Triple
T1127463
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mike Gartner |
E24751
|
entity |
| Predicate | scoredAtLeast30GoalsInSeason |
P9098
|
FINISHED |
| Object | 17 times |
—
|
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: 17 times | Statement: [Mike Gartner, scoredAtLeast30GoalsInSeason, 17 times]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: scoredAtLeast30GoalsInSeason Context triple: [Mike Gartner, scoredAtLeast30GoalsInSeason, 17 times]
-
A.
goalScorer
Indicates that the subject is the player who scored a particular goal in a game or match.
-
B.
numberOfGoals
chosen
Indicates the total count of goals scored or achieved by an entity in a given context.
-
C.
topScorer
Indicates that the subject is the individual with the highest score among a specified group or in a particular context.
-
D.
MVPGoals
Indicates that an entity has been recognized as the Most Valuable Player (MVP) based on achieving specific performance goals or criteria.
-
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_69a4940712c88190aa244f3fc6070a65 |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bc4bc21881909dcfe628f59f3e8c |
completed | March 1, 2026, 10:23 p.m. |
| PD | Predicate disambiguation | batch_69a4bb48de2081909a0dce005b1c9df1 |
completed | March 1, 2026, 10:18 p.m. |
Created at: March 1, 2026, 7:44 p.m.