Triple
T17520249
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Project Jupyter |
E426663
|
entity |
| Predicate | develops |
P73
|
FINISHED |
| Object | JupyterLab |
—
|
NE NERFINISHED |
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: JupyterLab | Statement: [Project Jupyter, develops, JupyterLab]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: JupyterLab Context triple: [Project Jupyter, develops, JupyterLab]
-
A.
JupyterLab
chosen
JupyterLab is a web-based interactive development environment for working with Jupyter notebooks, code, and data.
-
B.
Jupyter Notebook
Jupyter Notebook is an open-source web-based interactive computing environment that allows users to create and share documents containing live code, equations, visualizations, and narrative text.
-
C.
JupyterLite
JupyterLite is a lightweight, browser-based distribution of Jupyter that runs entirely in the web using WebAssembly and in-browser storage, enabling notebooks without a traditional server or local installation.
-
D.
Jupyter Server
Jupyter Server is the backend application that manages and serves Jupyter notebooks, kernels, and related services for frontends like JupyterLab.
-
E.
JupyterHub
JupyterHub is an open-source platform that enables multiple users to access and run Jupyter notebook environments on shared infrastructure, typically for education, research, and collaborative computing.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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. |
Created at: April 10, 2026, 5:49 a.m.