Triple

T16153162
Position Surface form Disambiguated ID Type / Status
Subject Columbus E391964 entity
Predicate hasVariant P455 FINISHED
Object Colón E738667 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: Colón | Statement: [Columbus, hasVariant, Colón]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Colón
Context triple: [Columbus, hasVariant, Colón]
  • A. Colón
    Colón is a municipality and city in western Cuba known for its agricultural surroundings and colonial-era architecture.
  • B. Colón
    Colón is a riverside city in Argentina known for its tourism, hot springs, and access to the Uruguay River.
  • C. Colón
    Colón is a small municipality located in Colombia's southern Amazonian region within the Putumayo Department.
  • D. Colón chosen
    Colón is a Spanish-origin surname commonly found in Hispanic communities and notably borne by figures such as comic book artist Ernie Colón.
  • E. Colón
    Colón is a city in northeastern Honduras known as a regional hub near the Caribbean coast and the Bay Islands.
  • 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_69d87f1c65e48190aa2b4c472e9bafc4 completed April 10, 2026, 4:39 a.m.
NER Named-entity recognition batch_69e21d98d08c8190a15d4aee40d47220 completed April 17, 2026, 11:46 a.m.
NED1 Entity disambiguation (via context triple) batch_69fff7ac6d1c8190a8553ceb5ec06119 completed May 10, 2026, 3:12 a.m.
Created at: April 10, 2026, 5:01 a.m.