Michael Caine       | Now You See Me 2                  | 83 (xsd:int) | 2016 (xsd:int) | 1933 (xsd:int) |

[Assistant]
answer

SELECT ?person ?movie (SAMPLE(?personLabel_) AS ?personLabel) (SAMPLE(?movieLabel_) AS ?movieLabel) (SAMPLE(?age_) AS ?age) (SAMPLE(?startYear_) AS ?startYear) (SAMPLE(?birthYear_) AS ?birthYear) WHERE {
  ?movie imdb:type "movie" .
  ?movie imdb:numVotes ?votes .
  FILTER(?votes >= 100000)
  ?movie imdb:startYear ?startYear_ .
  {
    ?movie imdb_principal:actor ?person .
  } UNION {
    ?movie imdb_principal:actress ?person .
  }
  ?person imdb:birthYear ?birthYear_ .
  BIND((xsd:int(?startYear_) - xsd:int(?birthYear_)) AS ?age_)
  OPTIONAL { ?person rdfs:label ?personLabel_ }
  OPTIONAL { ?movie rdfs:label ?movieLabel_ }
}
GROUP BY ?person ?movie
ORDER BY DESC(?age)
LIMIT 100

Answer: Here are the 100 oldest actors (including actresses) at the time of a movie (not series) with at least 100,000 votes. Due to data limitations, the IMDb knowledge graph does not provide a way to distinguish 'main' actors from supporting actors in the principal cast, so all actors and actresses are included. The results are deduplicated by person and movie to avoid alternate title duplicates. Only movies with imdb:type 'movie' are included, which should exclude TV movies and shorts. The age is calculated as the difference between the movie's start year and the person's birth year, which may not be exact if only years are available.


PREFIX imdb_principal: <https://www.imdb.com/principal/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX imdb: <https://www.imdb.com/>
SELECT ?person ?personLabel ( COUNT( DISTINCT ?title ) AS ?numTitles ) ( AVG( ?votes ) AS ?avgVotes ) WHERE {
  {
    ?title imdb_principal:actor ?person .
  } UNION {
    ?title imdb_principal:actress ?person .
  } UNION {
    ?title imdb_principal:director ?person .
  } UNION {
    ?title imdb_principal:producer ?person .
  } UNION {
    ?title imdb_principal:casting_director ?person .
  } UNION {
    ?title imdb_principal:cinematographer ?person .
  }
  ?title imdb:numVotes ?votes .
  OPTIONAL {
    ?person rdfs:label ?personLabel
  }
}
GROUP BY ?person ?personLabel
HAVING ( COUNT( DISTINCT ?title ) >= 3 )
ORDER BY DESC ( ?avgVotes )
LIMIT 10

Using properties:
- label (rdfs:label)
- num Votes (imdb:numVotes), also known as numVotes
- actor (imdb_principal:actor)
- actress (imdb_principal:actress)
- casting director (imdb_principal:casting_director), also known as casting_director
- cinematographer (imdb_principal:cinematographer)
- director (imdb_principal:director)
- producer (imdb_principal:producer)

Got 10 rows and 4 columns:
| person                                         | personLabel              | numTitles    | avgVotes                  |
| ---------------------------------------------- | ------------------------ | ------------ | ------------------------- |
| Emma Thomas (imdb_name:nm0858799)              | Emma Thomas              | 13 (xsd:int) | 1.27705e+06 (xsd:decimal) |
| Fran Walsh (imdb_name:nm0909638)               | Fran Walsh               | 10 (xsd:int) | 927457 (xsd:decimal)      |
| Christopher Nolan (imdb_name:nm0634240)        | Christopher Nolan        | 18 (xsd:int) | 879906 (xsd:decimal)      |
| Michael Conner Humphreys (imdb_name:nm0402011) | Michael Conner Humphreys | 3 (xsd:int)  | 802666 (xsd:decimal)      |
| Jonas Rivera (imdb_name:nm0729304)             | Jonas Rivera             | 3 (xsd:int)  | 791933 (xsd:decimal)      |
| Niki Marvin (imdb_name:nm0555550)              | Niki Marvin              | 4 (xsd:int)  | 771982 (xsd:decimal)      |
| Darla K. Anderson (imdb_name:nm0026565)        | Darla K. Anderson        | 5 (xsd:int)  | 696783 (xsd:decimal)      |
| Phyllis Carlyle (imdb_name:nm0138287)          | Phyllis Carlyle          | 3 (xsd:int)  | 645338 (xsd:decimal)      |
| Lorne Orleans (imdb_name:nm0650038)            | Lorne Orleans            | 16 (xsd:int) | 626320 (xsd:decimal)      |
| Noel Appleby (imdb_name:nm0032370)             | Noel Appleby             | 7 (xsd:int)  | 604764 (xsd:decimal)      |

Answer:
Here are the 10 persons with the highest average number of votes over at least 3 titles, including their average votes and number of titles:

1. Emma Thomas — 1,277,050 average votes (13 titles)
2. Fran Walsh — 927,457 average votes (10 titles)
3. Christopher Nolan — 879,906 average votes (18 titles)
4. Michael Conner Humphreys — 802,666 average votes (3 titles)
5. Jonas Rivera — 791,933 average votes (3 titles)
6. Niki Marvin — 771,982 average votes (4 titles)
7. Darla K. Anderson — 696,783 average votes (5 titles)
8. Phyllis Carlyle — 645,338 average votes (3 titles)
9. Lorne Orleans — 626,320 average votes (16 titles)
10. Noel Appleby — 604,764 average votes (7 titles)

Each person is listed with their name, average number of votes per title, and the number of titles considered.

Here are the 10 persons with the highest average number of votes over at least 3 titles, including their average votes and number of titles:

1. Emma Thomas — 1,277,050 average votes (13 titles)
2. Fran Walsh — 927,457 average votes (10 titles)
3. Christopher Nolan — 879,906 average votes (18 titles)
4. Michael Conner Humphreys — 802,666 average votes (3 titles)
5. Jonas Rivera — 791,933 average votes (3 titles)
6. Niki Marvin — 771,982 average votes (4 titles)
7. Darla K. Anderson — 696,783 average votes (5 titles)
8. Phyllis Carlyle — 645,338 average votes (3 titles)
9. Lorne Orleans — 626,320 average votes (16 titles)
10. Noel Appleby — 604,764 average votes (7 titles)

Each person is listed with their name, average number of votes per title, and the number of titles considered.

SPARQL query over imdb:
PREFIX imdb_principal: <https://www.imdb.com/principal/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX imdb: <https://www.imdb.com/>
SELECT ?person ?personLabel ( COUNT( DISTINCT ?title ) AS ?numTitles ) ( AVG( ?votes ) AS ?avgVotes ) WHERE {
  {
    ?title imdb_principal:actor ?person .
  } UNION {
    ?title imdb_principal:actress ?person .
  } UNION {
    ?title imdb_principal:director ?person .
  } UNION {
    ?title imdb_principal:producer ?person .
  } UNION {
    ?title imdb_principal:casting_director ?person .
  } UNION {
    ?title imdb_principal:cinematographer ?person .
  }
  ?title imdb:numVotes ?votes .
  OPTIONAL {
    ?person rdfs:label ?personLabel
  }
}
GROUP BY ?person ?personLabel
HAVING ( COUNT( DISTINCT ?title ) >= 3 )
ORDER BY DESC ( ?avgVotes )
LIMIT 10

Using properties:
- label (rdfs:label)
- num Votes (imdb:numVotes), also known as numVotes
- actor (imdb_principal:actor)
- actress (imdb_principal:actress)
- casting director (imdb_principal:casting_director), also known as casting_director
- cinematographer (imdb_principal:cinematographer)
- director (imdb_principal:director)
- producer (imdb_principal:producer)

Execution result:
Got 10 rows and 4 columns:
| person                                         | personLabel              | numTitles    | avgVotes                  |
| ---------------------------------------------- | ------------------------ | ------------ | ------------------------- |
| Emma Thomas (imdb_name:nm0858799)              | Emma Thomas              | 13 (xsd:int) | 1.27705e+06 (xsd:decimal) |
| Fran Walsh (imdb_name:nm0909638)               | Fran Walsh               | 10 (xsd:int) | 927457 (xsd:decimal)      |
| Christopher Nolan (imdb_name:nm0634240)        | Christopher Nolan        | 18 (xsd:int) | 879906 (xsd:decimal)      |
| Michael Conner Humphreys (imdb_name:nm0402011) | Michael Conner Humphreys | 3 (xsd:int)  | 802666 (xsd:decimal)      |
| Jonas Rivera (imdb_name:nm0729304)             | Jonas Rivera             | 3 (xsd:int)  | 791933 (xsd:decimal)      |
| Niki Marvin (imdb_name:nm0555550)              | Niki Marvin              | 4 (xsd:int)  | 771982 (xsd:decimal)      |
| Darla K. Anderson (imdb_name:nm0026565)        | Darla K. Anderson        | 5 (xsd:int)  | 696783 (xsd:decimal)      |
| Phyllis Carlyle (imdb_name:nm0138287)          | Phyllis Carlyle          | 3 (xsd:int)  | 645338 (xsd:decimal)      |
| Lorne Orleans (imdb_name:nm0650038)            | Lorne Orleans            | 16 (xsd:int) | 626320 (xsd:decimal)      |
| Noel Appleby (imdb_name:nm0032370)             | Noel Appleby             | 7 (xsd:int)  | 604764 (xsd:decimal)      |

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

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.

Available knowledge graphs:
- imdb at https://qlever.dev/api/imdb with prefix index for entities and similarity index for properties

Index types used for entities and properties:
- Prefix index: Retrieves items by overlap between query keywords and item words. The query keywords can match item words exactly or as prefixes.
- Similarity index: Retrieves items by cosine similarity between their embeddings and the query embedding.

SPARQL prefixes for use in function calls:
- bd: <http://www.bigdata.com/rdf#
- dc: <http://purl.org/dc/elements/1.1/
- dct: <http://purl.org/dc/terms/
- foaf: <http://xmlns.com/foaf/0.1/
- geo: <http://www.opengis.net/ont/geosparql#
- geosparql: <http://www.opengis.net/ont/geosparql#
- gn: <http://www.geonames.org/ontology#
- hint: <http://www.bigdata.com/queryHints#
- 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/
- owl: <http://www.w3.org/2002/07/owl#
- prov: <http://www.w3.org/ns/prov#
- qb: <http://purl.org/linked-data/cube#
- rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
- rdfs: <http://www.w3.org/2000/01/rdf-schema#
- schema: <http://schema.org/
- skos: <http://www.w3.org/2004/02/skos/core#
- void: <http://rdfs.org/ns/void#
- wikibase: <http://wikiba.se/ontology#
- xsd: <http://www.w3.org/2001/XMLSchema#

Additional rules to follow:
- Explain your thought process before and after each step and function call.
- Do not ask the user for clarification until you have made an initial attempt at completing the task. When the task input is incomplete or ambiguous, proceed based on reasonable assumptions.
- Do not use "SERVICE wikibase:label { bd:serviceParam wikibase:language ..." in SPARQL queries. It is unsupported by the used QLever SPARQL endpoints. Use rdfs:label or similar properties to get labels instead.
- Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
- 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.
- 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.
- 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.
- For questions with a "True" or "False" answer the SPARQL query should be an ASK query.

[User] The 10 persons with most votes on average over at least 3 titles

[Assistant]
Let's break down the question:

1. We are looking for persons (most likely actors, directors, etc.).
2. For each person, we want to consider all the titles (movies, shows, etc.) they are associated with.
3. For each person, we want to calculate the average number of votes across all their titles.
4. Only consider persons who have at least 3 titles.
5. Return the top 10 persons with the highest average votes.

Step 1: Identify entities and properties.
- "Person" likely corresponds to an entity of type person (e.g., imdb:Person).
- "Titles" are likely movies, TV shows, etc.
- The association between person and title is likely via a property like "acted in", "directed", or more generally "principal".
- "Votes" is a property of a title (e.g., imdb:ratingCount or similar).

Step 2: Search for relevant properties.
First, let's search for the property that links a person to a title.

Question: person title

Top 10 property alternatives:
1. parent Title (imdb:parentTitle), also known as parentTitle
2. self (imdb_principal:self)
3. known For Title (imdb:knownForTitle, matched via knownForTitle), also known as knownForTitle
4. profession (imdb:profession)
5. id (imdb:id)
6. primary Title (imdb:primaryTitle, matched via primaryTitle), also known as primaryTitle
7. actor (imdb_principal:actor)
8. type (imdb:type)
9. type (rdf:type)
10. birth Year (imdb:birthYear, matched via birthYear), also known as birthYear

[Assistant]
From the property search, "actor" (imdb_principal:actor) seems relevant, but it may only cover acting roles. There is also "known For Title" (imdb:knownForTitle), but that may not cover all titles a person is associated with.

The "principal" table in IMDb datasets usually links persons to titles in any principal role (actor, director, etc.). Let's check if there is a property or entity for "principal" that links persons and titles.

I'll search for "principal" as a property.

Question: principal

Top 10 property alternatives:
1. primary Title (imdb:primaryTitle), also known as primaryTitle
2. self (imdb_principal:self)
3. id (imdb:id)
4. actor (imdb_principal:actor)
5. label (rdfs:label)
6. type (imdb:type)
7. type (rdf:type)
8. profession (imdb:profession)
9. parent Title (imdb:parentTitle, matched via parentTitle), also known as parentTitle
10. editor (imdb_principal:editor)

[Assistant]
There isn't a direct "principal" property, but there are properties like imdb_principal:actor and imdb_principal:editor. These seem to be role-specific. To be comprehensive, we may need to consider all such role properties (actor, director, editor, etc.), but let's first check the structure of the data for a person entity to see how titles are linked.

I'll search for a well-known person, e.g., "Tom Hanks", and inspect their properties.

Question: Tom Hanks

Top 10 entity alternatives:
1. Tom Hanks (imdb_name:nm0000158)
2. Tom Hanks (imdb_title:tt1968908)
3. Tom Hanks (imdb_title:tt1432757)
4. Tom Hanks (imdb_title:tt0552918)
5. Tom Hanks (imdb_title:tt3268516)
6. Tom Hanks (imdb_title:tt3220718)
7.  LeBron James (imdb_title:tt12606104, matched via Tom Hanks), also known as  Sara Bareilles, Tom Hanks
8. Tom Hanks (imdb_title:tt27932333)
9. Tom Hanks (imdb_title:tt1614841)
10. Tom Hanks (imdb_title:tt9541280)

[Assistant]
list

imdb_name:nm0000158

Got 10 rows and 3 columns:
| subject                         | property                             | object                              |
| ------------------------------- | ------------------------------------ | ----------------------------------- |
| Tom Hanks (imdb_name:nm0000158) | label (rdfs:label)                   | Tom Hanks                           |
| Tom Hanks (imdb_name:nm0000158) | type (rdf:type)                      | Person (imdb:Person)                |
| Tom Hanks (imdb_name:nm0000158) | known For Title (imdb:knownForTitle) | Big (imdb_title:tt0094737)          |
| Tom Hanks (imdb_name:nm0000158) | profession (imdb:profession)         | actor                               |
| Tom Hanks (imdb_name:nm0000158) | birth Year (imdb:birthYear)          | 1956 (xsd:int)                      |
| Tom Hanks (imdb_name:nm0000158) | known For Title (imdb:knownForTitle) | Philadelphia (imdb_title:tt0107818) |
| Tom Hanks (imdb_name:nm0000158) | known For Title (imdb:knownForTitle) | Forrest Gump (imdb_title:tt0109830) |
| Tom Hanks (imdb_name:nm0000158) | known For Title (imdb:knownForTitle) | Cast Away (imdb_title:tt0162222)    |
| Tom Hanks (imdb_name:nm0000158) | profession (imdb:profession)         | producer                            |
| Tom Hanks (imdb_name:nm0000158) | profession (imdb:profession)         | writer                              |

[Assistant]
From the listing, Tom Hanks is linked to titles via "known For Title" (imdb:knownForTitle), but this only covers a few titles, not all. To get all titles a person is associated with, we likely need to use the "principal" table, which is common in IMDb datasets.

Let's try searching for properties of a title entity to see how persons are linked from the title side (e.g., actors, directors, etc.). I'll pick "Forrest Gump" as an example.

imdb_title:tt0109830

Got 64 rows and 3 columns, showing the first 10 rows below:
| subject                             | property                            | object                              |
| ----------------------------------- | ----------------------------------- | ----------------------------------- |
| Forrest Gump (imdb_title:tt0109830) | alt Label (skos:altLabel)           | Forrest Gump (lang:cs)              |
| Forrest Gump (imdb_title:tt0109830) | label (rdfs:label)                  | Forrest Gump                        |
| Forrest Gump (imdb_title:tt0109830) | type (rdf:type)                     | Movie (imdb:Movie)                  |
| Forrest Gump (imdb_title:tt0109830) | actor (imdb_principal:actor)        | Tom Hanks (imdb_name:nm0000158)     |
| Forrest Gump (imdb_title:tt0109830) | genre (imdb:genre)                  | Drama                               |
| Forrest Gump (imdb_title:tt0109830) | actress (imdb_principal:actress)    | Sally Field (imdb_name:nm0000398)   |
| Forrest Gump (imdb_title:tt0109830) | writer (imdb:writer)                | Winston Groom (imdb_name:nm0343165) |
| Forrest Gump (imdb_title:tt0109830) | primary Title (imdb:primaryTitle)   | Forrest Gump                        |
| Forrest Gump (imdb_title:tt0109830) | original Title (imdb:originalTitle) | Forrest Gump                        |
| Forrest Gump (imdb_title:tt0109830) | id (imdb:id)                        | tt0109830                           |
| ...                                 | ...                                 | ...                                 |

[Assistant]
From the title's properties, we see that persons are linked via role-specific properties: imdb_principal:actor, imdb_principal:actress, imdb:writer, etc. To get all persons associated with a title, we need to consider all these properties.

Now, let's check for the property that gives the number of votes for a title. I'll search for "votes" and "rating" properties.

Question: votes

Top 10 property alternatives:
1. num Votes (imdb:numVotes, matched via numVotes), also known as numVotes
2. self (imdb_principal:self)
3. id (imdb:id)
4. label (rdfs:label)
5. actor (imdb_principal:actor)
6. type (imdb:type)
7. type (rdf:type)
8. death Year (imdb:deathYear, matched via deathYear), also known as deathYear
9. editor (imdb_principal:editor)
10. genre (imdb:genre)

[Assistant]
The property imdb:numVotes is what we need for the number of votes a title has received.

Now, let's summarize the plan:
- For each person, find all titles they are associated with (via actor, actress, director, writer, editor, etc.).
- For each title, get imdb:numVotes.
- For each person, calculate the average numVotes over all their titles, considering only persons with at least 3 titles.
- Return the top 10 persons with the highest average votes.

To do this efficiently, we need to UNION the different role properties to get all person-title associations.

Let's first check what role properties exist by searching for properties containing "imdb_principal:".

Question: imdb_principal:

Top 10 property alternatives:
1. casting director (imdb_principal:casting_director, matched via casting_director), also known as casting_director
2. director (imdb_principal:director)
3. director (imdb:director)
4. actor (imdb_principal:actor)
5. known For Title (imdb:knownForTitle, matched via knownForTitle), also known as knownForTitle
6. producer (imdb_principal:producer)
7. primary Title (imdb:primaryTitle), also known as primaryTitle
8. id (imdb:id)
9. actress (imdb_principal:actress)
10. cinematographer (imdb_principal:cinematographer)

[Assistant]
The main role properties are:
- imdb_principal:actor
- imdb_principal:actress
- imdb_principal:director
- imdb_principal:producer
- imdb_principal:casting_director
- imdb_principal:cinematographer

To cover the majority of principal roles, let's use these properties in a UNION to get all person-title associations.

Now, let's write the SPARQL query step by step:
- For each person, get all titles via the above properties.
- For each title, get imdb:numVotes.
- Group by person, count titles, average votes, filter for at least 3 titles.
- Order by average votes descending, limit 10.

Let's construct the query.

execute

SELECT ?person (COUNT(DISTINCT ?title) AS ?numTitles) (AVG(?votes) AS ?avgVotes) WHERE {
  {
    ?title imdb_principal:actor ?person .
  } UNION {
    ?title imdb_principal:actress ?person .
  } UNION {
    ?title imdb_principal:director ?person .
  } UNION {
    ?title imdb_principal:producer ?person .
  } UNION {
    ?title imdb_principal:casting_director ?person .
  } UNION {
    ?title imdb_principal:cinematographer ?person .
  }
  ?title imdb:numVotes ?votes .
}
GROUP BY ?person
HAVING (COUNT(DISTINCT ?title) >= 3)
ORDER BY DESC(?avgVotes)
LIMIT 10

Got 10 rows and 3 columns:
| person                                         | numTitles    | avgVotes                  |
| ---------------------------------------------- | ------------ | ------------------------- |
| Emma Thomas (imdb_name:nm0858799)              | 13 (xsd:int) | 1.27705e+06 (xsd:decimal) |
| Fran Walsh (imdb_name:nm0909638)               | 10 (xsd:int) | 927457 (xsd:decimal)      |
| Christopher Nolan (imdb_name:nm0634240)        | 18 (xsd:int) | 879906 (xsd:decimal)      |
| Michael Conner Humphreys (imdb_name:nm0402011) | 3 (xsd:int)  | 802666 (xsd:decimal)      |
| Jonas Rivera (imdb_name:nm0729304)             | 3 (xsd:int)  | 791933 (xsd:decimal)      |
| Niki Marvin (imdb_name:nm0555550)              | 4 (xsd:int)  | 771982 (xsd:decimal)      |
| Darla K. Anderson (imdb_name:nm0026565)        | 5 (xsd:int)  | 696783 (xsd:decimal)      |
| Phyllis Carlyle (imdb_name:nm0138287)          | 3 (xsd:int)  | 645338 (xsd:decimal)      |
| Lorne Orleans (imdb_name:nm0650038)            | 16 (xsd:int) | 626320 (xsd:decimal)      |
| Noel Appleby (imdb_name:nm0032370)             | 7 (xsd:int)  | 604764 (xsd:decimal)      |

[Assistant]
answer

SELECT ?person (COUNT(DISTINCT ?title) AS ?numTitles) (AVG(?votes) AS ?avgVotes) WHERE {
  {
    ?title imdb_principal:actor ?person .
  } UNION {
    ?title imdb_principal:actress ?person .
  } UNION {
    ?title imdb_principal:director ?person .
  } UNION {
    ?title imdb_principal:producer ?person .
  } UNION {
    ?title imdb_principal:casting_director ?person .
  } UNION {
    ?title imdb_principal:cinematographer ?person .
  }
  ?title imdb:numVotes ?votes .
}
GROUP BY ?person
HAVING (COUNT(DISTINCT ?title) >= 3)
ORDER BY DESC(?avgVotes)
LIMIT 10

Answer:
The 10 persons with the highest average number of votes over at least 3 titles are:
1. Emma Thomas
2. Fran Walsh
3. Christopher Nolan
4. Michael Conner Humphreys
5. Jonas Rivera
6. Niki Marvin
7. Darla K. Anderson
8. Phyllis Carlyle
9. Lorne Orleans
10. Noel Appleby.
These persons have the highest average votes across all their associated titles (with at least 3 titles each).

[feedback]
Feedback (status=refine):
The query correctly computes the average number of votes per person over all their associated titles, considering only those with at least 3 titles, and returns the top 10. However, the answer only provides the names, omitting the actual average vote counts and the number of titles, which are present in the query results. For completeness and transparency, the answer should include these numerical values as well, since they are relevant to the ranking and requested by the user. Additionally, the query could be further refined by ensuring that only unique person-title pairs are counted (which is already handled by COUNT(DISTINCT ?title)), and by optionally including labels for the persons if available.

[Assistant]
Thank you for the feedback. I will refine the answer to include the average number of votes and the number of titles for each person, as returned by the query. Additionally, I will attempt to include the person's label (name) using an OPTIONAL clause for rdfs:label, to make the results more readable.

Here is the refined SPARQL query and answer plan:
- Select the person IRI, their label (if available), the number of titles, and the average number of votes.
- Ensure only unique person-title pairs are counted (already handled).
- Return the top 10 persons with the highest average votes, who have at least 3 titles.

Let's update the query to include the label.

execute

SELECT ?person ?personLabel (COUNT(DISTINCT ?title) AS ?numTitles) (AVG(?votes) AS ?avgVotes) WHERE {
  {
    ?title imdb_principal:actor ?person .
  } UNION {
    ?title imdb_principal:actress ?person .
  } UNION {
    ?title imdb_principal:director ?person .
  } UNION {
    ?title imdb_principal:producer ?person .
  } UNION {
    ?title imdb_principal:casting_director ?person .
  } UNION {
    ?title imdb_principal:cinematographer ?person .
  }
  ?title imdb:numVotes ?votes .
  OPTIONAL { ?person rdfs:label ?personLabel }
}
GROUP BY ?person ?personLabel
HAVING (COUNT(DISTINCT ?title) >= 3)
ORDER BY DESC(?avgVotes)
LIMIT 10

Got 10 rows and 4 columns:
| person                                         | personLabel              | numTitles    | avgVotes                  |
| ---------------------------------------------- | ------------------------ | ------------ | ------------------------- |
| Emma Thomas (imdb_name:nm0858799)              | Emma Thomas              | 13 (xsd:int) | 1.27705e+06 (xsd:decimal) |
| Fran Walsh (imdb_name:nm0909638)               | Fran Walsh               | 10 (xsd:int) | 927457 (xsd:decimal)      |
| Christopher Nolan (imdb_name:nm0634240)        | Christopher Nolan        | 18 (xsd:int) | 879906 (xsd:decimal)      |
| Michael Conner Humphreys (imdb_name:nm0402011) | Michael Conner Humphreys | 3 (xsd:int)  | 802666 (xsd:decimal)      |
| Jonas Rivera (imdb_name:nm0729304)             | Jonas Rivera             | 3 (xsd:int)  | 791933 (xsd:decimal)      |
| Niki Marvin (imdb_name:nm0555550)              | Niki Marvin              | 4 (xsd:int)  | 771982 (xsd:decimal)      |
| Darla K. Anderson (imdb_name:nm0026565)        | Darla K. Anderson        | 5 (xsd:int)  | 696783 (xsd:decimal)      |
| Phyllis Carlyle (imdb_name:nm0138287)          | Phyllis Carlyle          | 3 (xsd:int)  | 645338 (xsd:decimal)      |
| Lorne Orleans (imdb_name:nm0650038)            | Lorne Orleans            | 16 (xsd:int) | 626320 (xsd:decimal)      |
| Noel Appleby (imdb_name:nm0032370)             | Noel Appleby             | 7 (xsd:int)  | 604764 (xsd:decimal)      |

[Assistant]
answer

SELECT ?person ?personLabel (COUNT(DISTINCT ?title) AS ?numTitles) (AVG(?votes) AS ?avgVotes) WHERE {
  {
    ?title imdb_principal:actor ?person .
  } UNION {
    ?title imdb_principal:actress ?person .
  } UNION {
    ?title imdb_principal:director ?person .
  } UNION {
    ?title imdb_principal:producer ?person .
  } UNION {
    ?title imdb_principal:casting_director ?person .
  } UNION {
    ?title imdb_principal:cinematographer ?person .
  }
  ?title imdb:numVotes ?votes .
  OPTIONAL { ?person rdfs:label ?personLabel }
}
GROUP BY ?person ?personLabel
HAVING (COUNT(DISTINCT ?title) >= 3)
ORDER BY DESC(?avgVotes)
LIMIT 10

Answer:
Here are the 10 persons with the highest average number of votes over at least 3 titles, including their average votes and number of titles:

1. Emma Thomas — 1,277,050 average votes (13 titles)
2. Fran Walsh — 927,457 average votes (10 titles)
3. Christopher Nolan — 879,906 average votes (18 titles)
4. Michael Conner Humphreys — 802,666 average votes (3 titles)
5. Jonas Rivera — 791,933 average votes (3 titles)
6. Niki Marvin — 771,982 average votes (4 titles)
7. Darla K. Anderson — 696,783 average votes (5 titles)
8. Phyllis Carlyle — 645,338 average votes (3 titles)
9. Lorne Orleans — 626,320 average votes (16 titles)
10. Noel Appleby — 604,764 average votes (7 titles)

Each person is listed with their name, average number of votes per title, and the number of titles considered.

[feedback]
Feedback (status=done):
The SPARQL query correctly implements the requirements: it finds persons associated with at least 3 titles (using a variety of principal roles), calculates the average number of votes per title, and returns the top 10 by average votes. The answer matches the query result, and the relevant identifiers are included. No further refinement is needed.