Triple

T1102913
Position Surface form Disambiguated ID Type / Status
Subject Artemisa Province E25420 entity
Predicate hasMunicipality P847 FINISHED
Object Caimito E25672 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: Caimito | Statement: [Artemisa Province, hasMunicipality, Caimito]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Caimito
Context triple: [Artemisa Province, hasMunicipality, Caimito]
  • A. Caimito chosen
    Caimito is a municipality in western Cuba known for its agricultural activities and proximity to Havana.
  • B. La Ceiba
    La Ceiba is a prominent coastal city in northern Honduras known for its Caribbean port, vibrant nightlife, and annual carnival celebrations.
  • C. Canóvanas
    Canóvanas is a municipality in northeastern Puerto Rico known for its proximity to San Juan and its blend of suburban communities with rural, mountainous landscapes.
  • D. Mariquina
    Mariquina is a commune and town in southern Chile, located in the Los Ríos Region and known for its rural landscapes and Mapuche cultural presence.
  • E. Sopó
    Sopó is a small municipality in the department of Cundinamarca, Colombia, known for its scenic Andean landscapes and dairy production.
  • 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_69a49428d4448190b3b36991ceae87ce completed March 1, 2026, 7:31 p.m.
NER Named-entity recognition batch_69a4b9c375848190baec4d534f489616 completed March 1, 2026, 10:12 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac4c47dbf88190a1898d7bda32ecb2 completed March 7, 2026, 4:03 p.m.
Created at: March 1, 2026, 7:43 p.m.