Triple

T12492767
Position Surface form Disambiguated ID Type / Status
Subject Arabi Juba E298606 entity
Predicate influencedByLanguage P4183 FINISHED
Object Zande language E272630 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: Zande language | Statement: [Arabi Juba, influencedByLanguage, Zande language]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Zande language
Context triple: [Arabi Juba, influencedByLanguage, Zande language]
  • A. Zande language chosen
    The Zande language is a Central African language spoken primarily by the Azande people across parts of South Sudan, the Central African Republic, and the Democratic Republic of the Congo.
  • B. Ngada language
    The Ngada language is an Austronesian language spoken by the Ngada people in central Flores, Indonesia, known for its complex verbal morphology and rich oral tradition.
  • C. Tembe language
    The Tembe language is an indigenous Tupi-Guarani language spoken by the Tembé people of northern Brazil.
  • D. Banda-Ndélé language
    The Banda-Ndélé language is a Central Sudanic language spoken by the Banda people, primarily in the Central African Republic.
  • E. Sanglechi language
    The Sanglechi language is an Eastern Iranian language spoken by a small community in the Sanglech Valley region of Afghanistan and Tajikistan.
  • 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_69d6ada377208190a36011199a4d8558 completed April 8, 2026, 7:33 p.m.
NER Named-entity recognition batch_69d94de3076c81909640c982d520ca6b completed April 10, 2026, 7:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69f64bab99bc8190abe6dfb7c6a7fe6f completed May 2, 2026, 7:08 p.m.
Created at: April 8, 2026, 9:56 p.m.