Triple
T18835199
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Greta Onieogou |
E460645
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Greta Onieogou |
—
|
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: Greta Onieogou | Statement: [Greta Onieogou, name, Greta Onieogou]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Greta Onieogou Context triple: [Greta Onieogou, name, Greta Onieogou]
-
A.
Greta Onieogou
chosen
Greta Onieogou is a Canadian actress best known for her role as Layla Keating on the television drama series "All American."
-
B.
Nina Kristensen
Nina Kristensen is a video game developer and co-founder of the British studio Ninja Theory, known for cinematic action titles like Heavenly Sword and Hellblade: Senua’s Sacrifice.
-
C.
Emma Grede
Emma Grede is a British entrepreneur and fashion executive best known as the co-founder and CEO of Good American and a founding partner of Kim Kardashian’s shapewear brand SKIMS.
-
D.
Greta Lundgren
Greta Lundgren is a daughter of Swedish actor and martial artist Dolph Lundgren.
-
E.
Valeen Montenegro
Valeen Montenegro is a Filipino actress, comedian, and television personality known for her work on various GMA Network shows and comedy programs.
- 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_69d8dcfa11e4819090ab1ef5bdcd2b2e |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5a99d491c81909d8e55ac45621d44 |
completed | April 20, 2026, 4:20 a.m. |
Created at: April 10, 2026, 11:56 a.m.