Triple
T11767703
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 13th Army (Red Army) |
E279818
|
entity |
| Predicate | notableCommander |
P1197
|
FINISHED |
| Object | Konstantin Golubev |
—
|
NE NERFINISHED |
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: Konstantin Golubev | Statement: [13th Army (Red Army), notableCommander, Konstantin Golubev]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Konstantin Golubev Context triple: [13th Army (Red Army), notableCommander, Konstantin Golubev]
-
A.
Sergei Gerasimov
Sergei Gerasimov was a prominent Soviet film director, screenwriter, and actor who became one of the leading figures in Soviet cinema and film education.
-
B.
Konstantin Dmitrievich Golubev
chosen
Konstantin Dmitrievich Golubev was a Soviet military commander and general who held key leadership roles in the Red Army during World War II.
-
C.
Aleksandr Golikov
Aleksandr Golikov is a former Soviet ice hockey player best known for his career in the Soviet league and appearances with the USSR national team.
-
D.
Andrei Voronkov
Andrei Voronkov is a computer scientist known for his influential work in automated reasoning and theorem proving.
-
E.
Igor Shchyogolev
Igor Shchyogolev is a Russian politician and former Minister of Communications and Mass Media who has held several high-ranking government positions.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d6ab01d2688190ad8ed6bda487eaa5 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a526979c8190ad2089997906855b |
completed | April 10, 2026, 7:22 a.m. |
Created at: April 8, 2026, 9:41 p.m.