Triple
T18736512
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kim Porter |
E458176
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object | Sean John |
—
|
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: Sean John | Statement: [Kim Porter, employer, Sean John]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sean John Context triple: [Kim Porter, employer, Sean John]
-
A.
Sean John
chosen
Sean John is a fashion and lifestyle brand founded by hip-hop mogul Sean "Diddy" Combs, known for its urban-inspired clothing and accessories.
-
B.
Jonas Seaman
Jonas Seaman was an early American innkeeper best known for establishing what became the historic Golden Lamb Inn in Lebanon, Ohio.
-
C.
Matthew Johns
Matthew Johns is a former Australian rugby league footballer and media personality best known for his successful playing career with the Newcastle Knights and his later work as a television commentator and host.
-
D.
Sebastian Jones
Sebastian Jones is an editor known for his work on the book "A Hidden Life."
-
E.
Jonny Dymond
Jonny Dymond is a British journalist and broadcaster best known as a BBC correspondent and royal commentator.
- 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_69d8d394dc308190b6725073f5db324c |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e57689fa508190ad821d361cba9edf |
completed | April 20, 2026, 12:42 a.m. |
Created at: April 10, 2026, 11:51 a.m.