Triple

T8919248
Position Surface form Disambiguated ID Type / Status
Subject Suzuki E212368 entity
Predicate distinguishedFrom P1612 FINISHED
Object Suzuki (given name)
Suzuki is a Japanese given name that can be used for people of any gender, though it is far less common than its use as a surname.
 E766465 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: Suzuki (given name) | Statement: [Suzuki, distinguishedFrom, Suzuki (given name)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Suzuki (given name)
Context triple: [Suzuki, distinguishedFrom, Suzuki (given name)]
  • A. Kantarō Suzuki
    Kantarō Suzuki was a Japanese admiral and statesman who served as prime minister during the final months of World War II and oversaw Japan’s decision to surrender.
  • B. Hiroaki
    Hiroaki is a Japanese masculine given name that can be written with various kanji combinations and is borne by numerous notable figures in fields such as politics, sports, and the arts.
  • C. Daisuke
    Daisuke is a common Japanese masculine given name used by various notable figures in entertainment, sports, and other fields.
  • D. Shuji
    Shuji is a Japanese given name most notably borne by Nobel Prize–winning physicist Shuji Nakamura.
  • E. Hiroshi
    Hiroshi is a Japanese given name commonly used for males and borne by numerous notable figures in fields such as art, politics, and entertainment.
  • 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: Suzuki (given name)
Triple: [Suzuki, distinguishedFrom, Suzuki (given name)]
Generated description
Suzuki is a Japanese given name that can be used for people of any gender, though it is far less common than its use as a surname.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Suzuki (given name)
Target entity description: Suzuki is a Japanese given name that can be used for people of any gender, though it is far less common than its use as a surname.
  • A. Kantarō Suzuki
    Kantarō Suzuki was a Japanese admiral and statesman who served as prime minister during the final months of World War II and oversaw Japan’s decision to surrender.
  • B. Hiroaki
    Hiroaki is a Japanese masculine given name that can be written with various kanji combinations and is borne by numerous notable figures in fields such as politics, sports, and the arts.
  • C. Daisuke
    Daisuke is a common Japanese masculine given name used by various notable figures in entertainment, sports, and other fields.
  • D. Shuji
    Shuji is a Japanese given name most notably borne by Nobel Prize–winning physicist Shuji Nakamura.
  • E. Hiroshi
    Hiroshi is a Japanese given name commonly used for males and borne by numerous notable figures in fields such as art, politics, and entertainment.
  • 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_69ca8393b1808190bd4336787ffa2c40 completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc6613639881909090d060f388a865 completed April 1, 2026, 12:25 a.m.
NED1 Entity disambiguation (via context triple) batch_69cfba49d65c8190b9d9908822198cc0 completed April 3, 2026, 1:02 p.m.
NEDg Description generation batch_69cfbb35869c8190a75ddf6627667623 completed April 3, 2026, 1:05 p.m.
NED2 Entity disambiguation (via description) batch_69cfbb9585688190b3aa3d817bafba51 completed April 3, 2026, 1:07 p.m.
Created at: March 30, 2026, 6:56 p.m.