Triple

T12559419
Position Surface form Disambiguated ID Type / Status
Subject Marine Logistics Group E295301 entity
Predicate abbreviation P43 FINISHED
Object MLG
MLG is a Marine Logistics Group, a major U.S. Marine Corps unit responsible for providing comprehensive combat service support and logistics to Marine Air-Ground Task Forces.
 E991536 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: MLG | Statement: [Marine Logistics Group, abbreviation, MLG]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: MLG
Context triple: [Marine Logistics Group, abbreviation, MLG]
  • A. MLG
    MLG is the IATA airport code for Abdul Rachman Saleh Airport serving the Malang area in East Java, Indonesia.
  • B. MLN
    MLN is the IATA airport code for Melilla Airport, which serves the Spanish autonomous city of Melilla on the north coast of Africa.
  • C. 3d MLG
    3d MLG is a United States Marine Corps logistics unit that provides comprehensive combat service support to Marine Air-Ground Task Forces, primarily in the Indo-Pacific region.
  • D. MLY
    MLY is the National Rail station code for Morley railway station in West Yorkshire, England.
  • E. 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.
  • 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: MLG
Triple: [Marine Logistics Group, abbreviation, MLG]
Generated description
MLG is a Marine Logistics Group, a major U.S. Marine Corps unit responsible for providing comprehensive combat service support and logistics to Marine Air-Ground Task Forces.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: MLG
Target entity description: MLG is a Marine Logistics Group, a major U.S. Marine Corps unit responsible for providing comprehensive combat service support and logistics to Marine Air-Ground Task Forces.
  • A. MLG
    MLG is the IATA airport code for Abdul Rachman Saleh Airport serving the Malang area in East Java, Indonesia.
  • B. MLN
    MLN is the IATA airport code for Melilla Airport, which serves the Spanish autonomous city of Melilla on the north coast of Africa.
  • C. 3d MLG
    3d MLG is a United States Marine Corps logistics unit that provides comprehensive combat service support to Marine Air-Ground Task Forces, primarily in the Indo-Pacific region.
  • D. MLY
    MLY is the National Rail station code for Morley railway station in West Yorkshire, England.
  • E. 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.
  • 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_69d6ad9cac2c81908e8a7bed82d1e21d completed April 8, 2026, 7:33 p.m.
NER Named-entity recognition batch_69d95491d9688190a6b88a939124233e completed April 10, 2026, 7:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6558b307c81909ec5407af013de1d completed May 2, 2026, 7:50 p.m.
NEDg Description generation batch_69f656a6dafc81908acf59c0ba65189a completed May 2, 2026, 7:55 p.m.
NED2 Entity disambiguation (via description) batch_69f65b4d109c8190b48c71f664e7bb3f completed May 2, 2026, 8:15 p.m.
Created at: April 8, 2026, 11:48 p.m.