Triple

T11486667
Position Surface form Disambiguated ID Type / Status
Subject Kantōgun E272297 entity
Predicate headquartersLocation P62 FINISHED
Object Changchun E164950 NE FINISHED

How this triple was built (2 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: Changchun | Statement: [Kantōgun, headquartersLocation, Changchun]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Changchun
Context triple: [Kantōgun, headquartersLocation, Changchun]
  • A. Changchun chosen
    Changchun is a major city in northeastern China that served as the capital of the Japanese puppet state of Manchukuo during the early 20th century.
  • B. Jilin City
    Jilin City is a major industrial and transportation hub in northeastern China, situated along the Songhua River in central Jilin Province.
  • C. Shenyang
    Shenyang is a major industrial and historical city in northeastern China and the capital of Liaoning Province.
  • D. Liaoyuan
    Liaoyuan is a prefecture-level city in northeastern China known for its coal mining history and location in the central part of Jilin Province.
  • E. Harbin
    Harbin is a major city in northeastern China known for its Russian-influenced architecture and its internationally famous annual ice and snow festival.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d6aae1b09881909ce2ded3fa0c14fa completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d85a1fc9688190aacc2eed64229b79 completed April 10, 2026, 2:02 a.m.
NED1 Entity disambiguation (via context triple) batch_69e6046076d0819087766bf905825217 completed April 20, 2026, 10:48 a.m.
Created at: April 8, 2026, 9:36 p.m.