Triple
T21350787
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pavel Dybenko |
E526470
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Dybenko
Dybenko is a Slavic surname most notably associated with Pavel Dybenko, a prominent Bolshevik revolutionary and Soviet military leader.
|
E1479773
|
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: Dybenko | Statement: [Pavel Dybenko, familyName, Dybenko]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dybenko Context triple: [Pavel Dybenko, familyName, Dybenko]
-
A.
Dobrynin
Dobrynin is a Russian surname most prominently associated with Anatoly Dobrynin, the long-serving Soviet ambassador to the United States during the Cold War.
-
B.
Derevko
Derevko is the surname of Irina Derevko, a key character in the television series "Alias."
-
C.
Dyachenko
Dyachenko is a Russian surname most notably borne by Tatyana Dyachenko, the daughter and former adviser of Russia’s first president, Boris Yeltsin.
-
D.
Shcherbatsky
Shcherbatsky is the aristocratic Russian family name of a prominent noble household featured in Leo Tolstoy’s novel "Anna Karenina."
-
E.
Yuryatin
Yuryatin is a fictional Russian town in Boris Pasternak’s novel "Doctor Zhivago," serving as a key setting in Lara Antipova’s story.
- 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: Dybenko Triple: [Pavel Dybenko, familyName, Dybenko]
Generated description
Dybenko is a Slavic surname most notably associated with Pavel Dybenko, a prominent Bolshevik revolutionary and Soviet military leader.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Dybenko Target entity description: Dybenko is a Slavic surname most notably associated with Pavel Dybenko, a prominent Bolshevik revolutionary and Soviet military leader.
-
A.
Dobrynin
Dobrynin is a Russian surname most prominently associated with Anatoly Dobrynin, the long-serving Soviet ambassador to the United States during the Cold War.
-
B.
Derevko
Derevko is the surname of Irina Derevko, a key character in the television series "Alias."
-
C.
Dyachenko
Dyachenko is a Russian surname most notably borne by Tatyana Dyachenko, the daughter and former adviser of Russia’s first president, Boris Yeltsin.
-
D.
Shcherbatsky
Shcherbatsky is the aristocratic Russian family name of a prominent noble household featured in Leo Tolstoy’s novel "Anna Karenina."
-
E.
Yuryatin
Yuryatin is a fictional Russian town in Boris Pasternak’s novel "Doctor Zhivago," serving as a key setting in Lara Antipova’s story.
- 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_69e0b51cd5cc81909ac1187971e8a8ad |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e8ad31087481909d41e9d28286f04d |
completed | April 22, 2026, 11:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a09ad5e14188190a55aed4c72b72162 |
completed | May 17, 2026, 11:58 a.m. |
| NEDg | Description generation | batch_6a09adf244d481908bb02f99844f1cd0 |
completed | May 17, 2026, noon |
| NED2 | Entity disambiguation (via description) | batch_6a09ae91d9cc8190a677124f1a0e1e4e |
completed | May 17, 2026, 12:03 p.m. |
Created at: April 16, 2026, 5:04 p.m.