Triple
T6327742
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Real-time Kinematic Positioning |
E141899
|
entity |
| Predicate | typicalHorizontalAccuracy |
P70871
|
FINISHED |
| Object | 1–2 centimeters |
—
|
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: 1–2 centimeters | Statement: [Real-time Kinematic Positioning, typicalHorizontalAccuracy, 1–2 centimeters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalHorizontalAccuracy Context triple: [Real-time Kinematic Positioning, typicalHorizontalAccuracy, 1–2 centimeters]
-
A.
elevationAccuracy
Indicates the degree of precision or reliability associated with a measured or reported elevation value in the relationship.
-
B.
azimuthAccuracy
Indicates the degree of precision or allowable error in the measured or specified azimuth angle between entities.
-
C.
hasAccuracy
Indicates that something possesses a specified level or measure of correctness, precision, or exactness in relation to a standard or reference.
-
D.
minimumGeoTargetingAccuracy
Indicates the lowest acceptable level of geographic precision required when targeting locations for an action or relationship.
-
E.
accuracyCivilianTypical
Indicates the degree to which something typically achieves accurate results or effects when applied to civilians.
- 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_69c008d201748190917e69c41ba3f978 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c064e9532081908277f10ec380a486 |
completed | March 22, 2026, 9:53 p.m. |
| PD | Predicate disambiguation | batch_69c060e7e2d48190af9d004236466788 |
completed | March 22, 2026, 9:36 p.m. |
| PDg | Predicate description generation | batch_69c064c080148190a7c3218867f1f572 |
completed | March 22, 2026, 9:53 p.m. |
Created at: March 22, 2026, 4:29 p.m.