Triple
T4832612
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gutenberg |
E107978
|
entity |
| Predicate | replaces |
P101
|
FINISHED |
| Object | Classic Editor |
E452784
|
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: Classic Editor | Statement: [Gutenberg, replaces, Classic Editor]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Classic Editor Context triple: [Gutenberg, replaces, Classic Editor]
-
A.
TextEdit
TextEdit is a simple, built-in macOS application for creating and editing plain text and rich text documents.
-
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.
WordPad
WordPad is a basic word processing application for Microsoft Windows that offers more features than Notepad but fewer than full office suites like Microsoft Word.
-
D.
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.
-
E.
Gutenberg
Gutenberg is the block-based content editor introduced in WordPress to enable more flexible, visual page and post creation.
- 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_69bd43fac8188190803f0327190621e4 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6cc924e08190b03a7541c629aff9 |
completed | March 20, 2026, 3:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be4dd744688190a420580e3a8332ff |
completed | March 21, 2026, 7:50 a.m. |
Created at: March 20, 2026, 1:24 p.m.