Triple
T979250
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mario Lemieux |
E21128
|
entity |
| Predicate | returnedToPlayAfterIllness |
P22128
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Mario Lemieux, returnedToPlayAfterIllness, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: returnedToPlayAfterIllness Context triple: [Mario Lemieux, returnedToPlayAfterIllness, true]
-
A.
hasInjuries
Indicates that an entity has sustained one or more physical or bodily injuries.
-
B.
returnedFromRetirement
Indicates that an entity has resumed an activity, role, or position after having previously retired from it.
-
C.
injuryInvolvedPlayer
Indicates that a specific player is involved in, affected by, or associated with a particular injury event.
-
D.
replacedPlayer
Indicates that one player has been substituted out of play and replaced by another player.
-
E.
sportsCareer
Indicates a relationship where an entity’s professional involvement, roles, or achievements in sports are associated with a particular sport, team, period, or competitive level.
- F. None of above. chosen
Provenance (4 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_69a493c2b62c8190b616351789ec47f8 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b479e8f081908183448c36244e1f |
completed | March 1, 2026, 9:49 p.m. |
| PD | Predicate disambiguation | batch_69a4b2a8a3b08190b4538e119b13f7f5 |
completed | March 1, 2026, 9:42 p.m. |
| PDg | Predicate description generation | batch_69a4b344f6f48190ba03ce593c94176b |
completed | March 1, 2026, 9:44 p.m. |
Created at: March 1, 2026, 7:40 p.m.