Triple
T12562537
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | System R |
E295384
|
entity |
| Predicate | mainSubject |
P3
|
FINISHED |
| Object | structured query language |
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: structured query language | Statement: [System R, mainSubject, structured query language]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: structured query language Context triple: [System R, mainSubject, structured query language]
-
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.
SLQ
SLQ is the National Rail station code for St Leonards Warrior Square railway station in East Sussex, England.
-
C.
SLQ
SLQ is the IATA airport code for the small public airport serving the remote community of Sleetmute in western Alaska, United States.
-
D.
CQL
CQL (Contextual Query Language) is a formal query language designed for representing and expressing complex search queries in a human-readable, standards-based way, commonly used in information retrieval and library systems.
-
E.
DB
DB is the commonly used abbreviation for Deutsche Bahn, Germany’s national railway company and one of the largest rail operators in Europe.
- 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_69d6ad9cac2c81908e8a7bed82d1e21d |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d95494ae1c81908b9ee14b8ef92a65 |
completed | April 10, 2026, 7:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6558da7e0819086860bfaf394e2d8 |
completed | May 2, 2026, 7:50 p.m. |
Created at: April 8, 2026, 11:48 p.m.