Triple
T21899120
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Laurel Holloman |
E540759
|
entity |
| Predicate | yearsActiveActingStart |
P45002
|
FINISHED |
| Object | 1990s |
—
|
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: 1990s | Statement: [Laurel Holloman, yearsActiveActingStart, 1990s]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: yearsActiveActingStart Context triple: [Laurel Holloman, yearsActiveActingStart, 1990s]
-
A.
activeYearsInFilm
Indicates the span of years during which an entity was actively involved in film-related work or roles.
-
B.
startedActingCareer
chosen
Indicates that an entity began their professional work or involvement in acting at a specific time or event.
-
C.
leadActorDebutFilmFor
Indicates that a person’s first film as a lead actor is the specified movie.
-
D.
directorStartYear
Indicates the year in which an individual began serving as a director (e.g., of a film, organization, or project).
-
E.
filmIndustryDebut
Indicates the event or point in time when an entity first appears or participates in the film industry, such as through a first film role, production, or related professional activity.
- 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_69e0c47b4e8c81908c8076eaa4c8e4f2 |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f11fc8c2108190b55ff1ba3badc9fb |
completed | April 28, 2026, 8:59 p.m. |
| PD | Predicate disambiguation | batch_69e6be9a65888190a66598d62d20366c |
completed | April 21, 2026, 12:02 a.m. |
Created at: April 16, 2026, 7:07 p.m.