Triple
T32971401
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Academy Award for Best Actress for Reds |
E843527
|
entity |
| Predicate | filmSubjectMatter |
P150261
|
FINISHED |
| Object | American journalists and revolutionaries |
—
|
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: American journalists and revolutionaries | Statement: [Academy Award for Best Actress for Reds, filmSubjectMatter, American journalists and revolutionaries]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: filmSubjectMatter Context triple: [Academy Award for Best Actress for Reds, filmSubjectMatter, American journalists and revolutionaries]
-
A.
subjectOfFilm
Indicates that a person, character, or topic is the main focus or central topic depicted in a particular film.
-
B.
filmGenreOfRelatedWork
Indicates that a work is related to another work through sharing or being associated with the same film genre.
-
C.
filmType
Indicates the specific category or genre that a film belongs to.
-
D.
filmContent
chosen
Indicates that one entity is the substantive material or subject matter contained within a film.
-
E.
sourceFilmGenre
Indicates that a film is classified as belonging to a particular 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_69f3494b9fc48190bb61c955ba471275 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f6d74b20a48190900dda1014cc13a8 |
completed | May 3, 2026, 5:04 a.m. |
| PD | Predicate disambiguation | batch_69f6d26f27dc8190ae426a3e1573933e |
completed | May 3, 2026, 4:43 a.m. |
Created at: May 1, 2026, 1:21 a.m.