Triple
T33613158
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anna Meares |
E861042
|
entity |
| Predicate | sufferedSeriousInjury |
P171029
|
FINISHED |
| Object | fractured neck vertebra in 2008 crash |
—
|
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: fractured neck vertebra in 2008 crash | Statement: [Anna Meares, sufferedSeriousInjury, fractured neck vertebra in 2008 crash]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sufferedSeriousInjury Context triple: [Anna Meares, sufferedSeriousInjury, fractured neck vertebra in 2008 crash]
-
A.
woundedSeverely
chosen
Indicates that one entity has inflicted or suffered a level of injury on another that is serious, potentially life-threatening, or causes significant impairment.
-
B.
hasInjuredPerson
Indicates that an entity has a person who has been harmed or injured associated with it.
-
C.
injuredIn
Indicates that an entity sustained an injury as a result of a specified event, situation, or action.
-
D.
hasPlaceOfInjury
Indicates that an injury occurred at a specific place or location.
-
E.
majorInjuryTrack
Indicates that an entity has sustained a significant or severe injury that is being recorded or monitored over 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_69f3498037c88190a4500f002b5540e0 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f6fb19063c81909466b329655c8583 |
completed | May 3, 2026, 7:36 a.m. |
| PD | Predicate disambiguation | batch_69f6f96badb08190994442c2aba840b1 |
completed | May 3, 2026, 7:29 a.m. |
Created at: May 1, 2026, 1:41 a.m.