Triple
T15316855
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Taras Shevchenko |
E366180
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Kateryna |
E263535
|
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: Kateryna | Statement: [Taras Shevchenko, notableWork, Kateryna]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kateryna Context triple: [Taras Shevchenko, notableWork, Kateryna]
-
A.
Kateryna
chosen
Kateryna is a feminine given name, commonly used in Slavic countries, that is a variant of the name Katherine.
-
B.
Oleksandra
Oleksandra is a feminine given name commonly used in Slavic countries, particularly Ukraine, and is the female form of Oleksandr (Alexander).
-
C.
Pereyaslava Danylivna
Pereyaslava Danylivna was a medieval Ruthenian princess, the daughter of King Danylo of Halych in the Kingdom of Galicia–Volhynia.
-
D.
Zoriana Skaletska
Zoriana Skaletska is a Ukrainian lawyer and public health expert who briefly served as Ukraine’s Minister of Health in the government of Oleksiy Honcharuk.
-
E.
Kateryna Hrushevska
Kateryna Hrushevska was the daughter of prominent Ukrainian historian and statesman Mykhailo Hrushevsky and a member of an influential Ukrainian intellectual family.
- 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_69d85a121520819093dcce999fdefe1a |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03dd1d384819098f38402a8740d91 |
completed | April 16, 2026, 1:39 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fef8a688a48190848eb7f065aba146 |
completed | May 9, 2026, 9:04 a.m. |
Created at: April 10, 2026, 3:16 a.m.