Triple
T18704933
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SchemaGen |
E457344
|
entity |
| Predicate | output |
P490
|
FINISHED |
| Object |
TensorFlow Metadata schema
TensorFlow Metadata schema is a standardized, machine-readable specification that describes the structure, types, and constraints of data used in TensorFlow Extended (TFX) pipelines.
|
E1338346
|
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 Metadata schema | Statement: [SchemaGen, output, TensorFlow Metadata schema]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: TensorFlow Metadata schema Context triple: [SchemaGen, output, TensorFlow Metadata schema]
-
A.
TensorFlow Serving
TensorFlow Serving is a flexible, high-performance system for deploying and serving machine learning models in production, particularly those built with TensorFlow.
-
B.
TensorFlow Extended
TensorFlow Extended (TFX) is an end-to-end platform for deploying, managing, and scaling production machine learning pipelines built on TensorFlow.
-
C.
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.
-
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 GraphDef
TensorFlow GraphDef is a serialized protocol buffer format that represents the computational graph structure of a TensorFlow model, including its operations and data flow.
- 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 Metadata schema Triple: [SchemaGen, output, TensorFlow Metadata schema]
Generated description
TensorFlow Metadata schema is a standardized, machine-readable specification that describes the structure, types, and constraints of data used in TensorFlow Extended (TFX) pipelines.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: TensorFlow Metadata schema Target entity description: TensorFlow Metadata schema is a standardized, machine-readable specification that describes the structure, types, and constraints of data used in TensorFlow Extended (TFX) pipelines.
-
A.
TensorFlow Serving
TensorFlow Serving is a flexible, high-performance system for deploying and serving machine learning models in production, particularly those built with TensorFlow.
-
B.
TensorFlow Extended
TensorFlow Extended (TFX) is an end-to-end platform for deploying, managing, and scaling production machine learning pipelines built on TensorFlow.
-
C.
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.
-
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 GraphDef
TensorFlow GraphDef is a serialized protocol buffer format that represents the computational graph structure of a TensorFlow model, including its operations and data flow.
- 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_69d8d392aad081909fe31aa03e6e97d1 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e5671665bc8190b9b4a4ce4ec5b2eb |
completed | April 19, 2026, 11:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a052b38ab548190b46ecda128e93c9b |
completed | May 14, 2026, 1:54 a.m. |
| NEDg | Description generation | batch_6a052c9486908190a5cdb60f7cb65888 |
completed | May 14, 2026, 1:59 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a052cec9c788190a0c9396b35dd95c0 |
completed | May 14, 2026, 2:01 a.m. |
Created at: April 10, 2026, 11:49 a.m.