Triple
T300607
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | RESTCONF |
E6188
|
entity |
| Predicate | resourceModel |
P535
|
FINISHED |
| Object | YANG-defined datastore resources |
—
|
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: YANG-defined datastore resources | Statement: [RESTCONF, resourceModel, YANG-defined datastore resources]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: resourceModel Context triple: [RESTCONF, resourceModel, YANG-defined datastore resources]
-
A.
fundingModel
Indicates how an entity is financially supported or sustained, such as through specific revenue sources, payment structures, or funding mechanisms.
-
B.
dataModel
chosen
Indicates a relationship where an entity defines, uses, or is structured according to a specific data model or schema.
-
C.
operatingModel
Indicates how an organization structures and manages its processes, resources, and governance to deliver its products or services.
-
D.
model
Indicates that one entity serves as a representation, example, or simulation of another entity or concept.
-
E.
modelNumber
Indicates that one entity is the specific model identifier or code assigned to another entity (such as a product or device).
- 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_69a2e79114b081909490b3bf5a5dbb51 |
completed | Feb. 28, 2026, 1:03 p.m. |
| NER | Named-entity recognition | batch_69a2ea2fba548190a5aeb1597dca96bd |
completed | Feb. 28, 2026, 1:14 p.m. |
| PD | Predicate disambiguation | batch_69a2e93aff048190a633c8ae2b76a41f |
completed | Feb. 28, 2026, 1:10 p.m. |
Created at: Feb. 28, 2026, 1:06 p.m.