Triple

T11675595
Position Surface form Disambiguated ID Type / Status
Subject Mughal Subah of Bihar E277482 entity
Predicate hasOffice P1268 FINISHED
Object Bakshi
Bakshi was a key Mughal administrative and military officer responsible for managing pay, recruitment, and organization of troops within the empire.
 E940401 NE FINISHED

How this triple was built (4 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: Bakshi | Statement: [Mughal Subah of Bihar, hasOffice, Bakshi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Bakshi
Context triple: [Mughal Subah of Bihar, hasOffice, Bakshi]
  • A. Bidar Bakht
    Bidar Bakht was a Mughal prince of the early 18th century, known as a grandson of Emperor Aurangzeb through his father Azam Shah.
  • B. Gulani
    Gulani is a local government area in northeastern Nigeria known for its rural communities within Yobe State.
  • C. Surama Ghatak
    Surama Ghatak was the wife of renowned Indian filmmaker Ritwik Ghatak and a figure associated with his personal and artistic life.
  • D. Bawarchi
    Bawarchi is a 1972 Hindi comedy-drama film directed by Hrishikesh Mukherjee, known for its heartwarming story about a mysterious cook who transforms a quarrelsome joint family.
  • E. Shelkar
    Shelkar is a town in Tibet that serves as the administrative and historical center of Tingri County, known as a gateway to the Mount Everest region.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Bakshi
Triple: [Mughal Subah of Bihar, hasOffice, Bakshi]
Generated description
Bakshi was a key Mughal administrative and military officer responsible for managing pay, recruitment, and organization of troops within the empire.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Bakshi
Target entity description: Bakshi was a key Mughal administrative and military officer responsible for managing pay, recruitment, and organization of troops within the empire.
  • A. Bidar Bakht
    Bidar Bakht was a Mughal prince of the early 18th century, known as a grandson of Emperor Aurangzeb through his father Azam Shah.
  • B. Gulani
    Gulani is a local government area in northeastern Nigeria known for its rural communities within Yobe State.
  • C. Surama Ghatak
    Surama Ghatak was the wife of renowned Indian filmmaker Ritwik Ghatak and a figure associated with his personal and artistic life.
  • D. Bawarchi
    Bawarchi is a 1972 Hindi comedy-drama film directed by Hrishikesh Mukherjee, known for its heartwarming story about a mysterious cook who transforms a quarrelsome joint family.
  • E. Shelkar
    Shelkar is a town in Tibet that serves as the administrative and historical center of Tingri County, known as a gateway to the Mount Everest region.
  • F. None of above. chosen

Provenance (5 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_69d6aafd0a448190b44da30af8c6c519 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d8a44504c48190b519765a83ff9c5e completed April 10, 2026, 7:18 a.m.
NED1 Entity disambiguation (via context triple) batch_69ef13f12c2481909171a3237064c76d completed April 27, 2026, 7:44 a.m.
NEDg Description generation batch_69ef3551b9a88190a9b30bcb2592628b completed April 27, 2026, 10:07 a.m.
NED2 Entity disambiguation (via description) batch_69ef51c17078819083f05036f290ce09 completed April 27, 2026, 12:08 p.m.
Created at: April 8, 2026, 9:40 p.m.