Triple
T2745748
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Medina Police Department |
E60862
|
entity |
| Predicate | policesAreaType |
P6822
|
FINISHED |
| Object | suburban city |
—
|
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: suburban city | Statement: [Medina Police Department, policesAreaType, suburban city]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: policesAreaType Context triple: [Medina Police Department, policesAreaType, suburban city]
-
A.
policePrecinct
Indicates that a specified location, building, or area functions as or is designated as a police precinct.
-
B.
cityDistrictType
Indicates the type or classification of a city district within an urban or administrative structure.
-
C.
typeOfLawEnforcement
Indicates that one entity is a specific kind or category of law enforcement associated with another entity.
-
D.
coversPolicyArea
Indicates that a policy, document, or initiative includes or addresses a particular policy area or topic within its scope.
-
E.
hasAreaType
chosen
Indicates that an entity is associated with a specific kind or classification of area (e.g., urban, rural, coastal).
- 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_69ab4b79846081909096725374d65ce9 |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abdb4d37a481908cc2ad4666f3ac94 |
completed | March 7, 2026, 8:01 a.m. |
| PD | Predicate disambiguation | batch_69abd829f1e88190aab1d54f87c69714 |
completed | March 7, 2026, 7:47 a.m. |
Created at: March 6, 2026, 9:56 p.m.