Triple
T4549737
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sebastián Ramírez |
E110131
|
entity |
| Predicate | SQLModel |
P535
|
FINISHED |
| Object | is a library for interacting with SQL databases using Python type hints |
—
|
LITERAL 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: is a library for interacting with SQL databases using Python type hints | Statement: [Sebastián Ramírez, SQLModel, is a library for interacting with SQL databases using Python type hints]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: SQLModel Context triple: [Sebastián Ramírez, SQLModel, is a library for interacting with SQL databases using Python type hints]
-
A.
dataModel
chosen
Indicates a relationship where an entity defines, uses, or is structured according to a specific data model or schema.
-
B.
associatedModelGeneration
Indicates that one entity is responsible for creating, producing, or generating another related model or representation.
-
C.
databaseIntroducedIn
Indicates the point in time, version, or context in which a particular database was first introduced or made available.
-
D.
databaseType
Indicates the specific kind or category of database technology associated with an entity.
-
E.
usedInDatabases
Indicates that something is employed or implemented within one or more database systems or database contexts.
- F. None of above.
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_69bd4412524c8190be5bcc9ddee91848 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd57f3f8348190868e274ac4df87ce |
completed | March 20, 2026, 2:21 p.m. |
| PD | Predicate disambiguation | batch_69bd5223423c81908317351b58cff5f5 |
completed | March 20, 2026, 1:56 p.m. |
Created at: March 20, 2026, 1:05 p.m.