Triple
T5611215
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ronald Reagan UCLA Medical Center |
E147359
|
entity |
| Predicate | hasIntensiveCareUnits |
P29662
|
FINISHED |
| Object | multiple adult ICUs |
—
|
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: multiple adult ICUs | Statement: [Ronald Reagan UCLA Medical Center, hasIntensiveCareUnits, multiple adult ICUs]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasIntensiveCareUnits Context triple: [Ronald Reagan UCLA Medical Center, hasIntensiveCareUnits, multiple adult ICUs]
-
A.
hasIntensiveCareUnit
chosen
Indicates that a medical facility includes and operates an intensive care unit (ICU) for critically ill patients.
-
B.
hasInpatientBeds
Indicates that an entity provides or is equipped with beds designated for admitting and treating inpatients.
-
C.
numberOfHospitals
Indicates the total count of hospitals associated with a given entity or within a specified context.
-
D.
numberOfHospitalized
Indicates the count of individuals who have been admitted to a hospital for medical care.
-
E.
hasHospitalType
Indicates that a hospital is classified as belonging to a specific type or category (e.g., general, specialized, teaching).
- 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_69c0090500f881908374285baf0ac46f |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c0211fad448190b068b77ed25931d5 |
completed | March 22, 2026, 5:04 p.m. |
| PD | Predicate disambiguation | batch_69c01b1b3c98819080687d18ab10a914 |
completed | March 22, 2026, 4:38 p.m. |
Created at: March 22, 2026, 3:39 p.m.