Triple

T5033309
Position Surface form Disambiguated ID Type / Status
Subject Margareta E113357 entity
Predicate relatedName P3889 FINISHED
Object Margit E113357 NE 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: Margit | Statement: [Margareta, relatedName, Margit]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Margit
Context triple: [Margareta, relatedName, Margit]
  • A. Liesl
    Liesl is a feminine given name, commonly used as a diminutive of names like Elisabeth in German-speaking regions.
  • B. Margot Wendice
    Margot Wendice is the wealthy wife targeted in her husband's elaborate murder plot in Alfred Hitchcock's thriller "Dial M for Murder."
  • C. Jacoba
    Jacoba is a feminine given name of Dutch origin, historically borne by several notable women in the Netherlands and South Africa.
  • D. Margareta chosen
    Margareta is a feminine given name used in various European languages, closely related to and derived from the name Margaret.
  • E. Margot
    Margot is a feminine given name of French origin, often associated with Margot Frank, the elder sister of diarist Anne Frank.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

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_69bd443775e48190a646ffbfc4334723 completed March 20, 2026, 12:57 p.m.
NER Named-entity recognition batch_69bd73b68d8c8190b8e04fb406abdb0f completed March 20, 2026, 4:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69be9c71b2f081908c5c4c1d9ba1ccf4 completed March 21, 2026, 1:26 p.m.
Created at: March 20, 2026, 1:36 p.m.