Triple
T27128725
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | UNESCO-Japan Prize on Education for Sustainable Development |
E681506
|
entity |
| Predicate | numberOfLaureatesPerEdition |
P13298
|
FINISHED |
| Object | 3 |
—
|
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: 3 | Statement: [UNESCO-Japan Prize on Education for Sustainable Development, numberOfLaureatesPerEdition, 3]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfLaureatesPerEdition Context triple: [UNESCO-Japan Prize on Education for Sustainable Development, numberOfLaureatesPerEdition, 3]
-
A.
maximumNumberOfLaureatesPerEdition
Indicates the highest number of laureates that can be awarded in a single edition of a given prize or event.
-
B.
typicalNumberOfLaureatesPerCycle
chosen
Indicates the usual or average number of laureates associated with each award cycle or iteration.
-
C.
typicalNumberOfLaureatesPerYear
Indicates the usual or average number of laureates associated with a given award or context in a single year.
-
D.
maximumNumberOfLaureatesPerYear
Indicates the highest allowable or observed count of laureates associated with a given year.
-
E.
numberOfNobelPrizes
Indicates the count of Nobel Prizes that have been awarded to a given entity.
- 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_69eefacbcc2081909ebf00daa23f1981 |
completed | April 27, 2026, 5:57 a.m. |
| NER | Named-entity recognition | batch_69fe91383a1c81909266e40c3c3ede6c |
completed | May 9, 2026, 1:43 a.m. |
| PD | Predicate disambiguation | batch_69fe8fde094081908f0f121664fbb5c7 |
completed | May 9, 2026, 1:37 a.m. |
Created at: April 27, 2026, 9:03 a.m.