Triple
T2167555
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Serpukhovsko–Timiryazevskaya Line |
E46944
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Prazhskaya
Prazhskaya is a Moscow Metro station named after Prague, featuring Soviet-era architecture with Czech design influences.
|
E292995
|
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: Prazhskaya | Statement: [Serpukhovsko–Timiryazevskaya Line, hasStation, Prazhskaya]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Prazhskaya Context triple: [Serpukhovsko–Timiryazevskaya Line, hasStation, Prazhskaya]
-
A.
Kolín
Kolín is a historic industrial town and important transport hub on the Elbe River in the Central Bohemian Region of the Czech Republic.
-
B.
Nymburk
Nymburk is a historic town in the Czech Republic known for its medieval fortifications and location on the Elbe River.
-
C.
Prague 6
Prague 6 is a large municipal district of Prague, Czech Republic, known for its residential neighborhoods, diplomatic quarter, and proximity to Prague Castle and the airport.
-
D.
Plzeň
Plzeň is a major city in western Bohemia in the Czech Republic, known for its brewing tradition and industrial heritage.
-
E.
Pardubice
Pardubice is a city in the Czech Republic known for its ice hockey tradition, historic center, and as the hometown of legendary NHL goaltender Dominik Hašek.
- 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: Prazhskaya Triple: [Serpukhovsko–Timiryazevskaya Line, hasStation, Prazhskaya]
Generated description
Prazhskaya is a Moscow Metro station named after Prague, featuring Soviet-era architecture with Czech design influences.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Prazhskaya Target entity description: Prazhskaya is a Moscow Metro station named after Prague, featuring Soviet-era architecture with Czech design influences.
-
A.
Kolín
Kolín is a historic industrial town and important transport hub on the Elbe River in the Central Bohemian Region of the Czech Republic.
-
B.
Nymburk
Nymburk is a historic town in the Czech Republic known for its medieval fortifications and location on the Elbe River.
-
C.
Prague 6
Prague 6 is a large municipal district of Prague, Czech Republic, known for its residential neighborhoods, diplomatic quarter, and proximity to Prague Castle and the airport.
-
D.
Plzeň
Plzeň is a major city in western Bohemia in the Czech Republic, known for its brewing tradition and industrial heritage.
-
E.
Pardubice
Pardubice is a city in the Czech Republic known for its ice hockey tradition, historic center, and as the hometown of legendary NHL goaltender Dominik Hašek.
- 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_69a88a184cbc8190877791f6552c2484 |
completed | March 4, 2026, 7:38 p.m. |
| NER | Named-entity recognition | batch_69abbeac9d688190bfa68715e173771e |
completed | March 7, 2026, 5:59 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afb66209bc8190aa030b147e4d34cb |
completed | March 10, 2026, 6:12 a.m. |
| NEDg | Description generation | batch_69afb6f35a2881908a1023908184fd60 |
completed | March 10, 2026, 6:15 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69afb76a786081908add737721132edc |
completed | March 10, 2026, 6:17 a.m. |
Created at: March 4, 2026, 7:45 p.m.