Triple

T11239947
Position Surface form Disambiguated ID Type / Status
Subject Kevin Corrigan E266044 entity
Predicate hasNotableWork P4 FINISHED
Object Big Fan E749718 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: Big Fan | Statement: [Kevin Corrigan, hasNotableWork, Big Fan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Big Fan
Context triple: [Kevin Corrigan, hasNotableWork, Big Fan]
  • A. Big Fan chosen
    Big Fan is a dark comedy-drama film about an obsessive New York Giants fan whose life unravels after a violent encounter with his favorite player.
  • B. The Fan
    The Fan is a 1996 psychological thriller film about an obsessive baseball fan whose fixation on his favorite player turns dangerously violent.
  • C. 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.
  • D. Fan y Big
    Fan y Big is a prominent peak in the central Brecon Beacons of South Wales, known for its distinctive cliffs and panoramic views.
  • E. Fanatikerne
    Fanatikerne is a notable painting by Norwegian artist Adolph Tidemand depicting religious zealots in a dramatic, realist style.
  • 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_69d6aac656d48190b275efaa7d6074ee completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e918375081908c2a7ccb50cbf331 completed April 9, 2026, 5:59 p.m.
NED1 Entity disambiguation (via context triple) batch_69e4ad79e4788190af39186f37600a64 completed April 19, 2026, 10:24 a.m.
Created at: April 8, 2026, 9:30 p.m.