Triple

T4965131
Position Surface form Disambiguated ID Type / Status
Subject Futrono E111504 entity
Predicate countrySubdivision P766 FINISHED
Object Ranco Province E118979 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: Ranco Province | Statement: [Futrono, countrySubdivision, Ranco Province]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ranco Province
Context triple: [Futrono, countrySubdivision, Ranco Province]
  • A. Ranco Province chosen
    Ranco Province is an administrative division in southern Chile known for its lakes, rivers, and rural landscapes within the Los Ríos Region.
  • B. Gualivá Province
    Gualivá Province is an administrative subdivision of the Cundinamarca Department in central Colombia, known for its mountainous terrain and agricultural towns.
  • C. Itata Province
    Itata Province is an administrative division in Chile's Ñuble Region, known for its rural landscapes, wine production, and small provincial capital of Quirihue.
  • D. Caranavi Province
    Caranavi Province is an administrative province in Bolivia known for its coffee production and location within the Yungas region of the La Paz Department.
  • E. Orellana Province
    Orellana Province is an Amazonian region in northeastern Ecuador known for its vast tropical rainforests, rich biodiversity, and significant oil reserves.
  • 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_69bd4419393c819086319a6fe4bf8542 completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd71f693b48190b3523cd314303cbe completed March 20, 2026, 4:12 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf06982c5081908c275019c5d6b1c1 completed March 21, 2026, 8:59 p.m.
Created at: March 20, 2026, 1:32 p.m.