Triple

T6027215
Position Surface form Disambiguated ID Type / Status
Subject Waterloo Underground station E134209 entity
Predicate hasStationCode P1289 FINISHED
Object WLO
WLO is the National Rail station code used to identify London Waterloo Underground station in the UK rail network.
 E563495 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: WLO | Statement: [Waterloo Underground station, hasStationCode, WLO]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: WLO
Context triple: [Waterloo Underground station, hasStationCode, WLO]
  • A. WNLO
    WNLO is a major Chinese research institute specializing in optoelectronics and photonics, based in Wuhan.
  • B. WOB
    WOB is the vehicle registration code used on license plates for cars registered in Wolfsburg, Germany.
  • C. WLA
    WLA is an acronym commonly used for the Women's Land Army, a civilian organization of women who worked in agriculture to support food production during wartime, particularly in the United Kingdom.
  • D. WU
    WU is a leading European university in Vienna specializing in economics, business, and social sciences.
  • E. WU
    WU is the stock ticker symbol for Western Union, a global financial services company best known for its money transfer and payment services.
  • 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: WLO
Triple: [Waterloo Underground station, hasStationCode, WLO]
Generated description
WLO is the National Rail station code used to identify London Waterloo Underground station in the UK rail network.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: WLO
Target entity description: WLO is the National Rail station code used to identify London Waterloo Underground station in the UK rail network.
  • A. WNLO
    WNLO is a major Chinese research institute specializing in optoelectronics and photonics, based in Wuhan.
  • B. WOB
    WOB is the vehicle registration code used on license plates for cars registered in Wolfsburg, Germany.
  • C. WLA
    WLA is an acronym commonly used for the Women's Land Army, a civilian organization of women who worked in agriculture to support food production during wartime, particularly in the United Kingdom.
  • D. WU
    WU is a leading European university in Vienna specializing in economics, business, and social sciences.
  • E. WU
    WU is the stock ticker symbol for Western Union, a global financial services company best known for its money transfer and payment services.
  • 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_69c0087515148190a97475d412563865 completed March 22, 2026, 3:19 p.m.
NER Named-entity recognition batch_69c0560cdc308190b25ca8ecb42c4e4f completed March 22, 2026, 8:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69c113799d648190a08516a33a5f92b7 completed March 23, 2026, 10:18 a.m.
NEDg Description generation batch_69c113c9bc048190ab517300d56dd8e0 completed March 23, 2026, 10:19 a.m.
NED2 Entity disambiguation (via description) batch_69c1144e77f881908ab59a67160c1630 completed March 23, 2026, 10:22 a.m.
Created at: March 22, 2026, 4:07 p.m.