Triple
T35512654
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Queanbeyan Hospital |
E1026323
|
entity |
| Predicate | emergencyServicesAvailable |
P464
|
FINISHED |
| Object | 24-hour |
—
|
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: 24-hour | Statement: [Queanbeyan Hospital, emergencyServicesAvailable, 24-hour]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: emergencyServicesAvailable Context triple: [Queanbeyan Hospital, emergencyServicesAvailable, 24-hour]
-
A.
emergencyService
Indicates that one entity provides or is associated with urgent, time-critical assistance or response to another entity in emergency situations.
-
B.
hasEmergencyServices
chosen
Indicates that the subject provides or is equipped with emergency response services (such as police, fire, or medical assistance).
-
C.
emergencyService102
Indicates a relationship where an entity provides or is associated with emergency response or urgent assistance services.
-
D.
hasEmergencyServiceProvider
Indicates that an entity is associated with or served by a specific emergency service provider (such as police, fire, or medical services).
-
E.
emergencyServicesRegion
Indicates the geographic region within which specific emergency services are responsible for responding to incidents.
- 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_69f76dfd61208190b93ec6dc439cab41 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7b5ccbda481908fe1945c35e36ce8 |
completed | May 3, 2026, 8:53 p.m. |
| PD | Predicate disambiguation | batch_69f7b4c06f5881908f0b98cad6796478 |
completed | May 3, 2026, 8:49 p.m. |
Created at: May 3, 2026, 4:04 p.m.