Triple

T161266
Position Surface form Disambiguated ID Type / Status
Subject Web 2.0 E3289 entity
Predicate enabledBy P6232 FINISHED
Object XML E3756 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: XML | Statement: [Web 2.0, enabledBy, XML]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: XML
Context triple: [Web 2.0, enabledBy, XML]
  • A. XML chosen
    XML (Extensible Markup Language) is a flexible, text-based markup language designed for structuring, storing, and transporting data in a platform-independent way.
  • B. SGML
    SGML (Standard Generalized Markup Language) is a standardized metalanguage for defining markup languages used to structure and describe the content of electronic documents.
  • C. HTML
    HTML (HyperText Markup Language) is the standard markup language used to structure and present content on the World Wide Web.
  • D. SVG
    SVG (Scalable Vector Graphics) is an XML-based vector image format for two-dimensional graphics that supports interactivity and animation, widely used for web graphics due to its scalability and resolution independence.
  • 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_69a2527757ec819090b8becb2cf1a862 completed Feb. 28, 2026, 2:27 a.m.
NER Named-entity recognition batch_69a25bae79b48190abfd247410e5918f completed Feb. 28, 2026, 3:06 a.m.
NED1 Entity disambiguation (via context triple) batch_69a2d4cbaedc81908aa7cf4df2661ccc completed Feb. 28, 2026, 11:43 a.m.
Created at: Feb. 28, 2026, 2:31 a.m.