Triple

T4425382
Position Surface form Disambiguated ID Type / Status
Subject JAX E95194 entity
Predicate compatibleWith P203 FINISHED
Object Flax
Flax is a neural network library for JAX that provides a flexible, modular framework for building and training machine learning models in Python.
 E438356 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: Flax | Statement: [JAX, compatibleWith, Flax]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Flax
Context triple: [JAX, compatibleWith, Flax]
  • A. Hahnenklee
    Hahnenklee is a village in the Harz Mountains of Germany, known as a popular tourist resort for hiking, winter sports, and its distinctive stave church.
  • B. Avena fatua
    Avena fatua, commonly known as wild oat, is a widespread annual grass species often considered a weed in agricultural and disturbed habitats.
  • C. Fagopyrum
    Fagopyrum is a genus of flowering plants best known for species such as buckwheat, which are cultivated for their edible seeds and use as pseudocereals.
  • D. Dill
    The Dill is a river in central Germany that flows through Hesse and North Rhine-Westphalia before joining the Lahn.
  • E. Lucerne
    Lucerne is a picturesque Swiss city known for its preserved medieval architecture, lakeside setting on Lake Lucerne, and proximity to the Swiss Alps.
  • 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: Flax
Triple: [JAX, compatibleWith, Flax]
Generated description
Flax is a neural network library for JAX that provides a flexible, modular framework for building and training machine learning models in Python.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Flax
Target entity description: Flax is a neural network library for JAX that provides a flexible, modular framework for building and training machine learning models in Python.
  • A. Hahnenklee
    Hahnenklee is a village in the Harz Mountains of Germany, known as a popular tourist resort for hiking, winter sports, and its distinctive stave church.
  • B. Avena fatua
    Avena fatua, commonly known as wild oat, is a widespread annual grass species often considered a weed in agricultural and disturbed habitats.
  • C. Fagopyrum
    Fagopyrum is a genus of flowering plants best known for species such as buckwheat, which are cultivated for their edible seeds and use as pseudocereals.
  • D. Dill
    The Dill is a river in central Germany that flows through Hesse and North Rhine-Westphalia before joining the Lahn.
  • E. Lucerne
    Lucerne is a picturesque Swiss city known for its preserved medieval architecture, lakeside setting on Lake Lucerne, and proximity to the Swiss Alps.
  • 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_69b3453c2a0c8190926b574c90766db9 completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b3554e40ec8190982acc0948da2f42 completed March 13, 2026, 12:07 a.m.
NED1 Entity disambiguation (via context triple) batch_69b5f633a69c8190b062c2a78b0f8319 completed March 14, 2026, 11:58 p.m.
NEDg Description generation batch_69b5f6bcfa0481909d07ffb2a975a350 completed March 15, 2026, 12:01 a.m.
NED2 Entity disambiguation (via description) batch_69b5f733c660819081c68dc3ec342e12 completed March 15, 2026, 12:02 a.m.
Created at: March 12, 2026, 11:30 p.m.