Triple

T2792016
Position Surface form Disambiguated ID Type / Status
Subject Focsa Building E61950 entity
Predicate city P40 FINISHED
Object Havana E396 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: Havana | Statement: [Focsa Building, city, Havana]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Havana
Context triple: [Focsa Building, city, Havana]
  • A. Havana, Cuba chosen
    Havana, Cuba is the capital and largest city of Cuba, renowned for its historic architecture, vibrant culture, and significant political and economic role in the Caribbean.
  • B. Santiago de Cuba
    Santiago de Cuba is a major city in southeastern Cuba known for its rich Afro-Cuban cultural heritage, historic role in the Cuban Revolution, and vibrant music and carnival traditions.
  • C. Habana Vieja
    Habana Vieja is the historic old quarter of Havana, Cuba, renowned for its colonial architecture, cobblestone streets, and vibrant cultural life.
  • D. Santo Domingo
    Santo Domingo is a major city in Ecuador known as a commercial and transportation hub in the country’s central coastal region.
  • E. Cienfuegos
    Cienfuegos is a coastal city in central Cuba known for its French-influenced architecture and historic bay.
  • 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_69ab4b7f51d881908768300ebd2fbdae completed March 6, 2026, 9:47 p.m.
NER Named-entity recognition batch_69abddd107ac81908eb1a6946834eee3 completed March 7, 2026, 8:12 a.m.
NED1 Entity disambiguation (via context triple) batch_69afce859ffc8190b88f82af50238215 completed March 10, 2026, 7:55 a.m.
Created at: March 6, 2026, 9:58 p.m.