Triple

T18815931
Position Surface form Disambiguated ID Type / Status
Subject Love Field E460133 entity
Predicate shortName P43 FINISHED
Object Love Field 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: Love Field | Statement: [Love Field, shortName, Love Field]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Love Field
Context triple: [Love Field, shortName, Love Field]
  • A. Love Field
    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 chosen
    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 (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_69d8d398c7d4819091cb2f7e48948aeb completed April 10, 2026, 10:40 a.m.
NER Named-entity recognition batch_69e5a3e092e081908fc310c70f646e79 completed April 20, 2026, 3:56 a.m.
Created at: April 10, 2026, 11:53 a.m.