Triple

T1474175
Position Surface form Disambiguated ID Type / Status
Subject Calais E30799 entity
Predicate hasDemonym P191 FINISHED
Object Calaisien E30799 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: Calaisien | Statement: [Calais, hasDemonym, Calaisien]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Calaisien
Context triple: [Calais, hasDemonym, Calaisien]
  • A. Calais chosen
    Calais is a major French port city on the northern coast, serving as one of the primary crossing points between France and England.
  • B. Boulogne-sur-Seine
    Boulogne-sur-Seine was a former commune in the western suburbs of Paris, France, now part of Boulogne-Billancourt, known historically as a residential and industrial area along the Seine River.
  • C. Reims
    Reims is a historic city in northeastern France known for its Gothic cathedral, role in French coronations, and significance during both World Wars.
  • D. Cherbourg
    Cherbourg is a major French port city on the Cotentin Peninsula, known for its strategic naval harbor and cross-Channel ferry connections.
  • E. Valenciennes
    Valenciennes is a historic industrial city in northern France near the Belgian border, known for its former coal and steel industries and its rich artistic and architectural heritage.
  • 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_69a498fe55a88190ab7f9e40ace88e49 completed March 1, 2026, 7:52 p.m.
NER Named-entity recognition batch_69a4c6011d248190988380eca4ecf514 completed March 1, 2026, 11:04 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad15a98b4c819082bd7aa8e0c82de9 completed March 8, 2026, 6:22 a.m.
Created at: March 1, 2026, 8:11 p.m.