Triple
T8483260
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MVS |
E200568
|
entity |
| Predicate | supportsSubsystem |
P13398
|
FINISHED |
| Object | DB2 |
E35363
|
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: DB2 | Statement: [MVS, supportsSubsystem, DB2]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: DB2 Context triple: [MVS, supportsSubsystem, DB2]
-
A.
IBM DB2
chosen
IBM DB2 is a family of enterprise-grade relational database management systems developed by IBM, widely used for high-performance, scalable data storage and transaction processing across mainframe, distributed, and cloud environments.
-
B.
Sybase
Sybase is a pioneering enterprise software company best known for its relational database management systems and data management solutions, later acquired by SAP.
-
C.
SAP MaxDB
SAP MaxDB is a relational database management system developed by SAP, commonly used for enterprise applications and SAP solutions.
-
D.
Db2 Data Server Manager
Db2 Data Server Manager is IBM’s web-based administration and monitoring tool for managing, tuning, and analyzing Db2 database environments.
-
E.
Teradata
Teradata is an enterprise-grade relational database management system and data warehousing platform designed for large-scale analytics and business intelligence 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_69ca831b17988190a1f3f3413d57b820 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cc4576d48c8190a3e94d8ab3001b65 |
completed | March 31, 2026, 10:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce3a348a8481908a72c7ac15605022 |
completed | April 2, 2026, 9:43 a.m. |
Created at: March 30, 2026, 6:12 p.m.