Triple
T16073294
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oksana |
E389917
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Oksana Skidan
Oksana Skidan is a Russian athlete known for competing in track and field events at the international level.
|
E1219119
|
NE FINISHED |
How this triple was built (4 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 Skidan | Statement: [Oksana, hasNotableBearer, Oksana Skidan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Oksana Skidan Context triple: [Oksana, hasNotableBearer, Oksana Skidan]
-
A.
Oksana Yatskaya
Oksana Yatskaya is a Belarusian long-distance runner known for competing internationally in events such as the marathon.
-
B.
Oksana Skaldina
Oksana Skaldina is a former Soviet and Ukrainian rhythmic gymnast renowned for winning multiple World Championship titles and an Olympic bronze medal in the early 1990s.
-
C.
Oksana Akinshina
Oksana Akinshina is a Russian film actress known for her roles in movies such as "Lilya 4-ever," "The Bourne Supremacy," and "Hipsters."
-
D.
Oksana Shyshkova
Oksana Shyshkova is a Ukrainian Paralympic biathlete and cross-country skier who has won multiple medals at the Winter Paralympic Games.
-
E.
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.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Oksana Skidan Triple: [Oksana, hasNotableBearer, Oksana Skidan]
Generated description
Oksana Skidan is a Russian athlete known for competing in track and field events at the international level.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Oksana Skidan Target entity description: Oksana Skidan is a Russian athlete known for competing in track and field events at the international level.
-
A.
Oksana Yatskaya
Oksana Yatskaya is a Belarusian long-distance runner known for competing internationally in events such as the marathon.
-
B.
Oksana Skaldina
Oksana Skaldina is a former Soviet and Ukrainian rhythmic gymnast renowned for winning multiple World Championship titles and an Olympic bronze medal in the early 1990s.
-
C.
Oksana Akinshina
Oksana Akinshina is a Russian film actress known for her roles in movies such as "Lilya 4-ever," "The Bourne Supremacy," and "Hipsters."
-
D.
Oksana Shyshkova
Oksana Shyshkova is a Ukrainian Paralympic biathlete and cross-country skier who has won multiple medals at the Winter Paralympic Games.
-
E.
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.
- F. None of above. chosen
Provenance (5 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_69d86daf32ec8190a8c0466c8f49c3c0 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e183c0390c8190b0da263cccec14e5 |
completed | April 17, 2026, 12:50 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00606a3d5c8190a145ca35ce458f7e |
completed | May 10, 2026, 10:39 a.m. |
| NEDg | Description generation | batch_6a00625330e0819090db64974ceec4ea |
completed | May 10, 2026, 10:47 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0062b9dad88190b24be02e2a9ee7fd |
completed | May 10, 2026, 10:49 a.m. |
Created at: April 10, 2026, 4:57 a.m.