Triple
T23524586
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lanny McDonald |
E574597
|
entity |
| Predicate | careerAssistsNHL |
P153099
|
FINISHED |
| Object | 506 |
—
|
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: 506 | Statement: [Lanny McDonald, careerAssistsNHL, 506]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: careerAssistsNHL Context triple: [Lanny McDonald, careerAssistsNHL, 506]
-
A.
careerNHLGamesPlayed
Indicates the total number of NHL games an individual has played over the course of their entire career.
-
B.
careerOPS
Indicates a relationship where an entity’s career on-base plus slugging (OPS) statistic is recorded or associated with that entity.
-
C.
NHLAssists
Indicates that one player is credited with an assist on another player's goal in a National Hockey League (NHL) game.
-
D.
personReferredToNHLTeam
Indicates that a person has mentioned or made reference to a specific NHL (National Hockey League) team.
-
E.
numberOfNHLSeasons
Indicates the total count of seasons an entity has participated in the National Hockey League (NHL).
- 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_69e245bb3dcc8190ba9a2b35972b58d0 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1ac72bfb88190b7da3837e66e851c |
completed | April 29, 2026, 7 a.m. |
| PD | Predicate disambiguation | batch_69f1189d75b48190a1c01928a993c9fb |
completed | April 28, 2026, 8:29 p.m. |
| PDg | Predicate description generation | batch_69f12760784c8190aaeff002ef31febe |
completed | April 28, 2026, 9:32 p.m. |
Created at: April 17, 2026, 6:09 p.m.