Triple

T14841426
Position Surface form Disambiguated ID Type / Status
Subject SKEMA Business School E348973 entity
Predicate hasCampusIn P4623 FINISHED
Object Sophia Antipolis E158509 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: Sophia Antipolis | Statement: [SKEMA Business School, hasCampusIn, Sophia Antipolis]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sophia Antipolis
Context triple: [SKEMA Business School, hasCampusIn, Sophia Antipolis]
  • A. Sophia Antipolis chosen
    Sophia Antipolis is a major technology and research park in southeastern France, known as a European hub for telecommunications, information technology, and innovation.
  • B. Juan-les-Pins
    Juan-les-Pins is a seaside resort town on the French Riviera, known for its beaches, nightlife, and jazz festival.
  • C. Bures-sur-Yvette
    Bures-sur-Yvette is a suburban commune in the Île-de-France region of northern France, known for hosting part of the Paris-Saclay scientific and university cluster.
  • D. Garches
    Garches is a suburban commune in the western outskirts of Paris, France, known for its residential character and proximity to major Parisian business districts.
  • E. Palaiseau
    Palaiseau is a suburban commune in the southern outskirts of Paris, France, known for hosting major scientific and engineering institutions.
  • 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_69d822ec69008190a9232caa68836872 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69ded28fa49c81908d1059e6cafd607f completed April 14, 2026, 11:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69fe38a9eb9481908ca509f484007cf6 completed May 8, 2026, 7:25 p.m.
Created at: April 10, 2026, 1:53 a.m.