Triple

T310571
Position Surface form Disambiguated ID Type / Status
Subject Daniel Barenboim E6397 entity
Predicate placeOfBirth P1 FINISHED
Object Buenos Aires E5323 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: Buenos Aires | Statement: [Daniel Barenboim, placeOfBirth, Buenos Aires]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Buenos Aires
Context triple: [Daniel Barenboim, placeOfBirth, Buenos Aires]
  • A. Buenos Aires chosen
    Buenos Aires is the capital and largest city of Argentina, known for its rich European-influenced culture, tango music and dance, and vibrant urban life.
  • B. 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.
  • C. La Boca
    La Boca is a colorful, working-class neighborhood in Buenos Aires famous for its vividly painted houses, tango culture, and the Boca Juniors football stadium.
  • D. Bariloche
    Bariloche is a popular Argentine city in the Andean region known for its lakes, mountains, skiing, and Swiss-style alpine architecture.
  • E. Puerto Madero
    Puerto Madero is a revitalized waterfront neighborhood in Buenos Aires known for its modern high-rises, upscale dining, and contemporary urban development along the old port docks.
  • 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_69a2e79230508190b912ecb555aae17e completed Feb. 28, 2026, 1:03 p.m.
NER Named-entity recognition batch_69a2ea4778cc8190be7b648a82542891 completed Feb. 28, 2026, 1:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69a3d7e3015081908f494ff2a6df5c86 completed March 1, 2026, 6:08 a.m.
Created at: Feb. 28, 2026, 1:06 p.m.