Triple

T1058135
Position Surface form Disambiguated ID Type / Status
Subject Kyoto Prefecture E22843 entity
Predicate hasCity P316 FINISHED
Object Nantan
Nantan is a city in central Kyoto Prefecture, Japan, known for its rural landscapes, forests, and traditional cultural sites.
 E172607 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: Nantan | Statement: [Kyoto Prefecture, hasCity, Nantan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Nantan
Context triple: [Kyoto Prefecture, hasCity, Nantan]
  • A. Kyotanabe
    Kyotanabe is a city in Kyoto Prefecture, Japan, known for its residential suburbs, educational institutions, and location within the Kansai region.
  • B. Maishima
    Maishima is a man-made island in Osaka, Japan, known for its sports facilities, event venues, and waterfront recreational areas.
  • C. Takatsuki
    Takatsuki is a city in northern Osaka Prefecture, Japan, known as a residential and commercial hub between Osaka and Kyoto.
  • D. Daikanyama
    Daikanyama is a trendy, upscale neighborhood in Tokyo known for its stylish boutiques, cafes, and relaxed, residential atmosphere.
  • E. Kameoka
    Kameoka is a city in Kyoto Prefecture, Japan, known for its rural landscapes, historical sites, and proximity to Kyoto.
  • 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: Nantan
Triple: [Kyoto Prefecture, hasCity, Nantan]
Generated description
Nantan is a city in central Kyoto Prefecture, Japan, known for its rural landscapes, forests, and traditional cultural sites.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Nantan
Target entity description: Nantan is a city in central Kyoto Prefecture, Japan, known for its rural landscapes, forests, and traditional cultural sites.
  • A. Kyotanabe
    Kyotanabe is a city in Kyoto Prefecture, Japan, known for its residential suburbs, educational institutions, and location within the Kansai region.
  • B. Maishima
    Maishima is a man-made island in Osaka, Japan, known for its sports facilities, event venues, and waterfront recreational areas.
  • C. Takatsuki
    Takatsuki is a city in northern Osaka Prefecture, Japan, known as a residential and commercial hub between Osaka and Kyoto.
  • D. Daikanyama
    Daikanyama is a trendy, upscale neighborhood in Tokyo known for its stylish boutiques, cafes, and relaxed, residential atmosphere.
  • E. Kameoka
    Kameoka is a city in Kyoto Prefecture, Japan, known for its rural landscapes, historical sites, and proximity to Kyoto.
  • 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_69a493dada0481909c43649f9843ea91 completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4b8dc9e8c819099fbb192bcf80615 completed March 1, 2026, 10:08 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad230d2870819099001561e36078f5 completed March 8, 2026, 7:19 a.m.
NEDg Description generation batch_69ad24b98d6881908e31adebea66affb completed March 8, 2026, 7:26 a.m.
NED2 Entity disambiguation (via description) batch_69ad25732ca08190bc8cbb1692adf35e completed March 8, 2026, 7:29 a.m.
Created at: March 1, 2026, 7:42 p.m.