Triple
T277238
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SQL |
E5275
|
entity |
| Predicate | hasComponent |
P35
|
FINISHED |
| Object |
Data Query Language
Data Query Language (DQL) is the subset of SQL focused on retrieving and viewing data from databases, primarily through commands like SELECT.
|
E5275
|
NE FINISHED |
How this triple was built (4 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: Data Query Language | Statement: [SQL, hasComponent, Data Query Language]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Data Query Language Context triple: [SQL, hasComponent, Data Query Language]
-
A.
SQL
SQL (Structured Query Language) is a standardized programming language used to manage, query, and manipulate data in relational database management systems.
-
B.
SPARQL
SPARQL is a semantic query language and protocol used to retrieve and manipulate data stored in Resource Description Framework (RDF) format on the Semantic Web.
-
C.
Open Data Index
Open Data Index is a global initiative that evaluates and ranks the openness and accessibility of government data across countries.
-
D.
Google BigQuery
Google BigQuery is a fully managed, serverless cloud data warehouse from Google Cloud designed for fast SQL-based analytics on large-scale datasets.
-
E.
OWL 2 QL
OWL 2 QL is a lightweight profile of the Web Ontology Language designed to enable efficient query answering over large datasets using standard relational database technologies.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Data Query Language Triple: [SQL, hasComponent, Data Query Language]
Generated description
Data Query Language (DQL) is the subset of SQL focused on retrieving and viewing data from databases, primarily through commands like SELECT.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Data Query Language Target entity description: Data Query Language (DQL) is the subset of SQL focused on retrieving and viewing data from databases, primarily through commands like SELECT.
-
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.
SPARQL
SPARQL is a semantic query language and protocol used to retrieve and manipulate data stored in Resource Description Framework (RDF) format on the Semantic Web.
-
C.
Open Data Index
Open Data Index is a global initiative that evaluates and ranks the openness and accessibility of government data across countries.
-
D.
Google BigQuery
Google BigQuery is a fully managed, serverless cloud data warehouse from Google Cloud designed for fast SQL-based analytics on large-scale datasets.
-
E.
OWL 2 QL
OWL 2 QL is a lightweight profile of the Web Ontology Language designed to enable efficient query answering over large datasets using standard relational database technologies.
- F. None of above.
Provenance (5 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_69a257e6c8788190987dfe705ca2912a |
completed | Feb. 28, 2026, 2:50 a.m. |
| NER | Named-entity recognition | batch_69a25ded68c88190b1fc595ce329aeb9 |
completed | Feb. 28, 2026, 3:15 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a391525a7081909166884a8ea47ace |
completed | March 1, 2026, 1:07 a.m. |
| NEDg | Description generation | batch_69a391e734148190a56e6a07447af304 |
completed | March 1, 2026, 1:09 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a3924ca2148190811f9d376805d0cd |
completed | March 1, 2026, 1:11 a.m. |
Created at: Feb. 28, 2026, 2:59 a.m.