Triple
T9497318
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Soroka University Medical Center |
E229041
|
entity |
| Predicate | hasInternalMedicineDepartment |
P41890
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Soroka University Medical Center, hasInternalMedicineDepartment, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasInternalMedicineDepartment Context triple: [Soroka University Medical Center, hasInternalMedicineDepartment, true]
-
A.
hasPharmacyDepartment
Indicates that an entity includes or is associated with a dedicated pharmacy department or unit.
-
B.
hasClinicalUnit
chosen
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.
hasSpecialty
Indicates that an entity possesses a particular area of expertise, focus, or professional specialization.
-
E.
containsHospital
Indicates that one entity includes or encompasses a hospital within its boundaries or composition.
- 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_69ca84753660819098e8d416e89e26ae |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd95ecf4148190aa8f4733980166ae |
completed | April 1, 2026, 10:02 p.m. |
| PD | Predicate disambiguation | batch_69cca5651a588190a3cfebe249a223e5 |
completed | April 1, 2026, 4:56 a.m. |
Created at: March 30, 2026, 7:56 p.m.