PREFIX imdb: <https://www.imdb.com/>
SELECT ?movie ( SAMPLE( ?director ) AS ?leadDirector ) ?releaseYear ?birthYear ( ?releaseYear - ?birthYear AS ?age ) WHERE {
  ?movie imdb:type "movie" .
  ?movie imdb:numVotes ?votes .
  FILTER ( ?votes >= 100000 ) ?movie imdb:director ?director .
  ?movie imdb:startYear ?releaseYear .
  ?director imdb:birthYear ?birthYear .
  FILTER ( BOUND( ?birthYear ) && BOUND( ?releaseYear ) )
}
GROUP BY ?movie ?releaseYear ?birthYear
ORDER BY DESC ( ?age )
LIMIT 100

Using properties:
- birth Year (imdb:birthYear), also known as birthYear
- director (imdb:director)
- num Votes (imdb:numVotes), also known as numVotes
- start Year (imdb:startYear), also known as startYear
- type (imdb:type)

Got 100 rows and 5 columns, showing the first 5 and last 5 rows below:
| movie                                        | leadDirector                         | releaseYear    | birthYear      | age          |
| -------------------------------------------- | ------------------------------------ | -------------- | -------------- | ------------ |
| Juror #2 (imdb_title:tt27403986)             | Clint Eastwood (imdb_name:nm0000142) | 2024 (xsd:int) | 1930 (xsd:int) | 94 (xsd:int) |
| Richard Jewell (imdb_title:tt3513548)        | Clint Eastwood (imdb_name:nm0000142) | 2019 (xsd:int) | 1930 (xsd:int) | 89 (xsd:int) |
| The Mule (imdb_title:tt7959026)              | Clint Eastwood (imdb_name:nm0000142) | 2018 (xsd:int) | 1930 (xsd:int) | 88 (xsd:int) |
| Gladiator II (imdb_title:tt9218128)          | Ridley Scott (imdb_name:nm0000631)   | 2024 (xsd:int) | 1937 (xsd:int) | 87 (xsd:int) |
| Sully (imdb_title:tt3263904)                 | Clint Eastwood (imdb_name:nm0000142) | 2016 (xsd:int) | 1930 (xsd:int) | 86 (xsd:int) |
| ...                                          | ...                                  | ...            | ...            | ...          |
| Public Enemies (imdb_title:tt1152836)        | Michael Mann (imdb_name:nm0000520)   | 2009 (xsd:int) | 1943 (xsd:int) | 66 (xsd:int) |
| The Fox and the Hound (imdb_title:tt0082406) | Art Stevens (imdb_name:nm0828245)    | 1981 (xsd:int) | 1915 (xsd:int) | 66 (xsd:int) |
| Unstoppable (imdb_title:tt0477080)           | Tony Scott (imdb_name:nm0001716)     | 2010 (xsd:int) | 1944 (xsd:int) | 66 (xsd:int) |
| The Way Back (imdb_title:tt1023114)          | Peter Weir (imdb_name:nm0001837)     | 2010 (xsd:int) | 1944 (xsd:int) | 66 (xsd:int) |
| Savages (imdb_title:tt1615065)               | Oliver Stone (imdb_name:nm0000231)   | 2012 (xsd:int) | 1946 (xsd:int) | 66 (xsd:int) |

Answer: This query returns the 100 oldest directors (one per movie, using SAMPLE to approximate a 'lead' director) at the time of a movie (not series) with at least 100,000 votes, ordered by their age at the movie's release. Note: If a movie has multiple directors, only one is arbitrarily selected due to lack of ordering in the data; the knowledge graph does not distinguish a 'lead' or first-listed director. Filtering for imdb:type = 'movie' excludes series, TV movies, shorts, and other non-movie types. Only movies and directors with both release year and birth year present are included. Age is calculated as (release year - birth year), which may not be the exact age at release depending on the months involved.

This query returns the 100 oldest directors (one per movie, using SAMPLE to approximate a 'lead' director) at the time of a movie (not series) with at least 100,000 votes, ordered by their age at the movie's release. Note: If a movie has multiple directors, only one is arbitrarily selected due to lack of ordering in the data; the knowledge graph does not distinguish a 'lead' or first-listed director. Filtering for imdb:type = 'movie' excludes series, TV movies, shorts, and other non-movie types. Only movies and directors with both release year and birth year present are included. Age is calculated as (release year - birth year), which may not be the exact age at release depending on the months involved.

SPARQL query over imdb:
PREFIX imdb: <https://www.imdb.com/>
SELECT ?movie ( SAMPLE( ?director ) AS ?leadDirector ) ?releaseYear ?birthYear ( ?releaseYear - ?birthYear AS ?age ) WHERE {
  ?movie imdb:type "movie" .
  ?movie imdb:numVotes ?votes .
  FILTER ( ?votes >= 100000 ) ?movie imdb:director ?director .
  ?movie imdb:startYear ?releaseYear .
  ?director imdb:birthYear ?birthYear .
  FILTER ( BOUND( ?birthYear ) && BOUND( ?releaseYear ) )
}
GROUP BY ?movie ?releaseYear ?birthYear
ORDER BY DESC ( ?age )
LIMIT 100

Using properties:
- birth Year (imdb:birthYear), also known as birthYear
- director (imdb:director)
- num Votes (imdb:numVotes), also known as numVotes
- start Year (imdb:startYear), also known as startYear
- type (imdb:type)

Execution result:
Got 100 rows and 5 columns, showing the first 5 and last 5 rows below:
| movie                                        | leadDirector                         | releaseYear    | birthYear      | age          |
| -------------------------------------------- | ------------------------------------ | -------------- | -------------- | ------------ |
| Juror #2 (imdb_title:tt27403986)             | Clint Eastwood (imdb_name:nm0000142) | 2024 (xsd:int) | 1930 (xsd:int) | 94 (xsd:int) |
| Richard Jewell (imdb_title:tt3513548)        | Clint Eastwood (imdb_name:nm0000142) | 2019 (xsd:int) | 1930 (xsd:int) | 89 (xsd:int) |
| The Mule (imdb_title:tt7959026)              | Clint Eastwood (imdb_name:nm0000142) | 2018 (xsd:int) | 1930 (xsd:int) | 88 (xsd:int) |
| Gladiator II (imdb_title:tt9218128)          | Ridley Scott (imdb_name:nm0000631)   | 2024 (xsd:int) | 1937 (xsd:int) | 87 (xsd:int) |
| Sully (imdb_title:tt3263904)                 | Clint Eastwood (imdb_name:nm0000142) | 2016 (xsd:int) | 1930 (xsd:int) | 86 (xsd:int) |
| ...                                          | ...                                  | ...            | ...            | ...          |
| Public Enemies (imdb_title:tt1152836)        | Michael Mann (imdb_name:nm0000520)   | 2009 (xsd:int) | 1943 (xsd:int) | 66 (xsd:int) |
| The Fox and the Hound (imdb_title:tt0082406) | Art Stevens (imdb_name:nm0828245)    | 1981 (xsd:int) | 1915 (xsd:int) | 66 (xsd:int) |
| Unstoppable (imdb_title:tt0477080)           | Tony Scott (imdb_name:nm0001716)     | 2010 (xsd:int) | 1944 (xsd:int) | 66 (xsd:int) |
| The Way Back (imdb_title:tt1023114)          | Peter Weir (imdb_name:nm0001837)     | 2010 (xsd:int) | 1944 (xsd:int) | 66 (xsd:int) |
| Savages (imdb_title:tt1615065)               | Oliver Stone (imdb_name:nm0000231)   | 2012 (xsd:int) | 1946 (xsd:int) | 66 (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 lead directors 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 lead director.
3. For each director, we want their age at the time of the movie's release.
4. We want the 100 oldest such director-movie pairs.

Step 1: Identify entities and properties.
- "Movie (not series)": We need to distinguish between movies and series.
- "Votes": There should be a property for the number of votes.
- "Director": There should be a property linking a movie to its director(s).
- "Release date": Needed to calculate age.
- "Director's birth date": Needed to calculate age.

Step 2: Search for relevant properties.

Let's start by searching for the property for "votes".

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 "movie" from "series". This is likely a "type" property. Let's check what values are used for imdb:type.

Question: movie

No entity items found

No other items found

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

[Assistant]
Question: series

No entity items found

No other items found

No literal items found

[Assistant]
No entity items found

No other items found

No literal items found

[Assistant]
Question: movie

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

[Assistant]
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]
list

Got more than 131,072 rows and 3 columns, showing the first 10 rows below:
| subject                                                       | property         | object       |
| ------------------------------------------------------------- | ---------------- | ------------ |
| Days of Our Lives (imdb_title:tt0058796)                      | type (imdb:type) | tvSeries     |
| Lost in Space (imdb_title:tt0120738)                          | type (imdb:type) | movie        |
| From the Earth to the Moon (imdb_title:tt0120570)             | type (imdb:type) | tvMiniSeries |
| Wing Commander III: Heart of the Tiger (imdb_title:tt0111727) | type (imdb:type) | videoGame    |
| Back to the Future... The Ride (imdb_title:tt0101392)         | type (imdb:type) | video        |
| Crazy Horse (imdb_title:tt0115969)                            | type (imdb:type) | tvMovie      |
|  Mother (imdb_title:tt0097917)                                | type (imdb:type) | short        |
| Peter Gabriel: Secret World Live (imdb_title:tt0111109)       | type (imdb:type) | tvSpecial    |
| Dottie Gets Spanked (imdb_title:tt0106757)                    | type (imdb:type) | tvShort      |
| Pilot (imdb_title:tt0088692)                                  | type (imdb:type) | tvEpisode    |
| ...                                                           | ...              | ...          |

[Assistant]
The imdb:type property distinguishes between "movie", "tvSeries", "tvMiniSeries", "videoGame", "video", "tvMovie", "short", "tvSpecial", "tvShort", "tvEpisode", etc. For our query, we want only those with imdb:type "movie".

Next, let's find the property for "director".

Question: director

Top 10 property alternatives:
1. director (imdb_principal:director)
2. director (imdb:director)
3. casting director (imdb_principal:casting_director), also known as casting_director
4. producer (imdb_principal:producer)
5. cinematographer (imdb_principal:cinematographer)
6. production designer (imdb_principal:production_designer), also known as production_designer
7. writer (imdb_principal:writer)
8. writer (imdb:writer)
9. actor (imdb_principal:actor)
10. actress (imdb_principal:actress)

[Assistant]
Got more than 131,072 rows and 3 columns, showing the first 10 rows below:
| subject                                           | property                 | object                               |
| ------------------------------------------------- | ------------------------ | ------------------------------------ |
| Crossroads (imdb_title:tt0057741)                 | director (imdb:director) | Reg Watson (imdb_name:nm0914844)     |
| La constitución (imdb_title:tt0065284)            | director (imdb:director) | Ernesto Alonso (imdb_name:nm0022172) |
| Blue Peter (imdb_title:tt0051257)                 | director (imdb:director) | Joy Whitby (imdb_name:nm0924345)     |
| Days of Our Lives (imdb_title:tt0058796)          | director (imdb:director) | Jill Ackles (imdb_name:nm0010076)    |
| Coronation Street (imdb_title:tt0053494)          | director (imdb:director) | Michael Apted (imdb_name:nm0000776)  |
| Entertainment This Week (imdb_title:tt0081857)    | director (imdb:director) | Anthony Caleca (imdb_name:nm0129828) |
| The Young and the Restless (imdb_title:tt0069658) | director (imdb:director) | Paul Schneider (imdb_name:nm0004621) |
| As the World Turns (imdb_title:tt0048845)         | director (imdb:director) | Ellen Wheeler (imdb_name:nm0004098)  |
| Follow That Man (imdb_title:tt0041040)            | director (imdb:director) | Paul Alter (imdb_name:nm0022750)     |
| Today (imdb_title:tt0044298)                      | director (imdb:director) | Will Chase (imdb_name:nm0153855)     |
| ...                                               | ...                      | ...                                  |

[Assistant]
Got more than 131,072 rows and 3 columns, showing the first 10 rows below:
| subject                                       | property                           | object                                       |
| --------------------------------------------- | ---------------------------------- | -------------------------------------------- |
| Jock of the Bushveld (imdb_title:tt0104563)   | director (imdb_principal:director) | Danie Joubert (imdb_name:nm1083609)          |
| Boca (imdb_title:tt0109312)                   | director (imdb_principal:director) | Walter Avancini (imdb_name:nm0042771)        |
| Agapi kai aima (imdb_title:tt0135123)         | director (imdb_principal:director) | Nikos Foskolos (imdb_name:nm0287566)         |
| O Cangaceiro Trapalhão (imdb_title:tt0139095) | director (imdb_principal:director) | Daniel Filho (imdb_name:nm0276899)           |
| Screaming Skull (imdb_title:tt0070658)        | director (imdb_principal:director) | Gloria Monty (imdb_name:nm0600353)           |
| Noche de machos (imdb_title:tt0091643)        | director (imdb_principal:director) | Tito Rojas (imdb_name:nm0737880)             |
| Socorro (imdb_title:tt0120155)                | director (imdb_principal:director) | António Borges Correia (imdb_name:nm0096539) |
| Wing Commander (imdb_title:tt0080137)         | director (imdb_principal:director) | Franz Marx (imdb_name:nm0555609)             |
| Kalyani (imdb_title:tt0079388)                | director (imdb_principal:director) | Dasari Narayana Rao (imdb_name:nm0201927)    |
| Gallopin' Gals (imdb_title:tt0032504)         | director (imdb_principal:director) | Joseph Barbera (imdb_name:nm0053484)         |
| ...                                           | ...                                | ...                                          |

[Assistant]
Both imdb:director and imdb_principal:director link movies to directors. imdb:director seems to be the main property.

Next, let's find the properties for "release date" and "birth date" (for age calculation).

Question: release date

Top 10 property alternatives:
1. id (imdb:id)
2. death Year (imdb:deathYear), also known as deathYear
3. type (imdb:type)
4. type (rdf:type)
5. end Year (imdb:endYear, matched via endYear), also known as endYear
6. genre (imdb:genre)
7. episode Number (imdb:episodeNumber, matched via episodeNumber), also known as episodeNumber
8. label (rdfs:label)
9. birth Year (imdb:birthYear, matched via birthYear), also known as birthYear
10. archive footage (imdb_principal:archive_footage, matched via archive_footage), also known as archive_footage

[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]
Got more than 131,072 rows and 3 columns, showing the first 10 rows below:
| subject                                           | property                    | object         |
| ------------------------------------------------- | --------------------------- | -------------- |
| Days of Our Lives (imdb_title:tt0058796)          | start Year (imdb:startYear) | 1965 (xsd:int) |
| General Hospital (imdb_title:tt0056758)           | start Year (imdb:startYear) | 1963 (xsd:int) |
| Coronation Street (imdb_title:tt0053494)          | start Year (imdb:startYear) | 1960 (xsd:int) |
| The Young and the Restless (imdb_title:tt0069658) | start Year (imdb:startYear) | 1973 (xsd:int) |
| As the World Turns (imdb_title:tt0048845)         | start Year (imdb:startYear) | 1956 (xsd:int) |
| 60 Minutes (imdb_title:tt0123338)                 | start Year (imdb:startYear) | 1968 (xsd:int) |
| Today (imdb_title:tt0044298)                      | start Year (imdb:startYear) | 1952 (xsd:int) |
| Emmerdale (imdb_title:tt0068069)                  | start Year (imdb:startYear) | 1972 (xsd:int) |
| Wheel of Fortune (imdb_title:tt0072584)           | start Year (imdb:startYear) | 1983 (xsd:int) |
| Neighbours (imdb_title:tt0088580)                 | start Year (imdb:startYear) | 1985 (xsd:int) |
| ...                                               | ...                         | ...            |

[Assistant]
Got more than 131,072 rows and 3 columns, showing the first 10 rows below:
| subject                                       | property                    | object         |
| --------------------------------------------- | --------------------------- | -------------- |
| Ekta Kapoor (imdb_name:nm0438471)             | birth Year (imdb:birthYear) | 1975 (xsd:int) |
| William J. Bell (imdb_name:nm0068589)         | birth Year (imdb:birthYear) | 1927 (xsd:int) |
| David Letterman (imdb_name:nm0001468)         | birth Year (imdb:birthYear) | 1947 (xsd:int) |
| Doris Hursley (imdb_name:nm0403568)           | birth Year (imdb:birthYear) | 1898 (xsd:int) |
| Ernesto Alonso (imdb_name:nm0022172)          | birth Year (imdb:birthYear) | 1917 (xsd:int) |
| Frank Hursley (imdb_name:nm0403569)           | birth Year (imdb:birthYear) | 1902 (xsd:int) |
| Lee Phillip Bell (imdb_name:nm0068347)        | birth Year (imdb:birthYear) | 1928 (xsd:int) |
| Delia Fiallo (imdb_name:nm0275585)            | birth Year (imdb:birthYear) | 1924 (xsd:int) |
| Hans Christian Andersen (imdb_name:nm0026153) | birth Year (imdb:birthYear) | 1805 (xsd:int) |
| Bradley Bell (imdb_name:nm0068074)            | birth Year (imdb:birthYear) | 1964 (xsd:int) |
| ...                                           | ...                         | ...            |

[Assistant]
Got more than 131,072 rows and 3 columns, showing the first 10 rows below:
| subject                                           | property                  | object         |
| ------------------------------------------------- | ------------------------- | -------------- |
| Days of Our Lives (imdb_title:tt0058796)          | num Votes (imdb:numVotes) | 7896 (xsd:int) |
| General Hospital (imdb_title:tt0056758)           | num Votes (imdb:numVotes) | 6003 (xsd:int) |
| Coronation Street (imdb_title:tt0053494)          | num Votes (imdb:numVotes) | 6399 (xsd:int) |
| The Young and the Restless (imdb_title:tt0069658) | num Votes (imdb:numVotes) | 7189 (xsd:int) |
| As the World Turns (imdb_title:tt0048845)         | num Votes (imdb:numVotes) | 2424 (xsd:int) |
| Jeopardy! (imdb_title:tt0159881)                  | num Votes (imdb:numVotes) | 8740 (xsd:int) |
| 60 Minutes (imdb_title:tt0123338)                 | num Votes (imdb:numVotes) | 4707 (xsd:int) |
| Today (imdb_title:tt0044298)                      | num Votes (imdb:numVotes) | 2609 (xsd:int) |
| Emmerdale (imdb_title:tt0068069)                  | num Votes (imdb:numVotes) | 3779 (xsd:int) |
| Wheel of Fortune (imdb_title:tt0072584)           | num Votes (imdb:numVotes) | 3915 (xsd:int) |
| ...                                               | ...                       | ...            |

[Assistant]
We have all the necessary properties:
- imdb:type = "movie"
- imdb:numVotes >= 100000
- imdb:director (or imdb_principal:director)
- imdb:startYear (release year)
- imdb:birthYear (director's birth year)

Now, let's build the SPARQL query step by step.

First, let's select movies with imdb:type "movie" and imdb:numVotes >= 100000, and get their directors, release year, and director's birth year.

execute

SELECT ?movie ?director ?releaseYear ?birthYear WHERE {
  ?movie imdb:type "movie" .
  ?movie imdb:numVotes ?votes .
  FILTER(?votes >= 100000)
  ?movie imdb:director ?director .
  ?movie imdb:startYear ?releaseYear .
  ?director imdb:birthYear ?birthYear .
} LIMIT 10

Got 10 rows and 4 columns:
| movie                                       | director                               | releaseYear    | birthYear      |
| ------------------------------------------- | -------------------------------------- | -------------- | -------------- |
| Persona (imdb_title:tt0060827)              | Ingmar Bergman (imdb_name:nm0000005)   | 1966 (xsd:int) | 1918 (xsd:int) |
| Smultronstället (imdb_title:tt0050986)      | Ingmar Bergman (imdb_name:nm0000005)   | 1957 (xsd:int) | 1918 (xsd:int) |
| Det sjunde inseglet (imdb_title:tt0050976)  | Ingmar Bergman (imdb_name:nm0000005)   | 1957 (xsd:int) | 1918 (xsd:int) |
| 8½ (imdb_title:tt0056801)                   | Federico Fellini (imdb_name:nm0000019) | 1963 (xsd:int) | 1920 (xsd:int) |
| Strangers on a Train (imdb_title:tt0044079) | Alfred Hitchcock (imdb_name:nm0000033) | 1951 (xsd:int) | 1899 (xsd:int) |
| Rebecca (imdb_title:tt0032976)              | Alfred Hitchcock (imdb_name:nm0000033) | 1940 (xsd:int) | 1899 (xsd:int) |
| The Birds (imdb_title:tt0056869)            | Alfred Hitchcock (imdb_name:nm0000033) | 1963 (xsd:int) | 1899 (xsd:int) |
| Psycho (imdb_title:tt0054215)               | Alfred Hitchcock (imdb_name:nm0000033) | 1960 (xsd:int) | 1899 (xsd:int) |
| North by Northwest (imdb_title:tt0053125)   | Alfred Hitchcock (imdb_name:nm0000033) | 1959 (xsd:int) | 1899 (xsd:int) |
| Vertigo (imdb_title:tt0052357)              | Alfred Hitchcock (imdb_name:nm0000033) | 1958 (xsd:int) | 1899 (xsd:int) |

[Assistant]
The query works and returns the expected columns. Now, let's compute the director's age at the time of the movie (releaseYear - birthYear), and order by age descending, limiting to the top 100.

We'll also return the movie and director IRIs, and optionally their labels for clarity.

SELECT ?movie ?director ?releaseYear ?birthYear (?releaseYear - ?birthYear AS ?age) WHERE {
  ?movie imdb:type "movie" .
  ?movie imdb:numVotes ?votes .
  FILTER(?votes >= 100000)
  ?movie imdb:director ?director .
  ?movie imdb:startYear ?releaseYear .
  ?director imdb:birthYear ?birthYear .
} ORDER BY DESC(?age) LIMIT 100

Got 100 rows and 5 columns, showing the first 5 and last 5 rows below:
| movie                                 | director                               | releaseYear    | birthYear      | age          |
| ------------------------------------- | -------------------------------------- | -------------- | -------------- | ------------ |
| Juror #2 (imdb_title:tt27403986)      | Clint Eastwood (imdb_name:nm0000142)   | 2024 (xsd:int) | 1930 (xsd:int) | 94 (xsd:int) |
| Richard Jewell (imdb_title:tt3513548) | Clint Eastwood (imdb_name:nm0000142)   | 2019 (xsd:int) | 1930 (xsd:int) | 89 (xsd:int) |
| The Mule (imdb_title:tt7959026)       | Clint Eastwood (imdb_name:nm0000142)   | 2018 (xsd:int) | 1930 (xsd:int) | 88 (xsd:int) |
| Gladiator II (imdb_title:tt9218128)   | Ridley Scott (imdb_name:nm0000631)     | 2024 (xsd:int) | 1937 (xsd:int) | 87 (xsd:int) |
| Napoleon (imdb_title:tt13287846)      | Ridley Scott (imdb_name:nm0000631)     | 2023 (xsd:int) | 1937 (xsd:int) | 86 (xsd:int) |
| ...                                   | ...                                    | ...            | ...            | ...          |
| Red Eye (imdb_title:tt0421239)        | Wes Craven (imdb_name:nm0000127)       | 2005 (xsd:int) | 1939 (xsd:int) | 66 (xsd:int) |
| Savages (imdb_title:tt1615065)        | Oliver Stone (imdb_name:nm0000231)     | 2012 (xsd:int) | 1946 (xsd:int) | 66 (xsd:int) |
| The Ninth Gate (imdb_title:tt0142688) | Roman Polanski (imdb_name:nm0000591)   | 1999 (xsd:int) | 1933 (xsd:int) | 66 (xsd:int) |
| Lincoln (imdb_title:tt0443272)        | Steven Spielberg (imdb_name:nm0000229) | 2012 (xsd:int) | 1946 (xsd:int) | 66 (xsd:int) |
| Public Enemies (imdb_title:tt1152836) | Michael Mann (imdb_name:nm0000520)     | 2009 (xsd:int) | 1943 (xsd:int) | 66 (xsd:int) |

[Assistant]
answer

SELECT ?movie ?director ?releaseYear ?birthYear (?releaseYear - ?birthYear AS ?age) WHERE {
  ?movie imdb:type "movie" .
  ?movie imdb:numVotes ?votes .
  FILTER(?votes >= 100000)
  ?movie imdb:director ?director .
  ?movie imdb:startYear ?releaseYear .
  ?director imdb:birthYear ?birthYear .
} ORDER BY DESC(?age) LIMIT 100

Answer: These are the 100 oldest lead directors at the time of a movie (not series) with at least 100,000 votes, ordered by their age at the movie's release. The result includes the movie and director IRIs, release year, birth year, and calculated age at release.

[feedback]
Feedback (status=refine):
The query is mostly correct and produces a sensible result, but it does not fully guarantee that only the 'lead' director is selected in cases where a movie has multiple directors. The property imdb:director may return multiple values, and the query does not distinguish which director is the 'lead' if there are several. If the knowledge graph provides a way to identify the principal or first-listed director, that should be used. Otherwise, the query should at least document this limitation. Additionally, the query does not explicitly exclude series or other non-movie types beyond filtering for imdb:type = 'movie', which is likely sufficient but should be confirmed. Otherwise, the query logic and answer are correct.

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

1. Lead Director Selection:
- The property imdb:director may return multiple directors for a movie, but does not specify which is the "lead" or first-listed director.
- The property imdb_principal:director may provide a way to identify principal crew, so let's investigate if it distinguishes a lead director.

2. Exclusion of Non-Movie Types:
- imdb:type = "movie" appears to be sufficient, but let's confirm that this excludes all series, shorts, TV movies, etc.

Step 1: Investigate imdb_principal:director for lead/principal director information.

list

Got more than 131,072 rows and 3 columns, showing the first 10 rows below:
| subject                                       | property                           | object                                       |
| --------------------------------------------- | ---------------------------------- | -------------------------------------------- |
| Jock of the Bushveld (imdb_title:tt0104563)   | director (imdb_principal:director) | Danie Joubert (imdb_name:nm1083609)          |
| Boca (imdb_title:tt0109312)                   | director (imdb_principal:director) | Walter Avancini (imdb_name:nm0042771)        |
| Agapi kai aima (imdb_title:tt0135123)         | director (imdb_principal:director) | Nikos Foskolos (imdb_name:nm0287566)         |
| O Cangaceiro Trapalhão (imdb_title:tt0139095) | director (imdb_principal:director) | Daniel Filho (imdb_name:nm0276899)           |
| Screaming Skull (imdb_title:tt0070658)        | director (imdb_principal:director) | Gloria Monty (imdb_name:nm0600353)           |
| Noche de machos (imdb_title:tt0091643)        | director (imdb_principal:director) | Tito Rojas (imdb_name:nm0737880)             |
| Socorro (imdb_title:tt0120155)                | director (imdb_principal:director) | António Borges Correia (imdb_name:nm0096539) |
| Wing Commander (imdb_title:tt0080137)         | director (imdb_principal:director) | Franz Marx (imdb_name:nm0555609)             |
| Kalyani (imdb_title:tt0079388)                | director (imdb_principal:director) | Dasari Narayana Rao (imdb_name:nm0201927)    |
| Gallopin' Gals (imdb_title:tt0032504)         | director (imdb_principal:director) | Joseph Barbera (imdb_name:nm0053484)         |
| ...                                           | ...                                | ...                                          |

[Assistant]
imdb_title:tt0054215

Got 68 rows and 3 columns, showing the first 10 rows below:
| subject                       | property                            | object                              |
| ----------------------------- | ----------------------------------- | ----------------------------------- |
| Psycho (imdb_title:tt0054215) | alt Label (skos:altLabel)           | Alfred Hitchcock's Psycho (lang:en) |
| Psycho (imdb_title:tt0054215) | label (rdfs:label)                  | Psycho                              |
| Psycho (imdb_title:tt0054215) | type (rdf:type)                     | Movie (imdb:Movie)                  |
| Psycho (imdb_title:tt0054215) | actor (imdb_principal:actor)        | Martin Balsam (imdb_name:nm0000842) |
| Psycho (imdb_title:tt0054215) | genre (imdb:genre)                  | Drama                               |
| Psycho (imdb_title:tt0054215) | actress (imdb_principal:actress)    | Vera Miles (imdb_name:nm0587256)    |
| Psycho (imdb_title:tt0054215) | writer (imdb:writer)                | Robert Bloch (imdb_name:nm0088645)  |
| Psycho (imdb_title:tt0054215) | primary Title (imdb:primaryTitle)   | Psycho                              |
| Psycho (imdb_title:tt0054215) | original Title (imdb:originalTitle) | Psycho                              |
| Psycho (imdb_title:tt0054215) | id (imdb:id)                        | tt0054215                           |
| ...                           | ...                                 | ...                                 |

[Assistant] imdb_title:tt0054215

[Assistant] imdb_title:tt0054215

[Assistant] imdb_title:tt0110912

[Assistant] imdb_title:tt0110912

[Assistant] imdb_title:tt0120338

[Assistant] imdb_title:tt0120338

[Assistant] imdb_title:tt0068646

[Assistant] imdb_title:tt0068646

[Assistant] imdb_title:tt0108052

[Assistant] imdb_title:tt0108052

[Assistant] imdb_title:tt0061512

[Assistant] imdb_title:tt0061512

[Assistant] imdb_title:tt0062622

[Assistant] imdb_title:tt0062622

[Assistant] imdb_title:tt0061722

[Assistant] imdb_title:tt0061722

[Assistant] imdb_title:tt0064116

[Assistant] imdb_title:tt0064116

[Assistant] imdb_title:tt0066921

[Assistant] imdb_title:tt0066921

[Assistant] imdb_title:tt0063350

[Assistant] imdb_title:tt0063350

[Assistant] imdb_title:tt0112573

[Assistant] imdb_title:tt0112573

[Assistant] imdb_title:tt0062622

[Assistant] imdb_title:tt0062622

[Assistant] imdb_title:tt0061722

[Assistant] imdb_title:tt0061722

[Assistant]
imdb_title:tt0061722

Got 66 rows and 3 columns, showing the first 10 rows below:
| subject                             | property                            | object                              |
| ----------------------------------- | ----------------------------------- | ----------------------------------- |
| The Graduate (imdb_title:tt0061722) | alt Label (skos:altLabel)           | A Primeira Noite (lang:pt)          |
| The Graduate (imdb_title:tt0061722) | label (rdfs:label)                  | The Graduate                        |
| The Graduate (imdb_title:tt0061722) | type (rdf:type)                     | Movie (imdb:Movie)                  |
| The Graduate (imdb_title:tt0061722) | actor (imdb_principal:actor)        | Walter Brooke (imdb_name:nm0111754) |
| The Graduate (imdb_title:tt0061722) | genre (imdb:genre)                  | Comedy                              |
| The Graduate (imdb_title:tt0061722) | actress (imdb_principal:actress)    | Anne Bancroft (imdb_name:nm0000843) |
| The Graduate (imdb_title:tt0061722) | writer (imdb:writer)                | Buck Henry (imdb_name:nm0377750)    |
| The Graduate (imdb_title:tt0061722) | primary Title (imdb:primaryTitle)   | The Graduate                        |
| The Graduate (imdb_title:tt0061722) | original Title (imdb:originalTitle) | The Graduate                        |
| The Graduate (imdb_title:tt0061722) | id (imdb:id)                        | tt0061722                           |
| ...                                 | ...                                 | ...                                 |

[Assistant] imdb_title:tt0061722

[Assistant]
Based on the above checks, for all the tested movies (including those with only one director), both imdb:director and imdb_principal:director return the same single value. However, to check for movies with multiple directors, let's try a movie known to have more than one director, such as "The Matrix" (Wachowskis) or "No Country for Old Men" (Coen brothers).

Let's check "The Matrix" (tt0133093).

imdb_title:tt0133093

Got 2 rows and 3 columns:
| subject                           | property                 | object                                |
| --------------------------------- | ------------------------ | ------------------------------------- |
| The Matrix (imdb_title:tt0133093) | director (imdb:director) | Lilly Wachowski (imdb_name:nm0905152) |
| The Matrix (imdb_title:tt0133093) | director (imdb:director) | Lana Wachowski (imdb_name:nm0905154)  |

[Assistant]
imdb_title:tt0133093

Got 2 rows and 3 columns:
| subject                           | property                           | object                                |
| --------------------------------- | ---------------------------------- | ------------------------------------- |
| The Matrix (imdb_title:tt0133093) | director (imdb_principal:director) | Lilly Wachowski (imdb_name:nm0905152) |
| The Matrix (imdb_title:tt0133093) | director (imdb_principal:director) | Lana Wachowski (imdb_name:nm0905154)  |

[Assistant]
imdb_title:tt0477348

Got 2 rows and 3 columns:
| subject                                       | property                 | object                           |
| --------------------------------------------- | ------------------------ | -------------------------------- |
| No Country for Old Men (imdb_title:tt0477348) | director (imdb:director) | Ethan Coen (imdb_name:nm0001053) |
| No Country for Old Men (imdb_title:tt0477348) | director (imdb:director) | Joel Coen (imdb_name:nm0001054)  |

[Assistant]
imdb_title:tt0477348

Got 2 rows and 3 columns:
| subject                                       | property                           | object                           |
| --------------------------------------------- | ---------------------------------- | -------------------------------- |
| No Country for Old Men (imdb_title:tt0477348) | director (imdb_principal:director) | Ethan Coen (imdb_name:nm0001053) |
| No Country for Old Men (imdb_title:tt0477348) | director (imdb_principal:director) | Joel Coen (imdb_name:nm0001054)  |

[Assistant]
Both imdb:director and imdb_principal:director return all directors for movies with multiple directors, and do not distinguish a "lead" or first-listed director. There is no property in the knowledge graph that marks a principal or lead director, nor is there an ordering.

Therefore, the limitation is that for movies with multiple directors, all are included, and the query cannot guarantee only the "lead" director.

Regarding imdb:type, filtering for "movie" does exclude series, TV movies, shorts, etc., as confirmed by the earlier listing.

Final Query and Documentation:

I will provide the final query using imdb:director (or imdb_principal:director, which is equivalent here), and document the limitation regarding lead director selection.

answer

SELECT ?movie ?director ?releaseYear ?birthYear (?releaseYear - ?birthYear AS ?age) WHERE {
  ?movie imdb:type "movie" .
  ?movie imdb:numVotes ?votes .
  FILTER(?votes >= 100000)
  ?movie imdb:director ?director .
  ?movie imdb:startYear ?releaseYear .
  ?director imdb:birthYear ?birthYear .
} ORDER BY DESC(?age) LIMIT 100

Answer: This query returns the 100 oldest directors at the time of a movie (not series) with at least 100,000 votes, ordered by their age at the movie's release. Note: If a movie has multiple directors, all are included, as the knowledge graph does not distinguish a 'lead' or first-listed director. Filtering for imdb:type = 'movie' excludes series, TV movies, shorts, and other non-movie types.

[feedback]
Feedback (status=refine):
The query is mostly correct and returns sensible results, but there are a few points that could be improved for completeness and accuracy:

1. The user requested the 100 oldest 'lead' directors, but the query includes all directors for each movie. While the system notes that the knowledge graph does not distinguish a 'lead' director, it would be better to explicitly mention this limitation in the answer and, if possible, attempt to select only the first-listed director per movie (if such ordering is available in the data).

2. The query does not filter out movies where the director's birth year or the movie's release year is missing, which could result in incorrect age calculations or missing data. Adding a FILTER to ensure both ?birthYear and ?releaseYear are bound would improve result quality.

3. The query does not explicitly exclude TV movies, shorts, or other non-theatrical releases except by filtering for imdb:type = 'movie'. If the knowledge graph has more granular types, consider refining this filter.

4. The answer should clarify that the age is calculated as the difference between release year and birth year, which may not be the exact age at release depending on the months involved.

Overall, the query is close to correct, but a few refinements would make it more robust and the answer more precise.

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

1. Lead Director Selection:
- The knowledge graph does not provide ordering or a “lead” director. I will explicitly state this limitation in the answer.
- To approximate “lead” director, I can use GROUP BY to select only one director per movie (e.g., MIN or SAMPLE), but this is arbitrary and not guaranteed to be the actual lead. I will document this in the answer.

2. Missing Data:
- I will add FILTERs to ensure both ?birthYear and ?releaseYear are bound and not null.

3. Movie Type Filtering:
- imdb:type = "movie" appears to exclude TV movies, shorts, etc., but I will check for other possible types and document this in the answer.

4. Age Calculation:
- I will clarify that age is calculated as (release year - birth year), which may not be the exact age at release.

Let’s first check for other possible imdb:type values related to movies, to ensure we are not including TV movies, shorts, etc.

Question: movie

No entity items found

No other items found

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

[Assistant]
No entity items found

No other items found

No literal items found

[Assistant]
list

Got more than 131,072 rows and 3 columns, showing the first 10 rows below:
| subject                                                       | property         | object       |
| ------------------------------------------------------------- | ---------------- | ------------ |
| Days of Our Lives (imdb_title:tt0058796)                      | type (imdb:type) | tvSeries     |
| Lost in Space (imdb_title:tt0120738)                          | type (imdb:type) | movie        |
| From the Earth to the Moon (imdb_title:tt0120570)             | type (imdb:type) | tvMiniSeries |
| Wing Commander III: Heart of the Tiger (imdb_title:tt0111727) | type (imdb:type) | videoGame    |
| Back to the Future... The Ride (imdb_title:tt0101392)         | type (imdb:type) | video        |
| Crazy Horse (imdb_title:tt0115969)                            | type (imdb:type) | tvMovie      |
|  Mother (imdb_title:tt0097917)                                | type (imdb:type) | short        |
| Peter Gabriel: Secret World Live (imdb_title:tt0111109)       | type (imdb:type) | tvSpecial    |
| Dottie Gets Spanked (imdb_title:tt0106757)                    | type (imdb:type) | tvShort      |
| Pilot (imdb_title:tt0088692)                                  | type (imdb:type) | tvEpisode    |
| ...                                                           | ...              | ...          |

[Assistant]
The possible imdb:type values include: "movie", "tvSeries", "tvMiniSeries", "videoGame", "video", "tvMovie", "short", "tvSpecial", "tvShort", "tvEpisode", etc. Filtering for imdb:type = "movie" is sufficient to exclude TV movies, shorts, and other non-theatrical releases.

Now, let's write the improved query:
- Select only one director per movie using SAMPLE(?director) (with a note about arbitrariness).
- Add FILTERs to ensure ?birthYear and ?releaseYear are bound.
- Keep the rest of the logic as before.

Here is the improved query and answer.

execute

SELECT ?movie (SAMPLE(?director) AS ?leadDirector) ?releaseYear ?birthYear (?releaseYear - ?birthYear AS ?age) WHERE {
  ?movie imdb:type "movie" .
  ?movie imdb:numVotes ?votes .
  FILTER(?votes >= 100000)
  ?movie imdb:director ?director .
  ?movie imdb:startYear ?releaseYear .
  ?director imdb:birthYear ?birthYear .
  FILTER(BOUND(?birthYear) && BOUND(?releaseYear))
} GROUP BY ?movie ?releaseYear ?birthYear
ORDER BY DESC(?age)
LIMIT 100

Got 100 rows and 5 columns, showing the first 5 and last 5 rows below:
| movie                                        | leadDirector                         | releaseYear    | birthYear      | age          |
| -------------------------------------------- | ------------------------------------ | -------------- | -------------- | ------------ |
| Juror #2 (imdb_title:tt27403986)             | Clint Eastwood (imdb_name:nm0000142) | 2024 (xsd:int) | 1930 (xsd:int) | 94 (xsd:int) |
| Richard Jewell (imdb_title:tt3513548)        | Clint Eastwood (imdb_name:nm0000142) | 2019 (xsd:int) | 1930 (xsd:int) | 89 (xsd:int) |
| The Mule (imdb_title:tt7959026)              | Clint Eastwood (imdb_name:nm0000142) | 2018 (xsd:int) | 1930 (xsd:int) | 88 (xsd:int) |
| Gladiator II (imdb_title:tt9218128)          | Ridley Scott (imdb_name:nm0000631)   | 2024 (xsd:int) | 1937 (xsd:int) | 87 (xsd:int) |
| Sully (imdb_title:tt3263904)                 | Clint Eastwood (imdb_name:nm0000142) | 2016 (xsd:int) | 1930 (xsd:int) | 86 (xsd:int) |
| ...                                          | ...                                  | ...            | ...            | ...          |
| Public Enemies (imdb_title:tt1152836)        | Michael Mann (imdb_name:nm0000520)   | 2009 (xsd:int) | 1943 (xsd:int) | 66 (xsd:int) |
| The Fox and the Hound (imdb_title:tt0082406) | Art Stevens (imdb_name:nm0828245)    | 1981 (xsd:int) | 1915 (xsd:int) | 66 (xsd:int) |
| Unstoppable (imdb_title:tt0477080)           | Tony Scott (imdb_name:nm0001716)     | 2010 (xsd:int) | 1944 (xsd:int) | 66 (xsd:int) |
| The Way Back (imdb_title:tt1023114)          | Peter Weir (imdb_name:nm0001837)     | 2010 (xsd:int) | 1944 (xsd:int) | 66 (xsd:int) |
| Savages (imdb_title:tt1615065)               | Oliver Stone (imdb_name:nm0000231)   | 2012 (xsd:int) | 1946 (xsd:int) | 66 (xsd:int) |

[Assistant]
answer

SELECT ?movie (SAMPLE(?director) AS ?leadDirector) ?releaseYear ?birthYear (?releaseYear - ?birthYear AS ?age) WHERE {
  ?movie imdb:type "movie" .
  ?movie imdb:numVotes ?votes .
  FILTER(?votes >= 100000)
  ?movie imdb:director ?director .
  ?movie imdb:startYear ?releaseYear .
  ?director imdb:birthYear ?birthYear .
  FILTER(BOUND(?birthYear) && BOUND(?releaseYear))
} GROUP BY ?movie ?releaseYear ?birthYear
ORDER BY DESC(?age)
LIMIT 100

Answer: This query returns the 100 oldest directors (one per movie, using SAMPLE to approximate a 'lead' director) at the time of a movie (not series) with at least 100,000 votes, ordered by their age at the movie's release. Note: If a movie has multiple directors, only one is arbitrarily selected due to lack of ordering in the data; the knowledge graph does not distinguish a 'lead' or first-listed director. Filtering for imdb:type = 'movie' excludes series, TV movies, shorts, and other non-movie types. Only movies and directors with both release year and birth year present are included. Age is calculated as (release year - birth year), which may not be the exact age at release depending on the months involved.


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX imdb: <https://www.imdb.com/>
SELECT ?person ?personLabel ( COUNT( DISTINCT ?title ) AS ?numTitles ) WHERE {
  ?person imdb:knownForTitle ?title .
  ?title imdb:numVotes ?votes .
  FILTER ( ?votes > 1000000 ) OPTIONAL {
    ?person rdfs:label ?personLabel
  }
}
GROUP BY ?person ?personLabel
HAVING ( COUNT( DISTINCT ?title ) >= 3 )
LIMIT 100

Using properties:
- label (rdfs:label)
- known For Title (imdb:knownForTitle), also known as knownForTitle
- num Votes (imdb:numVotes), also known as numVotes

Got 100 rows and 3 columns, showing the first 5 and last 5 rows below:
| person                                  | personLabel       | numTitles   |
| --------------------------------------- | ----------------- | ----------- |
| Leonardo DiCaprio (imdb_name:nm0000138) | Leonardo DiCaprio | 4 (xsd:int) |
| Kevin Spacey (imdb_name:nm0000228)      | Kevin Spacey      | 3 (xsd:int) |
| Steven Spielberg (imdb_name:nm0000229)  | Steven Spielberg  | 3 (xsd:int) |
| Sean Astin (imdb_name:nm0000276)        | Sean Astin        | 3 (xsd:int) |
| Christian Bale (imdb_name:nm0000288)    | Christian Bale    | 3 (xsd:int) |
| ...                                     | ...               | ...         |
| Rewa Harre (imdb_name:nm0364066)        | Rewa Harre        | 3 (xsd:int) |
| Bob Hart (imdb_name:nm0366165)          | Bob Hart          | 3 (xsd:int) |
| Jim Henrikson (imdb_name:nm0377619)     | Jim Henrikson     | 3 (xsd:int) |
| Paul Herbert (imdb_name:nm0378609)      | Paul Herbert      | 3 (xsd:int) |
| Miki Herman (imdb_name:nm0379192)       | Miki Herman       | 4 (xsd:int) |

Answer: People who are known for at least 3 titles with more than 1 million votes include (for example): Leonardo DiCaprio, Kevin Spacey, Steven Spielberg, Sean Astin, Christian Bale, and others. The query returns their IMDB identifiers, their names (if available), and the count of such titles per person. Note: This list is not exhaustive due to the LIMIT 100 in the query.

People who are known for at least 3 titles with more than 1 million votes include (for example): Leonardo DiCaprio, Kevin Spacey, Steven Spielberg, Sean Astin, Christian Bale, and others. The query returns their IMDB identifiers, their names (if available), and the count of such titles per person. Note: This list is not exhaustive due to the LIMIT 100 in the query.

SPARQL query over imdb:
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX imdb: <https://www.imdb.com/>
SELECT ?person ?personLabel ( COUNT( DISTINCT ?title ) AS ?numTitles ) WHERE {
  ?person imdb:knownForTitle ?title .
  ?title imdb:numVotes ?votes .
  FILTER ( ?votes > 1000000 ) OPTIONAL {
    ?person rdfs:label ?personLabel
  }
}
GROUP BY ?person ?personLabel
HAVING ( COUNT( DISTINCT ?title ) >= 3 )
LIMIT 100

Using properties:
- label (rdfs:label)
- known For Title (imdb:knownForTitle), also known as knownForTitle
- num Votes (imdb:numVotes), also known as numVotes

Execution result:
Got 100 rows and 3 columns, showing the first 5 and last 5 rows below:
| person                                  | personLabel       | numTitles   |
| --------------------------------------- | ----------------- | ----------- |
| Leonardo DiCaprio (imdb_name:nm0000138) | Leonardo DiCaprio | 4 (xsd:int) |
| Kevin Spacey (imdb_name:nm0000228)      | Kevin Spacey      | 3 (xsd:int) |
| Steven Spielberg (imdb_name:nm0000229)  | Steven Spielberg  | 3 (xsd:int) |
| Sean Astin (imdb_name:nm0000276)        | Sean Astin        | 3 (xsd:int) |
| Christian Bale (imdb_name:nm0000288)    | Christian Bale    | 3 (xsd:int) |
| ...                                     | ...               | ...         |
| Rewa Harre (imdb_name:nm0364066)        | Rewa Harre        | 3 (xsd:int) |
| Bob Hart (imdb_name:nm0366165)          | Bob Hart          | 3 (xsd:int) |
| Jim Henrikson (imdb_name:nm0377619)     | Jim Henrikson     | 3 (xsd:int) |
| Paul Herbert (imdb_name:nm0378609)      | Paul Herbert      | 3 (xsd:int) |
| Miki Herman (imdb_name:nm0379192)       | Miki Herman       | 4 (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] People who are known for at least 3 titles with more than 1 M votes

[Assistant]
Let's break down the question:

- We are looking for people (likely actors, directors, etc.).
- They must be "known for" at least 3 titles.
- Each of those titles must have more than 1 million votes.

Step 1: Identify entities and properties.
- "People" are likely entities of type imdb:Person.
- "Known for" is a property linking people to titles (movies, series, etc.).
- "Votes" is a property of titles, representing the number of user votes.

Step 2: Search for relevant properties.
- Let's search for the "known for" property to see how people are linked to titles.

Question: known for

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

[Assistant]
The property imdb:knownForTitle links people to the titles they are known for.

Step 3: Find the property for "votes" on titles.
- Let's search for the "votes" property to see how to filter titles with more than 1 million votes.

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 represents the number of votes a title has received.

Step 4: Plan the query.
- For each person, get their imdb:knownForTitle titles.
- For each title, check imdb:numVotes > 1,000,000.
- Count the number of such titles per person.
- Filter for people with at least 3 such titles.

Step 5: Write the SPARQL query.
- We will select people (?person) who have at least 3 distinct knownForTitle (?title) with imdb:numVotes > 1,000,000.
- Optionally, we can retrieve their labels.

Let's construct the query.

execute

SELECT ?person (COUNT(DISTINCT ?title) AS ?numTitles) WHERE {
  ?person imdb:knownForTitle ?title .
  ?title imdb:numVotes ?votes .
  FILTER(?votes > 1000000)
} GROUP BY ?person HAVING(COUNT(DISTINCT ?title) >= 3)
LIMIT 100

Got 100 rows and 2 columns, showing the first 5 and last 5 rows below:
| person                                  | numTitles   |
| --------------------------------------- | ----------- |
| Leonardo DiCaprio (imdb_name:nm0000138) | 4 (xsd:int) |
| Kevin Spacey (imdb_name:nm0000228)      | 3 (xsd:int) |
| Steven Spielberg (imdb_name:nm0000229)  | 3 (xsd:int) |
| Sean Astin (imdb_name:nm0000276)        | 3 (xsd:int) |
| Christian Bale (imdb_name:nm0000288)    | 3 (xsd:int) |
| ...                                     | ...         |
| Rewa Harre (imdb_name:nm0364066)        | 3 (xsd:int) |
| Bob Hart (imdb_name:nm0366165)          | 3 (xsd:int) |
| Jim Henrikson (imdb_name:nm0377619)     | 3 (xsd:int) |
| Paul Herbert (imdb_name:nm0378609)      | 3 (xsd:int) |
| Miki Herman (imdb_name:nm0379192)       | 4 (xsd:int) |

[Assistant]
answer

SELECT ?person (COUNT(DISTINCT ?title) AS ?numTitles) WHERE {
  ?person imdb:knownForTitle ?title .
  ?title imdb:numVotes ?votes .
  FILTER(?votes > 1000000)
} GROUP BY ?person HAVING(COUNT(DISTINCT ?title) >= 3)
LIMIT 100

Answer: People who are known for at least 3 titles with more than 1 million votes include Leonardo DiCaprio, Kevin Spacey, Steven Spielberg, Sean Astin, Christian Bale, and others. The query returns their IMDB identifiers and the count of such titles per person.

[feedback]
Feedback (status=refine):
The query is mostly correct and returns people who are known for at least 3 titles with more than 1 million votes, as requested. It uses the correct properties and aggregates as needed. However, the answer could be improved by optionally including the labels (names) of the people for better readability, since only the IMDB identifiers are guaranteed to be returned. This can be done by adding an OPTIONAL clause to retrieve the person's label. Additionally, the answer should clarify that the list is not exhaustive due to the LIMIT 100 in the query. Otherwise, the logic and execution are correct.

[Assistant]
execute

SELECT ?person ?personLabel (COUNT(DISTINCT ?title) AS ?numTitles) WHERE {
  ?person imdb:knownForTitle ?title .
  ?title imdb:numVotes ?votes .
  FILTER(?votes > 1000000)
  OPTIONAL { ?person rdfs:label ?personLabel }
} GROUP BY ?person ?personLabel HAVING(COUNT(DISTINCT ?title) >= 3)
LIMIT 100

Got 100 rows and 3 columns, showing the first 5 and last 5 rows below:
| person                                  | personLabel       | numTitles   |
| --------------------------------------- | ----------------- | ----------- |
| Leonardo DiCaprio (imdb_name:nm0000138) | Leonardo DiCaprio | 4 (xsd:int) |
| Kevin Spacey (imdb_name:nm0000228)      | Kevin Spacey      | 3 (xsd:int) |
| Steven Spielberg (imdb_name:nm0000229)  | Steven Spielberg  | 3 (xsd:int) |
| Sean Astin (imdb_name:nm0000276)        | Sean Astin        | 3 (xsd:int) |
| Christian Bale (imdb_name:nm0000288)    | Christian Bale    | 3 (xsd:int) |
| ...                                     | ...               | ...         |
| Rewa Harre (imdb_name:nm0364066)        | Rewa Harre        | 3 (xsd:int) |
| Bob Hart (imdb_name:nm0366165)          | Bob Hart          | 3 (xsd:int) |
| Jim Henrikson (imdb_name:nm0377619)     | Jim Henrikson     | 3 (xsd:int) |
| Paul Herbert (imdb_name:nm0378609)      | Paul Herbert      | 3 (xsd:int) |
| Miki Herman (imdb_name:nm0379192)       | Miki Herman       | 4 (xsd:int) |

[Assistant]
answer

SELECT ?person ?personLabel (COUNT(DISTINCT ?title) AS ?numTitles) WHERE {
  ?person imdb:knownForTitle ?title .
  ?title imdb:numVotes ?votes .
  FILTER(?votes > 1000000)
  OPTIONAL { ?person rdfs:label ?personLabel }
} GROUP BY ?person ?personLabel HAVING(COUNT(DISTINCT ?title) >= 3)
LIMIT 100

Answer: People who are known for at least 3 titles with more than 1 million votes include (for example): Leonardo DiCaprio, Kevin Spacey, Steven Spielberg, Sean Astin, Christian Bale, and others. The query returns their IMDB identifiers, their names (if available), and the count of such titles per person. Note: This list is not exhaustive due to the LIMIT 100 in the query.

[feedback]
Feedback (status=done):
The SPARQL query correctly implements the requirements: it finds people who are known for at least 3 titles (using imdb:knownForTitle), where each title has more than 1 million votes (using imdb:numVotes > 1000000), and groups by person, filtering with HAVING to ensure at least 3 such titles. The answer is accurate, and the query returns the expected identifiers and optional labels. No further refinement is needed.


PREFIX imdb_title: <https://www.imdb.com/title/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX imdb: <https://www.imdb.com/>
SELECT ?episode ?title ?rating WHERE {
  ?episode imdb:parentTitle imdb_title:tt0903747 .
  ?episode imdb:seasonNumber 5 .
  ?episode imdb:averageRating ?rating .
  OPTIONAL {
    ?episode rdfs:label ?title
  }
}
ORDER BY DESC ( ?rating )
LIMIT 3

Using entities:
- Breaking Bad (imdb_title:tt0903747)

Using properties:
- label (rdfs:label)
- average Rating (imdb:averageRating), also known as averageRating
- parent Title (imdb:parentTitle), also known as parentTitle
- season Number (imdb:seasonNumber), also known as seasonNumber

Got 3 rows and 3 columns:
| episode                            | title       | rating            |
| ---------------------------------- | ----------- | ----------------- |
| Ozymandias (imdb_title:tt2301451)  | Ozymandias  | 10 (xsd:int)      |
| Felina (imdb_title:tt2301455)      | Felina      | 9.9 (xsd:decimal) |
| To'hajiilee (imdb_title:tt2301449) | To'hajiilee | 9.8 (xsd:decimal) |

Answer:
The top 3 episodes of the last season (season 5) of Breaking Bad, based on IMDb ratings, are:
1. Ozymandias (imdb_title:tt2301451) – Rating: 10
2. Felina (imdb_title:tt2301455) – Rating: 9.9
3. To'hajiilee (imdb_title:tt2301449) – Rating: 9.8

The top 3 episodes of the last season (season 5) of Breaking Bad, based on IMDb ratings, are:
1. Ozymandias (imdb_title:tt2301451) – Rating: 10
2. Felina (imdb_title:tt2301455) – Rating: 9.9
3. To'hajiilee (imdb_title:tt2301449) – Rating: 9.8

SPARQL query over imdb:
PREFIX imdb_title: <https://www.imdb.com/title/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX imdb: <https://www.imdb.com/>
SELECT ?episode ?title ?rating WHERE {
  ?episode imdb:parentTitle imdb_title:tt0903747 .
  ?episode imdb:seasonNumber 5 .
  ?episode imdb:averageRating ?rating .
  OPTIONAL {
    ?episode rdfs:label ?title
  }
}
ORDER BY DESC ( ?rating )
LIMIT 3

Using entities:
- Breaking Bad (imdb_title:tt0903747)

Using properties:
- label (rdfs:label)
- average Rating (imdb:averageRating), also known as averageRating
- parent Title (imdb:parentTitle), also known as parentTitle
- season Number (imdb:seasonNumber), also known as seasonNumber

Execution result:
Got 3 rows and 3 columns:
| episode                            | title       | rating            |
| ---------------------------------- | ----------- | ----------------- |
| Ozymandias (imdb_title:tt2301451)  | Ozymandias  | 10 (xsd:int)      |
| Felina (imdb_title:tt2301455)      | Felina      | 9.9 (xsd:decimal) |
| To'hajiilee (imdb_title:tt2301449) | To'hajiilee | 9.8 (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] Top 3 episodes of the last Breaking Bad season

[Assistant]
Let's break down the question:

1. Entities and properties:
   - Entity: "Breaking Bad" (TV series)
   - Property: "season" (specifically, the last season)
   - Property: "episode" (episodes belonging to the last season)
   - Property: "rating" or similar (to determine "top" episodes)
   - We need to sort episodes of the last season by rating and select the top 3.

2. Steps:
   - Find the entity for "Breaking Bad".
   - Find its seasons and identify the last one.
   - Get episodes of the last season.
   - Find the rating property for episodes.
   - Sort episodes by rating and select the top 3.

Let's start by searching for the "Breaking Bad" entity.

Question: Breaking Bad

Top 10 entity alternatives:
1. Breaking Bad (imdb_title:tt0903747)
2. Breaking Bad (imdb_title:tt12188568)
3. Breaking Bad (imdb_title:tt7017416)
4. Breaking Bad (imdb_title:tt3075216)
5. Breaking Bad (imdb_title:tt2931468)
6. Breaking Bad (imdb_title:tt4111020)
7. Breaking Bad (imdb_title:tt3105216)
8. Breaking Bad (imdb_title:tt2329627)
9. Breaking Bad (imdb_title:tt6949164)
10. Breaking Bad (imdb_title:tt11681716), also known as Breaking Bad - TV series review

[Assistant]
The most likely correct entity for the TV series "Breaking Bad" is imdb_title:tt0903747.

Next, I need to find the seasons of this series and identify the last season. To do this, I will look for properties related to "season" for this entity.

Question: season

Top 10 property alternatives:
1. end Year (imdb:endYear, matched via endYear), also known as endYear
2. type (imdb:type)
3. type (rdf:type)
4. id (imdb:id)
5. label (rdfs:label)
6. actor (imdb_principal:actor)
7. genre (imdb:genre)
8. start Year (imdb:startYear), also known as startYear
9. is Adult (imdb:isAdult, matched via isAdult), also known as isAdult
10. average Rating (imdb:averageRating, matched via averageRating), also known as averageRating

No other items found

[Assistant]
It appears that there is no direct "season" property listed for the "Breaking Bad" entity in the search results. However, TV series data in IMDb knowledge graphs often models episodes as entities linked to the series, possibly with season and episode numbers as properties.

Next, I will search for properties related to "season" for episodes, to see how season information is encoded for episodes.

Question: season

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

[Assistant]
The property imdb:seasonNumber is likely used to indicate the season number for episodes.

Now, I need to find all episodes of "Breaking Bad" and determine which have the highest season number (i.e., the last season). To do this, I will look for episodes linked to "Breaking Bad" and check their season numbers.

First, let's check how episodes are linked to the series. I'll search for properties related to "episode" for the "Breaking Bad" entity.

Question: episode

Top 10 property alternatives:
1. id (imdb:id)
2. actor (imdb_principal:actor)
3. primary Title (imdb:primaryTitle, matched via primaryTitle), also known as primaryTitle
4. genre (imdb:genre)
5. type (imdb:type)
6. type (rdf:type)
7. original Title (imdb:originalTitle, matched via originalTitle), also known as originalTitle
8. label (rdfs:label)
9. end Year (imdb:endYear, matched via endYear), also known as endYear
10. director (imdb:director)

No other items found

[Assistant]
There is no direct "episode" property listed for the series entity, but typically, episodes are modeled as entities with a property linking them to their parent series.

I will now search for the property "parent series" or similar, to see how episodes are linked to their series.

Question: parent series

Top 10 property alternatives:
1. parent Title (imdb:parentTitle, matched via parentTitle), also known as parentTitle
2. primary Title (imdb:primaryTitle), also known as primaryTitle
3. actor (imdb_principal:actor)
4. id (imdb:id)
5. known For Title (imdb:knownForTitle, matched via knownForTitle), also known as knownForTitle
6. episode Number (imdb:episodeNumber, matched via episodeNumber), also known as episodeNumber
7. type (imdb:type)
8. type (rdf:type)
9. death Year (imdb:deathYear), also known as deathYear
10. original Title (imdb:originalTitle, matched via originalTitle), also known as originalTitle

[Assistant]
The property imdb:parentTitle is likely used to link episodes to their parent series.

Now, I will look for all entities where imdb:parentTitle is imdb_title:tt0903747 (Breaking Bad), and retrieve their imdb:seasonNumber. This will allow me to determine the highest season number (the last season).

execute

SELECT ?season WHERE {
  ?episode imdb:parentTitle imdb_title:tt0903747 .
  ?episode imdb:seasonNumber ?season .
}
ORDER BY DESC(?season)
LIMIT 1

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

[Assistant]
The last season of "Breaking Bad" is season 5.

Next, I need to find all episodes of "Breaking Bad" with imdb:seasonNumber 5, and retrieve their ratings. Then, I will sort them by rating and select the top 3.

First, let's check the property for episode ratings. From earlier, imdb:averageRating is likely the correct property.

Now, I will construct a query to get all episodes of "Breaking Bad" season 5, their ratings, and titles, sorted by rating (descending), and limit to the top 3.

SELECT ?episode ?title ?rating WHERE {
  ?episode imdb:parentTitle imdb_title:tt0903747 .
  ?episode imdb:seasonNumber 5 .
  ?episode imdb:averageRating ?rating .
  OPTIONAL { ?episode rdfs:label ?title }
}
ORDER BY DESC(?rating)
LIMIT 3

Got 3 rows and 3 columns:
| episode                            | title       | rating            |
| ---------------------------------- | ----------- | ----------------- |
| Ozymandias (imdb_title:tt2301451)  | Ozymandias  | 10 (xsd:int)      |
| Felina (imdb_title:tt2301455)      | Felina      | 9.9 (xsd:decimal) |
| To'hajiilee (imdb_title:tt2301449) | To'hajiilee | 9.8 (xsd:decimal) |

[Assistant]
answer

SELECT ?episode ?title ?rating WHERE {
  ?episode imdb:parentTitle imdb_title:tt0903747 .
  ?episode imdb:seasonNumber 5 .
  ?episode imdb:averageRating ?rating .
  OPTIONAL { ?episode rdfs:label ?title }
}
ORDER BY DESC(?rating)
LIMIT 3

Answer:
The top 3 episodes of the last season (season 5) of Breaking Bad, based on IMDb ratings, are:
1. Ozymandias (imdb_title:tt2301451) – Rating: 10
2. Felina (imdb_title:tt2301455) – Rating: 9.9
3. To'hajiilee (imdb_title:tt2301449) – Rating: 9.8

[feedback]
Feedback (status=done):
The answer is correct and complete. The SPARQL query accurately retrieves the top 3 episodes of the last season (season 5) of Breaking Bad by IMDb rating, and the answer matches the query results. The query uses the correct entity for Breaking Bad, filters for season 5, sorts by rating in descending order, and limits the results to 3. No further refinement is needed.