Triple

T4446953
Position Surface form Disambiguated ID Type / Status
Subject Daijō-daijin E96311 entity
Predicate hasHigherRankThan P4476 FINISHED
Object Udaijin
Udaijin was a high-ranking ministerial post in Japan’s historical imperial court, typically serving as one of the chief advisors and administrators directly beneath the top chancellor.
 E442496 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: Udaijin | Statement: [Daijō-daijin, hasHigherRankThan, Udaijin]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Udaijin
Context triple: [Daijō-daijin, hasHigherRankThan, Udaijin]
  • A. Seishirō
    Seishirō is a Japanese given name commonly used for male individuals.
  • B. Keiyo
    Keiyo is a Southern Nilotic language spoken primarily by the Keiyo people of Kenya’s Rift Valley region.
  • C. Takayoshi
    Takayoshi is a Japanese given name notably borne by Kido Takayoshi, a key samurai and statesman of the Meiji Restoration.
  • D. Michinaga
    Michinaga is the given name of Fujiwara no Michinaga, a powerful Heian-period Japanese court noble who dominated imperial politics at the height of the Fujiwara clan’s influence.
  • E. Arinori
    Arinori was a given name of Mori Arinori, a prominent Meiji-era Japanese statesman and reformer known for modernizing Japan’s education system.
  • 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: Udaijin
Triple: [Daijō-daijin, hasHigherRankThan, Udaijin]
Generated description
Udaijin was a high-ranking ministerial post in Japan’s historical imperial court, typically serving as one of the chief advisors and administrators directly beneath the top chancellor.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Udaijin
Target entity description: Udaijin was a high-ranking ministerial post in Japan’s historical imperial court, typically serving as one of the chief advisors and administrators directly beneath the top chancellor.
  • A. Seishirō
    Seishirō is a Japanese given name commonly used for male individuals.
  • B. Keiyo
    Keiyo is a Southern Nilotic language spoken primarily by the Keiyo people of Kenya’s Rift Valley region.
  • C. Takayoshi
    Takayoshi is a Japanese given name notably borne by Kido Takayoshi, a key samurai and statesman of the Meiji Restoration.
  • D. Michinaga
    Michinaga is the given name of Fujiwara no Michinaga, a powerful Heian-period Japanese court noble who dominated imperial politics at the height of the Fujiwara clan’s influence.
  • E. Arinori
    Arinori was a given name of Mori Arinori, a prominent Meiji-era Japanese statesman and reformer known for modernizing Japan’s education system.
  • 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_69b345415ba481908df738e7174448ba completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b355d31e10819086590b9f828d50b0 completed March 13, 2026, 12:09 a.m.
NED1 Entity disambiguation (via context triple) batch_69b62818295481909c0ffa377570effc completed March 15, 2026, 3:31 a.m.
NEDg Description generation batch_69b628ffed1c819097d048712e9aafef completed March 15, 2026, 3:35 a.m.
NED2 Entity disambiguation (via description) batch_69b6298d1ff88190a58a6fc5992ef864 completed March 15, 2026, 3:37 a.m.
Created at: March 12, 2026, 11:32 p.m.