Triple

T4529590
Position Surface form Disambiguated ID Type / Status
Subject Moston railway station E106261 entity
Predicate hasStationCode P1289 FINISHED
Object MSO
MSO is the National Rail station code for Moston railway station in Greater Manchester, England.
 E449363 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: MSO | Statement: [Moston railway station, hasStationCode, MSO]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: MSO
Context triple: [Moston railway station, hasStationCode, MSO]
  • A. MSS
    MSS is the Mobile Servicing System, a Canadian-built robotic arm and handling system used on the International Space Station for assembly, maintenance, and payload operations.
  • B. MSH
    MSH is the vehicle registration code for the Mansfeld-Südharz district in the German state of Saxony-Anhalt.
  • C. MSA
    MSA is the standardized, literary form of Arabic used in formal writing, media, education, and official communication across the Arab world.
  • D. MSA
    MSA is the commonly used abbreviation for the Magnuson–Stevens Fishery Conservation and Management Act, the primary law governing marine fisheries management in U.S. federal waters.
  • E. MSA
    MSA is a common abbreviation for a metropolitan statistical area, a region defined by the U.S. Office of Management and Budget for statistical and demographic analysis.
  • 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: MSO
Triple: [Moston railway station, hasStationCode, MSO]
Generated description
MSO is the National Rail station code for Moston railway station in Greater Manchester, England.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: MSO
Target entity description: MSO is the National Rail station code for Moston railway station in Greater Manchester, England.
  • A. MSS
    MSS is the Mobile Servicing System, a Canadian-built robotic arm and handling system used on the International Space Station for assembly, maintenance, and payload operations.
  • B. MSH
    MSH is the vehicle registration code for the Mansfeld-Südharz district in the German state of Saxony-Anhalt.
  • C. MSA
    MSA is the standardized, literary form of Arabic used in formal writing, media, education, and official communication across the Arab world.
  • D. MSA
    MSA is a common abbreviation for a metropolitan statistical area, a region defined by the U.S. Office of Management and Budget for statistical and demographic analysis.
  • E. MSA
    MSA is the commonly used abbreviation for the Magnuson–Stevens Fishery Conservation and Management Act, the primary law governing marine fisheries management in U.S. federal waters.
  • 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_69bd43f3d6e08190a91824f833d51bbe completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd579ba4188190b4cef6e91772f7e5 completed March 20, 2026, 2:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69bda45eadd0819093429e14c88a161d completed March 20, 2026, 7:47 p.m.
NEDg Description generation batch_69bda4e4f5a081908892c4363b3a4989 completed March 20, 2026, 7:49 p.m.
NED2 Entity disambiguation (via description) batch_69bda556d4188190a00d6e6259139c10 completed March 20, 2026, 7:51 p.m.
Created at: March 20, 2026, 1:03 p.m.