Triple

T20215473
Position Surface form Disambiguated ID Type / Status
Subject Seaford railway station E493609 entity
Predicate hasStationCode P1289 FINISHED
Object SEF
SEF is the National Rail station code for Seaford railway station in East Sussex, England.
E1418515 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: SEF | Statement: [Seaford railway station, hasStationCode, SEF]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: SEF
Context triple: [Seaford railway station, hasStationCode, SEF]
  • A. SEF
    SEF is a German vehicle registration code used for cars registered in the district of Neustadt an der Aisch-Bad Windsheim in Bavaria.
  • B. SEDEF
    SEDEF is the Spanish Ministry of Defence’s Secretariat of State responsible for high-level planning, management, and administration of national defense policy and resources.
  • C. SE-F
    SE-F is the ISO 3166-2 subdivision code assigned to Jönköping County in Sweden.
  • D. SIF
    SIF is the governing body for ice hockey in Sweden, overseeing the national teams and domestic competitions.
  • E. SIF
    SIF is the Swiss federal body responsible for shaping and coordinating Switzerland’s international financial, tax, and monetary policy.
  • 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: SEF
Triple: [Seaford railway station, hasStationCode, SEF]
Generated description
SEF is the National Rail station code for Seaford railway station in East Sussex, England.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: SEF
Target entity description: SEF is the National Rail station code for Seaford railway station in East Sussex, England.
  • A. SEF
    SEF is a German vehicle registration code used for cars registered in the district of Neustadt an der Aisch-Bad Windsheim in Bavaria.
  • B. SEDEF
    SEDEF is the Spanish Ministry of Defence’s Secretariat of State responsible for high-level planning, management, and administration of national defense policy and resources.
  • C. SE-F
    SE-F is the ISO 3166-2 subdivision code assigned to Jönköping County in Sweden.
  • D. SIF
    SIF is the governing body for ice hockey in Sweden, overseeing the national teams and domestic competitions.
  • E. SIF
    SIF is the Swiss federal body responsible for shaping and coordinating Switzerland’s international financial, tax, and monetary policy.
  • 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_69da6269614c8190bb40475d9d477358 completed April 11, 2026, 3:02 p.m.
NER Named-entity recognition batch_69e66ed8cc8c8190889ecadc702010d8 completed April 20, 2026, 6:22 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0843f447b481908faa28cc504fcea9 completed May 16, 2026, 10:16 a.m.
NEDg Description generation batch_6a0845367ccc8190b1232173205e750d completed May 16, 2026, 10:21 a.m.
NED2 Entity disambiguation (via description) batch_6a0845e241048190ba7fb657d4dca3d7 completed May 16, 2026, 10:24 a.m.
Created at: April 11, 2026, 11:38 p.m.