Triple
T22793395
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Orrington Fire and Rescue |
E564173
|
entity |
| Predicate | emergencyServiceDomain |
P149739
|
FINISHED |
| Object | fire emergencies |
—
|
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: fire emergencies | Statement: [Orrington Fire and Rescue, emergencyServiceDomain, fire emergencies]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: emergencyServiceDomain Context triple: [Orrington Fire and Rescue, emergencyServiceDomain, fire emergencies]
-
A.
emergencyService
Indicates that one entity provides or is associated with urgent, time-critical assistance or response to another entity in emergency situations.
-
B.
emergencyServicesRegion
Indicates the geographic region within which specific emergency services are responsible for responding to incidents.
-
C.
emergencyOffice
Indicates that an office or location serves as an emergency contact point or coordination center for urgent or crisis situations.
-
D.
typeOfEmergencyService
Indicates the specific category or kind of emergency service associated with or provided in a given situation.
-
E.
hasEmergencyServices
Indicates that the subject provides or is equipped with emergency response services (such as police, fire, or medical assistance).
- F. None of above. chosen
Provenance (4 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_69e2458185f88190b0045227ee420411 |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17cd751f081909c7907c96c9906ea |
completed | April 29, 2026, 3:36 a.m. |
| PD | Predicate disambiguation | batch_69eed2c32e8c8190b73bb9965ed47d64 |
completed | April 27, 2026, 3:06 a.m. |
| PDg | Predicate description generation | batch_69eeeb5681f88190821129ced752f190 |
completed | April 27, 2026, 4:51 a.m. |
Created at: April 17, 2026, 3:30 p.m.