Triple

T6618175
Position Surface form Disambiguated ID Type / Status
Subject Shoichi Sakata E149606 entity
Predicate familyName P18 FINISHED
Object Sakata
Sakata is a Japanese surname borne by various notable individuals in fields such as physics, sports, and entertainment.
 E674242 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: Sakata | Statement: [Shoichi Sakata, familyName, Sakata]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Sakata
Context triple: [Shoichi Sakata, familyName, Sakata]
  • A. Takaishi
    Takaishi is a city in Osaka Prefecture, Japan, known as a small industrial and residential hub within the Osaka metropolitan area.
  • B. Sakae
    Sakae is a major downtown commercial and entertainment district in Nagoya, Japan, known for its shopping, nightlife, and landmark attractions.
  • C. Wakatsuki
    Wakatsuki was a Japanese destroyer of the Imperial Japanese Navy that served in World War II before being sunk during late-war Pacific naval operations.
  • D. Saito
    Saito is a Japanese surname commonly borne by notable figures in fields such as politics, sports, and the arts.
  • E. Nishiwaki
    Nishiwaki is a city in central Hyōgo Prefecture, Japan, known for its location near the geographic center of the country and its mix of industrial and rural landscapes.
  • 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: Sakata
Triple: [Shoichi Sakata, familyName, Sakata]
Generated description
Sakata is a Japanese surname borne by various notable individuals in fields such as physics, sports, and entertainment.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Sakata
Target entity description: Sakata is a Japanese surname borne by various notable individuals in fields such as physics, sports, and entertainment.
  • A. Takaishi
    Takaishi is a city in Osaka Prefecture, Japan, known as a small industrial and residential hub within the Osaka metropolitan area.
  • B. Sakae
    Sakae is a major downtown commercial and entertainment district in Nagoya, Japan, known for its shopping, nightlife, and landmark attractions.
  • C. Wakatsuki
    Wakatsuki was a Japanese destroyer of the Imperial Japanese Navy that served in World War II before being sunk during late-war Pacific naval operations.
  • D. Saito
    Saito is a Japanese surname commonly borne by notable figures in fields such as politics, sports, and the arts.
  • E. Nishiwaki
    Nishiwaki is a city in central Hyōgo Prefecture, Japan, known for its location near the geographic center of the country and its mix of industrial and rural landscapes.
  • 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_69c687ed8a9c81908bb671717cb192ef completed March 27, 2026, 1:36 p.m.
NER Named-entity recognition batch_69c6af5b21348190b7f09045e9ec7d63 completed March 27, 2026, 4:24 p.m.
NED1 Entity disambiguation (via context triple) batch_69c861218ac081909798edadae16162f completed March 28, 2026, 11:15 p.m.
NEDg Description generation batch_69c861d1255881909091426b62bfdd1e completed March 28, 2026, 11:18 p.m.
NED2 Entity disambiguation (via description) batch_69c8627e9a6c8190baf4e8bb113845f1 completed March 28, 2026, 11:21 p.m.
Created at: March 27, 2026, 1:58 p.m.