Triple
T31625251
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Altnagelvin Area Hospital |
E807003
|
entity |
| Predicate | has24HourEmergencyCare |
P59476
|
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, has24HourEmergencyCare, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: has24HourEmergencyCare Context triple: [Altnagelvin Area Hospital, has24HourEmergencyCare, yes]
-
A.
hasEmergencyCare
chosen
Indicates that an entity provides or is equipped with emergency medical care services for another entity or individuals.
-
B.
hasEmergencyServiceProvider
Indicates that an entity is associated with or served by a specific emergency service provider (such as police, fire, or medical services).
-
C.
hasEmergencyServices
Indicates that the subject provides or is equipped with emergency response services (such as police, fire, or medical assistance).
-
D.
hasCrisisFacility
Indicates that an entity possesses or includes a dedicated facility or service for handling crises or emergency situations.
-
E.
hasImmediateMedicalResponse
Indicates that an entity receives prompt medical attention or intervention immediately following an incident or onset of a medical condition.
- 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_69f6a8dfcc708190b19e7444a14cdbb9 |
completed | May 3, 2026, 1:46 a.m. |
| PD | Predicate disambiguation | batch_69f6a757c6e081908e37631e5d8d246b |
completed | May 3, 2026, 1:39 a.m. |
Created at: April 30, 2026, 10:43 p.m.