Triple

T10266855
Position Surface form Disambiguated ID Type / Status
Subject HKWorkout E240731 entity
Predicate definedIn P775 FINISHED
Object HealthKit framework E46912 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: HealthKit framework | Statement: [HKWorkout, definedIn, HealthKit framework]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: HealthKit framework
Context triple: [HKWorkout, definedIn, HealthKit framework]
  • A. HealthKit framework chosen
    HealthKit framework is an Apple software framework that enables apps to securely access, store, and share users’ health and fitness data on iOS devices.
  • B. Apple Health Research
    Apple Health Research is an Apple initiative that uses iPhone-based tools and apps to enable large-scale medical studies by collecting health data from participants.
  • C. CareKit
    CareKit is an open-source Apple framework that helps developers build iOS apps for tracking, managing, and visualizing users’ health and care plans.
  • D. Samsung Health
    Samsung Health is Samsung’s health and fitness tracking platform that monitors activity, sleep, heart rate, and other wellness metrics across its mobile devices and wearables.
  • E. CoreMotion
    CoreMotion is an Apple framework that provides access to motion and fitness data from device sensors such as accelerometers and gyroscopes.
  • 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_69d381a94c1881908fc38fc263d9b9c2 completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4d26d4dd88190bdd324bafa1e8e00 completed April 7, 2026, 9:46 a.m.
NED1 Entity disambiguation (via context triple) batch_69d794b9b1d881908c55aa50da68ae75 completed April 9, 2026, 11:59 a.m.
Created at: April 6, 2026, 11:34 a.m.