Triple
T8174837
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 小柴昌俊 |
E190914
|
entity |
| Predicate | 業績の影響 |
P81252
|
FINISHED |
| Object | ニュートリノを用いた宇宙観測の道を開いた |
—
|
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: ニュートリノを用いた宇宙観測の道を開いた | Statement: [小柴昌俊, 業績の影響, ニュートリノを用いた宇宙観測の道を開いた]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 業績の影響 Context triple: [小柴昌俊, 業績の影響, ニュートリノを用いた宇宙観測の道を開いた]
-
A.
impactOnPerformance
Indicates that one entity has an effect, influence, or consequence on the performance level or effectiveness of another entity.
-
B.
workImpact
Indicates that one entity’s work has an effect or influence on another entity, situation, or outcome.
-
C.
impactOnBusiness
Indicates the effect or influence that one factor, event, or action has on a business’s performance, operations, or outcomes.
-
D.
hasSignificantInfluenceIn
Indicates that one entity exerts a substantial impact or shaping effect on another entity within a particular domain, context, or outcome.
-
E.
indirectImpactOn
Indicates that one entity affects another entity’s state, condition, or outcome through one or more intermediate factors rather than through a direct interaction.
- F. None of above. chosen
Provenance (4 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_69ca82c1c0a08190bf8692b4d91a03ca |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb480a7ac4819088fabd5bec6ba2e5 |
completed | March 31, 2026, 4:05 a.m. |
| PD | Predicate disambiguation | batch_69cb36a7952481908f34e3e82f375a84 |
completed | March 31, 2026, 2:51 a.m. |
| PDg | Predicate description generation | batch_69cb45503eec8190aeef0da6c3324710 |
completed | March 31, 2026, 3:53 a.m. |
Created at: March 30, 2026, 5:40 p.m.