Triple
T19349403
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kamenskiy |
E483972
|
entity |
| Predicate | hasTransliterationVariant |
P5923
|
FINISHED |
| Object | Kamenskiĭ |
—
|
NE NERFINISHED |
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: Kamenskiĭ | Statement: [Kamenskiy, hasTransliterationVariant, Kamenskiĭ]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kamenskiĭ Context triple: [Kamenskiy, hasTransliterationVariant, Kamenskiĭ]
-
A.
Kamenskiy
chosen
Kamenskiy is a Slavic surname, commonly transliterated from Russian or related languages, borne by various individuals across Eastern Europe and the former Soviet Union.
-
B.
Yukhnov
Yukhnov is a small historic town in western Russia known for its location on the Ugra River and its role in regional trade and World War II history.
-
C.
Konstantinovka
Konstantinovka is an archaeological site in Ukraine notable for remains associated with the Eneolithic Sredny Stog culture, an early horse-using steppe society.
-
D.
Kuntsevskaya
Kuntsevskaya is a Moscow Metro station on the Big Circle Line serving the Kuntsevo District in western Moscow.
-
E.
Artyomovsky
Artyomovsky is a town in Russia’s Ural region known for its industrial base and role as a local administrative center.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e8d244f8819080eb1f3491300db2 |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e6185d54c0819081715ca13a5806b4 |
completed | April 20, 2026, 12:13 p.m. |
Created at: April 10, 2026, 1:34 p.m.