Triple

T12535971
Position Surface form Disambiguated ID Type / Status
Subject Beccles E299689 entity
Predicate hasTwinTown P919 FINISHED
Object Petit-Couronne
Petit-Couronne is a commune in the Seine-Maritime department of northern France, located near Rouen in the Normandy region.
E1022224 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: Petit-Couronne | Statement: [Beccles, hasTwinTown, Petit-Couronne]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Petit-Couronne
Context triple: [Beccles, hasTwinTown, Petit-Couronne]
  • A. Remigny
    Remigny is a small wine-producing village in the Burgundy region of eastern France, situated near the renowned appellation of Santenay.
  • B. Pommereuil
    Pommereuil is a small commune in the Nord department of northern France.
  • C. Ermontoise
    Ermontoise is the French demonym referring to a female inhabitant or native of the town of Ermont in France.
  • D. Boussy-Saint-Antoine
    Boussy-Saint-Antoine is a suburban commune in the Essonne department in the Île-de-France region of northern France.
  • E. Calvé
    Calvé is a well-known food brand, particularly recognized for its peanut butter and sauces, that forms part of Unilever’s global brand portfolio.
  • 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: Petit-Couronne
Triple: [Beccles, hasTwinTown, Petit-Couronne]
Generated description
Petit-Couronne is a commune in the Seine-Maritime department of northern France, located near Rouen in the Normandy region.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Petit-Couronne
Target entity description: Petit-Couronne is a commune in the Seine-Maritime department of northern France, located near Rouen in the Normandy region.
  • A. Remigny
    Remigny is a small wine-producing village in the Burgundy region of eastern France, situated near the renowned appellation of Santenay.
  • B. Pommereuil
    Pommereuil is a small commune in the Nord department of northern France.
  • C. Ermontoise
    Ermontoise is the French demonym referring to a female inhabitant or native of the town of Ermont in France.
  • D. Boussy-Saint-Antoine
    Boussy-Saint-Antoine is a suburban commune in the Essonne department in the Île-de-France region of northern France.
  • E. Calvé
    Calvé is a well-known food brand, particularly recognized for its peanut butter and sauces, that forms part of Unilever’s global brand portfolio.
  • 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_69d6ada707008190aaec1238117c9379 completed April 8, 2026, 7:33 p.m.
NER Named-entity recognition batch_69d9546d3b2081908d3e0659f8f13678 completed April 10, 2026, 7:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6e2588b448190b5dc5a5d37fd1897 completed May 3, 2026, 5:51 a.m.
NEDg Description generation batch_69f6e3fc6524819081fec59d7e31c816 completed May 3, 2026, 5:58 a.m.
NED2 Entity disambiguation (via description) batch_69f6e47b052c8190a6f6ca5a6bd210f5 completed May 3, 2026, 6 a.m.
Created at: April 8, 2026, 9:57 p.m.