Triple
T36182986
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Roar |
E1046764
|
entity |
| Predicate | notableCastInjury |
—
|
GENERATED |
| Object | Melanie Griffith suffered facial injuries from a lion attack |
—
|
UNRECOGNIZED GENERATED |
How this triple was built (1 step)
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.
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notableCastInjury Context triple: [Roar, notableCastInjury, Melanie Griffith suffered facial injuries from a lion attack]
-
A.
notableInjuryCount
Indicates the number of significant injuries associated with an entity, event, or period.
-
B.
injuredIn
chosen
Indicates that an entity sustained an injury as a result of a specified event, situation, or action.
-
C.
hasInjuries
Indicates that an entity has sustained one or more physical or bodily injuries.
-
D.
majorInjuryTrack
Indicates that an entity has sustained a significant or severe injury that is being recorded or monitored over time.
-
E.
injuryType
Indicates the specific kind or category of injury associated with an entity or event.
- F. None of above.
Provenance (1 batch)
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_69f76e3c1b10819081fc7a807a71cf84 |
completed | May 3, 2026, 3:48 p.m. |
Created at: May 3, 2026, 4:08 p.m.