Triple

T13622153
Position Surface form Disambiguated ID Type / Status
Subject Galvarino E325482 entity
Predicate hasMinorityLanguage P2267 FINISHED
Object Mapudungun E234600 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: Mapudungun | Statement: [Galvarino, hasMinorityLanguage, Mapudungun]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mapudungun
Context triple: [Galvarino, hasMinorityLanguage, Mapudungun]
  • A. Mapudungun chosen
    Mapudungun is an indigenous language of South America spoken primarily by the Mapuche people in Chile and Argentina.
  • B. Puel Mapu
    Puel Mapu is the eastern portion of the ancestral Mapuche territory, located mainly in what is now Argentina.
  • C. Mazabuka
    Mazabuka is a town in southern Zambia known for its sugar industry and agricultural production.
  • D. Mapusa
    Mapusa is a bustling commercial town in North Goa, India, known as a major market and transport hub near the popular beaches of the state.
  • E. Ngulu Mapu
    Ngulu Mapu is the eastern, Argentine sector of the ancestral Mapuche territory within the broader region known as Wallmapu.
  • 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_69d8076aae28819092cf636190ee5529 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbbe99ddc08190a8d79107c8e176fa completed April 12, 2026, 3:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69f77fa291f48190a0ee7a228ea303bc completed May 3, 2026, 5:02 p.m.
Created at: April 9, 2026, 9:50 p.m.