Triple
T37099019
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maimonides Medical Center |
E918643
|
entity |
| Predicate | hasAnesthesiologyDepartment |
P193974
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Maimonides Medical Center, hasAnesthesiologyDepartment, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAnesthesiologyDepartment Context triple: [Maimonides Medical Center, hasAnesthesiologyDepartment, yes]
-
A.
hasPharmacyDepartment
Indicates that an entity includes or is associated with a dedicated pharmacy department or unit.
-
B.
hasEmergencyDepartmentLevel
Indicates the specific classification or tier of emergency care capability associated with an emergency department.
-
C.
operatedTheatersIn
Indicates that an entity managed or ran the day-to-day operations of one or more theaters in a specified location or context.
-
D.
hasClinicalUnit
Indicates that an entity is associated with or belongs to a specific clinical unit or department within a healthcare setting.
-
E.
containsHospital
Indicates that one entity includes or encompasses a hospital within its boundaries or composition.
- 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_69f76e9a48bc8190a3947508d8bca408 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fd5bf69acc819092a01e4259785dc3 |
completed | May 8, 2026, 3:43 a.m. |
| PD | Predicate disambiguation | batch_69fd59b3f4ac8190a7f9dd3142da6e09 |
completed | May 8, 2026, 3:34 a.m. |
| PDg | Predicate description generation | batch_69fd5bf49288819098a12202411cba4f |
completed | May 8, 2026, 3:43 a.m. |
Created at: May 3, 2026, 4:14 p.m.