Triple
T186416
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | district courts of Japan |
E3990
|
entity |
| Predicate | criminalPanelComposition |
P1126
|
FINISHED |
| Object | one or three professional judges |
—
|
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: one or three professional judges | Statement: [district courts of Japan, criminalPanelComposition, one or three professional judges]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: criminalPanelComposition Context triple: [district courts of Japan, criminalPanelComposition, one or three professional judges]
-
A.
chamberInvolved
chosen
Indicates that a particular chamber (e.g., legislative or judicial body) participates in, is affected by, or plays a role in the referenced event, process, or relationship.
-
B.
committeeMember
Indicates that an entity serves as a member of a particular committee.
-
C.
numberOfPeopleAccused
Indicates the count of individuals who are formally alleged to have committed a particular act or offense.
-
D.
hasCommittee
Indicates that an entity is associated with or overseen by a specific committee.
-
E.
notableProsecutor
Indicates that the person served as a prosecutor in a way that is widely recognized as significant or noteworthy.
- 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_69a25497e2f08190a040f8c6e1842643 |
completed | Feb. 28, 2026, 2:36 a.m. |
| NER | Named-entity recognition | batch_69a2594809288190b3d3b1283e7e0d00 |
completed | Feb. 28, 2026, 2:56 a.m. |
| PD | Predicate disambiguation | batch_69a25670feb081908e26a2543ebe7b3a |
completed | Feb. 28, 2026, 2:44 a.m. |
Created at: Feb. 28, 2026, 2:40 a.m.