Triple
T10252020
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | New Fairfield Volunteer Fire Department |
E240366
|
entity |
| Predicate | staffingType |
P11922
|
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: [New Fairfield Volunteer Fire Department, staffingType, volunteer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: staffingType Context triple: [New Fairfield Volunteer Fire Department, staffingType, volunteer]
-
A.
staffingLevel
Indicates the degree or adequacy of personnel assigned to perform a particular function, task, or operation.
-
B.
personnelType
Indicates the classification or role category assigned to a person within an organization or system.
-
C.
hasWorkforceType
chosen
Indicates the type or category of workforce associated with an entity (such as permanent, temporary, contract, or part-time).
-
D.
employmentType
Indicates the specific kind or category of employment relationship that exists between an individual and an employer (e.g., full-time, part-time, contract).
-
E.
salaryType
Indicates the classification or structure of compensation associated with an entity, such as whether pay is salaried, hourly, commission-based, or another type.
- 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_69d381a7e198819090280d5ab885d59e |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d328272c8190a3548d7f7f38cfc4 |
completed | April 7, 2026, 9:49 a.m. |
| PD | Predicate disambiguation | batch_69d4d1ebd6c88190a1f3f4a72a99d6fe |
completed | April 7, 2026, 9:44 a.m. |
Created at: April 6, 2026, 11:29 a.m.