Triple

T11493837
Position Surface form Disambiguated ID Type / Status
Subject Hotel Berlin E272482 entity
Predicate castMember P1668 FINISHED
Object Faye Emerson E67606 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: Faye Emerson | Statement: [Hotel Berlin, castMember, Faye Emerson]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Faye Emerson
Context triple: [Hotel Berlin, castMember, Faye Emerson]
  • A. Faye Emerson chosen
    Faye Emerson was an American film and stage actress who became a popular early television personality in the 1940s and 1950s.
  • B. Faye Miller
    Faye Miller is a market research psychologist who becomes one of Don Draper’s significant romantic partners in the television series "Mad Men."
  • C. Faye Medwick
    Faye Medwick is a fictional character appearing in the work titled "Chapter Two."
  • D. Faye Webb Gardner
    Faye Webb Gardner was a prominent local benefactor and namesake whose support and influence were instrumental in the development of Gardner–Webb Junior College in North Carolina.
  • E. Emmy Brown
    Emmy Brown is a character in the 1941 romantic drama film "Hold Back the Dawn," serving as part of the story’s emotional and narrative development.
  • 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_69d6aae1b09881909ce2ded3fa0c14fa completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d85ddffdf88190a00e94ad5b8b91a5 completed April 10, 2026, 2:18 a.m.
NED1 Entity disambiguation (via context triple) batch_69ef822a90e48190874a87bd9a634685 completed April 27, 2026, 3:35 p.m.
Created at: April 8, 2026, 9:36 p.m.