Triple

T13297172
Position Surface form Disambiguated ID Type / Status
Subject Ayumi Ito E316714 entity
Predicate notableWork P4 FINISHED
Object Hana and Alice
Hana and Alice is a Japanese coming-of-age film that explores the complex friendship and emotional lives of two teenage girls.
 E1032669 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 and Alice | Statement: [Ayumi Ito, notableWork, Hana and Alice]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Hana and Alice
Context triple: [Ayumi Ito, notableWork, Hana and Alice]
  • A. Hana
    Hana is a common female given name of Hebrew origin, often associated with meanings like "grace" or "favor."
  • 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 Japanese restaurant located within Tokyo Disney Resort’s Disney Ambassador Hotel, offering themed dining to hotel guests and park visitors.
  • 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 and Alice
Triple: [Ayumi Ito, notableWork, Hana and Alice]
Generated description
Hana and Alice is a Japanese coming-of-age film that explores the complex friendship and emotional lives of two teenage girls.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Hana and Alice
Target entity description: Hana and Alice is a Japanese coming-of-age film that explores the complex friendship and emotional lives of two teenage girls.
  • 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 common female given name of Hebrew origin, often associated with meanings like "grace" or "favor."
  • C. Hana
    Hana is a person known primarily as the romantic partner of Kip.
  • 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 Japanese restaurant located within Tokyo Disney Resort’s Disney Ambassador Hotel, offering themed dining to hotel guests and park visitors.
  • 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_69d806b40ab4819094adf6c374f4811a completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d990a2f2708190a8f2aa7e7c0b92d2 completed April 11, 2026, 12:06 a.m.
NED1 Entity disambiguation (via context triple) batch_69f716dad3648190bf360955fbdfb2f0 completed May 3, 2026, 9:35 a.m.
NEDg Description generation batch_69f7177e07508190b46e6a12f09e7986 completed May 3, 2026, 9:38 a.m.
NED2 Entity disambiguation (via description) batch_69f717e72b988190927b628022bcbf12 completed May 3, 2026, 9:39 a.m.
Created at: April 9, 2026, 9:28 p.m.