Triple

T3207593
Position Surface form Disambiguated ID Type / Status
Subject Line 5 (Beijing Subway) E67200 entity
Predicate servesDistrict P82 FINISHED
Object Xicheng District E66174 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: Xicheng District | Statement: [Line 5 (Beijing Subway), servesDistrict, Xicheng District]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Xicheng District
Context triple: [Line 5 (Beijing Subway), servesDistrict, Xicheng District]
  • A. Xicheng District chosen
    Xicheng District is a central urban district of Beijing, China, known for its historic sites, government institutions, and cultural landmarks.
  • B. Xialu District
    Xialu District is an urban administrative district of the prefecture-level city of Huangshi in Hubei Province, China.
  • C. Jianye District
    Jianye District is an urban district of Nanjing, China, known for its historical significance and major memorial sites related to the Nanjing Massacre.
  • D. Tianxin District
    Tianxin District is a central urban district of Changsha, the capital city of Hunan Province in China, known for its historical sites and commercial areas.
  • E. Jinyuan District
    Jinyuan District is an urban administrative district of Taiyuan, the capital city of Shanxi Province in northern China.
  • 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_69ad858ac36c81909962589cd277d6e2 completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69adaa58340881908347d772cfa0ac4c completed March 8, 2026, 4:56 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4c3601894819082568a7ee8d6aabc completed March 14, 2026, 2:09 a.m.
Created at: March 8, 2026, 3:07 p.m.