Triple

T9788289
Position Surface form Disambiguated ID Type / Status
Subject Brian Dennehy as Django E237542 entity
Predicate characterName P36851 FINISHED
Object Django E331529 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: Django | Statement: [Brian Dennehy as Django, characterName, Django]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Django
Context triple: [Brian Dennehy as Django, characterName, Django]
  • A. Django
    Django is a high-level Python web framework that encourages rapid development and clean, pragmatic design for building secure, scalable web applications.
  • B. Django chosen
    Django is a 1966 Italian Spaghetti Western film directed by Sergio Corbucci and starring Franco Nero as a mysterious gunslinger, renowned for its gritty style and influential impact on the genre.
  • C. Django Strikes Again
    Django Strikes Again is a 1987 Italian Western film that serves as the official sequel to the classic 1966 Spaghetti Western Django, once again starring Franco Nero in the title role.
  • D. Flask
    Flask is a minor but tough and pugnacious third mate aboard the whaling ship Pequod in Herman Melville’s novel "Moby-Dick."
  • E. Flask
    Flask is a lightweight, flexible Python micro web framework designed for building web applications and APIs with minimal boilerplate.
  • 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_69ca84da927881909bda80caecad6010 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cda2131164819099e8644e40a3cab6 completed April 1, 2026, 10:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69d1c427cb2c81909fce8e1958ab3282 completed April 5, 2026, 2:08 a.m.
Created at: March 30, 2026, 8:27 p.m.