Triple
T678351
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cam Neely |
E13126
|
entity |
| Predicate | injuryHistory |
P3816
|
FINISHED |
| Object | chronic knee 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: chronic knee injuries | Statement: [Cam Neely, injuryHistory, chronic knee injuries]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: injuryHistory Context triple: [Cam Neely, injuryHistory, chronic knee injuries]
-
A.
hasInjuries
chosen
Indicates that an entity has sustained one or more physical or bodily injuries.
-
B.
causeOfInjury
Indicates that one entity is the source or reason that another entity sustained an injury.
-
C.
injuryOccurredAt
Indicates that an injury took place at a specific location or during a particular event or time.
-
D.
injuryInvolvedPlayer
Indicates that a specific player is involved in, affected by, or associated with a particular injury event.
-
E.
subsequentHistory
Indicates that one event, state, or record occurs or is recorded after another in time, reflecting its later historical development or outcome.
- 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_69a4933d3bf88190972041cd8cf143b9 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a4a04e17088190943d54977eb3f83a |
completed | March 1, 2026, 8:23 p.m. |
| PD | Predicate disambiguation | batch_69a49d1d79608190a849ba9ffad2879d |
completed | March 1, 2026, 8:10 p.m. |
Created at: March 1, 2026, 7:36 p.m.