Triple
T24026350
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arizona College of Optometry |
E594970
|
entity |
| Predicate | hasClinicType |
P2836
|
FINISHED |
| Object | optometry clinic |
—
|
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: optometry clinic | Statement: [Arizona College of Optometry, hasClinicType, optometry clinic]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasClinicType Context triple: [Arizona College of Optometry, hasClinicType, optometry clinic]
-
A.
hasFacilityType
chosen
Indicates that an entity possesses or is associated with a specific type or category of facility.
-
B.
hasHospitalType
Indicates that a hospital is classified as belonging to a specific type or category (e.g., general, specialized, teaching).
-
C.
hasNumberOfClinics
Indicates the quantity of clinics associated with or belonging to a given entity.
-
D.
hasFictionalClinic
Indicates that an entity is associated with or contains a clinic that exists only in a fictional or imaginary context.
-
E.
hasClinicalService
Indicates that an entity provides, offers, or is associated with a specific clinical service.
- 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_69e288be2c288190a3a46006945557f7 |
completed | April 17, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69f1d76c083c819090784ecb42effbae |
completed | April 29, 2026, 10:03 a.m. |
| PD | Predicate disambiguation | batch_69f17639d23c8190bed93434e2f9230a |
completed | April 29, 2026, 3:08 a.m. |
Created at: April 17, 2026, 9:54 p.m.