Triple

T5693849
Position Surface form Disambiguated ID Type / Status
Subject Irrfan Khan E125488 entity
Predicate givenName P17 FINISHED
Object Irfan
Irfan is the given name of the acclaimed Indian actor Irrfan Khan, known for his work in both Bollywood and international cinema.
 E541887 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: Irfan | Statement: [Irrfan Khan, givenName, Irfan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Irfan
Context triple: [Irrfan Khan, givenName, Irfan]
  • A. Sarfaraz Khan
    Sarfaraz Khan was an 18th-century Nawab of Bengal, Bihar, and Orissa in the Mughal era, known for his brief and turbulent rule that ended with his defeat by Alivardi Khan.
  • B. Hasnat Khan
    Hasnat Khan is a British-Pakistani heart surgeon best known for his romantic relationship with Diana, Princess of Wales.
  • C. Hafeez
    Hafeez is a male given name of Arabic origin, commonly used in South Asia and the Muslim world, meaning "guardian" or "protector."
  • D. Shahrukh Mirza
    Shahrukh Mirza was a 15th-century Timurid ruler who consolidated and governed much of Iran and Central Asia, fostering a flourishing of Persian culture, arts, and architecture.
  • E. Alim Khan
    Alim Khan was a prominent early 19th-century khan of the Kokand Khanate in Central Asia, known for expanding its territory and consolidating its political power.
  • 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: Irfan
Triple: [Irrfan Khan, givenName, Irfan]
Generated description
Irfan is the given name of the acclaimed Indian actor Irrfan Khan, known for his work in both Bollywood and international cinema.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Irfan
Target entity description: Irfan is the given name of the acclaimed Indian actor Irrfan Khan, known for his work in both Bollywood and international cinema.
  • A. Sarfaraz Khan
    Sarfaraz Khan was an 18th-century Nawab of Bengal, Bihar, and Orissa in the Mughal era, known for his brief and turbulent rule that ended with his defeat by Alivardi Khan.
  • B. Hasnat Khan
    Hasnat Khan is a British-Pakistani heart surgeon best known for his romantic relationship with Diana, Princess of Wales.
  • C. Hafeez
    Hafeez is a male given name of Arabic origin, commonly used in South Asia and the Muslim world, meaning "guardian" or "protector."
  • D. Shahrukh Mirza
    Shahrukh Mirza was a 15th-century Timurid ruler who consolidated and governed much of Iran and Central Asia, fostering a flourishing of Persian culture, arts, and architecture.
  • E. Alim Khan
    Alim Khan was a prominent early 19th-century khan of the Kokand Khanate in Central Asia, known for expanding its territory and consolidating its political power.
  • 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_69c0082bb19c8190823a4facd3cba79b completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c023e7dbe48190850b501f223614e3 completed March 22, 2026, 5:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69c05a528a348190a7f6fd4cc3b76c92 completed March 22, 2026, 9:08 p.m.
NEDg Description generation batch_69c05d8890148190a4f81b2c1ca70886 completed March 22, 2026, 9:22 p.m.
NED2 Entity disambiguation (via description) batch_69c0620ee1848190935f5f78abbed7ba completed March 22, 2026, 9:41 p.m.
Created at: March 22, 2026, 3:44 p.m.