Triple

T7250885
Position Surface form Disambiguated ID Type / Status
Subject Moxo language E157596 entity
Predicate region P40 FINISHED
Object Beni Department E124822 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: Beni Department | Statement: [Moxo language, region, Beni Department]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Beni Department
Context triple: [Moxo language, region, Beni Department]
  • A. Beni Department chosen
    Beni Department is a large, sparsely populated administrative region in northern Bolivia known for its vast Amazonian lowlands, wetlands, and cattle ranching.
  • B. Sud Department
    Sud Department is an administrative region in southern Haiti known for its coastal cities, beaches, and agricultural activities.
  • C. Ouest Department
    Ouest Department is an administrative region in western Haiti that includes the capital city, Port-au-Prince, and serves as the country’s political and economic center.
  • D. Ñeembucú Department
    Ñeembucú Department is a sparsely populated, rural department in southern Paraguay known for its wetlands, historical sites, and border location along the Paraguay and Paraná rivers.
  • E. Ngounié Province
    Ngounié Province is an inland administrative region in southwestern Gabon known for its forests, rivers, and ethnolinguistic diversity.
  • 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_69c6882d81d4819085f7ff862951ee4f completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6ea791fec8190aee56ab4503770be completed March 27, 2026, 8:37 p.m.
NED1 Entity disambiguation (via context triple) batch_69c8fa20137081909a21ac366c19407f completed March 29, 2026, 10:08 a.m.
Created at: March 27, 2026, 2:56 p.m.