Triple
T2504596
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Olga Korbut |
E52548
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Olga |
E136344
|
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: Olga | Statement: [Olga Korbut, givenName, Olga]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Olga Context triple: [Olga Korbut, givenName, Olga]
-
A.
Olga
chosen
Olga is a female given name of Russian origin, historically borne by several notable figures including Russian grand duchesses and saints.
-
B.
Lyudmila
Lyudmila is a Russian linguist and the former First Lady of Russia, known for being the ex-wife of President Vladimir Putin.
-
C.
Olga Loyev
Olga Loyev was the wife of the famed Yiddish writer Sholem Aleichem and a supportive partner throughout his literary career.
-
D.
Aloysya
Aloysya is a given name, typically a feminine variant of Aloysius, used in various cultures and languages.
-
E.
Tatyana
Tatyana is a feminine given name of Slavic origin, particularly common 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_69ab4957b3a88190adf968ae0c1b931c |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abd1cd2db0819087d21ec49ffd9585 |
completed | March 7, 2026, 7:20 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af2b8bc7708190a507136d1f9f34ca |
completed | March 9, 2026, 8:20 p.m. |
Created at: March 6, 2026, 9:46 p.m.