PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX imdb: <https://www.imdb.com/>
SELECT ?series ?seriesLabel ( COUNT( ?episode ) AS ?episodeCount ) ?votes WHERE {
  ?series imdb:type "tvSeries" .
  ?series imdb:numVotes ?votes .
  FILTER ( ?votes > 1000000 ) ?episode imdb:type "tvEpisode" .
  ?episode imdb:parentTitle ?series .
  OPTIONAL {
    ?series rdfs:label ?seriesLabel
  }
}
GROUP BY ?series ?seriesLabel ?votes
ORDER BY DESC ( ?episodeCount )
LIMIT 1

Using properties:
- label (rdfs:label)
- num Votes (imdb:numVotes), also known as numVotes
- parent Title (imdb:parentTitle), also known as parentTitle
- type (imdb:type)

Answer: The TV series with more than 1 million votes and the most episodes is 'Friends' (imdb_title:tt0108778), with 235 episodes and 1,155,756 votes.

The TV series with more than 1 million votes and the most episodes is 'Friends' (imdb_title:tt0108778), with 235 episodes and 1,155,756 votes.

SPARQL query over imdb:
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX imdb: <https://www.imdb.com/>
SELECT ?series ?seriesLabel ( COUNT( ?episode ) AS ?episodeCount ) ?votes WHERE {
  ?series imdb:type "tvSeries" .
  ?series imdb:numVotes ?votes .
  FILTER ( ?votes > 1000000 ) ?episode imdb:type "tvEpisode" .
  ?episode imdb:parentTitle ?series .
  OPTIONAL {
    ?series rdfs:label ?seriesLabel
  }
}
GROUP BY ?series ?seriesLabel ?votes
ORDER BY DESC ( ?episodeCount )
LIMIT 1

Using properties:
- label (rdfs:label)
- num Votes (imdb:numVotes), also known as numVotes
- parent Title (imdb:parentTitle), also known as parentTitle
- type (imdb:type)

Execution result:
Got 1 row and 4 columns:
| series                         | seriesLabel | episodeCount  | votes             |
| ------------------------------ | ----------- | ------------- | ----------------- |
| Friends (imdb_title:tt0108778) | Friends     | 235 (xsd:int) | 1155756 (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] Series with more than 1 M votes with most episodes

[Assistant]
Let's break down the question:

1. We are looking for TV series (not movies).
2. The series must have more than 1 million votes.
3. We want the series with the most episodes among those.

Step 1: Identify entities and properties.
- Entity: TV series.
- Property: Number of votes (likely "votes" or "numVotes").
- Property: Number of episodes.
- We need to filter for series with more than 1 million votes and then sort by the number of episodes.

Step 2: Search for relevant properties.
I'll start by searching for properties related to "votes" and "episodes".

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]
Question: episodes

Top 10 property alternatives:
1. episode Number (imdb:episodeNumber), also known as episodeNumber
2. id (imdb:id)
3. archive footage (imdb_principal:archive_footage), also known as archive_footage
4. archive sound (imdb_principal:archive_sound, matched via archive_sound
), also known as archive_sound

5. death Year (imdb:deathYear), also known as deathYear
6. actor (imdb_principal:actor)
7. known For Title (imdb:knownForTitle, matched via knownForTitle), also known as knownForTitle
8. primary Title (imdb:primaryTitle, matched via primaryTitle), also known as primaryTitle
9. self (imdb_principal:self)
10. type (imdb:type)

[Assistant]
Question: number of episodes

Top 10 property alternatives:
1. episode Number (imdb:episodeNumber, matched via episodeNumber), also known as episodeNumber
2. runtime Minutes (imdb:runtimeMinutes, matched via runtimeMinutes), also known as runtimeMinutes
3. season Number (imdb:seasonNumber), also known as seasonNumber
4. archive footage (imdb_principal:archive_footage, matched via archive_footage), also known as archive_footage
5. id (imdb:id)
6. end Year (imdb:endYear, matched via endYear), also known as endYear
7. type (imdb:type)
8. type (rdf:type)
9. archive sound (imdb_principal:archive_sound, matched via archive_sound
), also known as archive_sound

10. num Votes (imdb:numVotes, matched via numVotes), also known as numVotes

[Assistant]
Question: series

Top 10 property alternatives:
1. episode Number (imdb:episodeNumber, matched via episodeNumber), also known as episodeNumber
2. id (imdb:id)
3. genre (imdb:genre)
4. actor (imdb_principal:actor)
5. type (imdb:type)
6. type (rdf:type)
7. self (imdb_principal:self)
8. primary Title (imdb:primaryTitle, matched via primaryTitle), also known as primaryTitle
9. archive footage (imdb_principal:archive_footage), also known as archive_footage
10. known For Title (imdb:knownForTitle, matched via knownForTitle), also known as knownForTitle

[Assistant]
Question: series

No entity items found

No other items found

No literal items found

[Assistant]
Question: series

No entity items found

No other items found

No literal items found

[Assistant]
Question: tv

No entity items found

No other items found

Top 10 literal alternatives:
1. tvEpisode ("tvEpisode")
2. tvMiniSeries ("tvMiniSeries")
3. tvMovie ("tvMovie")
4. tvPilot ("tvPilot")
5. tvSeries ("tvSeries")
6. tvShort ("tvShort")
7. tvSpecial ("tvSpecial")

[Assistant]
list

Got more than 131,072 rows and 3 columns, showing the first 10 rows below:
| subject                                           | property         | object   |
| ------------------------------------------------- | ---------------- | -------- |
| NRK Nyheter (imdb_title:tt12164062)               | type (imdb:type) | tvSeries |
| Tatort (imdb_title:tt0806910)                     | type (imdb:type) | tvSeries |
| Days of Our Lives (imdb_title:tt0058796)          | type (imdb:type) | tvSeries |
| General Hospital (imdb_title:tt0056758)           | type (imdb:type) | tvSeries |
| Coronation Street (imdb_title:tt0053494)          | type (imdb:type) | tvSeries |
| The Young and the Restless (imdb_title:tt0069658) | type (imdb:type) | tvSeries |
| As the World Turns (imdb_title:tt0048845)         | type (imdb:type) | tvSeries |
| The NFL on CBS (imdb_title:tt0407423)             | type (imdb:type) | tvSeries |
| Jeopardy! (imdb_title:tt0159881)                  | type (imdb:type) | tvSeries |
| 60 Minutes (imdb_title:tt0123338)                 | type (imdb:type) | tvSeries |
| ...                                               | ...              | ...      |

[Assistant]
imdb_title:tt0058796

Got 295 rows and 3 columns, showing the first 10 rows below:
| subject                                  | property                            | object                                |
| ---------------------------------------- | ----------------------------------- | ------------------------------------- |
| Days of Our Lives (imdb_title:tt0058796) | alt Label (skos:altLabel)           |  passano i giorni (lang:it)           |
| Days of Our Lives (imdb_title:tt0058796) | label (rdfs:label)                  | Days of Our Lives                     |
| Days of Our Lives (imdb_title:tt0058796) | type (rdf:type)                     | Movie (imdb:Movie)                    |
| Days of Our Lives (imdb_title:tt0058796) | actor (imdb_principal:actor)        | Peter Reckell (imdb_name:nm0005340)   |
| Days of Our Lives (imdb_title:tt0058796) | genre (imdb:genre)                  | Drama                                 |
| Days of Our Lives (imdb_title:tt0058796) | actress (imdb_principal:actress)    | Deidre Hall (imdb_name:nm0004984)     |
| Days of Our Lives (imdb_title:tt0058796) | writer (imdb:writer)                | William J. Bell (imdb_name:nm0068589) |
| Days of Our Lives (imdb_title:tt0058796) | primary Title (imdb:primaryTitle)   | Days of Our Lives                     |
| Days of Our Lives (imdb_title:tt0058796) | original Title (imdb:originalTitle) | Days of Our Lives                     |
| Days of Our Lives (imdb_title:tt0058796) | id (imdb:id)                        | tt0058796                             |
| ...                                      | ...                                 | ...                                   |

[Assistant] imdb_title:tt0058796

[Assistant] imdb_title:tt0058796

[Assistant]
Question: number of episodes

Top 10 property alternatives:
1. episode Number (imdb:episodeNumber, matched via episodeNumber), also known as episodeNumber
2. runtime Minutes (imdb:runtimeMinutes, matched via runtimeMinutes), also known as runtimeMinutes
3. season Number (imdb:seasonNumber), also known as seasonNumber
4. archive footage (imdb_principal:archive_footage, matched via archive_footage), also known as archive_footage
5. id (imdb:id)
6. end Year (imdb:endYear, matched via endYear), also known as endYear
7. type (imdb:type)
8. type (rdf:type)
9. archive sound (imdb_principal:archive_sound, matched via archive_sound
), also known as archive_sound

10. num Votes (imdb:numVotes, matched via numVotes), also known as numVotes

[Assistant] imdb_title:tt0058796

[Assistant]
Question: episodes

Top 10 property alternatives:
1. episode Number (imdb:episodeNumber), also known as episodeNumber
2. id (imdb:id)
3. archive footage (imdb_principal:archive_footage), also known as archive_footage
4. archive sound (imdb_principal:archive_sound, matched via archive_sound
), also known as archive_sound

5. death Year (imdb:deathYear), also known as deathYear
6. actor (imdb_principal:actor)
7. known For Title (imdb:knownForTitle, matched via knownForTitle), also known as knownForTitle
8. primary Title (imdb:primaryTitle, matched via primaryTitle), also known as primaryTitle
9. self (imdb_principal:self)
10. type (imdb:type)

[Assistant]
Question: number of episodes

Top 10 property alternatives:
1. episode Number (imdb:episodeNumber, matched via episodeNumber), also known as episodeNumber
2. runtime Minutes (imdb:runtimeMinutes, matched via runtimeMinutes), also known as runtimeMinutes
3. season Number (imdb:seasonNumber), also known as seasonNumber
4. archive footage (imdb_principal:archive_footage, matched via archive_footage), also known as archive_footage
5. id (imdb:id)
6. end Year (imdb:endYear, matched via endYear), also known as endYear
7. type (imdb:type)
8. type (rdf:type)
9. archive sound (imdb_principal:archive_sound, matched via archive_sound
), also known as archive_sound

10. num Votes (imdb:numVotes, matched via numVotes), also known as numVotes

[Assistant]
Question: episode count

Top 10 property alternatives:
1. episode Number (imdb:episodeNumber, matched via episodeNumber), also known as episodeNumber
2. id (imdb:id)
3. archive footage (imdb_principal:archive_footage, matched via archive_footage), also known as archive_footage
4. runtime Minutes (imdb:runtimeMinutes, matched via runtimeMinutes), also known as runtimeMinutes
5. actor (imdb_principal:actor)
6. death Year (imdb:deathYear), also known as deathYear
7. type (imdb:type)
8. type (rdf:type)
9. end Year (imdb:endYear, matched via endYear), also known as endYear
10. season Number (imdb:seasonNumber, matched via seasonNumber), also known as seasonNumber

[Assistant]
Question: episodes total

Top 10 property alternatives:
1. episode Number (imdb:episodeNumber), also known as episodeNumber
2. archive footage (imdb_principal:archive_footage, matched via archive_footage), also known as archive_footage
3. runtime Minutes (imdb:runtimeMinutes, matched via runtimeMinutes), also known as runtimeMinutes
4. id (imdb:id)
5. primary Title (imdb:primaryTitle, matched via primaryTitle), also known as primaryTitle
6. known For Title (imdb:knownForTitle, matched via knownForTitle), also known as knownForTitle
7. end Year (imdb:endYear, matched via endYear), also known as endYear
8. archive sound (imdb_principal:archive_sound, matched via archive_sound
), also known as archive_sound

9. death Year (imdb:deathYear), also known as deathYear
10. type (imdb:type)

[Assistant]
It appears that the IMDb knowledge graph does not have a direct property for the total number of episodes for a TV series. The closest property found is "episodeNumber", which is likely used for individual episodes rather than for the series as a whole.

Given this, the best approach would be to:
- Identify all entities of type "tvSeries".
- For each series, count the number of associated episodes (entities of type "tvEpisode" with a link to the series).
- Filter for series with more than 1 million votes (using imdb:numVotes).
- Sort by the episode count and return the series with the most episodes.

I will now attempt to write a SPARQL query that:
1. Finds all tvSeries.
2. Counts the number of tvEpisodes linked to each series.
3. Filters for series with imdb:numVotes > 1000000.
4. Orders by episode count descending and returns the top result.

Let's proceed with the query.

execute

SELECT ?series ?seriesLabel (COUNT(?episode) AS ?episodeCount) ?votes WHERE {
  ?series imdb:type "tvSeries" .
  ?series imdb:numVotes ?votes .
  FILTER(?votes > 1000000)
  ?episode imdb:type "tvEpisode" .
  ?episode imdb:parentTitle ?series .
  OPTIONAL { ?series rdfs:label ?seriesLabel }
}
GROUP BY ?series ?seriesLabel ?votes
ORDER BY DESC(?episodeCount)
LIMIT 1

[Assistant]
answer

SELECT ?series ?seriesLabel (COUNT(?episode) AS ?episodeCount) ?votes WHERE {
  ?series imdb:type "tvSeries" .
  ?series imdb:numVotes ?votes .
  FILTER(?votes > 1000000)
  ?episode imdb:type "tvEpisode" .
  ?episode imdb:parentTitle ?series .
  OPTIONAL { ?series rdfs:label ?seriesLabel }
}
GROUP BY ?series ?seriesLabel ?votes
ORDER BY DESC(?episodeCount)
LIMIT 1

Answer: The TV series with more than 1 million votes and the most episodes is 'Friends' (imdb_title:tt0108778), with 235 episodes and 1,155,756 votes.

[feedback]
Feedback (status=done):
The SPARQL query correctly identifies TV series with more than 1 million votes, counts their episodes, and returns the one with the most episodes. The answer matches the query result, and all relevant constraints are included in the query. No further refinement is needed.


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

Using properties:
- average Rating (imdb:averageRating), also known as averageRating
- genre (imdb:genre)
- num Votes (imdb:numVotes), also known as numVotes
- start Year (imdb:startYear), also known as startYear
- type (imdb:type)

Got 11 rows and 3 columns, showing the first 5 and last 5 rows below:
| decade             | avgRating             | numMovies     |
| ------------------ | --------------------- | ------------- |
| 1920 (xsd:decimal) | 8.3 (xsd:decimal)     | 1 (xsd:int)   |
| 1930 (xsd:decimal) | 7.53333 (xsd:decimal) | 6 (xsd:int)   |
| 1940 (xsd:decimal) | 6.6 (xsd:decimal)     | 2 (xsd:int)   |
| 1950 (xsd:decimal) | 6.855 (xsd:decimal)   | 20 (xsd:int)  |
| 1960 (xsd:decimal) | 6.5 (xsd:decimal)     | 14 (xsd:int)  |
| ...                | ...                   | ...           |
| 1980 (xsd:decimal) | 6.49101 (xsd:decimal) | 89 (xsd:int)  |
| 1990 (xsd:decimal) | 6.06075 (xsd:decimal) | 107 (xsd:int) |
| 2000 (xsd:decimal) | 6.05767 (xsd:decimal) | 163 (xsd:int) |
| 2010 (xsd:decimal) | 6.22901 (xsd:decimal) | 262 (xsd:int) |
| 2020 (xsd:decimal) | 5.76154 (xsd:decimal) | 104 (xsd:int) |

Answer:
Here are the average ratings of Sci-Fi movies (not series) with over 10,000 votes, grouped by decade:

1920s: 8.3 (1 movie)
1930s: 7.53 (6 movies)
1940s: 6.6 (2 movies)
1950s: 6.86 (20 movies)
1960s: 6.5 (14 movies)
1970s: 6.91 (44 movies)
1980s: 6.49 (89 movies)
1990s: 6.06 (107 movies)
2000s: 6.06 (163 movies)
2010s: 6.23 (262 movies)
2020s: 5.76 (104 movies)

The average rating of Sci-Fi movies has generally declined over the decades, with the highest in the 1920s and 1930s, and the lowest in the 2020s.

Here are the average ratings of Sci-Fi movies (not series) with over 10,000 votes, grouped by decade:

1920s: 8.3 (1 movie)
1930s: 7.53 (6 movies)
1940s: 6.6 (2 movies)
1950s: 6.86 (20 movies)
1960s: 6.5 (14 movies)
1970s: 6.91 (44 movies)
1980s: 6.49 (89 movies)
1990s: 6.06 (107 movies)
2000s: 6.06 (163 movies)
2010s: 6.23 (262 movies)
2020s: 5.76 (104 movies)

The average rating of Sci-Fi movies has generally declined over the decades, with the highest in the 1920s and 1930s, and the lowest in the 2020s.

SPARQL query over imdb:
PREFIX imdb: <https://www.imdb.com/>
SELECT ?decade ( AVG( ?rating ) AS ?avgRating ) ( COUNT( ?movie ) AS ?numMovies ) WHERE {
  ?movie imdb:genre "Sci-Fi" .
  ?movie imdb:type "movie" .
  ?movie imdb:numVotes ?votes .
  FILTER ( ?votes > 10000 ) ?movie imdb:averageRating ?rating .
  ?movie imdb:startYear ?year .
  BIND( FLOOR( ?year / 10 ) * 10 AS ?decade )
}
GROUP BY ?decade
ORDER BY ?decade

Using properties:
- average Rating (imdb:averageRating), also known as averageRating
- genre (imdb:genre)
- num Votes (imdb:numVotes), also known as numVotes
- start Year (imdb:startYear), also known as startYear
- type (imdb:type)

Execution result:
Got 11 rows and 3 columns, showing the first 5 and last 5 rows below:
| decade             | avgRating             | numMovies     |
| ------------------ | --------------------- | ------------- |
| 1920 (xsd:decimal) | 8.3 (xsd:decimal)     | 1 (xsd:int)   |
| 1930 (xsd:decimal) | 7.53333 (xsd:decimal) | 6 (xsd:int)   |
| 1940 (xsd:decimal) | 6.6 (xsd:decimal)     | 2 (xsd:int)   |
| 1950 (xsd:decimal) | 6.855 (xsd:decimal)   | 20 (xsd:int)  |
| 1960 (xsd:decimal) | 6.5 (xsd:decimal)     | 14 (xsd:int)  |
| ...                | ...                   | ...           |
| 1980 (xsd:decimal) | 6.49101 (xsd:decimal) | 89 (xsd:int)  |
| 1990 (xsd:decimal) | 6.06075 (xsd:decimal) | 107 (xsd:int) |
| 2000 (xsd:decimal) | 6.05767 (xsd:decimal) | 163 (xsd:int) |
| 2010 (xsd:decimal) | 6.22901 (xsd:decimal) | 262 (xsd:int) |
| 2020 (xsd:decimal) | 5.76154 (xsd:decimal) | 104 (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] Average rating of Sci-Fi movies (not series) by decade, for movies with over 10k votes

[Assistant]
Let's break down the question:

1. We need to find Sci-Fi movies (not series).
2. For each movie, we need its average rating and number of votes.
3. Only include movies with over 10,000 votes.
4. Group the results by decade (e.g., 1980s, 1990s, etc.).
5. For each decade, compute the average of the average ratings.

Step 1: Identify how Sci-Fi movies are represented in the IMDb KG. Let's search for the "Sci-Fi" genre entity.

Question: Sci-Fi

Top 10 entity alternatives:
1. Sci-Fi (imdb_title:tt20860308)
2. Sci-Fi (imdb_title:tt7115780)
3. Sci-fi (imdb_title:tt13372198)
4. Sci-Fi (imdb_title:tt2922258)
5.  Horror & Hell (imdb_title:tt27052883, matched via  Sci-Fi), also known as  Sci-Fi, Melodrama
6. Sci-Fi (imdb_title:tt3857958)
7. Sci-fi (imdb_title:tt34383614)
8. Sci-Fi (imdb_title:tt0801128)
9. Sci-fi (imdb_title:tt1405242)
10. Sci-Fi (imdb_title:tt3024378)

[Assistant]
Question: genre

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

[Assistant]
Question: Sci-Fi

No entity items found

No other items found

Top 10 literal alternatives:
1. Sci-Fi ("Sci-Fi")

[Assistant]
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]
Question: average rating

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

[Assistant]
Question: release 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), also known as deathYear
4. birth Year (imdb:birthYear), also known as birthYear
5. season Number (imdb:seasonNumber), also known as seasonNumber
6. archive footage (imdb_principal:archive_footage, matched via archive_footage), also known as archive_footage
7. id (imdb:id)
8. is Adult (imdb:isAdult, matched via isAdult), also known as isAdult
9. genre (imdb:genre)
10. episode Number (imdb:episodeNumber, matched via episodeNumber), also known as episodeNumber

[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]
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]
Question: tv

No entity items found

No other items found

Top 10 literal alternatives:
1. tvEpisode ("tvEpisode")
2. tvMiniSeries ("tvMiniSeries")
3. tvMovie ("tvMovie")
4. tvPilot ("tvPilot")
5. tvSeries ("tvSeries")
6. tvShort ("tvShort")
7. tvSpecial ("tvSpecial")

[Assistant]
Question: title

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

[Assistant]
list

Got 122,251 rows and 3 columns, showing the first 10 rows below:
| subject                                                      | property           | object |
| ------------------------------------------------------------ | ------------------ | ------ |
| The Fantastic Four: First Steps (imdb_title:tt10676052)      | genre (imdb:genre) | Sci-Fi |
| Black Panther (imdb_title:tt1825683)                         | genre (imdb:genre) | Sci-Fi |
| Black Widow (imdb_title:tt3480822)                           | genre (imdb:genre) | Sci-Fi |
| Star Trek Beyond (imdb_title:tt2660888)                      | genre (imdb:genre) | Sci-Fi |
| Godzilla (imdb_title:tt0831387)                              | genre (imdb:genre) | Sci-Fi |
| The Hunger Games: Mockingjay - Part 2 (imdb_title:tt1951266) | genre (imdb:genre) | Sci-Fi |
| Captain America: The First Avenger (imdb_title:tt0458339)    | genre (imdb:genre) | Sci-Fi |
| Alita: Battle Angel (imdb_title:tt0437086)                   | genre (imdb:genre) | Sci-Fi |
| Avengers: Endgame (imdb_title:tt4154796)                     | genre (imdb:genre) | Sci-Fi |
| Smosh (imdb_title:tt1500910)                                 | genre (imdb:genre) | Sci-Fi |
| ...                                                          | ...                | ...    |

[Assistant]
imdb_title:tt4154796

Got 82 rows and 3 columns, showing the first 10 rows below:
| subject                                  | property                            | object                                   |
| ---------------------------------------- | ----------------------------------- | ---------------------------------------- |
| Avengers: Endgame (imdb_title:tt4154796) | alt Label (skos:altLabel)           | Avengers 4 (lang:en)                     |
| Avengers: Endgame (imdb_title:tt4154796) | label (rdfs:label)                  | Avengers: Endgame                        |
| Avengers: Endgame (imdb_title:tt4154796) | type (rdf:type)                     | Movie (imdb:Movie)                       |
| Avengers: Endgame (imdb_title:tt4154796) | actor (imdb_principal:actor)        | Don Cheadle (imdb_name:nm0000332)        |
| Avengers: Endgame (imdb_title:tt4154796) | genre (imdb:genre)                  | Action                                   |
| Avengers: Endgame (imdb_title:tt4154796) | actress (imdb_principal:actress)    | Scarlett Johansson (imdb_name:nm0424060) |
| Avengers: Endgame (imdb_title:tt4154796) | writer (imdb:writer)                | Stan Lee (imdb_name:nm0498278)           |
| Avengers: Endgame (imdb_title:tt4154796) | primary Title (imdb:primaryTitle)   | Avengers: Endgame                        |
| Avengers: Endgame (imdb_title:tt4154796) | original Title (imdb:originalTitle) | Avengers: Endgame                        |
| Avengers: Endgame (imdb_title:tt4154796) | id (imdb:id)                        | tt4154796                                |
| ...                                      | ...                                 | ...                                      |

[Assistant] imdb_title:tt4154796

[Assistant] imdb_title:tt4154796

[Assistant] imdb_title:tt4154796

[Assistant] imdb_title:tt4154796

[Assistant]
We have confirmed the following:
- Sci-Fi movies can be found by filtering for imdb:genre "Sci-Fi" and imdb:type "movie".
- The number of votes is given by imdb:numVotes.
- The average rating is given by imdb:averageRating.
- The release year is given by imdb:startYear.

Now, we need to:
- Select all movies with imdb:genre "Sci-Fi" and imdb:type "movie".
- Filter for imdb:numVotes > 10000.
- Group by decade, which we can compute from imdb:startYear.
- For each decade, compute the average of imdb:averageRating.

Let's write the SPARQL query to do this.

execute

SELECT (FLOOR(?year/10)*10 AS ?decade) (AVG(?rating) AS ?avgRating) (COUNT(?movie) AS ?numMovies) WHERE {
  ?movie imdb:genre "Sci-Fi" .
  ?movie imdb:type "movie" .
  ?movie imdb:numVotes ?votes .
  FILTER(?votes > 10000)
  ?movie imdb:averageRating ?rating .
  ?movie imdb:startYear ?year .
} GROUP BY (FLOOR(?year/10)*10) ORDER BY ?decade

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

[Assistant]
The error indicates that the GROUP BY clause needs to use the same expression as in the SELECT. Let's adjust the query so that the decade calculation is done in the GROUP BY and SELECT consistently.

I'll define the decade as (FLOOR(?year/10)*10) in both SELECT and GROUP BY.

SELECT ?decade (AVG(?rating) AS ?avgRating) (COUNT(?movie) AS ?numMovies) WHERE {
  ?movie imdb:genre "Sci-Fi" .
  ?movie imdb:type "movie" .
  ?movie imdb:numVotes ?votes .
  FILTER(?votes > 10000)
  ?movie imdb:averageRating ?rating .
  ?movie imdb:startYear ?year .
  BIND(FLOOR(?year/10)*10 AS ?decade)
} GROUP BY ?decade ORDER BY ?decade

Got 11 rows and 3 columns, showing the first 5 and last 5 rows below:
| decade             | avgRating             | numMovies     |
| ------------------ | --------------------- | ------------- |
| 1920 (xsd:decimal) | 8.3 (xsd:decimal)     | 1 (xsd:int)   |
| 1930 (xsd:decimal) | 7.53333 (xsd:decimal) | 6 (xsd:int)   |
| 1940 (xsd:decimal) | 6.6 (xsd:decimal)     | 2 (xsd:int)   |
| 1950 (xsd:decimal) | 6.855 (xsd:decimal)   | 20 (xsd:int)  |
| 1960 (xsd:decimal) | 6.5 (xsd:decimal)     | 14 (xsd:int)  |
| ...                | ...                   | ...           |
| 1980 (xsd:decimal) | 6.49101 (xsd:decimal) | 89 (xsd:int)  |
| 1990 (xsd:decimal) | 6.06075 (xsd:decimal) | 107 (xsd:int) |
| 2000 (xsd:decimal) | 6.05767 (xsd:decimal) | 163 (xsd:int) |
| 2010 (xsd:decimal) | 6.22901 (xsd:decimal) | 262 (xsd:int) |
| 2020 (xsd:decimal) | 5.76154 (xsd:decimal) | 104 (xsd:int) |

[Assistant]
answer

SELECT ?decade (AVG(?rating) AS ?avgRating) (COUNT(?movie) AS ?numMovies) WHERE {
  ?movie imdb:genre "Sci-Fi" .
  ?movie imdb:type "movie" .
  ?movie imdb:numVotes ?votes .
  FILTER(?votes > 10000)
  ?movie imdb:averageRating ?rating .
  ?movie imdb:startYear ?year .
  BIND(FLOOR(?year/10)*10 AS ?decade)
} GROUP BY ?decade ORDER BY ?decade

Answer:
Here are the average ratings of Sci-Fi movies (not series) with over 10,000 votes, grouped by decade:

1920s: 8.3 (1 movie)
1930s: 7.53 (6 movies)
1940s: 6.6 (2 movies)
1950s: 6.86 (20 movies)
1960s: 6.5 (14 movies)
1970s: 6.91 (44 movies)
1980s: 6.49 (89 movies)
1990s: 6.06 (107 movies)
2000s: 6.06 (163 movies)
2010s: 6.23 (262 movies)
2020s: 5.76 (104 movies)

The average rating of Sci-Fi movies has generally declined over the decades, with the highest in the 1920s and 1930s, and the lowest in the 2020s.

[feedback]
Feedback (status=done):
The SPARQL query correctly implements all requirements: it filters for Sci-Fi movies (not series), ensures more than 10,000 votes, groups by decade, and calculates the average rating and count per decade. The answer accurately summarizes the query results. No further refinement is needed.


PREFIX imdb: <https://www.imdb.com/>
SELECT ?series ( COUNT( ?episode ) AS ?numEpisodes ) ( AVG( ?rating ) AS ?avgRating ) WHERE {
  ?series imdb:type "tvSeries" .
  ?episode imdb:type "tvEpisode" .
  ?episode imdb:parentTitle ?series .
  ?episode imdb:averageRating ?rating .
  ?episode imdb:numVotes ?votes .
  FILTER ( ?votes >= 10000 )
}
GROUP BY ?series
HAVING ( COUNT( ?episode ) >= 20 )
ORDER BY DESC ( ?avgRating )
LIMIT 10

Using properties:
- average Rating (imdb:averageRating), also known as averageRating
- num Votes (imdb:numVotes), also known as numVotes
- parent Title (imdb:parentTitle), also known as parentTitle
- type (imdb:type)

Got 10 rows and 3 columns:
| series                                     | numEpisodes  | avgRating             |
| ------------------------------------------ | ------------ | --------------------- |
| One Piece: Wan pîsu (imdb_title:tt0388629) | 46 (xsd:int) | 9.47391 (xsd:decimal) |
| Attack on Titan (imdb_title:tt2560140)     | 91 (xsd:int) | 9.1033 (xsd:decimal)  |
| Mr. Robot (imdb_title:tt4158110)           | 26 (xsd:int) | 9.09615 (xsd:decimal) |
| Breaking Bad (imdb_title:tt0903747)        | 62 (xsd:int) | 8.95968 (xsd:decimal) |
| The Sopranos (imdb_title:tt0141842)        | 23 (xsd:int) | 8.91304 (xsd:decimal) |
| Dark (imdb_title:tt5753856)                | 26 (xsd:int) | 8.88462 (xsd:decimal) |
| Daredevil (imdb_title:tt3322312)           | 28 (xsd:int) | 8.875 (xsd:decimal)   |
| Doctor Who (imdb_title:tt0436992)          | 21 (xsd:int) | 8.8 (xsd:decimal)     |
| Better Call Saul (imdb_title:tt3032476)    | 63 (xsd:int) | 8.79841 (xsd:decimal) |
| Game of Thrones (imdb_title:tt0944947)     | 73 (xsd:int) | 8.73699 (xsd:decimal) |

Answer:
The top 10 series by average episode rating (for series with at least 20 episodes with at least 10,000 votes each) are:
1. One Piece: Wan pîsu
2. Attack on Titan
3. Mr. Robot
4. Breaking Bad
5. The Sopranos
6. Dark
7. Daredevil
8. Doctor Who
9. Better Call Saul
10. Game of Thrones

The top 10 series by average episode rating (for series with at least 20 episodes with at least 10,000 votes each) are:
1. One Piece: Wan pîsu
2. Attack on Titan
3. Mr. Robot
4. Breaking Bad
5. The Sopranos
6. Dark
7. Daredevil
8. Doctor Who
9. Better Call Saul
10. Game of Thrones

SPARQL query over imdb:
PREFIX imdb: <https://www.imdb.com/>
SELECT ?series ( COUNT( ?episode ) AS ?numEpisodes ) ( AVG( ?rating ) AS ?avgRating ) WHERE {
  ?series imdb:type "tvSeries" .
  ?episode imdb:type "tvEpisode" .
  ?episode imdb:parentTitle ?series .
  ?episode imdb:averageRating ?rating .
  ?episode imdb:numVotes ?votes .
  FILTER ( ?votes >= 10000 )
}
GROUP BY ?series
HAVING ( COUNT( ?episode ) >= 20 )
ORDER BY DESC ( ?avgRating )
LIMIT 10

Using properties:
- average Rating (imdb:averageRating), also known as averageRating
- num Votes (imdb:numVotes), also known as numVotes
- parent Title (imdb:parentTitle), also known as parentTitle
- type (imdb:type)

Execution result:
Got 10 rows and 3 columns:
| series                                     | numEpisodes  | avgRating             |
| ------------------------------------------ | ------------ | --------------------- |
| One Piece: Wan pîsu (imdb_title:tt0388629) | 46 (xsd:int) | 9.47391 (xsd:decimal) |
| Attack on Titan (imdb_title:tt2560140)     | 91 (xsd:int) | 9.1033 (xsd:decimal)  |
| Mr. Robot (imdb_title:tt4158110)           | 26 (xsd:int) | 9.09615 (xsd:decimal) |
| Breaking Bad (imdb_title:tt0903747)        | 62 (xsd:int) | 8.95968 (xsd:decimal) |
| The Sopranos (imdb_title:tt0141842)        | 23 (xsd:int) | 8.91304 (xsd:decimal) |
| Dark (imdb_title:tt5753856)                | 26 (xsd:int) | 8.88462 (xsd:decimal) |
| Daredevil (imdb_title:tt3322312)           | 28 (xsd:int) | 8.875 (xsd:decimal)   |
| Doctor Who (imdb_title:tt0436992)          | 21 (xsd:int) | 8.8 (xsd:decimal)     |
| Better Call Saul (imdb_title:tt3032476)    | 63 (xsd:int) | 8.79841 (xsd:decimal) |
| Game of Thrones (imdb_title:tt0944947)     | 73 (xsd:int) | 8.73699 (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 10 series by average episode rating, for series with at least 20 episodes with min 10k votes each

[Assistant]
Let's break down the requirements:

1. We need to find TV series.
2. Each series must have at least 20 episodes.
3. Each episode must have at least 10,000 votes.
4. For each qualifying series, calculate the average rating of its qualifying episodes.
5. Return the top 10 series by this average.

Step-by-step plan:
- Identify the entity for "TV series".
- Find the property linking series to episodes.
- Find properties for episode rating and vote count.
- Filter episodes with at least 10,000 votes.
- Group episodes by series, count episodes per series, and filter for at least 20.
- Compute average rating per series.
- Order by average rating and limit to top 10.

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

Question: tv series

Top 10 entity alternatives:
1. TV Series (imdb_title:tt36848431)
2. TV Series (imdb_title:tt2011675)
3. TV Series (imdb_title:tt11177420)
4. Enforcer the Movie (imdb_title:tt5206646, matched via Enforcer TV Series), also known as Enforcer TV Series
5. HTOWN TV Series (imdb_title:tt8571338), also known as MUDDY WATERS (TV SERIES)
6. Preacher (TV Series) (imdb_title:tt5821682)
7. Pilot TV Series (imdb_title:tt1561886)
8. Nabhadas TV Series (imdb_title:tt33888561)
9. Nabhadas TV Series (imdb_title:tt33888559)
10. Nabhadas TV Series (imdb_title:tt33888554)

[Assistant]
Question: episode

Top 10 property alternatives:
1. episode Number (imdb:episodeNumber), also known as episodeNumber
2. id (imdb:id)
3. actor (imdb_principal:actor)
4. death Year (imdb:deathYear), also known as deathYear
5. archive footage (imdb_principal:archive_footage), also known as archive_footage
6. archive sound (imdb_principal:archive_sound, matched via archive_sound
), also known as archive_sound

7. self (imdb_principal:self)
8. primary Title (imdb:primaryTitle, matched via primaryTitle), also known as primaryTitle
9. known For Title (imdb:knownForTitle, matched via knownForTitle), also known as knownForTitle
10. genre (imdb:genre)

[Assistant]
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]
Question: rating

Top 10 property alternatives:
1. average Rating (imdb:averageRating, matched via averageRating), also known as averageRating
2. actor (imdb_principal:actor)
3. label (rdfs:label)
4. id (imdb:id)
5. type (imdb:type)
6. type (rdf:type)
7. self (imdb_principal:self)
8. genre (imdb:genre)
9. num Votes (imdb:numVotes), also known as numVotes
10. known For Title (imdb:knownForTitle, matched via knownForTitle), also known as knownForTitle

[Assistant]
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]
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: tv series

No entity items found

No other items found

Top 10 literal alternatives:
1. tvEpisode ("tvEpisode")
2. tvMiniSeries ("tvMiniSeries")
3. tvMovie ("tvMovie")
4. tvPilot ("tvPilot")
5. tvSeries ("tvSeries")
6. tvShort ("tvShort")
7. tvSpecial ("tvSpecial")

[Assistant]
list

Got more than 131,072 rows and 3 columns, showing the first 10 rows below:
| subject                                           | property         | object   |
| ------------------------------------------------- | ---------------- | -------- |
| NRK Nyheter (imdb_title:tt12164062)               | type (imdb:type) | tvSeries |
| Tatort (imdb_title:tt0806910)                     | type (imdb:type) | tvSeries |
| Days of Our Lives (imdb_title:tt0058796)          | type (imdb:type) | tvSeries |
| General Hospital (imdb_title:tt0056758)           | type (imdb:type) | tvSeries |
| Coronation Street (imdb_title:tt0053494)          | type (imdb:type) | tvSeries |
| The Young and the Restless (imdb_title:tt0069658) | type (imdb:type) | tvSeries |
| As the World Turns (imdb_title:tt0048845)         | type (imdb:type) | tvSeries |
| The NFL on CBS (imdb_title:tt0407423)             | type (imdb:type) | tvSeries |
| Jeopardy! (imdb_title:tt0159881)                  | type (imdb:type) | tvSeries |
| 60 Minutes (imdb_title:tt0123338)                 | type (imdb:type) | tvSeries |
| ...                                               | ...              | ...      |

[Assistant]
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. director (imdb:director)
10. num Votes (imdb:numVotes), also known as numVotes

No other items found

[Assistant]
imdb_title:tt0058796

Got 295 rows and 3 columns, showing the first 10 rows below:
| subject                                  | property                            | object                                |
| ---------------------------------------- | ----------------------------------- | ------------------------------------- |
| Days of Our Lives (imdb_title:tt0058796) | alt Label (skos:altLabel)           |  passano i giorni (lang:it)           |
| Days of Our Lives (imdb_title:tt0058796) | label (rdfs:label)                  | Days of Our Lives                     |
| Days of Our Lives (imdb_title:tt0058796) | type (rdf:type)                     | Movie (imdb:Movie)                    |
| Days of Our Lives (imdb_title:tt0058796) | actor (imdb_principal:actor)        | Peter Reckell (imdb_name:nm0005340)   |
| Days of Our Lives (imdb_title:tt0058796) | genre (imdb:genre)                  | Drama                                 |
| Days of Our Lives (imdb_title:tt0058796) | actress (imdb_principal:actress)    | Deidre Hall (imdb_name:nm0004984)     |
| Days of Our Lives (imdb_title:tt0058796) | writer (imdb:writer)                | William J. Bell (imdb_name:nm0068589) |
| Days of Our Lives (imdb_title:tt0058796) | primary Title (imdb:primaryTitle)   | Days of Our Lives                     |
| Days of Our Lives (imdb_title:tt0058796) | original Title (imdb:originalTitle) | Days of Our Lives                     |
| Days of Our Lives (imdb_title:tt0058796) | id (imdb:id)                        | tt0058796                             |
| ...                                      | ...                                 | ...                                   |

[Assistant]
Question: parent title

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

[Assistant]
Got 15,227 rows and 3 columns, showing the first 10 rows below:
| subject                                 | property                        | object                                   |
| --------------------------------------- | ------------------------------- | ---------------------------------------- |
| Episode #1.1 (imdb_title:tt0555329)     | parent Title (imdb:parentTitle) | Days of Our Lives (imdb_title:tt0058796) |
| Episode #1.10001 (imdb_title:tt0555330) | parent Title (imdb:parentTitle) | Days of Our Lives (imdb_title:tt0058796) |
| Episode #1.10002 (imdb_title:tt0555331) | parent Title (imdb:parentTitle) | Days of Our Lives (imdb_title:tt0058796) |
| Episode #1.10006 (imdb_title:tt0555332) | parent Title (imdb:parentTitle) | Days of Our Lives (imdb_title:tt0058796) |
| Episode #1.10007 (imdb_title:tt0555333) | parent Title (imdb:parentTitle) | Days of Our Lives (imdb_title:tt0058796) |
| Episode #1.10009 (imdb_title:tt0555334) | parent Title (imdb:parentTitle) | Days of Our Lives (imdb_title:tt0058796) |
| Episode #1.10010 (imdb_title:tt0555335) | parent Title (imdb:parentTitle) | Days of Our Lives (imdb_title:tt0058796) |
| Episode #1.10012 (imdb_title:tt0555336) | parent Title (imdb:parentTitle) | Days of Our Lives (imdb_title:tt0058796) |
| Episode #1.10013 (imdb_title:tt0555337) | parent Title (imdb:parentTitle) | Days of Our Lives (imdb_title:tt0058796) |
| Episode #1.10014 (imdb_title:tt0555338) | parent Title (imdb:parentTitle) | Days of Our Lives (imdb_title:tt0058796) |
| ...                                     | ...                             | ...                                      |

[Assistant]
imdb_title:tt0555329

Got 44 rows and 3 columns, showing the first 10 rows below:
| subject                             | property                            | object                                |
| ----------------------------------- | ----------------------------------- | ------------------------------------- |
| Episode #1.1 (imdb_title:tt0555329) | alt Label (skos:altLabel)           | Episode #1.1 (lang:mul)               |
| Episode #1.1 (imdb_title:tt0555329) | label (rdfs:label)                  | Episode #1.1                          |
| Episode #1.1 (imdb_title:tt0555329) | type (rdf:type)                     | Movie (imdb:Movie)                    |
| Episode #1.1 (imdb_title:tt0555329) | actor (imdb_principal:actor)        | Macdonald Carey (imdb_name:nm0136994) |
| Episode #1.1 (imdb_title:tt0555329) | genre (imdb:genre)                  | Drama                                 |
| Episode #1.1 (imdb_title:tt0555329) | actress (imdb_principal:actress)    | Frances Reid (imdb_name:nm0717265)    |
| Episode #1.1 (imdb_title:tt0555329) | writer (imdb:writer)                | Irna Phillips (imdb_name:nm0680460)   |
| Episode #1.1 (imdb_title:tt0555329) | primary Title (imdb:primaryTitle)   | Episode #1.1                          |
| Episode #1.1 (imdb_title:tt0555329) | original Title (imdb:originalTitle) | Episode #1.1                          |
| Episode #1.1 (imdb_title:tt0555329) | id (imdb:id)                        | tt0555329                             |
| ...                                 | ...                                 | ...                                   |

[Assistant] imdb_title:tt0555329

[Assistant] imdb_title:tt0555329

[Assistant]
We have confirmed the following:
- TV series are identified by imdb:type = "tvSeries".
- Episodes are linked to their series via imdb:parentTitle.
- Episodes have imdb:averageRating and imdb:numVotes.
- We can filter episodes by imdb:numVotes >= 10000.
- We can group episodes by their parent series, count episodes, and average their ratings.

Now, let's build the SPARQL query step by step:
- Select all episodes (?episode) with imdb:type "tvEpisode", imdb:numVotes >= 10000, and get their imdb:parentTitle (?series).
- For each series, count the number of such episodes (must be >= 20).
- For each series, calculate the average imdb:averageRating of its qualifying episodes.
- Only include series where imdb:type = "tvSeries".
- Order by average rating descending, limit 10.

Let's write the query.

execute

SELECT ?series (COUNT(?episode) AS ?numEpisodes) (AVG(?rating) AS ?avgRating) (SAMPLE(?seriesLabel) AS ?seriesLabel)
WHERE {
  ?series imdb:type "tvSeries" .
  ?episode imdb:type "tvEpisode" .
  ?episode imdb:parentTitle ?series .
  ?episode imdb:averageRating ?rating .
  ?episode imdb:numVotes ?votes .
  FILTER(?votes >= 10000)
  OPTIONAL { ?series rdfs:label ?seriesLabel }
}
GROUP BY ?series
HAVING(COUNT(?episode) >= 20)
ORDER BY DESC(?avgRating)
LIMIT 10

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

[Assistant]
SELECT ?series (COUNT(?episode) AS ?numEpisodes) (AVG(?rating) AS ?avgRating)
WHERE {
  ?series imdb:type "tvSeries" .
  ?episode imdb:type "tvEpisode" .
  ?episode imdb:parentTitle ?series .
  ?episode imdb:averageRating ?rating .
  ?episode imdb:numVotes ?votes .
  FILTER(?votes >= 10000)
}
GROUP BY ?series
HAVING(COUNT(?episode) >= 20)
ORDER BY DESC(?avgRating)
LIMIT 10

Got 10 rows and 3 columns:
| series                                     | numEpisodes  | avgRating             |
| ------------------------------------------ | ------------ | --------------------- |
| One Piece: Wan pîsu (imdb_title:tt0388629) | 46 (xsd:int) | 9.47391 (xsd:decimal) |
| Attack on Titan (imdb_title:tt2560140)     | 91 (xsd:int) | 9.1033 (xsd:decimal)  |
| Mr. Robot (imdb_title:tt4158110)           | 26 (xsd:int) | 9.09615 (xsd:decimal) |
| Breaking Bad (imdb_title:tt0903747)        | 62 (xsd:int) | 8.95968 (xsd:decimal) |
| The Sopranos (imdb_title:tt0141842)        | 23 (xsd:int) | 8.91304 (xsd:decimal) |
| Dark (imdb_title:tt5753856)                | 26 (xsd:int) | 8.88462 (xsd:decimal) |
| Daredevil (imdb_title:tt3322312)           | 28 (xsd:int) | 8.875 (xsd:decimal)   |
| Doctor Who (imdb_title:tt0436992)          | 21 (xsd:int) | 8.8 (xsd:decimal)     |
| Better Call Saul (imdb_title:tt3032476)    | 63 (xsd:int) | 8.79841 (xsd:decimal) |
| Game of Thrones (imdb_title:tt0944947)     | 73 (xsd:int) | 8.73699 (xsd:decimal) |

[Assistant]
answer

SELECT ?series (COUNT(?episode) AS ?numEpisodes) (AVG(?rating) AS ?avgRating)
WHERE {
  ?series imdb:type "tvSeries" .
  ?episode imdb:type "tvEpisode" .
  ?episode imdb:parentTitle ?series .
  ?episode imdb:averageRating ?rating .
  ?episode imdb:numVotes ?votes .
  FILTER(?votes >= 10000)
}
GROUP BY ?series
HAVING(COUNT(?episode) >= 20)
ORDER BY DESC(?avgRating)
LIMIT 10

Answer:
The top 10 series by average episode rating (for series with at least 20 episodes with at least 10,000 votes each) are:
1. One Piece: Wan pîsu
2. Attack on Titan
3. Mr. Robot
4. Breaking Bad
5. The Sopranos
6. Dark
7. Daredevil
8. Doctor Who
9. Better Call Saul
10. Game of Thrones

[feedback]
Feedback (status=done):
The SPARQL query correctly implements all requirements: it selects TV series, aggregates episode ratings only for episodes with at least 10,000 votes, ensures at least 20 such episodes per series, and returns the top 10 by average rating. The answer matches the query result and includes the required information. No further refinement is needed.


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 DISTINCT ?person ?personLabel ?actorTitle ?directorTitle WHERE {
  ?actorTitle imdb_principal:actor ?person .
  ?actorTitle imdb:numVotes ?votes1 .
  FILTER ( ?votes1 > 100000 ) ?directorTitle imdb_principal:director ?person .
  ?directorTitle imdb:numVotes ?votes2 .
  FILTER ( ?votes2 > 100000 ) FILTER ( ?actorTitle != ?directorTitle ) OPTIONAL {
    ?person rdfs:label ?personLabel
  }
}
LIMIT 100

Using properties:
- label (rdfs:label)
- num Votes (imdb:numVotes), also known as numVotes
- actor (imdb_principal:actor)
- director (imdb_principal:director)

Got 100 rows and 4 columns, showing the first 5 and last 5 rows below:
| person                                 | personLabel      | actorTitle                          | directorTitle                               |
| -------------------------------------- | ---------------- | ----------------------------------- | ------------------------------------------- |
| Alfred Hitchcock (imdb_name:nm0000033) | Alfred Hitchcock | Rope (imdb_title:tt0040746)         | Rebecca (imdb_title:tt0032976)              |
| Alfred Hitchcock (imdb_name:nm0000033) | Alfred Hitchcock | Rope (imdb_title:tt0040746)         | Notorious (imdb_title:tt0038787)            |
| Alfred Hitchcock (imdb_name:nm0000033) | Alfred Hitchcock | Rope (imdb_title:tt0040746)         | Strangers on a Train (imdb_title:tt0044079) |
| Alfred Hitchcock (imdb_name:nm0000033) | Alfred Hitchcock | Rope (imdb_title:tt0040746)         | Dial M for Murder (imdb_title:tt0046912)    |
| Alfred Hitchcock (imdb_name:nm0000033) | Alfred Hitchcock | Rope (imdb_title:tt0040746)         | Rear Window (imdb_title:tt0047396)          |
| ...                                    | ...              | ...                                 | ...                                         |
| George Clooney (imdb_name:nm0000123)   | George Clooney   | Three Kings (imdb_title:tt0120188)  | The Ides of March (imdb_title:tt1124035)    |
| George Clooney (imdb_name:nm0000123)   | George Clooney   | Three Kings (imdb_title:tt0120188)  | The Monuments Men (imdb_title:tt2177771)    |
| George Clooney (imdb_name:nm0000123)   | George Clooney   | Out of Sight (imdb_title:tt0120780) |  and Good Luck. (imdb_title:tt0433383)      |
| George Clooney (imdb_name:nm0000123)   | George Clooney   | Out of Sight (imdb_title:tt0120780) | The Ides of March (imdb_title:tt1124035)    |
| George Clooney (imdb_name:nm0000123)   | George Clooney   | Out of Sight (imdb_title:tt0120780) | The Monuments Men (imdb_title:tt2177771)    |

Answer: People who were a principal actor in one title and a principal director in a different title, both with over 100,000 votes, include Alfred Hitchcock and George Clooney. The query returns pairs of titles for each such person, showing the title they acted in and the title they directed.

People who were a principal actor in one title and a principal director in a different title, both with over 100,000 votes, include Alfred Hitchcock and George Clooney. The query returns pairs of titles for each such person, showing the title they acted in and the title they directed.

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 DISTINCT ?person ?personLabel ?actorTitle ?directorTitle WHERE {
  ?actorTitle imdb_principal:actor ?person .
  ?actorTitle imdb:numVotes ?votes1 .
  FILTER ( ?votes1 > 100000 ) ?directorTitle imdb_principal:director ?person .
  ?directorTitle imdb:numVotes ?votes2 .
  FILTER ( ?votes2 > 100000 ) FILTER ( ?actorTitle != ?directorTitle ) OPTIONAL {
    ?person rdfs:label ?personLabel
  }
}
LIMIT 100

Using properties:
- label (rdfs:label)
- num Votes (imdb:numVotes), also known as numVotes
- actor (imdb_principal:actor)
- director (imdb_principal:director)

Execution result:
Got 100 rows and 4 columns, showing the first 5 and last 5 rows below:
| person                                 | personLabel      | actorTitle                          | directorTitle                               |
| -------------------------------------- | ---------------- | ----------------------------------- | ------------------------------------------- |
| Alfred Hitchcock (imdb_name:nm0000033) | Alfred Hitchcock | Rope (imdb_title:tt0040746)         | Rebecca (imdb_title:tt0032976)              |
| Alfred Hitchcock (imdb_name:nm0000033) | Alfred Hitchcock | Rope (imdb_title:tt0040746)         | Notorious (imdb_title:tt0038787)            |
| Alfred Hitchcock (imdb_name:nm0000033) | Alfred Hitchcock | Rope (imdb_title:tt0040746)         | Strangers on a Train (imdb_title:tt0044079) |
| Alfred Hitchcock (imdb_name:nm0000033) | Alfred Hitchcock | Rope (imdb_title:tt0040746)         | Dial M for Murder (imdb_title:tt0046912)    |
| Alfred Hitchcock (imdb_name:nm0000033) | Alfred Hitchcock | Rope (imdb_title:tt0040746)         | Rear Window (imdb_title:tt0047396)          |
| ...                                    | ...              | ...                                 | ...                                         |
| George Clooney (imdb_name:nm0000123)   | George Clooney   | Three Kings (imdb_title:tt0120188)  | The Ides of March (imdb_title:tt1124035)    |
| George Clooney (imdb_name:nm0000123)   | George Clooney   | Three Kings (imdb_title:tt0120188)  | The Monuments Men (imdb_title:tt2177771)    |
| George Clooney (imdb_name:nm0000123)   | George Clooney   | Out of Sight (imdb_title:tt0120780) |  and Good Luck. (imdb_title:tt0433383)      |
| George Clooney (imdb_name:nm0000123)   | George Clooney   | Out of Sight (imdb_title:tt0120780) | The Ides of March (imdb_title:tt1124035)    |
| George Clooney (imdb_name:nm0000123)   | George Clooney   | Out of Sight (imdb_title:tt0120780) | The Monuments Men (imdb_title:tt2177771)    |

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

[Assistant]
Let's break down the question:

1. We are looking for people who were both a principal actor and a principal director, but in different titles.
2. Both titles (the one they acted in and the one they directed) must have over 100,000 votes.

Step 1: Identify entities and properties.
- Entities: People (actors/directors), Titles (movies/shows).
- Properties:
  - Principal actor: likely a property linking a person to a title as an actor.
  - Principal director: likely a property linking a person to a title as a director.
  - Number of votes: a property of a title.

Step 2: Search for relevant properties.
First, let's search for properties related to "principal actor", "principal director", and "number of votes".

Question: principal actor

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

[Assistant]
Question: principal 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. primary Title (imdb:primaryTitle), also known as primaryTitle
5. producer (imdb_principal:producer)
6. actor (imdb_principal:actor)
7. production designer (imdb_principal:production_designer), also known as production_designer
8. parent Title (imdb:parentTitle, matched via parentTitle), also known as parentTitle
9. cinematographer (imdb_principal:cinematographer)
10. id (imdb:id)

[Assistant]
Question: number of votes

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

[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: principal cast

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

[Assistant]
Question: principal crew

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

[Assistant]
From the property searches, it appears that the IMDb knowledge graph uses imdb_principal:actor and imdb_principal:director for principal actors and directors, respectively. The property imdb:numVotes gives the number of votes for a title.

To find people who were a principal actor in one title and a principal director in another title (both with over 100k votes), we need to:
- Find people who are linked as imdb_principal:actor to a title with imdb:numVotes > 100000.
- Find people who are linked as imdb_principal:director to a different title with imdb:numVotes > 100000.
- Ensure the titles are different.

Let's try to construct a SPARQL query for this. I'll start by checking how the actor and director relationships are structured in the data.

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]
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]
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]
Now that we have confirmed the structure:
- Titles are linked to actors and directors via imdb_principal:actor and imdb_principal:director.
- Titles have a number of votes via imdb:numVotes.

We need to:
- Find people (?person) who are an actor in one title (?title1) with >100k votes.
- Find the same person as a director in a different title (?title2) with >100k votes.
- Ensure ?title1 != ?title2.

Let's construct the SPARQL query accordingly.

execute

SELECT DISTINCT ?person ?personLabel ?actorTitle ?directorTitle WHERE {
  ?actorTitle imdb_principal:actor ?person .
  ?actorTitle imdb:numVotes ?votes1 .
  FILTER(?votes1 > 100000)
  ?directorTitle imdb_principal:director ?person .
  ?directorTitle imdb:numVotes ?votes2 .
  FILTER(?votes2 > 100000)
  FILTER(?actorTitle != ?directorTitle)
  OPTIONAL { ?person rdfs:label ?personLabel }
} LIMIT 100

Got 100 rows and 4 columns, showing the first 5 and last 5 rows below:
| person                                 | personLabel      | actorTitle                          | directorTitle                               |
| -------------------------------------- | ---------------- | ----------------------------------- | ------------------------------------------- |
| Alfred Hitchcock (imdb_name:nm0000033) | Alfred Hitchcock | Rope (imdb_title:tt0040746)         | Rebecca (imdb_title:tt0032976)              |
| Alfred Hitchcock (imdb_name:nm0000033) | Alfred Hitchcock | Rope (imdb_title:tt0040746)         | Notorious (imdb_title:tt0038787)            |
| Alfred Hitchcock (imdb_name:nm0000033) | Alfred Hitchcock | Rope (imdb_title:tt0040746)         | Strangers on a Train (imdb_title:tt0044079) |
| Alfred Hitchcock (imdb_name:nm0000033) | Alfred Hitchcock | Rope (imdb_title:tt0040746)         | Dial M for Murder (imdb_title:tt0046912)    |
| Alfred Hitchcock (imdb_name:nm0000033) | Alfred Hitchcock | Rope (imdb_title:tt0040746)         | Rear Window (imdb_title:tt0047396)          |
| ...                                    | ...              | ...                                 | ...                                         |
| George Clooney (imdb_name:nm0000123)   | George Clooney   | Three Kings (imdb_title:tt0120188)  | The Ides of March (imdb_title:tt1124035)    |
| George Clooney (imdb_name:nm0000123)   | George Clooney   | Three Kings (imdb_title:tt0120188)  | The Monuments Men (imdb_title:tt2177771)    |
| George Clooney (imdb_name:nm0000123)   | George Clooney   | Out of Sight (imdb_title:tt0120780) |  and Good Luck. (imdb_title:tt0433383)      |
| George Clooney (imdb_name:nm0000123)   | George Clooney   | Out of Sight (imdb_title:tt0120780) | The Ides of March (imdb_title:tt1124035)    |
| George Clooney (imdb_name:nm0000123)   | George Clooney   | Out of Sight (imdb_title:tt0120780) | The Monuments Men (imdb_title:tt2177771)    |

[Assistant]
answer

SELECT DISTINCT ?person ?personLabel ?actorTitle ?directorTitle WHERE {
  ?actorTitle imdb_principal:actor ?person .
  ?actorTitle imdb:numVotes ?votes1 .
  FILTER(?votes1 > 100000)
  ?directorTitle imdb_principal:director ?person .
  ?directorTitle imdb:numVotes ?votes2 .
  FILTER(?votes2 > 100000)
  FILTER(?actorTitle != ?directorTitle)
  OPTIONAL { ?person rdfs:label ?personLabel }
} LIMIT 100

Answer: People who were a principal actor in one title and a principal director in a different title, both with over 100,000 votes, include Alfred Hitchcock and George Clooney. The query returns pairs of titles for each such person, showing the title they acted in and the title they directed.

[feedback]
Feedback (status=done):
The query correctly identifies people who were principal actors in one title and principal directors in a different title, both with over 100,000 votes. It ensures that the acting and directing roles are in different titles and applies the vote threshold to both. The answer summarizes the results accurately and the query returns the expected data. No further refinement is needed.