Triple

T7786774
Position Surface form Disambiguated ID Type / Status
Subject Lokomotiv Moscow E187265 entity
Predicate formerName P65 FINISHED
Object Stalinets
Stalinets was the former name of the Russian football club now known as Lokomotiv Moscow.
 E692840 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: Stalinets | Statement: [Lokomotiv Moscow, formerName, Stalinets]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Stalinets
Context triple: [Lokomotiv Moscow, formerName, Stalinets]
  • A. Taganana
    Taganana is a historic coastal village on Tenerife in Spain’s Canary Islands, known for its dramatic cliffs, traditional architecture, and location within the Anaga mountain range.
  • B. Chernoye Bratya
    Chernoye Bratya is a small volcanic islet in the Kuril Islands chain of Russia, located near the island of Chirpoi in the North Pacific.
  • C. Staritsa
    Staritsa is a historic town in Tver Oblast, Russia, known for its medieval monasteries and role as a regional center in the upper Volga region.
  • D. Rubtsovsk
    Rubtsovsk is an industrial city in Altai Krai, Russia, known as the birthplace of Raisa Gorbacheva and for its role as a regional agricultural and machinery center.
  • E. Klimowitschi
    Klimowitschi is a town in Belarus known in part for its international municipal partnership with Werder (Havel) in Germany.
  • 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: Stalinets
Triple: [Lokomotiv Moscow, formerName, Stalinets]
Generated description
Stalinets was the former name of the Russian football club now known as Lokomotiv Moscow.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Stalinets
Target entity description: Stalinets was the former name of the Russian football club now known as Lokomotiv Moscow.
  • A. Taganana
    Taganana is a historic coastal village on Tenerife in Spain’s Canary Islands, known for its dramatic cliffs, traditional architecture, and location within the Anaga mountain range.
  • B. Chernoye Bratya
    Chernoye Bratya is a small volcanic islet in the Kuril Islands chain of Russia, located near the island of Chirpoi in the North Pacific.
  • C. Staritsa
    Staritsa is a historic town in Tver Oblast, Russia, known for its medieval monasteries and role as a regional center in the upper Volga region.
  • D. Rubtsovsk
    Rubtsovsk is an industrial city in Altai Krai, Russia, known as the birthplace of Raisa Gorbacheva and for its role as a regional agricultural and machinery center.
  • E. Klimowitschi
    Klimowitschi is a town in Belarus known in part for its international municipal partnership with Werder (Havel) in Germany.
  • 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_69ca82af2d2c8190963861f5e0b8bf21 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cadf2462248190863f838f0e077923 completed March 30, 2026, 8:37 p.m.
NED1 Entity disambiguation (via context triple) batch_69caf6123ad48190a50339073e91748c completed March 30, 2026, 10:15 p.m.
NEDg Description generation batch_69caf81ff934819094f8089b0bd8dde7 completed March 30, 2026, 10:24 p.m.
NED2 Entity disambiguation (via description) batch_69cafa512e748190a52fe462d3d59f06 completed March 30, 2026, 10:33 p.m.
Created at: March 30, 2026, 4:24 p.m.