Triple

T4371835
Position Surface form Disambiguated ID Type / Status
Subject R E98913 entity
Predicate hasPackage P14571 FINISHED
Object ggplot2
ggplot2 is a widely used R package for creating elegant, layered, and highly customizable data visualizations based on the Grammar of Graphics.
E436330 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: ggplot2 | Statement: [R, hasPackage, ggplot2]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: ggplot2
Context triple: [R, hasPackage, ggplot2]
  • 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. Plotly
    Plotly is an interactive, open-source graphing and data visualization library widely used in Python for creating rich, web-based charts and dashboards.
  • C. pgfplots
    pgfplots is a LaTeX package for creating high-quality plots and graphs based on the PGF/TikZ drawing framework.
  • D. PowerChart
    PowerChart is Cerner's flagship electronic health record (EHR) solution used by healthcare providers to document, manage, and access patient clinical information.
  • E. Agg
    Agg is a high-quality, anti-aliased 2D graphics rendering engine commonly used as a backend in plotting and visualization libraries.
  • 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: ggplot2
Triple: [R, hasPackage, ggplot2]
Generated description
ggplot2 is a widely used R package for creating elegant, layered, and highly customizable data visualizations based on the Grammar of Graphics.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: ggplot2
Target entity description: ggplot2 is a widely used R package for creating elegant, layered, and highly customizable data visualizations based on the Grammar of Graphics.
  • A. tidyverse
    tidyverse is a collection of R packages designed for data science, emphasizing a consistent, human-readable grammar for data manipulation, visualization, and analysis.
  • 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. 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. pgfplots
    pgfplots is a LaTeX package for creating high-quality plots and graphs based on the PGF/TikZ drawing framework.
  • E. PowerChart
    PowerChart is Cerner's flagship electronic health record (EHR) solution used by healthcare providers to document, manage, and access patient clinical information.
  • 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_69b3454db3708190aeafd814413c4c3d completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b3521dffbc8190b9300a7f4f64bdc0 completed March 12, 2026, 11:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5e50bcc9481909b0b9d60198dce63 completed March 14, 2026, 10:45 p.m.
NEDg Description generation batch_69b5eeadd68881909820a75aaff9d8d5 completed March 14, 2026, 11:26 p.m.
NED2 Entity disambiguation (via description) batch_69b5ef36f2bc8190a21e0f2fadbdd697 completed March 14, 2026, 11:28 p.m.
Created at: March 12, 2026, 11:17 p.m.