Triple
T36803951
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 大隅良典 |
E909393
|
entity |
| Predicate | 業績の応用分野 |
P81586
|
FINISHED |
| Object | がん研究 |
—
|
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: がん研究 | Statement: [大隅良典, 業績の応用分野, がん研究]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 業績の応用分野 Context triple: [大隅良典, 業績の応用分野, がん研究]
-
A.
appliedWork
Indicates that an entity has put effort, skill, or labor into performing or producing a particular work or task.
-
B.
hasProfessionalApplication
Indicates that something is used or applied within a professional, occupational, or work-related context.
-
C.
usedInIndustry
Indicates that something is employed or applied within a particular industry or industrial sector.
-
D.
appliedPrimarilyTo
chosen
Indicates that something is used mainly or chiefly in relation to a particular target, context, or purpose, rather than being used broadly or equally elsewhere.
-
E.
appliesResearchTo
Indicates that an entity uses or implements research findings, methods, or insights in relation to another entity, context, or problem.
- 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_69f76e7b98888190899b6478a82ad6ae |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69f7ca93bd0481909d6eee9e950001a1 |
completed | May 3, 2026, 10:22 p.m. |
| PD | Predicate disambiguation | batch_69f7c89b528c8190bf80b230fc7c7108 |
completed | May 3, 2026, 10:13 p.m. |
Created at: May 3, 2026, 4:12 p.m.