Triple

T21615601
Position Surface form Disambiguated ID Type / Status
Subject Mountain Resort E533426 entity
Predicate locatedIn P40 FINISHED
Object Chengde NE NERFINISHED

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: Chengde | Statement: [Mountain Resort, locatedIn, Chengde]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Chengde
Context triple: [Mountain Resort, locatedIn, Chengde]
  • A. Chengde chosen
    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.
  • B. 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.
  • C. 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.
  • D. Zhangjiakou
    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.
  • E. Pinghu City
    Pinghu City is a county-level coastal city in northern Zhejiang Province, China, known for its manufacturing industry and proximity to Shanghai across Hangzhou Bay.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0c46411108190bba0d4176dffc9f3 completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69ef3baab9e88190bc02f27133ef32d6 completed April 27, 2026, 10:34 a.m.
Created at: April 16, 2026, 6:33 p.m.