Triple

T17964586
Position Surface form Disambiguated ID Type / Status
Subject Pingxifu station E449168 entity
Predicate publicTransportSystem P1288 FINISHED
Object Beijing Metro 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: Beijing Metro | Statement: [Pingxifu station, publicTransportSystem, Beijing Metro]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Beijing Metro
Context triple: [Pingxifu station, publicTransportSystem, Beijing Metro]
  • A. Beijing Subway chosen
    The Beijing Subway is one of the world’s largest and busiest rapid transit systems, forming the backbone of public transportation in China’s capital city.
  • B. Beijing MTR
    Beijing MTR is a railway and metro operating company responsible for running several lines of the Beijing Subway in partnership with the city government.
  • C. Shanghai Metro
    Shanghai Metro is one of the world’s largest and busiest rapid transit systems, serving the city of Shanghai with an extensive network of urban and suburban rail lines.
  • D. Beijing Subway Line 10
    Beijing Subway Line 10 is a major loop line in Beijing’s metro system that encircles the city center and connects numerous key business, residential, and transfer hubs.
  • E. Tianjin Metro
    Tianjin Metro is the rapid transit system serving the city of Tianjin, China, providing urban and suburban rail transportation across the municipality.
  • 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_69d8b9f9927c8190a006110c8b996e61 completed April 10, 2026, 8:51 a.m.
NER Named-entity recognition batch_69e4b136e4088190ac97fd92dc84a4b9 completed April 19, 2026, 10:40 a.m.
Created at: April 10, 2026, 10:22 a.m.