Triple
T37582599
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Peter Munk Cardiac Centre |
E935011
|
entity |
| Predicate | typeOfHospitalService |
P30483
|
FINISHED |
| Object | specialized cardiac centre |
—
|
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: specialized cardiac centre | Statement: [Peter Munk Cardiac Centre, typeOfHospitalService, specialized cardiac centre]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfHospitalService Context triple: [Peter Munk Cardiac Centre, typeOfHospitalService, specialized cardiac centre]
-
A.
healthcareType
Indicates the category or kind of healthcare service, system, or coverage associated with an entity.
-
B.
typeOfEmergencyService
Indicates the specific category or kind of emergency service associated with or provided in a given situation.
-
C.
hasHospitalType
chosen
Indicates that a hospital is classified as belonging to a specific type or category (e.g., general, specialized, teaching).
-
D.
hospitalFunction
Indicates the specific medical or administrative role, service, or operational purpose that a hospital performs.
-
E.
designatedAsFlagshipHospitalFor
Indicates that one hospital has been officially selected or recognized as the primary or leading flagship institution for another entity (such as a health system, region, or organization).
- 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_69f76ece61dc8190a0ab33f8d87d0a7e |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_6a037c8efcd4819088c2aeead65d93df |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a1553e08190bb7424c448cb1f33 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:17 p.m.