Triple
T15998790
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oksana Baiul |
E388043
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Oksana Baiul |
E83352
|
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: Oksana Baiul | Statement: [Oksana Baiul, name, Oksana Baiul]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Oksana Baiul Context triple: [Oksana Baiul, name, Oksana Baiul]
-
A.
Oksana Baiul
chosen
Oksana Baiul is a Ukrainian figure skater who became the 1994 Olympic ladies' singles champion and one of the sport's most celebrated performers.
-
B.
Oksana Markarova
Oksana Markarova is a Ukrainian economist and politician who served as Ukraine’s Minister of Finance and later became the country’s ambassador to the United States.
-
C.
Oksana Kravchuk
Oksana Kravchuk is a notable individual who bears the Ukrainian surname Kravchuk.
-
D.
Natalya Andrejchenko
Natalya Andrejchenko is a Russian actress best known for her title role in the 1984 Soviet film "Mary Poppins, Goodbye."
-
E.
Tatjana Masurenko
Tatjana Masurenko is a distinguished violist and pedagogue known for her international solo career and influential teaching in Europe.
- 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_69d86daa562c81908aacc179c0fe8fb5 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e157893ebc8190acb75ee05e450fae |
completed | April 16, 2026, 9:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffc3d99ca08190a3d07a0802b1b24a |
completed | May 9, 2026, 11:31 p.m. |
Created at: April 10, 2026, 4:55 a.m.