Triple
T18828939
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dash HTML Components |
E460470
|
entity |
| Predicate | usedWith |
P4791
|
FINISHED |
| Object | Plotly Dash |
—
|
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: Plotly Dash | Statement: [Dash HTML Components, usedWith, Plotly Dash]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Plotly Dash Context triple: [Dash HTML Components, usedWith, Plotly Dash]
-
A.
Plotly
Plotly is an interactive, open-source graphing and data visualization library widely used in Python for creating rich, web-based charts and dashboards.
-
B.
Streamlit
Streamlit is an open-source Python framework that lets developers quickly build and share interactive web apps for data science and machine learning.
-
C.
Grafana
Grafana is an open-source analytics and visualization platform used to create interactive dashboards and monitor metrics from various data sources.
-
D.
flexdashboard
flexdashboard is an R package that makes it easy to create interactive, web-based dashboards using R Markdown.
-
E.
Dash for Python
chosen
Dash for Python is an open-source framework for building interactive, web-based data visualization applications in Python, commonly used for creating analytical dashboards.
- 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_69d8dcf94c288190a06dea029ae4b223 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5a99554848190933dd2810f5c810f |
completed | April 20, 2026, 4:20 a.m. |
Created at: April 10, 2026, 11:56 a.m.