Triple

T5504544
Position Surface form Disambiguated ID Type / Status
Subject Staines railway station E144407 entity
Predicate hasStationCode P1289 FINISHED
Object SNS
SNS is the National Rail station code for Staines railway station in Surrey, England.
 E529282 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: SNS | Statement: [Staines railway station, hasStationCode, SNS]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: SNS
Context triple: [Staines railway station, hasStationCode, SNS]
  • A. SNA
    SNA is the IATA airport code for John Wayne Airport, a commercial and general aviation airport serving Orange County, California.
  • B. SNA
    SNA is the commonly used abbreviation for the United Nations System of National Accounts, the international standard framework for measuring a country’s economic activity.
  • C. sns
    sns is the conventional alias used when importing Seaborn, a popular Python data visualization library built on top of Matplotlib.
  • D. Facebook
    Facebook is a major global social networking platform that allows users to connect, share content, and communicate online.
  • E. Weibo
    Weibo is a major Chinese microblogging and social media platform widely used for news, entertainment, and public discourse.
  • 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: SNS
Triple: [Staines railway station, hasStationCode, SNS]
Generated description
SNS is the National Rail station code for Staines railway station in Surrey, England.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: SNS
Target entity description: SNS is the National Rail station code for Staines railway station in Surrey, England.
  • A. SNA
    SNA is the IATA airport code for John Wayne Airport, a commercial and general aviation airport serving Orange County, California.
  • B. SNA
    SNA is the commonly used abbreviation for the United Nations System of National Accounts, the international standard framework for measuring a country’s economic activity.
  • C. sns
    sns is the conventional alias used when importing Seaborn, a popular Python data visualization library built on top of Matplotlib.
  • D. Facebook
    Facebook is a major global social networking platform that allows users to connect, share content, and communicate online.
  • E. Weibo
    Weibo is a major Chinese microblogging and social media platform widely used for news, entertainment, and public discourse.
  • 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_69c008f6b5048190a09064116062cf69 completed March 22, 2026, 3:21 p.m.
NER Named-entity recognition batch_69c01f0d21848190ae8c41561eca6342 completed March 22, 2026, 4:55 p.m.
NED1 Entity disambiguation (via context triple) batch_69c027ae9a448190a927d39d30f0134b completed March 22, 2026, 5:32 p.m.
NEDg Description generation batch_69c037fca93881908d4d7403bfb1f866 completed March 22, 2026, 6:42 p.m.
NED2 Entity disambiguation (via description) batch_69c03898327c8190bd3b889bd7663003 completed March 22, 2026, 6:44 p.m.
Created at: March 22, 2026, 3:32 p.m.