Triple

T277377
Position Surface form Disambiguated ID Type / Status
Subject SAS E5277 entity
Predicate hasComponent P35 FINISHED
Object SAS/ACCESS E5277 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: SAS/ACCESS | Statement: [SAS, hasComponent, SAS/ACCESS]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: SAS/ACCESS
Context triple: [SAS, hasComponent, SAS/ACCESS]
  • A. SAS chosen
    SAS is a widely used statistical software suite for advanced analytics, business intelligence, data management, and predictive modeling.
  • B. SAS
    SAS is the School of Arts and Sciences at the University of Pennsylvania, encompassing the university’s core liberal arts and sciences departments and programs.
  • C. IBM DB2
    IBM DB2 is a family of enterprise-grade relational database management systems developed by IBM, widely used for high-performance, scalable data storage and transaction processing across mainframe, distributed, and cloud environments.
  • D. SAP
    SAP is a leading global enterprise software company best known for its ERP solutions that help organizations manage business operations and customer relations.
  • E. ODBC
    ODBC (Open Database Connectivity) is a standard API that enables applications to access and query data from a wide variety of relational and non-relational database management systems using a common interface.
  • 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_69a257e6c8788190987dfe705ca2912a completed Feb. 28, 2026, 2:50 a.m.
NER Named-entity recognition batch_69a25ded68c88190b1fc595ce329aeb9 completed Feb. 28, 2026, 3:15 a.m.
NED1 Entity disambiguation (via context triple) batch_69a394a13b688190951b070facd01d8c completed March 1, 2026, 1:21 a.m.
Created at: Feb. 28, 2026, 2:59 a.m.