Triple
T816621
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Matplotlib |
E17663
|
entity |
| Predicate | compatibleWith |
P203
|
FINISHED |
| Object | Jupyter Notebook |
E97066
|
NE 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: Jupyter Notebook | Statement: [Matplotlib, compatibleWith, Jupyter Notebook]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jupyter Notebook Context triple: [Matplotlib, compatibleWith, Jupyter Notebook]
-
A.
Jupyter Notebook
chosen
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.
-
B.
RStudio
RStudio is an integrated development environment (IDE) for the R programming language, widely used for data analysis, visualization, and statistical computing.
-
C.
Streamlit
Streamlit is an open-source Python framework that lets developers quickly build and share interactive web apps for data science and machine learning.
-
D.
Julia
Julia is a feminine given name of Latin origin, commonly used in many languages and cultures.
-
E.
Julia
Julia is a high-level, high-performance programming language designed for numerical computing, data science, and scientific research, combining the ease of dynamic languages with the speed of compiled languages.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69a4937bcaac8190a322524ac6f45a5a |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4ab621d2c819083f10bff4f66c482 |
completed | March 1, 2026, 9:10 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a7928f8a808190aaf5f2a2f3ee676f |
completed | March 4, 2026, 2:01 a.m. |
Created at: March 1, 2026, 7:38 p.m.