Triple

T18828929
Position Surface form Disambiguated ID Type / Status
Subject Dash Core Components E460469 entity
Predicate ecosystem P964 FINISHED
Object Plotly Dash ecosystem 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 ecosystem | Statement: [Dash Core Components, ecosystem, Plotly Dash ecosystem]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Plotly Dash ecosystem
Context triple: [Dash Core Components, ecosystem, Plotly Dash ecosystem]
  • 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. 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. 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.
  • E. 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.
  • 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.