Triple
T6360527
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1993–94 NHL season |
E143095
|
entity |
| Predicate | goalsLeaderTotalGoals |
P43996
|
FINISHED |
| Object | 60 |
—
|
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: 60 | Statement: [1993–94 NHL season, goalsLeaderTotalGoals, 60]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: goalsLeaderTotalGoals Context triple: [1993–94 NHL season, goalsLeaderTotalGoals, 60]
-
A.
totalGoalsRecord
Indicates the total number of goals that have been recorded for an entity across all relevant events or contexts.
-
B.
scoringLeaderGoals
chosen
Indicates that the subject is the leading scorer in terms of goals, having scored more goals than any other relevant participant in the given context.
-
C.
goalScorer
Indicates that the subject is the player who scored a particular goal in a game or match.
-
D.
topScorer
Indicates that the subject is the individual with the highest score among a specified group or in a particular context.
-
E.
topScorerPoints
Indicates the number of points scored by the top-scoring entity in a given context or event.
- 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_69c008d7a9c4819098d647ec47776917 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c067f8758081909c5ce40abf57dd2c |
completed | March 22, 2026, 10:06 p.m. |
| PD | Predicate disambiguation | batch_69c060ec091c8190912aac44e1b8b1c9 |
completed | March 22, 2026, 9:36 p.m. |
Created at: March 22, 2026, 4:32 p.m.