Triple

T3091492
Position Surface form Disambiguated ID Type / Status
Subject Sanyo Shinkansen E64491 entity
Predicate throughServices P26302 FINISHED
Object Sakura
Sakura is a Japanese high-speed Shinkansen train service that operates mainly on the Sanyo and Kyushu Shinkansen lines.
 E328289 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: Sakura | Statement: [Sanyo Shinkansen, throughServices, Sakura]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Sakura
Context triple: [Sanyo Shinkansen, throughServices, Sakura]
  • A. Hana
    Hana is a compassionate Canadian army nurse in Michael Ondaatje's novel "The English Patient," who cares for a badly burned man in an abandoned Italian villa during World War II.
  • B. Hana
    Hana is a small, remote town on the eastern coast of Maui, Hawaii, known for its lush landscapes, waterfalls, and the scenic Road to Hana.
  • C. Hana
    Hana is a person known primarily as the romantic partner of Kip.
  • D. Miraitowa
    Miraitowa is the futuristic, blue-and-white checkered character created as the official mascot of the Tokyo 2020 Summer Olympics, symbolizing tradition, innovation, and a hopeful future.
  • E. Asaka
    Asaka is a Japanese noble family name historically associated with a collateral branch of the Imperial Family, including Prince Asaka Yasuhiko.
  • 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: Sakura
Triple: [Sanyo Shinkansen, throughServices, Sakura]
Generated description
Sakura is a Japanese high-speed Shinkansen train service that operates mainly on the Sanyo and Kyushu Shinkansen lines.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Sakura
Target entity description: Sakura is a Japanese high-speed Shinkansen train service that operates mainly on the Sanyo and Kyushu Shinkansen lines.
  • A. Hana
    Hana is a compassionate Canadian army nurse in Michael Ondaatje's novel "The English Patient," who cares for a badly burned man in an abandoned Italian villa during World War II.
  • B. Hana
    Hana is a small, remote town on the eastern coast of Maui, Hawaii, known for its lush landscapes, waterfalls, and the scenic Road to Hana.
  • C. Hana
    Hana is a person known primarily as the romantic partner of Kip.
  • D. Miraitowa
    Miraitowa is the futuristic, blue-and-white checkered character created as the official mascot of the Tokyo 2020 Summer Olympics, symbolizing tradition, innovation, and a hopeful future.
  • E. Asaka
    Asaka is a Japanese noble family name historically associated with a collateral branch of the Imperial Family, including Prince Asaka Yasuhiko.
  • 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_69ad857c97d88190b26f9b1c90839c77 completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada437c5e08190af22f6fa11cf9252 completed March 8, 2026, 4:30 p.m.
NED1 Entity disambiguation (via context triple) batch_69b20364f8ac8190898b2aef195eb85f completed March 12, 2026, 12:05 a.m.
NEDg Description generation batch_69b207d7771c8190974bba03a63295c1 completed March 12, 2026, 12:24 a.m.
NED2 Entity disambiguation (via description) batch_69b20bc77c188190aa5c412cf9a9f914 completed March 12, 2026, 12:41 a.m.
Created at: March 8, 2026, 3:03 p.m.