Triple
T38064805
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yan Chai Hospital |
E950441
|
entity |
| Predicate | hospitalCluster |
P15635
|
FINISHED |
| Object |
Kowloon West Cluster
Kowloon West Cluster is a regional group of public hospitals and healthcare institutions in Hong Kong that provides coordinated medical and health services to residents in the western part of Kowloon and nearby areas.
|
E2255634
|
NE FINISHED |
How this triple was built (3 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: Kowloon West Cluster | Statement: [Yan Chai Hospital, hospitalCluster, Kowloon West Cluster]
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Kowloon West Cluster Triple: [Yan Chai Hospital, hospitalCluster, Kowloon West Cluster]
Generated description
Kowloon West Cluster is a regional group of public hospitals and healthcare institutions in Hong Kong that provides coordinated medical and health services to residents in the western part of Kowloon and nearby areas.
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hospitalCluster Context triple: [Yan Chai Hospital, hospitalCluster, Kowloon West Cluster]
-
A.
hospitalLocation
Indicates the geographic place or address where a hospital is situated.
-
B.
majorHospital
Indicates that a hospital holds a primary or leading status within a healthcare system or region, typically due to its size, capacity, or range of services.
-
C.
healthSystem
chosen
Indicates a relationship where an entity functions as, belongs to, or is managed within a particular health care system or network.
-
D.
containsHospital
Indicates that one entity includes or encompasses a hospital within its boundaries or composition.
-
E.
hospitalLevel
Indicates the classification or rank of a hospital within a defined healthcare system or hierarchy.
- F. None of above.
Provenance (6 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_69f76f01e63c819093b6012fc974f35a |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69fc4748843c8190931432653be4890c |
completed | May 7, 2026, 8:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a4168042f1c8190acb3c516afab9ebe |
completed | June 28, 2026, 6:29 p.m. |
| NEDg | Description generation | batch_6a416905565481909cedc046a031e725 |
completed | June 28, 2026, 6:33 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a41695966448190bc3f6d44933fe0f2 |
completed | June 28, 2026, 6:35 p.m. |
| PD | Predicate disambiguation | batch_69fc45646ce481908caf292ff9f06e15 |
completed | May 7, 2026, 7:55 a.m. |
Created at: May 3, 2026, 4:21 p.m.