Triple

T15682317
Position Surface form Disambiguated ID Type / Status
Subject Batong Line (Beijing Subway) E377607 entity
Predicate system P730 FINISHED
Object Beijing Subway E12220 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 Subway | Statement: [Batong Line (Beijing Subway), system, Beijing Subway]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Beijing Subway
Context triple: [Batong Line (Beijing Subway), system, Beijing Subway]
  • 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 Suburban Railway
    Beijing Suburban Railway is a commuter rail network serving the greater Beijing metropolitan area, connecting urban districts with surrounding suburban regions.
  • 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 (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_69d85cd2e28481909d4e975bee20872f completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e04f306a1c8190a819541a3cc51f5a completed April 16, 2026, 2:53 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff6ee4c8688190ae2fefb56171161a completed May 9, 2026, 5:29 p.m.
Created at: April 10, 2026, 4:16 a.m.