Triple

T22664098
Position Surface form Disambiguated ID Type / Status
Subject Employee of the Month E559737 entity
Predicate producer P490 FINISHED
Object Andrew Panay NE NERFINISHED

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: Andrew Panay | Statement: [Employee of the Month, producer, Andrew Panay]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Andrew Panay
Context triple: [Employee of the Month, producer, Andrew Panay]
  • A. Andrew Panay chosen
    Andrew Panay is a film producer best known for his work on hit comedies such as "Wedding Crashers."
  • B. John Amplas
    John Amplas is an American actor best known for his collaborations with director George A. Romero, particularly his lead role in the 1977 horror film "Martin."
  • C. James Galanis
    James Galanis is a soccer coach best known for his work in women's professional soccer, including coaching in the former Women's Professional Soccer league and training elite players.
  • D. Nick Patsaouras
    Nick Patsaouras is a Greek-American engineer and public transportation advocate known for his influential role in shaping transit policy and infrastructure in Los Angeles.
  • E. Jason Mantzoukas
    Jason Mantzoukas is an American actor, comedian, and podcaster known for his energetic, offbeat roles in film and television, including standout performances in projects like "The League," "Brooklyn Nine-Nine," and various comedy podcasts.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e2454a158c819093b8e35f5045efb6 completed April 17, 2026, 2:35 p.m.
NER Named-entity recognition batch_69f176617ed8819095a58a2c9f1e3918 completed April 29, 2026, 3:09 a.m.
Created at: April 17, 2026, 3:08 p.m.