Triple
T394132
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | High Courts of Japan |
E8942
|
entity |
| Predicate | typeOfCases |
P4217
|
FINISHED |
| Object | civil appeals |
—
|
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: civil appeals | Statement: [High Courts of Japan, typeOfCases, civil appeals]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfCases Context triple: [High Courts of Japan, typeOfCases, civil appeals]
-
A.
hasTypeOfCase
chosen
Indicates that an entity is associated with or classified under a particular type or category of case.
-
B.
typeOfLaw
Indicates that one entity is a specific category or kind of law to which the other entity pertains.
-
C.
hasTypeOfCourt
Indicates that an entity is associated with or classified by a specific type or category of court.
-
D.
hasNumberOfCasesApprox
Indicates that an entity is associated with an approximate (not exact) count of cases.
-
E.
legalCase
Indicates a relationship where a formal legal dispute or proceeding exists between parties, typically adjudicated by a court or similar authority.
- 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_69a2e7f55c60819097aff65ea2ca2832 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ec77a8a08190b6f96373aa8c1346 |
completed | Feb. 28, 2026, 1:24 p.m. |
| PD | Predicate disambiguation | batch_69a2e96bd3848190a66ca14dfbd26da5 |
completed | Feb. 28, 2026, 1:11 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.