Triple

T9437407
Position Surface form Disambiguated ID Type / Status
Subject Francisco Tamburini E227545 entity
Predicate residence P75 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: [Francisco Tamburini, residence, Buenos Aires]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Buenos Aires
Context triple: [Francisco Tamburini, residence, 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. Mar del Plata
    Mar del Plata is a major Argentine Atlantic coastal city renowned as a popular beach resort and tourist destination.
  • C. Colonia Buenos Aires
    Colonia Buenos Aires is a neighborhood located within the Cuauhtémoc borough in central Mexico City.
  • 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. Montevideo
    Montevideo is the capital and largest city of Uruguay, serving as the country’s main political, economic, and cultural center.
  • 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_69ca8437a7ac81908651de48f2d2141d completed March 30, 2026, 2:09 p.m.
NER Named-entity recognition batch_69cd7edff5e881909b72976e8909ba4b completed April 1, 2026, 8:24 p.m.
NED1 Entity disambiguation (via context triple) batch_69d2e4b5781c8190b33035b81dd74260 completed April 5, 2026, 10:39 p.m.
Created at: March 30, 2026, 7:50 p.m.