Triple
T22227748
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | California Teacher of the Year |
E549388
|
entity |
| Predicate | numberOfHonoreesPerYear |
P119394
|
FINISHED |
| Object | up to five |
—
|
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: up to five | Statement: [California Teacher of the Year, numberOfHonoreesPerYear, up to five]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfHonoreesPerYear Context triple: [California Teacher of the Year, numberOfHonoreesPerYear, up to five]
-
A.
numberOfHonoreesPerPeriod
chosen
Indicates the count of honorees associated with each defined time period.
-
B.
maximumNumberOfLaureatesPerYear
Indicates the highest allowable or observed count of laureates associated with a given year.
-
C.
numberOfAwardsPerYear
Indicates the number of awards associated with an entity within a given year.
-
D.
hasHonorees
Indicates that one entity is recognized or celebrated by listing another entity or entities as its honorees.
-
E.
typicalNumberOfLaureatesPerYear
Indicates the usual or average number of laureates associated with a given award or context in a single year.
- 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_69e11e403d6481909a94d0aaf157f6ef |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f12befe38c8190b547586e41b099b1 |
completed | April 28, 2026, 9:51 p.m. |
| PD | Predicate disambiguation | batch_69e71b4dcc408190a30429fb08fcf39e |
completed | April 21, 2026, 6:38 a.m. |
Created at: April 16, 2026, 8:37 p.m.