Triple
T193000
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | WAI-ARIA |
E3759
|
entity |
| Predicate | usedWith |
P4791
|
FINISHED |
| Object | JavaScript |
E3742
|
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: JavaScript | Statement: [WAI-ARIA, usedWith, JavaScript]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: JavaScript Context triple: [WAI-ARIA, usedWith, JavaScript]
-
A.
JavaScript
chosen
JavaScript is a high-level, dynamic programming language primarily used to create interactive and dynamic content on web pages.
-
B.
ECMAScript
ECMAScript is the official scripting language specification that defines the core features and behavior implemented by JavaScript and related languages.
-
C.
Node.js
Node.js is an open-source, cross-platform runtime environment that allows developers to execute JavaScript code on the server side.
-
D.
JavaScriptCore
JavaScriptCore is Apple’s high-performance JavaScript engine used primarily in the Safari web browser and WebKit-based applications.
-
E.
DOM
The Document Object Model (DOM) is a platform- and language-neutral interface that represents structured documents like HTML and XML as a tree of objects, enabling programs and scripts to dynamically access and update their content and structure.
- 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_69a2548debd48190ae3a06d6e65b53c6 |
completed | Feb. 28, 2026, 2:35 a.m. |
| NER | Named-entity recognition | batch_69a259669ba08190a5be1d2e10e70b27 |
completed | Feb. 28, 2026, 2:56 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a3161cdc4c8190bc67683a1a5d39d9 |
completed | Feb. 28, 2026, 4:21 p.m. |
Created at: Feb. 28, 2026, 2:41 a.m.