Triple
T13844902
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arbatskaya |
E332773
|
entity |
| Predicate | hasAdjacentStation |
P231
|
FINISHED |
| Object |
Smolenskaya
Smolenskaya is a Moscow Metro station on the Arbatsko–Pokrovskaya and Filyovskaya lines, located near the historic Arbat district.
|
E1076011
|
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: Smolenskaya | Statement: [Arbatskaya, hasAdjacentStation, Smolenskaya]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Smolenskaya Context triple: [Arbatskaya, hasAdjacentStation, Smolenskaya]
-
A.
Smolensk
Smolensk is a historic city in western Russia near the Belarusian border, known for its strategic location and centuries-old fortifications.
-
B.
Kolomenskaya
Kolomenskaya is a Moscow Metro station on the Zamoskvoretskaya Line, serving the Kolomenskoye area in the southern part of the city.
-
C.
Kolomna
Kolomna is a historic Russian city southeast of Moscow, known for its well-preserved kremlin, medieval architecture, and traditional pastila confectionery.
-
D.
Kaluzhskaya
Kaluzhskaya is a Moscow Metro station on the Kaluzhsko–Rizhskaya line, serving the southwestern part of the city.
-
E.
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.
- 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: Smolenskaya Triple: [Arbatskaya, hasAdjacentStation, Smolenskaya]
Generated description
Smolenskaya is a Moscow Metro station on the Arbatsko–Pokrovskaya and Filyovskaya lines, located near the historic Arbat district.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Smolenskaya Target entity description: Smolenskaya is a Moscow Metro station on the Arbatsko–Pokrovskaya and Filyovskaya lines, located near the historic Arbat district.
-
A.
Smolensk
Smolensk is a historic city in western Russia near the Belarusian border, known for its strategic location and centuries-old fortifications.
-
B.
Kolomenskaya
Kolomenskaya is a Moscow Metro station on the Zamoskvoretskaya Line, serving the Kolomenskoye area in the southern part of the city.
-
C.
Kolomna
Kolomna is a historic Russian city southeast of Moscow, known for its well-preserved kremlin, medieval architecture, and traditional pastila confectionery.
-
D.
Kaluzhskaya
Kaluzhskaya is a Moscow Metro station on the Kaluzhsko–Rizhskaya line, serving the southwestern part of the city.
-
E.
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.
- 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_69d81c5ba13c8190839315f54768acfd |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de02b1a25c8190a9f85ba43c421188 |
completed | April 14, 2026, 9:02 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fbc31a91608190a80a69be38ac7f71 |
completed | May 6, 2026, 10:39 p.m. |
| NEDg | Description generation | batch_69fc432c35ec8190bb7183d902bf7d62 |
completed | May 7, 2026, 7:45 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fc43e71f708190903a63388b664ba5 |
completed | May 7, 2026, 7:48 a.m. |
Created at: April 9, 2026, 10:13 p.m.