Triple
T15927162
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Don Shirley (Green Book character) |
E386231
|
entity |
| Predicate | filmThemeConnection |
P120580
|
FINISHED |
| Object | racism |
—
|
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: racism | Statement: [Don Shirley (Green Book character), filmThemeConnection, racism]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: filmThemeConnection Context triple: [Don Shirley (Green Book character), filmThemeConnection, racism]
-
A.
identifierForFilmTheme
Indicates that something serves as an identifying label or code specifically assigned to a film theme.
-
B.
linkedFilm
Indicates that there is an associated or connected film related to the given entity.
-
C.
filmSeriesRelation
Indicates a relationship where one film is part of, belongs to, or is connected within a larger film series or franchise.
-
D.
subjectOfFilm
Indicates that a person, character, or topic is the main focus or central topic depicted in a particular film.
-
E.
filmBase
Indicates the primary location or headquarters from which a film-related entity (such as a production, company, or operation) is based or operates.
- 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_69d86da750008190987eb26be3f6c118 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e172b48b308190bc430b2308cbc75b |
completed | April 16, 2026, 11:37 p.m. |
| PD | Predicate disambiguation | batch_69e142cf5c548190a931f7b58144cd31 |
completed | April 16, 2026, 8:13 p.m. |
| PDg | Predicate description generation | batch_69e172b213e481909ee0c05e16229a26 |
completed | April 16, 2026, 11:37 p.m. |
Created at: April 10, 2026, 4:52 a.m.