Triple
T14129193
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Brierley |
E340115
|
entity |
| Predicate | filmGenreAboutLife |
P96255
|
FINISHED |
| Object | biographical drama |
—
|
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: biographical drama | Statement: [John Brierley, filmGenreAboutLife, biographical drama]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: filmGenreAboutLife Context triple: [John Brierley, filmGenreAboutLife, biographical drama]
-
A.
filmType
Indicates the specific category or genre that a film belongs to.
-
B.
sourceFilmGenre
Indicates that a film is classified as belonging to a particular genre.
-
C.
filmBase
Indicates the primary location or headquarters from which a film-related entity (such as a production, company, or operation) is based or operates.
-
D.
featuredInFilmGenre
chosen
Indicates that an entity (such as a film, character, or work) appears in or is associated with a specific film genre.
-
E.
visualGenre
Indicates the visual or stylistic category to which something belongs, such as its artistic or cinematic genre.
- 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_69d81c6a95b481909e39111e0c1f31ee |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de610aa434819096671c5aabb9134a |
completed | April 14, 2026, 3:45 p.m. |
| PD | Predicate disambiguation | batch_69de05b5e7a08190a16be9ad8b92b80c |
completed | April 14, 2026, 9:15 a.m. |
Created at: April 9, 2026, 10:23 p.m.