Triple
T10630672
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tamara |
E250443
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Tamara (Cyrillic: Тамара) |
E250443
|
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: Tamara (Cyrillic: Тамара) | Statement: [Tamara, hasVariant, Tamara (Cyrillic: Тамара)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tamara (Cyrillic: Тамара) Context triple: [Tamara, hasVariant, Tamara (Cyrillic: Тамара)]
-
A.
Tamar (wife of Demetrius I of Georgia)
Tamar was a medieval Georgian queen consort, known as the wife of King Demetrius I of Georgia.
-
B.
Tamara
chosen
Tamara is a feminine given name of Hebrew origin, commonly used in various cultures and languages.
-
C.
Lyudmila
Lyudmila is a Russian linguist and the former First Lady of Russia, known for being the ex-wife of President Vladimir Putin.
-
D.
Lyudmila
Lyudmila is a common Russian female given name, notably borne by figures such as Soviet World War II sniper Lyudmila Pavlichenko.
-
E.
Svetlana
Svetlana is a feminine given name of Slavic origin, most notably borne by Svetlana Alliluyeva, the daughter of Soviet leader Joseph Stalin.
- 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_69d6aa5993448190a493b790b8f85010 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6df93a2b88190a0f3a52b8e88f54f |
completed | April 8, 2026, 11:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d96bb4bbf08190994ea9123c0b2dab |
completed | April 10, 2026, 9:29 p.m. |
Created at: April 8, 2026, 9:01 p.m.