Triple
T6778730
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mayo Clinic Hospital – Saint Marys Campus |
E155623
|
entity |
| Predicate | bedCapacity |
P29715
|
FINISHED |
| Object | over 1000 beds |
—
|
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 1000 beds | Statement: [Mayo Clinic Hospital – Saint Marys Campus, bedCapacity, over 1000 beds]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: bedCapacity Context triple: [Mayo Clinic Hospital – Saint Marys Campus, bedCapacity, over 1000 beds]
-
A.
bedCount
chosen
Indicates the number of beds associated with an entity, such as a room, facility, or accommodation.
-
B.
seatingCapacity
Indicates the maximum number of people that something (typically a venue or vehicle) is designed or allowed to seat.
-
C.
hasBedType
Indicates that an entity (such as a room or accommodation) is associated with a specific type or configuration of bed.
-
D.
numberOfBedrooms
Indicates the quantity of bedrooms associated with a given property or dwelling.
-
E.
capacityPerSide
Indicates the maximum quantity or volume that each individual side or unit in a pair can hold or accommodate.
- 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_69c688162bf8819088b664b5c3b5be7a |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d2689d408190bc2c1ce4ae9c1b13 |
completed | March 27, 2026, 6:54 p.m. |
| PD | Predicate disambiguation | batch_69c6d095dcac8190bb9b943f50a7f885 |
completed | March 27, 2026, 6:46 p.m. |
Created at: March 27, 2026, 2:14 p.m.