Triple
T4276886
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | tiangolo |
E97064
|
entity |
| Predicate | notableProject |
P4
|
FINISHED |
| Object | SQLModel |
E426657
|
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: SQLModel | Statement: [tiangolo, notableProject, SQLModel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SQLModel Context triple: [tiangolo, notableProject, SQLModel]
-
A.
sqlmodel
chosen
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.
-
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.
DC ORM
DC ORM is the abbreviated name for the District of Columbia Office of Risk Management, the agency responsible for managing risk, insurance, and related claims for the D.C. government.
-
D.
SQLite
SQLite is a lightweight, self-contained, serverless SQL database engine widely embedded in applications, operating systems, and devices.
-
E.
SQL
SQL (Structured Query Language) is a standardized programming language used to manage, query, and manipulate data in relational database management systems.
- 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_69b34544be3c819084d1ab82d29f90c5 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3501d677481909e7416a1d2b0008c |
completed | March 12, 2026, 11:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5c7237b608190ab5aca56027344c4 |
completed | March 14, 2026, 8:37 p.m. |
Created at: March 12, 2026, 11:07 p.m.