Triple
T7857710
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Markdown |
E182418
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | R Markdown |
E426693
|
NE FINISHED |
How this triple was built (2 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: [Markdown, hasVariant, R Markdown]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: R Markdown Context triple: [Markdown, hasVariant, 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.
knitr
knitr is an R package that enables dynamic report generation by integrating R code with documents in formats like R Markdown, LaTeX, and HTML.
-
C.
Sweave
Sweave is a tool in the R ecosystem that enables dynamic report generation by integrating statistical analysis code with LaTeX documents for reproducible research.
-
D.
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.
-
E.
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.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69ca82887fd48190975896bf38c4596b |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb1a76f8648190976b488d0d8658ef |
completed | March 31, 2026, 12:51 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cb5b32eaf88190aae55aaeb963c50b |
completed | March 31, 2026, 5:27 a.m. |
Created at: March 30, 2026, 4:52 p.m.