Triple

T10019329
Position Surface form Disambiguated ID Type / Status
Subject Jakarta Server Faces E200574 entity
Predicate usesMarkupLanguage P2177 FINISHED
Object XHTML E24914 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: XHTML | Statement: [Jakarta Server Faces, usesMarkupLanguage, XHTML]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: XHTML
Context triple: [Jakarta Server Faces, usesMarkupLanguage, XHTML]
  • A. XHTML chosen
    XHTML is a reformulation of HTML as an XML-based markup language designed to create structured, standards-compliant web pages.
  • B. XML
    XML (Extensible Markup Language) is a flexible, text-based markup language designed for structuring, storing, and transporting data in a platform-independent way.
  • C. XSLT
    XSLT is a language for transforming XML documents into other formats such as XML, HTML, or plain text using template-based rules.
  • D. HTM
    HTM is the public transport company that operates trams and buses in and around The Hague in the Netherlands.
  • E. DTD
    DTD (Document Type Definition) is an XML schema language used to define the legal structure, elements, and attributes of an XML document.
  • 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_69ca831c45f08190ac1505cc15076608 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cdcd4f3a988190892cc698109be8b8 completed April 2, 2026, 1:58 a.m.
NED1 Entity disambiguation (via context triple) batch_69d26aaa38188190aed8c18eccd8a79d completed April 5, 2026, 1:59 p.m.
Created at: March 30, 2026, 8:53 p.m.