Triple

T23395020
Position Surface form Disambiguated ID Type / Status
Subject Pontois E559332 entity
Predicate feminineForm P17779 FINISHED
Object Pontoise 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: Pontoise | Statement: [Pontois, feminineForm, Pontoise]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pontoise
Context triple: [Pontois, feminineForm, 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 (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_69e24549610c8190a069d6411ce5f661 completed April 17, 2026, 2:35 p.m.
NER Named-entity recognition batch_69f1a4db1c888190ace5d58bcc8645c1 completed April 29, 2026, 6:27 a.m.
Created at: April 17, 2026, 5:36 p.m.