Triple
T17520291
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Project Jupyter |
E426663
|
entity |
| Predicate | hasStandard |
P1371
|
FINISHED |
| Object | Jupyter messaging protocol |
—
|
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: Jupyter messaging protocol | Statement: [Project Jupyter, hasStandard, Jupyter messaging protocol]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jupyter messaging protocol Context triple: [Project Jupyter, hasStandard, Jupyter messaging protocol]
-
A.
Jupyter protocol
chosen
The Jupyter protocol is a messaging specification that enables interactive communication between computational kernels and front-end interfaces in the Jupyter ecosystem.
-
B.
Jupyter Server
Jupyter Server is the backend application that manages and serves Jupyter notebooks, kernels, and related services for frontends like JupyterLab.
-
C.
Jupyter kernels
Jupyter kernels are modular computation backends that execute code in specific programming languages for Jupyter notebooks and other Jupyter frontends.
-
D.
JupyterLab
JupyterLab is a web-based interactive development environment for working with Jupyter notebooks, code, and data.
-
E.
Jupyter Notebook file format .ipynb
The Jupyter Notebook file format (.ipynb) is a JSON-based document format that stores interactive computational notebooks combining executable code, rich text, outputs, and metadata for use in Jupyter environments.
- 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.