Triple
T15958788
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Anderton |
E387003
|
entity |
| Predicate | filmCharacterOccupation |
P114816
|
FINISHED |
| Object | chief of Precrime (Washington, D.C.) |
—
|
LITERAL 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: chief of Precrime (Washington, D.C.) | Statement: [John Anderton, filmCharacterOccupation, chief of Precrime (Washington, D.C.)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: filmCharacterOccupation Context triple: [John Anderton, filmCharacterOccupation, chief of Precrime (Washington, D.C.)]
-
A.
occupationInFilm
chosen
Indicates that an entity has a specific occupation or role within the context of a particular film.
-
B.
genreOfWorkCharacterIsIn
Indicates the specific genre of the creative work in which a given character appears.
-
C.
notableCharacterOccupation
Indicates that a notable character is associated with a specific occupation or professional role.
-
D.
mainCastMemberRole
Indicates that an entity’s role specifies the character or position they portray as a principal member of a production’s main cast.
-
E.
roleInFilmEcosystem
Indicates the specific function or position an entity holds within the broader network of activities, stakeholders, and processes that make up the film ecosystem.
- F. None of above.
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_69d86da882448190a82ea962fe343b79 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e173b3bf6c81909230170e833d7ce7 |
completed | April 16, 2026, 11:41 p.m. |
| PD | Predicate disambiguation | batch_69e142d6fb588190b4176eab4bbae774 |
completed | April 16, 2026, 8:13 p.m. |
Created at: April 10, 2026, 4:53 a.m.