Triple

T25075387
Position Surface form Disambiguated ID Type / Status
Subject The Nagasaki Vector E628031 entity
Predicate hasAlternateHistory P149009 FINISHED
Object yes 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: yes | Statement: [The Nagasaki Vector, hasAlternateHistory, yes]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: hasAlternateHistory
Context triple: [The Nagasaki Vector, hasAlternateHistory, yes]
  • A. hasAlternativeHistoryElements chosen
    Indicates that something incorporates elements of alternative or counterfactual historical scenarios into its content or structure.
  • B. hasAlternateTimeline
    Indicates that an entity exists or occurs in a different possible or parallel timeline relative to another reference timeline.
  • C. alternateTimelineEvent
    Indicates that an event occurs in, or is associated with, a timeline that diverges from the primary or original sequence of events.
  • D. alternateTimelineName
    Indicates that one entity is the name or designation used for another entity in an alternate or parallel timeline.
  • E. hasAlternateTimelineConnectionWith
    Indicates a relationship where two entities are linked through an alternate or parallel timeline, allowing events or states in one timeline to correspond to or influence those in the other.
  • 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_69e2ff2e73f881909992bf3eda5c25cb completed April 18, 2026, 3:49 a.m.
NER Named-entity recognition batch_69f55e519978819087a1676564a74630 completed May 2, 2026, 2:15 a.m.
PD Predicate disambiguation batch_69f4a0edd10c81908a052ab864d57c54 completed May 1, 2026, 12:47 p.m.
Created at: April 18, 2026, 6:21 a.m.