Triple
T666122
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fairfax County Police Department |
E12864
|
entity |
| Predicate | hasTypeOfEmployee |
P11922
|
FINISHED |
| Object | sworn police officers |
—
|
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: sworn police officers | Statement: [Fairfax County Police Department, hasTypeOfEmployee, sworn police officers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypeOfEmployee Context triple: [Fairfax County Police Department, hasTypeOfEmployee, sworn police officers]
-
A.
hasWorkforceType
chosen
Indicates the type or category of workforce associated with an entity (such as permanent, temporary, contract, or part-time).
-
B.
hasTypeOfOrganization
Indicates that an entity is classified as belonging to a particular type or category of organization.
-
C.
personnelType
Indicates the classification or role category assigned to a person within an organization or system.
-
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.
hasAffiliationType
Indicates that one entity is connected to another through a specified kind or category of affiliation or association.
- 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_69a493355dec819098d4244b2fa34885 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49fd4f4988190a0973ceb7329b4c9 |
completed | March 1, 2026, 8:21 p.m. |
| PD | Predicate disambiguation | batch_69a49d16cff881908c8d2c3fe4d1d6fb |
completed | March 1, 2026, 8:09 p.m. |
Created at: March 1, 2026, 7:36 p.m.