Triple
T3218729
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nagasaki Branch of Fukuoka High Court |
E67457
|
entity |
| Predicate | hasTypeOfJudge |
P10518
|
FINISHED |
| Object | high court judge |
—
|
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: high court judge | Statement: [Nagasaki Branch of Fukuoka High Court, hasTypeOfJudge, high court judge]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypeOfJudge Context triple: [Nagasaki Branch of Fukuoka High Court, hasTypeOfJudge, high court judge]
-
A.
hasJudgeType
chosen
Indicates that an entity is associated with a specific category or type of judge.
-
B.
hasJudge
Indicates that a legal case, proceeding, or decision is presided over or decided by a particular judge.
-
C.
hasJuryType
Indicates that an entity is associated with, or classified by, a specific type or category of jury.
-
D.
hasJudgesRole
Indicates that an entity serves in the capacity or role of a judge within a specified context or system.
-
E.
hasTypeOfCourt
Indicates that an entity is associated with or classified by a specific type or category of court.
- 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_69ad858b8adc8190ad989712c87a476b |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69adab0c48b481909d1bd9dc41dfa8c2 |
completed | March 8, 2026, 4:59 p.m. |
| PD | Predicate disambiguation | batch_69ad9e0bb6c48190a0659c67d40ee37c |
completed | March 8, 2026, 4:04 p.m. |
Created at: March 8, 2026, 3:08 p.m.