Triple
T32510572
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Target pharmacy and clinic businesses |
E830918
|
entity |
| Predicate | numberOfPharmaciesAtAcquisition |
—
|
GENERATED |
| Object | 1672 |
—
|
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: numberOfPharmaciesAtAcquisition Context triple: [Target pharmacy and clinic businesses, numberOfPharmaciesAtAcquisition, 1672]
-
A.
hasPharmacies
chosen
Indicates that one entity possesses, operates, or is associated with one or more pharmacies.
-
B.
openedAsPharmacy
Indicates that an entity originally began operation or was first established functioning as a pharmacy.
-
C.
hasPharmacyDepartment
Indicates that an entity includes or is associated with a dedicated pharmacy department or unit.
-
D.
successorInPharmaceuticalBusiness
Indicates that one entity has taken over or continued the pharmaceutical business operations or role previously held by another entity.
-
E.
numberOfDescribedDrugs
Indicates the quantity of drugs that are being described or specified in a given context.
- 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_69f3492318348190ba37fb6b5f1d67f4 |
completed | April 30, 2026, 12:20 p.m. |
Created at: May 1, 2026, 1 a.m.