Triple

T18395098
Position Surface form Disambiguated ID Type / Status
Subject TechMed Centre E449846 entity
Predicate name P16 FINISHED
Object TechMed Centre NE NERFINISHED

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: TechMed Centre | Statement: [TechMed Centre, name, TechMed Centre]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: TechMed Centre
Context triple: [TechMed Centre, name, TechMed Centre]
  • A. TechMed Centre chosen
    TechMed Centre is a University of Twente institute focused on research, innovation, and education in medical technology and healthcare solutions.
  • B. Healthineers
    Healthineers is the consumer-facing brand of Siemens Healthineers, associated with innovative medical technology and healthcare solutions.
  • C. SciMed
    SciMed is the official abbreviation for the Faculty of Science and Medicine at the University of Fribourg, a multidisciplinary faculty combining scientific and medical education and research.
  • D. VisTaTech Center
    VisTaTech Center is a modern multipurpose facility at Schoolcraft College that hosts culinary arts, conference, and technology-focused learning and event spaces.
  • E. Centre for Health Innovation
    The Centre for Health Innovation is a unit within the Norwegian Institute of Public Health focused on developing and implementing innovative approaches to improve public health services and outcomes.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8b9fab8a8819086a9ddc0871715e0 completed April 10, 2026, 8:51 a.m.
NER Named-entity recognition batch_69e51845d6708190bc96ec801e21b7a3 completed April 19, 2026, 6 p.m.
Created at: April 10, 2026, 10:46 a.m.