Triple
T26377742
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | REM (Road Experience Management) |
E660944
|
entity |
| Predicate | mapGranularity |
P109501
|
FINISHED |
| Object | high-resolution |
—
|
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: high-resolution | Statement: [REM (Road Experience Management), mapGranularity, high-resolution]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mapGranularity Context triple: [REM (Road Experience Management), mapGranularity, high-resolution]
-
A.
granularityLevel
chosen
Indicates the degree of detail or resolution at which something is specified, measured, or analyzed within a given context.
-
B.
scalingGranularity
Indicates the level of detail or resolution at which a quantity, process, or system is adjusted or scaled.
-
C.
shadingGranularity
Indicates the level of detail or fineness with which shading is applied or controlled in a given context.
-
D.
controlGranularity
Indicates the level of detail or fineness with which control or regulation is applied within a given process or system.
-
E.
mapScaleCategory
Indicates the classification of a map based on its scale range or level of detail.
- 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_69ee812a698881908d6a58265995fa39 |
completed | April 26, 2026, 9:18 p.m. |
| NER | Named-entity recognition | batch_69f61071a4c4819090729e0c9789cf21 |
completed | May 2, 2026, 2:55 p.m. |
| PD | Predicate disambiguation | batch_69f5f800fa9c8190aab0962669fde8ac |
completed | May 2, 2026, 1:11 p.m. |
Created at: April 26, 2026, 11:02 p.m.