Triple

T16591166
Position Surface form Disambiguated ID Type / Status
Subject Zhichun Road station E403088 entity
Predicate isInterchangeStationFor P15892 FINISHED
Object Line 13 E66476 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: Line 13 | Statement: [Zhichun Road station, isInterchangeStationFor, Line 13]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Line 13
Context triple: [Zhichun Road station, isInterchangeStationFor, Line 13]
  • A. Line 13 chosen
    Line 13 is a suburban loop line of the Beijing Subway that serves the northern part of the city and connects several major transfer stations.
  • B. Line 13
    Line 13 is a rapid transit line of the Guangzhou Metro system in Guangzhou, China.
  • C. Line 13
    Line 13 is a major rapid transit route in the Shanghai Metro system that serves key urban districts and supports heavy commuter traffic across the city.
  • D. Line 13
    Line 13 is a planned rapid transit line of the Shenzhen Metro system in Shenzhen, China.
  • E. Line 13
    Line 13 is one of the busiest and most congested lines of the Paris Métro, running north–south across the city and serving major hubs such as Saint-Lazare.
  • 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_69d88387363c8190a97a0c942130de97 completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e359a012e081909a0604dde3c04bbb completed April 18, 2026, 10:14 a.m.
NED1 Entity disambiguation (via context triple) batch_6a008a275910819086b49ce741045ecf completed May 10, 2026, 1:37 p.m.
Created at: April 10, 2026, 5:16 a.m.