Triple

T13910582
Position Surface form Disambiguated ID Type / Status
Subject Bangor railway station E334480 entity
Predicate hasStationCode P1289 FINISHED
Object BNG
BNG is the National Rail station code for Bangor railway station in Gwynedd, Wales.
 E1067170 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: BNG | Statement: [Bangor railway station, hasStationCode, BNG]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: BNG
Context triple: [Bangor railway station, hasStationCode, BNG]
  • A. BnG
    BnG is the commonly used abbreviation for Bòrd na Gàidhlig, the principal public body responsible for promoting and supporting the Scottish Gaelic language in Scotland.
  • B. BNC
    BNC is the commonly used abbreviation for Brasenose College, one of the constituent colleges of the University of Oxford.
  • C. BNC
    BNC is the three-letter National Rail station code for Burnley Central railway station in Lancashire, England.
  • D. BNC
    BNC is the IATA airport code for Beni Airport in the Democratic Republic of the Congo.
  • E. BPNG
    BPNG is the central bank of Papua New Guinea, responsible for issuing currency, formulating monetary policy, and overseeing the country’s financial 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: BNG
Triple: [Bangor railway station, hasStationCode, BNG]
Generated description
BNG is the National Rail station code for Bangor railway station in Gwynedd, Wales.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: BNG
Target entity description: BNG is the National Rail station code for Bangor railway station in Gwynedd, Wales.
  • A. BnG
    BnG is the commonly used abbreviation for Bòrd na Gàidhlig, the principal public body responsible for promoting and supporting the Scottish Gaelic language in Scotland.
  • B. BNC
    BNC is the commonly used abbreviation for Brasenose College, one of the constituent colleges of the University of Oxford.
  • C. BNC
    BNC is the three-letter National Rail station code for Burnley Central railway station in Lancashire, England.
  • D. BNC
    BNC is the IATA airport code for Beni Airport in the Democratic Republic of the Congo.
  • E. BPNG
    BPNG is the central bank of Papua New Guinea, responsible for issuing currency, formulating monetary policy, and overseeing the country’s financial 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_69d81c5eaa9c819083b1ff8689179565 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de2723461881908376b5509ee0d530 completed April 14, 2026, 11:38 a.m.
NED1 Entity disambiguation (via context triple) batch_69f7c72879e48190ac01d0a2023b098c completed May 3, 2026, 10:07 p.m.
NEDg Description generation batch_69f7c7b9e4888190822501d439df142a completed May 3, 2026, 10:10 p.m.
NED2 Entity disambiguation (via description) batch_69f7c83e31d4819094209406fc99456a completed May 3, 2026, 10:12 p.m.
Created at: April 9, 2026, 10:16 p.m.