Triple

T22819723
Position Surface form Disambiguated ID Type / Status
Subject AdaGrad E565192 entity
Predicate influenced P9 FINISHED
Object Adam
Adam is a popular stochastic optimization algorithm in machine learning that combines ideas from momentum and adaptive learning rates to efficiently train deep neural networks.
E701497 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: Adam | Statement: [AdaGrad, influenced, Adam]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Adam
Context triple: [AdaGrad, influenced, Adam]
  • A. Adam
    Adam is a common surname of Scottish and English origin, borne by various notable individuals including architects, politicians, and artists.
  • B. Adam
    "Adam" is a 1983 American television film based on the true story of the kidnapping and murder of Adam Walsh, in which JoBeth Williams stars as the boy’s mother.
  • C. Adam
    Adam is a young, inexperienced stripper who is mentored and drawn into the world of male entertainment in the film "Magic Mike."
  • D. Adam
    Adam is a person who enjoys or benefits from eating lasagne.
  • E. Adam
    "Adam" is a track featured on the album *Illuminations*, likely contributing to its overall atmospheric and thematic sound.
  • 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: Adam
Triple: [AdaGrad, influenced, Adam]
Generated description
Adam is a popular stochastic optimization algorithm in machine learning that combines ideas from momentum and adaptive learning rates to efficiently train deep neural networks.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Adam
Target entity description: Adam is a popular stochastic optimization algorithm in machine learning that combines ideas from momentum and adaptive learning rates to efficiently train deep neural networks.
  • A. Adam chosen
    Adam is a widely used stochastic optimization algorithm in machine learning that combines ideas from momentum and adaptive learning rates to efficiently train deep neural networks.
  • B. Adam
    Adam is a popular stochastic optimization algorithm widely used to train deep learning models by adaptively adjusting learning rates for each parameter.
  • C. Adam
    Adam is a masculine given name of Hebrew origin, commonly used in many cultures and languages.
  • D. Adam
    Adam is a renowned sculpture by Auguste Rodin, notable for its expressive depiction of the biblical figure and housed in the Rodin Museum.
  • E. Adam
    Adam is a common surname of Scottish and English origin, borne by various notable individuals including architects, politicians, and artists.
  • F. None of above.

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_69e2458426188190b58b8ab4844fe420 completed April 17, 2026, 2:36 p.m.
NER Named-entity recognition batch_69f17dcf39a88190bec26affc304236d completed April 29, 2026, 3:41 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0ba7a674908190a3d08c2c7210028e completed May 18, 2026, 11:58 p.m.
NEDg Description generation batch_6a0ba82b00648190887a3164ececc66d completed May 19, 2026, midnight
NED2 Entity disambiguation (via description) batch_6a0ba87ed5148190a96c0ea7e1cbb865 completed May 19, 2026, 12:02 a.m.
Created at: April 17, 2026, 3:33 p.m.