Triple

T2385987
Position Surface form Disambiguated ID Type / Status
Subject Kip E48823 entity
Predicate hasRomanticRelationshipWith P9994 FINISHED
Object Hana
Hana is a person known primarily as the romantic partner of Kip.
 E260879 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: Hana | Statement: [Kip, hasRomanticRelationshipWith, Hana]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Hana
Context triple: [Kip, hasRomanticRelationshipWith, Hana]
  • A. 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.
  • 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. 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.
  • D. Hani
    The Hani are an ethnic minority group in China, primarily known for their terraced rice farming, distinctive traditional dress, and rich folk culture in the mountainous regions of Yunnan.
  • E. Yuriko
    Yuriko is the given name of Japanese actress Rinko Kikuchi, known for her roles in films such as "Babel" and "Pacific Rim."
  • 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: Hana
Triple: [Kip, hasRomanticRelationshipWith, Hana]
Generated description
Hana is a person known primarily as the romantic partner of Kip.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Hana
Target entity description: Hana is a person known primarily as the romantic partner of Kip.
  • 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. 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.
  • D. Hani
    The Hani are an ethnic minority group in China, primarily known for their terraced rice farming, distinctive traditional dress, and rich folk culture in the mountainous regions of Yunnan.
  • E. Yuriko
    Yuriko is the given name of Japanese actress Rinko Kikuchi, known for her roles in films such as "Babel" and "Pacific Rim."
  • 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_69a88aa5f63081908d07fd302029fcbd completed March 4, 2026, 7:40 p.m.
NER Named-entity recognition batch_69abc7d9d8148190bb8aa16fd4364aba completed March 7, 2026, 6:38 a.m.
NED1 Entity disambiguation (via context triple) batch_69aea8bcb8c88190b57fd4d0a76209a5 completed March 9, 2026, 11:02 a.m.
NEDg Description generation batch_69aeaaf53f3881909901cd204e45a5a2 completed March 9, 2026, 11:11 a.m.
NED2 Entity disambiguation (via description) batch_69aeab6f9bd48190a5873527991b2ce1 completed March 9, 2026, 11:13 a.m.
Created at: March 4, 2026, 7:57 p.m.