Triple
T239419
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | IANA time zone database |
E4894
|
entity |
| Predicate | usedBy |
P260
|
FINISHED |
| Object | MySQL |
E17668
|
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: MySQL | Statement: [IANA time zone database, usedBy, MySQL]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MySQL Context triple: [IANA time zone database, usedBy, MySQL]
-
A.
MySQL
chosen
MySQL is a widely used open-source relational database management system known for its reliability, performance, and role in powering many web applications and services.
-
B.
SQL
SQL (Structured Query Language) is a standardized programming language used to manage, query, and manipulate data in relational database management systems.
-
C.
SQL Server
SQL Server is Microsoft's enterprise-grade relational database management system used for storing, managing, and analyzing data in a wide range of applications.
-
D.
PostgreSQL
PostgreSQL is a powerful open-source relational database management system known for its robustness, extensibility, and strong standards compliance.
-
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.
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_69a257c3d0708190b0871c4269d273e6 |
completed | Feb. 28, 2026, 2:49 a.m. |
| NER | Named-entity recognition | batch_69a25ceaecdc81909e9ff49cb6a4e02a |
completed | Feb. 28, 2026, 3:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a3695ec8cc8190a070462cd0022f6a |
completed | Feb. 28, 2026, 10:17 p.m. |
Created at: Feb. 28, 2026, 2:53 a.m.