Triple
T13637727
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Raytown Fire Protection District |
E325892
|
entity |
| Predicate | typeOfEMS |
P33503
|
FINISHED |
| Object | pre-hospital emergency medical care |
—
|
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: pre-hospital emergency medical care | Statement: [Raytown Fire Protection District, typeOfEMS, pre-hospital emergency medical care]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfEMS Context triple: [Raytown Fire Protection District, typeOfEMS, pre-hospital emergency medical care]
-
A.
typeOfEmergencyService
chosen
Indicates the specific category or kind of emergency service associated with or provided in a given situation.
-
B.
typeOfRescue
Indicates the specific method or category of rescue operation performed in a rescue event.
-
C.
emissionType
Indicates the specific category or kind of emission associated with an entity or activity.
-
D.
terminusType
Indicates the specific kind or role of an endpoint or terminal within a route, network, or process.
-
E.
equipmentTypeTrainedOn
Indicates the type of equipment on which an entity has received training or is qualified to operate.
- 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_69d8076beddc8190a53156f5bea77f5e |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbc60635d08190899806fe8936f02a |
completed | April 12, 2026, 4:19 p.m. |
| PD | Predicate disambiguation | batch_69dbbe85e1c4819095194f4b7f9f6118 |
completed | April 12, 2026, 3:47 p.m. |
Created at: April 9, 2026, 9:51 p.m.