Triple
T12785038
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anatoly Lukyanov |
E305601
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Anatoly |
E443521
|
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: Anatoly | Statement: [Anatoly Lukyanov, givenName, Anatoly]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anatoly Context triple: [Anatoly Lukyanov, givenName, Anatoly]
-
A.
Anatoly
chosen
Anatoly is a masculine given name of Slavic origin, commonly used in Russian-speaking countries.
-
B.
Semyon
Semyon is a masculine given name of Russian origin, commonly used in Slavic countries.
-
C.
Leonid
Leonid is a masculine given name of Slavic origin, notably borne by Soviet leader Leonid Brezhnev.
-
D.
Yevgeny
Yevgeny is a masculine given name of Slavic origin, commonly used in Russian-speaking countries.
-
E.
Nikolay
Nikolay is a masculine given name of Slavic origin, commonly used in Russian-speaking countries and equivalent to Nicholas in English.
- 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_69d7bdf2b43c819098ae5aa68e61ea58 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96e5cb3c08190b8e1e22de8b96e17 |
completed | April 10, 2026, 9:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7b05474bc8190a42e2a9540055c47 |
completed | May 3, 2026, 8:30 p.m. |
Created at: April 9, 2026, 5:29 p.m.