Triple

T13914943
Position Surface form Disambiguated ID Type / Status
Subject Tiangongyuan station E334596 entity
Predicate partOf P40 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: [Tiangongyuan station, partOf, Beijing Subway]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Beijing Subway
Context triple: [Tiangongyuan station, partOf, 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. 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. Beijing Subway Line 16
    Beijing Subway Line 16 is a rapid transit line in Beijing’s subway network that runs generally north–south, connecting the city’s northern suburbs with central and southwestern districts.
  • 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_69d81c5eaa9c819083b1ff8689179565 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de27260ae08190be45b4b15898e365 completed April 14, 2026, 11:38 a.m.
NED1 Entity disambiguation (via context triple) batch_69fbc31a91608190a80a69be38ac7f71 completed May 6, 2026, 10:39 p.m.
Created at: April 9, 2026, 10:16 p.m.