Triple

T344333
Position Surface form Disambiguated ID Type / Status
Subject Manchester Airports Group E6905 entity
Predicate alsoKnownAs P39 FINISHED
Object MAG
MAG is a major British airport operator that owns and manages several UK airports, including Manchester Airport.
 E43437 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: MAG | Statement: [Manchester Airports Group, alsoKnownAs, MAG]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: MAG
Context triple: [Manchester Airports Group, alsoKnownAs, MAG]
  • A. MagE
    MagE is a medium-resolution optical echellette spectrograph used on the Magellan telescopes for detailed spectroscopic studies of astronomical objects.
  • B. MGA
    MGA is the commonly used abbreviation for the Maryland General Assembly, the state’s bicameral legislative body.
  • C. Swiss Army
    The Swiss Army is Switzerland’s national military force, responsible for the country’s defense and organized primarily as a militia with a small professional core.
  • D. MAB
    MAB is a German bibliographic data format used for cataloging and exchanging library records, closely related to and historically aligned with MARC standards.
  • E. Armor Branch
    The Armor Branch is the United States Army’s combat arms branch responsible for armored and cavalry forces, specializing in tank and mechanized warfare.
  • 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: MAG
Triple: [Manchester Airports Group, alsoKnownAs, MAG]
Generated description
MAG is a major British airport operator that owns and manages several UK airports, including Manchester Airport.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: MAG
Target entity description: MAG is a major British airport operator that owns and manages several UK airports, including Manchester Airport.
  • A. MagE
    MagE is a medium-resolution optical echellette spectrograph used on the Magellan telescopes for detailed spectroscopic studies of astronomical objects.
  • B. MGA
    MGA is the commonly used abbreviation for the Maryland General Assembly, the state’s bicameral legislative body.
  • C. Swiss Army
    The Swiss Army is Switzerland’s national military force, responsible for the country’s defense and organized primarily as a militia with a small professional core.
  • D. MAB
    MAB is a German bibliographic data format used for cataloging and exchanging library records, closely related to and historically aligned with MARC standards.
  • E. Armor Branch
    The Armor Branch is the United States Army’s combat arms branch responsible for armored and cavalry forces, specializing in tank and mechanized warfare.
  • 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_69a2e7951ba08190960e90823b5078f3 completed Feb. 28, 2026, 1:03 p.m.
NER Named-entity recognition batch_69a2eb01261c81909280128b5ce75eff completed Feb. 28, 2026, 1:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69a3d4ea324c8190acd07727ca0ac193 completed March 1, 2026, 5:55 a.m.
NEDg Description generation batch_69a3d593afbc8190b7148201f890ac77 completed March 1, 2026, 5:58 a.m.
NED2 Entity disambiguation (via description) batch_69a3d659a1308190bad45dc2af33b3f3 completed March 1, 2026, 6:02 a.m.
Created at: Feb. 28, 2026, 1:08 p.m.