Triple
T5815885
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Krasnov |
E128983
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Oleg Krasnov
Oleg Krasnov is a person notable enough to be recognized as a significant bearer of the Krasnov surname.
|
E863035
|
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: Oleg Krasnov | Statement: [Krasnov, hasNotableBearer, Oleg Krasnov]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Oleg Krasnov Context triple: [Krasnov, hasNotableBearer, Oleg Krasnov]
-
A.
Oleg Baklanov
Oleg Baklanov was a Soviet politician and high-ranking official who played a key role as one of the hardline plotters in the failed 1991 coup attempt against Mikhail Gorbachev.
-
B.
Oleg Klimov
Oleg Klimov is a researcher known for his contributions to the development and analysis of Proximal Policy Optimization (PPO) algorithms in reinforcement learning.
-
C.
Andrei Voronkov
Andrei Voronkov is a computer scientist known for his influential work in automated reasoning and theorem proving.
-
D.
Mikhail Zharov
Mikhail Zharov was a prominent Soviet film and theater actor known for his character roles in classic Russian cinema.
-
E.
Sergey Sokolov
Sergey Sokolov was a Soviet military leader and Marshal of the Soviet Union who served as the USSR’s Minister of Defense during the 1980s.
- 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: Oleg Krasnov Triple: [Krasnov, hasNotableBearer, Oleg Krasnov]
Generated description
Oleg Krasnov is a person notable enough to be recognized as a significant bearer of the Krasnov surname.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Oleg Krasnov Target entity description: Oleg Krasnov is a person notable enough to be recognized as a significant bearer of the Krasnov surname.
-
A.
Oleg Baklanov
Oleg Baklanov was a Soviet politician and high-ranking official who played a key role as one of the hardline plotters in the failed 1991 coup attempt against Mikhail Gorbachev.
-
B.
Oleg Klimov
Oleg Klimov is a researcher known for his contributions to the development and analysis of Proximal Policy Optimization (PPO) algorithms in reinforcement learning.
-
C.
Andrei Voronkov
Andrei Voronkov is a computer scientist known for his influential work in automated reasoning and theorem proving.
-
D.
Mikhail Zharov
Mikhail Zharov was a prominent Soviet film and theater actor known for his character roles in classic Russian cinema.
-
E.
Sergey Sokolov
Sergey Sokolov was a Soviet military leader and Marshal of the Soviet Union who served as the USSR’s Minister of Defense during the 1980s.
- 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_69c0084869e881908d7859492183ca7b |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c0336344148190bcf417c0b9617cb9 |
completed | March 22, 2026, 6:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d87dfc46e08190bea27c11b987cb6d |
completed | April 10, 2026, 4:35 a.m. |
| NEDg | Description generation | batch_69d8837e70508190b03e8983b2617eac |
completed | April 10, 2026, 4:58 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d889cc40648190a1d80b955e676ea5 |
completed | April 10, 2026, 5:25 a.m. |
Created at: March 22, 2026, 3:53 p.m.