Triple
T838419
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Valeri Kamensky |
E18122
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Valeri |
E18122
|
NE FINISHED |
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: Valeri | Statement: [Valeri Kamensky, givenName, Valeri]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Valeri Context triple: [Valeri Kamensky, givenName, Valeri]
-
A.
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.
-
B.
Valeri Kamensky
chosen
Valeri Kamensky is a former Russian ice hockey star and Stanley Cup champion known for his prolific scoring in both the NHL and international play.
-
C.
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.
-
D.
Andrei Voronkov
Andrei Voronkov is a computer scientist known for his influential work in automated reasoning and theorem proving.
-
E.
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.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69a49389f44881909a608fb27d89f247 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4abd0e8bc8190afe29cd4745c2f86 |
completed | March 1, 2026, 9:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a7c0144a70819098aa4872a02b62b7 |
completed | March 4, 2026, 5:16 a.m. |
Created at: March 1, 2026, 7:38 p.m.