Triple
T14439630
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Spring Framework |
E358052
|
entity |
| Predicate | component |
P35
|
FINISHED |
| Object | Spring Context |
E358053
|
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: Spring Context | Statement: [Spring Framework, component, Spring Context]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Spring Context Context triple: [Spring Framework, component, Spring Context]
-
A.
Spring Framework
Spring Framework is a widely used open-source Java application framework that provides comprehensive infrastructure support for building enterprise-grade, modular, and testable applications.
-
B.
Spring Cocoon
Spring Cocoon is a distinctive multi-purpose sports and entertainment complex in Shenzhen, China, known for its cocoon-like architectural design.
-
C.
Spring Boot
chosen
Spring Boot is a Java-based framework that simplifies building and deploying production-ready Spring applications with minimal configuration.
-
D.
InitialContext
InitialContext is the primary JNDI class that provides the starting point for performing naming and directory operations in Java applications.
-
E.
Bean
Bean is a 1997 British-American comedy film based on Rowan Atkinson’s Mr. Bean character, following his chaotic misadventures in the United States.
- 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_69d8279402a88190821ffa39ae15bccf |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de914c1398819090fa2a74d257ba3e |
completed | April 14, 2026, 7:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd5bd7f46881908df1a1cea7b6af9b |
completed | May 8, 2026, 3:43 a.m. |
Created at: April 10, 2026, 1:18 a.m.