Triple

T9299887
Position Surface form Disambiguated ID Type / Status
Subject Battle of Quebec (1690) E223730 entity
Predicate frenchOutcome P76199 FINISHED
Object maintenance of control over Quebec City 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: maintenance of control over Quebec City | Statement: [Battle of Quebec (1690), frenchOutcome, maintenance of control over Quebec City]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: frenchOutcome
Context triple: [Battle of Quebec (1690), frenchOutcome, maintenance of control over Quebec City]
  • A. FrenchOutcome
    Indicates that an event, action, or process results in an outcome that is specifically French in nature, context, or origin.
  • B. resultForFrance chosen
    Indicates the outcome or result of an event, action, or process specifically as it pertains to France.
  • C. FrenchObjective
    Indicates that an entity serves as the goal, target, or object of an action or relation specifically within a French linguistic or contextual framework.
  • D. sideOutcomeForFrenchUnion
    Indicates a secondary or indirect result that occurs specifically in relation to the French Union.
  • E. FrenchSupport
    Indicates that one entity provides support, assistance, or backing to another in a specifically French context (e.g., by French actors, in France, or involving the French language or institutions).
  • 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_69ca8423edb08190bc0c91287a484768 completed March 30, 2026, 2:09 p.m.
NER Named-entity recognition batch_69cd08d070c881908bed41aada6f85ae completed April 1, 2026, noon
PD Predicate disambiguation batch_69cc7a5ef1908190bc5ca166bb895af6 completed April 1, 2026, 1:52 a.m.
Created at: March 30, 2026, 7:36 p.m.