Triple
T11370015
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ted Ligety |
E269315
|
entity |
| Predicate | sponsorsOrPartners |
P1807
|
FINISHED |
| Object | Shred Optics |
E921819
|
NE 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: Shred Optics | Statement: [Ted Ligety, sponsorsOrPartners, Shred Optics]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shred Optics Context triple: [Ted Ligety, sponsorsOrPartners, Shred Optics]
-
A.
Shred Optics
chosen
Shred Optics is a sports eyewear and protective gear brand known for its innovative ski and snowboard goggles, helmets, and accessories.
-
B.
Optica
Optica is a leading scientific society dedicated to advancing the study and application of optics and photonics worldwide.
-
C.
Opti
Opti is a friendly, futuristic robot character that served as one of the official mascots of Expo 2020 Dubai.
-
D.
Oakley
Oakley is a city in Contra Costa County, California, located in the eastern San Francisco Bay Area.
-
E.
Oakley
Oakley is a surname most notably associated with Violet Oakley, an American artist and pioneering female muralist of the early 20th century.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d6aacca1048190b39dbbc2174616fa |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d808855a7481909314f90ad92aae68 |
completed | April 9, 2026, 8:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e58bdaabd48190ab533c1c7f3b5fd8 |
completed | April 20, 2026, 2:13 a.m. |
Created at: April 8, 2026, 9:33 p.m.