Triple

T27762327
Position Surface form Disambiguated ID Type / Status
Subject Batch Normalization E701500 entity
Predicate appliedBetween P18583 FINISHED
Object linear transformation and nonlinearity LITERAL FINISHED

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: linear transformation and nonlinearity | Statement: [Batch Normalization, appliedBetween, linear transformation and nonlinearity]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: appliedBetween
Context triple: [Batch Normalization, appliedBetween, linear transformation and nonlinearity]
  • A. appliesBetween chosen
    Indicates that a specified condition, rule, or relationship holds specifically between two or more entities, rather than for each entity individually.
  • B. appliedWhen
    Indicates the condition, time, or circumstances under which something (such as a rule, action, or effect) becomes applicable or is carried out.
  • C. formedBetween
    Indicates a relationship in which a connection, bond, or structure has come into existence linking two or more entities.
  • D. usedBetween
    Indicates that something serves as a means, medium, or shared resource connecting or operating jointly between two entities.
  • E. appliesAt
    Indicates that an action, rule, or condition is relevant to or in effect at a specific location, context, or point in time.
  • F. None of above.

Provenance (3 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_69ef6a5193808190816eb7d0020b2d87 completed April 27, 2026, 1:53 p.m.
NER Named-entity recognition batch_69f7cec454a88190a9f3bbee2b856636 completed May 3, 2026, 10:40 p.m.
PD Predicate disambiguation batch_69f7c8977c288190997a892ec5f756ed completed May 3, 2026, 10:13 p.m.
Created at: April 27, 2026, 4:28 p.m.