Triple
T18828838
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jupyter Server |
E460468
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | Jupyter ecosystem component |
C16180
|
CONCEPT FINISHED |
How this triple was built (1 step)
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.
CD
Concept disambiguation
gpt-5-mini-2025-08-07
Target class: Jupyter ecosystem component Context triple: [Jupyter Server, instanceOf, Jupyter ecosystem component]
-
A.
Jupyter ecosystem project
chosen
A Jupyter ecosystem project is a software component, tool, or extension that integrates with and enhances the Jupyter environment for interactive computing, data analysis, and reproducible research.
-
B.
collaborative data science platform
A collaborative data science platform is an integrated environment where multiple users can jointly develop, run, and share data workflows, analyses, and models using shared datasets, tools, and computational resources.
-
C.
integrated development environment component
An integrated development environment component is a modular tool or feature within an IDE that provides specific functionality—such as code editing, debugging, or project management—to support and streamline software development.
-
D.
interactive plotting backend
An interactive plotting backend is a software component that connects a plotting library to a graphical user interface or environment, enabling dynamic visualization features such as zooming, panning, and real-time updates.
-
E.
machine learning platform component
A machine learning platform component is a modular software element that provides specific functionality—such as data processing, model training, deployment, or monitoring—within an integrated ML lifecycle system.
- F. None of above.
Provenance (1 batch)
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_69d8dcf94c288190a06dea029ae4b223 |
completed | April 10, 2026, 11:20 a.m. |
Created at: April 10, 2026, 11:56 a.m.