Triple
T17521452
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Streamlit Community Cloud |
E426686
|
entity |
| Predicate | supportsFramework |
P9089
|
FINISHED |
| Object | Streamlit |
—
|
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: Streamlit | Statement: [Streamlit Community Cloud, supportsFramework, Streamlit]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Streamlit Context triple: [Streamlit Community Cloud, supportsFramework, Streamlit]
-
A.
Streamlit
chosen
Streamlit is an open-source Python framework that lets developers quickly build and share interactive web apps for data science and machine learning.
-
B.
Streamlit Community Cloud
Streamlit Community Cloud is a hosted platform that lets users easily deploy, share, and manage Streamlit data apps directly from their code repositories.
-
C.
Plotly
Plotly is an interactive, open-source graphing and data visualization library widely used in Python for creating rich, web-based charts and dashboards.
-
D.
Datalore
Datalore is JetBrains’ collaborative data science and analytics platform that combines notebooks, computation, and team features for working with code and data in the cloud.
-
E.
Seaborn
Seaborn is a Python data visualization library built on top of Matplotlib that provides a high-level interface for creating attractive and informative statistical graphics.
- 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_69e452d2f79881909556894728e255ab |
completed | April 19, 2026, 3:58 a.m. |
Created at: April 10, 2026, 5:49 a.m.