Triple

T8790874
Position Surface form Disambiguated ID Type / Status
Subject Constitución station E209160 entity
Predicate ownedBy P347 FINISHED
Object City of 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: City of Buenos Aires | Statement: [Constitución station, ownedBy, City of Buenos Aires]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: City of Buenos Aires
Context triple: [Constitución station, ownedBy, 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. Mar del Plata
    Mar del Plata is a major Argentine Atlantic coastal city renowned as a popular beach resort and tourist destination.
  • D. 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.
  • E. Greater Buenos Aires
    Greater Buenos Aires is the vast, densely populated metropolitan region surrounding Argentina’s capital city, encompassing Buenos Aires and its many suburban municipalities.
  • 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_69ca836168108190bb43d3dc235c1f55 completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cc5f8d25f881908863d636fa57a8a2 completed March 31, 2026, 11:58 p.m.
NED1 Entity disambiguation (via context triple) batch_69d4eae224cc8190ac596ff9f8921cda completed April 7, 2026, 11:30 a.m.
Created at: March 30, 2026, 6:43 p.m.