Triple

T4925279
Position Surface form Disambiguated ID Type / Status
Subject Giuseppe Garibaldi E110560 entity
Predicate residence P75 FINISHED
Object Montevideo E47651 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: Montevideo | Statement: [Giuseppe Garibaldi, residence, Montevideo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Montevideo
Context triple: [Giuseppe Garibaldi, residence, Montevideo]
  • A. Montevideo chosen
    Montevideo is the capital and largest city of Uruguay, serving as the country’s main political, economic, and cultural center.
  • B. Buenos Aires
    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.
  • C. Ciudad del Este
    Ciudad del Este is a major commercial city in eastern Paraguay, known as a busy border trading hub near the tri-border area with Brazil and Argentina.
  • D. Bahía Blanca
    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.
  • E. Caxias do Sul
    Caxias do Sul is a major city in southern Brazil known for its strong European immigrant heritage, particularly German and Italian influences, and its significant industrial and wine-producing sectors.
  • 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_69bd4413f9908190afcff44d7929cc4c completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd7033c430819088e62f14979aa83c completed March 20, 2026, 4:05 p.m.
NED1 Entity disambiguation (via context triple) batch_69bea46996c481908ec7b783ac9a20b1 completed March 21, 2026, 2 p.m.
Created at: March 20, 2026, 1:30 p.m.