Triple

T7467750
Position Surface form Disambiguated ID Type / Status
Subject Bushu Dima E176416 entity
Predicate languageContext P36 FINISHED
Object Dimasa language E166026 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: Dimasa language | Statement: [Bushu Dima, languageContext, Dimasa language]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dimasa language
Context triple: [Bushu Dima, languageContext, Dimasa language]
  • A. Dimasa language chosen
    Dimasa is a Tibeto-Burman language spoken primarily by the Dimasa people in the Indian states of Assam and Nagaland.
  • B. Damana language
    The Damana language is an indigenous Chibchan tongue spoken by the Wiwa people of the Sierra Nevada de Santa Marta region in northern Colombia.
  • C. Madura language
    The Madura language is an Austronesian language spoken primarily on Madura Island and in parts of East Java, Indonesia, by the Madurese people.
  • D. Bima language
    Bima language is an Austronesian language spoken primarily on Sumbawa Island in Indonesia, known for its distinct grammar and vocabulary within the region.
  • E. Betawi language
    Betawi language is an Austronesian language variety spoken primarily in Jakarta, Indonesia, known for blending Malay with influences from Javanese, Sundanese, Chinese, Arabic, and Dutch.
  • 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_69c69f223fd88190b4c69b95d7cbeeda completed March 27, 2026, 3:15 p.m.
NER Named-entity recognition batch_69c6f3f589cc81909f25268838c7c964 completed March 27, 2026, 9:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69c83475392c8190a51d24e1530c0c83 completed March 28, 2026, 8:05 p.m.
Created at: March 27, 2026, 3:40 p.m.