Triple

T11634716
Position Surface form Disambiguated ID Type / Status
Subject Northumberland County E276486 entity
Predicate hasAdministrativeCentre P1474 FINISHED
Object Cobourg E661380 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: Cobourg | Statement: [Northumberland County, hasAdministrativeCentre, Cobourg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Cobourg
Context triple: [Northumberland County, hasAdministrativeCentre, Cobourg]
  • A. Cobourg chosen
    Cobourg is a small town in Ontario, Canada, known for its historic downtown, sandy beach, and picturesque waterfront along Lake Ontario.
  • B. Brockville
    Brockville is a small city in Eastern Ontario, Canada, located along the St. Lawrence River and known as one of the region’s historic riverfront communities.
  • C. Kitchener
    Kitchener is a mid-sized city in southwestern Ontario, Canada, known for its manufacturing history and annual Oktoberfest celebration.
  • D. Alliston
    Alliston is a community in New Tecumseth, Ontario, Canada, known historically as the birthplace of insulin co-discoverer Sir Frederick Banting.
  • E. Fort Frances
    Fort Frances is a small Canadian town in northwestern Ontario located on the Rainy River along the U.S. border opposite International Falls, Minnesota.
  • 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_69d6aafa51148190ab84940694c00235 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d8a25c0b00819095898d2b2445ecfb completed April 10, 2026, 7:10 a.m.
NED1 Entity disambiguation (via context triple) batch_69f13010e394819099b648db07f1193c completed April 28, 2026, 10:09 p.m.
Created at: April 8, 2026, 9:39 p.m.