Triple

T2497377
Position Surface form Disambiguated ID Type / Status
Subject MG 42 E52182 entity
Predicate influenced P9 FINISHED
Object FN MAG
The FN MAG is a Belgian-designed 7.62×51mm NATO general-purpose machine gun widely used by military forces around the world for its reliability and versatility in both infantry and vehicle-mounted roles.
 E271695 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: FN MAG | Statement: [MG 42, influenced, FN MAG]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: FN MAG
Context triple: [MG 42, influenced, FN MAG]
  • A. MAG
    MAG is a major British airport operator that owns and manages several UK airports, including Manchester Airport.
  • B. FMG
    FMG is the Faculty of Social and Behavioural Sciences at the University of Amsterdam, encompassing disciplines such as psychology, sociology, political science, and communication science.
  • C. Forum Magnum
    Forum Magnum is the Latin name for the Roman Forum, the central public and political hub of ancient Rome.
  • D. MF
    MF is the two-letter IATA airline designator assigned to XiamenAir, a major Chinese carrier based in Xiamen.
  • E. MagE
    MagE is a medium-resolution optical echellette spectrograph used on the Magellan telescopes for detailed spectroscopic studies of astronomical objects.
  • 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: FN MAG
Triple: [MG 42, influenced, FN MAG]
Generated description
The FN MAG is a Belgian-designed 7.62×51mm NATO general-purpose machine gun widely used by military forces around the world for its reliability and versatility in both infantry and vehicle-mounted roles.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: FN MAG
Target entity description: The FN MAG is a Belgian-designed 7.62×51mm NATO general-purpose machine gun widely used by military forces around the world for its reliability and versatility in both infantry and vehicle-mounted roles.
  • A. MAG
    MAG is a major British airport operator that owns and manages several UK airports, including Manchester Airport.
  • B. FMG
    FMG is the Faculty of Social and Behavioural Sciences at the University of Amsterdam, encompassing disciplines such as psychology, sociology, political science, and communication science.
  • C. Forum Magnum
    Forum Magnum is the Latin name for the Roman Forum, the central public and political hub of ancient Rome.
  • D. MF
    MF is the two-letter IATA airline designator assigned to XiamenAir, a major Chinese carrier based in Xiamen.
  • E. MagE
    MagE is a medium-resolution optical echellette spectrograph used on the Magellan telescopes for detailed spectroscopic studies of astronomical objects.
  • 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_69ab4955111c8190835bf619adec21ff completed March 6, 2026, 9:38 p.m.
NER Named-entity recognition batch_69abd1ad2f8c81908853e97d75081e84 completed March 7, 2026, 7:20 a.m.
NED1 Entity disambiguation (via context triple) batch_69af1f9be594819099a03a2784691124 completed March 9, 2026, 7:29 p.m.
NEDg Description generation batch_69af200e2db4819085851a45213edc89 completed March 9, 2026, 7:31 p.m.
NED2 Entity disambiguation (via description) batch_69af208dfab081909d706aad8ff5f615 completed March 9, 2026, 7:33 p.m.
Created at: March 6, 2026, 9:46 p.m.