Triple
T300496
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | YANG modeling language |
E6186
|
entity |
| Predicate | updatedStandardPublicationYear |
P7939
|
FINISHED |
| Object | 2016 |
—
|
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: 2016 | Statement: [YANG modeling language, updatedStandardPublicationYear, 2016]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: updatedStandardPublicationYear Context triple: [YANG modeling language, updatedStandardPublicationYear, 2016]
-
A.
publicationYear
Indicates the specific calendar year in which a work was formally published or released.
-
B.
standardizedInYear
chosen
Indicates the specific year in which something was formally standardized or adopted as a standard.
-
C.
publicationPeriod
Indicates the span of time during which something is published, active in publication, or valid as a published work.
-
D.
becameStandardIssueByYear
Indicates that an item started being officially issued as standard equipment by a specified year.
-
E.
originYear
Indicates the year in which something first originated, was created, or began.
- 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.