Triple

T8943488
Position Surface form Disambiguated ID Type / Status
Subject Garo Hills E212961 entity
Predicate hasLanguage P15 FINISHED
Object Garo language E644025 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: Garo language | Statement: [Garo Hills, hasLanguage, Garo language]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Garo language
Context triple: [Garo Hills, hasLanguage, Garo language]
  • A. Garo language chosen
    The Garo language is a Tibeto-Burman language spoken primarily by the Garo people of northeastern India and neighboring Bangladesh.
  • B. Gurma language
    Gurma language is a Gur language spoken primarily in parts of Burkina Faso, Togo, Benin, and neighboring West African countries.
  • C. Sanglechi language
    The Sanglechi language is an Eastern Iranian language spoken by a small community in the Sanglech Valley region of Afghanistan and Tajikistan.
  • D. Murle language
    The Murle language is an Eastern Sudanic language spoken primarily by the Murle people of South Sudan.
  • E. Saho language
    The Saho language is an Afroasiatic Cushitic language spoken primarily by the Saho people in Eritrea and northern Ethiopia.
  • 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_69ca839694c88190b324ffeb43d23b08 completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc66da71808190b4454b2f95aae0bd completed April 1, 2026, 12:29 a.m.
NED1 Entity disambiguation (via context triple) batch_69cfc1f87db481909d40ed6fb0a4c8c9 completed April 3, 2026, 1:34 p.m.
Created at: March 30, 2026, 6:58 p.m.