Triple
T9164382
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oregon justice courts |
E219909
|
entity |
| Predicate | typeOfCasesAreUsually |
P10545
|
FINISHED |
| Object | high-volume |
—
|
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-volume | Statement: [Oregon justice courts, typeOfCasesAreUsually, high-volume]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfCasesAreUsually Context triple: [Oregon justice courts, typeOfCasesAreUsually, high-volume]
-
A.
typeOfCasesHandled
Indicates the categories or kinds of cases that an entity (such as a person, organization, or system) is responsible for managing or processing.
-
B.
caseTypes
Indicates the types or categories of cases associated with or applicable to an entity or situation.
-
C.
typicalCaseTypes
chosen
Indicates the kinds or categories of cases that are most commonly associated with or handled by a given entity.
-
D.
numberOfCases
Indicates the total count of individual instances, occurrences, or records associated with a particular situation, condition, or category.
-
E.
typeOfAppeals
Indicates the specific category or kind of appeals associated with or applied to a given case, decision, or legal action.
- 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_69ca83e3633c81908688a9fa2306ba99 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69ccaa2ee64c8190a9a5abafe5d0b086 |
completed | April 1, 2026, 5:16 a.m. |
| PD | Predicate disambiguation | batch_69cc6605c6808190a30d92da006206ac |
completed | April 1, 2026, 12:25 a.m. |
Created at: March 30, 2026, 7:21 p.m.