Triple
T27449256
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Helen Vinson |
E692385
|
entity |
| Predicate | filmographyCountApprox |
P8980
|
FINISHED |
| Object | over 40 feature films |
—
|
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: over 40 feature films | Statement: [Helen Vinson, filmographyCountApprox, over 40 feature films]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: filmographyCountApprox Context triple: [Helen Vinson, filmographyCountApprox, over 40 feature films]
-
A.
hasFilmographyType
Indicates the type or category of film-related work associated with an entity (e.g., actor, director, producer) within its filmography.
-
B.
numberOfFilmsAppearedIn
chosen
Indicates the total count of distinct films in which a given entity has appeared.
-
C.
numberOfFilmsWorkedOn
Indicates the total count of films on which the subject has worked or participated.
-
D.
composedForNumberOfFilms
Indicates the number of films for which an entity has composed music or a score.
-
E.
hasFilmCareer
Indicates that an entity has been professionally involved in the film industry as a career.
- 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_69ef5206c9248190b5975c2a7f9d229c |
completed | April 27, 2026, 12:09 p.m. |
| NER | Named-entity recognition | batch_69f62dc4980481909e303ade433c7d61 |
completed | May 2, 2026, 5 p.m. |
| PD | Predicate disambiguation | batch_69f623aaf40081909f947431424a1d55 |
completed | May 2, 2026, 4:17 p.m. |
Created at: April 27, 2026, 12:47 p.m.