Triple
T6819745
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Natalia Sergeyevna Wulfert |
E156866
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Natalia |
E281523
|
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: Natalia | Statement: [Natalia Sergeyevna Wulfert, givenName, Natalia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Natalia Context triple: [Natalia Sergeyevna Wulfert, givenName, Natalia]
-
A.
Natalia
Natalia was a short-lived Boer republic established in the 1830s in what is now KwaZulu-Natal, South Africa.
-
B.
Natalya
chosen
Natalya is a feminine given name of Slavic origin, commonly used in Russian-speaking countries and derived from the Latin name Natalia.
-
C.
Nadya
Nadya is a feminine given name, often used as a diminutive of Nadezhda in Slavic cultures.
-
D.
Yelena
Yelena is a feminine given name of Slavic origin, commonly used in Russian-speaking countries and equivalent to Helen or Helena in English.
-
E.
Nina
Nina is a feminine given name used in various cultures, often as a short form of names like Antonina or Giannina, and borne by numerous notable figures in the arts and public life.
- 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_69c688298a288190af3f285d57f76bbe |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d35781e88190a45d1386706d4422 |
completed | March 27, 2026, 6:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c723e797908190bb0a2d22556b5906 |
completed | March 28, 2026, 12:42 a.m. |
Created at: March 27, 2026, 2:17 p.m.