Triple

T10939737
Position Surface form Disambiguated ID Type / Status
Subject Kagawa Prefecture E258435 entity
Predicate hasCity P316 FINISHED
Object Kan’onji
Kan’onji is a coastal city in western Kagawa Prefecture on Japan’s Shikoku Island, known for its historic temples and scenic views of the Seto Inland Sea.
 E952086 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: Kan’onji | Statement: [Kagawa Prefecture, hasCity, Kan’onji]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Kan’onji
Context triple: [Kagawa Prefecture, hasCity, Kan’onji]
  • A. Kanmaki
    Kanmaki is a town in Nara Prefecture, Japan, known as a residential community within the Kansai region.
  • B. Nakanai
    Nakanai is an Austronesian language spoken on the island of New Britain in Papua New Guinea, known for its role in the linguistic diversity of the Bismarck Archipelago.
  • C. Takarano
    Takarano is a small settlement on the atoll of Tabiteuea in the island nation of Kiribati, located in the central Pacific Ocean.
  • D. Takarano
    Takarano is a village located on the atoll of Abaiang in the island nation of Kiribati.
  • E. Marunouchi
    Marunouchi is a central Tokyo business district known for its concentration of corporate headquarters, upscale offices, and proximity to Tokyo Station and the Imperial Palace.
  • 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: Kan’onji
Triple: [Kagawa Prefecture, hasCity, Kan’onji]
Generated description
Kan’onji is a coastal city in western Kagawa Prefecture on Japan’s Shikoku Island, known for its historic temples and scenic views of the Seto Inland Sea.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Kan’onji
Target entity description: Kan’onji is a coastal city in western Kagawa Prefecture on Japan’s Shikoku Island, known for its historic temples and scenic views of the Seto Inland Sea.
  • A. Kanmaki
    Kanmaki is a town in Nara Prefecture, Japan, known as a residential community within the Kansai region.
  • B. Nakanai
    Nakanai is an Austronesian language spoken on the island of New Britain in Papua New Guinea, known for its role in the linguistic diversity of the Bismarck Archipelago.
  • C. Takarano
    Takarano is a small settlement on the atoll of Tabiteuea in the island nation of Kiribati, located in the central Pacific Ocean.
  • D. Takarano
    Takarano is a village located on the atoll of Abaiang in the island nation of Kiribati.
  • E. Marunouchi
    Marunouchi is a central Tokyo business district known for its concentration of corporate headquarters, upscale offices, and proximity to Tokyo Station and the Imperial Palace.
  • 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_69d6aa8769b4819082bfe5e61b9017f0 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d770c1389881909341170984211810 completed April 9, 2026, 9:26 a.m.
NED1 Entity disambiguation (via context triple) batch_69f2802d74f081909c7af34bf266ae01 completed April 29, 2026, 10:03 p.m.
NEDg Description generation batch_69f28b7db84c8190b4c2b22a7465cf97 completed April 29, 2026, 10:51 p.m.
NED2 Entity disambiguation (via description) batch_69f2a0e8861c8190a9ac8e0f488f4a95 completed April 30, 2026, 12:23 a.m.
Created at: April 8, 2026, 9:23 p.m.