Triple
T6888761
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Xenia |
E158990
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Ksenya |
E627620
|
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: Ksenya | Statement: [Xenia, hasVariant, Ksenya]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ksenya Context triple: [Xenia, hasVariant, Ksenya]
-
A.
Ksenia
chosen
Ksenia is a feminine given name, commonly used in Slavic countries and derived from the Greek name Xenia, meaning "hospitality" or "guest-friendship."
-
B.
Nadya
Nadya is a feminine given name, often used as a diminutive of Nadezhda in Slavic cultures.
-
C.
Yelena
Yelena is a feminine given name of Slavic origin, commonly used in Russian-speaking countries and equivalent to Helen or Helena in English.
-
D.
Katya
Katya is a diminutive and affectionate form of the given name Catherine, commonly used in Slavic and other European cultures.
-
E.
Natalya
Natalya is a feminine given name of Slavic origin, commonly used in Russian-speaking countries and derived from the Latin name Natalia.
- 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_69c6883568c8819081db6407e892cccc |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d9101e30819084695ba0003a255c |
completed | March 27, 2026, 7:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7511d44ec819093b3779dd1eda5f1 |
completed | March 28, 2026, 3:55 a.m. |
Created at: March 27, 2026, 2:23 p.m.