Triple
T2454231
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kim Pegula |
E53779
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Kim |
E113831
|
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: Kim | Statement: [Kim Pegula, givenName, Kim]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kim Context triple: [Kim Pegula, givenName, Kim]
-
A.
Kim
chosen
Kim is the given name of American singer-songwriter Kim Carnes, best known for her hit song "Bette Davis Eyes."
-
B.
Kim
Kim is a common Korean surname shared by many notable figures in Korean history and contemporary society.
-
C.
Kang
Kang is a recurring alien character from the animated television series "The Simpsons," often appearing in satirical and Halloween-themed episodes alongside his companion Kodos.
-
D.
Ken
Ken is the iconic male doll character and Barbie’s counterpart, portrayed in the 2023 film as a comically self-aware and insecure figure exploring identity and patriarchy.
-
E.
Ken
Ken is the nickname of Ken Dryden, the legendary Canadian Hall of Fame goaltender best known for backstopping the Montreal Canadiens to multiple Stanley Cup championships in the 1970s.
- 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_69ab495d227c8190b26ae6548eeb1019 |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abd0f7f85c8190a60970b6adb7fe80 |
completed | March 7, 2026, 7:17 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69aef0c7c90c8190ba5ed3cece5e049f |
completed | March 9, 2026, 4:09 p.m. |
Created at: March 6, 2026, 9:44 p.m.