Triple
T5442794
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Criminal Division |
E122175
|
entity |
| Predicate | caseTypesInclude |
P10545
|
FINISHED |
| Object | arraignments |
—
|
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: arraignments | Statement: [Criminal Division, caseTypesInclude, arraignments]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: caseTypesInclude Context triple: [Criminal Division, caseTypesInclude, arraignments]
-
A.
typicalCaseTypes
chosen
Indicates the kinds or categories of cases that are most commonly associated with or handled by a given entity.
-
B.
hasTypeOfCase
Indicates that an entity is associated with or classified under a particular type or category of case.
-
C.
classificationIncludes
Indicates that a broader classification category encompasses or contains a specified subclass, member, or element within its scope.
-
D.
hasCase
Indicates that one entity is involved in, associated with, or characterized by a particular case, instance, or occurrence represented by another entity.
-
E.
includesRouteType
Indicates that one entity’s set of routes contains or covers a specific type or category of route associated with another entity.
- 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_69bd46400768819092925d461c0b8432 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd922f66bc8190b7d47fd68d2fcf2e |
completed | March 20, 2026, 6:30 p.m. |
| PD | Predicate disambiguation | batch_69bd919aeb048190b786f814177d6cd9 |
completed | March 20, 2026, 6:27 p.m. |
Created at: March 20, 2026, 2:07 p.m.