Triple

T5679830
Position Surface form Disambiguated ID Type / Status
Subject sot E125172 entity
Predicate ISO639-2Equivalent P65937 FINISHED
Object sot LITERAL 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: sot | Statement: [sot, ISO639-2Equivalent, sot]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: ISO639-2Equivalent
Context triple: [sot, ISO639-2Equivalent, sot]
  • A. sharesISO639-3CodeWith
    Indicates that two language entities share the same ISO 639-3 code, meaning they are treated as the same language in that coding system.
  • B. ISO639-3CodeOfLanguage
    Indicates that one entity is the ISO 639-3 three-letter language code assigned to the language represented by the other entity.
  • C. ISO639CollectiveCode
    Indicates that the relationship assigns or associates an ISO 639 collective language code (a code representing a group of related languages) to the relevant language entity or set of languages.
  • D. ISO639Macrolanguage
    Indicates that a language variety is part of a broader ISO 639-defined macrolanguage grouping that encompasses multiple closely related individual languages.
  • E. ISO639Scope
    Indicates the classification of a language according to its scope, such as whether it represents an individual language, a macrolanguage, or a collection of languages.
  • F. None of above. chosen

Provenance (4 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_69c008295c808190acfe78915e7d656a completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c025303860819093e51f176babed71 completed March 22, 2026, 5:21 p.m.
PD Predicate disambiguation batch_69c021bc3894819084f37d14ba4b2644 completed March 22, 2026, 5:07 p.m.
PDg Predicate description generation batch_69c0252e18988190a8f3aa0684c12fb8 completed March 22, 2026, 5:21 p.m.
Created at: March 22, 2026, 3:44 p.m.