Triple

T13477832
Position Surface form Disambiguated ID Type / Status
Subject Shandong Normal University E318293 entity
Predicate province P604 FINISHED
Object Shandong E12524 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: Shandong | Statement: [Shandong Normal University, province, Shandong]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Shandong
Context triple: [Shandong Normal University, province, Shandong]
  • A. Shandong chosen
    Shandong is a coastal province in eastern China that has historically been a significant political, military, and cultural center, notably during various conflicts in modern Chinese history.
  • B. Liaoning
    Liaoning is a northeastern coastal province of China known for its heavy industry, port cities, and role as a gateway to the Korean Peninsula.
  • C. Kiaochow
    Kiaochow is the former German colonial name for the Chinese port city now known as Qingdao on the Shandong Peninsula.
  • D. Hebei
    Hebei is a northern Chinese province surrounding Beijing and Tianjin, historically significant as a major political, military, and industrial region.
  • E. Jiangsu
    Jiangsu is a populous and economically significant coastal province in eastern China, known for its rich history, dense urbanization, and major cities such as Nanjing and Suzhou.
  • 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_69d806b6bfec819089222715b2e86c8e completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69dbaf266c508190930d30776c09ce35 completed April 12, 2026, 2:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69f7b05a6d808190bae503b177816718 completed May 3, 2026, 8:30 p.m.
Created at: April 9, 2026, 9:42 p.m.