Triple
T23255180
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Coyote Ridge Corrections Center |
E581847
|
entity |
| Predicate | hasEmployerImpact |
P91516
|
FINISHED |
| Object | local economy |
—
|
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: local economy | Statement: [Coyote Ridge Corrections Center, hasEmployerImpact, local economy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEmployerImpact Context triple: [Coyote Ridge Corrections Center, hasEmployerImpact, local economy]
-
A.
effectOnEmployers
chosen
Indicates the impact or consequences that something has on employers, such as changes to their responsibilities, costs, or working conditions.
-
B.
workImpact
Indicates that one entity’s work has an effect or influence on another entity, situation, or outcome.
-
C.
hasIndustrialEmployer
Indicates that an entity is employed by, or has an employment relationship with, an industrial organization or company.
-
D.
hasMajorEmployer
Indicates that an entity has a primary or most significant employer with which it is chiefly affiliated for work or occupation.
-
E.
hasImpactArea
Indicates that an entity affects, influences, or has consequences within a specific area, domain, or scope.
- 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_69e246079f58819085eaa9c260906880 |
completed | April 17, 2026, 2:39 p.m. |
| NER | Named-entity recognition | batch_69f193fadac881908579b5729718ee41 |
completed | April 29, 2026, 5:15 a.m. |
| PD | Predicate disambiguation | batch_69effce4d704819092826931d430e8c4 |
completed | April 28, 2026, 12:18 a.m. |
Created at: April 17, 2026, 4:11 p.m.