Triple
T35885803
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dan Henderson vs Michael Bisping |
E1037638
|
entity |
| Predicate | BispingNickname |
P159779
|
FINISHED |
| Object | The Count |
—
|
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: The Count | Statement: [Dan Henderson vs Michael Bisping, BispingNickname, The Count]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: BispingNickname Context triple: [Dan Henderson vs Michael Bisping, BispingNickname, The Count]
-
A.
BispingCorner
Indicates that one entity serves as the corner coach or cornerman for the fighter Bisping during a bout.
-
B.
nicknameOfBoxer
chosen
Indicates that one entity is the nickname used to refer to a particular boxer.
-
C.
opponentNickname
Indicates that one entity is the nickname used to refer to another entity in a competitive or adversarial context.
-
D.
isOfficialNicknameOf
Indicates that one name is the formally recognized nickname or informal moniker used to refer to another entity.
-
E.
nicknameOfAssociatedWrestler
Indicates that one entity is a nickname that is used to refer to an associated professional wrestler.
- 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_69f76e1f4d748190bb55594d8441d70e |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7be53890081909b1d93f30a8f31c6 |
completed | May 3, 2026, 9:29 p.m. |
| PD | Predicate disambiguation | batch_69f7bccacbac8190978976324c67db28 |
completed | May 3, 2026, 9:23 p.m. |
Created at: May 3, 2026, 4:06 p.m.