Triple

T2976519
Position Surface form Disambiguated ID Type / Status
Subject U.S. Army North E80410 entity
Predicate abbreviation P43 FINISHED
Object ARNORTH
ARNORTH is the U.S. Army’s component of U.S. Northern Command responsible for homeland defense, civil support, and security cooperation within North America.
 E316122 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: ARNORTH | Statement: [U.S. Army North, abbreviation, ARNORTH]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: ARNORTH
Context triple: [U.S. Army North, abbreviation, ARNORTH]
  • A. Farguson
    Farguson is an alternative spelling of the surname Ferguson, which is of Scottish origin.
  • B. Ain
    Ain is a department in eastern France known for its diverse landscapes, historic towns, and proximity to both the Alps and the Swiss border.
  • C. ANE
    ANE is Apple's dedicated on-device neural processing unit designed to accelerate machine learning tasks efficiently on Apple hardware.
  • D. Norris
    Norris is a surname most notably associated with influential American politician George W. Norris, a progressive-era U.S. senator from Nebraska.
  • E. Hannington
    Hannington is a small rural village in Wiltshire, England, known for its traditional English countryside setting and historic character.
  • 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: ARNORTH
Triple: [U.S. Army North, abbreviation, ARNORTH]
Generated description
ARNORTH is the U.S. Army’s component of U.S. Northern Command responsible for homeland defense, civil support, and security cooperation within North America.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: ARNORTH
Target entity description: ARNORTH is the U.S. Army’s component of U.S. Northern Command responsible for homeland defense, civil support, and security cooperation within North America.
  • A. Farguson
    Farguson is an alternative spelling of the surname Ferguson, which is of Scottish origin.
  • B. Ain
    Ain is a department in eastern France known for its diverse landscapes, historic towns, and proximity to both the Alps and the Swiss border.
  • C. ANE
    ANE is Apple's dedicated on-device neural processing unit designed to accelerate machine learning tasks efficiently on Apple hardware.
  • D. Norris
    Norris is a surname most notably associated with influential American politician George W. Norris, a progressive-era U.S. senator from Nebraska.
  • E. Hannington
    Hannington is a small rural village in Wiltshire, England, known for its traditional English countryside setting and historic character.
  • 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_69ad8b15f6ac8190be5fd16a33edcb4f completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad998c589c8190b4530f3fb8975187 completed March 8, 2026, 3:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69b108e99ea881908cccc6656e8653c3 completed March 11, 2026, 6:17 a.m.
NEDg Description generation batch_69b109fc0f308190b1a0105a67491c54 completed March 11, 2026, 6:21 a.m.
NED2 Entity disambiguation (via description) batch_69b10a78414481908689ac3ba5265388 completed March 11, 2026, 6:23 a.m.
Created at: March 8, 2026, 2:58 p.m.