Triple
T31625244
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Altnagelvin Area Hospital |
E807003
|
entity |
| Predicate | hasDiagnosticServices |
P73149
|
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, hasDiagnosticServices, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDiagnosticServices Context triple: [Altnagelvin Area Hospital, hasDiagnosticServices, yes]
-
A.
hasDiagnosticService
chosen
Indicates that an entity provides, is associated with, or makes use of a diagnostic service for detecting, analyzing, or identifying issues or conditions.
-
B.
hasClinicalService
Indicates that an entity provides, offers, or is associated with a specific clinical service.
-
C.
hasHealthcareServicesIn
Indicates that a healthcare provider or organization offers or operates healthcare services within a specified location or area.
-
D.
hasLaboratoryServices
Indicates that one entity provides or makes available laboratory services to another entity or within a given context.
-
E.
hasDiagnosticFinding
Indicates that a subject (e.g., a patient, test, or examination) is associated with a specific diagnostic observation, result, or abnormality identified during medical evaluation.
- 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_69fe2f078c24819082ba396b56f02808 |
completed | May 8, 2026, 6:44 p.m. |
| PD | Predicate disambiguation | batch_69fe228fe1988190baf3bb34897f3dbe |
completed | May 8, 2026, 5:51 p.m. |
Created at: April 30, 2026, 10:43 p.m.