Triple

T2016210
Position Surface form Disambiguated ID Type / Status
Subject Cambridge, Ontario E43999 entity
Predicate regionSeat P29359 FINISHED
Object Kitchener, Ontario E32080 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: Kitchener, Ontario | Statement: [Cambridge, Ontario, regionSeat, Kitchener, Ontario]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kitchener, Ontario
Context triple: [Cambridge, Ontario, regionSeat, Kitchener, Ontario]
  • A. Kitchener chosen
    Kitchener is a mid-sized city in southwestern Ontario, Canada, known for its manufacturing history and annual Oktoberfest celebration.
  • B. Waterloo, Ontario
    Waterloo, Ontario is a Canadian city in the Regional Municipality of Waterloo best known as a major tech and innovation hub and home to the University of Waterloo and Wilfrid Laurier University.
  • C. Kingston, Ontario
    Kingston, Ontario is a historic Canadian city on the northeastern shore of Lake Ontario, known for its 19th-century limestone architecture, military and political heritage, and as home to Queen’s University.
  • D. Guelph
    Guelph is a mid-sized Canadian city known for its strong manufacturing base, historic architecture, and the University of Guelph.
  • E. Markham, Ontario
    Markham, Ontario is a rapidly growing city in the Greater Toronto Area known for its diverse population, high-tech industry hub, and blend of urban and suburban communities.
  • 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_69a8891201bc8190aca837be6de41579 completed March 4, 2026, 7:33 p.m.
NER Named-entity recognition batch_69abb8cb16048190bc626685fbb5f707 completed March 7, 2026, 5:34 a.m.
NED1 Entity disambiguation (via context triple) batch_69afaf266ed881909206c87c69a2ea95 completed March 10, 2026, 5:41 a.m.
Created at: March 4, 2026, 7:38 p.m.