Triple

T19226120
Position Surface form Disambiguated ID Type / Status
Subject Revolution of 1880 E480741 entity
Predicate location P40 FINISHED
Object City of Buenos Aires 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: City of Buenos Aires | Statement: [Revolution of 1880, location, City of Buenos Aires]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: City of Buenos Aires
Context triple: [Revolution of 1880, location, City of 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. Colonia Buenos Aires
    Colonia Buenos Aires is a neighborhood located within the Cuauhtémoc borough in central Mexico City.
  • C. city of Rosario
    The city of Rosario is a major urban center in Argentina, known for its significant port on the Paraná River, vibrant cultural life, and role as an important industrial and commercial hub.
  • D. Campana, Buenos Aires
    Campana is an industrial port city in the Buenos Aires Province of Argentina, located on the Paraná River northwest of Buenos Aires city.
  • E. Mar del Plata
    Mar del Plata is a major Argentine Atlantic coastal city renowned as a popular beach resort and tourist destination.
  • 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_69d8e8ccb8f48190ad420098e74fb1db completed April 10, 2026, 12:10 p.m.
NER Named-entity recognition batch_69e5fa988afc81909c6a751a148bea16 completed April 20, 2026, 10:06 a.m.
Created at: April 10, 2026, 1:24 p.m.