Triple

T865345
Position Surface form Disambiguated ID Type / Status
Subject Russian Railways E18688 entity
Predicate hasBrand P1500 FINISHED
Object Sapsan
Sapsan is a high-speed passenger train service in Russia operated by Russian Railways, primarily running between Moscow and St. Petersburg.
 E101288 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: Sapsan | Statement: [Russian Railways, hasBrand, Sapsan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Sapsan
Context triple: [Russian Railways, hasBrand, Sapsan]
  • A. Atossa
    Atossa was a prominent Achaemenid Persian queen, daughter of Cyrus the Great and later wife of Darius I, who played a significant role in the early Persian Empire.
  • B. Yunaska
    Yunaska is the maiden surname of Lara Trump, who is married to Eric Trump, son of former U.S. President Donald Trump.
  • C. Stempenyu
    Stempenyu is a Yiddish novel by Sholem Aleichem that portrays the life and romantic entanglements of a charismatic klezmer violinist in a Jewish shtetl.
  • D. Bezymianny
    Bezymianny is an active stratovolcano on Russia’s Kamchatka Peninsula, known for its catastrophic 1956 eruption and ongoing explosive activity.
  • E. Tsageri
    Tsageri is a small town in western Georgia that serves as an administrative and cultural center of the mountainous Racha-Lechkhumi region.
  • 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: Sapsan
Triple: [Russian Railways, hasBrand, Sapsan]
Generated description
Sapsan is a high-speed passenger train service in Russia operated by Russian Railways, primarily running between Moscow and St. Petersburg.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Sapsan
Target entity description: Sapsan is a high-speed passenger train service in Russia operated by Russian Railways, primarily running between Moscow and St. Petersburg.
  • A. Atossa
    Atossa was a prominent Achaemenid Persian queen, daughter of Cyrus the Great and later wife of Darius I, who played a significant role in the early Persian Empire.
  • B. Yunaska
    Yunaska is the maiden surname of Lara Trump, who is married to Eric Trump, son of former U.S. President Donald Trump.
  • C. Stempenyu
    Stempenyu is a Yiddish novel by Sholem Aleichem that portrays the life and romantic entanglements of a charismatic klezmer violinist in a Jewish shtetl.
  • D. Bezymianny
    Bezymianny is an active stratovolcano on Russia’s Kamchatka Peninsula, known for its catastrophic 1956 eruption and ongoing explosive activity.
  • E. Tsageri
    Tsageri is a small town in western Georgia that serves as an administrative and cultural center of the mountainous Racha-Lechkhumi region.
  • 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_69a4938ce8688190a24bdfef82ba7d21 completed March 1, 2026, 7:29 p.m.
NER Named-entity recognition batch_69a4ac6acc148190bcc00a1e939ace77 completed March 1, 2026, 9:15 p.m.
NED1 Entity disambiguation (via context triple) batch_69a7a3c7999c81908b2f27610c263b7f completed March 4, 2026, 3:15 a.m.
NEDg Description generation batch_69a7a5a452f88190ad560d33ab42d71f completed March 4, 2026, 3:23 a.m.
NED2 Entity disambiguation (via description) batch_69a7a63398f081908b63b4f79aa81f85 completed March 4, 2026, 3:25 a.m.
Created at: March 1, 2026, 7:39 p.m.