Triple
T28003929
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Watford General Hospital |
E707221
|
entity |
| Predicate | hasEmergencyCareType |
P124008
|
FINISHED |
| Object | 24-hour A&E |
—
|
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 A&E | Statement: [Watford General Hospital, hasEmergencyCareType, 24-hour A&E]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEmergencyCareType Context triple: [Watford General Hospital, hasEmergencyCareType, 24-hour A&E]
-
A.
hasEmergencyCare
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.
hasEmergencyDepartmentLevel
chosen
Indicates the specific classification or tier of emergency care capability associated with an emergency department.
-
D.
hasEmergencyServices
Indicates that the subject provides or is equipped with emergency response services (such as police, fire, or medical assistance).
-
E.
hasEmergencyTransport
Indicates that an entity provides, is equipped with, or has access to emergency transportation services or vehicles.
- 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_69ef96ba350c81908230d0b501b974c4 |
completed | April 27, 2026, 5:02 p.m. |
| NER | Named-entity recognition | batch_69f64dbbaefc8190952b8320bf4397d8 |
completed | May 2, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_69f64cacd2c08190aed8a1761d0da679 |
completed | May 2, 2026, 7:12 p.m. |
Created at: April 27, 2026, 7:59 p.m.