Triple

T7186511
Position Surface form Disambiguated ID Type / Status
Subject Central Savanna Province E167585 entity
Predicate containsMunicipality P852 FINISHED
Object Gachancipá E227047 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: Gachancipá | Statement: [Central Savanna Province, containsMunicipality, Gachancipá]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gachancipá
Context triple: [Central Savanna Province, containsMunicipality, Gachancipá]
  • A. Gachancipá chosen
    Gachancipá is a municipality in the Cundinamarca Department of Colombia, located in the central highlands near Bogotá.
  • B. Sibaté
    Sibaté is a municipality in central Colombia known for its agricultural production and proximity to Bogotá within the Cundinamarca Department.
  • C. Comayagüela
    Comayagüela is a major urban district of Honduras that, together with Tegucigalpa, forms the country’s capital area.
  • D. Iramuco
    Iramuco is a town in Mexico known for its cultural and municipal links with international partner cities such as the London Borough of Ealing.
  • E. Pacasmayo
    Pacasmayo is a coastal city in northern Peru known for its long pier, surfing beaches, and colonial-era architecture.
  • 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_69c6888b5248819090499a884ee3ec39 completed March 27, 2026, 1:39 p.m.
NER Named-entity recognition batch_69c6e8e0f2f48190a4ddf8637f556934 completed March 27, 2026, 8:30 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7b950f90c81908391a96071594ea6 completed March 28, 2026, 11:19 a.m.
Created at: March 27, 2026, 2:49 p.m.