Triple

T9548876
Position Surface form Disambiguated ID Type / Status
Subject Melanie Lynskey E230366 entity
Predicate employer P7 FINISHED
Object CBS (for Two and a Half Men) E6070 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: CBS (for Two and a Half Men) | Statement: [Melanie Lynskey, employer, CBS (for Two and a Half Men)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: CBS (for Two and a Half Men)
Context triple: [Melanie Lynskey, employer, CBS (for Two and a Half Men)]
  • A. CBS chosen
    CBS is a major American broadcast television network known for airing a wide range of popular news, sports, and entertainment programming nationwide.
  • B. CBS
    CBS is a leading Danish university in Copenhagen specializing in business and economics education and research.
  • C. CBS
    CBS is a college within the University of California, Davis that focuses on education and research in the biological sciences.
  • D. CBS
    CBS is a Harvard University research center dedicated to advancing the understanding of the brain through interdisciplinary neuroscience studies.
  • E. CBS
    CBS is the commonly used abbreviation for the Commission for Basic Systems, a specialized body focused on foundational infrastructure and standards, likely within an international or governmental organizational context.
  • 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_69ca847c70b8819088a0a0bad64a50d6 completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd99059138819088ae54b26df979cf completed April 1, 2026, 10:15 p.m.
NED1 Entity disambiguation (via context triple) batch_69d14c82c98c8190a4fd6fc3ceb4173d completed April 4, 2026, 5:38 p.m.
Created at: March 30, 2026, 8:02 p.m.