Triple
T37657085
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SQL Server error numbers |
E937628
|
entity |
| Predicate | exampleErrorNumber |
P189087
|
FINISHED |
| Object | 2627 |
—
|
LITERAL 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: 2627 | Statement: [SQL Server error numbers, exampleErrorNumber, 2627]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: exampleErrorNumber Context triple: [SQL Server error numbers, exampleErrorNumber, 2627]
-
A.
errorType
Indicates the specific category or kind of error associated with an event, action, or entity.
-
B.
errorDescription
Indicates a textual explanation that describes the nature or details of an error that has occurred.
-
C.
errorCodeSource
Indicates the origin or component responsible for generating a particular error code.
-
D.
definesErrorCode
Indicates that one entity specifies or assigns the error code used to represent a particular error condition in another entity.
-
E.
errorModel
Indicates the specific model or framework used to represent, quantify, or simulate errors in a process, system, or prediction.
- F. None of above. chosen
Provenance (4 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_69f76ed6df7c8190b018e5baea716ceb |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fbb084760c8190a1554985d3c3cb7a |
completed | May 6, 2026, 9:20 p.m. |
| PD | Predicate disambiguation | batch_69fbadf3cb548190ba3b7514f76b790a |
completed | May 6, 2026, 9:09 p.m. |
| PDg | Predicate description generation | batch_69fbb083ab708190a18b045311106f27 |
completed | May 6, 2026, 9:20 p.m. |
Created at: May 3, 2026, 4:18 p.m.