Triple
T24854141
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Danville Correctional Center |
E621974
|
entity |
| Predicate | hasVocationalProgram |
P2489
|
FINISHED |
| Object | vocational training |
—
|
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: vocational training | Statement: [Danville Correctional Center, hasVocationalProgram, vocational training]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasVocationalProgram Context triple: [Danville Correctional Center, hasVocationalProgram, vocational training]
-
A.
hasEducationalProgram
chosen
Indicates that an entity offers, runs, or is associated with a specific educational program.
-
B.
hasAcademicProgramsWith
Indicates that two institutions or entities offer or participate in the same or jointly organized academic programs.
-
C.
hasWorkProgram
Indicates that an entity offers, participates in, or is associated with a specific work-related program or scheme.
-
D.
offersProfessionalPrograms
Indicates that an entity provides formal, career-oriented educational or training programs to others.
-
E.
hasIndustryProgram
Indicates that an entity offers, participates in, or is associated with a structured program involving collaboration or engagement with industry organizations or sectors.
- 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_69e2fac297e481909d3aedc75f585e42 |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f453035f508190be83a3d521723acf |
completed | May 1, 2026, 7:15 a.m. |
| PD | Predicate disambiguation | batch_69f44d6ef33081908f5d36ba1ae5f473 |
completed | May 1, 2026, 6:51 a.m. |
Created at: April 18, 2026, 5:21 a.m.