Triple

T5404327
Position Surface form Disambiguated ID Type / Status
Subject Racing Club de Avellaneda E120854 entity
Predicate partOf P40 FINISHED
Object Gran Buenos Aires E288011 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: Gran Buenos Aires | Statement: [Racing Club de Avellaneda, partOf, Gran Buenos Aires]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gran Buenos Aires
Context triple: [Racing Club de Avellaneda, partOf, Gran Buenos Aires]
  • A. 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.
  • B. Colonia Buenos Aires
    Colonia Buenos Aires is a neighborhood located within the Cuauhtémoc borough in central Mexico 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. Greater Buenos Aires chosen
    Greater Buenos Aires is the vast, densely populated metropolitan region surrounding Argentina’s capital city, encompassing Buenos Aires and its many suburban municipalities.
  • E. Count of Buenos Aires
    Count of Buenos Aires is a Spanish noble title historically associated with Santiago de Liniers, a key colonial-era figure in the defense and governance of Buenos Aires.
  • 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_69bd46391c0c81909fa484446732b6a3 completed March 20, 2026, 1:06 p.m.
NER Named-entity recognition batch_69bd8774cd8881909b3437ba02018444 completed March 20, 2026, 5:44 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf339037b4819084423826b7252002 completed March 22, 2026, 12:10 a.m.
Created at: March 20, 2026, 2:05 p.m.