Triple
T16652905
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Silvaplana |
E404651
|
entity |
| Predicate | hasPopularSport |
P251
|
FINISHED |
| Object | snowkiting |
—
|
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: snowkiting | Statement: [Silvaplana, hasPopularSport, snowkiting]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPopularSport Context triple: [Silvaplana, hasPopularSport, snowkiting]
-
A.
hasMajorSport
Indicates that an entity is associated with or participates in a primary or most prominent sport.
-
B.
popularSport
chosen
Indicates that a sport is widely liked, followed, or played by many people within a certain group or region.
-
C.
includesSport
Indicates that one entity contains, offers, or features a particular sport as part of its activities, content, or composition.
-
D.
hasLocalSport
Indicates that a place or community is associated with, supports, or regularly engages in a particular sport at the local level.
-
E.
hasSportingContext
Indicates that something is associated with, occurs within, or is relevant to a particular sporting or athletic context.
- 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_69d8838b5fbc81908c6575c132b82e80 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e37bf861348190b2b0b5574d4ddb4f |
completed | April 18, 2026, 12:41 p.m. |
| PD | Predicate disambiguation | batch_69e319b1d7f08190b5ecb4a68c636c15 |
completed | April 18, 2026, 5:42 a.m. |
Created at: April 10, 2026, 5:18 a.m.