Triple

T15965016
Position Surface form Disambiguated ID Type / Status
Subject Donald E. Thorin E387163 entity
Predicate notableWork P4 FINISHED
Object The Fan E101099 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: The Fan | Statement: [Donald E. Thorin, notableWork, The Fan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: The Fan
Context triple: [Donald E. Thorin, notableWork, The Fan]
  • A. The Fan chosen
    The Fan is a 1996 psychological thriller film about an obsessive baseball fan whose fixation on his favorite player turns dangerously violent.
  • B. The Fan
    The Fan is the popular nickname for Beijing's National Indoor Stadium, a major multi-purpose arena known for hosting events during the 2008 and 2022 Olympic Games.
  • C. Fan of a Fan
    Fan of a Fan is a collaborative hip hop mixtape by Tyga and Chris Brown that helped boost both artists' profiles with its club-ready tracks and catchy hooks.
  • D. The Biggest Fan
    The Biggest Fan is a 2002 teen comedy film centered on a high school girl who becomes obsessed with a pop star after he unexpectedly ends up hiding in her bedroom.
  • E. The Fan Club
    The Fan Club is a 1974 thriller novel by Irving Wallace about a group of obsessed fans who kidnap a famous Hollywood actress.
  • 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_69d86da94ccc819083d187f5dc6a123e completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e157258b3c8190a72c868bd055ed94 completed April 16, 2026, 9:39 p.m.
NED1 Entity disambiguation (via context triple) batch_69ffbe84f4888190b3fdb5f32763f78d completed May 9, 2026, 11:08 p.m.
Created at: April 10, 2026, 4:54 a.m.