Triple
T9542
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | School of Nursing |
E192
|
entity |
| Predicate | contributesTo |
P477
|
FINISHED |
| Object | healthcare workforce development |
—
|
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: healthcare workforce development | Statement: [School of Nursing, contributesTo, healthcare workforce development]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: contributesTo Context triple: [School of Nursing, contributesTo, healthcare workforce development]
-
A.
notableContribution
chosen
Indicates that an entity has made a significant, recognized contribution to another entity, field, work, or endeavor.
-
B.
relatedTo
Indicates a general, non-specific relationship or association exists between two entities.
-
C.
worksWith
Indicates that two entities collaborate or perform tasks together in a shared work-related context.
-
D.
hasConcept
Indicates that an entity includes, embodies, or is associated with a particular concept.
-
E.
grantedTo
Indicates that a right, permission, or resource has been formally given or assigned by one party to another.
- 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_69a23bb612708190b09f25385e4b63d1 |
completed | Feb. 28, 2026, 12:49 a.m. |
| NER | Named-entity recognition | batch_69a240b249788190af8dbf7e80e9c91b |
completed | Feb. 28, 2026, 1:11 a.m. |
| PD | Predicate disambiguation | batch_69a23fe52ec48190a4d24101c91434ed |
completed | Feb. 28, 2026, 1:07 a.m. |
Created at: Feb. 28, 2026, 12:54 a.m.