Triple
T18825028
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Girl Number 9 |
E460361
|
entity |
| Predicate | productionCompany |
P490
|
FINISHED |
| Object |
Hazelhurst Films
Hazelhurst Films is a British independent production company known for creating the crime thriller web series "Girl Number 9."
|
E1347344
|
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: Hazelhurst Films | Statement: [Girl Number 9, productionCompany, Hazelhurst Films]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hazelhurst Films Context triple: [Girl Number 9, productionCompany, Hazelhurst Films]
-
A.
Vinson Films
Vinson Films is a film production company known for producing the 2019 horror-comedy thriller "Ready or Not."
-
B.
Hartswood Films
Hartswood Films is a British television production company best known internationally for creating the hit detective series "Sherlock."
-
C.
Waverly Films
Waverly Films is a Brooklyn-based film and video production collective known for its offbeat comedy shorts, music videos, and collaborations with major studios and brands.
-
D.
Linson Films
Linson Films is a film production company founded by producer Art Linson, known for backing notable movies such as "Fight Club."
-
E.
Hawk Films
Hawk Films was a British film production company best known for producing several of Stanley Kubrick’s films, including "The Shining."
- 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: Hazelhurst Films Triple: [Girl Number 9, productionCompany, Hazelhurst Films]
Generated description
Hazelhurst Films is a British independent production company known for creating the crime thriller web series "Girl Number 9."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hazelhurst Films Target entity description: Hazelhurst Films is a British independent production company known for creating the crime thriller web series "Girl Number 9."
-
A.
Vinson Films
Vinson Films is a film production company known for producing the 2019 horror-comedy thriller "Ready or Not."
-
B.
Hartswood Films
Hartswood Films is a British television production company best known internationally for creating the hit detective series "Sherlock."
-
C.
Waverly Films
Waverly Films is a Brooklyn-based film and video production collective known for its offbeat comedy shorts, music videos, and collaborations with major studios and brands.
-
D.
Linson Films
Linson Films is a film production company founded by producer Art Linson, known for backing notable movies such as "Fight Club."
-
E.
Hawk Films
Hawk Films was a British film production company best known for producing several of Stanley Kubrick’s films, including "The Shining."
- 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_69d8dcf94c288190a06dea029ae4b223 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5a6bdefac8190892d6fd5c20a431e |
completed | April 20, 2026, 4:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0575ac249c8190989b9de40eb8a444 |
completed | May 14, 2026, 7:11 a.m. |
| NEDg | Description generation | batch_6a0576d2baac8190a9cd366978471809 |
completed | May 14, 2026, 7:16 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0577ca320481909b26700820ad4820 |
completed | May 14, 2026, 7:20 a.m. |
Created at: April 10, 2026, 11:56 a.m.