Triple

T14676712
Position Surface form Disambiguated ID Type / Status
Subject Arve E344664 entity
Predicate flowsThrough P225 FINISHED
Object Annemasse E93133 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: Annemasse | Statement: [Arve, flowsThrough, Annemasse]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Annemasse
Context triple: [Arve, flowsThrough, Annemasse]
  • A. Annemasse chosen
    Annemasse is a French town in the Haute-Savoie department near the Swiss border, functioning as a key commuter suburb of Geneva and a regional transport hub.
  • B. Obernai
    Obernai is a historic Alsatian town in northeastern France known for its well-preserved medieval architecture, wine production, and picturesque setting along the Alsace Wine Route.
  • C. Fallières
    Fallières is a French surname most notably borne by Armand Fallières, who served as President of France in the early 20th century.
  • D. Brumath
    Brumath is a small commune in northeastern France’s Grand Est region, known for its historical roots dating back to Roman times.
  • E. Sarreguemines
    Sarreguemines is a town in northeastern France near the German border, historically known for its ceramics and faience production.
  • 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_69d822e34b348190ada4d1cdb6c7c226 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69deb567c2b88190a9639e61b6fba7df completed April 14, 2026, 9:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69fdf07e355881908a2e75ccff4f0590 completed May 8, 2026, 2:17 p.m.
Created at: April 10, 2026, 1:27 a.m.