Triple
T10266795
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CareKit |
E240729
|
entity |
| Predicate | relatedTo |
P37
|
FINISHED |
| Object | HealthKit |
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 | Statement: [CareKit, relatedTo, HealthKit]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: HealthKit Context triple: [CareKit, relatedTo, HealthKit]
-
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.
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.
-
D.
CareKit
CareKit is an open-source Apple framework that helps developers build iOS apps for tracking, managing, and visualizing users’ health and care plans.
-
E.
Google Fit
Google Fit is a health and fitness tracking platform by Google that aggregates activity, workout, and wellness data across devices and apps.
- 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_69d71ce5ac10819092c287bc435ae010 |
completed | April 9, 2026, 3:28 a.m. |
Created at: April 6, 2026, 11:34 a.m.