Triple
T36879886
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Drifter |
E911448
|
entity |
| Predicate | hasLeadActorOccupation |
P110410
|
FINISHED |
| Object | cowboy actor |
—
|
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: cowboy actor | Statement: [The Drifter, hasLeadActorOccupation, cowboy actor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLeadActorOccupation Context triple: [The Drifter, hasLeadActorOccupation, cowboy actor]
-
A.
leadActorOccupation
chosen
Indicates that the occupation specified is the primary professional role of the lead actor in a given work or context.
-
B.
actorNotableOccupation
Indicates that a person (typically an actor) is associated with a particular occupation or professional role for which they are especially well known.
-
C.
leadRoleActor
Indicates that an actor performs a leading or principal role in a work or production.
-
D.
originalLeadActorRole
Indicates the role originally played by a particular lead actor in a given production or work.
-
E.
associatedWithLeadActorOfFilm
Indicates a relationship where one entity is connected or linked in some relevant way to the lead actor of a specified film.
- 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_69f76e82339881909607a65c0503d941 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fe6fea4a288190bf8615c5d6bf41b4 |
completed | May 8, 2026, 11:21 p.m. |
| PD | Predicate disambiguation | batch_69fe6f774de08190975a2393b9a1fd22 |
completed | May 8, 2026, 11:19 p.m. |
Created at: May 3, 2026, 4:13 p.m.