Triple

T9668657
Position Surface form Disambiguated ID Type / Status
Subject User-based Security Model E233769 entity
Predicate securityModelIdentifier P35862 FINISHED
Object usm(3) E189499 NE FINISHED

How this triple was built (2 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: usm(3) | Statement: [User-based Security Model, securityModelIdentifier, usm(3)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: usm(3)
Context triple: [User-based Security Model, securityModelIdentifier, usm(3)]
  • A. USM
    USM is a public research university located in Hattiesburg, Mississippi, known for its programs in the arts, sciences, and education.
  • B. USM
    USM is the stock ticker symbol for United States Cellular Corporation, a regional wireless telecommunications provider in the United States.
  • C. USM
    USM is the abbreviation for the U.S. Department of State’s Under Secretary for Management, the senior official overseeing the department’s administrative, budgetary, and logistical functions.
  • D. USM
    USM is the IATA airport code for Samui International Airport serving Ko Samui in Thailand.
  • E. USM chosen
    USM (User-based Security Model) is the SNMPv3 security framework that provides user-level authentication, privacy (encryption), and access control for Simple Network Management Protocol communications.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69ca848d3b6c8190ae98ea554dea58df completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd9c3d5b3481908c8c66a3528875aa completed April 1, 2026, 10:29 p.m.
NED1 Entity disambiguation (via context triple) batch_69d18a208798819088db055e44d288e3 completed April 4, 2026, 10:01 p.m.
Created at: March 30, 2026, 8:15 p.m.