Triple
T5963752
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rookie of the Year Award |
E132701
|
entity |
| Predicate | numberOfWinnersPerYear |
P66376
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [Rookie of the Year Award, numberOfWinnersPerYear, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfWinnersPerYear Context triple: [Rookie of the Year Award, numberOfWinnersPerYear, 2]
-
A.
numberOfWinnersPerSeason
chosen
Indicates the relationship that specifies how many winners are associated with each individual season.
-
B.
maximumNumberOfLaureatesPerYear
Indicates the highest allowable or observed count of laureates associated with a given year.
-
C.
mostWinsYears
Indicates the years during which an entity achieved the highest number of wins compared to others or compared to its own performance in other years.
-
D.
typicalNumberOfLaureatesPerYear
Indicates the usual or average number of laureates associated with a given award or context in a single year.
-
E.
yearOfWin
Indicates the specific calendar year in which an entity achieved a particular victory or win.
- 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_69c0086c2364819091e9fe2f58fa2517 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c03fb7f8a88190a8bd45208bda4a03 |
completed | March 22, 2026, 7:15 p.m. |
| PD | Predicate disambiguation | batch_69c0335a635881909c58c1ef0f97f1e8 |
completed | March 22, 2026, 6:22 p.m. |
Created at: March 22, 2026, 4:03 p.m.