Triple

T6409965
Position Surface form Disambiguated ID Type / Status
Subject Cateau-Cambrésis E127678 entity
Predicate near P350 FINISHED
Object Cambrai E110945 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: Cambrai | Statement: [Cateau-Cambrésis, near, Cambrai]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Cambrai
Context triple: [Cateau-Cambrésis, near, Cambrai]
  • A. Cambrai chosen
    Cambrai is a historic city in northern France known for its medieval heritage, role in World War I, and traditional confectionery.
  • B. Saint-Omer
    Saint-Omer is a historic town in northern France known for its medieval architecture, strategic military importance, and role in Franco-Spanish conflicts.
  • C. Arras
    Arras is a historic city in northern France renowned for its Flemish-Baroque architecture, grand squares, and role as a strategic site in both World Wars.
  • D. Thérouanne
    Thérouanne is a historic town in northern France that once served as an important medieval religious center and episcopal seat.
  • 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_69c0083723d88190b1e37b19df162c08 completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c068cf81508190bc09e58ec45bc858 completed March 22, 2026, 10:10 p.m.
NED1 Entity disambiguation (via context triple) batch_69c638b7582481909640965acf261dff completed March 27, 2026, 7:58 a.m.
Created at: March 22, 2026, 4:41 p.m.