Triple
T4400732
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sergei Makarov |
E93608
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Sergei |
E64229
|
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: Sergei | Statement: [Sergei Makarov, givenName, Sergei]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sergei Context triple: [Sergei Makarov, givenName, Sergei]
-
A.
Sergei
chosen
Sergei is a masculine given name of Slavic origin, commonly used in Russia and other Eastern European countries.
-
B.
Anatoly
Anatoly is a masculine given name of Slavic origin, commonly used in Russian-speaking countries.
-
C.
Юрий
Юрий is a common Russian male given name, often rendered in English as Yuri and borne by numerous notable figures in Russian and Soviet history and culture.
-
D.
Leonid
Leonid is a masculine given name of Slavic origin, notably borne by Soviet leader Leonid Brezhnev.
-
E.
Yevgeny
Yevgeny 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_69b345158c748190a2c040fce2da9980 |
completed | March 12, 2026, 10:58 p.m. |
| NER | Named-entity recognition | batch_69b352cd71ec8190a62320fd072f744f |
completed | March 12, 2026, 11:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf410bd6b08190a0ad4af6e5f4b0b8 |
completed | March 22, 2026, 1:08 a.m. |
Created at: March 12, 2026, 11:28 p.m.