Triple

T9724073
Position Surface form Disambiguated ID Type / Status
Subject Kiyosumi-Shirakawa E235555 entity
Predicate near P350 FINISHED
Object Kiba
Kiba is a district in Tokyo’s Kōtō ward known for its history as a lumberyard area and its large urban green space, Kiba Park.
 E817388 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: Kiba | Statement: [Kiyosumi-Shirakawa, near, Kiba]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Kiba
Context triple: [Kiyosumi-Shirakawa, near, Kiba]
  • A. Ryūō
    Ryūō is a town in Shiga Prefecture, Japan, known for its location near Lake Biwa and its blend of rural landscapes with growing commercial development.
  • B. Shinya
    Shinya is a Japanese given name commonly used for males.
  • C. Takehiro
    Takehiro is a central character in Ryūnosuke Akutagawa’s short story “In a Grove,” whose ambiguous fate is revealed through conflicting eyewitness testimonies.
  • D. Kinsaku
    Kinsaku is the birth name of Matsuo Bashō, the renowned 17th-century Japanese haiku poet and literary figure.
  • E. Ichirō
    Ichirō is a common Japanese masculine given name that can be written with various kanji and is often associated with first-born sons.
  • 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: Kiba
Triple: [Kiyosumi-Shirakawa, near, Kiba]
Generated description
Kiba is a district in Tokyo’s Kōtō ward known for its history as a lumberyard area and its large urban green space, Kiba Park.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Kiba
Target entity description: Kiba is a district in Tokyo’s Kōtō ward known for its history as a lumberyard area and its large urban green space, Kiba Park.
  • A. Ryūō
    Ryūō is a town in Shiga Prefecture, Japan, known for its location near Lake Biwa and its blend of rural landscapes with growing commercial development.
  • B. Shinya
    Shinya is a Japanese given name commonly used for males.
  • C. Takehiro
    Takehiro is a central character in Ryūnosuke Akutagawa’s short story “In a Grove,” whose ambiguous fate is revealed through conflicting eyewitness testimonies.
  • D. Kinsaku
    Kinsaku is the birth name of Matsuo Bashō, the renowned 17th-century Japanese haiku poet and literary figure.
  • E. Ichirō
    Ichirō is a common Japanese masculine given name that can be written with various kanji and is often associated with first-born sons.
  • 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_69ca84d0123c819096f9dc3b6abb0881 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cd9e77096481908ffd315fecb1d5ec completed April 1, 2026, 10:38 p.m.
NED1 Entity disambiguation (via context triple) batch_69d19faa064081909c1d23044984a17c completed April 4, 2026, 11:32 p.m.
NEDg Description generation batch_69d1a3cc5420819091ee338da5afe4b7 completed April 4, 2026, 11:50 p.m.
NED2 Entity disambiguation (via description) batch_69d1a5f265148190af432e3640221a33 completed April 4, 2026, 11:59 p.m.
Created at: March 30, 2026, 8:21 p.m.