Triple
T5017340
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oracle Data Integrator |
E112765
|
entity |
| Predicate | supports |
P516
|
FINISHED |
| Object | Teradata |
E233571
|
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: Teradata | Statement: [Oracle Data Integrator, supports, Teradata]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Teradata Context triple: [Oracle Data Integrator, supports, Teradata]
-
A.
Teradata
chosen
Teradata is an enterprise-grade relational database management system and data warehousing platform designed for large-scale analytics and business intelligence workloads.
-
B.
Actian
Actian is a data management and analytics company known for its hybrid data platforms and database technologies used in enterprise applications.
-
C.
Sybase
Sybase is a pioneering enterprise software company best known for its relational database management systems and data management solutions, later acquired by SAP.
-
D.
IBM DB2
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.
-
E.
AnalyticDB
AnalyticDB is Alibaba Cloud’s distributed cloud-native data warehousing and analytics service designed for high-performance, real-time analysis of large-scale data.
- 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_69bd4434acb8819086679dbeccc2fe54 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd734037a88190a950db814412a023 |
completed | March 20, 2026, 4:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be9278421c8190b142466c099c7d1c |
completed | March 21, 2026, 12:43 p.m. |
Created at: March 20, 2026, 1:35 p.m.