Triple

T3282725
Position Surface form Disambiguated ID Type / Status
Subject Daisy Buchanan E68908 entity
Predicate relationshipStatusWithJayGatsby P26473 FINISHED
Object former romantic interest LITERAL 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: former romantic interest | Statement: [Daisy Buchanan, relationshipStatusWithJayGatsby, former romantic interest]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: relationshipStatusWithJayGatsby
Context triple: [Daisy Buchanan, relationshipStatusWithJayGatsby, former romantic interest]
  • A. relationshipStatusDuringFilm
    Indicates the type or state of a relationship between entities specifically during the time period in which a film takes place or is produced.
  • B. hasRomanticTensionWith chosen
    Indicates a mutual or one-sided romantic attraction or unresolved romantic interest existing between two entities.
  • C. companionshipStatus
    Indicates the current state or condition of a relationship of companionship between two or more entities.
  • D. relationshipDynamic
    Indicates a changing or evolving pattern of interaction between entities, such as shifts in their roles, closeness, or influence over time.
  • E. hasProtagonistRelationship
    Indicates that there exists a central, story-driving relationship involving the protagonist and another entity within a narrative.
  • F. None of above.

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_69ad859c463481909ca4be267336c290 completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adb0345d448190a1f936abe7748e33 completed March 8, 2026, 5:21 p.m.
PD Predicate disambiguation batch_69ada421fadc8190b7c7d3c8afd20061 completed March 8, 2026, 4:30 p.m.
Created at: March 8, 2026, 3:10 p.m.