Triple
T12818431
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ochsner Medical Center – North Shore |
E306462
|
entity |
| Predicate | offersInpatientBeds |
P30112
|
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: [Ochsner Medical Center – North Shore, offersInpatientBeds, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: offersInpatientBeds Context triple: [Ochsner Medical Center – North Shore, offersInpatientBeds, true]
-
A.
hasInpatientBeds
chosen
Indicates that an entity provides or is equipped with beds designated for admitting and treating inpatients.
-
B.
numberOfHospitalized
Indicates the count of individuals who have been admitted to a hospital for medical care.
-
C.
numberOfHospitals
Indicates the total count of hospitals associated with a given entity or within a specified context.
-
D.
containsHospital
Indicates that one entity includes or encompasses a hospital within its boundaries or composition.
-
E.
hospitalizedIn
Indicates that a person or patient is admitted for medical care and staying as an inpatient in a specified hospital or healthcare 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_69d7bdf46c448190b1faa55aaacb6317 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96e9d00088190ac0f5d60e1de7a7c |
completed | April 10, 2026, 9:41 p.m. |
| PD | Predicate disambiguation | batch_69d964100f7481909a197396003d4a71 |
completed | April 10, 2026, 8:56 p.m. |
Created at: April 9, 2026, 5:31 p.m.