Triple

T8770846
Position Surface form Disambiguated ID Type / Status
Subject 120th Division E208455 entity
Predicate areaOfOperations P710 FINISHED
Object Hebei E11863 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: Hebei | Statement: [120th Division, areaOfOperations, Hebei]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hebei
Context triple: [120th Division, areaOfOperations, Hebei]
  • A. Hebei chosen
    Hebei is a northern Chinese province surrounding Beijing and Tianjin, historically significant as a major political, military, and industrial region.
  • 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. Hubei Province
    Hubei Province is a landlocked region in central China known for its capital city Wuhan, major role in industry and transportation, and significant historical and cultural heritage.
  • D. Shandong
    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.
  • E. Kansu
    Kansu is a Turkish surname most notably associated with Şevket Aziz Kansu, a prominent Turkish academic and anthropologist.
  • 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_69ca835edb4481909b4aafb616dc5eb7 completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cc5f2b08f881909f3d4fab2eda1d67 completed March 31, 2026, 11:56 p.m.
NED1 Entity disambiguation (via context triple) batch_69cfd0771920819081f191567c7e9fd2 completed April 3, 2026, 2:36 p.m.
Created at: March 30, 2026, 6:41 p.m.