Triple
T6440437
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Midwestern University |
E138202
|
entity |
| Predicate | hasClinicalFacilities |
P12416
|
FINISHED |
| Object | multispecialty clinics |
—
|
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: multispecialty clinics | Statement: [Midwestern University, hasClinicalFacilities, multispecialty clinics]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasClinicalFacilities Context triple: [Midwestern University, hasClinicalFacilities, multispecialty clinics]
-
A.
hasFacilities
chosen
Indicates that an entity possesses, provides, or is equipped with certain facilities or physical resources.
-
B.
hasClinicalUnit
Indicates that an entity is associated with or belongs to a specific clinical unit or department within a healthcare setting.
-
C.
hasMedicalCenter
Indicates that an entity possesses, hosts, or is associated with a medical center facility.
-
D.
hasNumberOfClinics
Indicates the quantity of clinics associated with or belonging to a given entity.
-
E.
hasCompanyFacility
Indicates that a company possesses, operates, or is responsible for a particular facility.
- 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_69c008aa61ac8190bc96715ed79fe2d8 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c06968c6a88190a623da9bd3152a66 |
completed | March 22, 2026, 10:12 p.m. |
| PD | Predicate disambiguation | batch_69c060f96980819091bab9335922a457 |
completed | March 22, 2026, 9:36 p.m. |
Created at: March 22, 2026, 4:46 p.m.