Triple
T10837410
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Okawa Prize |
E255797
|
entity |
| Predicate | domainOfContribution |
P9961
|
FINISHED |
| Object | information and telecommunications technologies |
—
|
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: information and telecommunications technologies | Statement: [Okawa Prize, domainOfContribution, information and telecommunications technologies]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: domainOfContribution Context triple: [Okawa Prize, domainOfContribution, information and telecommunications technologies]
-
A.
scopeOfContribution
chosen
Indicates the specific area, domain, or extent within which an entity’s contribution or involvement applies.
-
B.
fieldOfSignificance
Indicates that something holds particular importance, relevance, or impact within a specified domain, context, or area of interest.
-
C.
notableContributionField
Indicates the field or domain in which an entity has made a significant or noteworthy contribution.
-
D.
fieldOfWork
Indicates the professional or academic domain in which an entity is primarily engaged or specializes.
-
E.
regionOfStudy
Indicates the academic or research area that is the focus of someone’s study or investigation.
- 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_69d6aa81a5d08190aa86689061d1ddd2 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d746ff70148190b844ab92d796af6c |
completed | April 9, 2026, 6:28 a.m. |
| PD | Predicate disambiguation | batch_69d70d25280c8190b648d7d1958b413a |
completed | April 9, 2026, 2:21 a.m. |
Created at: April 8, 2026, 9:19 p.m.