Triple

T13917480
Position Surface form Disambiguated ID Type / Status
Subject Gur-e-Amir E334658 entity
Predicate builtFor P1261 FINISHED
Object Muhammad Sultan E1079136 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: Muhammad Sultan | Statement: [Gur-e-Amir, builtFor, Muhammad Sultan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Muhammad Sultan
Context triple: [Gur-e-Amir, builtFor, Muhammad Sultan]
  • A. Muhammad Sultan chosen
    Muhammad Sultan was a Timurid prince and grandson of the conqueror Timur, remembered as one of the early heirs apparent of the Timurid Empire.
  • B. Abdullah Ma'ayat Shah
    Abdullah Ma'ayat Shah was a Sultan of Johor in the Malay Peninsula during the 16th century, known for his role in the early history of the Johor Sultanate following the fall of Malacca.
  • C. Sultan Ahmad Khan
    Sultan Ahmad Khan was a Central Asian Timurid-era prince from the royal lineage associated with the Chagatai Khanate.
  • D. Nasiruddin Nasrat Shah
    Nasiruddin Nasrat Shah was a 16th-century Sultan of Bengal known for consolidating the Hussain Shahi dynasty’s power and overseeing a period of relative stability and cultural flourishing.
  • E. Raja Mohammad
    Raja Mohammad is an Indian film editor known for his work on acclaimed Tamil-language movies.
  • 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_69d81c5f739081908bc05b2461f54828 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de272753e48190bc609482635280ff completed April 14, 2026, 11:38 a.m.
NED1 Entity disambiguation (via context triple) batch_69fcdef1e4608190a137f340e06d5ddb completed May 7, 2026, 6:50 p.m.
Created at: April 9, 2026, 10:16 p.m.