Triple
T13147981
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amulet |
E312388
|
entity |
| Predicate | productionCompany |
P490
|
FINISHED |
| Object |
Kreo Films FZ
Kreo Films FZ is a film production company known for producing the movie "Amulet."
|
E1024661
|
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: Kreo Films FZ | Statement: [Amulet, productionCompany, Kreo Films FZ]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kreo Films FZ Context triple: [Amulet, productionCompany, Kreo Films FZ]
-
A.
Cinelou Films
Cinelou Films is an independent American film production company known for producing character-driven dramas such as the 2014 film "Cake."
-
B.
Swaka Films
Swaka Films is a film production company known for producing the 2013 adaptation of "Romeo & Juliet."
-
C.
B-Reel Films
B-Reel Films is a Swedish film and television production company known for producing documentaries and narrative features, including the climate-focused film "I Am Greta."
-
D.
Diaphana Films
Diaphana Films is a French film distribution and production company known for handling acclaimed international and auteur cinema.
-
E.
Canana Films
Canana Films is a Mexican film production company known for producing socially conscious and critically acclaimed Latin American cinema.
- 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: Kreo Films FZ Triple: [Amulet, productionCompany, Kreo Films FZ]
Generated description
Kreo Films FZ is a film production company known for producing the movie "Amulet."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kreo Films FZ Target entity description: Kreo Films FZ is a film production company known for producing the movie "Amulet."
-
A.
Cinelou Films
Cinelou Films is an independent American film production company known for producing character-driven dramas such as the 2014 film "Cake."
-
B.
Swaka Films
Swaka Films is a film production company known for producing the 2013 adaptation of "Romeo & Juliet."
-
C.
B-Reel Films
B-Reel Films is a Swedish film and television production company known for producing documentaries and narrative features, including the climate-focused film "I Am Greta."
-
D.
Diaphana Films
Diaphana Films is a French film distribution and production company known for handling acclaimed international and auteur cinema.
-
E.
Canana Films
Canana Films is a Mexican film production company known for producing socially conscious and critically acclaimed Latin American cinema.
- 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_69d806aabde48190899e13e41659cae5 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98bd0f5b08190ab700c5de1c8e138 |
completed | April 10, 2026, 11:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6eae834908190aecb825db1d705ff |
completed | May 3, 2026, 6:27 a.m. |
| NEDg | Description generation | batch_69f6eb9ff2a881908004cc060b892f48 |
completed | May 3, 2026, 6:30 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f6ec67be70819087d6c49a85d163bc |
completed | May 3, 2026, 6:34 a.m. |
Created at: April 9, 2026, 9:10 p.m.