Triple

T1157116
Position Surface form Disambiguated ID Type / Status
Subject Potosí E24407 entity
Predicate locatedIn P40 FINISHED
Object Potosí Department E146260 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: Potosí Department | Statement: [Potosí, locatedIn, Potosí Department]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Potosí Department
Context triple: [Potosí, locatedIn, Potosí Department]
  • A. Potosí Department chosen
    Potosí Department is a high-altitude region in southwestern Bolivia known for its historic silver mining city of Potosí and vast Andean landscapes.
  • B. Sucre Department
    Sucre Department is an administrative region in northern Colombia, known for its Caribbean coastline, agricultural economy, and capital city Sincelejo.
  • C. Oruro Department
    Oruro Department is a high-altitude administrative region in western Bolivia known for its Andean landscapes, mining heritage, and vibrant Carnival of Oruro.
  • D. La Paz Department
    La Paz Department is a highland administrative region in western Bolivia that includes the city of La Paz and encompasses part of the Lake Titicaca basin in the Andes.
  • 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_69a494060e148190abb42f971242c197 completed March 1, 2026, 7:31 p.m.
NER Named-entity recognition batch_69a4bc92ae008190a587c12ecc9a502a completed March 1, 2026, 10:24 p.m.
NED1 Entity disambiguation (via context triple) batch_69acb2f3df34819090d942aad0a409fa completed March 7, 2026, 11:21 p.m.
Created at: March 1, 2026, 7:44 p.m.