Triple

T16058856
Position Surface form Disambiguated ID Type / Status
Subject Clorindo Testa E389554 entity
Predicate basedIn P40 FINISHED
Object Buenos Aires, Argentina 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, Argentina | Statement: [Clorindo Testa, basedIn, Buenos Aires, Argentina]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Buenos Aires, Argentina
Context triple: [Clorindo Testa, basedIn, Buenos Aires, Argentina]
  • 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. 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.
  • C. Mar del Plata
    Mar del Plata is a major Argentine Atlantic coastal city renowned as a popular beach resort and tourist destination.
  • D. Luján, Buenos Aires
    Luján, Buenos Aires is an Argentine city renowned as a major Catholic pilgrimage center, home to the Basilica of Our Lady of Luján.
  • E. Santa Fe, Argentina
    Santa Fe, Argentina is a major river port city and the capital of Santa Fe Province, located in northeastern Argentina along the Paraná and Salado rivers.
  • 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_69d86dae698881908327ef2d67706cb9 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e1837729e4819086e7429e0a76b0d7 completed April 17, 2026, 12:48 a.m.
NED1 Entity disambiguation (via context triple) batch_69ffeb8b1b2c8190949943b20f2f8574 completed May 10, 2026, 2:20 a.m.
Created at: April 10, 2026, 4:57 a.m.