Triple
T921339
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Virginia Community College System |
E19889
|
entity |
| Predicate | credentialFocus |
P6396
|
FINISHED |
| Object | short-term workforce credentials |
—
|
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: short-term workforce credentials | Statement: [Virginia Community College System, credentialFocus, short-term workforce credentials]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: credentialFocus Context triple: [Virginia Community College System, credentialFocus, short-term workforce credentials]
-
A.
credentialType
chosen
Indicates the specific kind or category of credential associated with an entity or relationship.
-
B.
identityConcept
Indicates that two concepts are the same in identity, representing exactly the same underlying idea or meaning.
-
C.
subjectIdentity
Indicates that two or more references correspond to the same underlying entity or individual identity.
-
D.
recognizedFor
Indicates that one entity is acknowledged, credited, or honored for a particular achievement, quality, contribution, or work associated with another entity.
-
E.
creditRecognition
Indicates that one party formally acknowledges and accepts academic or professional credits earned from another source.
- 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_69a493a099788190a696d9d8408cbaf4 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b313cb908190ad78b3a54e4f2eb7 |
completed | March 1, 2026, 9:43 p.m. |
| PD | Predicate disambiguation | batch_69a4b295b02481908e5f53bfcb83cc94 |
completed | March 1, 2026, 9:41 p.m. |
Created at: March 1, 2026, 7:40 p.m.