Triple
T10890749
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | KiKi Layne |
E257166
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | KiKi Layne |
E257166
|
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: KiKi Layne | Statement: [KiKi Layne, name, KiKi Layne]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: KiKi Layne Context triple: [KiKi Layne, name, KiKi Layne]
-
A.
KiKi Layne
chosen
KiKi Layne is an American actress known for her breakout role in "If Beale Street Could Talk" and performances in films such as "The Old Guard" and "Don't Worry Darling."
-
B.
Tamrat Layne
Tamrat Layne is an Ethiopian former rebel leader and politician who served as Prime Minister during the transitional government that followed the fall of the Derg regime.
-
C.
Kelly Ray
Kelly Ray is a musical track featured on the album "The Way I See It."
-
D.
Kim Kaswell
Kim Kaswell is a driven, sharp-tongued attorney on the legal dramedy series "Drop Dead Diva," known for her ambition, wit, and complicated relationships with her colleagues.
-
E.
Layla Moore
Layla Moore is the central protagonist of the thriller series "The Recruit," around whom the story’s espionage-driven plot and character dynamics revolve.
- 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_69d6aa8550c8819095508a2ed9acf3db |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d7520550c4819081296546c8f534f1 |
completed | April 9, 2026, 7:15 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e15500bb7881908b9799d7653aec72 |
completed | April 16, 2026, 9:30 p.m. |
Created at: April 8, 2026, 9:21 p.m.