Triple
T23559059
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vera Cicero |
E579176
|
entity |
| Predicate | depictedInGenreContext |
P151347
|
FINISHED |
| Object | gangster milieu |
—
|
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: gangster milieu | Statement: [Vera Cicero, depictedInGenreContext, gangster milieu]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: depictedInGenreContext Context triple: [Vera Cicero, depictedInGenreContext, gangster milieu]
-
A.
depictsGenre
Indicates that one entity visually represents or portrays the genre category associated with another entity.
-
B.
partOfWorkGenreContext
chosen
Indicates that something occurs within or is associated with the genre-related context of a particular work.
-
C.
coveredInGenre
Indicates that a work or item is associated with, categorized under, or treated within a particular genre.
-
D.
genreOfAppearance
Indicates the genre or type of creative work in which an entity appears.
-
E.
featuredInFilmGenre
Indicates that an entity (such as a film, character, or work) appears in or is associated with a specific film 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_69e245fe24588190888f3aec8407d8e3 |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f1af6663e0819081d654da38cf3aa9 |
completed | April 29, 2026, 7:12 a.m. |
| PD | Predicate disambiguation | batch_69f118afabd88190bd88f49597d120e8 |
completed | April 28, 2026, 8:29 p.m. |
Created at: April 17, 2026, 6:12 p.m.