Triple

T20214217
Position Surface form Disambiguated ID Type / Status
Subject Babel E493573 entity
Predicate hasChoreographer P12049 FINISHED
Object Yuriko
Yuriko was a prominent Japanese-American dancer and choreographer best known for her long association with the Martha Graham Dance Company and her influential work on Broadway.
 E1565872 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: Yuriko | Statement: [Babel, hasChoreographer, Yuriko]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Yuriko
Context triple: [Babel, hasChoreographer, Yuriko]
  • A. Yuriko
    Yuriko is the given name of Japanese actress Rinko Kikuchi, known for her roles in films such as "Babel" and "Pacific Rim."
  • B. Yuriko
    Yuriko is a creator known for developing the project or work titled "Babel."
  • C. Yukiko
    Yukiko is a character in Haruki Murakami’s novel "South of the Border, West of the Sun," serving as a figure from the protagonist’s past whose reappearance profoundly affects his adult life and relationships.
  • D. Yoriko
    Yoriko is a Japanese feminine given name commonly borne by women and girls in Japan.
  • E. Ayako
    Ayako is a Japanese feminine given name commonly used for women and girls in Japan.
  • 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: Yuriko
Triple: [Babel, hasChoreographer, Yuriko]
Generated description
Yuriko was a prominent Japanese-American dancer and choreographer best known for her long association with the Martha Graham Dance Company and her influential work on Broadway.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Yuriko
Target entity description: Yuriko was a prominent Japanese-American dancer and choreographer best known for her long association with the Martha Graham Dance Company and her influential work on Broadway.
  • A. Yuriko
    Yuriko is the given name of Japanese actress Rinko Kikuchi, known for her roles in films such as "Babel" and "Pacific Rim."
  • B. Yuriko
    Yuriko is a creator known for developing the project or work titled "Babel."
  • C. Yukiko
    Yukiko is a character in Haruki Murakami’s novel "South of the Border, West of the Sun," serving as a figure from the protagonist’s past whose reappearance profoundly affects his adult life and relationships.
  • D. Yoriko
    Yoriko is a Japanese feminine given name commonly borne by women and girls in Japan.
  • E. Ayako
    Ayako is a Japanese feminine given name commonly used for women and girls in Japan.
  • 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_69da6269614c8190bb40475d9d477358 completed April 11, 2026, 3:02 p.m.
NER Named-entity recognition batch_69e66ed8101081908e53a8bde48624b1 completed April 20, 2026, 6:22 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0bd35543848190992a934d417ac1c4 completed May 19, 2026, 3:04 a.m.
NEDg Description generation batch_6a0bdc2d1ad0819089e0cdff6f1d3266 completed May 19, 2026, 3:42 a.m.
NED2 Entity disambiguation (via description) batch_6a0bdc7fc8488190ab3250cea734c6da completed May 19, 2026, 3:43 a.m.
Created at: April 11, 2026, 11:38 p.m.