Triple

T14028921
Position Surface form Disambiguated ID Type / Status
Subject Tinker Bell and the Legend of the NeverBeast E337534 entity
Predicate writer P1360 FINISHED
Object Steve Loter E1074856 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: Steve Loter | Statement: [Tinker Bell and the Legend of the NeverBeast, writer, Steve Loter]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Steve Loter
Context triple: [Tinker Bell and the Legend of the NeverBeast, writer, Steve Loter]
  • A. Steve Loter chosen
    Steve Loter is an American animation director and producer best known for his work on various Disney projects, including films in the Tinker Bell franchise and animated television series.
  • B. Joe Klotz
    Joe Klotz is an American film editor best known for his acclaimed work on the drama film "Precious."
  • C. Larry Kellner
    Larry Kellner is an American business executive best known for leading Continental Airlines as its chief executive officer.
  • D. Larry Cipa
    Larry Cipa is a former American football quarterback best known for his play in the short-lived World Football League, particularly with the Chicago Fire.
  • E. Barry Kroeger
    Barry Kroeger was an American character actor known for his distinctive villainous roles in mid-20th-century film and television.
  • 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_69d81c6543a48190bd5ba93d7419e797 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de2fa9f8248190930954d609dee5f1 completed April 14, 2026, 12:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69fcb657ab348190ab51ec0e8caa2c4f completed May 7, 2026, 3:57 p.m.
Created at: April 9, 2026, 10:20 p.m.