Triple
T678352
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cam Neely |
E13126
|
entity |
| Predicate | causeOfRetirement |
P10984
|
FINISHED |
| Object | 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: knee injuries | Statement: [Cam Neely, causeOfRetirement, knee injuries]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: causeOfRetirement Context triple: [Cam Neely, causeOfRetirement, knee injuries]
-
A.
causeOfEarlyRetirement
chosen
Indicates that one entity is the reason or contributing factor leading another entity to retire earlier than the typical or expected time.
-
B.
reasonForLeaving
Indicates the cause, motivation, or circumstance that led an entity to depart or discontinue an association, position, or place.
-
C.
retiredFrom
Indicates that an entity has permanently stopped working for or being active in a specified organization, role, or activity.
-
D.
forcedToRetire
Indicates that an entity was compelled, rather than choosing voluntarily, to end their professional role or career.
-
E.
retired
Indicates that an entity has permanently stopped working in their former occupation or role, typically after reaching a certain age or service duration.
- 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.