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.