Triple
T4816942
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Prince Andrei Bolkonsky |
E107610
|
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: [Prince Andrei Bolkonsky, givenName, Andrei]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Andrei Context triple: [Prince Andrei Bolkonsky, 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.
Rodion
Rodion is a masculine given name of Slavic origin, most notably borne by Soviet military commander Rodion Malinovsky.
-
E.
Anatoly
Anatoly 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_69bd43f9efa081908314cb3e94fa1695 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6c947b18819086c3af556bb7591c |
completed | March 20, 2026, 3:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bfb1324c2c8190ba2a1c5708ba88e5 |
completed | March 22, 2026, 9:06 a.m. |
Created at: March 20, 2026, 1:23 p.m.