Triple
T4654866
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TensorFlow Extended |
E102383
|
entity |
| Predicate | hasComponent |
P35
|
FINISHED |
| Object |
SchemaGen
SchemaGen is a TensorFlow Extended (TFX) component that automatically infers and generates data schemas by analyzing example datasets for use in machine learning pipelines.
|
E457344
|
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: SchemaGen | Statement: [TensorFlow Extended, hasComponent, SchemaGen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SchemaGen Context triple: [TensorFlow Extended, hasComponent, SchemaGen]
-
A.
Gerar
Gerar is an ancient Philistine city mentioned in the Hebrew Bible, associated with the patriarchs Abraham and Isaac in the region of the Negev.
-
B.
StarDraw
StarDraw is a vector graphics and diagramming application that was part of the StarOffice productivity suite.
-
C.
CREA
CREA is a large reference corpus of contemporary Spanish used for linguistic research and language analysis.
-
D.
SuiteBuilder
SuiteBuilder is a NetSuite configuration tool that lets users customize forms, fields, records, and user interface elements without needing to write code.
-
E.
InGen
InGen is the fictional bioengineering corporation in the Jurassic Park franchise responsible for cloning dinosaurs and creating the dinosaur theme parks.
- 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: SchemaGen Triple: [TensorFlow Extended, hasComponent, SchemaGen]
Generated description
SchemaGen is a TensorFlow Extended (TFX) component that automatically infers and generates data schemas by analyzing example datasets for use in machine learning pipelines.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: SchemaGen Target entity description: SchemaGen is a TensorFlow Extended (TFX) component that automatically infers and generates data schemas by analyzing example datasets for use in machine learning pipelines.
-
A.
Gerar
Gerar is an ancient Philistine city mentioned in the Hebrew Bible, associated with the patriarchs Abraham and Isaac in the region of the Negev.
-
B.
StarDraw
StarDraw is a vector graphics and diagramming application that was part of the StarOffice productivity suite.
-
C.
CREA
CREA is a large reference corpus of contemporary Spanish used for linguistic research and language analysis.
-
D.
SuiteBuilder
SuiteBuilder is a NetSuite configuration tool that lets users customize forms, fields, records, and user interface elements without needing to write code.
-
E.
InGen
InGen is the fictional bioengineering corporation in the Jurassic Park franchise responsible for cloning dinosaurs and creating the dinosaur theme parks.
- 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_69bd43d823288190952279faa0d1d066 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd6317ba70819089145766d3462e57 |
completed | March 20, 2026, 3:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bdfaef125c819097d79f25608302dc |
completed | March 21, 2026, 1:57 a.m. |
| NEDg | Description generation | batch_69bdfc0964c881909e6b98a1c8ea747f |
completed | March 21, 2026, 2:01 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69bdfce1be788190ae3418df301e5136 |
completed | March 21, 2026, 2:05 a.m. |
Created at: March 20, 2026, 1:14 p.m.