Triple

T13155256
Position Surface form Disambiguated ID Type / Status
Subject Kaluzhsko–Rizhskaya Line E312566 entity
Predicate hasStation P35 FINISHED
Object Yasenevo
Yasenevo is a Moscow Metro station serving the Yasenevo District in the south-western part of Moscow, Russia.
 E1056815 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: Yasenevo | Statement: [Kaluzhsko–Rizhskaya Line, hasStation, Yasenevo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Yasenevo
Context triple: [Kaluzhsko–Rizhskaya Line, hasStation, Yasenevo]
  • A. Yuzovka
    Yuzovka was the original name of the industrial settlement in eastern Ukraine that later developed into the city of Donetsk.
  • B. Rumyantsevo
    Rumyantsevo is a Moscow Metro station serving the southwestern part of the city near the Troparevo-Nikulino area.
  • C. Yuryev
    Yuryev is a historical name for the Estonian city now known as Tartu, reflecting its past under various regional powers.
  • D. Yelizovo
    Yelizovo is a town on Russia’s Kamchatka Peninsula that functions as a key regional hub and gateway to the area’s volcanic and natural attractions.
  • E. Yartsevo
    Yartsevo is a town in western Russia known as an industrial center within Smolensk Oblast.
  • 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: Yasenevo
Triple: [Kaluzhsko–Rizhskaya Line, hasStation, Yasenevo]
Generated description
Yasenevo is a Moscow Metro station serving the Yasenevo District in the south-western part of Moscow, Russia.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Yasenevo
Target entity description: Yasenevo is a Moscow Metro station serving the Yasenevo District in the south-western part of Moscow, Russia.
  • A. Yuzovka
    Yuzovka was the original name of the industrial settlement in eastern Ukraine that later developed into the city of Donetsk.
  • B. Rumyantsevo
    Rumyantsevo is a Moscow Metro station serving the southwestern part of the city near the Troparevo-Nikulino area.
  • C. Yuryev
    Yuryev is a historical name for the Estonian city now known as Tartu, reflecting its past under various regional powers.
  • D. Yelizovo
    Yelizovo is a town on Russia’s Kamchatka Peninsula that functions as a key regional hub and gateway to the area’s volcanic and natural attractions.
  • E. Yartsevo
    Yartsevo is a town in western Russia known as an industrial center within Smolensk Oblast.
  • 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_69d806aabde48190899e13e41659cae5 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d98c06ccb881909390df18e1a6f7ed completed April 10, 2026, 11:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69f79d3398b08190a0fc4b6044576e0a completed May 3, 2026, 7:08 p.m.
NEDg Description generation batch_69f79e77b5e88190a85f4061c8abb8a4 completed May 3, 2026, 7:13 p.m.
NED2 Entity disambiguation (via description) batch_69f79fa5249481909b2c046ed9801371 completed May 3, 2026, 7:19 p.m.
Created at: April 9, 2026, 9:12 p.m.