Triple
T10422912
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oleksandra |
E245703
|
entity |
| Predicate | relatedName |
P3889
|
FINISHED |
| Object | Aleksandra |
E260502
|
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: Aleksandra | Statement: [Oleksandra, relatedName, Aleksandra]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aleksandra Context triple: [Oleksandra, relatedName, Aleksandra]
-
A.
Aleksandra
chosen
Aleksandra is a feminine given name of Slavic origin, commonly used in various Eastern and Central European countries.
-
B.
Svetlana
Svetlana is a feminine given name of Slavic origin, most notably borne by Svetlana Alliluyeva, the daughter of Soviet leader Joseph Stalin.
-
C.
Elisaveta
Elisaveta is a feminine given name of Slavic origin, commonly used in Eastern Europe as a variant of Elizabeth.
-
D.
Ludmila
Ludmila is the heroine of Alexander Pushkin’s narrative poem "Ruslan and Ludmila," known as a beautiful Kievan princess whose abduction sets the story’s adventurous plot in motion.
-
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_69d381bf3dc08190bf35a2643e4e8f22 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4ea2de4d48190aee65b3f6ec3cc48 |
completed | April 7, 2026, 11:27 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d8dc374f6c8190a44b9da27be343e4 |
completed | April 10, 2026, 11:17 a.m. |
Created at: April 6, 2026, 12:12 p.m.