Triple
T5551083
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Carradine |
E145525
|
entity |
| Predicate | appearedInNumberOfFilms |
P8980
|
FINISHED |
| Object | over 200 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 200 feature films | Statement: [John Carradine, appearedInNumberOfFilms, over 200 feature films]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: appearedInNumberOfFilms Context triple: [John Carradine, appearedInNumberOfFilms, over 200 feature films]
-
A.
numberOfFilmsAppearedIn
chosen
Indicates the total count of distinct films in which a given entity has appeared.
-
B.
portrayedInFranchise
Indicates that an entity is depicted as a character or element within a specific media franchise.
-
C.
includedInFilm
Indicates that one entity (such as a scene, segment, or element) is contained within or forms part of a particular film.
-
D.
partOfFilmographyOf
Indicates that a work (such as a film, show, or role) is included in the body of screen-related works credited to a particular person.
-
E.
numberOfFilmsDirected
Indicates the total count of films that a given entity has directed.
- 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_69c008fb879c81909f5bfa56fadc1d46 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c01fe3e7788190aa5361b083197c17 |
completed | March 22, 2026, 4:59 p.m. |
| PD | Predicate disambiguation | batch_69c01b0e72f08190bf705d8fe1639401 |
completed | March 22, 2026, 4:38 p.m. |
Created at: March 22, 2026, 3:35 p.m.