Triple

T1058130
Position Surface form Disambiguated ID Type / Status
Subject Kyoto Prefecture E22843 entity
Predicate hasCity P316 FINISHED
Object Muko
Muko is a small city in Japan’s Kyoto Prefecture, known for its residential character and proximity to Kyoto City.
E129411 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: Muko | Statement: [Kyoto Prefecture, hasCity, Muko]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Muko
Context triple: [Kyoto Prefecture, hasCity, Muko]
  • A. Mariinka
    Mariinka is a renowned historic opera and ballet theatre in Saint Petersburg, Russia, celebrated for its world-class performances and prestigious resident companies.
  • B. Sastamala
    Sastamala is a town and municipality in southwestern Finland known for its historical churches, cultural heritage, and scenic lakeside landscapes.
  • C. Tumpa
    Tumpa is a song featured on the album "Legend of the Sun Virgin."
  • D. Tikkakoski
    Tikkakoski is a district in Jyväskylä, Finland, known for its military air base and role as a key center for the Finnish Air Force.
  • E. Oulainen
    Oulainen is a small town and municipality in Northern Ostrobothnia, Finland, known for its rural character and local cultural events.
  • 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: Muko
Triple: [Kyoto Prefecture, hasCity, Muko]
Generated description
Muko is a small city in Japan’s Kyoto Prefecture, known for its residential character and proximity to Kyoto City.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Muko
Target entity description: Muko is a small city in Japan’s Kyoto Prefecture, known for its residential character and proximity to Kyoto City.
  • A. Mariinka
    Mariinka is a renowned historic opera and ballet theatre in Saint Petersburg, Russia, celebrated for its world-class performances and prestigious resident companies.
  • B. Sastamala
    Sastamala is a town and municipality in southwestern Finland known for its historical churches, cultural heritage, and scenic lakeside landscapes.
  • C. Tumpa
    Tumpa is a song featured on the album "Legend of the Sun Virgin."
  • D. Tikkakoski
    Tikkakoski is a district in Jyväskylä, Finland, known for its military air base and role as a key center for the Finnish Air Force.
  • E. Oulainen
    Oulainen is a small town and municipality in Northern Ostrobothnia, Finland, known for its rural character and local cultural events.
  • 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_69ac599c08f481908b720e2cc7c4a5ef completed March 7, 2026, 5 p.m.
NEDg Description generation batch_69ac5a6b43cc81908086f4c4e2bb34c8 completed March 7, 2026, 5:03 p.m.
NED2 Entity disambiguation (via description) batch_69ac5add366c819086a01654c6cdcf38 completed March 7, 2026, 5:05 p.m.
Created at: March 1, 2026, 7:42 p.m.