Triple

T106490
Position Surface form Disambiguated ID Type / Status
Subject Union Pacific Railroad E2147 entity
Predicate reportingMarks P2130 FINISHED
Object UPP
UPP is a reporting mark used by the Union Pacific Railroad to identify certain passenger cars and related rolling stock in its fleet.
 E11745 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: UPP | Statement: [Union Pacific Railroad, reportingMarks, UPP]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: UPP
Context triple: [Union Pacific Railroad, reportingMarks, UPP]
  • A. UPY
    UPY is a reporting mark used by Union Pacific Railroad, primarily identifying its yard and switching locomotives.
  • B. UP
    UP is the standard reporting mark used to identify rail equipment owned or operated by the Union Pacific Railroad in North America.
  • C. HUP
    HUP is a major academic medical center in Philadelphia that serves as the flagship teaching hospital of the University of Pennsylvania's health system.
  • D. LU
    LU is the two-letter ISO 3166-1 alpha-2 country code assigned to Luxembourg for international identification and data standards.
  • E. PRR
    PRR is the abbreviation commonly used for the Pennsylvania Railroad Company, once one of the largest and most influential railroads in the United States.
  • 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: UPP
Triple: [Union Pacific Railroad, reportingMarks, UPP]
Generated description
UPP is a reporting mark used by the Union Pacific Railroad to identify certain passenger cars and related rolling stock in its fleet.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: UPP
Target entity description: UPP is a reporting mark used by the Union Pacific Railroad to identify certain passenger cars and related rolling stock in its fleet.
  • A. UPY
    UPY is a reporting mark used by Union Pacific Railroad, primarily identifying its yard and switching locomotives.
  • B. UP
    UP is the standard reporting mark used to identify rail equipment owned or operated by the Union Pacific Railroad in North America.
  • C. HUP
    HUP is a major academic medical center in Philadelphia that serves as the flagship teaching hospital of the University of Pennsylvania's health system.
  • D. LU
    LU is the two-letter ISO 3166-1 alpha-2 country code assigned to Luxembourg for international identification and data standards.
  • E. PRR
    PRR is the abbreviation commonly used for the Pennsylvania Railroad Company, once one of the largest and most influential railroads in the United States.
  • 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_69a24e0a5b7c81908d52da08c60dabc4 completed Feb. 28, 2026, 2:08 a.m.
NER Named-entity recognition batch_69a25b7e2c188190b1dd8aafd4507a99 completed Feb. 28, 2026, 3:05 a.m.
NED1 Entity disambiguation (via context triple) batch_69a27c030ddc8190af2ebb672237bf18 completed Feb. 28, 2026, 5:24 a.m.
NEDg Description generation batch_69a27d8a52188190ae00d52340839bbc completed Feb. 28, 2026, 5:30 a.m.
NED2 Entity disambiguation (via description) batch_69a27e7633d88190a622115c4e29fd9c completed Feb. 28, 2026, 5:34 a.m.
Created at: Feb. 28, 2026, 2:12 a.m.