Triple
T5435125
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Clinton Correctional Facility |
E121988
|
entity |
| Predicate | hasInmateLabor |
P64045
|
FINISHED |
| Object | industrial work programs |
—
|
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: industrial work programs | Statement: [Clinton Correctional Facility, hasInmateLabor, industrial work programs]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasInmateLabor Context triple: [Clinton Correctional Facility, hasInmateLabor, industrial work programs]
-
A.
hasPrisoners
Indicates that an entity holds or contains one or more individuals who are imprisoned or detained.
-
B.
hasPrison
Indicates that one entity possesses, contains, or is the location of a prison associated with another entity.
-
C.
hasPrisonService
Indicates that an entity provides, manages, or is responsible for prison-related services or operations for another entity.
-
D.
hasNotableCategoryOfPrisoners
Indicates that a prison is known for housing a specific, notable category or type of prisoners.
-
E.
usedForcedLabor
Indicates that an entity compelled people to work against their will, typically under coercion, threat, or without fair compensation.
- 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_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. |
| PDg | Predicate description generation | batch_69bd922dc688819092bf33589ebc6d50 |
completed | March 20, 2026, 6:30 p.m. |
Created at: March 20, 2026, 2:06 p.m.