Triple

T10242134
Position Surface form Disambiguated ID Type / Status
Subject Constantin Wilhelm Lambert Gloger E243618 entity
Predicate hasGivenName P17 FINISHED
Object Lambert E255544 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: Lambert | Statement: [Constantin Wilhelm Lambert Gloger, hasGivenName, Lambert]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lambert
Context triple: [Constantin Wilhelm Lambert Gloger, hasGivenName, Lambert]
  • A. Lambert chosen
    Lambert is a masculine given name of Germanic origin, historically borne by various saints, nobles, and notable figures in Europe.
  • B. Lemery
    Lemery is a coastal municipality in the province of Batangas in the Philippines, known for its commercial activity and proximity to Taal Lake and Volcano.
  • C. Laudon
    Laudon is a German-language surname most notably associated with the 18th-century Austrian field marshal Ernst Gideon von Laudon.
  • D. Lindberg
    Lindberg is a small municipality in the Regen district of Bavaria, Germany, known for its location in the Bavarian Forest region.
  • E. Nantz
    Nantz is the surname of Jim Nantz, a prominent American sportscaster best known for his long-running work with CBS Sports covering events like the NFL, NCAA basketball, and The Masters.
  • 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_69d381b0f97c819085c9b45799a5fb7c completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4d229c1ac8190a86e911aea47a56d completed April 7, 2026, 9:45 a.m.
NED1 Entity disambiguation (via context triple) batch_69d6f78a6efc819091f8303a6cfe4c8b completed April 9, 2026, 12:49 a.m.
Created at: April 6, 2026, 11:25 a.m.