Triple

T8769222
Position Surface form Disambiguated ID Type / Status
Subject Pingjin Campaign E208413 entity
Predicate location P40 FINISHED
Object Zhangjiakou E144793 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: Zhangjiakou | Statement: [Pingjin Campaign, location, Zhangjiakou]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Zhangjiakou
Context triple: [Pingjin Campaign, location, Zhangjiakou]
  • A. Zhangjiakou chosen
    Zhangjiakou is a major city in northern China known as a key gateway between Beijing and Inner Mongolia and as one of the host locations for the 2022 Winter Olympics.
  • B. Chengde
    Chengde is a historic city in northeastern China best known for its Qing dynasty Mountain Resort, a vast imperial summer retreat and UNESCO World Heritage Site.
  • C. Baoding
    Baoding is a historic prefecture-level city in central Hebei Province, China, known as a regional transportation hub and former military and administrative center.
  • D. Langfang
    Langfang is a prefecture-level city in northern China situated between Beijing and Tianjin, known for its strategic location and growing industrial and service sectors.
  • E. Lingang
    Lingang is a rapidly developing industrial and high-tech district in Shanghai, China, known for hosting major manufacturing facilities such as Tesla’s Gigafactory Shanghai.
  • 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_69cc5eedc7188190a67d959b9af53837 completed March 31, 2026, 11:55 p.m.
NED1 Entity disambiguation (via context triple) batch_69cf51af01e48190af674a03d0768f3b completed April 3, 2026, 5:35 a.m.
Created at: March 30, 2026, 6:41 p.m.