Triple
T8325412
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 岸信介 |
E194938
|
entity |
| Predicate | 影響を与えた分野 |
P19800
|
FINISHED |
| Object | 日本の安全保障政策 |
—
|
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: 日本の安全保障政策 | Statement: [岸信介, 影響を与えた分野, 日本の安全保障政策]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 影響を与えた分野 Context triple: [岸信介, 影響を与えた分野, 日本の安全保障政策]
-
A.
hasInfluenceOnDiscipline
chosen
Indicates that one entity exerts an effect, shaping force, or contributing impact on the development, direction, or state of a particular discipline.
-
B.
influenced
Indicates that one entity has affected, shaped, or altered another entity’s state, behavior, or characteristics.
-
C.
influencedPolicyArea
Indicates that one entity has affected, shaped, or guided the development, direction, or implementation of a particular policy area associated with another entity.
-
D.
placeOfInfluence
Indicates the location or area where an entity exerts significant impact, authority, or cultural, social, or intellectual influence.
-
E.
hasEnduringInfluenceOn
Indicates that one entity exerts a lasting, long-term impact on another entity’s state, development, or behavior.
- 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_69ca82e7a8a88190a32bb5cc0feb012d |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb7f7fba688190b696593dfb2cde5d |
completed | March 31, 2026, 8:02 a.m. |
| PD | Predicate disambiguation | batch_69cb70c3231c81909e3d463192c9de22 |
completed | March 31, 2026, 6:59 a.m. |
Created at: March 30, 2026, 5:56 p.m.