Triple
T22903541
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Institute of Medical Sciences, Banaras Hindu University |
E568386
|
entity |
| Predicate | hasHospitalBeds |
P30112
|
FINISHED |
| Object | over 1000 beds (approximate) |
—
|
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 (approximate) | Statement: [Institute of Medical Sciences, Banaras Hindu University, hasHospitalBeds, over 1000 beds (approximate)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasHospitalBeds Context triple: [Institute of Medical Sciences, Banaras Hindu University, hasHospitalBeds, over 1000 beds (approximate)]
-
A.
hasInpatientBeds
chosen
Indicates that an entity provides or is equipped with beds designated for admitting and treating inpatients.
-
B.
numberOfHospitals
Indicates the total count of hospitals associated with a given entity or within a specified context.
-
C.
numberOfHospitalized
Indicates the count of individuals who have been admitted to a hospital for medical care.
-
D.
containsHospital
Indicates that one entity includes or encompasses a hospital within its boundaries or composition.
-
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_69e2458cd9e48190943ad2e34485d939 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f18017a0208190a7e6f1638fc17b01 |
completed | April 29, 2026, 3:50 a.m. |
| PD | Predicate disambiguation | batch_69ef3b6b2e2481908258156937b5a745 |
completed | April 27, 2026, 10:33 a.m. |
Created at: April 17, 2026, 3:41 p.m.