Triple

T16944954
Position Surface form Disambiguated ID Type / Status
Subject Gongyixiqiao station E411043 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: [Gongyixiqiao station, system, Beijing Subway]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Beijing Subway
Context triple: [Gongyixiqiao station, 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. 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.
  • D. 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.
  • E. Beijing Suburban Railway
    Beijing Suburban Railway is a commuter rail network serving the greater Beijing metropolitan area, connecting urban districts with surrounding suburban regions.
  • 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_69d886c886688190967be07322597ac9 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3cfb14b788190be5b7f9c00c3e7ea completed April 18, 2026, 6:38 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00dc04f60081909b7b276a4010c321 completed May 10, 2026, 7:27 p.m.
Created at: April 10, 2026, 5:31 a.m.