Triple
T34212152
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lugoff, South Carolina |
E877683
|
entity |
| Predicate | hasPrimaryHospitalNearby |
—
|
GENERATED |
| Object | KershawHealth Medical Center in Camden |
—
|
UNRECOGNIZED GENERATED |
How this triple was built (1 step)
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.
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPrimaryHospitalNearby Context triple: [Lugoff, South Carolina, hasPrimaryHospitalNearby, KershawHealth Medical Center in Camden]
-
A.
hasNearbyFacility
chosen
Indicates that one entity is located close to or in the vicinity of a particular facility.
-
B.
connectsToHospital
Indicates that one entity has a direct linkage, route, or interface to a hospital, enabling access or interaction between them.
-
C.
hasHealthCareInstitutionType
Indicates that an entity is classified as a specific type or category of healthcare institution.
-
D.
hasMedicalCenter
Indicates that an entity possesses, hosts, or is associated with a medical center facility.
-
E.
hasRegionalCenterNearby
Indicates that a regional center is located in close proximity to the referenced entity.
- F. None of above.
Provenance (1 batch)
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_69f349b0b4bc819088c1552424089ee9 |
completed | April 30, 2026, 12:23 p.m. |
Created at: May 1, 2026, 1:55 a.m.