Triple
T19349407
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kamenskiy |
E483972
|
entity |
| Predicate | hasFeminineForm |
P1613
|
FINISHED |
| Object | Kamenskaia |
—
|
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: Kamenskaia | Statement: [Kamenskiy, hasFeminineForm, Kamenskaia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kamenskaia Context triple: [Kamenskiy, hasFeminineForm, Kamenskaia]
-
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.
Kuntsevskaya
Kuntsevskaya is a Moscow Metro station on the Big Circle Line serving the Kuntsevo District in western Moscow.
-
C.
Kastrychnitskaya
Kastrychnitskaya is a central Minsk Metro station known for serving the heart of Belarus’s capital near key administrative and cultural landmarks.
-
D.
Skobelevskaya
Skobelevskaya is a Moscow Metro station serving the Severnoye Butovo District in the south of Moscow.
-
E.
Grushevskaya
Grushevskaya is a Russian-language surname of Slavic origin.
- 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.