Triple

T36280897
Position Surface form Disambiguated ID Type / Status
Subject Canada (fictional setting: Toronto) E892936 entity
Predicate usesContrastWith P123206 FINISHED
Object real-world Toronto to highlight themes LITERAL 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: real-world Toronto to highlight themes | Statement: [Canada (fictional setting: Toronto), usesContrastWith, real-world Toronto to highlight themes]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: usesContrastWith
Context triple: [Canada (fictional setting: Toronto), usesContrastWith, real-world Toronto to highlight themes]
  • A. providesContrastWith chosen
    Indicates that one entity is used to highlight differences or distinctions when compared with another entity.
  • B. achievesContrast
    Indicates that one entity creates or enhances a visual or conceptual difference relative to another entity.
  • C. createsContrastIn
    Indicates a relationship where one element is used to highlight or emphasize differences with another element within a given context.
  • D. contrastUse
    Indicates that one entity is used in opposition or distinction to another to highlight differences between them.
  • E. registerContrast
    Indicates that an entity records or establishes a distinction or difference between two or more items or states.
  • F. None of above.

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_69f76e4955c08190b8cfddca34fc0242 completed May 3, 2026, 3:48 p.m.
NER Named-entity recognition batch_69fe08d2b2e48190ac7be6d62d4a44a3 completed May 8, 2026, 4:01 p.m.
PD Predicate disambiguation batch_69fe06cd3af08190ae25de0dc0cdd573 completed May 8, 2026, 3:52 p.m.
Created at: May 3, 2026, 4:09 p.m.