Triple

T9726186
Position Surface form Disambiguated ID Type / Status
Subject Tsaritsyno E235617 entity
Predicate formerName P65 FINISHED
Object Lenino
Lenino was the former name of the Tsaritsyno district in Moscow, Russia, used during the Soviet era before the area reverted to its historical designation.
 E816286 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: Lenino | Statement: [Tsaritsyno, formerName, Lenino]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lenino
Context triple: [Tsaritsyno, formerName, Lenino]
  • A. Lenino
    Lenino is a village in eastern Belarus best known as the site of a major World War II battle between Soviet and Polish forces against Nazi Germany.
  • B. Gus-Khrustalny
    Gus-Khrustalny is a town in western Russia known for its historic glass-making industry and status as a local industrial center.
  • C. Chruściel
    Chruściel is a Polish surname most notably borne by Antoni Chruściel, a Polish military officer and leader in the Warsaw Uprising during World War II.
  • D. Godunov
    Godunov is a Russian surname most famously associated with Boris Godunov, the tsar who ruled Russia at the turn of the 17th century.
  • E. Tomsky
    Tomsky is a character in Alexander Pushkin’s novella "The Queen of Spades," known for recounting the mysterious tale of a secret winning card formula that drives the plot.
  • 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: Lenino
Triple: [Tsaritsyno, formerName, Lenino]
Generated description
Lenino was the former name of the Tsaritsyno district in Moscow, Russia, used during the Soviet era before the area reverted to its historical designation.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Lenino
Target entity description: Lenino was the former name of the Tsaritsyno district in Moscow, Russia, used during the Soviet era before the area reverted to its historical designation.
  • A. Lenino
    Lenino is a village in eastern Belarus best known as the site of a major World War II battle between Soviet and Polish forces against Nazi Germany.
  • B. Gus-Khrustalny
    Gus-Khrustalny is a town in western Russia known for its historic glass-making industry and status as a local industrial center.
  • C. Chruściel
    Chruściel is a Polish surname most notably borne by Antoni Chruściel, a Polish military officer and leader in the Warsaw Uprising during World War II.
  • D. Godunov
    Godunov is a Russian surname most famously associated with Boris Godunov, the tsar who ruled Russia at the turn of the 17th century.
  • E. Tomsky
    Tomsky is a character in Alexander Pushkin’s novella "The Queen of Spades," known for recounting the mysterious tale of a secret winning card formula that drives the plot.
  • 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_69ca84d0fad481909cdd45aa77416c48 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cd9e798c348190885b79d7dffc9d8d completed April 1, 2026, 10:38 p.m.
NED1 Entity disambiguation (via context triple) batch_69d19fadc2948190b405fb7f249b82aa completed April 4, 2026, 11:33 p.m.
NEDg Description generation batch_69d1a0988f00819094b5fe6e4d328688 completed April 4, 2026, 11:36 p.m.
NED2 Entity disambiguation (via description) batch_69d1a0f811fc8190b6a46a0441159089 completed April 4, 2026, 11:38 p.m.
Created at: March 30, 2026, 8:21 p.m.