Triple
T18015972
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NumFOCUS |
E430998
|
entity |
| Predicate | supportsProject |
P12986
|
FINISHED |
| Object | IPython |
—
|
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: IPython | Statement: [NumFOCUS, supportsProject, IPython]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: IPython Context triple: [NumFOCUS, supportsProject, IPython]
-
A.
IPython
chosen
IPython is an enhanced interactive Python shell and toolkit that provides powerful introspection, rich media, and parallel computing features, and serves as the core interactive engine behind Jupyter.
-
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.
JupyterLab
JupyterLab is a web-based interactive development environment for working with Jupyter notebooks, code, and data.
-
D.
Jupyter Server
Jupyter Server is the backend application that manages and serves Jupyter notebooks, kernels, and related services for frontends like JupyterLab.
-
E.
Project Jupyter
Project Jupyter is an open-source initiative that develops interactive computing tools and standards, most notably the Jupyter ecosystem for creating and sharing computational notebooks across multiple programming languages.
- 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_69d8b904530081908bf341d842464856 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4b523f588819097389e067dda7f23 |
completed | April 19, 2026, 10:57 a.m. |
Created at: April 10, 2026, 10:24 a.m.