Triple

T5013957
Position Surface form Disambiguated ID Type / Status
Subject Azam Shah E112694 entity
Predicate spouse P13 FINISHED
Object Zeb-un-Nissa E97359 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: Zeb-un-Nissa | Statement: [Azam Shah, spouse, Zeb-un-Nissa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Zeb-un-Nissa
Context triple: [Azam Shah, spouse, Zeb-un-Nissa]
  • A. Zeb-un-Nissa chosen
    Zeb-un-Nissa was a Mughal princess and noted Persian-language poet renowned for her literary works and intellectual pursuits in 17th-century India.
  • B. Juwayriya
    Juwayriya was a wife of the Prophet Muhammad and is regarded as one of the Mothers of the Believers in Islamic tradition.
  • C. Rawdah
    Rawdah is the revered area between the Prophet Muhammad’s tomb and his pulpit in Al-Masjid an-Nabawi in Medina, considered one of the holiest sites in Islam.
  • D. Zubeidaa
    Zubeidaa is a 2001 Indian biographical drama film directed by Shyam Benegal, known for its portrayal of a free-spirited actress who becomes entangled in royal politics and personal tragedy.
  • E. Nuzha
    Nuzha is a residential district in Kuwait City known for its quiet neighborhoods and local amenities.
  • 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_69bd4434acb8819086679dbeccc2fe54 completed March 20, 2026, 12:57 p.m.
NER Named-entity recognition batch_69bd7310c5b08190a5c9ab0f9fe9569f completed March 20, 2026, 4:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69be9271eccc8190bbe9bdb876b41cb8 completed March 21, 2026, 12:43 p.m.
Created at: March 20, 2026, 1:35 p.m.