Triple

T5374982
Position Surface form Disambiguated ID Type / Status
Subject Kakori train robbery E108938 entity
Predicate participant P858 FINISHED
Object Rajendra Lahiri E125301 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: Rajendra Lahiri | Statement: [Kakori train robbery, participant, Rajendra Lahiri]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rajendra Lahiri
Context triple: [Kakori train robbery, participant, Rajendra Lahiri]
  • A. Rajendra Lahiri chosen
    Rajendra Lahiri was an Indian revolutionary freedom fighter associated with the Hindustan Socialist Republican Association, known for his role in anti-British activities during the independence movement.
  • B. Nripendra Misra
    Nripendra Misra is an Indian civil servant and former top bureaucrat who served as a key aide and principal advisor to Prime Minister Narendra Modi.
  • C. Taraknath Bose
    Taraknath Bose was an Indian revolutionary associated with the early nationalist and anti-colonial movements against British rule.
  • D. B. N. Chakravarty
    B. N. Chakravarty was an Indian civil servant and administrator who served as a Governor in the Republic of India.
  • E. Pradip Bose
    Pradip Bose is a computer engineer and researcher known for his contributions to microprocessor architecture and performance analysis, particularly at IBM.
  • 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_69bd440c77948190aad2a5f39b7b80f5 completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd86aed2a8819089d9e699f53563db completed March 20, 2026, 5:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf3a96fba481909659b13425951068 completed March 22, 2026, 12:40 a.m.
Created at: March 20, 2026, 2:03 p.m.