Triple

T1622578
Position Surface form Disambiguated ID Type / Status
Subject Montevideo City Torque E35064 entity
Predicate shortName P43 FINISHED
Object Montevideo City E47651 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: Montevideo City | Statement: [Montevideo City Torque, shortName, Montevideo City]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Montevideo City
Context triple: [Montevideo City Torque, shortName, Montevideo City]
  • A. Montevideo chosen
    Montevideo is the capital and largest city of Uruguay, serving as the country’s main political, economic, and cultural center.
  • B. Colonia del Sacramento
    Colonia del Sacramento is a historic Uruguayan city famed for its well-preserved colonial quarter, a UNESCO World Heritage Site that reflects a blend of Portuguese and Spanish architectural influences.
  • C. Ciudad del Este
    Ciudad del Este is a major commercial city in eastern Paraguay, known as a busy border trading hub near the tri-border area with Brazil and Argentina.
  • D. 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.
  • E. Colonia Buenos Aires
    Colonia Buenos Aires is a neighborhood located within the Cuauhtémoc borough in central Mexico City.
  • 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_69a886023194819080a3fccd6e325d0e completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a909cf3c7481909ddbe6a6596bb0c8 completed March 5, 2026, 4:42 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae02fd8f68819080a4b39ce3ad1198 completed March 8, 2026, 11:15 p.m.
Created at: March 4, 2026, 7:28 p.m.