Triple

T8351619
Position Surface form Disambiguated ID Type / Status
Subject The Birdcage E196171 entity
Predicate producer P490 FINISHED
Object Neil A. Machlis E443520 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: Neil A. Machlis | Statement: [The Birdcage, producer, Neil A. Machlis]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Neil A. Machlis
Context triple: [The Birdcage, producer, Neil A. Machlis]
  • A. Neil A. Machlis chosen
    Neil A. Machlis is a film producer best known for his work on major Hollywood comedies, including the classic road-trip film "Planes, Trains and Automobiles."
  • B. Allen G. Siegler
    Allen G. Siegler was an American cinematographer active during the early to mid-20th century, known for his work on numerous Hollywood films.
  • C. Edward A. Garmatz
    Edward A. Garmatz was a long-serving U.S. Congressman from Maryland who represented Baltimore in the House of Representatives in the mid-20th century.
  • D. Philip M. Kaiser
    Philip M. Kaiser was an American diplomat and public servant who held several key ambassadorial posts during the Cold War era.
  • E. 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.
  • 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_69ca82edd63c8190b876b8465464c5fa completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cb8019fb308190a3edc744bd473a5b completed March 31, 2026, 8:04 a.m.
NED1 Entity disambiguation (via context triple) batch_69d2e4b5781c8190b33035b81dd74260 completed April 5, 2026, 10:39 p.m.
Created at: March 30, 2026, 5:59 p.m.