Triple
T8685262
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | VA Iowa City Health Care System |
E206141
|
entity |
| Predicate | healthCareSystemType |
P7500
|
FINISHED |
| Object | public health care system |
—
|
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: public health care system | Statement: [VA Iowa City Health Care System, healthCareSystemType, public health care system]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: healthCareSystemType Context triple: [VA Iowa City Health Care System, healthCareSystemType, public health care system]
-
A.
healthcareType
chosen
Indicates the category or kind of healthcare service, system, or coverage associated with an entity.
-
B.
healthSystem
Indicates a relationship where an entity functions as, belongs to, or is managed within a particular health care system or network.
-
C.
healthSystemLevel
Indicates the level or tier within a health system at which a given action, service, or relationship occurs (e.g., local, regional, national).
-
D.
hasHospitalType
Indicates that a hospital is classified as belonging to a specific type or category (e.g., general, specialized, teaching).
-
E.
basedInHospitalSystem
Indicates that an entity operates within, is affiliated with, or is organizationally part of a particular hospital system.
- 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_69ca835379688190aa06b9d98e684d58 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc4aec1f8c8190a9ab1a73c2dbcd3c |
completed | March 31, 2026, 10:30 p.m. |
| PD | Predicate disambiguation | batch_69cc4569f9048190b9c86b4c81103d35 |
completed | March 31, 2026, 10:06 p.m. |
Created at: March 30, 2026, 6:32 p.m.