Triple
T7512970
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | VA Medical Center Philadelphia |
E177566
|
entity |
| Predicate | hasInsurancePolicy |
P12185
|
FINISHED |
| Object | primarily VA-covered care |
—
|
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: primarily VA-covered care | Statement: [VA Medical Center Philadelphia, hasInsurancePolicy, primarily VA-covered care]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasInsurancePolicy Context triple: [VA Medical Center Philadelphia, hasInsurancePolicy, primarily VA-covered care]
-
A.
insuranceType
chosen
Indicates the specific category or kind of insurance coverage associated with an entity or relationship.
-
B.
isOnPolicy
Indicates that an action, configuration, or behavior complies with and adheres to a specified policy or set of rules.
-
C.
hasCoverage
Indicates that one entity provides insurance or protection coverage for another entity or subject.
-
D.
hasPlan
Indicates that an entity possesses or is associated with a specific plan or course of action.
-
E.
hasBenefit
Indicates that one entity provides an advantage, improvement, or positive outcome to another entity.
- 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_69c69f276b108190af2cc790b6554544 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f5d52b2c8190ba32b1575756fa7c |
completed | March 27, 2026, 9:25 p.m. |
| PD | Predicate disambiguation | batch_69c6f4d44e9481909813e073b194f6f4 |
completed | March 27, 2026, 9:21 p.m. |
Created at: March 27, 2026, 3:45 p.m.