Triple
T18828945
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dash HTML Components |
E460470
|
entity |
| Predicate | creator |
P184
|
FINISHED |
| Object | Plotly |
—
|
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 | Statement: [Dash HTML Components, creator, Plotly]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Plotly Context triple: [Dash HTML Components, creator, Plotly]
-
A.
Plotly
chosen
Plotly is an interactive, open-source graphing and data visualization library widely used in Python for creating rich, web-based charts and dashboards.
-
B.
Vega-Lite
Vega-Lite is a high-level grammar of interactive graphics that enables users to concisely create and share data visualizations, developed under the guidance of computer scientist Jeff Heer.
-
C.
HoloViews
HoloViews is a high-level Python library for building complex, interactive visualizations and data explorations with minimal code, often used in conjunction with plotting backends like Bokeh and Matplotlib.
-
D.
Grafana
Grafana is an open-source analytics and visualization platform used to create interactive dashboards and monitor metrics from various data sources.
-
E.
BokehJS
BokehJS is the JavaScript library that powers Bokeh’s interactive visualizations directly in the browser, enabling rich, client-side plotting and data exploration.
- 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.