Triple

T18711121
Position Surface form Disambiguated ID Type / Status
Subject County of Luxembourg E457515 entity
Predicate hasHistoricalRegion P915 FINISHED
Object Gaume NE NERFINISHED

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: Gaume | Statement: [County of Luxembourg, hasHistoricalRegion, Gaume]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gaume
Context triple: [County of Luxembourg, hasHistoricalRegion, Gaume]
  • A. Gaume chosen
    Gaume is a culturally distinct region in southern Belgium known for its milder microclimate, French-speaking population, and characteristic rural landscapes.
  • B. Dagneux
    Dagneux is a commune in eastern France’s Ain department, known for its residential character and proximity to the Lyon metropolitan area.
  • C. Baugnez
    Baugnez is a small hamlet in eastern Belgium best known as the site of the World War II Malmedy massacre during the Battle of the Bulge.
  • D. Gavisse
    Gavisse is a small commune in northeastern France, located in the Moselle department near the border with Luxembourg and Germany.
  • E. Garmes
    Garmes is a surname most notably associated with Lee Garmes, an influential American cinematographer of Hollywood’s classic era.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8d392aad081909fe31aa03e6e97d1 completed April 10, 2026, 10:40 a.m.
NER Named-entity recognition batch_69e5671b34508190b6180f7d6ad50a58 completed April 19, 2026, 11:36 p.m.
Created at: April 10, 2026, 11:50 a.m.