Triple

T1693054
Position Surface form Disambiguated ID Type / Status
Subject Chongqing E36590 entity
Predicate hasMetroSystem P522 FINISHED
Object Chongqing Rail Transit E213296 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: Chongqing Rail Transit | Statement: [Chongqing, hasMetroSystem, Chongqing Rail Transit]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Chongqing Rail Transit
Context triple: [Chongqing, hasMetroSystem, Chongqing Rail Transit]
  • A. Chongqing Metro chosen
    Chongqing Metro is the rapid transit system serving the mountainous megacity of Chongqing, China, known for its extensive use of straddle-beam monorail lines and dramatic elevated tracks.
  • 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. Wuhan Metro
    Wuhan Metro is the rapid transit system serving the city of Wuhan, China, providing urban rail transportation across its major districts.
  • D. Tianjin Metro
    Tianjin Metro is the rapid transit system serving the city of Tianjin, China, providing urban and suburban rail transportation across the municipality.
  • E. Guangzhou Metro
    Guangzhou Metro is the rapid transit system serving Guangzhou, China, forming one of the country’s largest and busiest urban rail networks.
  • 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_69a886151508819084fa7f1ce6e05577 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69aa62b228988190a7d19003ddf10ce5 completed March 6, 2026, 5:14 a.m.
NED1 Entity disambiguation (via context triple) batch_69adf3b9a2f4819082ca2e9f838f7b9e completed March 8, 2026, 10:10 p.m.
Created at: March 4, 2026, 7:29 p.m.