Triple
T3984894
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Will Keith Kellogg |
E86846
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object | WK Kellogg |
E86846
|
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: WK Kellogg | Statement: [Will Keith Kellogg, alsoKnownAs, WK Kellogg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: WK Kellogg Context triple: [Will Keith Kellogg, alsoKnownAs, WK Kellogg]
-
A.
Jeff Kellogg
Jeff Kellogg is a former Major League Baseball umpire who worked numerous high-profile games, including postseason and World Series matchups.
-
B.
Will Keith Kellogg
chosen
Will Keith Kellogg was an American industrialist and philanthropist best known for founding the Kellogg Company, a leading breakfast cereal manufacturer.
-
C.
Michael Sloan
Michael Sloan is a television writer and producer best known for co-creating the action-crime series "The Equalizer."
-
D.
Jerome Kellogg
Jerome Kellogg was a physicist known as a notable student of Nobel laureate Isidor Isaac Rabi.
-
E.
David Keller
David Keller is a fictional character from the film "The Names."
- 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_69aed93fd9d4819085d3b2137d2346cb |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aef9dfaf28819081b547836d79b889 |
completed | March 9, 2026, 4:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5402bbe208190947321353c309c98 |
completed | March 14, 2026, 11:02 a.m. |
Created at: March 9, 2026, 3:33 p.m.