Triple

T246736
Position Surface form Disambiguated ID Type / Status
Subject Norman Bethune E5053 entity
Predicate medicalInnovation P7500 FINISHED
Object early adoption of socialized medicine practices 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: early adoption of socialized medicine practices | Statement: [Norman Bethune, medicalInnovation, early adoption of socialized medicine practices]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: medicalInnovation
Context triple: [Norman Bethune, medicalInnovation, early adoption of socialized medicine practices]
  • A. innovation
    Indicates the introduction or development of something new or significantly improved compared to existing methods, products, or ideas.
  • B. researchArm
    Indicates that an entity is a specific study group or treatment arm within a research or clinical trial design.
  • C. inspiredDevelopmentOf
    Indicates that one entity served as a motivating influence or creative stimulus leading to the development or creation of another entity.
  • D. technologyParadigm
    Indicates a relationship where one entity represents or defines the overarching technological model, framework, or approach within which another entity operates or is categorized.
  • E. healthcareType chosen
    Indicates the category or kind of healthcare service, system, or coverage associated with an entity.
  • 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_69a257c4bf688190a46ebbf411ab7473 completed Feb. 28, 2026, 2:49 a.m.
NER Named-entity recognition batch_69a25d13b8088190a3f48f0388d57496 completed Feb. 28, 2026, 3:12 a.m.
PD Predicate disambiguation batch_69a25b63b0bc8190864d7324d339fb48 completed Feb. 28, 2026, 3:05 a.m.
Created at: Feb. 28, 2026, 2:54 a.m.