Triple
T608296
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Boston Children’s Hospital |
E12041
|
entity |
| Predicate | beds |
P17054
|
FINISHED |
| Object | approximately 400 |
—
|
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: approximately 400 | Statement: [Boston Children’s Hospital, beds, approximately 400]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: beds Context triple: [Boston Children’s Hospital, beds, approximately 400]
-
A.
numberOfBedrooms
Indicates the quantity of bedrooms associated with a given property or dwelling.
-
B.
lays
Indicates that one entity deposits or places something, typically eggs or objects, onto a surface or in a location.
-
C.
numberOfBathrooms
Indicates the total count of bathrooms associated with an entity (such as a property or unit).
-
D.
chairType
Indicates the specific kind or category of chair that an entity is classified as.
-
E.
furnishingType
Indicates the type or category of furnishings associated with an entity, such as a property or room.
- 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_69a493309df48190a327f748e88049a6 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49df54eec8190af3f5f04c01d5d2a |
completed | March 1, 2026, 8:13 p.m. |
| PD | Predicate disambiguation | batch_69a49cf8fc1c81908a9c7df552aa1a59 |
completed | March 1, 2026, 8:09 p.m. |
| PDg | Predicate description generation | batch_69a49def31ec81909dc53e70f4a36eda |
completed | March 1, 2026, 8:13 p.m. |
Created at: March 1, 2026, 7:35 p.m.