Triple
T23140212
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brooklyn's Finest |
E577439
|
entity |
| Predicate | productionCompany |
P490
|
FINISHED |
| Object |
Langley Films
Langley Films is a film production company best known for producing the 2009 crime drama "Brooklyn's Finest."
|
E1574022
|
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: Langley Films | Statement: [Brooklyn's Finest, productionCompany, Langley Films]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Langley Films Context triple: [Brooklyn's Finest, productionCompany, Langley Films]
-
A.
Vinson Films
Vinson Films is a film production company known for producing the 2019 horror-comedy thriller "Ready or Not."
-
B.
Langley Productions
Langley Productions is an American television production company best known for creating and producing the long-running reality TV series "Cops."
-
C.
Linson Films
Linson Films is a film production company founded by producer Art Linson, known for backing notable movies such as "Fight Club."
-
D.
Barwood Films
Barwood Films is a film production company co-founded by Barbra Streisand, known for producing several of her starring and directing projects.
-
E.
Kestrel Films
Kestrel Films is a British film production company best known for producing Ken Loach’s acclaimed 1969 drama "Kes."
- 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: Langley Films Triple: [Brooklyn's Finest, productionCompany, Langley Films]
Generated description
Langley Films is a film production company best known for producing the 2009 crime drama "Brooklyn's Finest."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Langley Films Target entity description: Langley Films is a film production company best known for producing the 2009 crime drama "Brooklyn's Finest."
-
A.
Vinson Films
Vinson Films is a film production company known for producing the 2019 horror-comedy thriller "Ready or Not."
-
B.
Langley Productions
Langley Productions is an American television production company best known for creating and producing the long-running reality TV series "Cops."
-
C.
Linson Films
Linson Films is a film production company founded by producer Art Linson, known for backing notable movies such as "Fight Club."
-
D.
Barwood Films
Barwood Films is a film production company co-founded by Barbra Streisand, known for producing several of her starring and directing projects.
-
E.
Kestrel Films
Kestrel Films is a British film production company best known for producing Ken Loach’s acclaimed 1969 drama "Kes."
- 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_69e245f8e6248190ba3d58e068b4dccb |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f18ec922b481908084eee6a95aef83 |
completed | April 29, 2026, 4:53 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0c308db8288190b036cd297820da7c |
completed | May 19, 2026, 9:42 a.m. |
| NEDg | Description generation | batch_6a0c32dc646081909d3c36d089d61c00 |
completed | May 19, 2026, 9:52 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0c35636e908190ae11d96deb15e1f5 |
completed | May 19, 2026, 10:03 a.m. |
Created at: April 17, 2026, 4 p.m.