Triple
T4416580
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Literate Programming |
E94988
|
entity |
| Predicate | influenced |
P9
|
FINISHED |
| Object | org‑mode Babel |
E59977
|
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: org‑mode Babel | Statement: [Literate Programming, influenced, org‑mode Babel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: org‑mode Babel Context triple: [Literate Programming, influenced, org‑mode Babel]
-
A.
org-mode
chosen
org-mode is a powerful Emacs-based system for organizing notes, tasks, and documents using plain-text outlines and embedded markup.
-
B.
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.
-
C.
Jupyter Notebook
Jupyter Notebook is an open-source web-based interactive computing environment that allows users to create and share documents containing live code, equations, visualizations, and narrative text.
-
D.
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.
-
E.
JupyterLab
JupyterLab is a web-based interactive development environment for working with Jupyter notebooks, code, and data.
- 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_69b3453a36908190b95a79a297ca083c |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3551afb448190a2ce2000193808ac |
completed | March 13, 2026, 12:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5f61b56a8819099b5302f1b53f76d |
completed | March 14, 2026, 11:58 p.m. |
Created at: March 12, 2026, 11:29 p.m.