Triple

T4540403
Position Surface form Disambiguated ID Type / Status
Subject Seaborn Cotton E107513 entity
Predicate givenName P17 FINISHED
Object Seaborn
Seaborn is a masculine given name of English origin, historically used in colonial America and associated with individuals such as Seaborn Cotton.
 E450091 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: Seaborn | Statement: [Seaborn Cotton, givenName, Seaborn]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Seaborn
Context triple: [Seaborn Cotton, givenName, Seaborn]
  • A. 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.
  • B. Matplotlib
    Matplotlib is a widely used Python plotting library for creating static, animated, and interactive visualizations.
  • 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. Streamlit
    Streamlit is an open-source Python framework that lets developers quickly build and share interactive web apps for data science and machine learning.
  • E. pandas
    pandas is a popular open-source Python library that provides powerful, easy-to-use data structures and tools for data analysis and manipulation.
  • 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: Seaborn
Triple: [Seaborn Cotton, givenName, Seaborn]
Generated description
Seaborn is a masculine given name of English origin, historically used in colonial America and associated with individuals such as Seaborn Cotton.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Seaborn
Target entity description: Seaborn is a masculine given name of English origin, historically used in colonial America and associated with individuals such as Seaborn Cotton.
  • A. 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.
  • B. Matplotlib
    Matplotlib is a widely used Python plotting library for creating static, animated, and interactive visualizations.
  • 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. Streamlit
    Streamlit is an open-source Python framework that lets developers quickly build and share interactive web apps for data science and machine learning.
  • E. pandas
    pandas is a popular open-source Python library that provides powerful, easy-to-use data structures and tools for data analysis and manipulation.
  • 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_69bd43f922788190b7edfa294e39b178 completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd57bb5c0c819092ebb2dd3310f5f8 completed March 20, 2026, 2:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69bdad01b2e48190806f9490b912ac66 completed March 20, 2026, 8:24 p.m.
NEDg Description generation batch_69bdadfe8a8881908de60c24b65af489 completed March 20, 2026, 8:28 p.m.
NED2 Entity disambiguation (via description) batch_69bdae46cae48190b313aa9d4703e2a8 completed March 20, 2026, 8:29 p.m.
Created at: March 20, 2026, 1:04 p.m.