Triple

T18704993
Position Surface form Disambiguated ID Type / Status
Subject ExampleValidator E457345 entity
Predicate integratesWith P1075 FINISHED
Object ExampleGen NE NERFINISHED

How this triple was built (2 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: [ExampleValidator, integratesWith, ExampleGen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: ExampleGen
Context triple: [ExampleValidator, integratesWith, ExampleGen]
  • A. ExampleGen chosen
    ExampleGen is a TensorFlow Extended (TFX) component responsible for ingesting and converting raw data into standardized examples for machine learning pipelines.
  • B. 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.
  • C. InGen
    InGen is the fictional bioengineering corporation in the Jurassic Park franchise responsible for cloning dinosaurs and creating the dinosaur theme parks.
  • D. Generator
    "Generator" is a 1992 punk rock album by Bad Religion that marked a darker, more experimental turn in the band's melodic hardcore sound.
  • E. Genera
    Genera is an advanced object-oriented Lisp-based operating environment created by Symbolics for its line of Lisp machines.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

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_69d8d392aad081909fe31aa03e6e97d1 completed April 10, 2026, 10:40 a.m.
NER Named-entity recognition batch_69e5671665bc8190b9b4a4ce4ec5b2eb completed April 19, 2026, 11:36 p.m.
Created at: April 10, 2026, 11:49 a.m.