Triple
T15613274
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LAPD Crime Suppression Platoons |
E375350
|
entity |
| Predicate | focusesOnAreaType |
P6822
|
FINISHED |
| Object | high-crime areas |
—
|
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: high-crime areas | Statement: [LAPD Crime Suppression Platoons, focusesOnAreaType, high-crime areas]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: focusesOnAreaType Context triple: [LAPD Crime Suppression Platoons, focusesOnAreaType, high-crime areas]
-
A.
includesAreaType
Indicates that one entity encompasses or contains another entity of a specified area type within its scope or boundaries.
-
B.
hasAreaType
chosen
Indicates that an entity is associated with a specific kind or classification of area (e.g., urban, rural, coastal).
-
C.
connectsTypeOfAreas
Indicates a relationship where one entity serves as a link or connector between two different types of areas.
-
D.
typeOfAreaRepresented
Indicates that one entity specifies the kind or category of area that another entity represents.
-
E.
categoryFocus
Indicates that one entity is the primary subject, theme, or focal point within the broader category defined by the other 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_69d85ccf2794819096cda4cbcb02d478 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04e8148a0819087d6d69cc84487ca |
completed | April 16, 2026, 2:50 a.m. |
| PD | Predicate disambiguation | batch_69deda844af081909e658ebc9d9b403d |
completed | April 15, 2026, 12:23 a.m. |
Created at: April 10, 2026, 4:13 a.m.