Triple

T277213
Position Surface form Disambiguated ID Type / Status
Subject SQL E5275 entity
Predicate hasStandard P1371 FINISHED
Object SQL-86
SQL-86 is the first formal ANSI standard version of the SQL database query language, established in 1986.
 E5275 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: SQL-86 | Statement: [SQL, hasStandard, SQL-86]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: SQL-86
Context triple: [SQL, hasStandard, SQL-86]
  • A. SQL
    SQL (Structured Query Language) is a standardized programming language used to manage, query, and manipulate data in relational database management systems.
  • B. 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.
  • C. PostgreSQL
    PostgreSQL is a powerful open-source relational database management system known for its robustness, extensibility, and strong standards compliance.
  • D. SQL Server
    SQL Server is Microsoft's enterprise-grade relational database management system used for storing, managing, and analyzing data in a wide range of applications.
  • E. SAS
    SAS is a widely used statistical software suite for advanced analytics, business intelligence, data management, and predictive modeling.
  • 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: SQL-86
Triple: [SQL, hasStandard, SQL-86]
Generated description
SQL-86 is the first formal ANSI standard version of the SQL database query language, established in 1986.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: SQL-86
Target entity description: SQL-86 is the first formal ANSI standard version of the SQL database query language, established in 1986.
  • A. SQL chosen
    SQL (Structured Query Language) is a standardized programming language used to manage, query, and manipulate data in relational database management systems.
  • B. 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.
  • C. PostgreSQL
    PostgreSQL is a powerful open-source relational database management system known for its robustness, extensibility, and strong standards compliance.
  • D. SQL Server
    SQL Server is Microsoft's enterprise-grade relational database management system used for storing, managing, and analyzing data in a wide range of applications.
  • E. SAS
    SAS is a widely used statistical software suite for advanced analytics, business intelligence, data management, and predictive modeling.
  • F. None of above.

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_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_69a391525a7081909166884a8ea47ace completed March 1, 2026, 1:07 a.m.
NEDg Description generation batch_69a391e734148190a56e6a07447af304 completed March 1, 2026, 1:09 a.m.
NED2 Entity disambiguation (via description) batch_69a3924ca2148190811f9d376805d0cd completed March 1, 2026, 1:11 a.m.
Created at: Feb. 28, 2026, 2:59 a.m.