Triple

T1155739
Position Surface form Disambiguated ID Type / Status
Subject Emperor Meiji E23778 entity
Predicate spouse P13 FINISHED
Object Haruko
Haruko, better known as Empress Shōken, was the consort of Emperor Meiji and a prominent Japanese empress noted for her support of modernization and social welfare.
 E156880 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: Haruko | Statement: [Emperor Meiji, spouse, Haruko]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Haruko
Context triple: [Emperor Meiji, spouse, Haruko]
  • A. Atsuko
    Atsuko is a Japanese feminine given name commonly borne by women and princesses in Japan, with meanings that vary depending on the kanji used.
  • B. Yuriko
    Yuriko is the given name of Japanese actress Rinko Kikuchi, known for her roles in films such as "Babel" and "Pacific Rim."
  • C. Kazuko
    Kazuko is a Japanese feminine given name commonly borne by women, including members of the imperial family.
  • D. 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.
  • E. 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.
  • 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: Haruko
Triple: [Emperor Meiji, spouse, Haruko]
Generated description
Haruko, better known as Empress Shōken, was the consort of Emperor Meiji and a prominent Japanese empress noted for her support of modernization and social welfare.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Haruko
Target entity description: Haruko, better known as Empress Shōken, was the consort of Emperor Meiji and a prominent Japanese empress noted for her support of modernization and social welfare.
  • A. Atsuko
    Atsuko is a Japanese feminine given name commonly borne by women and princesses in Japan, with meanings that vary depending on the kanji used.
  • B. Yuriko
    Yuriko is the given name of Japanese actress Rinko Kikuchi, known for her roles in films such as "Babel" and "Pacific Rim."
  • C. Kazuko
    Kazuko is a Japanese feminine given name commonly borne by women, including members of the imperial family.
  • D. 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.
  • E. 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.
  • 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_69a493f0d32c8190ac74bad3c87f2641 completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4bc912300819084c9c69783055a70 completed March 1, 2026, 10:24 p.m.
NED1 Entity disambiguation (via context triple) batch_69acce57fbe081908a2060344c19141d completed March 8, 2026, 1:18 a.m.
NEDg Description generation batch_69acd06d000481909f6d934e857236f0 completed March 8, 2026, 1:27 a.m.
NED2 Entity disambiguation (via description) batch_69acd17b8c508190812b241d7906992b completed March 8, 2026, 1:31 a.m.
Created at: March 1, 2026, 7:44 p.m.