Triple
T9165005
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Don Davis |
E219930
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object | Hollywood film industry |
E247
|
NE FINISHED |
How this triple was built (2 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: Hollywood film industry | Statement: [Don Davis, employer, Hollywood film industry]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hollywood film industry Context triple: [Don Davis, employer, Hollywood film industry]
-
A.
Hollywood studios
Hollywood studios are major American film production companies based in Hollywood that dominate the global movie industry through large-scale financing, production, and distribution of films.
-
B.
Hollywood
chosen
Hollywood is a famous Los Angeles neighborhood internationally recognized as the historic center of the American film and entertainment industry.
-
C.
Hollywood
Hollywood is a residential neighborhood in the city of College Park, Maryland, known for its suburban character and proximity to the University of Maryland.
-
D.
Hollywood
Hollywood is a residential neighborhood in Homewood, Alabama, known for its historic homes and suburban character just outside Birmingham.
-
E.
Hollywood
Hollywood is a coastal city in southeastern Florida known for its beaches, boardwalk, and proximity to Miami.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69ca83e3633c81908688a9fa2306ba99 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69ccaa2ee64c8190a9a5abafe5d0b086 |
completed | April 1, 2026, 5:16 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d05484c2688190a5c64b5b54bedbb5 |
completed | April 4, 2026, midnight |
Created at: March 30, 2026, 7:22 p.m.