Triple
T4654864
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TensorFlow Extended |
E102383
|
entity |
| Predicate | hasComponent |
P35
|
FINISHED |
| Object |
ExampleGen
ExampleGen is a TensorFlow Extended (TFX) component responsible for ingesting and converting raw data into standardized examples for machine learning pipelines.
|
E457342
|
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: ExampleGen | Statement: [TensorFlow Extended, hasComponent, ExampleGen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ExampleGen Context triple: [TensorFlow Extended, hasComponent, ExampleGen]
-
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.
InGen
InGen is the fictional bioengineering corporation in the Jurassic Park franchise responsible for cloning dinosaurs and creating the dinosaur theme parks.
-
C.
Geneta
Geneta is a residential district and suburb within Södertälje Municipality in Sweden.
-
D.
Advanced Génifique
Advanced Génifique is a popular Lancôme skincare line focused on improving skin’s radiance, texture, and youthful appearance through serum-based formulations.
-
E.
Genn
Genn is a surname most notably associated with British actor and barrister Leo Genn.
- 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: ExampleGen Triple: [TensorFlow Extended, hasComponent, ExampleGen]
Generated description
ExampleGen is a TensorFlow Extended (TFX) component responsible for ingesting and converting raw data into standardized examples for machine learning pipelines.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: ExampleGen Target entity description: ExampleGen is a TensorFlow Extended (TFX) component responsible for ingesting and converting raw data into standardized examples for 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.
InGen
InGen is the fictional bioengineering corporation in the Jurassic Park franchise responsible for cloning dinosaurs and creating the dinosaur theme parks.
-
C.
Geneta
Geneta is a residential district and suburb within Södertälje Municipality in Sweden.
-
D.
Advanced Génifique
Advanced Génifique is a popular Lancôme skincare line focused on improving skin’s radiance, texture, and youthful appearance through serum-based formulations.
-
E.
Genn
Genn is a surname most notably associated with British actor and barrister Leo Genn.
- 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.