Triple
T15958787
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Anderton |
E387003
|
entity |
| Predicate | filmAdaptationMedium |
P112039
|
FINISHED |
| Object | feature film |
—
|
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: feature film | Statement: [John Anderton, filmAdaptationMedium, feature film]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: filmAdaptationMedium Context triple: [John Anderton, filmAdaptationMedium, feature film]
-
A.
filmAdaptationFormat
chosen
Indicates the specific medium or format (e.g., feature film, TV movie, short film) in which a work has been adapted into a film.
-
B.
inFilmAdaptation
Indicates that one work or element appears within, or is incorporated into, a film adaptation of another work.
-
C.
filmAdaptationGenre
Indicates that a film adaptation belongs to or is categorized under a particular genre.
-
D.
mediumAdaptation
Indicates that one work has been adapted into another form or medium (e.g., book to film, comic to TV series).
-
E.
filmAdaptationStarred
Indicates that a particular person played a starring role in a specific film adaptation of a work.
- 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_69d86da882448190a82ea962fe343b79 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e173b3bf6c81909230170e833d7ce7 |
completed | April 16, 2026, 11:41 p.m. |
| PD | Predicate disambiguation | batch_69e142d6fb588190b4176eab4bbae774 |
completed | April 16, 2026, 8:13 p.m. |
Created at: April 10, 2026, 4:53 a.m.