Triple
T5091553
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zeb |
E114761
|
entity |
| Predicate | conflictWith |
P4897
|
FINISHED |
| Object | HelthWyzer |
E492539
|
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: HelthWyzer | Statement: [Zeb, conflictWith, HelthWyzer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: HelthWyzer Context triple: [Zeb, conflictWith, HelthWyzer]
-
A.
HelthWyzer
chosen
HelthWyzer is a powerful biotech and pharmaceutical corporation in Margaret Atwood’s MaddAddam trilogy, known for its unethical genetic engineering and role in triggering a global pandemic.
-
B.
One Medical
One Medical is a membership-based primary care provider that offers technology-enabled, patient-centered medical services through both in-person clinics and virtual care.
-
C.
Cloud Healthcare API
Cloud Healthcare API is a Google Cloud service that enables secure storage, management, and exchange of healthcare data using standard formats like HL7, FHIR, and DICOM.
-
D.
Health Connect
Health Connect is a unified health and fitness data platform on Android that lets apps securely share and manage users’ wellness information in one place.
-
E.
CareKit
CareKit is an open-source Apple framework that helps developers build iOS apps for tracking, managing, and visualizing users’ health and care plans.
- 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_69bd443e941881908eb4e8c685b6f656 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd7541b2bc8190b58c2a23733b7825 |
completed | March 20, 2026, 4:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69beba72a7b88190a118ff5f31079eff |
completed | March 21, 2026, 3:34 p.m. |
Created at: March 20, 2026, 1:40 p.m.