Triple

T1646976
Position Surface form Disambiguated ID Type / Status
Subject Clayton Hall tram stop E35603 entity
Predicate hasStationCode P1289 FINISHED
Object CLH
CLH is the station code for Clayton Hall tram stop on the Manchester Metrolink light rail system in Greater Manchester, England.
E185637 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: CLH | Statement: [Clayton Hall tram stop, hasStationCode, CLH]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: CLH
Context triple: [Clayton Hall tram stop, hasStationCode, CLH]
  • A. LCH
    LCH is a leading global clearing house that provides central counterparty clearing services for a wide range of financial markets and asset classes.
  • B. CH-LU
    CH-LU is the ISO 3166-2 code designating the Swiss canton of Lucerne.
  • C. CL-CO
    CL-CO is the ISO 3166-2 subdivision code assigned to the commune of Salamanca in Chile’s Coquimbo Region.
  • D. CLB
    CLB is the abbreviated short name commonly used for the Major League Soccer club Columbus Crew.
  • E. CLB
    CLB is the abbreviated name for Japan’s Cabinet Legislation Bureau, the government body that reviews and drafts legislation and advises the Cabinet on legal matters.
  • 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: CLH
Triple: [Clayton Hall tram stop, hasStationCode, CLH]
Generated description
CLH is the station code for Clayton Hall tram stop on the Manchester Metrolink light rail system in Greater Manchester, England.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: CLH
Target entity description: CLH is the station code for Clayton Hall tram stop on the Manchester Metrolink light rail system in Greater Manchester, England.
  • A. LCH
    LCH is a leading global clearing house that provides central counterparty clearing services for a wide range of financial markets and asset classes.
  • B. CH-LU
    CH-LU is the ISO 3166-2 code designating the Swiss canton of Lucerne.
  • C. CL-CO
    CL-CO is the ISO 3166-2 subdivision code assigned to the commune of Salamanca in Chile’s Coquimbo Region.
  • D. CLB
    CLB is the abbreviated short name commonly used for the Major League Soccer club Columbus Crew.
  • E. CLB
    CLB is the abbreviated name for Japan’s Cabinet Legislation Bureau, the government body that reviews and drafts legislation and advises the Cabinet on legal matters.
  • 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_69a8860568888190a32cd9f70acbba42 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a90a62b26c8190bf97bb80c228b47e completed March 5, 2026, 4:45 a.m.
NED1 Entity disambiguation (via context triple) batch_69ad60a4bd5481908b46f44364c15592 completed March 8, 2026, 11:42 a.m.
NEDg Description generation batch_69ad617fea508190ae6fa86cf9ea814a completed March 8, 2026, 11:46 a.m.
NED2 Entity disambiguation (via description) batch_69ad62028a448190a0c3f0dadae6a741 completed March 8, 2026, 11:48 a.m.
Created at: March 4, 2026, 7:28 p.m.