Triple

T2039025
Position Surface form Disambiguated ID Type / Status
Subject Koltsevaya Line E44698 entity
Predicate hasStation P35 FINISHED
Object 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.
 E269778 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: Krasnopresnenskaya | Statement: [Koltsevaya Line, hasStation, Krasnopresnenskaya]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Krasnopresnenskaya
Context triple: [Koltsevaya Line, hasStation, Krasnopresnenskaya]
  • A. Khoroshevskaya
    Khoroshevskaya is a Moscow Metro station located on the Big Circle Line, serving the Khoroshyovsky District of the city.
  • B. Paveletskaya
    Paveletskaya is a Moscow Metro station named after the nearby Paveletsky railway terminal, serving as a key transport hub in the city’s network.
  • C. Rizhskaya
    Rizhskaya is a Moscow Metro station on the Big Circle Line serving the Rizhsky railway terminal area.
  • 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: Krasnopresnenskaya
Triple: [Koltsevaya Line, hasStation, Krasnopresnenskaya]
Generated description
Krasnopresnenskaya is a Moscow Metro station on the city’s circular Koltsevaya Line, known for its deep-level construction and Soviet-era architectural design.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Krasnopresnenskaya
Target entity description: Krasnopresnenskaya is a Moscow Metro station on the city’s circular Koltsevaya Line, known for its deep-level construction and Soviet-era architectural design.
  • A. Khoroshevskaya
    Khoroshevskaya is a Moscow Metro station located on the Big Circle Line, serving the Khoroshyovsky District of the city.
  • B. Paveletskaya
    Paveletskaya is a Moscow Metro station named after the nearby Paveletsky railway terminal, serving as a key transport hub in the city’s network.
  • C. Rizhskaya
    Rizhskaya is a Moscow Metro station on the Big Circle Line serving the Rizhsky railway terminal area.
  • 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_69a889159ec481908f9e4472d9f480c7 completed March 4, 2026, 7:33 p.m.
NER Named-entity recognition batch_69abb951870481909dbdd8fc8b0c02fe completed March 7, 2026, 5:36 a.m.
NED1 Entity disambiguation (via context triple) batch_69af1757f4ac8190a894d8a35053eea2 completed March 9, 2026, 6:54 p.m.
NEDg Description generation batch_69af18e9d328819096130bc1631a5005 completed March 9, 2026, 7 p.m.
NED2 Entity disambiguation (via description) batch_69af19608cd48190aae972259090e0ee completed March 9, 2026, 7:02 p.m.
Created at: March 4, 2026, 7:39 p.m.