Triple

T16051672
Position Surface form Disambiguated ID Type / Status
Subject Snow Ruyi E389366 entity
Predicate locatedIn 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: [Snow Ruyi, locatedIn, Zhangjiakou]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Zhangjiakou
Context triple: [Snow Ruyi, locatedIn, 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_69d86dae698881908327ef2d67706cb9 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e183627bd88190bf94054de13a7733 completed April 17, 2026, 12:48 a.m.
NED1 Entity disambiguation (via context triple) batch_69ffeb8b1b2c8190949943b20f2f8574 completed May 10, 2026, 2:20 a.m.
Created at: April 10, 2026, 4:56 a.m.