Triple
T11655558
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | DQS Server |
E277004
|
entity |
| Predicate | usedBy |
P260
|
FINISHED |
| Object | DQS Client |
E277005
|
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: DQS Client | Statement: [DQS Server, usedBy, DQS Client]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: DQS Client Context triple: [DQS Server, usedBy, DQS Client]
-
A.
DQS Client
chosen
DQS Client is a graphical tool in SQL Server Data Quality Services that lets users create, manage, and apply data quality knowledge for profiling, cleansing, and matching data.
-
B.
DQS Server
DQS Server is the server-side component of SQL Server Data Quality Services that hosts and executes data quality knowledge bases, matching, and cleansing operations.
-
C.
DQS_MAIN
DQS_MAIN is the primary SQL Server database that stores knowledge bases, rules, and related metadata for Microsoft Data Quality Services.
-
D.
DQI
DQI is the American Physical Society’s Division of Quantum Information, a professional unit focused on advancing research and collaboration in quantum information science.
-
E.
QDR
QDR is a major U.S. Department of Defense strategic review conducted every four years to assess and guide long-term defense priorities, capabilities, and force structure.
- 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_69d6aafbb3c081908a9cdb4ecb8d981d |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a3cee010819089cffdbefe5a6efb |
completed | April 10, 2026, 7:16 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ee88166900819095063f045be44bed |
completed | April 26, 2026, 9:48 p.m. |
Created at: April 8, 2026, 9:39 p.m.