Triple
T399118
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aleksandr Vasilevsky |
E9239
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Vasilevsky
Vasilevsky is a Russian surname most prominently associated with Aleksandr Vasilevsky, a leading Soviet military commander and Marshal of the Soviet Union during World War II.
|
E81275
|
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: Vasilevsky | Statement: [Aleksandr Vasilevsky, familyName, Vasilevsky]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vasilevsky Context triple: [Aleksandr Vasilevsky, familyName, Vasilevsky]
-
A.
Valentin Pavlov
Valentin Pavlov was a Soviet politician and economist who briefly served as the last Prime Minister of the Soviet Union during its final months before dissolution.
-
B.
Grigory Morozov
Grigory Morozov was a Soviet engineer best known as the first husband of Joseph Stalin’s daughter, Svetlana Alliluyeva.
-
C.
Sergei
Sergei is a masculine given name of Slavic origin, commonly used in Russia and other Eastern European countries.
-
D.
Igor Babuschkin
Igor Babuschkin is an AI researcher and engineer known for his work on large language models at organizations such as DeepMind, OpenAI, and later xAI.
-
E.
Pavel
Pavel is a Slavic given name, equivalent to the English name Paul.
- 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: Vasilevsky Triple: [Aleksandr Vasilevsky, familyName, Vasilevsky]
Generated description
Vasilevsky is a Russian surname most prominently associated with Aleksandr Vasilevsky, a leading Soviet military commander and Marshal of the Soviet Union during World War II.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Vasilevsky Target entity description: Vasilevsky is a Russian surname most prominently associated with Aleksandr Vasilevsky, a leading Soviet military commander and Marshal of the Soviet Union during World War II.
-
A.
Valentin Pavlov
Valentin Pavlov was a Soviet politician and economist who briefly served as the last Prime Minister of the Soviet Union during its final months before dissolution.
-
B.
Grigory Morozov
Grigory Morozov was a Soviet engineer best known as the first husband of Joseph Stalin’s daughter, Svetlana Alliluyeva.
-
C.
Sergei
Sergei is a masculine given name of Slavic origin, commonly used in Russia and other Eastern European countries.
-
D.
Igor Babuschkin
Igor Babuschkin is an AI researcher and engineer known for his work on large language models at organizations such as DeepMind, OpenAI, and later xAI.
-
E.
Pavel
Pavel is a Slavic given name, equivalent to the English name Paul.
- 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_69a2e8004cb88190b92ed1add6abf41a |
completed | Feb. 28, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69a2ec8d0ca881909d786e8eed9b6748 |
completed | Feb. 28, 2026, 1:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a58030d0e48190a6390478e62f8659 |
completed | March 2, 2026, 12:18 p.m. |
| NEDg | Description generation | batch_69a582410b008190a4f354b354e27c31 |
completed | March 2, 2026, 12:27 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69a582eb73c08190982a7fa6bbee536f |
completed | March 2, 2026, 12:30 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.