Triple
T26377723
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | REM (Road Experience Management) |
E660944
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | mapping and localization technology |
C51486
|
CONCEPT FINISHED |
How this triple was built (1 step)
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.
CD
Concept disambiguation
gpt-5-mini-2025-08-07
Target class: mapping and localization technology Context triple: [REM (Road Experience Management), instanceOf, mapping and localization technology]
-
A.
robot localization method
A robot localization method is a computational technique that enables a robot to estimate its position and orientation within an environment using sensor data and a motion model.
-
B.
robot localization algorithm
A robot localization algorithm is a computational method that enables a robot to estimate its position and orientation within an environment using sensor data and a model of its motion and surroundings.
-
C.
autonomous navigation system
An autonomous navigation system is a self-directed control framework that enables vehicles or robots to perceive their environment, plan routes, and move safely to a destination without human intervention.
-
D.
terrestrial navigation system
A terrestrial navigation system is a ground-based infrastructure and associated equipment that determine a vehicle’s position, velocity, and course using signals or references located on or near the Earth’s surface.
-
E.
mapping
A mapping is an abstract association that pairs each element from a source set with one or more corresponding elements in a target set according to a defined rule.
- F. None of above. chosen
Provenance (1 batch)
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_69ee812a698881908d6a58265995fa39 |
completed | April 26, 2026, 9:18 p.m. |
Created at: April 26, 2026, 11:02 p.m.