Triple
T17521898
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Posit PBC |
E426694
|
entity |
| Predicate | product |
P490
|
FINISHED |
| Object | R Markdown |
—
|
NE NERFINISHED |
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: [Posit PBC, product, R Markdown]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: R Markdown Context triple: [Posit PBC, product, 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.
MultiMarkdown
MultiMarkdown is an extended version of the Markdown markup language that adds features like tables, footnotes, citations, and document metadata for more complex publishing needs.
-
E.
Pandoc
Pandoc is a powerful open-source document converter that can transform files between numerous markup and word-processing formats, widely used for working with Markdown and other text formats.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d889de677081909b22d2657b1f0292 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e452d2f79881909556894728e255ab |
completed | April 19, 2026, 3:58 a.m. |
Created at: April 10, 2026, 5:49 a.m.