Triple

T3803508
Position Surface form Disambiguated ID Type / Status
Subject Hadano E91746 entity
Predicate hasNeighboringMunicipality P224 FINISHED
Object Ōi
Ōi is a town in Kanagawa Prefecture, Japan, known for its residential communities and proximity to the city of Hadano.
 E391132 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: Ōi | Statement: [Hadano, hasNeighboringMunicipality, Ōi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Ōi
Context triple: [Hadano, hasNeighboringMunicipality, Ōi]
  • A. Oyugis
    Oyugis is a town in western Kenya that serves as a key commercial and administrative center in the former Rachuonyo District of Homa Bay County.
  • B. Ihi
    Ihi is an ancient Egyptian child god linked to music and joy, often depicted playing the sistrum and associated with the goddess Hathor.
  • C. Ōiso
    Ōiso is a coastal town in Kanagawa Prefecture, Japan, known as a historic seaside resort and former political retreat.
  • D. Ōhira
    Ōhira is a Japanese surname most notably associated with Masayoshi Ōhira, a former Prime Minister of Japan.
  • E. Onikan
    Onikan is a historic neighborhood on Lagos Island in Lagos, Nigeria, known for its cultural landmarks, sports and event venues, and proximity to the city’s central business and administrative districts.
  • 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: Ōi
Triple: [Hadano, hasNeighboringMunicipality, Ōi]
Generated description
Ōi is a town in Kanagawa Prefecture, Japan, known for its residential communities and proximity to the city of Hadano.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Ōi
Target entity description: Ōi is a town in Kanagawa Prefecture, Japan, known for its residential communities and proximity to the city of Hadano.
  • A. Oyugis
    Oyugis is a town in western Kenya that serves as a key commercial and administrative center in the former Rachuonyo District of Homa Bay County.
  • B. Ihi
    Ihi is an ancient Egyptian child god linked to music and joy, often depicted playing the sistrum and associated with the goddess Hathor.
  • C. Ōiso
    Ōiso is a coastal town in Kanagawa Prefecture, Japan, known as a historic seaside resort and former political retreat.
  • D. Ōhira
    Ōhira is a Japanese surname most notably associated with Masayoshi Ōhira, a former Prime Minister of Japan.
  • E. Onikan
    Onikan is a historic neighborhood on Lagos Island in Lagos, Nigeria, known for its cultural landmarks, sports and event venues, and proximity to the city’s central business and administrative districts.
  • 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_69aed96354f48190a768966d6bd19b04 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aee7bacf2881908198a77063d15d16 completed March 9, 2026, 3:31 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4fb270b008190bbd87cbddacb7204 completed March 14, 2026, 6:07 a.m.
NEDg Description generation batch_69b4fcb41dac8190ad127d0c23bef877 completed March 14, 2026, 6:14 a.m.
NED2 Entity disambiguation (via description) batch_69b4fdd7201c819087e710ad64216fc5 completed March 14, 2026, 6:19 a.m.
Created at: March 9, 2026, 3:15 p.m.