Triple
T34212152
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lugoff, South Carolina |
E877683
|
entity |
| Predicate | hasPrimaryHospitalNearby |
P5648
|
FINISHED |
| Object | KershawHealth Medical Center in Camden |
E657729
|
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: KershawHealth Medical Center in Camden | Statement: [Lugoff, South Carolina, hasPrimaryHospitalNearby, KershawHealth Medical Center in Camden]
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 (4 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_69f349b0b4bc819088c1552424089ee9 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_6a02fc3890a881908b69b0f2d7cc1ce1 |
completed | May 12, 2026, 10:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a36cc918f088190983dfd1483a75c0e |
completed | June 20, 2026, 5:23 p.m. |
| PD | Predicate disambiguation | batch_6a02fb98438c8190938896c00216b9ec |
completed | May 12, 2026, 10:06 a.m. |
Created at: May 1, 2026, 1:55 a.m.