Triple

T1991244
Position Surface form Disambiguated ID Type / Status
Subject Zamoskvoretskaya Line E43255 entity
Predicate hasTerminusStation P15150 FINISHED
Object Alma-Atinskaya
Alma-Atinskaya is a southern terminus station of the Moscow Metro, serving as one endpoint of the Zamoskvoretskaya Line.
 E239946 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: Alma-Atinskaya | Statement: [Zamoskvoretskaya Line, hasTerminusStation, Alma-Atinskaya]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Alma-Atinskaya
Context triple: [Zamoskvoretskaya Line, hasTerminusStation, Alma-Atinskaya]
  • A. Kaspiysk
    Kaspiysk is a coastal city on the Caspian Sea in the Republic of Dagestan, Russia, known for its industrial base and strategic naval facilities.
  • B. Kazan
    Kazan is a major city in western Russia and the capital of the Republic of Tatarstan, known for its rich Tatar-Russian cultural heritage and historic Kremlin.
  • C. Karaganda
    Karaganda is a large industrial city in central Kazakhstan known for its coal mining industry and Soviet-era history.
  • D. Karshi
    Karshi is a city in southern Uzbekistan known as an important regional center for industry, agriculture, and transportation.
  • E. Sofya
    Sofya is the Russian given name of Sophia Tolstaya, the wife and muse of novelist Leo Tolstoy.
  • 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: Alma-Atinskaya
Triple: [Zamoskvoretskaya Line, hasTerminusStation, Alma-Atinskaya]
Generated description
Alma-Atinskaya is a southern terminus station of the Moscow Metro, serving as one endpoint of the Zamoskvoretskaya Line.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Alma-Atinskaya
Target entity description: Alma-Atinskaya is a southern terminus station of the Moscow Metro, serving as one endpoint of the Zamoskvoretskaya Line.
  • A. Kaspiysk
    Kaspiysk is a coastal city on the Caspian Sea in the Republic of Dagestan, Russia, known for its industrial base and strategic naval facilities.
  • B. Kazan
    Kazan is a major city in western Russia and the capital of the Republic of Tatarstan, known for its rich Tatar-Russian cultural heritage and historic Kremlin.
  • C. Karaganda
    Karaganda is a large industrial city in central Kazakhstan known for its coal mining industry and Soviet-era history.
  • D. Karshi
    Karshi is a city in southern Uzbekistan known as an important regional center for industry, agriculture, and transportation.
  • E. Sofya
    Sofya is the Russian given name of Sophia Tolstaya, the wife and muse of novelist Leo Tolstoy.
  • 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_69a88714cf2c819081644be450b8356e completed March 4, 2026, 7:25 p.m.
NER Named-entity recognition batch_69abb8451fe8819093531052f4533c36 completed March 7, 2026, 5:31 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae58bda3fc81908bfff1707e777dc1 completed March 9, 2026, 5:21 a.m.
NEDg Description generation batch_69ae59c3e0008190942715e0c44ca206 completed March 9, 2026, 5:25 a.m.
NED2 Entity disambiguation (via description) batch_69ae5a42675c8190a019034e8a6bda21 completed March 9, 2026, 5:27 a.m.
Created at: March 4, 2026, 7:37 p.m.