Triple
T24577386
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1986–87 NHL season |
E608150
|
entity |
| Predicate | lemieuxPointsTotal |
P17024
|
FINISHED |
| Object | 107 |
—
|
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: 107 | Statement: [1986–87 NHL season, lemieuxPointsTotal, 107]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lemieuxPointsTotal Context triple: [1986–87 NHL season, lemieuxPointsTotal, 107]
-
A.
ChelseaPoints
Indicates the number of points associated with Chelsea, typically in a competitive or scoring context.
-
B.
totalPointsScored
chosen
Indicates the total number of points accumulated or scored by an entity over a defined period, event, or context.
-
C.
pointsLeader
Indicates that the subject entity is the current leader in points relative to other entities in a given context or competition.
-
D.
totalPointsAvailable
Indicates the complete number of points that can be obtained or assigned within a given context or activity.
-
E.
pointsForWin
Indicates the number of points awarded to an entity for achieving a win in a given context or competition.
- 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_69e2c4cdab6c8190aae6e5d3de55c95e |
completed | April 17, 2026, 11:39 p.m. |
| NER | Named-entity recognition | batch_69f2a97be2ac8190aecf5e54a37e266a |
completed | April 30, 2026, 12:59 a.m. |
| PD | Predicate disambiguation | batch_69f2a6c1f07081908edf0b521767e79b |
completed | April 30, 2026, 12:48 a.m. |
Created at: April 18, 2026, 2:29 a.m.