Triple

T340804
Position Surface form Disambiguated ID Type / Status
Subject Hausa E6832 entity
Predicate hasNounClasses P5217 FINISHED
Object no productive noun class system 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: no productive noun class system | Statement: [Hausa, hasNounClasses, no productive noun class system]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: hasNounClasses
Context triple: [Hausa, hasNounClasses, no productive noun class system]
  • A. hasNounClassSystem chosen
    Indicates that an entity possesses a grammatical system in which nouns are categorized into distinct classes that affect their agreement with other elements in the language.
  • B. hasNounEnding
    Indicates that something possesses or exhibits a particular noun-forming ending or suffix.
  • C. hasGrammaticalGender
    Indicates that one entity assigns or possesses a specific grammatical gender in relation to another entity (such as a word, phrase, or linguistic unit).
  • D. hasDefinitenessDistinction
    Indicates that a language or system grammatically distinguishes between definite and indefinite (or otherwise specified) reference in its expressions.
  • E. hasLinguisticFeature
    Indicates that an entity possesses a particular linguistic property, trait, or characteristic.
  • 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_69a2e7951ba08190960e90823b5078f3 completed Feb. 28, 2026, 1:03 p.m.
NER Named-entity recognition batch_69a2eae611f88190955fbebe2b01835b completed Feb. 28, 2026, 1:17 p.m.
PD Predicate disambiguation batch_69a2e95197fc8190820e8ebd0d7d27fa completed Feb. 28, 2026, 1:10 p.m.
Created at: Feb. 28, 2026, 1:08 p.m.