Triple
T16929240
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Teatralnaya |
E410658
|
entity |
| Predicate | adjacentStation |
P5707
|
FINISHED |
| Object | Novokuznetskaya |
—
|
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: Novokuznetskaya | Statement: [Teatralnaya, adjacentStation, Novokuznetskaya]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Novokuznetskaya Context triple: [Teatralnaya, adjacentStation, Novokuznetskaya]
-
A.
Novokuznetskaya
chosen
Novokuznetskaya is a Moscow Metro station known for its distinctive Stalinist architecture and richly decorated interiors.
-
B.
Novokuznetsk
Novokuznetsk is a major industrial city in southwestern Siberia, Russia, known for its large metallurgical and coal-mining industries.
-
C.
Kuznetsk
Kuznetsk is a city in Penza Oblast, Russia, known as an industrial and transport center in the Volga region.
-
D.
Nizhnekamsk
Nizhnekamsk is a major industrial city in Russia known for its large petrochemical and oil refining complexes.
-
E.
Omsk
Omsk is one of the largest cities in southwestern Siberia, Russia, serving as a major industrial, cultural, and transportation hub on the Irtysh River.
- 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_69d886c886688190967be07322597ac9 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3cf23873c8190bdc9121d6c3850e2 |
completed | April 18, 2026, 6:36 p.m. |
Created at: April 10, 2026, 5:30 a.m.