Triple
T17965365
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kyla Ross |
E449190
|
entity |
| Predicate | headCoach |
P256
|
FINISHED |
| Object | Chris Waller |
—
|
NE NERFINISHED |
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: Chris Waller | Statement: [Kyla Ross, headCoach, Chris Waller]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chris Waller Context triple: [Kyla Ross, headCoach, Chris Waller]
-
A.
Chris Waller
chosen
Chris Waller is an American gymnastics coach and former UCLA gymnast who has served as the head coach of the UCLA Bruins women's gymnastics team.
-
B.
Darrell Waltrip
Darrell Waltrip is a former NASCAR Cup Series champion who became a prominent television race analyst and commentator.
-
C.
Tom Drysdale
Tom Drysdale is a notable individual recognized as a prominent bearer of the Drysdale surname.
-
D.
Chris Dickerson
Chris Dickerson was an American professional bodybuilder best known for winning the 1982 Mr. Olympia title and for being one of the sport’s pioneering African-American and openly gay champions.
-
E.
Nick George
Nick George is the idealistic lawyer protagonist of the television drama "Dirty Sexy Money," who becomes entangled in the corrupt world of a wealthy New York family.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b9f9927c8190a006110c8b996e61 |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e4b136e4088190ac97fd92dc84a4b9 |
completed | April 19, 2026, 10:40 a.m. |
Created at: April 10, 2026, 10:22 a.m.