Triple
T18242123
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Makarov |
E436841
|
entity |
| Predicate | hasFeminineForm |
P1613
|
FINISHED |
| Object | Makarova |
E911404
|
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: Makarova | Statement: [Makarov, hasFeminineForm, Makarova]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Makarova Context triple: [Makarov, hasFeminineForm, Makarova]
-
A.
Makarova
chosen
Makarova is a Russian surname most prominently associated with the celebrated ballerina and choreographer Natalia Makarova.
-
B.
Astankova
Astankova is a Slavic surname most notably borne by individuals such as Oksana Astankova.
-
C.
Shcherbatskaya
Shcherbatskaya is the surname of Ekaterina Alexandrovna, a fictional Russian noblewoman featured in Leo Tolstoy’s novel "Anna Karenina."
-
D.
Gorjacheva
Gorjacheva is a transliterated Russian surname, typically borne by women and derived from the masculine form Goryachev.
-
E.
Karsavina
Karsavina is the surname of Tamara Karsavina, a renowned Russian prima ballerina of the early 20th century.
- 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_69d8b91104e08190a8241f7d260a5162 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4f7e387f481909d72574fb7d17923 |
completed | April 19, 2026, 3:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a03ac7124f88190bfc0064e00946586 |
completed | May 12, 2026, 10:40 p.m. |
Created at: April 10, 2026, 10:33 a.m.