Triple

T169642
Position Surface form Disambiguated ID Type / Status
Subject Kobe E3089 entity
Predicate twinCity P1072 FINISHED
Object Rio de Janeiro E6266 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: Rio de Janeiro | Statement: [Kobe, twinCity, Rio de Janeiro]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rio de Janeiro
Context triple: [Kobe, twinCity, Rio de Janeiro]
  • A. Rio de Janeiro chosen
    Rio de Janeiro is a major Brazilian coastal city famed for its stunning beaches, dramatic landscape, Carnival festival, and iconic Christ the Redeemer statue.
  • B. São Paulo
    São Paulo is Brazil’s largest city and a major global financial, cultural, and industrial center in South America.
  • C. Belém
    Belém is a historic riverside district of Lisbon, Portugal, known for its monuments of the Age of Discoveries, including the Belém Tower and Jerónimos Monastery.
  • 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. Santiago
    Santiago is the capital and primary economic, political, and cultural center of Chile, located in the country’s central valley.
  • 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_69a2524ce1e48190ab066bf72859f474 completed Feb. 28, 2026, 2:26 a.m.
NER Named-entity recognition batch_69a258b6f4f88190b1264bbbeb19a29e completed Feb. 28, 2026, 2:53 a.m.
NED1 Entity disambiguation (via context triple) batch_69a2e0f9ee948190947393acc2d52224 completed Feb. 28, 2026, 12:35 p.m.
Created at: Feb. 28, 2026, 2:34 a.m.