Triple

T2396410
Position Surface form Disambiguated ID Type / Status
Subject Moscow Central Circle E47660 entity
Predicate hasStation P35 FINISHED
Object Baltiyskaya MCC station
Baltiyskaya MCC station is a passenger rail station on Moscow’s orbital Moscow Central Circle urban rail line.
 E263159 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: Baltiyskaya MCC station | Statement: [Moscow Central Circle, hasStation, Baltiyskaya MCC station]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Baltiyskaya MCC station
Context triple: [Moscow Central Circle, hasStation, Baltiyskaya MCC station]
  • A. Khimvolokno station
    Khimvolokno station is a stop on the Volgograd Metrotram light rail system in Volgograd, Russia.
  • B. Traktorozavodskaya station
    Traktorozavodskaya station is a stop on Volgograd’s Metrotram system serving the industrial Traktorozavodsky district of the city.
  • C. Kachinskaya station
    Kachinskaya station is a stop on the Volgograd Metrotram light rail system in Volgograd, Russia.
  • D. Yuzhnaya station
    Yuzhnaya station is a stop on the Volgograd Metrotram light rail system in Volgograd, Russia.
  • E. Yelshanka station
    Yelshanka station is a stop on the Volgograd Metrotram light rail system in Volgograd, Russia.
  • 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: Baltiyskaya MCC station
Triple: [Moscow Central Circle, hasStation, Baltiyskaya MCC station]
Generated description
Baltiyskaya MCC station is a passenger rail station on Moscow’s orbital Moscow Central Circle urban rail line.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Baltiyskaya MCC station
Target entity description: Baltiyskaya MCC station is a passenger rail station on Moscow’s orbital Moscow Central Circle urban rail line.
  • A. Khimvolokno station
    Khimvolokno station is a stop on the Volgograd Metrotram light rail system in Volgograd, Russia.
  • B. Traktorozavodskaya station
    Traktorozavodskaya station is a stop on Volgograd’s Metrotram system serving the industrial Traktorozavodsky district of the city.
  • C. Kachinskaya station
    Kachinskaya station is a stop on the Volgograd Metrotram light rail system in Volgograd, Russia.
  • D. Yuzhnaya station
    Yuzhnaya station is a stop on the Volgograd Metrotram light rail system in Volgograd, Russia.
  • E. Yelshanka station
    Yelshanka station is a stop on the Volgograd Metrotram light rail system in Volgograd, Russia.
  • 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_69a88a1c450c81909f61abb8b6863885 completed March 4, 2026, 7:38 p.m.
NER Named-entity recognition batch_69abc8c4a8bc819086892a75caac0207 completed March 7, 2026, 6:42 a.m.
NED1 Entity disambiguation (via context triple) batch_69aeb3de3d548190b3eda939fa5f72b3 completed March 9, 2026, 11:49 a.m.
NEDg Description generation batch_69aeb4b83ec48190b2852daef0767ac8 completed March 9, 2026, 11:53 a.m.
NED2 Entity disambiguation (via description) batch_69aeb57f0e90819093b096955f9cc2b5 completed March 9, 2026, 11:56 a.m.
Created at: March 4, 2026, 7:57 p.m.