Triple
T18528849
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TinyMCE |
E452784
|
entity |
| Predicate | hasVersion |
P455
|
FINISHED |
| Object | TinyMCE 4 |
—
|
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: TinyMCE 4 | Statement: [TinyMCE, hasVersion, TinyMCE 4]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: TinyMCE 4 Context triple: [TinyMCE, hasVersion, TinyMCE 4]
-
A.
TinyMCE
chosen
TinyMCE is a popular open-source WYSIWYG rich text editor written in JavaScript that can be embedded in web applications to provide word processor–like content editing in the browser.
-
B.
Magic Editor
Magic Editor is an AI-powered photo editing feature on Google Pixel devices that lets users easily reframe, reposition, and enhance elements within their images.
-
C.
Sam text editor
Sam text editor is a powerful, programmable text editor from Bell Labs, designed by Rob Pike with a structural regular-expression-based command language and tight integration between its command and editing interfaces.
-
D.
Texmaker
Texmaker is a free, cross-platform LaTeX editor that provides an integrated environment with tools for writing, compiling, and previewing LaTeX documents.
-
E.
LaTeXiT
LaTeXiT is a macOS application that lets users quickly typeset LaTeX equations and export them as images or PDFs for use in other documents and presentations.
- 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_69d8d387b5548190aa030dad2cb4947e |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e533fad2d081908395914d6a6b4eb1 |
completed | April 19, 2026, 7:58 p.m. |
Created at: April 10, 2026, 11:37 a.m.