Triple
T4197410
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Union County, Florida |
E89185
|
entity |
| Predicate | hasCorrectionalIndustry |
P21915
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Union County, Florida, hasCorrectionalIndustry, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCorrectionalIndustry Context triple: [Union County, Florida, hasCorrectionalIndustry, true]
-
A.
containsIndustry
Indicates that one entity includes or encompasses a particular industry within its scope, structure, or operations.
-
B.
hasPrison
chosen
Indicates that one entity possesses, contains, or is the location of a prison associated with another entity.
-
C.
hasIndustryProgram
Indicates that an entity offers, participates in, or is associated with a structured program involving collaboration or engagement with industry organizations or sectors.
-
D.
isCriminalizedIn
Indicates that a specific behavior, action, or condition is prohibited and subject to legal penalties within a particular jurisdiction or legal system.
-
E.
hasPrincipalIndustry
Indicates that an entity’s main or primary industry of operation is the specified industry.
- 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_69aed9569a4481908b6c1fcec2a11e21 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af0360bc8081908ceb2483eef89174 |
completed | March 9, 2026, 5:29 p.m. |
| PD | Predicate disambiguation | batch_69af01959c4881909eb1adcb3bdadbe6 |
completed | March 9, 2026, 5:21 p.m. |
Created at: March 9, 2026, 3:46 p.m.