Triple
T5057592
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stanislaus County Sheriff’s Office |
E113940
|
entity |
| Predicate | correctionsFunction |
P56109
|
FINISHED |
| Object | inmate housing |
—
|
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: inmate housing | Statement: [Stanislaus County Sheriff’s Office, correctionsFunction, inmate housing]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: correctionsFunction Context triple: [Stanislaus County Sheriff’s Office, correctionsFunction, inmate housing]
-
A.
requiresCorrection
Indicates that something is identified as needing modification, adjustment, or fixing to correct an error or deficiency.
-
B.
usesCorrectorType
Indicates that one entity applies or employs a corrector of the specified type in performing an action or process.
-
C.
concernsFunction
chosen
Indicates that something is related to, involves, or pertains to a particular function or functional aspect.
-
D.
affectedFunction
Indicates that one entity has an impact on, alters, or impairs the operation or behavior of another entity’s function.
-
E.
aberrationCorrection
Indicates the application or presence of a process that corrects optical or imaging aberrations in a system or setup.
- 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_69bd443aa1f88190abb992d138f2cf42 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd7450312881908d4e3576ca65f7fb |
completed | March 20, 2026, 4:22 p.m. |
| PD | Predicate disambiguation | batch_69bd715622b48190a3e8e49a5ef62b4a |
completed | March 20, 2026, 4:09 p.m. |
Created at: March 20, 2026, 1:38 p.m.