Triple

T6995978
Position Surface form Disambiguated ID Type / Status
Subject Zezuru E162214 entity
Predicate ISO639-3Scope P27504 FINISHED
Object covered under Shona (sna) 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: covered under Shona (sna) | Statement: [Zezuru, ISO639-3Scope, covered under Shona (sna)]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: ISO639-3Scope
Context triple: [Zezuru, ISO639-3Scope, covered under Shona (sna)]
  • A. ISO639Scope chosen
    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.
  • 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. 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.
  • D. ISO639_3Status
    Indicates the classification or status assigned to a language according to the ISO 639-3 standard (e.g., active, extinct, historical, constructed).
  • E. ISO639Macrolanguage
    Indicates that a language variety is part of a broader ISO 639-defined macrolanguage grouping that encompasses multiple closely related individual languages.
  • F. None of above.

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_69c68857ffc08190857dc62cd5253777 completed March 27, 2026, 1:38 p.m.
NER Named-entity recognition batch_69c6dbedafa48190af0d2b47e3a1e17e completed March 27, 2026, 7:35 p.m.
PD Predicate disambiguation batch_69c6d7c4a18881908d267137daed828b completed March 27, 2026, 7:17 p.m.
Created at: March 27, 2026, 2:32 p.m.