Triple
T31625255
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Altnagelvin Area Hospital |
E807003
|
entity |
| Predicate | hasCoronaryCareUnit |
P74282
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Altnagelvin Area Hospital, hasCoronaryCareUnit, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCoronaryCareUnit Context triple: [Altnagelvin Area Hospital, hasCoronaryCareUnit, yes]
-
A.
hasCardiacCareUnit
chosen
Indicates that an entity (such as a hospital or medical facility) includes or is equipped with a specialized cardiac care unit for treating heart-related conditions.
-
B.
hasIntensiveCareUnit
Indicates that a medical facility includes and operates an intensive care unit (ICU) for critically ill patients.
-
C.
hasClinicalUnit
Indicates that an entity is associated with or belongs to a specific clinical unit or department within a healthcare setting.
-
D.
containsHospital
Indicates that one entity includes or encompasses a hospital within its boundaries or composition.
-
E.
hasEmergencyDepartmentLevel
Indicates the specific classification or tier of emergency care capability associated with an emergency department.
- 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_69f348d7883c8190b6c13ab92b7ef076 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_6a0301c45274819083b0dd9d335f7ee0 |
completed | May 12, 2026, 10:32 a.m. |
| PD | Predicate disambiguation | batch_6a03015c272481908a7bfe81befb1764 |
completed | May 12, 2026, 10:30 a.m. |
Created at: April 30, 2026, 10:43 p.m.