Triple

T19903
Position Surface form Disambiguated ID Type / Status
Subject Havana E396 entity
Predicate demonym P191 FINISHED
Object Habanero 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: Habanero | Statement: [Havana, demonym, Habanero]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Habanero
Context triple: [Havana, demonym, Habanero]
  • A. Lick
    Lick is the nickname of Joseph Carl Robnett Licklider, a pioneering American computer scientist whose ideas helped lay the foundations for interactive computing and the internet.
  • B. 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.
  • C. Ojos del Salado
    Ojos del Salado is a massive stratovolcano in the Andes on the Argentina–Chile border, recognized as the highest active volcano in the world.
  • D. 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.
  • E. Cottonopolis
    Cottonopolis is a historical nickname for Manchester, England, reflecting its prominence as a major center of the cotton and textile industry during the Industrial Revolution.
  • 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_69a240778d288190815c0052ebbbcc91 completed Feb. 28, 2026, 1:10 a.m.
NER Named-entity recognition batch_69a24669427481908b3369f090ea8edc completed Feb. 28, 2026, 1:35 a.m.
NED1 Entity disambiguation (via context triple) batch_69a248e88e588190a704e7b83d3dc07c completed Feb. 28, 2026, 1:46 a.m.
Created at: Feb. 28, 2026, 1:14 a.m.