Triple

T7730218
Position Surface form Disambiguated ID Type / Status
Subject Uttam Kumar E175228 entity
Predicate notableWork P4 FINISHED
Object Harano Sur E496027 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: Harano Sur | Statement: [Uttam Kumar, notableWork, Harano Sur]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Harano Sur
Context triple: [Uttam Kumar, notableWork, Harano Sur]
  • A. Harano Sur chosen
    Harano Sur is a classic Bengali romantic drama film starring Suchitra Sen, celebrated for its poignant love story and memorable music.
  • B. Hanazono
    Hanazono is a popular ski and outdoor recreation area within the Niseko resort region of Hokkaido, Japan, known for its powder snow and winter sports facilities.
  • C. Hanazono
    Hanazono is a historic rugby stadium in Higashiosaka, Japan, renowned as a major venue for high school and professional rugby matches.
  • D. Haruna
    Haruna was a Japanese Kongō-class fast battleship that served in the Imperial Japanese Navy during both World Wars and saw extensive action in the Pacific Theater.
  • E. Yukio
    Yukio is a Japanese given name commonly used for males and borne by several notable figures in politics, arts, and entertainment.
  • 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_69c6995e912c81909a49a2657103f786 completed March 27, 2026, 2:51 p.m.
NER Named-entity recognition batch_69c703358cf881909df8496d943d6de7 completed March 27, 2026, 10:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69c8b52e176481908595fea4ace7a607 completed March 29, 2026, 5:14 a.m.
Created at: March 27, 2026, 4:06 p.m.