Triple

T8409158
Position Surface form Disambiguated ID Type / Status
Subject Canada Pension Plan E198577 entity
Predicate abbreviation P43 FINISHED
Object YMPE
YMPE is a key earnings ceiling used by the Canada Pension Plan to determine maximum pensionable earnings and benefit calculations.
 E731453 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: YMPE | Statement: [Canada Pension Plan, abbreviation, YMPE]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: YMPE
Context triple: [Canada Pension Plan, abbreviation, YMPE]
  • A. YTEM
    YTEM is the ICAO airport code for Temora Airport, a regional airfield located in Temora, New South Wales, Australia.
  • B. MYEM
    MYEM is the ICAO airport code for Governor's Harbour Airport in the Bahamas.
  • C. YMEN
    YMEN is the ICAO airport code assigned to Essendon Airport in Melbourne, Australia.
  • D. YEM
    YEM is the three-letter ISO 3166-1 alpha-3 country code assigned to Yemen for international identification and data standards.
  • E. Vympel
    Vympel is an elite Russian special forces unit known for high-risk counterterrorism, covert operations, and strategic missions often under the purview of intelligence 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: YMPE
Triple: [Canada Pension Plan, abbreviation, YMPE]
Generated description
YMPE is a key earnings ceiling used by the Canada Pension Plan to determine maximum pensionable earnings and benefit calculations.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: YMPE
Target entity description: YMPE is a key earnings ceiling used by the Canada Pension Plan to determine maximum pensionable earnings and benefit calculations.
  • A. YTEM
    YTEM is the ICAO airport code for Temora Airport, a regional airfield located in Temora, New South Wales, Australia.
  • B. MYEM
    MYEM is the ICAO airport code for Governor's Harbour Airport in the Bahamas.
  • C. YMEN
    YMEN is the ICAO airport code assigned to Essendon Airport in Melbourne, Australia.
  • D. YEM
    YEM is the three-letter ISO 3166-1 alpha-3 country code assigned to Yemen for international identification and data standards.
  • E. Vympel
    Vympel is an elite Russian special forces unit known for high-risk counterterrorism, covert operations, and strategic missions often under the purview of intelligence 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_69ca831201b481909e137936ef99ff11 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cb8317045c8190b69cc99854b633be completed March 31, 2026, 8:17 a.m.
NED1 Entity disambiguation (via context triple) batch_69ce030dccf08190a70c0abf0bdcf244 completed April 2, 2026, 5:47 a.m.
NEDg Description generation batch_69ce07808098819087e896b87320aefd completed April 2, 2026, 6:06 a.m.
NED2 Entity disambiguation (via description) batch_69ce08759e1c81909c96caf3b571e1ca completed April 2, 2026, 6:11 a.m.
Created at: March 30, 2026, 6:05 p.m.