Triple

T13238958
Position Surface form Disambiguated ID Type / Status
Subject Leura railway station E315227 entity
Predicate hasStationCode P1289 FINISHED
Object LRA
LRA is the station code for Leura railway station in New South Wales, Australia.
 E1028921 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: LRA | Statement: [Leura railway station, hasStationCode, LRA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: LRA
Context triple: [Leura railway station, hasStationCode, LRA]
  • A. LRIA
    LRIA is the ICAO airport code for Iași International Airport, a major air transport hub serving the city of Iași in northeastern Romania.
  • B. LR
    LR is a German vehicle registration code assigned to the Ortenaukreis district in the state of Baden-Württemberg.
  • C. LR
    LR is the ISO 3166-1 alpha-2 country code for Liberia, a West African nation on the Atlantic coast.
  • D. LR
    LR is the commonly used abbreviation for Les Républicains, a major center-right political party in France.
  • E. LR
    LR is the stock ticker symbol for Legrand, a global specialist in electrical and digital building infrastructure.
  • 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: LRA
Triple: [Leura railway station, hasStationCode, LRA]
Generated description
LRA is the station code for Leura railway station in New South Wales, Australia.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: LRA
Target entity description: LRA is the station code for Leura railway station in New South Wales, Australia.
  • A. LRIA
    LRIA is the ICAO airport code for Iași International Airport, a major air transport hub serving the city of Iași in northeastern Romania.
  • B. LR
    LR is a German vehicle registration code assigned to the Ortenaukreis district in the state of Baden-Württemberg.
  • C. LR
    LR is the ISO 3166-1 alpha-2 country code for Liberia, a West African nation on the Atlantic coast.
  • D. LR
    LR is the stock ticker symbol for Legrand, a global specialist in electrical and digital building infrastructure.
  • E. LR
    LR is the commonly used abbreviation for Les Républicains, a major center-right political party in France.
  • 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_69d806b1072881909e46bd212259c5f0 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d98d5850ac8190849a51da39efe5be completed April 10, 2026, 11:52 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6ff323a3c8190b46b24e69e653105 completed May 3, 2026, 7:54 a.m.
NEDg Description generation batch_69f7013a368c8190a768f837f6551f5b completed May 3, 2026, 8:03 a.m.
NED2 Entity disambiguation (via description) batch_69f7038563848190ae96538e30ecafd5 completed May 3, 2026, 8:12 a.m.
Created at: April 9, 2026, 9:23 p.m.