Triple
T27609205
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Besisahar |
E700267
|
entity |
| Predicate | hasHealthCareInstitutionType |
P177112
|
FINISHED |
| Object | hospitals |
—
|
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: hospitals | Statement: [Besisahar, hasHealthCareInstitutionType, hospitals]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasHealthCareInstitutionType Context triple: [Besisahar, hasHealthCareInstitutionType, hospitals]
-
A.
hasHospitalType
Indicates that a hospital is classified as belonging to a specific type or category (e.g., general, specialized, teaching).
-
B.
hasHealthcareProvider
Indicates that one entity receives healthcare services or medical oversight from another entity acting as its healthcare provider.
-
C.
hasHealthcareServicesIn
Indicates that a healthcare provider or organization offers or operates healthcare services within a specified location or area.
-
D.
hasMedicalCenter
Indicates that an entity possesses, hosts, or is associated with a medical center facility.
-
E.
hasAffiliatedHospital
Indicates that one entity (typically a medical professional, clinic, or organization) is formally connected or associated with a particular hospital for professional or operational purposes.
- 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_69ef6a4e2e208190b63b7268f405785c |
completed | April 27, 2026, 1:53 p.m. |
| NER | Named-entity recognition | batch_69f6f85bfba48190aba95b40642a8ca7 |
completed | May 3, 2026, 7:25 a.m. |
| PD | Predicate disambiguation | batch_69f6f65fd1d08190b88e5e68ba268500 |
completed | May 3, 2026, 7:16 a.m. |
| PDg | Predicate description generation | batch_69f6f854486c81909396d944a55e03ab |
completed | May 3, 2026, 7:25 a.m. |
Created at: April 27, 2026, 2:10 p.m.