Triple
T2975429
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Andrei Vasilevskiy |
E80382
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Andrei |
E2779
|
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: Andrei | Statement: [Andrei Vasilevskiy, givenName, Andrei]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Andrei Context triple: [Andrei Vasilevskiy, givenName, Andrei]
-
A.
Andrei
chosen
Andrei is a masculine given name commonly used in Slavic and Eastern European countries, equivalent to the English name Andrew.
-
B.
Yevgeny
Yevgeny is a masculine given name of Slavic origin, commonly used in Russian-speaking countries.
-
C.
Sergei
Sergei is a masculine given name of Slavic origin, commonly used in Russia and other Eastern European countries.
-
D.
Anatoly
Anatoly is a masculine given name of Slavic origin, commonly used in Russian-speaking countries.
-
E.
Vasily
Vasily is a masculine given name of Slavic origin, commonly used in Russian-speaking countries.
- 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_69ad8b14ffe881908ffed62f9595c867 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad998ad5308190a012ec4940eb46cb |
completed | March 8, 2026, 3:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b6720e877481908d870653bbb09820 |
completed | March 15, 2026, 8:47 a.m. |
Created at: March 8, 2026, 2:58 p.m.