Triple

T16472879
Position Surface form Disambiguated ID Type / Status
Subject Montmagny E400108 entity
Predicate departmentSeat P3522 FINISHED
Object Pontoise E70703 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: Pontoise | Statement: [Montmagny, departmentSeat, Pontoise]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pontoise
Context triple: [Montmagny, departmentSeat, Pontoise]
  • A. Pontoise chosen
    Pontoise is a historic commune in the northwestern suburbs of Paris, France, known for its picturesque setting on the River Oise and its association with Impressionist painters.
  • B. Crépy-en-Valois
    Crépy-en-Valois is a commune in the Oise department of northern France, known for its historic medieval center and role as a regional rail terminus.
  • C. Pontois
    Pontois is the French demonym referring to inhabitants of the commune of Pont-de-l’Isère in southeastern France.
  • D. Armentières
    Armentières is a commune in northern France near the Belgian border, historically known for its textile industry and World War I significance.
  • E. Bazeilles
    Bazeilles is a commune in northeastern France, historically notable as a battlefield during the Franco-Prussian War.
  • 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_69d87f2dac988190b74d6e185fa88ba4 completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e32dd266c48190991a1484eb2f7bcc completed April 18, 2026, 7:08 a.m.
NED1 Entity disambiguation (via context triple) batch_6a00581c24508190b4888357828fed80 completed May 10, 2026, 10:04 a.m.
Created at: April 10, 2026, 5:11 a.m.