Triple
T32510572
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Target pharmacy and clinic businesses |
E830918
|
entity |
| Predicate | numberOfPharmaciesAtAcquisition |
P107963
|
FINISHED |
| Object | 1672 |
—
|
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: 1672 | Statement: [Target pharmacy and clinic businesses, numberOfPharmaciesAtAcquisition, 1672]
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 (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_69f3492318348190ba37fb6b5f1d67f4 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_6a01c41dfe248190a68f4a262591b30f |
completed | May 11, 2026, 11:57 a.m. |
| PD | Predicate disambiguation | batch_6a01c375d14c8190a15bfa12623588a9 |
completed | May 11, 2026, 11:54 a.m. |
Created at: May 1, 2026, 1 a.m.