Triple
T27128727
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | UNESCO-Japan Prize on Education for Sustainable Development |
E681506
|
entity |
| Predicate | totalAnnualMonetaryValue |
P482
|
FINISHED |
| Object | 150000 US dollars |
—
|
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: 150000 US dollars | Statement: [UNESCO-Japan Prize on Education for Sustainable Development, totalAnnualMonetaryValue, 150000 US dollars]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: totalAnnualMonetaryValue Context triple: [UNESCO-Japan Prize on Education for Sustainable Development, totalAnnualMonetaryValue, 150000 US dollars]
-
A.
monetaryValue
chosen
Indicates the amount of money associated with an entity, event, or transaction.
-
B.
hasMonetaryComponent
Indicates that something includes, involves, or is associated with a monetary or financial element.
-
C.
monetaryComponent
Indicates that something is a financial element or part of a larger monetary value, structure, or transaction.
-
D.
totalAidAmountUSD
Indicates the total monetary value of aid provided, expressed in U.S. dollars.
-
E.
monetaryValueSponsor
Indicates that a sponsor provides or assigns a specific monetary value (amount of funding) to another entity or activity.
- 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_69f65f7731e4819099d5bd3d915ee266 |
completed | May 2, 2026, 8:32 p.m. |
| PD | Predicate disambiguation | batch_69f65c1f94ac8190bc6fbc7916fc0d82 |
completed | May 2, 2026, 8:18 p.m. |
Created at: April 27, 2026, 9:03 a.m.