Triple
T17520611
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pipeline (scikit-learn) |
E426670
|
entity |
| Predicate | importExample |
P30248
|
FINISHED |
| Object | from sklearn.pipeline import Pipeline |
—
|
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: from sklearn.pipeline import Pipeline | Statement: [Pipeline (scikit-learn), importExample, from sklearn.pipeline import Pipeline]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: importExample Context triple: [Pipeline (scikit-learn), importExample, from sklearn.pipeline import Pipeline]
-
A.
baseExamples
Indicates that something serves as a fundamental or illustrative example for understanding or demonstrating another concept, item, or case.
-
B.
hasExample
Indicates that one entity serves as an instance, illustration, or concrete example of another entity.
-
C.
codeExample
chosen
Indicates that one entity provides a snippet or sample of source code that illustrates how to use, implement, or demonstrate another entity.
-
D.
exampleType
Indicates that one entity serves as a representative or illustrative instance of the type or category defined by another entity.
-
E.
backendExample
Indicates that something serves as an example or illustrative instance within a backend or server-side context.
- 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_69d889de677081909b22d2657b1f0292 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e452d23cf08190925510344fa36f57 |
completed | April 19, 2026, 3:58 a.m. |
| PD | Predicate disambiguation | batch_69e3b4f8b9888190aa8a45e09acf4319 |
completed | April 18, 2026, 4:44 p.m. |
Created at: April 10, 2026, 5:49 a.m.