Triple

T10483667
Position Surface form Disambiguated ID Type / Status
Subject Atuot language E247235 entity
Predicate neighboringLanguage P16383 FINISHED
Object Dinka language E60468 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: Dinka language | Statement: [Atuot language, neighboringLanguage, Dinka language]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dinka language
Context triple: [Atuot language, neighboringLanguage, Dinka language]
  • A. Dinka language chosen
    The Dinka language is a Western Nilotic language spoken primarily by the Dinka people of South Sudan, known for its complex system of tones and vowel lengths.
  • B. Nuer language
    Nuer language is a Western Nilotic language spoken primarily by the Nuer people of South Sudan and western Ethiopia.
  • C. Kipsigis language
    The Kipsigis language is a Southern Nilotic language spoken by the Kipsigis people of Kenya, forming part of the broader Kalenjin language cluster.
  • D. Didinga language
    The Didinga language is a Surmic language spoken primarily by the Didinga people in the Eastern Equatoria region of South Sudan.
  • E. Marakwet language
    The Marakwet language is a Southern Nilotic language spoken by the Marakwet people of Kenya and is closely related to other Kalenjin languages such as Kipsigis.
  • 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_69d381c309b88190af78aa681cf6a4c2 completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d509678ac88190984f18a2162e2dcf completed April 7, 2026, 1:40 p.m.
NED1 Entity disambiguation (via context triple) batch_69d96b31de8c8190996df69ae02278f8 completed April 10, 2026, 9:27 p.m.
Created at: April 6, 2026, 12:22 p.m.