Triple

T10485775
Position Surface form Disambiguated ID Type / Status
Subject Paris Métro Line 8 E247292 entity
Predicate hasStation P35 FINISHED
Object Lourmel
Lourmel is a Paris Métro station in the 15th arrondissement, serving local residential and commercial areas on the city’s Left Bank.
 E870780 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: Lourmel | Statement: [Paris Métro Line 8, hasStation, Lourmel]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lourmel
Context triple: [Paris Métro Line 8, hasStation, Lourmel]
  • A. Ermontoise
    Ermontoise is the French demonym referring to a female inhabitant or native of the town of Ermont in France.
  • B. Les Breuleux
    Les Breuleux is a small Swiss municipality and village in the Jura region, known for its watchmaking tradition and rural alpine setting.
  • C. Longueau
    Longueau is a commune in northern France that forms part of the suburban area of Amiens in the Somme department.
  • D. Orléat
    Orléat is a small commune in central France’s Puy-de-Dôme department, known for its rural character within the Auvergne region.
  • E. Lantheuil
    Lantheuil is a small commune in the Calvados department of the Normandy region in northwestern France.
  • 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: Lourmel
Triple: [Paris Métro Line 8, hasStation, Lourmel]
Generated description
Lourmel is a Paris Métro station in the 15th arrondissement, serving local residential and commercial areas on the city’s Left Bank.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Lourmel
Target entity description: Lourmel is a Paris Métro station in the 15th arrondissement, serving local residential and commercial areas on the city’s Left Bank.
  • A. Ermontoise
    Ermontoise is the French demonym referring to a female inhabitant or native of the town of Ermont in France.
  • B. Les Breuleux
    Les Breuleux is a small Swiss municipality and village in the Jura region, known for its watchmaking tradition and rural alpine setting.
  • C. Longueau
    Longueau is a commune in northern France that forms part of the suburban area of Amiens in the Somme department.
  • D. Orléat
    Orléat is a small commune in central France’s Puy-de-Dôme department, known for its rural character within the Auvergne region.
  • E. Lantheuil
    Lantheuil is a small commune in the Calvados department of the Normandy region in northwestern France.
  • 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_69d381c309b88190af78aa681cf6a4c2 completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d5096988ec81908d7518b09256c145 completed April 7, 2026, 1:40 p.m.
NED1 Entity disambiguation (via context triple) batch_69d933c5caa08190a5fba92ebf4b0ff9 completed April 10, 2026, 5:30 p.m.
NEDg Description generation batch_69d93802a4488190aa86ae209650d4e7 completed April 10, 2026, 5:48 p.m.
NED2 Entity disambiguation (via description) batch_69d938fcc3c48190a4acaaf75c1aa304 completed April 10, 2026, 5:53 p.m.
Created at: April 6, 2026, 12:23 p.m.