Triple

T15612277
Position Surface form Disambiguated ID Type / Status
Subject Mwanga District E375326 entity
Predicate hasLanguage P15 FINISHED
Object Pare language E1131075 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: Pare language | Statement: [Mwanga District, hasLanguage, Pare language]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pare language
Context triple: [Mwanga District, hasLanguage, Pare language]
  • A. Pare language chosen
    Pare language is a Bantu language of northeastern Tanzania spoken primarily by the Pare people in the Pare Mountains region.
  • B. Paresí language
    The Paresí language is an indigenous Arawakan language spoken by the Paresí (Haliti) people of Brazil’s Mato Grosso region.
  • C. Parji language
    The Parji language is a lesser-known Dravidian language spoken primarily by tribal communities in central India, particularly in parts of Chhattisgarh and Odisha.
  • D. Pattaeʼ language
    The Pattaeʼ language is an Austronesian language spoken by the Pattaeʼ people of West Sulawesi, Indonesia.
  • E. Paha language
    Paha language is a lesser-known Kra language spoken by an ethnic minority community in parts of southern China.
  • 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_69d85ccf2794819096cda4cbcb02d478 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e04e8148a0819087d6d69cc84487ca completed April 16, 2026, 2:50 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff56d91f208190a11f0d208970145b completed May 9, 2026, 3:46 p.m.
Created at: April 10, 2026, 4:13 a.m.