Triple
T9900098
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MVU |
E182260
|
entity |
| Predicate | influenced |
P9
|
FINISHED |
| Object | Redux |
E131759
|
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: Redux | Statement: [MVU, influenced, Redux]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Redux Context triple: [MVU, influenced, Redux]
-
A.
Redux
chosen
Redux is a predictable state management library for JavaScript applications, commonly used with React to centralize and control application state through a unidirectional data flow.
-
B.
Redux Toolkit
Redux Toolkit is the official, opinionated toolset for efficient Redux development, providing simplified APIs and best practices for state management in JavaScript applications.
-
C.
React-Redux
React-Redux is the official React binding library for Redux, providing tools to efficiently connect React components to a centralized state store.
-
D.
Redux Thunk
Redux Thunk is a popular middleware for Redux that enables writing action creators as functions to handle asynchronous logic and side effects in a Redux application.
-
E.
Redux Saga
Redux Saga is a JavaScript library for managing complex asynchronous side effects in Redux applications using generator functions and a declarative effects model.
- 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_69ca82876f8081909cf75df0f99bb13f |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cdb4e0705c8190bd17e36aff615cdd |
completed | April 2, 2026, 12:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1eb1b9534819093c5150f1ed8f685 |
completed | April 5, 2026, 4:54 a.m. |
Created at: March 30, 2026, 8:40 p.m.