Triple

T5329249
Position Surface form Disambiguated ID Type / Status
Subject Nice Port E123262 entity
Predicate partOf P40 FINISHED
Object City of Nice E140630 NE FINISHED

How this triple was built (2 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: City of Nice | Statement: [Nice Port, partOf, City of Nice]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: City of Nice
Context triple: [Nice Port, partOf, City of Nice]
  • A. City of Nice chosen
    The City of Nice is a major coastal city on the French Riviera, renowned for its Mediterranean climate, historic old town, and rich artistic and cultural heritage.
  • B. Vieux-Nice
    Vieux-Nice is the historic old town of Nice, France, known for its narrow winding streets, colorful buildings, bustling markets, and vibrant Mediterranean atmosphere.
  • C. Grasse
    Grasse is a town in southeastern France renowned as the world’s perfume capital and a historic center of the fragrance industry.
  • D. Villefranche-sur-Mer
    Villefranche-sur-Mer is a picturesque coastal town in southeastern France known for its deep natural harbor, colorful old town, and scenic setting on the Mediterranean Sea.
  • E. Old Town (Vieux-Nice)
    Old Town (Vieux-Nice) is the historic quarter of Nice, France, known for its narrow winding streets, colorful facades, bustling markets, and vibrant Mediterranean atmosphere.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69bd859552d8819080758bdd7c43c66a completed March 20, 2026, 5:36 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf21bebea0819083e6deae67f3e834 completed March 21, 2026, 10:54 p.m.
Created at: March 20, 2026, 2 p.m.