Triple

T6081915
Position Surface form Disambiguated ID Type / Status
Subject Vivienne Michel E135542 entity
Predicate relationshipToSeries P34778 FINISHED
Object unusual viewpoint character in James Bond canon 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: unusual viewpoint character in James Bond canon | Statement: [Vivienne Michel, relationshipToSeries, unusual viewpoint character in James Bond canon]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: relationshipToSeries
Context triple: [Vivienne Michel, relationshipToSeries, unusual viewpoint character in James Bond canon]
  • A. relationshipToCharacter
    Indicates the specific type of personal, social, or narrative connection that one entity has to a given character.
  • B. airedWithSeriesFrom
    Indicates that one series was broadcast together or in conjunction with another series during its airing period.
  • C. inRelationshipWith
    Indicates that two entities are mutually involved in a defined personal, romantic, or partnership relationship with each other.
  • D. termRelationTo chosen
    Indicates a general relational association between one term and another, without specifying the exact nature of that relationship.
  • E. relationshipType
    Indicates the specific kind of relationship that exists between two or more entities.
  • 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_69c0087ad31c8190ab936e0ff28614b6 completed March 22, 2026, 3:19 p.m.
NER Named-entity recognition batch_69c05774bc948190a446b27e83f7079b completed March 22, 2026, 8:56 p.m.
PD Predicate disambiguation batch_69c049f21fe08190995df3c5c05fb8ea completed March 22, 2026, 7:58 p.m.
Created at: March 22, 2026, 4:11 p.m.