Triple

T2422580
Position Surface form Disambiguated ID Type / Status
Subject Shenzhen Airlines E53450 entity
Predicate focusCity P164 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: [Shenzhen Airlines, focusCity, Changchun]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Changchun
Context triple: [Shenzhen Airlines, focusCity, 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. Harbin
    Harbin is a major city in northeastern China known for its Russian-influenced architecture and its internationally famous annual ice and snow festival.
  • E. Dalian
    Dalian is a major port city in northeastern China known for its strategic location on the Liaodong Peninsula, maritime trade, and modern urban development.
  • 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_69ab495c44d48190b7235b23719bc3f6 completed March 6, 2026, 9:38 p.m.
NER Named-entity recognition batch_69abc972934481909e05bd6f31162f9d completed March 7, 2026, 6:45 a.m.
NED1 Entity disambiguation (via context triple) batch_69af1f7f0738819091d06a7279f45d61 completed March 9, 2026, 7:29 p.m.
Created at: March 6, 2026, 9:42 p.m.