Triple
T32330413
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Parole and Probation Administration |
E826033
|
entity |
| Predicate | typeOfCorrectionalModel |
P202100
|
FINISHED |
| Object | community corrections |
—
|
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: community corrections | Statement: [Parole and Probation Administration, typeOfCorrectionalModel, community corrections]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfCorrectionalModel Context triple: [Parole and Probation Administration, typeOfCorrectionalModel, community corrections]
-
A.
typeOfConvict
Indicates the specific category or classification of a convict in relation to their conviction or legal status.
-
B.
prisonType
Indicates the specific category or classification of a prison associated with an entity.
-
C.
usesCorrectorType
Indicates that one entity applies or employs a corrector of the specified type in performing an action or process.
-
D.
typeOfProbation
Indicates the specific category or form of probation that applies to an entity within a legal or supervisory context.
-
E.
prisonerType
Indicates the classification or category assigned to a prisoner within a correctional or detention system.
- F. None of above. chosen
Provenance (4 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_69f34913d9048190befaa634025232be |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_6a00512437d48190ad20324968ead5f4 |
completed | May 10, 2026, 9:34 a.m. |
| PD | Predicate disambiguation | batch_6a0050227350819099f41369c3d168be |
completed | May 10, 2026, 9:30 a.m. |
| PDg | Predicate description generation | batch_6a0051234cc08190adae6e2cfae2f8cc |
completed | May 10, 2026, 9:34 a.m. |
Created at: May 1, 2026, 12:47 a.m.