Triple
T383153
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Catherine |
E8723
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Katerina |
E61396
|
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: Katerina | Statement: [Catherine, hasVariant, Katerina]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Katerina Context triple: [Catherine, hasVariant, Katerina]
-
A.
Luisa
Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
-
B.
Katya
chosen
Katya is a diminutive and affectionate form of the given name Catherine, commonly used in Slavic and other European cultures.
-
C.
Varvara
Varvara is the Slavic form of the female given name Barbara, commonly used in Russian and other Eastern European languages.
-
D.
Vera
Vera Rubin was an influential American astronomer whose pioneering work on galaxy rotation curves provided key evidence for the existence of dark matter.
-
E.
Clementina
Clementina is a feminine given name, often considered a variant of Clementine, used in various European and Latin American cultures.
- 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_69a2e7f47dd08190a4e294ccbbe46cd4 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ec40ff8c81909306eb2dfe1512af |
completed | Feb. 28, 2026, 1:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a49eb65d548190899347f23ccee2e5 |
completed | March 1, 2026, 8:16 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.