Triple

T20027192
Position Surface form Disambiguated ID Type / Status
Subject Nueva Segovia E495014 entity
Predicate hasMunicipality P847 FINISHED
Object Dipilto NE NERFINISHED

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: Dipilto | Statement: [Nueva Segovia, hasMunicipality, Dipilto]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dipilto
Context triple: [Nueva Segovia, hasMunicipality, Dipilto]
  • A. Dipilto chosen
    Dipilto is a small Nicaraguan town in the mountainous Nueva Segovia region, known for its cool climate, coffee production, and location near the border with Honduras.
  • B. Dipico
    Dipico is a surname of likely African origin, notably borne by South African politician Manne Dipico.
  • C. Pasochoa
    Pasochoa is an extinct volcanic mountain in Ecuador known for its lush cloud forests and rich biodiversity within a protected ecological reserve.
  • D. Tupiza
    Tupiza is a small historic town in southern Bolivia known for its dramatic red-rock canyons and as a gateway to Andean landscapes and mining regions.
  • E. Pitalito
    Pitalito is a major town and coffee-producing hub in southern Colombia, known as one of the country’s most important centers for high-quality coffee.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69da626bfd288190aa5d65098b6433ae completed April 11, 2026, 3:02 p.m.
NER Named-entity recognition batch_69e6628e1eec81908e4c9b2b0b68f0e4 completed April 20, 2026, 5:29 p.m.
Created at: April 11, 2026, 3:35 p.m.