Triple

T4371863
Position Surface form Disambiguated ID Type / Status
Subject R E98913 entity
Predicate supportsReproducibleResearch P24615 FINISHED
Object R Markdown E426693 NE FINISHED

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: R Markdown | Statement: [R, supportsReproducibleResearch, R Markdown]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: R Markdown
Context triple: [R, supportsReproducibleResearch, R Markdown]
  • A. R Markdown chosen
    R Markdown is a file format and authoring framework that combines R code with narrative text to create dynamic, reproducible documents, reports, and presentations.
  • B. Markdown
    Markdown is a lightweight markup language that uses plain-text formatting syntax to create structured documents, most commonly used for README files, documentation, and web content.
  • C. LaTeX
    LaTeX is a widely used, high-quality typesetting system particularly popular in academia for producing technical and scientific documents with precise control over layout and mathematical notation.
  • D. LyX
    LyX is an open-source, document processor and graphical front-end for LaTeX that lets users create structured, professional-quality documents without directly writing LaTeX code.
  • E. reStructuredText
    reStructuredText is a lightweight, plaintext markup language commonly used in the Python ecosystem for documentation, including PEPs and Sphinx-based docs.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: supportsReproducibleResearch
Context triple: [R, supportsReproducibleResearch, R Markdown]
  • A. supportsResearchIn
    Indicates that one entity provides resources, assistance, or infrastructure that enables or advances research activities in a particular field or area for another entity.
  • B. supportedResearch
    Indicates that one entity provided assistance, resources, or backing to enable or advance the research activities of another entity.
  • C. hasResearchUse chosen
    Indicates that an entity is used for, or associated with, conducting research activities or purposes.
  • D. researchSupportType
    Indicates the specific kind of support provided for research activities, such as funding, resources, or services.
  • E. supportsOpenSource
    Indicates that one entity actively endorses, contributes to, or otherwise promotes open-source software or open-source initiatives.
  • F. None of above.

Provenance (4 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_69b5e50ec35481908cf1e1afffda19cb completed March 14, 2026, 10:45 p.m.
PD Predicate disambiguation batch_69b34f557fe8819085032bf7f0cea5dc completed March 12, 2026, 11:42 p.m.
Created at: March 12, 2026, 11:17 p.m.