Triple
T17524029
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kane Brown |
E426748
|
entity |
| Predicate | collaboratedWith |
P435
|
FINISHED |
| Object | Katelyn Brown |
—
|
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: Katelyn Brown | Statement: [Kane Brown, collaboratedWith, Katelyn Brown]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Katelyn Brown Context triple: [Kane Brown, collaboratedWith, Katelyn Brown]
-
A.
Katelyn Jae Brown
chosen
Katelyn Jae Brown is an American singer and music manager best known as the wife of country music star Kane Brown.
-
B.
Kaitlyn Dunn
Kaitlyn Dunn is a person notable enough to be specifically referenced as a bearer of the surname Dunn.
-
C.
Kelsey Burrell
Kelsey Burrell is known as the child of the Jamaican-American reggae fusion singer and rapper Shaggy.
-
D.
Kaitlyn Robrock
Kaitlyn Robrock is an American voice actress best known for portraying iconic animated characters, including serving as the current voice of Minnie Mouse for Disney.
-
E.
Katie Brown
Katie Brown is an American television host, author, and lifestyle expert known for her home and gardening shows and practical entertaining advice.
- 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_69d889de677081909b22d2657b1f0292 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e452d4db60819096a03dbc4254850f |
completed | April 19, 2026, 3:58 a.m. |
Created at: April 10, 2026, 5:49 a.m.