Triple
T22448121
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gunpowder |
E554916
|
entity |
| Predicate | productionCompany |
P490
|
FINISHED |
| Object |
Thriker Films
Thriker Films is a film production company known for producing the movie "Gunpowder."
|
E1536653
|
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: Thriker Films | Statement: [Gunpowder, productionCompany, Thriker Films]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Thriker Films Context triple: [Gunpowder, productionCompany, Thriker Films]
-
A.
Kestrel Films
Kestrel Films is a British film production company best known for producing Ken Loach’s acclaimed 1969 drama "Kes."
-
B.
Maverick Films
Maverick Films is a film production company known for backing independent and genre-driven movies, including the crime comedy-drama "Gridlock'd."
-
C.
Brio Films
Brio Films is a French film production company known for producing imaginative and visually distinctive movies such as Michel Gondry’s "Mood Indigo."
-
D.
Shoebox Films
Shoebox Films is a British film production company known for producing independent and auteur-driven movies, including the 2019 thriller "Serenity."
-
E.
Maddock Films
Maddock Films is an Indian film production company known for backing popular Hindi movies across genres, including comedies, thrillers, and offbeat dramas.
- 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: Thriker Films Triple: [Gunpowder, productionCompany, Thriker Films]
Generated description
Thriker Films is a film production company known for producing the movie "Gunpowder."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Thriker Films Target entity description: Thriker Films is a film production company known for producing the movie "Gunpowder."
-
A.
Kestrel Films
Kestrel Films is a British film production company best known for producing Ken Loach’s acclaimed 1969 drama "Kes."
-
B.
Maverick Films
Maverick Films is a film production company known for backing independent and genre-driven movies, including the crime comedy-drama "Gridlock'd."
-
C.
Brio Films
Brio Films is a French film production company known for producing imaginative and visually distinctive movies such as Michel Gondry’s "Mood Indigo."
-
D.
Shoebox Films
Shoebox Films is a British film production company known for producing independent and auteur-driven movies, including the 2019 thriller "Serenity."
-
E.
Maddock Films
Maddock Films is an Indian film production company known for backing popular Hindi movies across genres, including comedies, thrillers, and offbeat dramas.
- 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_69e11e5113208190ab58c6b595f9d1d0 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15b48be0481909f4601b732424e5b |
completed | April 29, 2026, 1:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0b0c7640fc8190a2dd1643a7c2a5b0 |
completed | May 18, 2026, 12:56 p.m. |
| NEDg | Description generation | batch_6a0b0cd3f7bc8190bdc1e241ea1c642f |
completed | May 18, 2026, 12:57 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0b0d40e0c8819083a430e30dba1848 |
completed | May 18, 2026, 12:59 p.m. |
Created at: April 16, 2026, 8:48 p.m.