Triple
T16016704
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 山口那津男 |
E388484
|
entity |
| Predicate | 法学の専門性 |
P6403
|
FINISHED |
| Object | 憲法・行政法などの公法分野 |
—
|
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: 憲法・行政法などの公法分野 | Statement: [山口那津男, 法学の専門性, 憲法・行政法などの公法分野]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 法学の専門性 Context triple: [山口那津男, 法学の専門性, 憲法・行政法などの公法分野]
-
A.
legalSystemSpecialization
Indicates that a legal system is specialized or tailored to address a particular domain, issue, or type of case within the broader legal framework.
-
B.
lawReview
Indicates a relationship where an entity is associated with a law review, typically as its subject, source, or venue of publication within legal scholarship.
-
C.
branchOfLaw
chosen
Indicates a relationship where one legal field or discipline is a subdivision or specialized area within a broader body of law.
-
D.
studiedLawBy
Indicates that one entity pursued or received legal education under the instruction, supervision, or at the institution represented by the other entity.
-
E.
subjectOfLaw
Indicates that a law, legal document, or legal provision is about, concerns, or applies to the referenced subject.
- 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_69d86dabcb7c8190b6a39d6831d2fa1b |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e1858a00888190b8505071575dc56f |
completed | April 17, 2026, 12:57 a.m. |
| PD | Predicate disambiguation | batch_69e1826a4f7c8190aba6d4f1075141b0 |
completed | April 17, 2026, 12:44 a.m. |
Created at: April 10, 2026, 4:55 a.m.