Triple

T816685
Position Surface form Disambiguated ID Type / Status
Subject Plotly E17664 entity
Predicate integratesWith P1075 FINISHED
Object Streamlit
Streamlit is an open-source Python framework that lets developers quickly build and share interactive web apps for data science and machine learning.
 E97082 NE FINISHED

How this triple was built (4 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: [Plotly, integratesWith, Streamlit]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Streamlit
Context triple: [Plotly, integratesWith, Streamlit]
  • 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. 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.
  • C. Flask
    Flask is a minor but tough and pugnacious third mate aboard the whaling ship Pequod in Herman Melville’s novel "Moby-Dick."
  • D. Flask
    Flask is a lightweight, flexible Python micro web framework designed for building web applications and APIs with minimal boilerplate.
  • E. Data Studio
    Data Studio is Google's free data visualization and business intelligence tool that lets users create interactive, shareable reports and dashboards from multiple data sources.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Streamlit
Triple: [Plotly, integratesWith, Streamlit]
Generated description
Streamlit is an open-source Python framework that lets developers quickly build and share interactive web apps for data science and machine learning.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Streamlit
Target entity description: Streamlit is an open-source Python framework that lets developers quickly build and share interactive web apps for data science and machine learning.
  • 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. 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.
  • C. Flask
    Flask is a lightweight, flexible Python micro web framework designed for building web applications and APIs with minimal boilerplate.
  • D. Flask
    Flask is a minor but tough and pugnacious third mate aboard the whaling ship Pequod in Herman Melville’s novel "Moby-Dick."
  • E. Data Studio
    Data Studio is Google's free data visualization and business intelligence tool that lets users create interactive, shareable reports and dashboards from multiple data sources.
  • F. None of above. chosen

Provenance (5 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_69a76d8d1a448190be8494fa2776615a completed March 3, 2026, 11:23 p.m.
NEDg Description generation batch_69a78bd0a1d48190907434a17853dfb1 completed March 4, 2026, 1:33 a.m.
NED2 Entity disambiguation (via description) batch_69a78c3a57d88190a994ed44bcb2d8d1 completed March 4, 2026, 1:34 a.m.
Created at: March 1, 2026, 7:38 p.m.