Triple
T6154444
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | bengoshi |
E137283
|
entity |
| Predicate | professionalTitleInJapanese |
P57913
|
FINISHED |
| Object | 弁護士 |
—
|
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: 弁護士 | Statement: [bengoshi, professionalTitleInJapanese, 弁護士]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: professionalTitleInJapanese Context triple: [bengoshi, professionalTitleInJapanese, 弁護士]
-
A.
officeHolderTitleInJapanese
Indicates the official title or designation of an office holder as expressed in the Japanese language.
-
B.
titleInJapanese
Indicates that one entity is the title of another entity expressed specifically in the Japanese language.
-
C.
equivalentTitleInJapanese
chosen
Indicates that one entity has a corresponding or matching title in Japanese that is equivalent in meaning or usage to the other entity’s title.
-
D.
professionalTitleAbbreviation
Indicates that one entity is an abbreviated form of a professional title associated with another entity.
-
E.
nameInJapaneseKana
Indicates that an entity’s name is written or represented using Japanese kana characters.
- 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_69c008a45d008190832a9e19f5d63406 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c05d01ddb0819085b5f5338b86a25d |
completed | March 22, 2026, 9:20 p.m. |
| PD | Predicate disambiguation | batch_69c055f39e0881909ae56444b1b48929 |
completed | March 22, 2026, 8:49 p.m. |
Created at: March 22, 2026, 4:17 p.m.