Triple
T7013629
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Armonk Volunteer Ambulance Corps |
E162644
|
entity |
| Predicate | hasVolunteerType |
P47074
|
FINISHED |
| Object | emergency medical technician |
—
|
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: emergency medical technician | Statement: [Armonk Volunteer Ambulance Corps, hasVolunteerType, emergency medical technician]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasVolunteerType Context triple: [Armonk Volunteer Ambulance Corps, hasVolunteerType, emergency medical technician]
-
A.
hasVolunteerStatus
Indicates that an entity holds a particular volunteer-related status or role within a specified context.
-
B.
typeOfVolunteerUnit
chosen
Indicates that one entity is a specific kind or category of volunteer unit in relation to another entity.
-
C.
hasVolunteerProgram
Indicates that an organization or entity offers an organized program through which individuals can volunteer their time or services.
-
D.
volunteeredFor
Indicates that an entity willingly offered their time or services to support or participate in an activity, cause, or organization.
-
E.
hasVolunteerBase
Indicates that an entity maintains or relies on a group of volunteers as a foundational support resource.
- 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_69c6885a127c8190867b059bdccf13ff |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6dc59cbfc8190bba9ebd14143d43c |
completed | March 27, 2026, 7:36 p.m. |
| PD | Predicate disambiguation | batch_69c6d7c790288190b7cbbaa4a5f9c91d |
completed | March 27, 2026, 7:17 p.m. |
Created at: March 27, 2026, 2:34 p.m.