Triple

T15102300
Position Surface form Disambiguated ID Type / Status
Subject Kamran Mirza E360698 entity
Predicate givenName P17 FINISHED
Object Kamran E1039835 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: Kamran | Statement: [Kamran Mirza, givenName, Kamran]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kamran
Context triple: [Kamran Mirza, givenName, Kamran]
  • A. Kamran chosen
    Kamran is a central romantic lead in the classic Turkish novel and TV adaptations of "Çalıkuşu," known for his complex, often tumultuous relationship with the heroine Feride.
  • B. Zafar
    Zafar was the pen name of Bahadur Shah II, the last Mughal emperor of India and a noted Urdu poet.
  • C. Zafar
    Zafar was an important ancient South Arabian city that served as the political and cultural center of the Himyarite Kingdom in what is now Yemen.
  • D. Kamran Mirza
    Kamran Mirza was a Mughal prince, the second son of Emperor Babur and a prominent political figure in the early Mughal Empire known for his repeated rebellions against his brother Humayun.
  • E. Asif
    Asif is a common male given name used in South Asian and Middle Eastern cultures, notably borne by Pakistani politician Asif Ali Zardari.
  • 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_69d85a0491ec8190830960be8fafb994 completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e00551521c8190b48d1a074bb4bdfc completed April 15, 2026, 9:38 p.m.
NED1 Entity disambiguation (via context triple) batch_69feae2571f48190b73f0aecd113fed6 completed May 9, 2026, 3:46 a.m.
Created at: April 10, 2026, 3:05 a.m.