Triple

T9200570
Position Surface form Disambiguated ID Type / Status
Subject Burlington GO Station E220826 entity
Predicate hasStationCode P1289 FINISHED
Object BR
BR is the official station code used to identify Burlington GO Station in Ontario, Canada.
 E784231 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: BR | Statement: [Burlington GO Station, hasStationCode, BR]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: BR
Context triple: [Burlington GO Station, hasStationCode, BR]
  • A. BR
    BR is the IATA airline designator for EVA Air, a major Taiwanese international carrier based in Taoyuan.
  • B. BR
    BR is a postcode area in southeast London and parts of northwest Kent, covering towns such as Bromley and Beckenham.
  • C. BR
    BR is the two-letter ISO 3166-1 alpha-2 country code that uniquely identifies Brazil in international standards and systems.
  • D. BR
    BR is the commonly used abbreviation for Banco de la República, Colombia’s central bank responsible for monetary policy and currency issuance.
  • E. BR
    BR is the abbreviation for the Radiocommunication Bureau, the specialized ITU body responsible for managing global radio-frequency spectrum and satellite orbits.
  • 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: BR
Triple: [Burlington GO Station, hasStationCode, BR]
Generated description
BR is the official station code used to identify Burlington GO Station in Ontario, Canada.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: BR
Target entity description: BR is the official station code used to identify Burlington GO Station in Ontario, Canada.
  • A. BR
    BR is the official vehicle registration code assigned to the Indian state of Bihar.
  • B. BR
    BR is the two-letter ISO 3166-1 alpha-2 country code that uniquely identifies Brazil in international standards and systems.
  • C. BR
    BR is the abbreviation for the Radiocommunication Bureau, the specialized ITU body responsible for managing global radio-frequency spectrum and satellite orbits.
  • D. BR
    BR is the commonly used abbreviation for Banco de la República, Colombia’s central bank responsible for monetary policy and currency issuance.
  • E. BR
    BR is the IATA airline designator for EVA Air, a major Taiwanese international carrier based in Taoyuan.
  • 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_69ca83e8e9248190862cf3e41693b310 completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69ccd9429b448190a078e9cdfedd4918 completed April 1, 2026, 8:37 a.m.
NED1 Entity disambiguation (via context triple) batch_69d05c451210819091188151d799e4cb completed April 4, 2026, 12:33 a.m.
NEDg Description generation batch_69d05dbbe0d08190a15107c17948167f completed April 4, 2026, 12:39 a.m.
NED2 Entity disambiguation (via description) batch_69d05e34ae6881908172770c6097a5e5 completed April 4, 2026, 12:41 a.m.
Created at: March 30, 2026, 7:25 p.m.