Triple

T5329275
Position Surface form Disambiguated ID Type / Status
Subject Nice Port E123262 entity
Predicate hasQuay P15921 FINISHED
Object Quai Lunel
Quai Lunel is a waterfront quay in Nice, France, forming part of the city’s historic port area along the Mediterranean.
 E512604 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: Quai Lunel | Statement: [Nice Port, hasQuay, Quai Lunel]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Quai Lunel
Context triple: [Nice Port, hasQuay, Quai Lunel]
  • A. Lunel
    Lunel is a commune in southern France known for its historic center and location between Montpellier and Nîmes in the Occitanie region.
  • B. Marignane
    Marignane is a commune in southern France near Marseille, known for hosting Marseille Provence Airport and its proximity to the Mediterranean coast.
  • C. Draguignan
    Draguignan is a town in southeastern France’s Var department, known as a former prefecture and gateway to the Provence region.
  • D. Lavezares
    Lavezares is a coastal municipality in the province of Northern Samar in the Philippines, known for its fishing communities and island landscapes.
  • E. Aigues-Mortes
    Aigues-Mortes is a historic fortified town in southern France, renowned for its well-preserved medieval walls and proximity to the salt marshes of the Camargue.
  • 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: Quai Lunel
Triple: [Nice Port, hasQuay, Quai Lunel]
Generated description
Quai Lunel is a waterfront quay in Nice, France, forming part of the city’s historic port area along the Mediterranean.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Quai Lunel
Target entity description: Quai Lunel is a waterfront quay in Nice, France, forming part of the city’s historic port area along the Mediterranean.
  • A. Lunel
    Lunel is a commune in southern France known for its historic center and location between Montpellier and Nîmes in the Occitanie region.
  • B. Marignane
    Marignane is a commune in southern France near Marseille, known for hosting Marseille Provence Airport and its proximity to the Mediterranean coast.
  • C. Draguignan
    Draguignan is a town in southeastern France’s Var department, known as a former prefecture and gateway to the Provence region.
  • D. Lavezares
    Lavezares is a coastal municipality in the province of Northern Samar in the Philippines, known for its fishing communities and island landscapes.
  • E. Aigues-Mortes
    Aigues-Mortes is a historic fortified town in southern France, renowned for its well-preserved medieval walls and proximity to the salt marshes of the Camargue.
  • 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_69bd46477f9081909d242a327d749466 completed March 20, 2026, 1:06 p.m.
NER Named-entity recognition batch_69bd91aab9348190a373b30bb305f933 completed March 20, 2026, 6:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf18b396c08190be60bcb9ac933b5e completed March 21, 2026, 10:16 p.m.
NEDg Description generation batch_69bf1b23cdbc8190bec3b7bc7f70770c completed March 21, 2026, 10:26 p.m.
NED2 Entity disambiguation (via description) batch_69bf1ba465948190aac6bfc406bae806 completed March 21, 2026, 10:28 p.m.
Created at: March 20, 2026, 2 p.m.