Triple

T18222071
Position Surface form Disambiguated ID Type / Status
Subject ggplot2 E436330 entity
Predicate basedOn P98 FINISHED
Object Grammar of Graphics NE NERFINISHED

How this triple was built (3 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: Grammar of Graphics | Statement: [ggplot2, basedOn, Grammar of Graphics]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Grammar of Graphics
Context triple: [ggplot2, basedOn, Grammar of Graphics]
  • A. ggplot2
    ggplot2 is a widely used R package for creating elegant, layered, and highly customizable data visualizations based on the Grammar of Graphics.
  • B. Vega visualization grammar
    Vega visualization grammar is a declarative language and toolkit for creating, sharing, and reproducing interactive data visualizations on the web.
  • C. The Craft of Information Visualization
    The Craft of Information Visualization is a foundational book that compiles influential research papers and case studies on designing and evaluating effective visual representations of data and information.
  • D. Visual Strategies: A Practical Guide to Graphics for Scientists and Engineers
    "Visual Strategies: A Practical Guide to Graphics for Scientists and Engineers" is a handbook that teaches scientists and engineers how to design clear, effective, and visually compelling graphics to communicate their data and ideas.
  • E. 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.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Grammar of Graphics
Target entity description: Grammar of Graphics is a theoretical framework for data visualization that defines graphics as mappings from data to aesthetic attributes through layered, composable components.
  • A. ggplot2
    ggplot2 is a widely used R package for creating elegant, layered, and highly customizable data visualizations based on the Grammar of Graphics.
  • B. Vega visualization grammar
    Vega visualization grammar is a declarative language and toolkit for creating, sharing, and reproducing interactive data visualizations on the web.
  • C. The Craft of Information Visualization
    The Craft of Information Visualization is a foundational book that compiles influential research papers and case studies on designing and evaluating effective visual representations of data and information.
  • D. Visual Strategies: A Practical Guide to Graphics for Scientists and Engineers
    "Visual Strategies: A Practical Guide to Graphics for Scientists and Engineers" is a handbook that teaches scientists and engineers how to design clear, effective, and visually compelling graphics to communicate their data and ideas.
  • E. 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.
  • F. None of above. chosen

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_69d8b9103a8081908bbb0836fef10efd completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e4e47c85108190bd9707b40bdfdb38 completed April 19, 2026, 2:19 p.m.
Created at: April 10, 2026, 10:32 a.m.