Triple
T18503961
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shaun Livingston |
E452151
|
entity |
| Predicate | injuryDetails |
P23450
|
FINISHED |
| Object | tore ACL, PCL, and lateral meniscus in left knee |
—
|
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: tore ACL, PCL, and lateral meniscus in left knee | Statement: [Shaun Livingston, injuryDetails, tore ACL, PCL, and lateral meniscus in left knee]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: injuryDetails Context triple: [Shaun Livingston, injuryDetails, tore ACL, PCL, and lateral meniscus in left knee]
-
A.
injuryType
chosen
Indicates the specific kind or category of injury associated with an entity or event.
-
B.
causeOfInjury
Indicates that one entity is the source or reason that another entity sustained an injury.
-
C.
injuriesApprox
Indicates an approximate or estimated number or extent of injuries associated with an event or entity.
-
D.
injuredIn
Indicates that an entity sustained an injury as a result of a specified event, situation, or action.
-
E.
injuryOccurredAt
Indicates that an injury took place at a specific location or during a particular event or time.
- 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_69d8d386df84819092355ebb260d848e |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e53341063c81908361b0eb78794145 |
completed | April 19, 2026, 7:55 p.m. |
| PD | Predicate disambiguation | batch_69e469dbf5208190b6fc49e02a087f54 |
completed | April 19, 2026, 5:36 a.m. |
Created at: April 10, 2026, 11:36 a.m.