Triple

T531013
Position Surface form Disambiguated ID Type / Status
Subject Beijing Subway E12220 entity
Predicate hasStation P35 FINISHED
Object Dongzhimen transport hub E68474 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: Dongzhimen transport hub | Statement: [Beijing Subway, hasStation, Dongzhimen transport hub]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dongzhimen transport hub
Context triple: [Beijing Subway, hasStation, Dongzhimen transport hub]
  • A. Dongzhimen station chosen
    Dongzhimen station is a major Beijing Subway interchange hub connecting multiple lines and serving as a key gateway to the city’s northeastern districts and the airport rail link.
  • B. Fuxingmen station
    Fuxingmen station is a major interchange stop on the Beijing Subway, serving as a key transfer point between multiple central city lines.
  • C. Jianguomen station
    Jianguomen station is a major interchange stop on the Beijing Subway, serving as a key transfer point between central city lines near the historic Jianguomen area.
  • D. Wangfujing station
    Wangfujing station is a major underground metro station in central Beijing serving the busy Wangfujing commercial and shopping district.
  • E. Xizhimen station
    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_69a4933208e88190891f5debab1b776d completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a494dda58c8190870305056838a2b2 completed March 1, 2026, 7:34 p.m.
NED1 Entity disambiguation (via context triple) batch_69a5154d3a70819097de31be3b753523 completed March 2, 2026, 4:42 a.m.
Created at: March 1, 2026, 7:32 p.m.