Triple

T7715146
Position Surface form Disambiguated ID Type / Status
Subject 和光市 E174860 entity
Predicate hasVehicleRegistrationCode P1173 FINISHED
Object Ōmiya
Ōmiya is a district in Saitama, Japan, historically known as a post town and now a major commercial and transportation hub in the region.
 E250535 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: Ōmiya | Statement: [和光市, hasVehicleRegistrationCode, Ōmiya]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Ōmiya
Context triple: [和光市, hasVehicleRegistrationCode, Ōmiya]
  • A. Omiya
    Omiya is a major commercial and transportation hub in Saitama Prefecture, Japan, known for its busy railway station and urban center.
  • B. Toshima
    Toshima is a special ward in northwest Tokyo known for the major commercial and entertainment hub of Ikebukuro and its dense urban residential districts.
  • C. Maizuru
    Maizuru is a coastal city in northern Kyoto Prefecture, Japan, known for its natural harbor, former naval base, and role as a key repatriation port after World War II.
  • D. Akishima
    Akishima is a city in western Tokyo, Japan, known as part of the Tama area and characterized by its residential neighborhoods and light industry.
  • E. Utsunomiya
    Utsunomiya is a city in Tochigi Prefecture, Japan, known as a regional commercial center and for its specialty gyoza (dumplings).
  • 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: Ōmiya
Triple: [和光市, hasVehicleRegistrationCode, Ōmiya]
Generated description
Ōmiya is a district in Saitama, Japan, historically known as a post town and now a major commercial and transportation hub in the region.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Ōmiya
Target entity description: Ōmiya is a district in Saitama, Japan, historically known as a post town and now a major commercial and transportation hub in the region.
  • A. Omiya chosen
    Omiya is a major commercial and transportation hub in Saitama Prefecture, Japan, known for its busy railway station and urban center.
  • B. Toshima
    Toshima is a special ward in northwest Tokyo known for the major commercial and entertainment hub of Ikebukuro and its dense urban residential districts.
  • C. Maizuru
    Maizuru is a coastal city in northern Kyoto Prefecture, Japan, known for its natural harbor, former naval base, and role as a key repatriation port after World War II.
  • D. Akishima
    Akishima is a city in western Tokyo, Japan, known as part of the Tama area and characterized by its residential neighborhoods and light industry.
  • E. Utsunomiya
    Utsunomiya is a city in Tochigi Prefecture, Japan, known as a regional commercial center and for its specialty gyoza (dumplings).
  • F. None of above.

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_69c6995c463c8190a14458036249d419 completed March 27, 2026, 2:51 p.m.
NER Named-entity recognition batch_69c702cbe74081908502ac670515fa3c completed March 27, 2026, 10:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69d05422b25c819098189ac202c20123 completed April 3, 2026, 11:58 p.m.
NEDg Description generation batch_69d054c5c324819084adb6da1c76ce76 completed April 4, 2026, 12:01 a.m.
NED2 Entity disambiguation (via description) batch_69d055512f4081908d6c972e8402910b completed April 4, 2026, 12:03 a.m.
Created at: March 27, 2026, 4:04 p.m.