Triple
T18291108
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mont Fort |
E438115
|
entity |
| Predicate | hasSkiRunDifficulty |
P24163
|
FINISHED |
| Object | expert |
—
|
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: expert | Statement: [Mont Fort, hasSkiRunDifficulty, expert]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSkiRunDifficulty Context triple: [Mont Fort, hasSkiRunDifficulty, expert]
-
A.
hasTrailDifficulty
chosen
Indicates the level of challenge or effort required to traverse a particular trail or route.
-
B.
hasSkiRunsLength_km
Indicates the total length, in kilometers, of the ski runs associated with an entity.
-
C.
hasSkiAreaVerticalDrop
Indicates the vertical distance in elevation between the highest and lowest points of a ski area.
-
D.
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.
-
E.
numberOfSkiRuns
Indicates the total count of ski runs associated with a given entity.
- 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_69d8b914530c8190b4474d862a2b2a1b |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e500fee5248190928e68ddaa4d90d7 |
completed | April 19, 2026, 4:21 p.m. |
| PD | Predicate disambiguation | batch_69e44fdf43d08190bbcfb6b1fe3cc0ee |
completed | April 19, 2026, 3:45 a.m. |
Created at: April 10, 2026, 10:35 a.m.