Triple

T22561820
Position Surface form Disambiguated ID Type / Status
Subject ISO/IEC 15897 E557832 entity
Predicate relatedStandard P37 FINISHED
Object ISO 639 NE NERFINISHED

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: ISO 639 | Statement: [ISO/IEC 15897, relatedStandard, ISO 639]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: ISO 639
Context triple: [ISO/IEC 15897, relatedStandard, ISO 639]
  • A. ISO 639 chosen
    ISO 639 is an international standard that defines codes for the representation of names of languages.
  • B. ISO 639-3
    ISO 639-3 is an international standard that assigns three-letter codes to uniquely identify the world’s languages, including many lesser-known and endangered ones.
  • C. ISO 639-3 Registration Authority
    The ISO 639-3 Registration Authority is the organization responsible for maintaining and updating the ISO 639-3 standard, which assigns three-letter codes to the world’s languages.
  • D. ISO 15924
    ISO 15924 is an international standard that assigns four-letter codes to the world’s writing systems and scripts for use in information processing and interchange.
  • E. IETF BCP 47
    IETF BCP 47 is the Internet standard that defines the structure and use of language tags for identifying human languages and related variants in digital systems.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e11e5ae4ac8190b1f503457603d969 completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f15fa6a928819083925ea23aaaf725 completed April 29, 2026, 1:32 a.m.
Created at: April 16, 2026, 8:52 p.m.