Triple
T36908816
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Peotone Police Department |
E912850
|
entity |
| Predicate | hasPolicingModel |
P21773
|
FINISHED |
| Object | local policing |
—
|
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: local policing | Statement: [Peotone Police Department, hasPolicingModel, local policing]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPolicingModel Context triple: [Peotone Police Department, hasPolicingModel, local policing]
-
A.
policingModel
chosen
Indicates the approach, strategy, or framework used to organize and conduct policing activities or law enforcement operations.
-
B.
usesPolicyModel
Indicates that one entity applies, relies on, or operates according to a particular policy model.
-
C.
hasEnforcementMethod
Indicates that an entity applies, uses, or is associated with a specific method or mechanism for enforcing rules, obligations, or constraints.
-
D.
hasPolicySupport
Indicates that one entity provides endorsement, backing, or approval for a specific policy associated with another entity.
-
E.
enforcementModel
Indicates the method or framework by which rules, policies, or constraints are applied, monitored, and enforced within a system or interaction.
- 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_69f76e879768819085c2fb31a6a5b44b |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69ffa5f31c8881908c26e2aa52df6ece |
completed | May 9, 2026, 9:24 p.m. |
| PD | Predicate disambiguation | batch_69ffa42aee408190ad1a5f285688b338 |
completed | May 9, 2026, 9:16 p.m. |
Created at: May 3, 2026, 4:13 p.m.