Triple

T9887875
Position Surface form Disambiguated ID Type / Status
Subject Edogawa E181380 entity
Predicate hasFacility P105 FINISHED
Object Edogawa City Hall
Edogawa City Hall is the main municipal government building and administrative center serving Tokyo’s Edogawa ward.
 E827448 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: Edogawa City Hall | Statement: [Edogawa, hasFacility, Edogawa City Hall]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Edogawa City Hall
Context triple: [Edogawa, hasFacility, Edogawa City Hall]
  • A. Hadano City Hall
    Hadano City Hall is the main municipal government building and administrative center serving the city of Hadano in Kanagawa Prefecture, Japan.
  • B. Mishima City Hall
    Mishima City Hall is the main municipal government building and administrative center serving the city of Mishima in Shizuoka Prefecture, Japan.
  • C. Wako City Hall
    Wako City Hall is the main municipal government building and administrative center serving the city of Wako in Saitama Prefecture, Japan.
  • D. Hamura City Hall
    Hamura City Hall is the main municipal government building and administrative center serving the city of Hamura in Tokyo, Japan.
  • E. Hachiōji City Hall
    Hachiōji City Hall is the main municipal government building and administrative center serving the city of Hachiōji in Tokyo, Japan.
  • 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: Edogawa City Hall
Triple: [Edogawa, hasFacility, Edogawa City Hall]
Generated description
Edogawa City Hall is the main municipal government building and administrative center serving Tokyo’s Edogawa ward.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Edogawa City Hall
Target entity description: Edogawa City Hall is the main municipal government building and administrative center serving Tokyo’s Edogawa ward.
  • A. Hadano City Hall
    Hadano City Hall is the main municipal government building and administrative center serving the city of Hadano in Kanagawa Prefecture, Japan.
  • B. Mishima City Hall
    Mishima City Hall is the main municipal government building and administrative center serving the city of Mishima in Shizuoka Prefecture, Japan.
  • C. Wako City Hall
    Wako City Hall is the main municipal government building and administrative center serving the city of Wako in Saitama Prefecture, Japan.
  • D. Hamura City Hall
    Hamura City Hall is the main municipal government building and administrative center serving the city of Hamura in Tokyo, Japan.
  • E. Hachiōji City Hall
    Hachiōji City Hall is the main municipal government building and administrative center serving the city of Hachiōji in Tokyo, Japan.
  • 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_69ca8283a6708190801af7a25a7ebb9f completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69cdb4592db881909b134834dde614d8 completed April 2, 2026, 12:12 a.m.
NED1 Entity disambiguation (via context triple) batch_69d1eb0314108190b35906592a11727e completed April 5, 2026, 4:54 a.m.
NEDg Description generation batch_69d1ebec25508190ac4c0adb629f79b0 completed April 5, 2026, 4:58 a.m.
NED2 Entity disambiguation (via description) batch_69d1ec65d5e881909e4caa180b0f8867 completed April 5, 2026, 5 a.m.
Created at: March 30, 2026, 8:39 p.m.