Triple

T21909351
Position Surface form Disambiguated ID Type / Status
Subject Muna Madan E541021 entity
Predicate featuresCharacter P626 FINISHED
Object Madan
Madan is the tragic protagonist of the classic Nepali narrative poem "Muna Madan," whose journey and fate highlight themes of love, hardship, and social injustice.
 E1508239 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: Madan | Statement: [Muna Madan, featuresCharacter, Madan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Madan
Context triple: [Muna Madan, featuresCharacter, Madan]
  • A. Madan
    Madan is a small town in southern Bulgaria known for its mountainous surroundings and mining heritage.
  • B. Maddan
    Maddan is an alternative spelling of the name Madden, which is most commonly associated with the long-running American football video game series and the surname of legendary NFL coach and commentator John Madden.
  • C. Chudamani
    Chudamani is a key supporting character in the 1994 Hindi film "Krantiveer," contributing to the movie’s intense social and emotional drama.
  • D. Madana
    Madana is an Indian film in which actress Zarina Wahab delivered one of her notable performances.
  • E. Mandarmani
    Mandarmani is a seaside resort village in West Bengal, India, known for its long, drivable beach and growing popularity as a quieter alternative to busier coastal destinations.
  • 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: Madan
Triple: [Muna Madan, featuresCharacter, Madan]
Generated description
Madan is the tragic protagonist of the classic Nepali narrative poem "Muna Madan," whose journey and fate highlight themes of love, hardship, and social injustice.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Madan
Target entity description: Madan is the tragic protagonist of the classic Nepali narrative poem "Muna Madan," whose journey and fate highlight themes of love, hardship, and social injustice.
  • A. Madan
    Madan is a small town in southern Bulgaria known for its mountainous surroundings and mining heritage.
  • B. Maddan
    Maddan is an alternative spelling of the name Madden, which is most commonly associated with the long-running American football video game series and the surname of legendary NFL coach and commentator John Madden.
  • C. Chudamani
    Chudamani is a key supporting character in the 1994 Hindi film "Krantiveer," contributing to the movie’s intense social and emotional drama.
  • D. Madana
    Madana is an Indian film in which actress Zarina Wahab delivered one of her notable performances.
  • E. Mandarmani
    Mandarmani is a seaside resort village in West Bengal, India, known for its long, drivable beach and growing popularity as a quieter alternative to busier coastal destinations.
  • 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_69e0c47b4e8c81908c8076eaa4c8e4f2 completed April 16, 2026, 11:14 a.m.
NER Named-entity recognition batch_69f121d8c3108190a178ec6b3857da3f completed April 28, 2026, 9:08 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0a5a0d542c8190879307fd9228d2b6 completed May 18, 2026, 12:15 a.m.
NEDg Description generation batch_6a0a5a8228a4819082857231202487e6 completed May 18, 2026, 12:17 a.m.
NED2 Entity disambiguation (via description) batch_6a0a5af2b0188190b6221d1a2f94c5af completed May 18, 2026, 12:18 a.m.
Created at: April 16, 2026, 7:39 p.m.