Triple
T6327698
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Real-time Kinematic Positioning |
E141899
|
entity |
| Predicate | usesCorrectionDataFrom |
P11520
|
FINISHED |
| Object | reference stations |
—
|
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: reference stations | Statement: [Real-time Kinematic Positioning, usesCorrectionDataFrom, reference stations]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesCorrectionDataFrom Context triple: [Real-time Kinematic Positioning, usesCorrectionDataFrom, reference stations]
-
A.
requiresCorrection
Indicates that something is identified as needing modification, adjustment, or fixing to correct an error or deficiency.
-
B.
canBeCorrectedBy
Indicates that something has the potential to be made accurate, fixed, or improved through the intervention or action of a specified agent or method.
-
C.
usesForwardErrorCorrection
Indicates that one entity applies forward error correction techniques to detect and correct errors in data transmitted to or received from another entity.
-
D.
usedDataFrom
chosen
Indicates that one entity utilized or relied on data originating from another entity.
-
E.
aberrationCorrection
Indicates the application or presence of a process that corrects optical or imaging aberrations in a system or setup.
- 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_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. |
Created at: March 22, 2026, 4:29 p.m.