Triple

T3164328
Position Surface form Disambiguated ID Type / Status
Subject Xicheng District E66174 entity
Predicate contains P35 FINISHED
Object Xizhimen E68686 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: Xizhimen | Statement: [Xicheng District, contains, Xizhimen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Xizhimen
Context triple: [Xicheng District, contains, Xizhimen]
  • A. Chongwenmen
    Chongwenmen is a historic gate area in central Beijing that once formed part of the old city wall and now serves as a major commercial and transportation hub.
  • B. Yongdingmen
    Yongdingmen is a historic southern gate site of Beijing’s old city wall, now a reconstructed landmark and traffic node at the southern end of the city’s central axis.
  • C. Nanluoguxiang
    Nanluoguxiang is a historic hutong alley and popular cultural and tourist street in central Beijing, known for its traditional courtyard architecture, shops, cafes, and nightlife.
  • D. Dongsi
    Dongsi is a historic neighborhood and street-crossroads area in central Beijing known for its traditional hutong lanes and long-standing commercial streets.
  • E. Xizhimen station chosen
    Xizhimen station is a major interchange hub in the Beijing Subway system, connecting multiple lines and serving the busy Xizhimen commercial and transport area.
  • 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_69ad85850c1481908a9e9c6242238de2 completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada61ba98881909106951c8ceeb959 completed March 8, 2026, 4:38 p.m.
NED1 Entity disambiguation (via context triple) batch_69b235d823948190b1fbe0dbf303133d completed March 12, 2026, 3:41 a.m.
Created at: March 8, 2026, 3:06 p.m.