Triple
T9326410
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eric Blore as Bates |
E224396
|
entity |
| Predicate | blackAndWhiteFilmCharacter |
P87540
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Eric Blore as Bates, blackAndWhiteFilmCharacter, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: blackAndWhiteFilmCharacter Context triple: [Eric Blore as Bates, blackAndWhiteFilmCharacter, true]
-
A.
filmPortrayer
Indicates that one entity portrays or plays the role of another entity (such as a character or person) in a film.
-
B.
madeFamousByFilm
Indicates that something became widely known or gained significant public recognition as a result of being featured in a film.
-
C.
characterIn
Indicates that an entity appears as a character within a specified work, story, or narrative.
-
D.
characterInFocus
Indicates that a particular character is the primary subject or focal point within a given context, scene, or narrative segment.
-
E.
portraysCharacterInGenre
Indicates that an entity depicts or plays a character within works belonging to a specified genre.
- F. None of above. chosen
Provenance (4 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_69ca8427a0c08190b749831d5ea98f02 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd36f88e988190bb896a3d7c3c723c |
completed | April 1, 2026, 3:17 p.m. |
| PD | Predicate disambiguation | batch_69cc7a643924819097f01144734901cf |
completed | April 1, 2026, 1:52 a.m. |
| PDg | Predicate description generation | batch_69cc94b796788190816b71b1e9996288 |
completed | April 1, 2026, 3:44 a.m. |
Created at: March 30, 2026, 7:39 p.m.