Triple

T13572108
Position Surface form Disambiguated ID Type / Status
Subject Gotō Keita E324188 entity
Predicate givenName P17 FINISHED
Object Keita
Keita is a Japanese given name commonly used for males and borne by various notable figures in fields such as sports and entertainment.
 E1048228 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: Keita | Statement: [Gotō Keita, givenName, Keita]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Keita
Context triple: [Gotō Keita, givenName, Keita]
  • A. Keita Goto
    Keita Goto was a prominent Japanese businessman best known as the founder and longtime leader of the Tokyu Group, a major private railway and retail conglomerate centered in the Tokyo area.
  • B. Takahito
    Takahito, better known by his title Prince Mikasa, was a member of the Japanese imperial family and the youngest son of Emperor Taishō.
  • C. Takatoshi
    Takatoshi is a masculine Japanese given name that can be written with various kanji combinations and is borne by multiple notable individuals in Japan.
  • D. Taisuke
    Taisuke is a Japanese given name notably borne by historical figures such as the Meiji-era politician Itagaki Taisuke.
  • E. Mako Kamitsuna
    Mako Kamitsuna is a Japanese-born film editor and filmmaker known for her work on critically acclaimed independent films, including the period drama "Mudbound."
  • 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: Keita
Triple: [Gotō Keita, givenName, Keita]
Generated description
Keita is a Japanese given name commonly used for males and borne by various notable figures in fields such as sports and entertainment.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Keita
Target entity description: Keita is a Japanese given name commonly used for males and borne by various notable figures in fields such as sports and entertainment.
  • A. Keita Goto
    Keita Goto was a prominent Japanese businessman best known as the founder and longtime leader of the Tokyu Group, a major private railway and retail conglomerate centered in the Tokyo area.
  • B. Takahito
    Takahito, better known by his title Prince Mikasa, was a member of the Japanese imperial family and the youngest son of Emperor Taishō.
  • C. Takatoshi
    Takatoshi is a masculine Japanese given name that can be written with various kanji combinations and is borne by multiple notable individuals in Japan.
  • D. Taisuke
    Taisuke is a Japanese given name notably borne by historical figures such as the Meiji-era politician Itagaki Taisuke.
  • E. Mako Kamitsuna
    Mako Kamitsuna is a Japanese-born film editor and filmmaker known for her work on critically acclaimed independent films, including the period drama "Mudbound."
  • 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_69d80769100c819099111274614f5ed2 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbb0106cb48190b20eb9bda131a68a completed April 12, 2026, 2:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69f76bb827d48190958e5710d554cd04 completed May 3, 2026, 3:37 p.m.
NEDg Description generation batch_69f77641e5308190a75bcffeb9bfd7b4 completed May 3, 2026, 4:22 p.m.
NED2 Entity disambiguation (via description) batch_69f7791add908190af69b23a54eb7560 completed May 3, 2026, 4:34 p.m.
Created at: April 9, 2026, 9:48 p.m.