Triple
T2161901
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Weno |
E46818
|
entity |
| Predicate | hasHealthFacility |
P10262
|
FINISHED |
| Object | hospital in Weno |
—
|
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: hospital in Weno | Statement: [Weno, hasHealthFacility, hospital in Weno]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasHealthFacility Context triple: [Weno, hasHealthFacility, hospital in Weno]
-
A.
hasMedicalCenter
chosen
Indicates that an entity possesses, hosts, or is associated with a medical center facility.
-
B.
hasNearbyFacility
Indicates that one entity is located close to or in the vicinity of a particular facility.
-
C.
hasHospitalType
Indicates that a hospital is classified as belonging to a specific type or category (e.g., general, specialized, teaching).
-
D.
hasTraumaCenter
Indicates that an entity (such as a hospital or facility) includes or is equipped with a designated trauma center capable of providing specialized emergency care for severe injuries.
-
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.
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_69a88a184cbc8190877791f6552c2484 |
completed | March 4, 2026, 7:38 p.m. |
| NER | Named-entity recognition | batch_69abbe8b9c0881908373eabc7f81c394 |
completed | March 7, 2026, 5:58 a.m. |
| PD | Predicate disambiguation | batch_69abbd9c90408190b6b65498ca43ce26 |
completed | March 7, 2026, 5:54 a.m. |
Created at: March 4, 2026, 7:45 p.m.