Triple
T11694358
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chicago (1927 film) |
E277952
|
entity |
| Predicate | hasFilmGenre |
P93695
|
FINISHED |
| Object | silent crime-comedy 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: silent crime-comedy drama | Statement: [Chicago (1927 film), hasFilmGenre, silent crime-comedy drama]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFilmGenre Context triple: [Chicago (1927 film), hasFilmGenre, silent crime-comedy drama]
-
A.
featuredInFilmGenre
Indicates that an entity (such as a film, character, or work) appears in or is associated with a specific film genre.
-
B.
sourceFilmGenre
chosen
Indicates that a film is classified as belonging to a particular genre.
-
C.
keyGenreFilm
Indicates that a particular genre is the primary or defining genre associated with a given film.
-
D.
accompaniesGenreOfFilm
Indicates that one thing is typically associated with or goes along with a particular film genre.
-
E.
hasFilmStyle
Indicates that a film exhibits or is characterized by a particular cinematic style or aesthetic approach.
- 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_69d6aafe02d881909900d54ad7d4af84 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a47b9eb48190976a35e91e25b56b |
completed | April 10, 2026, 7:19 a.m. |
| PD | Predicate disambiguation | batch_69d88a7b30948190b616a9db5c5488d5 |
completed | April 10, 2026, 5:28 a.m. |
Created at: April 8, 2026, 9:40 p.m.