Triple
T31771910
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Office of Police Complaints (Washington, D.C.) |
E810962
|
entity |
| Predicate | complaintsSubjectMatter |
P450
|
FINISHED |
| Object | police misconduct |
—
|
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: police misconduct | Statement: [Office of Police Complaints (Washington, D.C.), complaintsSubjectMatter, police misconduct]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: complaintsSubjectMatter Context triple: [Office of Police Complaints (Washington, D.C.), complaintsSubjectMatter, police misconduct]
-
A.
complaintsTopic
Indicates that the complaints or grievances expressed are about, or pertain to, a particular topic or subject.
-
B.
complainsAbout
Indicates that one entity expresses dissatisfaction, criticism, or grievances regarding another entity or situation.
-
C.
complaintsAre
Indicates that one entity expresses or contains complaints directed toward or concerning another entity.
-
D.
subjectOfConcernFor
Indicates that one entity is regarded as a matter of worry, interest, or attention for another entity.
-
E.
subjectMatter
chosen
Indicates the topic, theme, or content area that something (such as a work, document, or discussion) is about.
- 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_69f348e463e08190b902d4819195e1f0 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69f707f7959881908f037f0d6b1d0c36 |
completed | May 3, 2026, 8:31 a.m. |
| PD | Predicate disambiguation | batch_69f700fc274c8190a128593dc7c7abd0 |
completed | May 3, 2026, 8:02 a.m. |
Created at: April 30, 2026, 11:33 p.m.