Triple

T16280235
Position Surface form Disambiguated ID Type / Status
Subject The Valley E395243 entity
Predicate hasAbbreviation P43 FINISHED
Object MVC E133615 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: MVC | Statement: [The Valley, hasAbbreviation, MVC]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: MVC
Context triple: [The Valley, hasAbbreviation, MVC]
  • A. MVC chosen
    MVC refers to the Missouri Valley Conference, one of the oldest NCAA Division I athletic conferences in the United States.
  • B. MVC
    MVC is a community college in Moreno Valley, California, that is part of the Riverside Community College District.
  • C. Model-View-Controller
    Model-View-Controller (MVC) is a software architectural pattern that separates an application into three interconnected components—model, view, and controller—to improve modularity, testability, and maintainability.
  • D. Model-View-Presenter
    Model-View-Presenter is a software architectural pattern that separates an application into model, view, and presenter components to improve testability and maintainability, particularly in user interface code.
  • E. MVVM
    MVVM (Model–View–ViewModel) is a software architectural pattern that separates an application's user interface from its business logic and data models to improve testability, maintainability, and modularity.
  • 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_69d87f22c7248190a54c949738441e2e completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e24611926c81909b276ca3f406f15d completed April 17, 2026, 2:39 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0017c48e5c8190a387a4158362417a completed May 10, 2026, 5:29 a.m.
Created at: April 10, 2026, 5:05 a.m.