Triple
T36277741
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Catholic Church in India |
E892851
|
entity |
| Predicate | hasHealthcareInstitutions |
P72534
|
FINISHED |
| Object | Catholic hospitals in India |
—
|
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: Catholic hospitals in India | Statement: [Catholic Church in India, hasHealthcareInstitutions, Catholic hospitals in India]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasHealthcareInstitutions Context triple: [Catholic Church in India, hasHealthcareInstitutions, Catholic hospitals in India]
-
A.
hasHealthCareInstitutionType
Indicates that an entity is classified as a specific type or category of healthcare institution.
-
B.
hasHealthcareInfrastructure
chosen
Indicates that an entity possesses facilities, systems, and resources necessary to deliver healthcare services.
-
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.
hasHealthcareProvider
Indicates that one entity receives healthcare services or medical oversight from another entity acting as its healthcare provider.
- 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_69f76e488f34819083e254dbe288c27a |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69fdbc5ef46c8190bbcfb9798f4615b7 |
completed | May 8, 2026, 10:35 a.m. |
| PD | Predicate disambiguation | batch_69fdbb270338819082ce3f73903e884f |
completed | May 8, 2026, 10:29 a.m. |
Created at: May 3, 2026, 4:09 p.m.