Triple
T1991258
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zamoskvoretskaya Line |
E43255
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Krasnogvardeyskaya
Krasnogvardeyskaya is a Moscow Metro station on the Zamoskvoretskaya Line serving the southern part of the city.
|
E251188
|
NE FINISHED |
How this triple was built (4 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: Krasnogvardeyskaya | Statement: [Zamoskvoretskaya Line, hasStation, Krasnogvardeyskaya]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Krasnogvardeyskaya Context triple: [Zamoskvoretskaya Line, hasStation, Krasnogvardeyskaya]
-
A.
Khoroshevskaya
Khoroshevskaya is a Moscow Metro station located on the Big Circle Line, serving the Khoroshyovsky District of the city.
-
B.
Krasnov
Krasnov is a Russian surname borne by various notable figures in military, political, and cultural history.
-
C.
Chernyakhovsky
Chernyakhovsky is a Slavic surname most notably associated with Soviet General Ivan Chernyakhovsky, a prominent commander during World War II.
-
D.
Kashirskaya
Kashirskaya is a Moscow Metro station that serves as an interchange point on the system’s Big Circle Line.
-
E.
Novoslobodskaya
Novoslobodskaya is a Moscow Metro station famed for its distinctive stained-glass panels and ornate, cathedral-like interior design.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Krasnogvardeyskaya Triple: [Zamoskvoretskaya Line, hasStation, Krasnogvardeyskaya]
Generated description
Krasnogvardeyskaya is a Moscow Metro station on the Zamoskvoretskaya Line serving the southern part of the city.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Krasnogvardeyskaya Target entity description: Krasnogvardeyskaya is a Moscow Metro station on the Zamoskvoretskaya Line serving the southern part of the city.
-
A.
Khoroshevskaya
Khoroshevskaya is a Moscow Metro station located on the Big Circle Line, serving the Khoroshyovsky District of the city.
-
B.
Krasnov
Krasnov is a Russian surname borne by various notable figures in military, political, and cultural history.
-
C.
Chernyakhovsky
Chernyakhovsky is a Slavic surname most notably associated with Soviet General Ivan Chernyakhovsky, a prominent commander during World War II.
-
D.
Kashirskaya
Kashirskaya is a Moscow Metro station that serves as an interchange point on the system’s Big Circle Line.
-
E.
Novoslobodskaya
Novoslobodskaya is a Moscow Metro station famed for its distinctive stained-glass panels and ornate, cathedral-like interior design.
- F. None of above. chosen
Provenance (5 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_69a88714cf2c819081644be450b8356e |
completed | March 4, 2026, 7:25 p.m. |
| NER | Named-entity recognition | batch_69abb8451fe8819093531052f4533c36 |
completed | March 7, 2026, 5:31 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae71a548408190b8c8c97c94336e2d |
completed | March 9, 2026, 7:07 a.m. |
| NEDg | Description generation | batch_69ae727388f48190bbb516e1cc907689 |
completed | March 9, 2026, 7:10 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae733c44008190ac1429da66cf77aa |
completed | March 9, 2026, 7:14 a.m. |
Created at: March 4, 2026, 7:37 p.m.