Triple

T2438971
Position Surface form Disambiguated ID Type / Status
Subject 2nd Marine Logistics Group E53227 entity
Predicate hasAbbreviation P43 FINISHED
Object 2d MLG
2d MLG is a major logistics formation of the United States Marine Corps responsible for providing supply, maintenance, transportation, and support services to Marine forces.
 E266870 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: 2d MLG | Statement: [2nd Marine Logistics Group, hasAbbreviation, 2d MLG]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: 2d MLG
Context triple: [2nd Marine Logistics Group, hasAbbreviation, 2d MLG]
  • A. D2
    D2 is a line of the Moscow Central Diameters suburban rail system, providing cross-city commuter rail service through Moscow and its surrounding areas.
  • B. L2M
    L2M is a DARPA research initiative focused on developing AI systems capable of continuous, lifelong learning and adaptation.
  • C. Division 2
    Division 2 is a designated subdivision within Section VIII, typically representing a specific category or set of rules in a larger organizational or regulatory framework.
  • D. MML
    MML is a major inter-city rail route in England connecting London with key cities in the East Midlands and South Yorkshire.
  • E. MLI
    MLI is the three-letter ISO 3166-1 alpha-3 country code assigned to Mali.
  • 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: 2d MLG
Triple: [2nd Marine Logistics Group, hasAbbreviation, 2d MLG]
Generated description
2d MLG is a major logistics formation of the United States Marine Corps responsible for providing supply, maintenance, transportation, and support services to Marine forces.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: 2d MLG
Target entity description: 2d MLG is a major logistics formation of the United States Marine Corps responsible for providing supply, maintenance, transportation, and support services to Marine forces.
  • A. D2
    D2 is a line of the Moscow Central Diameters suburban rail system, providing cross-city commuter rail service through Moscow and its surrounding areas.
  • B. L2M
    L2M is a DARPA research initiative focused on developing AI systems capable of continuous, lifelong learning and adaptation.
  • C. Division 2
    Division 2 is a designated subdivision within Section VIII, typically representing a specific category or set of rules in a larger organizational or regulatory framework.
  • D. MML
    MML is a major inter-city rail route in England connecting London with key cities in the East Midlands and South Yorkshire.
  • E. MLI
    MLI is the three-letter ISO 3166-1 alpha-3 country code assigned to Mali.
  • 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_69ab495b6dac8190ac82661aa1452222 completed March 6, 2026, 9:38 p.m.
NER Named-entity recognition batch_69abc9f62ad081909373134c5adf65d9 completed March 7, 2026, 6:47 a.m.
NED1 Entity disambiguation (via context triple) batch_69aef0b0e920819088bd7ee3684c81fe completed March 9, 2026, 4:09 p.m.
NEDg Description generation batch_69aef4d2cd3c81908773ac7ff9e4b28d completed March 9, 2026, 4:26 p.m.
NED2 Entity disambiguation (via description) batch_69aef54c3f1c819085019bd8e48d7db7 completed March 9, 2026, 4:29 p.m.
Created at: March 6, 2026, 9:43 p.m.