Triple
T10299508
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vorontsovskaya |
E241588
|
entity |
| Predicate | fareIntegrationWith |
P3494
|
FINISHED |
| Object | Kaluzhskaya |
E856329
|
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: Kaluzhskaya | Statement: [Vorontsovskaya, fareIntegrationWith, Kaluzhskaya]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kaluzhskaya Context triple: [Vorontsovskaya, fareIntegrationWith, Kaluzhskaya]
-
A.
Kaluzhskaya
chosen
Kaluzhskaya is a Moscow Metro station on the Kaluzhsko–Rizhskaya line, serving the southwestern part of the city.
-
B.
Khoroshevskaya
Khoroshevskaya is a Moscow Metro station located on the Big Circle Line, serving the Khoroshyovsky District of the city.
-
C.
Krasnopresnenskaya
Krasnopresnenskaya is a Moscow Metro station on the city’s circular Koltsevaya Line, known for its deep-level construction and Soviet-era architectural design.
-
D.
Kashirskaya
Kashirskaya is a Moscow Metro station that serves as an interchange point on the system’s Big Circle Line.
-
E.
Rizhskaya
Rizhskaya is a Moscow Metro station on the Big Circle Line serving the Rizhsky railway terminal area.
- 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_69d381aaafc08190af475ef58dc16aba |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d2ee10f88190b1615c49b8f24a26 |
completed | April 7, 2026, 9:48 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d7501f76e081908a7509a877c72b82 |
completed | April 9, 2026, 7:07 a.m. |
Created at: April 6, 2026, 11:44 a.m.