Triple

T11152461
Position Surface form Disambiguated ID Type / Status
Subject Professor Maximilian Arturo E263819 entity
Predicate creator P184 FINISHED
Object Robert K. Weiss E332323 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: Robert K. Weiss | Statement: [Professor Maximilian Arturo, creator, Robert K. Weiss]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Robert K. Weiss
Context triple: [Professor Maximilian Arturo, creator, Robert K. Weiss]
  • A. Robert K. Weiss chosen
    Robert K. Weiss is an American film and television producer best known for his work on comedy projects such as "The Naked Gun" series and collaborations with the Zucker brothers.
  • B. Daniel H. Weiss
    Daniel H. Weiss is an American art historian and academic leader who served as president and CEO of New York’s Metropolitan Museum of Art.
  • C. John Weiss
    John Weiss is a relatively obscure individual whose specific notability is not clearly established from the given information.
  • D. Peter J. Weinberger
    Peter J. Weinberger is an American computer scientist known for his contributions to programming languages and tools at Bell Labs, including co-creating the AWK programming language.
  • E. David N. Weiss
    David N. Weiss is an American screenwriter best known for co-writing popular family and animated films such as "Shrek 2" and "The Smurfs."
  • 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_69d6aa9ccddc8190868998c8b7beb060 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e872ffbc8190b8a3bbd912115342 completed April 9, 2026, 5:57 p.m.
NED1 Entity disambiguation (via context triple) batch_69f12fa07fc081909a42f9c19ad38511 completed April 28, 2026, 10:07 p.m.
Created at: April 8, 2026, 9:28 p.m.