Triple
T35896589
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monte Generoso Railway |
E1038240
|
entity |
| Predicate | maximumGradient_percent |
P121368
|
FINISHED |
| Object | 22 |
—
|
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: 22 | Statement: [Monte Generoso Railway, maximumGradient_percent, 22]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: maximumGradient_percent Context triple: [Monte Generoso Railway, maximumGradient_percent, 22]
-
A.
maximumGradient
Indicates the greatest rate of change or steepest slope that occurs within a given function, surface, or dataset.
-
B.
roadMaximumGradient
Indicates the steepest allowable slope or incline specified for a road segment.
-
C.
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.
-
D.
railwayGradient
chosen
Indicates the slope or steepness of a railway track along its route.
-
E.
averageGradient
Indicates the mean rate of change (slope) of a quantity over a specified interval or region.
- 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_69f76e2190f88190beb2eed798a4ef01 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69fd474b7e788190a9bb9b542d878f60 |
completed | May 8, 2026, 2:15 a.m. |
| PD | Predicate disambiguation | batch_69fd46d8b2f0819099d92d72c902f60e |
completed | May 8, 2026, 2:13 a.m. |
Created at: May 3, 2026, 4:06 p.m.