Triple
T6955659
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Memorial Regional Hospital |
E161235
|
entity |
| Predicate | hasCardiacCareUnit |
P74282
|
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: [Memorial Regional Hospital, hasCardiacCareUnit, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCardiacCareUnit Context triple: [Memorial Regional Hospital, hasCardiacCareUnit, yes]
-
A.
hasIntensiveCareUnit
Indicates that a medical facility includes and operates an intensive care unit (ICU) for critically ill patients.
-
B.
hasClinicalUnit
Indicates that an entity is associated with or belongs to a specific clinical unit or department within a healthcare setting.
-
C.
containsHospital
Indicates that one entity includes or encompasses a hospital within its boundaries or composition.
-
D.
hasHospitalType
Indicates that a hospital is classified as belonging to a specific type or category (e.g., general, specialized, teaching).
-
E.
hasEmergencyCare
Indicates that an entity provides or is equipped with emergency medical care services for another entity or individuals.
- 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_69c68852a9a0819097797e31d492e273 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6dacf8c8c8190a25dbacebeb4b66e |
completed | March 27, 2026, 7:30 p.m. |
| PD | Predicate disambiguation | batch_69c6d7bf0a7c8190b5ed4aca22ba9b97 |
completed | March 27, 2026, 7:17 p.m. |
| PDg | Predicate description generation | batch_69c6dacb524c81909aade2282a4c0c01 |
completed | March 27, 2026, 7:30 p.m. |
Created at: March 27, 2026, 2:29 p.m.