Triple

T10330292
Position Surface form Disambiguated ID Type / Status
Subject DITA E242854 entity
Predicate hasComponent P35 FINISHED
Object DITA DTDs E127654 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: DITA DTDs | Statement: [DITA, hasComponent, DITA DTDs]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: DITA DTDs
Context triple: [DITA, hasComponent, DITA DTDs]
  • A. DITA
    DITA (Darwin Information Typing Architecture) is an XML-based standard for authoring, structuring, and publishing modular technical documentation.
  • B. DTD
    DTD is the abbreviated name for the Division of Training and Development, an organizational unit focused on designing and delivering training and professional development programs.
  • C. DTD chosen
    DTD (Document Type Definition) is an XML schema language used to define the legal structure, elements, and attributes of an XML document.
  • D. ISO/IEC 19757 (Document Schema Definition Languages, DSDL)
    ISO/IEC 19757 (Document Schema Definition Languages, DSDL) is a multi-part international standard that defines a modular framework of languages and mechanisms for validating and describing the structure and semantics of XML documents.
  • E. DocBook
    DocBook is a semantic markup language, originally based on SGML and now commonly used in XML form, designed for authoring and publishing technical documentation and books in a platform-independent way.
  • 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_69d381af787481908bc401325c760a88 completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4d7fb77348190ac8ff887f6f03450 completed April 7, 2026, 10:10 a.m.
NED1 Entity disambiguation (via context triple) batch_69d71dbc7df48190b8a11a92f946fd30 completed April 9, 2026, 3:32 a.m.
Created at: April 6, 2026, 11:52 a.m.