Triple
T735621
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Most Outstanding Player of the College World Series |
E14923
|
entity |
| Predicate | hasWinnerCountPerYear |
P6983
|
FINISHED |
| Object | 1 |
—
|
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: 1 | Statement: [Most Outstanding Player of the College World Series, hasWinnerCountPerYear, 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWinnerCountPerYear Context triple: [Most Outstanding Player of the College World Series, hasWinnerCountPerYear, 1]
-
A.
winnerCount
chosen
Indicates the number of entities that are designated as winners in a given context or event.
-
B.
firstWinnerYear
Indicates the year in which an entity first won a particular competition, award, or title.
-
C.
numberOfMedalsPerYear
Indicates the quantity of medals associated with or awarded in a given year.
-
D.
hallOfFameEligibilityYear
Indicates the specific year in which an entity becomes eligible for induction into a hall of fame.
-
E.
gamesWonBy
Indicates the number of games that have been won by a particular entity in a given context.
- 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_69a4934d9930819099eed80096b0597d |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a66820548190b373deb117187c2c |
completed | March 1, 2026, 8:49 p.m. |
| PD | Predicate disambiguation | batch_69a4a4fafee081909bf356854c09aaff |
completed | March 1, 2026, 8:43 p.m. |
Created at: March 1, 2026, 7:37 p.m.