Triple
T5892451
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Providence Health & Services |
E131021
|
entity |
| Predicate | hasNumberOfHospitals |
P59459
|
FINISHED |
| Object | over 50 |
—
|
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: over 50 | Statement: [Providence Health & Services, hasNumberOfHospitals, over 50]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfHospitals Context triple: [Providence Health & Services, hasNumberOfHospitals, over 50]
-
A.
numberOfHospitals
chosen
Indicates the total count of hospitals associated with a given entity or within a specified context.
-
B.
designatedAsFlagshipHospitalFor
Indicates that one hospital has been officially selected or recognized as the primary or leading flagship institution for another entity (such as a health system, region, or organization).
-
C.
numberOfHospitalized
Indicates the count of individuals who have been admitted to a hospital for medical care.
-
D.
operatedHospitalsIn
Indicates that an entity managed or ran the operations of one or more hospitals located in a specified place or context.
-
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_69c00857439c819095950754176aa58a |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c0400f1af881908d376ea4793f6dea |
completed | March 22, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_69c0334dc8248190b7394dcece362d52 |
completed | March 22, 2026, 6:22 p.m. |
Created at: March 22, 2026, 3:58 p.m.