Triple
T755093
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | University Hospital Zurich |
E15535
|
entity |
| Predicate | hasMedicalStaffApprox |
P19125
|
FINISHED |
| Object | 1400 |
—
|
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: 1400 | Statement: [University Hospital Zurich, hasMedicalStaffApprox, 1400]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMedicalStaffApprox Context triple: [University Hospital Zurich, hasMedicalStaffApprox, 1400]
-
A.
hasMedicalCenter
Indicates that an entity possesses, hosts, or is associated with a medical center facility.
-
B.
hasPatient
Indicates that an action, event, or process involves a specific entity as the one undergoing or receiving its effects (the patient).
-
C.
containsMedicalDistrict
Indicates that one administrative or geographic area includes a designated medical district within its boundaries.
-
D.
staffIncluded
Indicates that staff members are included or provided as part of the associated entity, service, or arrangement.
-
E.
hasSpecialty
Indicates that an entity possesses a particular area of expertise, focus, or professional specialization.
- F. None of above. chosen
Provenance (4 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_69a493599a0081908da65f3407af1ef2 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a66820548190b373deb117187c2c |
completed | March 1, 2026, 8:49 p.m. |
| PD | Predicate disambiguation | batch_69a4a501c4cc81908de6d63e3d4f60d7 |
completed | March 1, 2026, 8:43 p.m. |
| PDg | Predicate description generation | batch_69a4a5bed20c81909ecc28bf42594e72 |
completed | March 1, 2026, 8:46 p.m. |
Created at: March 1, 2026, 7:37 p.m.