Triple

T36490175
Position Surface form Disambiguated ID Type / Status
Subject Neural Machine Translation by Jointly Learning to Align and Translate E899030 entity
Predicate attentionScoreFunction P185597 FINISHED
Object feedforward neural network 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: feedforward neural network | Statement: [Neural Machine Translation by Jointly Learning to Align and Translate, attentionScoreFunction, feedforward neural network]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: attentionScoreFunction
Context triple: [Neural Machine Translation by Jointly Learning to Align and Translate, attentionScoreFunction, feedforward neural network]
  • A. rankingImpact
    Indicates how an entity’s position or level in a ranking is affected or influenced by another factor or action.
  • B. rankingAuthority
    Indicates that one entity serves as the official source or body responsible for assigning rankings or ordered evaluations to another entity.
  • C. rankSignificance
    Indicates how important or influential one entity is relative to others within a specified context or ordering.
  • D. relevanceIn
    Indicates that something is pertinent or applicable within a specified context, scope, or domain.
  • E. featuresScoreBy
    Indicates a scoring relationship where a feature or set of features is evaluated and assigned a score according to a specified criterion or entity.
  • F. None of above. chosen

Provenance (4 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_69f76e5ad4588190bdbce60c52fbb785 completed May 3, 2026, 3:48 p.m.
NER Named-entity recognition batch_69f7be9d07ac8190adf796cbef60daf6 completed May 3, 2026, 9:31 p.m.
PD Predicate disambiguation batch_69f7bccf05bc8190b61fdb2b2a315811 completed May 3, 2026, 9:23 p.m.
PDg Predicate description generation batch_69f7be9b9ab481908328e0e8d8ac73d4 completed May 3, 2026, 9:31 p.m.
Created at: May 3, 2026, 4:10 p.m.