Triple
T17521000
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TensorFlow SavedModel (via conversion) |
E426677
|
entity |
| Predicate | compatibleWith |
P203
|
FINISHED |
| Object | TensorFlow.js converter |
—
|
NE NERFINISHED |
How this triple was built (3 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: TensorFlow.js converter | Statement: [TensorFlow SavedModel (via conversion), compatibleWith, TensorFlow.js converter]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: TensorFlow.js converter Context triple: [TensorFlow SavedModel (via conversion), compatibleWith, TensorFlow.js converter]
-
A.
TensorFlow.js
TensorFlow.js is a JavaScript library that enables training and running machine learning models directly in the browser and in Node.js using TensorFlow.
-
B.
TensorFlow SavedModel (via conversion)
TensorFlow SavedModel (via conversion) is a serialized model format from the core TensorFlow ecosystem that can be transformed into a TensorFlow.js-compatible model for deployment in JavaScript environments.
-
C.
TensorFlow Serving
TensorFlow Serving is a flexible, high-performance system for deploying and serving machine learning models in production, particularly those built with TensorFlow.
-
D.
TensorFlow Transform
TensorFlow Transform is a TensorFlow-based library for performing scalable, full-pass data preprocessing and feature engineering that can be applied consistently in both training and serving.
-
E.
TensorFlow Hub
TensorFlow Hub is a library and online repository of reusable machine learning models and components designed to simplify sharing and deploying pretrained models in TensorFlow applications.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: TensorFlow.js converter Target entity description: TensorFlow.js converter is a tool that transforms trained TensorFlow models into formats optimized for execution in JavaScript environments such as web browsers and Node.js.
-
A.
TensorFlow.js
chosen
TensorFlow.js is a JavaScript library that enables training and running machine learning models directly in the browser and in Node.js using TensorFlow.
-
B.
TensorFlow SavedModel (via conversion)
TensorFlow SavedModel (via conversion) is a serialized model format from the core TensorFlow ecosystem that can be transformed into a TensorFlow.js-compatible model for deployment in JavaScript environments.
-
C.
TensorFlow Serving
TensorFlow Serving is a flexible, high-performance system for deploying and serving machine learning models in production, particularly those built with TensorFlow.
-
D.
TensorFlow Transform
TensorFlow Transform is a TensorFlow-based library for performing scalable, full-pass data preprocessing and feature engineering that can be applied consistently in both training and serving.
-
E.
TensorFlow Hub
TensorFlow Hub is a library and online repository of reusable machine learning models and components designed to simplify sharing and deploying pretrained models in TensorFlow applications.
- F. None of above.
Provenance (2 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_69d889de677081909b22d2657b1f0292 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e452d23cf08190925510344fa36f57 |
completed | April 19, 2026, 3:58 a.m. |
Created at: April 10, 2026, 5:49 a.m.