Triple

T18839925
Position Surface form Disambiguated ID Type / Status
Subject CBS E460764 entity
Predicate hasProgram P178 FINISHED
Object CSI: Crime Scene Investigation 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: CSI: Crime Scene Investigation | Statement: [CBS, hasProgram, CSI: Crime Scene Investigation]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: CSI: Crime Scene Investigation
Context triple: [CBS, hasProgram, CSI: Crime Scene Investigation]
  • A. CSI: Crime Scene Investigation chosen
    CSI: Crime Scene Investigation is a popular American procedural drama series that follows forensic investigators as they use scientific techniques to solve crimes.
  • B. CSI: NY
    CSI: NY is an American police procedural television series that follows a team of forensic investigators solving crimes in New York City as part of the CSI franchise.
  • C. CSI
    CSI is the commonly used abbreviation for the College of Staten Island, a public institution within the City University of New York (CUNY) system.
  • D. CSI
    CSI is a post-nominal title indicating a Companion of the Order of the Star of India, a chivalric order of British India.
  • E. CSI
    CSI is an industry-standard interface specification that enables container orchestration platforms like Kubernetes to expose and manage diverse storage systems in a consistent, plugin-based way.
  • 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_69d8dcfa11e4819090ab1ef5bdcd2b2e completed April 10, 2026, 11:20 a.m.
NER Named-entity recognition batch_69e5b8e8f57081909edbbcaf56189816 completed April 20, 2026, 5:26 a.m.
Created at: April 10, 2026, 11:56 a.m.