Triple

T4112088
Position Surface form Disambiguated ID Type / Status
Subject Mutianyu section E90199 entity
Predicate locatedIn P40 FINISHED
Object Beijing Municipality E2312 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: Beijing Municipality | Statement: [Mutianyu section, locatedIn, Beijing Municipality]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Beijing Municipality
Context triple: [Mutianyu section, locatedIn, Beijing Municipality]
  • A. Tianjin
    Tianjin is a major port city and industrial hub in northern China, located near Beijing along the Bohai Sea.
  • B. Beijing chosen
    Beijing is the capital city of China, a major political, cultural, and economic center known for its rich history and rapid modern development.
  • C. Tiāntán
    Tiāntán is the Chinese pinyin name for the Temple of Heaven, a historic imperial religious complex in Beijing where Ming and Qing dynasty emperors performed annual ceremonies to pray for good harvests.
  • D. Zhongdu
    Zhongdu was the historical capital city of the Jurchen-led Jin dynasty in northern China, located in what is now part of modern Beijing.
  • 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_69aed95c080881908125e30c5dcdc6f8 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69af01decd808190b4e5a5f76b090b0a completed March 9, 2026, 5:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69b589db323881908ac7c0cd11798c24 completed March 14, 2026, 4:16 p.m.
Created at: March 9, 2026, 3:41 p.m.