Triple

T5289321
Position Surface form Disambiguated ID Type / Status
Subject Norwegian System of Patient Injury Compensation E119702 entity
Predicate compensatesFor P26699 FINISHED
Object economic loss due to patient injury 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: economic loss due to patient injury | Statement: [Norwegian System of Patient Injury Compensation, compensatesFor, economic loss due to patient injury]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: compensatesFor
Context triple: [Norwegian System of Patient Injury Compensation, compensatesFor, economic loss due to patient injury]
  • A. compensated chosen
    Indicates that one entity provides payment or some form of recompense to another entity in return for goods, services, or loss incurred.
  • B. dragCompensation
    Indicates that one entity adjusts or counteracts the drag force experienced by another entity or system.
  • C. compensationModel
    Indicates the type or structure of payment or rewards provided in exchange for work, services, or performance.
  • D. complements
    Indicates that one entity enhances, completes, or improves another by providing qualities or functions that fit well together.
  • E. laterComplementedBy
    Indicates that an earlier entity is subsequently supplemented, enhanced, or completed by a later 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_69bd446de5648190b313a90bd96730d2 completed March 20, 2026, 12:58 p.m.
NER Named-entity recognition batch_69bd8682d18c8190bbb35cc75c8a7c12 completed March 20, 2026, 5:40 p.m.
PD Predicate disambiguation batch_69bd844dfdac819086efedd1cbebff84 completed March 20, 2026, 5:30 p.m.
Created at: March 20, 2026, 1:52 p.m.