Triple
T216241
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Gaucho |
E4110
|
entity |
| Predicate | filmType |
P9709
|
FINISHED |
| Object | black-and-white silent feature |
—
|
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: black-and-white silent feature | Statement: [The Gaucho, filmType, black-and-white silent feature]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: filmType Context triple: [The Gaucho, filmType, black-and-white silent feature]
-
A.
televisionFilm
Indicates that the subject is a television film (a movie produced for or originally distributed via television).
-
B.
genre
Indicates the artistic or thematic category to which a work (such as a book, film, or song) belongs.
-
C.
filmSeries
Indicates that a film is part of, or associated with, a larger film series or franchise.
-
D.
popularFilmIndustry
Indicates that an entity has a widely recognized and well-liked film industry that attracts significant audience interest and attention.
-
E.
genreFeatures
Indicates that a particular genre is characterized or defined by certain features or attributes.
- 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_69a2573508588190b522c2476d91acfe |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25dcd2b208190855d5d8d70a3acfc |
completed | Feb. 28, 2026, 3:15 a.m. |
| PD | Predicate disambiguation | batch_69a25b52190481908f299d26122bafd2 |
completed | Feb. 28, 2026, 3:04 a.m. |
| PDg | Predicate description generation | batch_69a25dcba5148190ab80fd14c7cf4bb4 |
completed | Feb. 28, 2026, 3:15 a.m. |
Created at: Feb. 28, 2026, 2:53 a.m.