Triple
T7999601
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Microsoft Access |
E186212
|
entity |
| Predicate | supports |
P516
|
FINISHED |
| Object | SQL |
E5275
|
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: SQL | Statement: [Microsoft Access, supports, SQL]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SQL Context triple: [Microsoft Access, supports, SQL]
-
A.
SQL
chosen
SQL (Structured Query Language) is a standardized programming language used to manage, query, and manipulate data in relational database management systems.
-
B.
DB
DB is the commonly used abbreviation for Deutsche Bahn, Germany’s national railway company and one of the largest rail operators in Europe.
-
C.
DB
DB is the standard abbreviation for "Deutsche Biographie," a major German biographical reference work documenting notable figures from German history and culture.
-
D.
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.
-
E.
SLQ
SLQ is the National Rail station code for St Leonards Warrior Square railway station in East Sussex, England.
- 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_69ca82aaaf24819084b94d18f699ba53 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3c9b67988190a8a4a2c960017400 |
completed | March 31, 2026, 3:16 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cc56910dd4819084ffe3350f15d95d |
completed | March 31, 2026, 11:19 p.m. |
Created at: March 30, 2026, 5:17 p.m.