PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX imdb: <https://www.imdb.com/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
SELECT ?title ?titleLabel ?director ?directorLabel ?startYear ?birthYear ( ( xsd:int ( ?startYear ) - xsd:int ( ?birthYear ) ) AS ?age ) WHERE {
  ?title rdf:type imdb:Movie ; imdb:director ?director ; imdb:startYear ?startYear ; imdb:numVotes ?numVotes .
  ?director imdb:birthYear ?birthYear .
  FILTER ( ?numVotes >= 100000 ) OPTIONAL {
    ?title rdfs:label ?titleLabel
  }
  OPTIONAL {
    ?director rdfs:label ?directorLabel
  }
}
ORDER BY DESC ( ?age )
LIMIT 100

Using entities:
Movie (imdb:Movie)

Using properties:
type (rdf:type)
label (rdfs:label)
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

Got 100 rows and 7 columns, showing the first 5 and last 5 rows below:
| title                                      | titleLabel                   | director                             | directorLabel  | startYear      | birthYear      | age          |
| ------------------------------------------ | ---------------------------- | ------------------------------------ | -------------- | -------------- | -------------- | ------------ |
| Juror #2 (imdb_title:tt27403986)           | Juror #2                     | Clint Eastwood (imdb_name:nm0000142) | Clint Eastwood | 2024 (xsd:int) | 1930 (xsd:int) | 94 (xsd:int) |
| Richard Jewell (imdb_title:tt3513548)      | Richard Jewell               | Clint Eastwood (imdb_name:nm0000142) | Clint Eastwood | 2019 (xsd:int) | 1930 (xsd:int) | 89 (xsd:int) |
| Richard Jewell (imdb_title:tt3513548)      | The Ballad of Richard Jewell | Clint Eastwood (imdb_name:nm0000142) | Clint Eastwood | 2019 (xsd:int) | 1930 (xsd:int) | 89 (xsd:int) |
| The Mule (imdb_title:tt7959026)            | The Mule                     | Clint Eastwood (imdb_name:nm0000142) | Clint Eastwood | 2018 (xsd:int) | 1930 (xsd:int) | 88 (xsd:int) |
| Gladiator II (imdb_title:tt9218128)        | Gladiator II                 | Ridley Scott (imdb_name:nm0000631)   | Ridley Scott   | 2024 (xsd:int) | 1937 (xsd:int) | 87 (xsd:int) |
| ...                                        | ...                          | ...                                  | ...            | ...            | ...            | ...          |
| Mad Max: Fury Road (imdb_title:tt1392190)  | Mad Max: Fury Road           | George Miller (imdb_name:nm0004306)  | George Miller  | 2015 (xsd:int) | 1945 (xsd:int) | 70 (xsd:int) |
| Legends of Tomorrow (imdb_title:tt4532368) | Legends of Tomorrow          | Joe Dante (imdb_name:nm0001102)      | Joe Dante      | 2016 (xsd:int) | 1946 (xsd:int) | 70 (xsd:int) |
| Match Point (imdb_title:tt0416320)         | Match Point                  | Woody Allen (imdb_name:nm0000095)    | Woody Allen    | 2005 (xsd:int) | 1935 (xsd:int) | 70 (xsd:int) |
| American Gangster (imdb_title:tt0765429)   | American Gangster            | Ridley Scott (imdb_name:nm0000631)   | Ridley Scott   | 2007 (xsd:int) | 1937 (xsd:int) | 70 (xsd:int) |
| Batoru rowaiaru (imdb_title:tt0266308)     | Batoru rowaiaru              | Kinji Fukasaku (imdb_name:nm0297935) | Kinji Fukasaku | 2000 (xsd:int) | 1930 (xsd:int) | 70 (xsd:int) |

Answer:
Approach and reasoning:
- Goal: list the 100 oldest lead directors (age at movie release) for movies (not series) that have >= 100,000 votes.
- Important decisions: the IMDB KG contains two director properties: imdb:director and imdb_principal:director. imdb:director is used on titles to indicate the main/lead director in many cases; imdb_principal:director lists director principals (often multiple). To identify the lead director consistently, I used imdb:director.
- For each movie I require: rdf:type imdb:Movie, imdb:director present, imdb:startYear (movie release year), imdb:numVotes >= 100000, and director's imdb:birthYear to compute age.
- Age is computed as (xsd:int(startYear) - xsd:int(birthYear)) inside SPARQL; results are ordered by age descending and limited to 100.

Notes and caveats:
- Movies without an imdb:director value (but that do have imdb_principal:director) are not included because there is no reliable "lead" marker in the principal triples. If you want to include those and treat any imdb_principal:director as lead, we can adapt the query but it may include co-directors without a clear lead ordering.
- The query returns the IRIs of the titles (?title) and directors (?director) as required, plus optional labels and the computed age.

I executed the query on the IMDB KG and it returned 100 rows. Examples from the results include (IRI and label shown):
- imdb_title:tt27403986 — "Juror #2" directed by imdb_name:nm0000142 (Clint Eastwood) — age 94
- imdb_title:tt3513548 — "Richard Jewell" / "The Ballad of Richard Jewell" directed by imdb_name:nm0000142 (Clint Eastwood) — age 89
- imdb_title:tt7959026 — "The Mule" directed by imdb_name:nm0000142 (Clint Eastwood) — age 88

You can run the SPARQL query above against the IMDB endpoint to retrieve the full 100 results (it returns IRIs as well as labels and ages).

100 oldest lead directors at the time of a movie (not series) with >= 100k votes

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

You 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.

You have access to the following knowledge graphs:
imdb at https://qlever.cs.uni-freiburg.de/api/imdb without notes

You are provided with the following notes across all knowledge graphs:
No notes available

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

You should follow these rules:
- Explain your thought process before and after each step and function call.
- Do not just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
- Do not use "SERVICE wikibase:label { bd:serviceParam wikibase:language ..." in SPARQL queries. It is not SPARQL standard and unsupported by the used QLever SPARQL endpoints. Use rdfs:label or similar properties to get labels instead.
- 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] I'll first explore the IMDB KG to find the relevant classes and properties for titles (movies), votes, directors, director positions (lead), and release date or year/birth dates so I can compute director age at movie release. I'll run targeted searches for likely entities and properties (e.g., title types, vote counts, director roles, principal entries, birth date). I'll explain my reasoning and show each query's purpose and results as I go.

[Tool]
Top 10 entity alternatives:
1. Movie (imdb:Movie)
2.  Movie (imdb_title:tt1190894), also known as Road, Untitled Road Movie
3. Movie (imdb_title:tt1814259)
4. Movie (imdb_title:tt0649253)
5.  Amood (imdb_title:tt2953736, matched via  Movie), also known as  Dancer,  Movie, Pole
6.  Awesomest (imdb_title:tt6755970, matched via  Movie), also known as  Movie, CD's the Coolest
7. Movie (imdb_title:tt1853619)
8. Movie (imdb_title:tt12390722)
9. Movie (imdb_title:tt34754666)
10. Movie (imdb_title:tt1040023)

[Assistant] I'll search for properties related to votes and director/principal roles.

[Tool]
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] Search for director-related properties.

[Tool]
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] We need director's birth date and title's release date to compute age. Search for birthDate/birthYear.

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

[Assistant] Search for title release date/year.

[Tool]
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)

[Tool]
Got 65,536 rows and 3 columns, showing the first 10 rows below:
| subject                                                        | property                 | object                                 |
| -------------------------------------------------------------- | ------------------------ | -------------------------------------- |
| Blue Peter (imdb_title:tt0051257)                              | director (imdb:director) | Joy Whitby (imdb_name:nm0924345)       |
| Coronation Street (imdb_title:tt0053494)                       | director (imdb:director) | Michael Apted (imdb_name:nm0000776)    |
| 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)       |
| The First Hundred Years (imdb_title:tt0042104)                 | director (imdb:director) | Gloria Monty (imdb_name:nm0600353)     |
| The Tonight Show Starring Johnny Carson (imdb_title:tt0055708) | director (imdb:director) | Jerry Lewis (imdb_name:nm0001471)      |
| Search for Tomorrow (imdb_title:tt0043229)                     | director (imdb:director) | Peter L. Andrews (imdb_name:nm0028814) |
| The Philco Television Playhouse (imdb_title:tt0040049)         | director (imdb:director) | Ira Skutch (imdb_name:nm0804973)       |
| Gallopin' Gals (imdb_title:tt0032504)                          | director (imdb:director) | Joseph Barbera (imdb_name:nm0053484)   |
| ...                                                            | ...                      | ...                                    |

[Assistant] I'll search for a well-known movie to inspect how director principals and ordering are modeled (to identify the "lead" director). I'll look up "The Godfather".

[Tool]
Top 10 entity alternatives:
1. The Godfather Part III (imdb_title:tt0099674, matched via The Godfather), also known as  Part III, El Padrino. Parte III, Godfather: The Tragedy of Michael Corleone, Le parrain III, Mario Puzo's The Godfather: Part III, etc.
2. The Godfather (imdb_title:tt0068646), also known as El padrino, Kum, Le parrain, Mario Puzo's The Godfather
3. Al Arrab: Nadi Al Sharq (imdb_title:tt5817932, matched via The Godfather), also known as The Godfather, The Godfather: The Orient Club
4. Pedarkhandeh (imdb_title:tt26629467, matched via The Godfather), also known as The Godfather
5. The Godfather (imdb_title:tt0442674), also known as The Godfather: The Game, V Godfather
6. The Godfather (imdb_title:tt0691993)
7. The Godfather (imdb_title:tt4445128)
8. The Godfather (imdb_title:tt1540460)
9. The Godfather (imdb_title:tt0642397)
10. The Godfather (imdb_title:tt0644021)

[Tool]
Got 76 rows and 3 columns, showing the first 10 rows below:
| subject                              | property                            | object                                     |
| ------------------------------------ | ----------------------------------- | ------------------------------------------ |
| The Godfather (imdb_title:tt0068646) | alt Label (skos:altLabel)           | A keresztapa (hu)                          |
| The Godfather (imdb_title:tt0068646) | label (rdfs:label)                  | The Godfather                              |
| The Godfather (imdb_title:tt0068646) | type (rdf:type)                     | Movie (imdb:Movie)                         |
| The Godfather (imdb_title:tt0068646) | actor (imdb_principal:actor)        | James Caan (imdb_name:nm0001001)           |
| The Godfather (imdb_title:tt0068646) | genre (imdb:genre)                  | Crime                                      |
| The Godfather (imdb_title:tt0068646) | actress (imdb_principal:actress)    | Diane Keaton (imdb_name:nm0000473)         |
| The Godfather (imdb_title:tt0068646) | writer (imdb:writer)                | Francis Ford Coppola (imdb_name:nm0000338) |
| The Godfather (imdb_title:tt0068646) | primary Title (imdb:primaryTitle)   | The Godfather                              |
| The Godfather (imdb_title:tt0068646) | original Title (imdb:originalTitle) | The Godfather                              |
| The Godfather (imdb_title:tt0068646) | id (imdb:id)                        | tt0068646                                  |
| ...                                  | ...                                 | ...                                        |

[Tool]
Top 10 property alternatives:
1. num Votes (imdb:numVotes, matched via numVotes), also known as numVotes
2. id (imdb:id)
3. self (imdb_principal:self)
4. end Year (imdb:endYear, matched via endYear), also known as endYear
5. label (rdfs:label)
6. death Year (imdb:deathYear, matched via deathYear), also known as deathYear
7. type (imdb:type)
8. type (rdf:type)
9. average Rating (imdb:averageRating, matched via averageRating), also known as averageRating
10. actor (imdb_principal:actor)

[Assistant] Inspect properties of the title to find release date or year property.

[Tool]
Got 1 row and 3 columns:
| subject                              | property                    | object         |
| ------------------------------------ | --------------------------- | -------------- |
| The Godfather (imdb_title:tt0068646) | start Year (imdb:startYear) | 1972 (xsd:int) |

[Assistant] Director position: need to identify "lead director". The IMDB ontology likely has principal roles with ordering but maybe director property multiple. There's imdb_principal:director which might include role order info. Let's inspect an example principal entry.

[Tool] Got no rows and 3 columns

[Assistant] Search for principals involving director for The Godfather.

[Tool]
Top 10 entity alternatives:
1. Al Arrab: Nadi Al Sharq (imdb_title:tt5817932, matched via The Godfather: The Orient Club), also known as The Godfather, The Godfather: The Orient Club
2. Zenra kantoku (imdb_title:tt10477528, matched via The Naked Director), also known as The Naked Director
3. The Godfather Part III (imdb_title:tt0099674), also known as  Part III, El Padrino. Parte III, Godfather: The Tragedy of Michael Corleone, Le parrain III, Mario Puzo's The Godfather: Part III, etc.
4. The Godfather Part II (imdb_title:tt0071562), also known as  II deo,  II. del, Boter, El Padrino: Parte II, Kum, etc.
5. Avenging Disco Godfather (imdb_title:tt0074412, matched via The Avenging Disco Godfather), also known as Disco Godfather, Disco Music Godfather, The Avenging Disco Godfather, The Disco Godfather, The Disco Music Godfather
6. The Godfather (imdb_title:tt0068646), also known as El padrino, Kum, Le parrain, Mario Puzo's The Godfather
7. Kei zik (imdb_title:tt0098019, matched via Miracles: The Canton Godfather), also known as Black Dragon, Čuda, Miracles, Miracles: The Canton Godfather, The Canton Godfather
8. The Godfather of Fentanyl (imdb_title:tt33254831)
9. Bonanno: A Godfather's Story (imdb_title:tt0179804, matched via Bonanno: The Youngest Godfather), also known as Bonanno: The Youngest Godfather, Family: The Life and Times of Joseph Bonanno, The Family, The Youngest Godfather
10. Pedarkhandeh (imdb_title:tt26629467, matched via The Godfather), also known as The Godfather

[Assistant] Let's inspect director-related triples for The Godfather specifically.

[Tool]
Got 1 row and 3 columns:
| subject                              | property                 | object                                     |
| ------------------------------------ | ------------------------ | ------------------------------------------ |
| The Godfather (imdb_title:tt0068646) | director (imdb:director) | Francis Ford Coppola (imdb_name:nm0000338) |

[Assistant] I'll search for the property imdb_principal:director usage to see if it contains ordering or role info that helps identify a "lead" director. I'll list triples with that property.

[Tool]
Got 65,536 rows and 3 columns, showing the first 10 rows below:
| subject                                        | property                           | object                                   |
| ---------------------------------------------- | ---------------------------------- | ---------------------------------------- |
| Gallopin' Gals (imdb_title:tt0032504)          | director (imdb_principal:director) | Joseph Barbera (imdb_name:nm0053484)     |
| Blue Monday (imdb_title:tt0029928)             | director (imdb_principal:director) | William Hanna (imdb_name:nm0360253)      |
| The Bells of St. Mary's (imdb_title:tt0052616) | director (imdb_principal:director) | Tom Donovan (imdb_name:nm0233071)        |
| Los días duros (imdb_title:tt0065679)          | director (imdb_principal:director) | Julio César Mármol (imdb_name:nm0549373) |
| Cold Turkey (imdb_title:tt0066927)             | director (imdb_principal:director) | Norman Lear (imdb_name:nm0005131)        |
| Two Thousand Weeks (imdb_title:tt0063971)      | director (imdb_principal:director) | Tim Burstall (imdb_name:nm0123364)       |
| Uncle Tom's Cabin (imdb_title:tt0000472)       | director (imdb_principal:director) | Siegmund Lubin (imdb_name:nm0002615)     |
| El quelite (imdb_title:tt0064864)              | director (imdb_principal:director) | Jorge Fons (imdb_name:nm0284639)         |
| Time Piece (imdb_title:tt0059807)              | director (imdb_principal:director) | Jim Henson (imdb_name:nm0001345)         |
| Saiyûki (imdb_title:tt0054265)                 | director (imdb_principal:director) | Osamu Tezuka (imdb_name:nm0856804)       |
| ...                                            | ...                                | ...                                      |

[Assistant] It seems imdb:director and imdb_principal:director both exist. But how to identify "lead director"? Often for multiple directors, there's an ordering property for principals. Let's inspect the principal node for a title to see if there's an intermediate principal resource. For a movie with multiple directors, find a title with more than one director. I'll search for titles having multiple imdb:director values.

[Tool]
Got 10 rows and 2 columns:
| title                                                              | cnt         |
| ------------------------------------------------------------------ | ----------- |
| Corbett and Courtney Before the Kinetograph (imdb_title:tt0000007) | 2 (xsd:int) |
| L'arrivée d'un train à La Ciotat (imdb_title:tt0000012)            | 2 (xsd:int) |
| Italienischer Bauerntanz (imdb_title:tt0000017)                    | 2 (xsd:int) |
| Rough Sea at Dover (imdb_title:tt0000030)                          | 2 (xsd:int) |
| Départ de Jérusalem en chemin de fer (imdb_title:tt0000089)        | 2 (xsd:int) |
| Melbourne (imdb_title:tt0000093)                                   | 2 (xsd:int) |
| King John (imdb_title:tt0000247)                                   | 3 (xsd:int) |
| The Clown and the Alchemist (imdb_title:tt0000287)                 | 2 (xsd:int) |
| Soldiers of the Cross (imdb_title:tt0000335)                       | 2 (xsd:int) |
| La belle au bois dormant (imdb_title:tt0000380)                    | 2 (xsd:int) |

[Assistant] Let's inspect one such title to see representation. I'll inspect tt0000007.

[Tool]
Got 34 rows and 3 columns, showing the first 10 rows below:
| subject                                                            | property                            | object                                      |
| ------------------------------------------------------------------ | ----------------------------------- | ------------------------------------------- |
| Corbett and Courtney Before the Kinetograph (imdb_title:tt0000007) | alt Label (skos:altLabel)           | A Corbett-Courtney bokszmeccs (hu)          |
| Corbett and Courtney Before the Kinetograph (imdb_title:tt0000007) | label (rdfs:label)                  | Corbett and Courtney Before the Kinetograph |
| Corbett and Courtney Before the Kinetograph (imdb_title:tt0000007) | type (rdf:type)                     | Movie (imdb:Movie)                          |
| Corbett and Courtney Before the Kinetograph (imdb_title:tt0000007) | actor (imdb_principal:actor)        | James J. Corbett (imdb_name:nm0179163)      |
| Corbett and Courtney Before the Kinetograph (imdb_title:tt0000007) | genre (imdb:genre)                  | Short                                       |
| Corbett and Courtney Before the Kinetograph (imdb_title:tt0000007) | primary Title (imdb:primaryTitle)   | Corbett and Courtney Before the Kinetograph |
| Corbett and Courtney Before the Kinetograph (imdb_title:tt0000007) | original Title (imdb:originalTitle) | Corbett and Courtney Before the Kinetograph |
| Corbett and Courtney Before the Kinetograph (imdb_title:tt0000007) | id (imdb:id)                        | tt0000007                                   |
| Corbett and Courtney Before the Kinetograph (imdb_title:tt0000007) | is Adult (imdb:isAdult)             | false (xsd:boolean)                         |
| Corbett and Courtney Before the Kinetograph (imdb_title:tt0000007) | type (imdb:type)                    | short                                       |
| ...                                                                | ...                                 | ...                                         |

[Assistant] It shows imdb_principal actor but director may be under imdb_principal:director as well. List director properties.

[Tool]
Got 2 rows and 3 columns:
| subject                                                            | property                           | object                                     |
| ------------------------------------------------------------------ | ---------------------------------- | ------------------------------------------ |
| Corbett and Courtney Before the Kinetograph (imdb_title:tt0000007) | director (imdb_principal:director) | William K.L. Dickson (imdb_name:nm0005690) |
| Corbett and Courtney Before the Kinetograph (imdb_title:tt0000007) | director (imdb_principal:director) | William Heise (imdb_name:nm0374658)        |

[Assistant] No explicit ordering info here. The user asked "100 oldest lead directors at the time of a movie (not series) with >= 100k votes". That implies for each movie with numVotes >= 100000, find the lead director (if multiple directors, choose the lead). But dataset doesn't include "lead" marker. How to detect lead? Perhaps imdb_principal entries have ordering via imdb_principal:self or principal entries? Let's inspect the director person nodes for properties linking back. Look at director principal entity triple patterns? Alternatively, maybe use imdb:director (not principal) which for The Godfather had the single director. Maybe imdb:director indicates the primary director (lead), while imdb_principal:director lists all principals including secondary directors. So for titles with imdb:director property use that as the lead. For titles where imdb:director missing but imdb_principal:director present, maybe treat first? But we can't get "first" ordering. Let's check how many titles have imdb:director vs imdb_principal:director.

[Tool]
Got 1 row and 1 column:
| nLead             |
| ----------------- |
| 6678646 (xsd:int) |

[Tool]
Got 1 row and 1 column:
| nPrincipal        |
| ----------------- |
| 6383781 (xsd:int) |

[Assistant] Now I'll run a SPARQL query that selects movies (rdf:type imdb:Movie) with a lead director (imdb:director), at least 100,000 votes (imdb:numVotes), and with both the movie's startYear and the director's birthYear present. The query computes age = startYear - birthYear, orders by age descending, and returns the top 100. I'll execute the query to verify results.

[Tool]
Got 100 rows and 7 columns, showing the first 5 and last 5 rows below:
| title                                      | titleLabel                   | director                             | directorLabel  | startYear      | birthYear      | age          |
| ------------------------------------------ | ---------------------------- | ------------------------------------ | -------------- | -------------- | -------------- | ------------ |
| Juror #2 (imdb_title:tt27403986)           | Juror #2                     | Clint Eastwood (imdb_name:nm0000142) | Clint Eastwood | 2024 (xsd:int) | 1930 (xsd:int) | 94 (xsd:int) |
| Richard Jewell (imdb_title:tt3513548)      | Richard Jewell               | Clint Eastwood (imdb_name:nm0000142) | Clint Eastwood | 2019 (xsd:int) | 1930 (xsd:int) | 89 (xsd:int) |
| Richard Jewell (imdb_title:tt3513548)      | The Ballad of Richard Jewell | Clint Eastwood (imdb_name:nm0000142) | Clint Eastwood | 2019 (xsd:int) | 1930 (xsd:int) | 89 (xsd:int) |
| The Mule (imdb_title:tt7959026)            | The Mule                     | Clint Eastwood (imdb_name:nm0000142) | Clint Eastwood | 2018 (xsd:int) | 1930 (xsd:int) | 88 (xsd:int) |
| Gladiator II (imdb_title:tt9218128)        | Gladiator II                 | Ridley Scott (imdb_name:nm0000631)   | Ridley Scott   | 2024 (xsd:int) | 1937 (xsd:int) | 87 (xsd:int) |
| ...                                        | ...                          | ...                                  | ...            | ...            | ...            | ...          |
| Mad Max: Fury Road (imdb_title:tt1392190)  | Mad Max: Fury Road           | George Miller (imdb_name:nm0004306)  | George Miller  | 2015 (xsd:int) | 1945 (xsd:int) | 70 (xsd:int) |
| Legends of Tomorrow (imdb_title:tt4532368) | Legends of Tomorrow          | Joe Dante (imdb_name:nm0001102)      | Joe Dante      | 2016 (xsd:int) | 1946 (xsd:int) | 70 (xsd:int) |
| Match Point (imdb_title:tt0416320)         | Match Point                  | Woody Allen (imdb_name:nm0000095)    | Woody Allen    | 2005 (xsd:int) | 1935 (xsd:int) | 70 (xsd:int) |
| American Gangster (imdb_title:tt0765429)   | American Gangster            | Ridley Scott (imdb_name:nm0000631)   | Ridley Scott   | 2007 (xsd:int) | 1937 (xsd:int) | 70 (xsd:int) |
| Batoru rowaiaru (imdb_title:tt0266308)     | Batoru rowaiaru              | Kinji Fukasaku (imdb_name:nm0297935) | Kinji Fukasaku | 2000 (xsd:int) | 1930 (xsd:int) | 70 (xsd:int) |

[Tool] Stopping


PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX imdb: <https://www.imdb.com/>
SELECT ?person ( SAMPLE( ?personLabel ) AS ?personLabelSample ) ( COUNT( DISTINCT ?title ) AS ?count ) WHERE {
  ?person imdb:knownForTitle ?title .
  ?title imdb:numVotes ?numVotes .
  FILTER ( xsd:integer ( ?numVotes ) > 1000000 ) OPTIONAL {
    ?person rdfs:label ?personLabel
  }
}
GROUP BY ?person
HAVING ( COUNT( DISTINCT ?title ) >= 3 )
ORDER BY DESC ( ?count )
LIMIT 200

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

Got 200 rows and 3 columns, showing the first 5 and last 5 rows below:
| person                                 | personLabelSample | count       |
| -------------------------------------- | ----------------- | ----------- |
| Aaron Caskey (imdb_name:nm1401769)     | Aaron Caskey      | 4 (xsd:int) |
| Greg Allison (imdb_name:nm1405060)     | Greg Allison      | 4 (xsd:int) |
| Pete Bebb (imdb_name:nm0064825)        | Pete Bebb         | 4 (xsd:int) |
| Nathan Alexander (imdb_name:nm3488740) | Nathan Alexander  | 4 (xsd:int) |
| Paul J. Franklin (imdb_name:nm0291518) | Paul J. Franklin  | 4 (xsd:int) |
| ...                                    | ...               | ...         |
| Sidney Kombo (imdb_name:nm1894691)     | Sidney Kombo      | 3 (xsd:int) |
| Stephen Crene (imdb_name:nm1937310)    | Stephen Crene     | 3 (xsd:int) |
| Justo Dieguez (imdb_name:nm1954113)    | Justo Dieguez     | 3 (xsd:int) |
| Jordan Goldberg (imdb_name:nm2044373)  | Jordan Goldberg   | 3 (xsd:int) |
| Chris Cloud (imdb_name:nm2107747)      | Chris Cloud       | 3 (xsd:int) |

Answer:
I ran the query that returns person IRIs (?person) who are known for at least 3 distinct titles that each have more than 1,000,000 votes (imdb:numVotes > 1,000,000). The query groups by person and counts distinct qualifying titles, keeping only those with count >= 3. I executed this query to verify results; it returned 200 rows (limited to 200). Sample results from the executed query (first and last rows shown by the execution):

Top results (highest counts):
- imdb_name:nm1401769 — Aaron Caskey — count 4
- imdb_name:nm1405060 — Greg Allison — count 4
- imdb_name:nm0064825 — Pete Bebb — count 4
- imdb_name:nm3488740 — Nathan Alexander — count 4
- imdb_name:nm0291518 — Paul J. Franklin — count 4

Some later rows (counts = 3):
- imdb_name:nm1894691 — Sidney Kombo — count 3
- imdb_name:nm1937310 — Stephen Crene — count 3
- imdb_name:nm1954113 — Justo Dieguez — count 3
- imdb_name:nm2044373 — Jordan Goldberg — count 3
- imdb_name:nm2107747 — Chris Cloud — count 3

You can run the provided SPARQL query (copied above) against the IMDb endpoint to retrieve the full list (up to the LIMIT). If you want the complete list without a limit or sorted differently, tell me how you'd like it adjusted.

People who are known for at least 3 titles with more than 1 M votes

[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.

You have access to the following knowledge graphs:
imdb at https://qlever.cs.uni-freiburg.de/api/imdb without notes

You are provided with the following notes across all knowledge graphs:
No notes available

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

You should follow these rules:
- Explain your thought process before and after each step and function call.
- Do not just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
- Do not use "SERVICE wikibase:label { bd:serviceParam wikibase:language ..." in SPARQL queries. It is not SPARQL standard and unsupported by the used QLever SPARQL endpoints. Use rdfs:label or similar properties to get labels instead.
- 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] I'll start by searching the IMDb KG for properties related to "known for" or "known" on person entities, and also look for how votes are represented on title entities (to filter titles with more than 1,000,000 votes). I'll run targeted searches for properties and inspect a sample person to confirm property names. I'll explain my thought process before and after each call. I'll first search for properties that relate to "known for".

[Tool]
Top 10 property alternatives:
1. known For Title (imdb:knownForTitle, matched via knownForTitle), also known as knownForTitle
2. label (rdfs:label)
3. type (rdf:type)
4. profession (imdb:profession)
5. death Year (imdb:deathYear, matched via deathYear), also known as deathYear
6. birth Year (imdb:birthYear, matched via birthYear), also known as birthYear

No other items found

[Assistant] Next I'll search properties for a title entity to find how votes or vote counts are stored (e.g., "votes", "numVotes", "voteCount"). I'll query properties of a sample title like imdb_title:tt0000001. I'll explain results after.

[Tool]
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. type (imdb:type)
6. type (rdf:type)
7. genre (imdb:genre)
8. average Rating (imdb:averageRating, matched via averageRating), also known as averageRating
9. primary Title (imdb:primaryTitle, matched via primaryTitle), also known as primaryTitle
10. alt Label (skos:altLabel, matched via altLabel), also known as altLabel

No other items found

[Assistant] I'll construct a SPARQL query that finds person entities (?person) with imdb:knownForTitle linking to titles (?title) whose imdb:numVotes is greater than 1,000,000, then count distinct such titles per person and retain persons with count >= 3. I'll include an optional rdfs:label for readability and order by the count descending. I'll execute the query to verify results. Now I'll run that query.

[Tool]
Got 200 rows and 3 columns, showing the first 5 and last 5 rows below:
| person                                 | personLabelSample | count       |
| -------------------------------------- | ----------------- | ----------- |
| Aaron Caskey (imdb_name:nm1401769)     | Aaron Caskey      | 4 (xsd:int) |
| Greg Allison (imdb_name:nm1405060)     | Greg Allison      | 4 (xsd:int) |
| Pete Bebb (imdb_name:nm0064825)        | Pete Bebb         | 4 (xsd:int) |
| Nathan Alexander (imdb_name:nm3488740) | Nathan Alexander  | 4 (xsd:int) |
| Paul J. Franklin (imdb_name:nm0291518) | Paul J. Franklin  | 4 (xsd:int) |
| ...                                    | ...               | ...         |
| Sidney Kombo (imdb_name:nm1894691)     | Sidney Kombo      | 3 (xsd:int) |
| Stephen Crene (imdb_name:nm1937310)    | Stephen Crene     | 3 (xsd:int) |
| Justo Dieguez (imdb_name:nm1954113)    | Justo Dieguez     | 3 (xsd:int) |
| Jordan Goldberg (imdb_name:nm2044373)  | Jordan Goldberg   | 3 (xsd:int) |
| Chris Cloud (imdb_name:nm2107747)      | Chris Cloud       | 3 (xsd:int) |

[Assistant]
Planned final steps and explanation before finalizing:
- I will present the SPARQL query that finds person IRIs (?person) who have at least 3 distinct imdb:knownForTitle ?title where the title's imdb:numVotes > 1,000,000.
- I already executed this query to verify it returns results. It returned 200 rows (I limited to 200) and I inspected the top and bottom rows to ensure the query behaves as intended.
- I'll now provide the final query and a concise summary of the results (including a few sample persons from the executed query).

Now I will provide the final SPARQL query and the answer summary.

[Tool] Stopping


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX imdb_title: <https://www.imdb.com/title/>
PREFIX imdb: <https://www.imdb.com/>
SELECT ?episode ?episodeLabel ?season ?epnum ?rating WHERE {
  ?episode imdb:parentTitle imdb_title:tt0903747 .
  ?episode imdb:seasonNumber ?season .
  ?episode imdb:episodeNumber ?epnum .
  OPTIONAL {
    ?episode imdb:averageRating ?rating
  }
  OPTIONAL {
    ?episode rdfs:label ?episodeLabel
  }
  {
    SELECT ( MAX( ?s ) AS ?maxSeason ) WHERE {
      ?e imdb:parentTitle imdb_title:tt0903747 .
      ?e imdb:seasonNumber ?s .
    }
  }
  FILTER ( ?season = ?maxSeason )
}
ORDER BY DESC ( ?rating ) ?epnum
LIMIT 3

Using entities:
Breaking Bad (imdb_title:tt0903747)

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

Got 3 rows and 5 columns:
| episode                            | episodeLabel | season      | epnum        | rating            |
| ---------------------------------- | ------------ | ----------- | ------------ | ----------------- |
| Ozymandias (imdb_title:tt2301451)  | Ozymandias   | 5 (xsd:int) | 14 (xsd:int) | 10 (xsd:int)      |
| Felina (imdb_title:tt2301455)      | Felina       | 5 (xsd:int) | 16 (xsd:int) | 9.9 (xsd:decimal) |
| To'hajiilee (imdb_title:tt2301449) | To'hajiilee  | 5 (xsd:int) | 13 (xsd:int) | 9.8 (xsd:decimal) |

Answer:
Top 3 episodes of the last Breaking Bad season (season 5), ordered by IMDb average rating:
1. Ozymandias (imdb_title:tt2301451) — season 5, episode 14 — rating 10
2. Felina (imdb_title:tt2301455) — season 5, episode 16 — rating 9.9
3. To'hajiilee (imdb_title:tt2301449) — season 5, episode 13 — rating 9.8

Top 3 episodes of the last Breaking Bad season

[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.

You have access to the following knowledge graphs:
imdb at https://qlever.cs.uni-freiburg.de/api/imdb without notes

You are provided with the following notes across all knowledge graphs:
No notes available

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

You should follow these rules:
- Explain your thought process before and after each step and function call.
- Do not just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
- Do not use "SERVICE wikibase:label { bd:serviceParam wikibase:language ..." in SPARQL queries. It is not SPARQL standard and unsupported by the used QLever SPARQL endpoints. Use rdfs:label or similar properties to get labels instead.
- 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] I will first find the Breaking Bad series entity in the IMDb graph. I'll search for "Breaking Bad" to get its IRI. Then I'll find its seasons, identify the last season (highest season number), list episodes of that season with their ratings, and return the top 3 episodes by rating. I'll start by searching for the series entity. I'll run a search_entity call for "Breaking Bad".

[Tool]
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] I'll look up properties of the Breaking Bad title entity (tt0903747) to find how seasons and episodes are represented. I'll search properties of that entity for terms like "season", "episode", or "seasons".

[Tool]
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] I'll search for properties related to "episode" in the whole IMDB graph to see how episodes are linked.

[Tool]
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] I'll search for properties that link titles to other titles or seasons, like "parentTitle", "series", "seasonNumber", or similar.

[Tool]
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. start Year (imdb:startYear), also known as startYear
4. death Year (imdb:deathYear), also known as deathYear
5. id (imdb:id)
6. episode Number (imdb:episodeNumber, matched via episodeNumber), also known as episodeNumber
7. birth Year (imdb:birthYear, matched via birthYear), also known as birthYear
8. self (imdb_principal:self)
9. type (imdb:type)
10. type (rdf:type)

[Assistant] I'll retrieve triples for the Breaking Bad title entity (tt0903747) to see what properties it has that indicate relationship to episodes or seasons.

[Tool]
Got 89 rows and 3 columns, showing the first 10 rows below:
| subject                             | property                            | object                               |
| ----------------------------------- | ----------------------------------- | ------------------------------------ |
| Breaking Bad (imdb_title:tt0903747) | alt Label (skos:altLabel)           | Breaking bad (ro)                    |
| Breaking Bad (imdb_title:tt0903747) | label (rdfs:label)                  | Breaking Bad                         |
| Breaking Bad (imdb_title:tt0903747) | type (rdf:type)                     | Movie (imdb:Movie)                   |
| Breaking Bad (imdb_title:tt0903747) | actor (imdb_principal:actor)        | Bob Odenkirk (imdb_name:nm0644022)   |
| Breaking Bad (imdb_title:tt0903747) | genre (imdb:genre)                  | Crime                                |
| Breaking Bad (imdb_title:tt0903747) | actress (imdb_principal:actress)    | Anna Gunn (imdb_name:nm0348152)      |
| Breaking Bad (imdb_title:tt0903747) | writer (imdb:writer)                | Vince Gilligan (imdb_name:nm0319213) |
| Breaking Bad (imdb_title:tt0903747) | primary Title (imdb:primaryTitle)   | Breaking Bad                         |
| Breaking Bad (imdb_title:tt0903747) | original Title (imdb:originalTitle) | Breaking Bad                         |
| Breaking Bad (imdb_title:tt0903747) | id (imdb:id)                        | tt0903747                            |
| ...                                 | ...                                 | ...                                  |

[Assistant] I'll search for properties that indicate a parent series or TV series relationship for titles, like "series" or "parent series".

[Tool]
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] I'll inspect triples for known episodes of Breaking Bad to see how episodes are represented. For example, episode "Ozymandias" (S5E14) has IRI tt2301451. I'll list that subject.

[Tool]
Got 45 rows and 3 columns, showing the first 10 rows below:
| subject                           | property                            | object                               |
| --------------------------------- | ----------------------------------- | ------------------------------------ |
| Ozymandias (imdb_title:tt2301451) | alt Label (skos:altLabel)           | Ozymandias (en)                      |
| Ozymandias (imdb_title:tt2301451) | label (rdfs:label)                  | Ozymandias                           |
| Ozymandias (imdb_title:tt2301451) | type (rdf:type)                     | Movie (imdb:Movie)                   |
| Ozymandias (imdb_title:tt2301451) | actor (imdb_principal:actor)        | Bob Odenkirk (imdb_name:nm0644022)   |
| Ozymandias (imdb_title:tt2301451) | genre (imdb:genre)                  | Crime                                |
| Ozymandias (imdb_title:tt2301451) | actress (imdb_principal:actress)    | Betsy Brandt (imdb_name:nm1336827)   |
| Ozymandias (imdb_title:tt2301451) | writer (imdb:writer)                | Vince Gilligan (imdb_name:nm0319213) |
| Ozymandias (imdb_title:tt2301451) | primary Title (imdb:primaryTitle)   | Ozymandias                           |
| Ozymandias (imdb_title:tt2301451) | original Title (imdb:originalTitle) | Ozymandias                           |
| Ozymandias (imdb_title:tt2301451) | id (imdb:id)                        | tt2301451                            |
| ...                               | ...                                 | ...                                  |

[Assistant] I'll search for properties like "seasonNumber" and "episodeNumber" on the Ozymandias item to see how season/episode metadata is stored.

[Tool]
Top 10 property alternatives:
1. season Number (imdb:seasonNumber, matched via seasonNumber), also known as seasonNumber
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] I'll list the triples specifically for seasonNumber and episodeNumber for Ozymandias to confirm their values.

[Tool]
Got 1 row and 3 columns:
| subject                           | property                          | object      |
| --------------------------------- | --------------------------------- | ----------- |
| Ozymandias (imdb_title:tt2301451) | season Number (imdb:seasonNumber) | 5 (xsd:int) |

[Tool]
Got 1 row and 3 columns:
| subject                           | property                            | object       |
| --------------------------------- | ----------------------------------- | ------------ |
| Ozymandias (imdb_title:tt2301451) | episode Number (imdb:episodeNumber) | 14 (xsd:int) |

[Assistant] Good. Episodes have imdb:seasonNumber and imdb:episodeNumber. They also have imdb:averageRating. I'll retrieve the averageRating for Ozymandias to confirm.

[Tool]
Got 1 row and 3 columns:
| subject                           | property                            | object       |
| --------------------------------- | ----------------------------------- | ------------ |
| Ozymandias (imdb_title:tt2301451) | average Rating (imdb:averageRating) | 10 (xsd:int) |

[Assistant] Now I'll construct a SPARQL query to find all episodes of Breaking Bad (tt0903747) from the last season. To do that I need to: find episodes with seasonNumber equal to the maximum seasonNumber among episodes whose series is Breaking Bad. But I need a way to ensure episodes belong to Breaking Bad. How are episodes linked to the series? There may be a property like "is part of series" or "parentTitle". I'll inspect an episode like Ozymandias for properties that link to the series.

[Tool]
Got 45 rows and 3 columns, showing the first 10 rows below:
| subject                           | property                            | object                               |
| --------------------------------- | ----------------------------------- | ------------------------------------ |
| Ozymandias (imdb_title:tt2301451) | alt Label (skos:altLabel)           | Ozymandias (en)                      |
| Ozymandias (imdb_title:tt2301451) | label (rdfs:label)                  | Ozymandias                           |
| Ozymandias (imdb_title:tt2301451) | type (rdf:type)                     | Movie (imdb:Movie)                   |
| Ozymandias (imdb_title:tt2301451) | actor (imdb_principal:actor)        | Bob Odenkirk (imdb_name:nm0644022)   |
| Ozymandias (imdb_title:tt2301451) | genre (imdb:genre)                  | Crime                                |
| Ozymandias (imdb_title:tt2301451) | actress (imdb_principal:actress)    | Betsy Brandt (imdb_name:nm1336827)   |
| Ozymandias (imdb_title:tt2301451) | writer (imdb:writer)                | Vince Gilligan (imdb_name:nm0319213) |
| Ozymandias (imdb_title:tt2301451) | primary Title (imdb:primaryTitle)   | Ozymandias                           |
| Ozymandias (imdb_title:tt2301451) | original Title (imdb:originalTitle) | Ozymandias                           |
| Ozymandias (imdb_title:tt2301451) | id (imdb:id)                        | tt2301451                            |
| ...                               | ...                                 | ...                                  |

[Assistant] I don't see an explicit property linking to the series in this list snippet. However, IMDb often encodes series membership by having episodes' primaryTitle and also a property "series" or having a parent title via "imdb:parentTitle" or similar. I'll search for properties like "parent", "partOf", "series" in the episode.

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

No other items found

[Tool]
Got 1 row and 3 columns:
| subject                           | property                        | object                              |
| --------------------------------- | ------------------------------- | ----------------------------------- |
| Ozymandias (imdb_title:tt2301451) | parent Title (imdb:parentTitle) | Breaking Bad (imdb_title:tt0903747) |

[Assistant]
Great — episodes have imdb:parentTitle linking to the series. So the plan:

1. Find the Breaking Bad series IRI imdb_title:tt0903747 (verified).
2. Find episodes with imdb:parentTitle = that IRI.
3. Compute the maximum imdb:seasonNumber among those episodes to identify the last season.
4. Filter episodes to that seasonNumber.
5. Order by imdb:averageRating desc then maybe by episodeNumber or voteCount? The user asked top 3 episodes of the last season — I'll order by averageRating desc and take top 3. Need to ensure we return IRIs of episodes and optionally labels and ratings.
6. Build SPARQL SELECT query that returns episode IRI, label, seasonNumber, episodeNumber, averageRating. Use subquery to compute max seasonNumber.

I'll construct the SPARQL now and run it.

[Tool] Got no rows and 5 columns

[Assistant] I'll modify the query to reference the series IRI explicitly inside the subquery (to avoid scoping issues) and run it again.

[Tool]
Got 3 rows and 5 columns:
| episode                            | episodeLabel | season      | epnum        | rating            |
| ---------------------------------- | ------------ | ----------- | ------------ | ----------------- |
| Ozymandias (imdb_title:tt2301451)  | Ozymandias   | 5 (xsd:int) | 14 (xsd:int) | 10 (xsd:int)      |
| Felina (imdb_title:tt2301455)      | Felina       | 5 (xsd:int) | 16 (xsd:int) | 9.9 (xsd:decimal) |
| To'hajiilee (imdb_title:tt2301449) | To'hajiilee  | 5 (xsd:int) | 13 (xsd:int) | 9.8 (xsd:decimal) |

[Assistant] The query returned the top 3 episodes in season 5 ordered by rating. This looks correct. I'll now finalize by returning the SPARQL query and the answer.

[Tool] Stopping