Triple
T13036924
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eniko Parrish |
E326584
|
entity |
| Predicate | hasChild |
P369
|
FINISHED |
| Object | Kenzo Kash Hart |
E255786
|
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: Kenzo Kash Hart | Statement: [Eniko Parrish, hasChild, Kenzo Kash Hart]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kenzo Kash Hart Context triple: [Eniko Parrish, hasChild, Kenzo Kash Hart]
-
A.
Kenzo Kash Hart
chosen
Kenzo Kash Hart is the young son of American comedian and actor Kevin Hart and his wife Eniko Parrish.
-
B.
Kenzo Nakamura
Kenzo Nakamura is a Japanese judoka and Olympic gold medalist known for his achievements in the lightweight divisions during the 1990s.
-
C.
Kenzo Kitakata
Kenzo Kitakata is a Japanese crime and hardboiled novelist renowned for his gritty, psychologically rich portrayals of yakuza and urban underworld life.
-
D.
Kenzo Takada
Kenzo Takada was a pioneering Japanese fashion designer who founded the Paris-based luxury brand Kenzo, known for its vibrant prints and East-meets-West aesthetic.
-
E.
Kenzo Yashima
Kenzo Yashima is a person notable for bearing the Japanese given name Kenzo.
- 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_69d8076cc45c81908123123f43e69266 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97f2a71a0819098bb6cf8a4b2208a |
completed | April 10, 2026, 10:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6cbd12eec81908ad5dae638c2210e |
completed | May 3, 2026, 4:15 a.m. |
Created at: April 9, 2026, 8:55 p.m.