Triple
T11280787
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Broad Channel Volunteer Fire Department |
E267057
|
entity |
| Predicate | hasStaffingModel |
P98903
|
FINISHED |
| Object | volunteer |
—
|
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: volunteer | Statement: [Broad Channel Volunteer Fire Department, hasStaffingModel, volunteer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasStaffingModel Context triple: [Broad Channel Volunteer Fire Department, hasStaffingModel, volunteer]
-
A.
hasSupportStaff
Indicates that an entity is associated with one or more staff members who provide assistance or support services to it.
-
B.
hasStaffedHours
Indicates that specific hours or time periods are assigned during which staff are present and available.
-
C.
staffingLevel
Indicates the degree or adequacy of personnel assigned to perform a particular function, task, or operation.
-
D.
hasWorkforceType
Indicates the type or category of workforce associated with an entity (such as permanent, temporary, contract, or part-time).
-
E.
hasCareModel
Indicates that one entity uses, follows, or is governed by a particular model or approach to providing care.
- 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_69d6aac8c2f48190ad0596f1f89f0470 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e969b3448190940e2bd499d2d7de |
completed | April 9, 2026, 6:01 p.m. |
| PD | Predicate disambiguation | batch_69d787a240588190aa097298f951c915 |
completed | April 9, 2026, 11:04 a.m. |
| PDg | Predicate description generation | batch_69d796cf74308190a5b29d0dd82954a2 |
completed | April 9, 2026, 12:08 p.m. |
Created at: April 8, 2026, 9:31 p.m.