Triple

T610672
Position Surface form Disambiguated ID Type / Status
Subject Hokkaido E12089 entity
Predicate hasCity P316 FINISHED
Object Muroran
Muroran is an industrial port city in southern Hokkaido, Japan, known for its steel industry and scenic coastal landscapes.
 E80590 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: Muroran | Statement: [Hokkaido, hasCity, Muroran]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Muroran
Context triple: [Hokkaido, hasCity, Muroran]
  • A. Vyborg
    Vyborg is a historic port city in northwestern Russia near the Finnish border, known for its medieval castle and long-contested status between Sweden, Finland, and Russia.
  • B. Kostroma
    Kostroma is a historic Russian city northeast of Moscow, known as part of the Golden Ring and for its well-preserved medieval architecture and monasteries.
  • C. Astapovo
    Astapovo is a small Russian railway station village historically known as the place where the writer Leo Tolstoy died in 1910.
  • D. Yakutsk
    Yakutsk is a major city in northeastern Siberia, Russia, known as one of the coldest large cities in the world and a key administrative and cultural center of the Sakha Republic.
  • E. Magadan
    Magadan is a remote port city in Russia’s Far East, known historically as a gateway to the Kolyma region and its former Gulag labor camps.
  • 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: Muroran
Triple: [Hokkaido, hasCity, Muroran]
Generated description
Muroran is an industrial port city in southern Hokkaido, Japan, known for its steel industry and scenic coastal landscapes.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Muroran
Target entity description: Muroran is an industrial port city in southern Hokkaido, Japan, known for its steel industry and scenic coastal landscapes.
  • A. Vyborg
    Vyborg is a historic port city in northwestern Russia near the Finnish border, known for its medieval castle and long-contested status between Sweden, Finland, and Russia.
  • B. Kostroma
    Kostroma is a historic Russian city northeast of Moscow, known as part of the Golden Ring and for its well-preserved medieval architecture and monasteries.
  • C. Astapovo
    Astapovo is a small Russian railway station village historically known as the place where the writer Leo Tolstoy died in 1910.
  • D. Yakutsk
    Yakutsk is a major city in northeastern Siberia, Russia, known as one of the coldest large cities in the world and a key administrative and cultural center of the Sakha Republic.
  • E. Magadan
    Magadan is a remote port city in Russia’s Far East, known historically as a gateway to the Kolyma region and its former Gulag labor camps.
  • 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_69a493309df48190a327f748e88049a6 completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a49df7c088819082eb70de4f0f4fbf completed March 1, 2026, 8:13 p.m.
NED1 Entity disambiguation (via context triple) batch_69a577891a148190ae1364191f7f63bb completed March 2, 2026, 11:42 a.m.
NEDg Description generation batch_69a57a2852c881909f5623847e2fd85d completed March 2, 2026, 11:53 a.m.
NED2 Entity disambiguation (via description) batch_69a57a82f3248190b3eced48edf7a4da completed March 2, 2026, 11:54 a.m.
Created at: March 1, 2026, 7:35 p.m.