Triple

T16830097
Position Surface form Disambiguated ID Type / Status
Subject Arrondissement of Annecy E409125 entity
Predicate contains P35 FINISHED
Object Poisy
Poisy is a small French commune located in the Haute-Savoie department in the Auvergne-Rhône-Alpes region of southeastern France.
 E1235447 NE FINISHED

How this triple was built (4 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: Poisy | Statement: [Arrondissement of Annecy, contains, Poisy]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Poisy
Context triple: [Arrondissement of Annecy, contains, Poisy]
  • A. Olivette
    Olivette is a diminutive given name derived from Olive, often associated with peace and nature.
  • B. Zibelle
    Zibelle is a village in eastern Germany, historically part of Lusatia, known in this context as the place where physicist Walther Nernst died.
  • C. Mignon
    Mignon is a mysterious, ethereal child of Italian origin who becomes one of the most poignant and symbolically rich figures in Goethe’s novel "Wilhelm Meister’s Apprenticeship."
  • D. Capucine
    Capucine was a French fashion model and film actress best known for her elegant screen presence in 1960s comedies and dramas, including roles in films like The Pink Panther.
  • E. Lilou
    Lilou is a renowned French-Algerian b-boy and world champion breakdancer known for his appearances in international dance competitions and films.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Poisy
Triple: [Arrondissement of Annecy, contains, Poisy]
Generated description
Poisy is a small French commune located in the Haute-Savoie department in the Auvergne-Rhône-Alpes region of southeastern France.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Poisy
Target entity description: Poisy is a small French commune located in the Haute-Savoie department in the Auvergne-Rhône-Alpes region of southeastern France.
  • A. Olivette
    Olivette is a diminutive given name derived from Olive, often associated with peace and nature.
  • B. Zibelle
    Zibelle is a village in eastern Germany, historically part of Lusatia, known in this context as the place where physicist Walther Nernst died.
  • C. Mignon
    Mignon is a mysterious, ethereal child of Italian origin who becomes one of the most poignant and symbolically rich figures in Goethe’s novel "Wilhelm Meister’s Apprenticeship."
  • D. Capucine
    Capucine was a French fashion model and film actress best known for her elegant screen presence in 1960s comedies and dramas, including roles in films like The Pink Panther.
  • E. Lilou
    Lilou is a renowned French-Algerian b-boy and world champion breakdancer known for his appearances in international dance competitions and films.
  • F. None of above. chosen

Provenance (5 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_69d88394566c8190b3dcbdc72935f7fa completed April 10, 2026, 4:59 a.m.
NER Named-entity recognition batch_69e3b316acc881909c686add53d72388 completed April 18, 2026, 4:36 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00b2a0ac148190a7a7edebcb67c040 completed May 10, 2026, 4:30 p.m.
NEDg Description generation batch_6a00b35ea8f88190ae33e8a2f906d133 completed May 10, 2026, 4:33 p.m.
NED2 Entity disambiguation (via description) batch_6a00b3d14b3c819081f435777f47eca3 completed May 10, 2026, 4:35 p.m.
Created at: April 10, 2026, 5:23 a.m.