Triple

T16658721
Position Surface form Disambiguated ID Type / Status
Subject Xiangshan MRT Station E404801 entity
Predicate hasStationCode P1289 FINISHED
Object R02
R02 is the station code for Xiangshan MRT Station, a metro stop on Taipei's Tamsui–Xinyi (Red) Line.
 E1225685 NE FINISHED

How this triple was built (4 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: R02 | Statement: [Xiangshan MRT Station, hasStationCode, R02]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: R02
Context triple: [Xiangshan MRT Station, hasStationCode, R02]
  • A. R08
    R08 is the pennant number of HMS Queen Elizabeth, the lead ship of the Royal Navy’s Queen Elizabeth-class aircraft carriers and one of the largest warships ever built for the United Kingdom.
  • B. R09
    R09 is the station code assigned to the 36th Avenue subway station in the New York City Transit system.
  • C. R01
    R01 is the station code used by the New York City Subway for the Astoria–Ditmars Boulevard terminal station on the BMT Astoria Line in Queens.
  • D. R2
    R2 is the MBTA station code used to identify Ashmont station on Boston's Red Line transit system.
  • E. R2
    R2 is a classification for U.S. doctoral universities characterized by high levels of research activity, as defined by the Carnegie Classification system.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: R02
Triple: [Xiangshan MRT Station, hasStationCode, R02]
Generated description
R02 is the station code for Xiangshan MRT Station, a metro stop on Taipei's Tamsui–Xinyi (Red) Line.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: R02
Target entity description: R02 is the station code for Xiangshan MRT Station, a metro stop on Taipei's Tamsui–Xinyi (Red) Line.
  • A. R08
    R08 is the pennant number of HMS Queen Elizabeth, the lead ship of the Royal Navy’s Queen Elizabeth-class aircraft carriers and one of the largest warships ever built for the United Kingdom.
  • B. R09
    R09 is the station code assigned to the 36th Avenue subway station in the New York City Transit system.
  • C. R01
    R01 is the station code used by the New York City Subway for the Astoria–Ditmars Boulevard terminal station on the BMT Astoria Line in Queens.
  • D. R2
    R2 is a classification for U.S. doctoral universities characterized by high levels of research activity, as defined by the Carnegie Classification system.
  • E. R2
    R2 is the MBTA station code used to identify Ashmont station on Boston's Red Line transit system.
  • F. None of above. chosen

Provenance (5 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_69d8838b5fbc81908c6575c132b82e80 completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e37bfcbb6881909c0419174dd017dc completed April 18, 2026, 12:41 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0084ccbc888190816cdf0ea67b0a90 completed May 10, 2026, 1:14 p.m.
NEDg Description generation batch_6a008576b0bc81909fdf0b7d26f4c2c1 completed May 10, 2026, 1:17 p.m.
NED2 Entity disambiguation (via description) batch_6a0085e4b6ec81908383085ff08f0dce completed May 10, 2026, 1:19 p.m.
Created at: April 10, 2026, 5:18 a.m.