Triple

T6104229
Position Surface form Disambiguated ID Type / Status
Subject Chūō, Tokyo, Japan E136076 entity
Predicate containsAdministrativeTerritorialEntity P747 FINISHED
Object Harumi
Harumi is a waterfront district in Tokyo’s Chūō ward known for its high-rise residential towers and role in the Tokyo 2020 Olympic and Paralympic Village.
 E569186 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: Harumi | Statement: [Chūō, Tokyo, Japan, containsAdministrativeTerritorialEntity, Harumi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Harumi
Context triple: [Chūō, Tokyo, Japan, containsAdministrativeTerritorialEntity, Harumi]
  • A. 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.
  • B. 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.
  • C. 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.
  • D. Hana
    Hana is a person known primarily as the romantic partner of Kip.
  • E. Hana
    Hana is a common female given name of Hebrew origin, often associated with meanings like "grace" or "favor."
  • 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: Harumi
Triple: [Chūō, Tokyo, Japan, containsAdministrativeTerritorialEntity, Harumi]
Generated description
Harumi is a waterfront district in Tokyo’s Chūō ward known for its high-rise residential towers and role in the Tokyo 2020 Olympic and Paralympic Village.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Harumi
Target entity description: Harumi is a waterfront district in Tokyo’s Chūō ward known for its high-rise residential towers and role in the Tokyo 2020 Olympic and Paralympic Village.
  • A. 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.
  • B. Hana
    Hana is a person known primarily as the romantic partner of Kip.
  • C. 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.
  • 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 common female given name of Hebrew origin, often associated with meanings like "grace" or "favor."
  • 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_69c0087dee9881909e3655be88208c01 completed March 22, 2026, 3:19 p.m.
NER Named-entity recognition batch_69c05b3f8e5481909e85a60aaf319f66 completed March 22, 2026, 9:12 p.m.
NED1 Entity disambiguation (via context triple) batch_69c12553f1d4819096de40514ef4d2cb completed March 23, 2026, 11:34 a.m.
NEDg Description generation batch_69c125d888cc819092b765d47f1d9f9f completed March 23, 2026, 11:36 a.m.
NED2 Entity disambiguation (via description) batch_69c126f308988190ab6cb6c79ea12877 completed March 23, 2026, 11:41 a.m.
Created at: March 22, 2026, 4:13 p.m.