Triple
T27764244
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Allegra EMU (ABe 8/12) |
E701555
|
entity |
| Predicate | maximumGradientCapability |
P46516
|
FINISHED |
| Object | 70 ‰ |
—
|
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: 70 ‰ | Statement: [Allegra EMU (ABe 8/12), maximumGradientCapability, 70 ‰]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: maximumGradientCapability Context triple: [Allegra EMU (ABe 8/12), maximumGradientCapability, 70 ‰]
-
A.
maximumGradient
chosen
Indicates the greatest rate of change or steepest slope that occurs within a given function, surface, or dataset.
-
B.
minimumGradient
Indicates the smallest rate of change or steepness value that a quantity, function, or surface is allowed or observed to have within a given context.
-
C.
maximumOperatingMach
Indicates the highest machine operating level, capacity, or intensity at which the related entity is allowed or designed to function.
-
D.
maximumResolution
Indicates the highest level of detail or fineness at which something (such as an image, display, or measurement) can be represented or processed.
-
E.
maximumCeiling
Indicates the highest allowable or achievable limit or value that something cannot exceed.
- 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_69ef6a52fa708190934a32308d2c92dc |
completed | April 27, 2026, 1:53 p.m. |
| NER | Named-entity recognition | batch_69f6b903538481909cffcb6cc1cc0e70 |
completed | May 3, 2026, 2:54 a.m. |
| PD | Predicate disambiguation | batch_69f6b626120c819097c9ad04487570d7 |
completed | May 3, 2026, 2:42 a.m. |
Created at: April 27, 2026, 4:29 p.m.