Triple

T23052018
Position Surface form Disambiguated ID Type / Status
Subject The Vogues E574044 entity
Predicate notableWork P4 FINISHED
Object Magic Town 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: Magic Town | Statement: [The Vogues, notableWork, Magic Town]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Magic Town
Context triple: [The Vogues, notableWork, Magic Town]
  • A. Magic Town chosen
    Magic Town is a 1947 American comedy film starring James Stewart as a pollster who exploits a statistically average small town, directed by William A. Wellman and written by Robert Riskin.
  • B. Happy Town
    Happy Town is an American mystery drama television series that follows a small town plagued by abductions and dark secrets.
  • C. Tiny Town
    Tiny Town is a miniature, child-sized city attraction where kids can explore scaled-down buildings, streets, and activities designed for imaginative play.
  • D. Storyland
    Storyland is a whimsical, fairy-tale themed children's playground and attraction located within New Orleans City Park.
  • E. Storyland
    Storyland is a film and television production company known for producing the mystery-thriller series "Wayward Pines."
  • 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_69e245ba7ae48190be606dbc54120e39 completed April 17, 2026, 2:37 p.m.
NER Named-entity recognition batch_69f1867d010c8190bc5dba6758d0b797 completed April 29, 2026, 4:18 a.m.
Created at: April 17, 2026, 3:54 p.m.