Triple

T9097292
Position Surface form Disambiguated ID Type / Status
Subject KDE Applications E218058 entity
Predicate includes P1393 FINISHED
Object KMag
KMag is a screen magnifier utility for the KDE desktop environment that enlarges portions of the screen to improve readability and accessibility.
 E777938 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: KMag | Statement: [KDE Applications, includes, KMag]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: KMag
Context triple: [KDE Applications, includes, KMag]
  • A. CMAG
    CMAG is the abbreviated name for the Commonwealth Ministerial Action Group, a body of foreign ministers that addresses serious or persistent violations of Commonwealth political values by member states.
  • B. MAG
    MAG is the abbreviated name used to represent Magic Gaming, the NBA 2K League affiliate of the Orlando Magic.
  • C. MAG
    MAG is the parent company of Malaysia Airlines and related aviation businesses, overseeing the group’s airline, cargo, and aviation services operations.
  • D. MAG
    MAG is the magnetometer instrument aboard the European Space Agency’s Venus Express spacecraft, designed to measure Venus’s magnetic field and its interaction with the solar wind.
  • E. MAG
    MAG is a major British airport operator that owns and manages several UK airports, including Manchester Airport.
  • 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: KMag
Triple: [KDE Applications, includes, KMag]
Generated description
KMag is a screen magnifier utility for the KDE desktop environment that enlarges portions of the screen to improve readability and accessibility.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: KMag
Target entity description: KMag is a screen magnifier utility for the KDE desktop environment that enlarges portions of the screen to improve readability and accessibility.
  • A. CMAG
    CMAG is the abbreviated name for the Commonwealth Ministerial Action Group, a body of foreign ministers that addresses serious or persistent violations of Commonwealth political values by member states.
  • B. MAG
    MAG is the abbreviated name used to represent Magic Gaming, the NBA 2K League affiliate of the Orlando Magic.
  • C. MAG
    MAG is the parent company of Malaysia Airlines and related aviation businesses, overseeing the group’s airline, cargo, and aviation services operations.
  • D. MAG
    MAG is the magnetometer instrument aboard the European Space Agency’s Venus Express spacecraft, designed to measure Venus’s magnetic field and its interaction with the solar wind.
  • E. MAG
    MAG is a major British airport operator that owns and manages several UK airports, including Manchester Airport.
  • 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_69ca83d9844081908e561e367fda6d45 completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69cc96b7d0d48190a3b15f35bef087e3 completed April 1, 2026, 3:53 a.m.
NED1 Entity disambiguation (via context triple) batch_69d0181a9ae88190ab80d4e80e919f42 completed April 3, 2026, 7:42 p.m.
NEDg Description generation batch_69d019652fe8819096cccb8cff431261 completed April 3, 2026, 7:47 p.m.
NED2 Entity disambiguation (via description) batch_69d01a290de881909482b7eb70bef0e3 completed April 3, 2026, 7:51 p.m.
Created at: March 30, 2026, 7:15 p.m.