Triple

T12709040
Position Surface form Disambiguated ID Type / Status
Subject Yuko Tanaka E303665 entity
Predicate givenName P17 FINISHED
Object Yuko
Yuko is a common Japanese feminine given name borne by numerous notable figures in fields such as entertainment, sports, and the arts.
 E1001577 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: Yuko | Statement: [Yuko Tanaka, givenName, Yuko]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Yuko
Context triple: [Yuko Tanaka, givenName, Yuko]
  • A. Yuko
    Yuko is an alternate name for the Yukpa language, an indigenous language spoken by the Yukpa people of Colombia and Venezuela.
  • B. Yukie
    Yukie is a Japanese film featuring Ken Watanabe in a prominent role.
  • C. Yuki
    The Yuki are a Native American people indigenous to what is now Northern California, traditionally living in the upper Eel River region with distinct languages and cultural practices.
  • D. Takako
    Takako is a Japanese feminine given name borne by various notable figures in politics, arts, and entertainment.
  • E. Kyoko
    Kyoko is a mysterious, mostly silent android in the science fiction film "Ex Machina," serving as both assistant and unsettling presence within the reclusive inventor Nathan's isolated research facility.
  • 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: Yuko
Triple: [Yuko Tanaka, givenName, Yuko]
Generated description
Yuko is a common Japanese feminine given name borne by numerous notable figures in fields such as entertainment, sports, and the arts.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Yuko
Target entity description: Yuko is a common Japanese feminine given name borne by numerous notable figures in fields such as entertainment, sports, and the arts.
  • A. Yuko
    Yuko is an alternate name for the Yukpa language, an indigenous language spoken by the Yukpa people of Colombia and Venezuela.
  • B. Yukie
    Yukie is a Japanese film featuring Ken Watanabe in a prominent role.
  • C. Yuki
    The Yuki are a Native American people indigenous to what is now Northern California, traditionally living in the upper Eel River region with distinct languages and cultural practices.
  • D. Takako
    Takako is a Japanese feminine given name borne by various notable figures in politics, arts, and entertainment.
  • E. Kyoko
    Kyoko is a mysterious, mostly silent android in the science fiction film "Ex Machina," serving as both assistant and unsettling presence within the reclusive inventor Nathan's isolated research facility.
  • 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_69d7bdf084148190ab9d513dc0735af4 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d96207b2d881908314efc3e350aa78 completed April 10, 2026, 8:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69f684e43424819080659ab152caae52 completed May 2, 2026, 11:12 p.m.
NEDg Description generation batch_69f685dac5cc8190b4bc2d81186c9266 completed May 2, 2026, 11:16 p.m.
NED2 Entity disambiguation (via description) batch_69f6869156048190b548ecd04561deb8 completed May 2, 2026, 11:19 p.m.
Created at: April 9, 2026, 5:23 p.m.