Triple
T17615606
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | K2 |
E429073
|
entity |
| Predicate | sponsorshipActivity |
P737
|
FINISHED |
| Object | professional skiers |
—
|
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: professional skiers | Statement: [K2, sponsorshipActivity, professional skiers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sponsorshipActivity Context triple: [K2, sponsorshipActivity, professional skiers]
-
A.
sponsorshipRenamedFrom
Indicates that a sponsorship currently known by one name previously existed under a different, earlier name.
-
B.
sportsSponsored
chosen
Indicates that one entity provides financial or material sponsorship to support another entity’s sports-related activities or events.
-
C.
sponsorshipName
Indicates the name or title associated with a sponsorship relationship between entities.
-
D.
sponsorshipIndustry
Indicates a relationship where one entity sponsors another specifically within a given industry or sector context.
-
E.
sponsorEnd
Indicates the point in time or condition at which a sponsorship relationship or sponsorship-related action comes to an end.
- 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_69d889e1c6148190ba76241e74688f8b |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e46d3174008190a2b5bb1b061ea4df |
completed | April 19, 2026, 5:50 a.m. |
| PD | Predicate disambiguation | batch_69e3cdd7da34819099bc9481c5a79bab |
completed | April 18, 2026, 6:30 p.m. |
Created at: April 10, 2026, 5:51 a.m.