Triple

T21944704
Position Surface form Disambiguated ID Type / Status
Subject Maachis E541905 entity
Predicate editor P1954 FINISHED
Object Sanjib Datta
Sanjib Datta is an Indian film editor best known for his work on acclaimed Hindi films such as the political drama "Maachis."
E1513029 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: Sanjib Datta | Statement: [Maachis, editor, Sanjib Datta]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sanjib Datta
Context triple: [Maachis, editor, Sanjib Datta]
  • A. Sanjib Basu
    Sanjib Basu is a statistician known for his contributions to Bayesian methods and statistical theory.
  • B. Sanjib Saha
    Sanjib Saha is a person notable enough to be recognized as a namesake of the surname Saha.
  • C. Sanjib Bose
    Sanjib Bose is an individual notable for bearing the surname Bose, associated with the broader Bose family name.
  • D. Sanjib Bhattacharyya
    Sanjib Bhattacharyya is an economist and academic recognized as a notable scholar associated with the Delhi School of Economics.
  • E. Sudip Bandyopadhyay
    Sudip Bandyopadhyay is an Indian politician and long-time parliamentarian from West Bengal known for his senior leadership role in the Trinamool Congress.
  • 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: Sanjib Datta
Triple: [Maachis, editor, Sanjib Datta]
Generated description
Sanjib Datta is an Indian film editor best known for his work on acclaimed Hindi films such as the political drama "Maachis."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Sanjib Datta
Target entity description: Sanjib Datta is an Indian film editor best known for his work on acclaimed Hindi films such as the political drama "Maachis."
  • A. Sanjib Basu
    Sanjib Basu is a statistician known for his contributions to Bayesian methods and statistical theory.
  • B. Sanjib Saha
    Sanjib Saha is a person notable enough to be recognized as a namesake of the surname Saha.
  • C. Sanjib Bose
    Sanjib Bose is an individual notable for bearing the surname Bose, associated with the broader Bose family name.
  • D. Sanjib Bhattacharyya
    Sanjib Bhattacharyya is an economist and academic recognized as a notable scholar associated with the Delhi School of Economics.
  • E. Sudip Bandyopadhyay
    Sudip Bandyopadhyay is an Indian politician and long-time parliamentarian from West Bengal known for his senior leadership role in the Trinamool Congress.
  • 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_69e0c47e2e5c81909a7f74ce3de50911 completed April 16, 2026, 11:14 a.m.
NER Named-entity recognition batch_69f1242688988190a7b8f033c49368de completed April 28, 2026, 9:18 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0a736619388190b9f1bd7cc4cf97e8 completed May 18, 2026, 2:03 a.m.
NEDg Description generation batch_6a0a744b20d48190a5dc20d9442f075b completed May 18, 2026, 2:07 a.m.
NED2 Entity disambiguation (via description) batch_6a0a75011178819089d94f745a38a6e6 completed May 18, 2026, 2:10 a.m.
Created at: April 16, 2026, 7:56 p.m.