Triple

T1566780
Position Surface form Disambiguated ID Type / Status
Subject Disneyland Paris E33448 entity
Predicate nearCity P350 FINISHED
Object Serris
Serris is a French suburban town in the Île-de-France region best known for hosting the Val d'Europe area adjacent to Disneyland Paris.
 E268191 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: Serris | Statement: [Disneyland Paris, nearCity, Serris]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Serris
Context triple: [Disneyland Paris, nearCity, Serris]
  • A. Poissy
    Poissy is a commune in the western suburbs of Paris, France, known for hosting Le Corbusier’s iconic modernist Villa Savoye.
  • B. Choisy-le-Roi
    Choisy-le-Roi is a suburban commune in the southeastern outskirts of Paris, France, situated along the River Seine in the Val-de-Marne department.
  • C. Gonesse
    Gonesse is a commune in the northeastern suburbs of Paris, France, known historically as a rural town and now as part of the greater Paris metropolitan area.
  • D. Saint-Germain-en-Laye
    Saint-Germain-en-Laye is a historic town in the western suburbs of Paris, France, known for its royal château and long association with the French monarchy.
  • E. Nanterre
    Nanterre is a western suburb of Paris in the Hauts-de-Seine department of France, known as an important administrative and educational center.
  • 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: Serris
Triple: [Disneyland Paris, nearCity, Serris]
Generated description
Serris is a French suburban town in the Île-de-France region best known for hosting the Val d'Europe area adjacent to Disneyland Paris.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Serris
Target entity description: Serris is a French suburban town in the Île-de-France region best known for hosting the Val d'Europe area adjacent to Disneyland Paris.
  • A. Poissy
    Poissy is a commune in the western suburbs of Paris, France, known for hosting Le Corbusier’s iconic modernist Villa Savoye.
  • B. Choisy-le-Roi
    Choisy-le-Roi is a suburban commune in the southeastern outskirts of Paris, France, situated along the River Seine in the Val-de-Marne department.
  • C. Gonesse
    Gonesse is a commune in the northeastern suburbs of Paris, France, known historically as a rural town and now as part of the greater Paris metropolitan area.
  • D. Saint-Germain-en-Laye
    Saint-Germain-en-Laye is a historic town in the western suburbs of Paris, France, known for its royal château and long association with the French monarchy.
  • E. Nanterre
    Nanterre is a western suburb of Paris in the Hauts-de-Seine department of France, known as an important administrative and educational center.
  • 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_69a885f11b048190935025a035302715 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a908a0314c8190a5ce3e32dd9035db completed March 5, 2026, 4:37 a.m.
NED1 Entity disambiguation (via context triple) batch_69aef06b043481909eb0195456f1f7fa completed March 9, 2026, 4:08 p.m.
NEDg Description generation batch_69aef5f1aafc8190b3f08ba728b73122 completed March 9, 2026, 4:31 p.m.
NED2 Entity disambiguation (via description) batch_69aef6c34e748190b079d7600f6f7ed5 completed March 9, 2026, 4:35 p.m.
Created at: March 4, 2026, 7:27 p.m.