Triple

T5028129
Position Surface form Disambiguated ID Type / Status
Subject RC2 E113227 entity
Predicate alsoKnownAs P39 FINISHED
Object ARC2
ARC2 is a deep learning model architecture designed for efficient and accurate text classification tasks.
 E487731 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: ARC2 | Statement: [RC2, alsoKnownAs, ARC2]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: ARC2
Context triple: [RC2, alsoKnownAs, ARC2]
  • A. ARC
    ARC is a family of configurable 32-bit RISC processor architectures commonly used in embedded and SoC designs.
  • B. ARC
    ARC is the commonly used acronym for the Augmentation Research Center, a pioneering research group known for its early work on interactive computing and human–computer interaction.
  • C. ARCIC
    ARCIC is an international ecumenical body that fosters theological dialogue and seeks closer unity between the Anglican Communion and the Roman Catholic Church.
  • D. ARCX
    ARCX is the Market Identifier Code for the NYSE Arca exchange, an electronic securities trading platform operated by the New York Stock Exchange.
  • E. Arc
    Arc is the main protagonist of the role-playing game Arc the Lad, a young hero who embarks on a quest to save his world from impending destruction.
  • 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: ARC2
Triple: [RC2, alsoKnownAs, ARC2]
Generated description
ARC2 is a deep learning model architecture designed for efficient and accurate text classification tasks.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: ARC2
Target entity description: ARC2 is a deep learning model architecture designed for efficient and accurate text classification tasks.
  • A. ARC
    ARC is a family of configurable 32-bit RISC processor architectures commonly used in embedded and SoC designs.
  • B. ARC
    ARC is the commonly used acronym for the Augmentation Research Center, a pioneering research group known for its early work on interactive computing and human–computer interaction.
  • C. ARCIC
    ARCIC is an international ecumenical body that fosters theological dialogue and seeks closer unity between the Anglican Communion and the Roman Catholic Church.
  • D. ARCX
    ARCX is the Market Identifier Code for the NYSE Arca exchange, an electronic securities trading platform operated by the New York Stock Exchange.
  • E. Arc
    Arc is the main protagonist of the role-playing game Arc the Lad, a young hero who embarks on a quest to save his world from impending destruction.
  • 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_69bd443775e48190a646ffbfc4334723 completed March 20, 2026, 12:57 p.m.
NER Named-entity recognition batch_69bd738d852c8190a122354f1e1f5343 completed March 20, 2026, 4:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69be9c64db5c81909224c82ae9d9e0ab completed March 21, 2026, 1:25 p.m.
NEDg Description generation batch_69be9ce7959081908b9ddb4c677c477c completed March 21, 2026, 1:28 p.m.
NED2 Entity disambiguation (via description) batch_69be9d7e00f88190b2d12e872fadc181 completed March 21, 2026, 1:30 p.m.
Created at: March 20, 2026, 1:36 p.m.