Triple

T22248381
Position Surface form Disambiguated ID Type / Status
Subject Olimpo E549906 entity
Predicate location P40 FINISHED
Object Bahía Blanca 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: Bahía Blanca | Statement: [Olimpo, location, Bahía Blanca]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bahía Blanca
Context triple: [Olimpo, location, Bahía Blanca]
  • A. Bahía Blanca chosen
    Bahía Blanca is a major port city in southern Buenos Aires Province, Argentina, known for its industrial activity and strategic location on the Atlantic coast.
  • B. Mar del Plata
    Mar del Plata is a major Argentine Atlantic coastal city renowned as a popular beach resort and tourist destination.
  • C. Comodoro Rivadavia
    Comodoro Rivadavia is a coastal city in southern Argentina known as a key oil industry hub and one of the main urban centers of Patagonia.
  • D. San Antonio de los Buenos
    San Antonio de los Buenos is a borough of the city of Tijuana in Baja California, Mexico, known primarily as a residential and hillside area within the municipality.
  • E. Gualeguaychú
    Gualeguaychú is a city in eastern Argentina known for its vibrant Carnival celebrations and riverside tourism.
  • 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_69e11e41d9408190bd770cf282e22753 completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f13219dc3481908dd987c4e98623e6 completed April 28, 2026, 10:18 p.m.
Created at: April 16, 2026, 8:38 p.m.