Triple

T3176474
Position Surface form Disambiguated ID Type / Status
Subject Line 13 (Beijing Subway) E66476 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: [Line 13 (Beijing Subway), system, Beijing Subway]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Beijing Subway
Context triple: [Line 13 (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. 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.
  • C. Tianjin Metro
    Tianjin Metro is the rapid transit system serving the city of Tianjin, China, providing urban and suburban rail transportation across the municipality.
  • D. Shenzhen Metro
    Shenzhen Metro is the rapid transit system serving the city of Shenzhen, China, known for its extensive, modern network and role in supporting the region’s fast-paced urban development.
  • E. Shijiazhuang Metro
    Shijiazhuang Metro is the rapid transit system serving the city of Shijiazhuang, providing urban rail transportation across the capital of Hebei Province in 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_69ad8586a34c8190944c63ec11a8de1a completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada69b0bec8190957913b44d876079 completed March 8, 2026, 4:40 p.m.
NED1 Entity disambiguation (via context triple) batch_69b37e49fa3c8190baa956a311b1a716 completed March 13, 2026, 3:02 a.m.
Created at: March 8, 2026, 3:06 p.m.