Triple
T24352357
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | U.S. Route 101 |
E613825
|
entity |
| Predicate | urbanFreewaySegment |
P28220
|
FINISHED |
| Object | Hollywood Freeway through central Los Angeles |
—
|
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: Hollywood Freeway through central Los Angeles | Statement: [U.S. Route 101, urbanFreewaySegment, Hollywood Freeway through central Los Angeles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: urbanFreewaySegment Context triple: [U.S. Route 101, urbanFreewaySegment, Hollywood Freeway through central Los Angeles]
-
A.
hasFreewaySegments
Indicates that one entity includes, contains, or is associated with specific freeway segments as part of its structure or network.
-
B.
isFreeway
Indicates that a given road segment functions as a freeway, typically designed for high-speed, limited-access vehicular traffic.
-
C.
highwaySegment
Indicates a road section that forms part of a larger highway route.
-
D.
isUrbanFreeway
chosen
Indicates that a roadway segment functions as a high-capacity, limited-access freeway located within an urban area.
-
E.
urbanSegmentName
Indicates the specific name assigned to a segment within an urban area or city layout.
- 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_69e2d7ddd29481909e7f539a6072bd71 |
completed | April 18, 2026, 1:01 a.m. |
| NER | Named-entity recognition | batch_69f293467a508190937b9614870e243e |
completed | April 29, 2026, 11:24 p.m. |
| PD | Predicate disambiguation | batch_69f287bb1b2c81909c2e7fcc392ad143 |
completed | April 29, 2026, 10:35 p.m. |
Created at: April 18, 2026, 1:59 a.m.