Triple
T7898274
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Prisma |
E183385
|
entity |
| Predicate | hasComponent |
P35
|
FINISHED |
| Object | Prisma Client |
E183385
|
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: Prisma Client | Statement: [Prisma, hasComponent, Prisma Client]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Prisma Client Context triple: [Prisma, hasComponent, Prisma Client]
-
A.
Prisma
chosen
Prisma is a modern TypeScript ORM and database toolkit that provides a type-safe and intuitive way to work with databases in Node.js applications.
-
B.
DBArtisan
DBArtisan is a commercial database administration tool that provides cross-platform management, performance tuning, and development capabilities for various relational database systems.
-
C.
PostgreSQL
PostgreSQL is a powerful open-source relational database management system known for its robustness, extensibility, and strong standards compliance.
-
D.
Prismo
Prismo is a powerful wish-granting cosmic being from Adventure Time who exists outside normal time and space and influences major events across the multiverse.
-
E.
TypeORM
TypeORM is a popular TypeScript-based Object-Relational Mapper for Node.js that provides a high-level, decorator-driven way to work with relational databases like PostgreSQL, MySQL, and SQLite.
- 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_69ca828d13088190b222be7aa9f9315c |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3a2ae5048190a6824d34b582c366 |
completed | March 31, 2026, 3:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cbdfd2dbbc8190b7b1e45b7f0b7515 |
completed | March 31, 2026, 2:53 p.m. |
Created at: March 30, 2026, 5:01 p.m.