Triple

T2835746
Position Surface form Disambiguated ID Type / Status
Subject Chessie System E62343 entity
Predicate usedReportingMarks P42928 FINISHED
Object WM
WM was the reporting mark for the Western Maryland Railway, a regional U.S. railroad that later became part of the Chessie System.
 E303448 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: WM | Statement: [Chessie System, usedReportingMarks, WM]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: WM
Context triple: [Chessie System, usedReportingMarks, WM]
  • A. WN
    WN is the IATA airline designator used to identify Southwest Airlines in flight schedules, ticketing, and aviation operations.
  • B. MW
    MW is the two-letter ISO 3166-1 alpha-2 country code assigned to Malawi.
  • C. WD
    WD is a consumer-facing brand of Western Digital known for its hard drives, solid-state drives, and other data storage products.
  • D. WB
    WB is the official vehicle registration code used for motor vehicles registered in the Indian state of West Bengal.
  • E. W
    W is one of the iconic white capital letters that make up the famous Hollywood Sign overlooking Los Angeles.
  • 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: WM
Triple: [Chessie System, usedReportingMarks, WM]
Generated description
WM was the reporting mark for the Western Maryland Railway, a regional U.S. railroad that later became part of the Chessie System.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: WM
Target entity description: WM was the reporting mark for the Western Maryland Railway, a regional U.S. railroad that later became part of the Chessie System.
  • A. WN
    WN is the IATA airline designator used to identify Southwest Airlines in flight schedules, ticketing, and aviation operations.
  • B. MW
    MW is the two-letter ISO 3166-1 alpha-2 country code assigned to Malawi.
  • C. WD
    WD is a consumer-facing brand of Western Digital known for its hard drives, solid-state drives, and other data storage products.
  • D. WB
    WB is the official vehicle registration code used for motor vehicles registered in the Indian state of West Bengal.
  • E. W
    W is one of the iconic white capital letters that make up the famous Hollywood Sign overlooking Los Angeles.
  • 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_69ab4c3c39188190955b9c49d98463d8 completed March 6, 2026, 9:50 p.m.
NER Named-entity recognition batch_69abe08ae5048190a0a3b573d9a5fdbc completed March 7, 2026, 8:23 a.m.
NED1 Entity disambiguation (via context triple) batch_69afe8c890508190868f50f4e5e1d642 completed March 10, 2026, 9:47 a.m.
NEDg Description generation batch_69afe9ba068881908727c82d5eddb974 completed March 10, 2026, 9:51 a.m.
NED2 Entity disambiguation (via description) batch_69b00412e7448190898050f18f64ea9a completed March 10, 2026, 11:44 a.m.
Created at: March 6, 2026, 10:01 p.m.