Triple
T12992886
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fayette County jail |
E321958
|
entity |
| Predicate | inmateTurnover |
P107932
|
FINISHED |
| Object | high turnover rate |
—
|
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 turnover rate | Statement: [Fayette County jail, inmateTurnover, high turnover rate]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: inmateTurnover Context triple: [Fayette County jail, inmateTurnover, high turnover rate]
-
A.
inmates
Indicates that one entity is confined or held as a prisoner within an institution or facility associated with another entity.
-
B.
supervisesInmateMovement
Indicates that one entity oversees, directs, or monitors the movement of an inmate from one location to another.
-
C.
lastRemainingInmate
Indicates that the subject is the final inmate still present or remaining in a given context, after all others are gone.
-
D.
estimatedPrisonerCount
Indicates the estimated number of prisoners associated with a particular context, such as a location, time period, or event.
-
E.
peakIncarcerationPeriod
Indicates the time period during which incarceration levels for the referenced entity were at their highest.
- 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_69d8076479b8819090afce3591939cdf |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97f2a71a0819098bb6cf8a4b2208a |
completed | April 10, 2026, 10:52 p.m. |
| PD | Predicate disambiguation | batch_69d97dbdd94c8190ac4bbecca02dc77b |
completed | April 10, 2026, 10:46 p.m. |
| PDg | Predicate description generation | batch_69d97f1badac8190a59e60751f47b8d6 |
completed | April 10, 2026, 10:52 p.m. |
Created at: April 9, 2026, 8:44 p.m.