Triple

T5471659
Position Surface form Disambiguated ID Type / Status
Subject Gyumri E122847 entity
Predicate formerName P65 FINISHED
Object Leninakan
Leninakan was the Soviet-era name of Gyumri, Armenia’s second-largest city and a major cultural and industrial center.
 E523403 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: Leninakan | Statement: [Gyumri, formerName, Leninakan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Leninakan
Context triple: [Gyumri, formerName, Leninakan]
  • A. Odintsovo
    Odintsovo is a town in western Russia that serves as an important suburban center just outside Moscow.
  • B. Vorkuta
    Vorkuta is a remote Arctic city in Russia historically known as one of the largest centers of the Soviet Gulag labor camp system.
  • C. Kirovakan
    Kirovakan is the former name of Vanadzor, a major industrial city in northern Armenia.
  • 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. Komsomolskaya
    Komsomolskaya is one of Moscow Metro’s most famous and ornate stations, renowned for its grand Baroque-style decor and elaborate mosaics.
  • 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: Leninakan
Triple: [Gyumri, formerName, Leninakan]
Generated description
Leninakan was the Soviet-era name of Gyumri, Armenia’s second-largest city and a major cultural and industrial center.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Leninakan
Target entity description: Leninakan was the Soviet-era name of Gyumri, Armenia’s second-largest city and a major cultural and industrial center.
  • A. Odintsovo
    Odintsovo is a town in western Russia that serves as an important suburban center just outside Moscow.
  • B. Vorkuta
    Vorkuta is a remote Arctic city in Russia historically known as one of the largest centers of the Soviet Gulag labor camp system.
  • C. Kirovakan
    Kirovakan is the former name of Vanadzor, a major industrial city in northern Armenia.
  • 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. Komsomolskaya
    Komsomolskaya is one of Moscow Metro’s most famous and ornate stations, renowned for its grand Baroque-style decor and elaborate mosaics.
  • 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_69bd46459ff48190823377457bcf7128 completed March 20, 2026, 1:06 p.m.
NER Named-entity recognition batch_69bd921d02188190b5c1eee7205ea88e completed March 20, 2026, 6:29 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf4895b77c819089629d2296a230bc completed March 22, 2026, 1:40 a.m.
NEDg Description generation batch_69bf4c43b8bc81908b28e1b8287400c7 completed March 22, 2026, 1:56 a.m.
NED2 Entity disambiguation (via description) batch_69bf4cadc9508190a901a6fe6702f1b7 completed March 22, 2026, 1:58 a.m.
Created at: March 20, 2026, 2:09 p.m.