Triple
T10682405
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cake (2014 film) |
E251789
|
entity |
| Predicate | productionCompany |
P490
|
FINISHED |
| Object |
Cinelou Films
Cinelou Films is an independent American film production company known for producing character-driven dramas such as the 2014 film "Cake."
|
E878903
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Cinelou Films | Statement: [Cake (2014 film), productionCompany, Cinelou Films]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cinelou Films Context triple: [Cake (2014 film), productionCompany, Cinelou Films]
-
A.
Valoria Films
Valoria Films is a film distribution company known for handling the release of various international and independent movies.
-
B.
Nala Films
Nala Films is an independent film production company known for financing and producing critically acclaimed feature films.
-
C.
Diaphana Films
Diaphana Films is a French film distribution and production company known for handling acclaimed international and auteur cinema.
-
D.
Canana Films
Canana Films is a Mexican film production company known for producing socially conscious and critically acclaimed Latin American cinema.
-
E.
Athos Films
Athos Films is a French film distribution company known for handling the release of notable art-house and New Wave films.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Cinelou Films Triple: [Cake (2014 film), productionCompany, Cinelou Films]
Generated description
Cinelou Films is an independent American film production company known for producing character-driven dramas such as the 2014 film "Cake."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Cinelou Films Target entity description: Cinelou Films is an independent American film production company known for producing character-driven dramas such as the 2014 film "Cake."
-
A.
Valoria Films
Valoria Films is a film distribution company known for handling the release of various international and independent movies.
-
B.
Nala Films
Nala Films is an independent film production company known for financing and producing critically acclaimed feature films.
-
C.
Diaphana Films
Diaphana Films is a French film distribution and production company known for handling acclaimed international and auteur cinema.
-
D.
Canana Films
Canana Films is a Mexican film production company known for producing socially conscious and critically acclaimed Latin American cinema.
-
E.
Athos Films
Athos Films is a French film distribution company known for handling the release of notable art-house and New Wave films.
- F. None of above. chosen
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d6aa5bd7c08190a816e733b4045c23 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6fcc30be481909922844b539b622d |
completed | April 9, 2026, 1:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d9888cf7b481909de6a4fecb48cf4b |
completed | April 10, 2026, 11:32 p.m. |
| NEDg | Description generation | batch_69d98aea391c81909ec64a29053c35c1 |
completed | April 10, 2026, 11:42 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d98c013348819094bde38a057257b4 |
completed | April 10, 2026, 11:47 p.m. |
Created at: April 8, 2026, 9:10 p.m.