Triple

T14005081
Position Surface form Disambiguated ID Type / Status
Subject Arjumand Banu Begum E336925 entity
Predicate spouse P13 FINISHED
Object Prince Khurram E340934 NE FINISHED

How this triple was built (2 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: Prince Khurram | Statement: [Arjumand Banu Begum, spouse, Prince Khurram]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Prince Khurram
Context triple: [Arjumand Banu Begum, spouse, Prince Khurram]
  • A. Prince Khurram chosen
    Prince Khurram, later known as the Mughal emperor Shah Jahan, was a 17th-century ruler of the Mughal Empire famed for commissioning the Taj Mahal.
  • B. Muhammad Khan Bangash
    Muhammad Khan Bangash was an 18th-century Pashtun military leader and Mughal noble who rose to prominence as a powerful regional ruler in northern India.
  • C. Raja Zulqarnain Khan
    Raja Zulqarnain Khan is a Pakistani politician who served as the President of Azad Jammu and Kashmir.
  • D. Sultan Daniyal Mirza
    Sultan Daniyal Mirza was a Mughal prince, the third son of Emperor Akbar, known for his military campaigns in the Deccan and his ultimately tragic early death.
  • E. Fazal Shah
    Fazal Shah was a Punjabi poet renowned for his romantic narrative poetry, particularly his celebrated retellings of classic love legends.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d81c645c5c8190b1fd16a285a1b78a completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de2ed1d2548190bb46d6b7cba4ffde completed April 14, 2026, 12:10 p.m.
NED1 Entity disambiguation (via context triple) batch_69fcb652305c81908ea097d4f36a05c0 completed May 7, 2026, 3:57 p.m.
Created at: April 9, 2026, 10:19 p.m.