Triple
T33670302
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cole Trickle |
E862598
|
entity |
| Predicate | suffersFromInFilm |
P177643
|
FINISHED |
| Object | racing accident injuries |
—
|
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: racing accident injuries | Statement: [Cole Trickle, suffersFromInFilm, racing accident injuries]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: suffersFromInFilm Context triple: [Cole Trickle, suffersFromInFilm, racing accident injuries]
-
A.
inheritedByInFilm
Indicates that a character, role, or attribute is passed on or taken over by another character within the narrative of a film.
-
B.
hasTypeOfUseInFilm
Indicates that something is associated with a specific manner or category of use within the context of a film.
-
C.
mentionedInFilm
Indicates that an entity is referenced or talked about within the content of a film.
-
D.
hasOutcomeInFilm
Indicates that a particular event, action, or situation results in a specific outcome within the context of a film.
-
E.
featuredInFilmBy
Indicates that an entity is prominently included or showcased within a film that is created, directed, or produced by a specified person or organization.
- 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_69f34984c4008190bb82f33a7819da64 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f7009d39508190af7301f824615e88 |
completed | May 3, 2026, 8 a.m. |
| PD | Predicate disambiguation | batch_69f6fc5740fc81909774a4f65201a3ff |
completed | May 3, 2026, 7:42 a.m. |
| PDg | Predicate description generation | batch_69f6ffb7554881908993d6d2ffbcf8f5 |
completed | May 3, 2026, 7:56 a.m. |
Created at: May 1, 2026, 1:42 a.m.