Triple
T29751059
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jet Black |
E752898
|
entity |
| Predicate | bodyModificationReason |
P167840
|
FINISHED |
| Object | injury sustained on duty as a cop |
—
|
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: injury sustained on duty as a cop | Statement: [Jet Black, bodyModificationReason, injury sustained on duty as a cop]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: bodyModificationReason Context triple: [Jet Black, bodyModificationReason, injury sustained on duty as a cop]
-
A.
bodyTransformation
Indicates a change in an entity’s physical form, structure, or appearance into a different bodily state.
-
B.
bodyTreatment
Indicates a treatment or therapeutic procedure that is applied to a person's body.
-
C.
abolishedBodyCharacteristic
Indicates that an entity has eliminated or discontinued a previously existing physical or bodily characteristic.
-
D.
bodyworkBy
Indicates that one entity performs, creates, or is responsible for the bodywork (such as physical treatment, structural work, or crafted body structure) of another entity.
-
E.
bodyPartsTransformedInto
Indicates that one or more body parts of an entity are changed or converted into different body parts or forms.
- 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_69f0d62c84cc8190846f80ae04fdf8ec |
completed | April 28, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_69f6736ad5e48190bab89c27970a8e74 |
completed | May 2, 2026, 9:58 p.m. |
| PD | Predicate disambiguation | batch_69f66ac1a4fc81909740d2e52fbe6970 |
completed | May 2, 2026, 9:21 p.m. |
| PDg | Predicate description generation | batch_69f66c59de9881909ebbb7b0ae7ab495 |
completed | May 2, 2026, 9:27 p.m. |
Created at: April 28, 2026, 7:54 p.m.