Triple

T3176743
Position Surface form Disambiguated ID Type / Status
Subject Lutfunnisa Begum E66482 entity
Predicate residence P75 FINISHED
Object Murshidabad E66134 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: Murshidabad | Statement: [Lutfunnisa Begum, residence, Murshidabad]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Murshidabad
Context triple: [Lutfunnisa Begum, residence, Murshidabad]
  • A. Murshidabad chosen
    Murshidabad is a historic city in West Bengal, India, that served as the capital of the Nawabs of Bengal and a major political and commercial center during the Mughal and early British periods.
  • B. Islampur
    Islampur is a town in the Indian state of West Bengal, known as a local commercial and administrative center in the northern part of the state.
  • C. Sonargaon
    Sonargaon is a historic city in present-day Bangladesh that served as a major political and commercial center in medieval Bengal, renowned for its role in regional trade and Islamic culture.
  • D. Mohiuddinnagar
    Mohiuddinnagar is a town in the Indian state of Bihar, situated within the Samastipur district.
  • E. Chandannagar
    Chandannagar is a former French colonial town in West Bengal, India, known for its historic riverside architecture and cultural blend of French and Bengali influences.
  • 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_69ad8586a34c8190944c63ec11a8de1a completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada69b0bec8190957913b44d876079 completed March 8, 2026, 4:40 p.m.
NED1 Entity disambiguation (via context triple) batch_69b2f3b513248190a94c2f7642151515 completed March 12, 2026, 5:11 p.m.
Created at: March 8, 2026, 3:06 p.m.