Triple

T17326214
Position Surface form Disambiguated ID Type / Status
Subject 西行 E420692 entity
Predicate 影響を与えた人物 P38337 FINISHED
Object 藤原定家
藤原定家は、『新古今和歌集』の撰者として知られる鎌倉時代初期の公家・歌人であり、和歌理論と美意識に大きな影響を与えた人物である。
E1263498 NE FINISHED

How this triple was built (5 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: 藤原定家 | Statement: [西行, 影響を与えた人物, 藤原定家]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: 藤原定家
Context triple: [西行, 影響を与えた人物, 藤原定家]
  • A. 曽禰荒助
    曽禰荒助は、明治期の日本において外務大臣や朝鮮統治に関わる要職を歴任した政治家・外交官である。
  • B. 藤原道長
    藤原道長は、平安時代中期に摂関政治の最盛期を築き上げた藤原氏の有力貴族であり、娘たちを天皇の后に立てることで絶大な権勢を振るった人物である。
  • C. 内藤多仲
    内藤多仲 was a pioneering Japanese structural engineer renowned as the “father of Japanese skyscrapers” for designing many of the country’s early high-rise and tower structures, including several iconic broadcasting towers.
  • D. 最澄
    最澄は、平安時代初期に比叡山延暦寺を拠点として日本天台宗を開いた僧で、日本仏教史に大きな影響を与えた高僧である。
  • E. 井上馨
    井上馨は、明治時代に外務大臣や内務大臣などを歴任し、近代日本の外交・内政の基盤づくりに大きな影響を与えた政治家・元長州藩士である。
  • 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: 藤原定家
Triple: [西行, 影響を与えた人物, 藤原定家]
Generated description
藤原定家は、『新古今和歌集』の撰者として知られる鎌倉時代初期の公家・歌人であり、和歌理論と美意識に大きな影響を与えた人物である。
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: 藤原定家
Target entity description: 藤原定家は、『新古今和歌集』の撰者として知られる鎌倉時代初期の公家・歌人であり、和歌理論と美意識に大きな影響を与えた人物である。
  • A. 曽禰荒助
    曽禰荒助は、明治期の日本において外務大臣や朝鮮統治に関わる要職を歴任した政治家・外交官である。
  • B. 藤原道長
    藤原道長は、平安時代中期に摂関政治の最盛期を築き上げた藤原氏の有力貴族であり、娘たちを天皇の后に立てることで絶大な権勢を振るった人物である。
  • C. 内藤多仲
    内藤多仲 was a pioneering Japanese structural engineer renowned as the “father of Japanese skyscrapers” for designing many of the country’s early high-rise and tower structures, including several iconic broadcasting towers.
  • D. 最澄
    最澄は、平安時代初期に比叡山延暦寺を拠点として日本天台宗を開いた僧で、日本仏教史に大きな影響を与えた高僧である。
  • E. 井上馨
    井上馨は、明治時代に外務大臣や内務大臣などを歴任し、近代日本の外交・内政の基盤づくりに大きな影響を与えた政治家・元長州藩士である。
  • F. None of above. chosen
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: 影響を与えた人物
Context triple: [西行, 影響を与えた人物, 藤原定家]
  • A. influencedPerson chosen
    Indicates that one entity has affected, shaped, or guided the thoughts, behavior, or development of another person.
  • B. hadInfluenceOn
    Indicates that one entity affected, shaped, or contributed to the development, behavior, or characteristics of another entity.
  • C. hasEnduringInfluenceOn
    Indicates that one entity exerts a lasting, long-term impact on another entity’s state, development, or behavior.
  • D. wereInfluencedBy
    Indicates that one entity’s ideas, actions, or characteristics were shaped or affected by another entity.
  • E. influencedNameOf
    Indicates that one entity has affected or shaped the naming or choice of name of another entity.
  • F. None of above.

Provenance (6 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_69d889d3adc881909319f1edb8d2a956 completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e439d24e548190a766dd246a4d63d4 completed April 19, 2026, 2:11 a.m.
NED1 Entity disambiguation (via context triple) batch_6a018c4c2dc08190b60982abc9ac7c9c completed May 11, 2026, 7:59 a.m.
NEDg Description generation batch_6a018f2358b481908226aa84a7bd9d7f completed May 11, 2026, 8:11 a.m.
NED2 Entity disambiguation (via description) batch_6a0190125abc8190ad4d1f500e3513b3 completed May 11, 2026, 8:15 a.m.
PD Predicate disambiguation batch_69e3b021a5bc81909ae55406f9d0b37f completed April 18, 2026, 4:24 p.m.
Created at: April 10, 2026, 5:43 a.m.