Triple
T36879835
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Phantom Thunderbolt |
E911446
|
entity |
| Predicate | hasActorOccupation |
P114816
|
FINISHED |
| Object | Ken Maynard: 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: Ken Maynard: cowboy actor | Statement: [The Phantom Thunderbolt, hasActorOccupation, Ken Maynard: cowboy actor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasActorOccupation Context triple: [The Phantom Thunderbolt, hasActorOccupation, Ken Maynard: cowboy actor]
-
A.
actorNotableOccupation
Indicates that a person (typically an actor) is associated with a particular occupation or professional role for which they are especially well known.
-
B.
leadActorOccupation
Indicates that the occupation specified is the primary professional role of the lead actor in a given work or context.
-
C.
occupationInFilm
chosen
Indicates that an entity has a specific occupation or role within the context of a particular film.
-
D.
hasFilmographyType
Indicates the type or category of film-related work associated with an entity (e.g., actor, director, producer) within its filmography.
-
E.
employedActor
Indicates that one entity has hired or currently employs another entity to perform work or services.
- 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_69fd05ba6b2c81909c62b46237d10365 |
completed | May 7, 2026, 9:35 p.m. |
| PD | Predicate disambiguation | batch_69fd03039e48819082b6e12c5453885a |
completed | May 7, 2026, 9:24 p.m. |
Created at: May 3, 2026, 4:13 p.m.