Triple
T13775975
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Westerly Hospital |
E331006
|
entity |
| Predicate | hasBedCountApproximate |
P29715
|
FINISHED |
| Object | 60 |
—
|
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: 60 | Statement: [Westerly Hospital, hasBedCountApproximate, 60]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBedCountApproximate Context triple: [Westerly Hospital, hasBedCountApproximate, 60]
-
A.
bedCount
chosen
Indicates the number of beds associated with an entity, such as a room, facility, or accommodation.
-
B.
hasBedType
Indicates that an entity (such as a room or accommodation) is associated with a specific type or configuration of bed.
-
C.
numberOfBedrooms
Indicates the quantity of bedrooms associated with a given property or dwelling.
-
D.
approximateNumberOfRooms
Indicates an estimated or not precisely known count of rooms associated with an entity.
-
E.
hasBedMaterial
Indicates that one entity has, contains, or is characterized by a particular bed material (e.g., the substance forming the base or bedding of that entity).
- 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_69d81c583b0081909e408a17db517a21 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de0238bdbc8190a946e6e5431632a5 |
completed | April 14, 2026, 9 a.m. |
| PD | Predicate disambiguation | batch_69dbbe97846c819093b00ea117b64e0d |
completed | April 12, 2026, 3:47 p.m. |
Created at: April 9, 2026, 10:10 p.m.