Triple

T1962897
Position Surface form Disambiguated ID Type / Status
Subject Kabuliwala E42626 entity
Predicate mainCharacter P1183 FINISHED
Object Rahmat
Rahmat is the central character of Rabindranath Tagore’s short story "Kabuliwala," an Afghan fruit seller in Kolkata whose poignant bond with a young girl highlights themes of love, separation, and humanity.
 E218899 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: Rahmat | Statement: [Kabuliwala, mainCharacter, Rahmat]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Rahmat
Context triple: [Kabuliwala, mainCharacter, Rahmat]
  • A. Harun
    Harun is the Islamic prophet Aaron, brother of Moses, revered for his prophethood and leadership among the Israelites.
  • B. Rashid
    Rashid is one of the child mascots created to represent the themes of innovation and optimism at Expo 2020 Dubai.
  • C. Mirza
    Mirza is a historical noble title of Persian and Central Asian origin, commonly borne by princes and high-ranking members of royal and aristocratic families.
  • D. Thadiq
    Thadiq is a town in central Saudi Arabia known for its traditional architecture and location within the Riyadh administrative region.
  • E. Hamed
    Hamed is a masculine given name commonly used in Arabic-speaking and Muslim-majority cultures.
  • 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: Rahmat
Triple: [Kabuliwala, mainCharacter, Rahmat]
Generated description
Rahmat is the central character of Rabindranath Tagore’s short story "Kabuliwala," an Afghan fruit seller in Kolkata whose poignant bond with a young girl highlights themes of love, separation, and humanity.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Rahmat
Target entity description: Rahmat is the central character of Rabindranath Tagore’s short story "Kabuliwala," an Afghan fruit seller in Kolkata whose poignant bond with a young girl highlights themes of love, separation, and humanity.
  • A. Harun
    Harun is the Islamic prophet Aaron, brother of Moses, revered for his prophethood and leadership among the Israelites.
  • B. Rashid
    Rashid is one of the child mascots created to represent the themes of innovation and optimism at Expo 2020 Dubai.
  • C. Mirza
    Mirza is a historical noble title of Persian and Central Asian origin, commonly borne by princes and high-ranking members of royal and aristocratic families.
  • D. Thadiq
    Thadiq is a town in central Saudi Arabia known for its traditional architecture and location within the Riyadh administrative region.
  • E. Hamed
    Hamed is a masculine given name commonly used in Arabic-speaking and Muslim-majority cultures.
  • 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_69a88711151c8190940b2572095059d7 completed March 4, 2026, 7:25 p.m.
NER Named-entity recognition batch_69abb3ac31a08190abaecac8badc52c7 completed March 7, 2026, 5:12 a.m.
NED1 Entity disambiguation (via context triple) batch_69adfbd133908190add1c78d91a813ef completed March 8, 2026, 10:44 p.m.
NEDg Description generation batch_69adfc6d7fb08190959ee247d0060f4c completed March 8, 2026, 10:47 p.m.
NED2 Entity disambiguation (via description) batch_69adfd2f68f48190ad23db5ca6ae663a completed March 8, 2026, 10:50 p.m.
Created at: March 4, 2026, 7:36 p.m.