Triple

T23236522
Position Surface form Disambiguated ID Type / Status
Subject Sebastián de Ocampo E581312 entity
Predicate explored P1562 FINISHED
Object Cuba NE NERFINISHED

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: Cuba | Statement: [Sebastián de Ocampo, explored, Cuba]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Cuba
Context triple: [Sebastián de Ocampo, explored, Cuba]
  • A. Cuba chosen
    Cuba is a Caribbean island nation known for its communist government, historic Havana architecture, classic cars, and influential music and culture.
  • B. Cuba
    Cuba is a municipality in Portugal’s Beja District, known for its rural Alentejo landscape and traditional wine production.
  • C. Cubão
    Cubão is a locality in the state of São Paulo, Brazil, situated near the industrial city of São Bernardo do Campo.
  • D. Dominican Republic
    The Dominican Republic is a Caribbean nation on the island of Hispaniola known for its beaches, mountainous interior, and vibrant blend of Spanish, African, and Taíno cultural influences.
  • E. San Domingo
    San Domingo is a historic site on Kunta Kinteh Island in The Gambia, associated with the transatlantic slave trade and colonial-era fortifications.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e2460556f88190be1744a84a84173f completed April 17, 2026, 2:39 p.m.
NER Named-entity recognition batch_69f192e98dec8190a23385600bed9ae0 completed April 29, 2026, 5:11 a.m.
Created at: April 17, 2026, 4:09 p.m.