Triple
T14828033
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Local Area Commands (LACs) |
E348625
|
entity |
| Predicate | hasGeographicBasis |
P115992
|
FINISHED |
| Object | defined police local area boundaries |
—
|
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: defined police local area boundaries | Statement: [Local Area Commands (LACs), hasGeographicBasis, defined police local area boundaries]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGeographicBasis Context triple: [Local Area Commands (LACs), hasGeographicBasis, defined police local area boundaries]
-
A.
hasGeoculturalBasis
Indicates that something is grounded in, derived from, or shaped by a particular geographic and cultural context.
-
B.
hasTypicalGeographicOrigin
Indicates that an entity is commonly or characteristically associated with originating from a particular geographic location.
-
C.
isGeographicalEntity
Indicates that something exists as a distinct geographic feature, area, or place within physical space.
-
D.
containsGeographicalArea
Indicates that one geographical area spatially encompasses or includes another geographical area within its boundaries.
-
E.
geographicalRepresentation
Indicates that one entity serves as a geographic depiction, model, or mapping of another entity’s location, area, or spatial characteristics.
- F. None of above. chosen
Provenance (4 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_69d822eb8f588190bf53445e730a934f |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69ded0737d4c8190a49bf6b013da208c |
completed | April 14, 2026, 11:40 p.m. |
| PD | Predicate disambiguation | batch_69de8c13418c819088ff9905ace1416a |
completed | April 14, 2026, 6:48 p.m. |
| PDg | Predicate description generation | batch_69de90806f3881908fcbfec5bd4ab4d2 |
completed | April 14, 2026, 7:07 p.m. |
Created at: April 10, 2026, 1:51 a.m.