Triple

T7878192
Position Surface form Disambiguated ID Type / Status
Subject Kappa E182908 entity
Predicate hasCharacter P2308 FINISHED
Object Maggu
Maggu is a character from the Indian comic series "Chacha Chaudhary," known as one of the recurring goons who often clash with the protagonists.
 E701626 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: Maggu | Statement: [Kappa, hasCharacter, Maggu]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Maggu
Context triple: [Kappa, hasCharacter, Maggu]
  • A. Jajaghu
    Jajaghu is an alternative name for the Jago Temple, an ancient religious structure in Indonesia known for its historical and architectural significance.
  • B. Mugatu
    Mugatu is the flamboyant, villainous fashion designer portrayed by Will Ferrell in the comedy film "Zoolander."
  • C. Magar
    Magar are an indigenous ethnic group of Nepal known for their distinct language, culture, and significant presence in the country’s military history.
  • D. Munnik
    Munnik is a given name associated with J. B. M. Hertzog, a prominent early 20th-century South African prime minister and political leader.
  • E. Masmo
    Masmo is a residential district in the southern suburbs of Stockholm, Sweden, known for its metro station on the red line and proximity to green areas and Lake Mälaren.
  • 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: Maggu
Triple: [Kappa, hasCharacter, Maggu]
Generated description
Maggu is a character from the Indian comic series "Chacha Chaudhary," known as one of the recurring goons who often clash with the protagonists.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Maggu
Target entity description: Maggu is a character from the Indian comic series "Chacha Chaudhary," known as one of the recurring goons who often clash with the protagonists.
  • A. Jajaghu
    Jajaghu is an alternative name for the Jago Temple, an ancient religious structure in Indonesia known for its historical and architectural significance.
  • B. Mugatu
    Mugatu is the flamboyant, villainous fashion designer portrayed by Will Ferrell in the comedy film "Zoolander."
  • C. Magar
    Magar are an indigenous ethnic group of Nepal known for their distinct language, culture, and significant presence in the country’s military history.
  • D. Munnik
    Munnik is a given name associated with J. B. M. Hertzog, a prominent early 20th-century South African prime minister and political leader.
  • E. Masmo
    Masmo is a residential district in the southern suburbs of Stockholm, Sweden, known for its metro station on the red line and proximity to green areas and Lake Mälaren.
  • 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_69ca828a17248190b46defe758bc5ad3 completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69cb39bd64e481909f699e7dd2818b8f completed March 31, 2026, 3:04 a.m.
NED1 Entity disambiguation (via context triple) batch_69cb5b7fef308190bbc74e13f4205192 completed March 31, 2026, 5:28 a.m.
NEDg Description generation batch_69cb7630b8908190a0b8f4856bceea0a completed March 31, 2026, 7:22 a.m.
NED2 Entity disambiguation (via description) batch_69cbbfb894588190971ade076acdbd5c completed March 31, 2026, 12:36 p.m.
Created at: March 30, 2026, 4:57 p.m.