Triple
T3627167
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maryna Poroshenko |
E76866
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Maryna |
E370383
|
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: Maryna | Statement: [Maryna Poroshenko, givenName, Maryna]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maryna Context triple: [Maryna Poroshenko, givenName, Maryna]
-
A.
Kateryna
Kateryna is a feminine given name, commonly used in Slavic countries, that is a variant of the name Katherine.
-
B.
Sylwia
Sylwia is a feminine given name, primarily used in Poland, that is a cognate of the name Sylvia.
-
C.
Romeyka
Romeyka is an endangered Greek dialect spoken mainly in northeastern Turkey, notable for preserving many archaic features of Ancient Greek.
-
D.
Ewelina
Ewelina is a feminine given name of Slavic origin, commonly used in Poland and other Central and Eastern European countries.
-
E.
Marya
chosen
Marya is a feminine given name, often considered a variant of Mary and used in various cultures and languages.
- 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_69ad85dc03948190b35b7189e4175bcc |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc2ddccc881909ae13dca3dd8a11d |
completed | March 8, 2026, 6:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b433270ddc81908080698604009737 |
completed | March 13, 2026, 3:54 p.m. |
Created at: March 8, 2026, 3:23 p.m.