Triple
T36492615
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ann Arbor Police Department |
E899089
|
entity |
| Predicate | policyIncludes |
P130292
|
FINISHED |
| Object | complaint and oversight procedures |
—
|
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: complaint and oversight procedures | Statement: [Ann Arbor Police Department, policyIncludes, complaint and oversight procedures]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: policyIncludes Context triple: [Ann Arbor Police Department, policyIncludes, complaint and oversight procedures]
-
A.
policyName
Indicates the specific name or title assigned to a policy associated with an entity.
-
B.
policyDetail
chosen
Indicates that there is specific descriptive or explanatory information associated with a particular policy.
-
C.
guaranteeCoverage
Indicates that one party commits to providing financial or protective coverage for another party or specified situation.
-
D.
providesCoverage
Indicates that one entity supplies protection, insurance, or service coverage to another entity or for a specified risk or scope.
-
E.
benefitProtection
Indicates that one entity provides protective advantages or safeguards that benefit another entity.
- 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_69f76e5ad4588190bdbce60c52fbb785 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69fd02680d948190a3463fb119ba8556 |
completed | May 7, 2026, 9:21 p.m. |
| PD | Predicate disambiguation | batch_69fcf89c69b4819082bbc564bd15137d |
completed | May 7, 2026, 8:39 p.m. |
Created at: May 3, 2026, 4:10 p.m.