Triple

T17028790
Position Surface form Disambiguated ID Type / Status
Subject Leytonstone High Road railway station E413135 entity
Predicate stationCode P1289 FINISHED
Object LER
LER is the National Rail station code for Leytonstone High Road railway station in London.
 E1246823 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: LER | Statement: [Leytonstone High Road railway station, stationCode, LER]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: LER
Context triple: [Leytonstone High Road railway station, stationCode, LER]
  • A. LER
    LER is the vehicle registration code assigned to the German island municipality of Borkum.
  • B. LR
    LR is the stock ticker symbol for Legrand, a global specialist in electrical and digital building infrastructure.
  • 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 a German vehicle registration code assigned to the Ortenaukreis district in the state of Baden-Württemberg.
  • E. LR
    LR is the commonly used abbreviation for Les Républicains, a major center-right political party in France.
  • 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: LER
Triple: [Leytonstone High Road railway station, stationCode, LER]
Generated description
LER is the National Rail station code for Leytonstone High Road railway station in London.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: LER
Target entity description: LER is the National Rail station code for Leytonstone High Road railway station in London.
  • A. LER
    LER is the vehicle registration code assigned to the German island municipality of Borkum.
  • 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 stock ticker symbol for Legrand, a global specialist in electrical and digital building infrastructure.
  • D. LR
    LR is the ISO 3166-1 alpha-2 country code for Liberia, a West African nation on the Atlantic coast.
  • 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_69d886cc4170819093deddc7b8b4b6a7 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3d5d858448190acfe81f10d83ed4b completed April 18, 2026, 7:04 p.m.
NED1 Entity disambiguation (via context triple) batch_6a011b553c588190b46785b85142dce5 completed May 10, 2026, 11:57 p.m.
NEDg Description generation batch_6a011d2651e88190b6a57fa11e29bb21 completed May 11, 2026, 12:04 a.m.
NED2 Entity disambiguation (via description) batch_6a011d99a038819086924a1b2af55967 completed May 11, 2026, 12:06 a.m.
Created at: April 10, 2026, 5:33 a.m.