Triple

T18222931
Position Surface form Disambiguated ID Type / Status
Subject Divide and Conquer E436349 entity
Predicate hasTitle P38 FINISHED
Object Divide and Conquer NE NERFINISHED

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: Divide and Conquer | Statement: [Divide and Conquer, hasTitle, Divide and Conquer]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Divide and Conquer
Context triple: [Divide and Conquer, hasTitle, Divide and Conquer]
  • A. Divide and Conquer chosen
    "Divide and Conquer" is a World War II-era American propaganda documentary in the "Why We Fight" series that chronicles Nazi Germany’s invasion and subjugation of Western Europe.
  • B. Divide
    Divide is a small unincorporated community located in Nolan County, Texas.
  • C. Meet in the Middle
    "Meet in the Middle" is a 1991 country song by Diamond Rio that became their breakthrough hit and is known for its clever lyrics about compromise in relationships.
  • D. Division
    Division is a Chicago Transit Authority 'L' station on the Blue Line serving the city's Near North Side.
  • E. Partition
    Partition is a 2007 historical drama film set during the 1947 partition of India, focusing on the tragic human consequences of the subcontinent’s division.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8b9103a8081908bbb0836fef10efd completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e4e47d9b348190897d5a1e70b39ec5 completed April 19, 2026, 2:19 p.m.
Created at: April 10, 2026, 10:32 a.m.