Triple
T10442487
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nadia |
E246202
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Nadya |
E616360
|
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: Nadya | Statement: [Nadia, hasVariant, Nadya]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nadya Context triple: [Nadia, hasVariant, Nadya]
-
A.
Nadya
chosen
Nadya is a feminine given name, often used as a diminutive of Nadezhda in Slavic cultures.
-
B.
Natalia
Natalia was a short-lived Boer republic established in the 1830s in what is now KwaZulu-Natal, South Africa.
-
C.
Natalya
Natalya is a feminine given name of Slavic origin, commonly used in Russian-speaking countries and derived from the Latin name Natalia.
-
D.
Ksenia
Ksenia is a feminine given name, commonly used in Slavic countries and derived from the Greek name Xenia, meaning "hospitality" or "guest-friendship."
-
E.
Yulia
Yulia is a feminine given name, commonly used in Slavic countries as a form of the name Julia.
- 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_69d381c04fe08190957c26c526a3b05a |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4fdbd731c819084dfff83b4481ae8 |
completed | April 7, 2026, 12:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d90d9ed4988190b4fee055eaad39c5 |
completed | April 10, 2026, 2:47 p.m. |
Created at: April 6, 2026, 12:15 p.m.