Triple

T12514712
Position Surface form Disambiguated ID Type / Status
Subject GNU As E299165 entity
Predicate supportsTarget P5090 FINISHED
Object OR1K
OR1K is the OpenRISC 1000, an open-source RISC processor architecture used for research, education, and embedded systems development.
 E986965 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: OR1K | Statement: [GNU As, supportsTarget, OR1K]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: OR1K
Context triple: [GNU As, supportsTarget, OR1K]
  • A. OR-1
    OR-1 is the commonly used shorthand for Oregon's 1st congressional district, a U.S. House of Representatives district in northwestern Oregon.
  • B. OR-5
    OR-5 is the NATO-standard enlisted rank level corresponding to mid-grade non-commissioned officers such as sergeants in various armed forces.
  • C. ORU
    ORU is the IATA airport code for Juan Mendoza Airport, a regional airport serving the city of Oruro in Bolivia.
  • D. ORU
    ORU is a private Christian liberal arts university in Tulsa, Oklahoma, founded by evangelist Oral Roberts and known for integrating faith-based education with a diverse range of academic programs.
  • E. OROLSI
    OROLSI is a United Nations office responsible for supporting rule of law, security sector reform, and related institution-building in conflict and post-conflict settings.
  • 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: OR1K
Triple: [GNU As, supportsTarget, OR1K]
Generated description
OR1K is the OpenRISC 1000, an open-source RISC processor architecture used for research, education, and embedded systems development.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: OR1K
Target entity description: OR1K is the OpenRISC 1000, an open-source RISC processor architecture used for research, education, and embedded systems development.
  • A. OR-1
    OR-1 is the commonly used shorthand for Oregon's 1st congressional district, a U.S. House of Representatives district in northwestern Oregon.
  • B. OR-5
    OR-5 is the NATO-standard enlisted rank level corresponding to mid-grade non-commissioned officers such as sergeants in various armed forces.
  • C. ORU
    ORU is the IATA airport code for Juan Mendoza Airport, a regional airport serving the city of Oruro in Bolivia.
  • D. ORU
    ORU is a private Christian liberal arts university in Tulsa, Oklahoma, founded by evangelist Oral Roberts and known for integrating faith-based education with a diverse range of academic programs.
  • E. OROLSI
    OROLSI is a United Nations office responsible for supporting rule of law, security sector reform, and related institution-building in conflict and post-conflict settings.
  • 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_69d6ada5cdd48190860d9ce30aff69be completed April 8, 2026, 7:33 p.m.
NER Named-entity recognition batch_69d9541e752c8190bf12d2b5a37b53df completed April 10, 2026, 7:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69f64bbd58b88190baeb99380babf64f completed May 2, 2026, 7:08 p.m.
NEDg Description generation batch_69f64ce1b0ec8190bcbd245255e548b5 completed May 2, 2026, 7:13 p.m.
NED2 Entity disambiguation (via description) batch_69f64da735f48190b051ce173c13e5b2 completed May 2, 2026, 7:16 p.m.
Created at: April 8, 2026, 9:57 p.m.