Triple
T27219229
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Peak Practice |
E681222
|
entity |
| Predicate | hasMedicalTheme |
P152334
|
FINISHED |
| Object | rural healthcare |
—
|
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: rural healthcare | Statement: [Peak Practice, hasMedicalTheme, rural healthcare]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMedicalTheme Context triple: [Peak Practice, hasMedicalTheme, rural healthcare]
-
A.
isMedicallyRelevant
chosen
Indicates that something has significance, impact, or applicability within a medical or clinical context.
-
B.
usesMedicalKnowledge
Indicates that an entity applies or relies on medical knowledge in performing an action or making a decision.
-
C.
medicalBackground
Indicates that an entity has a history of prior medical conditions, treatments, or health-related experiences relevant to its current state or context.
-
D.
medicalField
Indicates that one entity is a branch or specialty of medicine in which the other entity is practiced, studied, or categorized.
-
E.
hasMedicalCenter
Indicates that an entity possesses, hosts, or is associated with a medical center facility.
- 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_69eefac9f64c8190a07490fe0c8b72a3 |
completed | April 27, 2026, 5:57 a.m. |
| NER | Named-entity recognition | batch_69f74c70fd248190a9d5543afcb08211 |
completed | May 3, 2026, 1:24 p.m. |
| PD | Predicate disambiguation | batch_69f7478e3b548190a51d5d436e2bb036 |
completed | May 3, 2026, 1:03 p.m. |
Created at: April 27, 2026, 9:42 a.m.