Triple
T21270162
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bologna–Florence railway |
E524232
|
entity |
| Predicate | maximumInclineApprox |
P121368
|
FINISHED |
| Object | 26 per mille |
—
|
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: 26 per mille | Statement: [Bologna–Florence railway, maximumInclineApprox, 26 per mille]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: maximumInclineApprox Context triple: [Bologna–Florence railway, maximumInclineApprox, 26 per mille]
-
A.
hasIncline
Indicates that one entity possesses or exhibits a slope, tilt, or upward/downward angle relative to another reference.
-
B.
hasSlopeRating
Indicates that something (typically a golf course or hole) is associated with a specific slope rating value that quantifies its relative difficulty for bogey golfers compared to scratch golfers.
-
C.
totalAscent
Indicates the total cumulative elevation gained over the course of a movement, route, or activity.
-
D.
maximumGradient
Indicates the greatest rate of change or steepest slope that occurs within a given function, surface, or dataset.
-
E.
railwayGradient
chosen
Indicates the slope or steepness of a railway track along its route.
- 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_69e0b516293c819089458ea2ec85f85e |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e73651c9208190a87d45acd6fafaaa |
completed | April 21, 2026, 8:33 a.m. |
| PD | Predicate disambiguation | batch_69e5f6161dac8190b06009cd180e3ff7 |
completed | April 20, 2026, 9:47 a.m. |
Created at: April 16, 2026, 4:01 p.m.