Triple

T17520000
Position Surface form Disambiguated ID Type / Status
Subject SQLModel E426657 entity
Predicate compatibleWith P203 FINISHED
Object SQLAlchemy 1.4+ NE NERFINISHED

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: SQLAlchemy 1.4+ | Statement: [SQLModel, compatibleWith, SQLAlchemy 1.4+]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: SQLAlchemy 1.4+
Context triple: [SQLModel, compatibleWith, SQLAlchemy 1.4+]
  • A. SQLAlchemy chosen
    SQLAlchemy is a powerful Python SQL toolkit and Object-Relational Mapping (ORM) library that provides a high-level, flexible interface for working with relational databases.
  • B. Flask-SQLAlchemy
    Flask-SQLAlchemy is a popular Flask extension that integrates the SQLAlchemy ORM with Flask applications to simplify database configuration and usage.
  • C. sqlmodel
    SQLModel is a Python library by Sebastián Ramírez (tiangolo) that combines SQLAlchemy and Pydantic to provide an easy, type-safe way to define and interact with SQL databases.
  • D. Flask-Migrate
    Flask-Migrate is a Flask extension that integrates Alembic-based database schema migrations into Flask applications.
  • E. Orm
    Orm is a prominent DC Comics supervillain and half-brother of Aquaman, often depicted as the Ocean Master and one of Atlantis’s chief antagonists.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d889de677081909b22d2657b1f0292 completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e452d18c1c81908bb843bbddb44ca1 completed April 19, 2026, 3:58 a.m.
Created at: April 10, 2026, 5:49 a.m.