Triple

T816284
Position Surface form Disambiguated ID Type / Status
Subject Django E17657 entity
Predicate supportsDatabase P11254 FINISHED
Object PostgreSQL E17669 NE FINISHED

How this triple was built (3 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: PostgreSQL | Statement: [Django, supportsDatabase, PostgreSQL]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: PostgreSQL
Context triple: [Django, supportsDatabase, PostgreSQL]
  • A. PostgreSQL chosen
    PostgreSQL is a powerful open-source relational database management system known for its robustness, extensibility, and strong standards compliance.
  • B. MariaDB
    MariaDB is an open-source relational database management system, forked from MySQL, known for its compatibility, performance, and community-driven development.
  • 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. Amazon Redshift
    Amazon Redshift is a fully managed, cloud-based data warehousing service from Amazon Web Services designed for fast querying and analysis of large datasets using SQL.
  • E. Oracle Database
    Oracle Database is a widely used enterprise relational database management system known for its scalability, reliability, and robust support for complex data workloads.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: supportsDatabase
Context triple: [Django, supportsDatabase, PostgreSQL]
  • A. hasDatabase
    Indicates that an entity possesses, uses, or is associated with a specific database.
  • B. supportsUse
    Indicates that one entity enables, allows, or is compatible with the use or operation of another entity.
  • C. supportsFeature
    Indicates that one entity provides, enables, or is compatible with a particular feature or capability of another.
  • D. supportsDatastoreType chosen
    Indicates that one entity is capable of working with, handling, or being compatible with a specified type of datastore.
  • E. isSupportedBy
    Indicates that an entity is upheld, sustained, or enabled by another entity, which provides necessary assistance, resources, or justification.
  • F. None of above.

Provenance (4 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_69a4937bcaac8190a322524ac6f45a5a completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4ab5157b08190b6c8f2fd455f261e completed March 1, 2026, 9:10 p.m.
NED1 Entity disambiguation (via context triple) batch_69a76d8d1a448190be8494fa2776615a completed March 3, 2026, 11:23 p.m.
PD Predicate disambiguation batch_69a4aa756920819080ae82948974c876 completed March 1, 2026, 9:07 p.m.
Created at: March 1, 2026, 7:38 p.m.