Triple
T10876142
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Svetlana |
E256802
|
entity |
| Predicate | hasVariantTransliteration |
P5923
|
FINISHED |
| Object | Swetlana |
E256802
|
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: Swetlana | Statement: [Svetlana, hasVariantTransliteration, Swetlana]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Swetlana Context triple: [Svetlana, hasVariantTransliteration, Swetlana]
-
A.
Svetlana
chosen
Svetlana is a feminine given name of Slavic origin, most notably borne by Svetlana Alliluyeva, the daughter of Soviet leader Joseph Stalin.
-
B.
Yulia
Yulia is a feminine given name, commonly used in Slavic countries as a form of the name Julia.
-
C.
Galina
Galina is a feminine given name of Slavic origin, commonly used in Russia and other Eastern European countries.
-
D.
Shcherbatskaya
Shcherbatskaya is the surname of Ekaterina Alexandrovna, a fictional Russian noblewoman featured in Leo Tolstoy’s novel "Anna Karenina."
-
E.
Xenia Chizh
Xenia Chizh is known primarily as the spouse of Anton Denikin.
- 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_69d6aa848804819081b2713ca0bedf06 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d751ac901881909938cabe4d21bdbf |
completed | April 9, 2026, 7:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e3c7e869748190991e06d9df50faa9 |
completed | April 18, 2026, 6:05 p.m. |
Created at: April 8, 2026, 9:21 p.m.