Triple

T94015
Position Surface form Disambiguated ID Type / Status
Subject Industriales E1889 entity
Predicate basedIn 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: [Industriales, basedIn, Havana]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Havana
Context triple: [Industriales, basedIn, 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. Vedado district of Havana
    The Vedado district of Havana is a central, upscale neighborhood known for its wide avenues, modernist architecture, cultural venues, and vibrant nightlife.
  • C. Panama City
    Panama City is the largest urban center and economic hub of Panama, known for its modern skyline, historic Casco Viejo district, and proximity to the Panama Canal.
  • D. San Juan
    San Juan is the largest city and main cultural, economic, and tourism hub of Puerto Rico, known for its historic colonial architecture and vibrant coastal setting.
  • E. San Salvador
    San Salvador is the largest city of El Salvador and its political, cultural, and economic center.
  • 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_69a24d4862f881908cc8b89d3a78031d completed Feb. 28, 2026, 2:04 a.m.
NER Named-entity recognition batch_69a24fd28e988190bde699647ee5b16b completed Feb. 28, 2026, 2:15 a.m.
NED1 Entity disambiguation (via context triple) batch_69a2db50ac3881908088683967e9ae9f completed Feb. 28, 2026, 12:10 p.m.
Created at: Feb. 28, 2026, 2:09 a.m.