Triple

T10635298
Position Surface form Disambiguated ID Type / Status
Subject Michelle Pfeiffer E250563 entity
Predicate notableWork P4 FINISHED
Object Love Field E174844 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: Love Field | Statement: [Michelle Pfeiffer, notableWork, Love Field]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Love Field
Context triple: [Michelle Pfeiffer, notableWork, Love Field]
  • A. Love Field chosen
    Love Field is a 1992 American drama film set around the aftermath of President John F. Kennedy’s assassination, starring Michelle Pfeiffer and featuring Dennis Haysbert in a prominent role.
  • B. Love Field
    Love Field is a public airport in Dallas, Texas, historically known as the city’s primary airport before the opening of Dallas/Fort Worth International Airport.
  • C. Berry Field
    Berry Field was the original name of what is now Nashville International Airport, a major air travel hub serving Nashville, Tennessee.
  • D. Brown Field
    Brown Field is a military training area associated with the United States Marine Corps Officer Candidates School.
  • E. Bugle Field
    Bugle Field was a historic Negro league baseball park in Baltimore, Maryland, that served as a key venue for African American baseball during the early 20th century.
  • 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_69d6aa5993448190a493b790b8f85010 completed April 8, 2026, 7:19 p.m.
NER Named-entity recognition batch_69d6dfac70f481908363f9ac0b651fbe completed April 8, 2026, 11:07 p.m.
NED1 Entity disambiguation (via context triple) batch_69d96bc57a8081908abd73f4273d0666 completed April 10, 2026, 9:29 p.m.
Created at: April 8, 2026, 9:03 p.m.