Triple

T9312198
Position Surface form Disambiguated ID Type / Status
Subject Carter–Wegman MACs E224031 entity
Predicate influenced P9 FINISHED
Object UMAC
UMAC is a high-speed, universal hash-based message authentication code designed for efficient and secure data integrity verification in modern cryptographic systems.
 E792085 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: UMAC | Statement: [Carter–Wegman MACs, influenced, UMAC]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: UMAC
Context triple: [Carter–Wegman MACs, influenced, UMAC]
  • A. UMI
    UMI is the three-letter ISO 3166-1 alpha-3 country code assigned to Kingman Reef, an uninhabited U.S. territory in the central Pacific Ocean.
  • B. UM
    UM is the stock ticker symbol for MRU, the Canadian food and pharmacy retail company Metro Inc.
  • C. UM
    UM is the commonly used abbreviation for Universitas Negeri Malang, a public university in Malang, Indonesia known for its strong focus on education and teacher training.
  • D. UM
    UM is the commonly used abbreviation for the University of Miami, a private research university located in Coral Gables, Florida.
  • E. UM
    UM is the regional vehicle registration code used for the district of Uckermark in the German state of Brandenburg.
  • 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: UMAC
Triple: [Carter–Wegman MACs, influenced, UMAC]
Generated description
UMAC is a high-speed, universal hash-based message authentication code designed for efficient and secure data integrity verification in modern cryptographic systems.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: UMAC
Target entity description: UMAC is a high-speed, universal hash-based message authentication code designed for efficient and secure data integrity verification in modern cryptographic systems.
  • A. UMI
    UMI is the three-letter ISO 3166-1 alpha-3 country code assigned to Kingman Reef, an uninhabited U.S. territory in the central Pacific Ocean.
  • B. UM
    UM is the commonly used abbreviation for the University of Miami, a private research university located in Coral Gables, Florida.
  • C. UM
    UM is the regional vehicle registration code used for the district of Uckermark in the German state of Brandenburg.
  • D. UM
    UM is a public research university in Winnipeg, Canada, known as the University of Manitoba.
  • E. UM
    UM is the stock ticker symbol for MRU, the Canadian food and pharmacy retail company Metro Inc.
  • 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_69ca8425f4fc81909c1c586e9a5b7530 completed March 30, 2026, 2:09 p.m.
NER Named-entity recognition batch_69cd20ae96e481909a1af9ea1c91f2b2 completed April 1, 2026, 1:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69d0c797640c8190be003e321faf3b86 completed April 4, 2026, 8:11 a.m.
NEDg Description generation batch_69d0cb9605608190b0c5f7149b2194a9 completed April 4, 2026, 8:28 a.m.
NED2 Entity disambiguation (via description) batch_69d0cd0028048190a94cd7e8971f8940 completed April 4, 2026, 8:34 a.m.
Created at: March 30, 2026, 7:37 p.m.