Triple

T9660300
Position Surface form Disambiguated ID Type / Status
Subject Teradata E233571 entity
Predicate supportsLanguage P2177 FINISHED
Object Teradata SQL dialect E233571 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: Teradata SQL dialect | Statement: [Teradata, supportsLanguage, Teradata SQL dialect]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Teradata SQL dialect
Context triple: [Teradata, supportsLanguage, Teradata SQL dialect]
  • A. Teradata chosen
    Teradata is an enterprise-grade relational database management system and data warehousing platform designed for large-scale analytics and business intelligence workloads.
  • B. AnalyticDB
    AnalyticDB is Alibaba Cloud’s distributed cloud-native data warehousing and analytics service designed for high-performance, real-time analysis of large-scale data.
  • C. IBM Netezza Performance Server
    IBM Netezza Performance Server is a high-performance, cloud-ready data warehouse and analytics platform designed for fast, large-scale data processing and advanced analytics workloads.
  • D. SAP HANA
    SAP HANA is an in-memory, column-oriented relational database management system developed by SAP for high-performance transactional and analytical processing.
  • E. Greenplum
    Greenplum is a massively parallel, open-source data warehouse and analytics platform designed for large-scale business intelligence and big data workloads.
  • 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_69ca848c1ba88190b84b410cd14627fc completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd9c08d0d0819086426ad6891b18db completed April 1, 2026, 10:28 p.m.
NED1 Entity disambiguation (via context triple) batch_69d18a0b84a0819083191beeaf8d968b completed April 4, 2026, 10 p.m.
Created at: March 30, 2026, 8:14 p.m.