Triple
T17598485
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TensorFlow ecosystem |
E428633
|
entity |
| Predicate | includesComponent |
P1393
|
FINISHED |
| Object |
TensorFlow I/O
TensorFlow I/O is an extension library for TensorFlow that provides specialized input/output operations and dataset integrations for a wide range of file formats and data sources beyond the core framework’s built-in support.
|
E1278742
|
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: TensorFlow I/O | Statement: [TensorFlow ecosystem, includesComponent, TensorFlow I/O]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: TensorFlow I/O Context triple: [TensorFlow ecosystem, includesComponent, TensorFlow I/O]
-
A.
TensorFlow Extended
TensorFlow Extended (TFX) is an end-to-end platform for deploying, managing, and scaling production machine learning pipelines built on TensorFlow.
-
B.
TensorFlow Serving
TensorFlow Serving is a flexible, high-performance system for deploying and serving machine learning models in production, particularly those built with TensorFlow.
-
C.
TensorFlow ecosystem
The TensorFlow ecosystem is a comprehensive suite of tools, libraries, and extensions built around the TensorFlow machine learning framework to support model development, training, deployment, and visualization.
-
D.
TensorFlow
TensorFlow is an open-source, end-to-end machine learning and deep learning framework widely used for building, training, and deploying neural network models at scale.
-
E.
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.
- 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: TensorFlow I/O Triple: [TensorFlow ecosystem, includesComponent, TensorFlow I/O]
Generated description
TensorFlow I/O is an extension library for TensorFlow that provides specialized input/output operations and dataset integrations for a wide range of file formats and data sources beyond the core framework’s built-in support.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: TensorFlow I/O Target entity description: TensorFlow I/O is an extension library for TensorFlow that provides specialized input/output operations and dataset integrations for a wide range of file formats and data sources beyond the core framework’s built-in support.
-
A.
TensorFlow Extended
TensorFlow Extended (TFX) is an end-to-end platform for deploying, managing, and scaling production machine learning pipelines built on TensorFlow.
-
B.
TensorFlow Serving
TensorFlow Serving is a flexible, high-performance system for deploying and serving machine learning models in production, particularly those built with TensorFlow.
-
C.
TensorFlow ecosystem
The TensorFlow ecosystem is a comprehensive suite of tools, libraries, and extensions built around the TensorFlow machine learning framework to support model development, training, deployment, and visualization.
-
D.
TensorFlow
TensorFlow is an open-source, end-to-end machine learning and deep learning framework widely used for building, training, and deploying neural network models at scale.
-
E.
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.
- F. None of above. chosen
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_69d889e1030481909950e140c63255b9 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e46c474e5481909d2736241b592dab |
completed | April 19, 2026, 5:46 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a01e819cec88190b79a7e5c1ced3e18 |
completed | May 11, 2026, 2:30 p.m. |
| NEDg | Description generation | batch_6a01eed32bbc81908ea9e2588e3741f8 |
completed | May 11, 2026, 2:59 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a01ef6edb7c81908bdbf4d42dd6b9aa |
completed | May 11, 2026, 3:02 p.m. |
Created at: April 10, 2026, 5:51 a.m.