Triple

T1878067
Position Surface form Disambiguated ID Type / Status
Subject Luise E39188 entity
Predicate equivalentFormInFrench P6538 FINISHED
Object Louise E5411 NE FINISHED

How this triple was built (3 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: Louise | Statement: [Luise, equivalentFormInFrench, Louise]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Louise
Context triple: [Luise, equivalentFormInFrench, Louise]
  • A. Louise chosen
    Louise is a feminine given name of French origin, traditionally associated with nobility and widely used in many European and English-speaking countries.
  • B. Marie
    Marie is a widely used European given name, especially common in French-speaking countries, derived from the Hebrew name Miryam (Mary).
  • C. Emilie
    Emilie is a young French girl in Michael Morpurgo’s novel and its film adaptation "War Horse," who befriends and cares for the horses Joey and Topthorn during World War I.
  • D. Madeleine
    Madeleine is a feminine given name, commonly used in French and English, derived from Magdalene and often associated with literary and cultural figures.
  • E. Jeanne
    Jeanne was a common French female given name historically borne by notable figures such as queens, saints, and writers.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: equivalentFormInFrench
Context triple: [Luise, equivalentFormInFrench, Louise]
  • A. equivalentTitleInFrench
    Indicates that one entity’s title is the equivalent or corresponding title of another entity, specifically expressed in French.
  • B. languageEquivalent
    Indicates that two linguistic expressions convey the same meaning or function across different languages or language varieties.
  • C. nameInFrench chosen
    Indicates that an entity is known or referred to by a specific name expressed in the French language.
  • D. equivalentInZapotec
    Indicates that two linguistic elements are equivalent in meaning or function within the Zapotec language.
  • 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 (4 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_69a8862f7074819096afe7fe65e179e9 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abb0f79fbc819085c54f3189a552d9 completed March 7, 2026, 5 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae1fcb534881908415237d24b4d8b9 completed March 9, 2026, 1:18 a.m.
PD Predicate disambiguation batch_69abafe2b56c81909e13d543982e6e13 completed March 7, 2026, 4:56 a.m.
Created at: March 4, 2026, 7:34 p.m.