Triple

T3070342
Position Surface form Disambiguated ID Type / Status
Subject Yokohama DeNA BayStars E64003 entity
Predicate hasMascot P52 FINISHED
Object DB. Kirara
DB. Kirara is a cheerleader-style female mascot character for the Yokohama DeNA BayStars professional baseball team in Japan.
 E324140 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: DB. Kirara | Statement: [Yokohama DeNA BayStars, hasMascot, DB. Kirara]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: DB. Kirara
Context triple: [Yokohama DeNA BayStars, hasMascot, DB. Kirara]
  • A. Garai
    Garai is a surname most notably associated with English actress and director Romola Garai.
  • B. Hikari
    Hikari is a high-speed Shinkansen train service in Japan that operates on the Tokaido and Sanyo Shinkansen lines, offering fast intercity travel with fewer stops than local services.
  • C. Shinkiari
    Shinkiari is a town in Pakistan’s Khyber Pakhtunkhwa province, known for its agricultural surroundings and its location along the Karakoram Highway near Mansehra.
  • D. Hana
    Hana is a person known primarily as the romantic partner of Kip.
  • E. 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.
  • 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: DB. Kirara
Triple: [Yokohama DeNA BayStars, hasMascot, DB. Kirara]
Generated description
DB. Kirara is a cheerleader-style female mascot character for the Yokohama DeNA BayStars professional baseball team in Japan.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: DB. Kirara
Target entity description: DB. Kirara is a cheerleader-style female mascot character for the Yokohama DeNA BayStars professional baseball team in Japan.
  • A. Garai
    Garai is a surname most notably associated with English actress and director Romola Garai.
  • B. Hikari
    Hikari is a high-speed Shinkansen train service in Japan that operates on the Tokaido and Sanyo Shinkansen lines, offering fast intercity travel with fewer stops than local services.
  • C. Shinkiari
    Shinkiari is a town in Pakistan’s Khyber Pakhtunkhwa province, known for its agricultural surroundings and its location along the Karakoram Highway near Mansehra.
  • D. Hana
    Hana is a person known primarily as the romantic partner of Kip.
  • E. 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.
  • 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_69ad857a8aec8190bfdfd9c14554ac5a completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada100f0b8819095da366fdc6803a8 completed March 8, 2026, 4:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69b1f87f6a3881908ae313f62ff13159 completed March 11, 2026, 11:19 p.m.
NEDg Description generation batch_69b1f908ee3081909126bc3797658fca completed March 11, 2026, 11:21 p.m.
NED2 Entity disambiguation (via description) batch_69b1f99f7aec8190bb35ef4ddf64735c completed March 11, 2026, 11:24 p.m.
Created at: March 8, 2026, 3:02 p.m.