Triple
T37099013
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maimonides Medical Center |
E918643
|
entity |
| Predicate | hasOrthopedicsDepartment |
P130509
|
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, hasOrthopedicsDepartment, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOrthopedicsDepartment Context triple: [Maimonides Medical Center, hasOrthopedicsDepartment, yes]
-
A.
orthopedicCenter
chosen
Indicates a relationship where a medical facility or unit specializes in the diagnosis, treatment, and care of musculoskeletal and orthopedic conditions.
-
B.
hasAnesthesiologyDepartment
Indicates that an entity includes or is associated with a department specializing in anesthesiology services.
-
C.
hasPharmacyDepartment
Indicates that an entity includes or is associated with a dedicated pharmacy department or unit.
-
D.
hasPediatricsDepartment
Indicates that an institution or facility includes or is equipped with a pediatrics department providing medical care for children.
-
E.
hasMedicalCenter
Indicates that an entity possesses, hosts, or is associated with a medical center 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_69f76e9a48bc8190a3947508d8bca408 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69ff17be6ad48190963206f2619b1b28 |
completed | May 9, 2026, 11:17 a.m. |
| PD | Predicate disambiguation | batch_69ff1724ba24819092c928fcbcb286ec |
completed | May 9, 2026, 11:14 a.m. |
Created at: May 3, 2026, 4:14 p.m.