Triple

T4571484
Position Surface form Disambiguated ID Type / Status
Subject Léon Azéma E123039 entity
Predicate employer P7 FINISHED
Object City of Paris E568 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 Paris | Statement: [Léon Azéma, employer, City of Paris]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: City of Paris
Context triple: [Léon Azéma, employer, City of Paris]
  • A. Parigi
    Parigi is a coastal town that serves as the administrative center of Parigi Moutong Regency in Central Sulawesi, Indonesia.
  • B. Paris chosen
    Paris is the capital and largest city of France, renowned for its historic architecture, art, fashion, and cultural influence worldwide.
  • C. Paris
    Paris is a prince of Troy in Greek mythology, best known for judging the beauty contest of the goddesses and for abducting Helen, which sparked the Trojan War.
  • D. Cité
    Cité is a Paris Métro station located on the Île de la Cité in the historic center of Paris.
  • E. Metropolis of France
    The Metropolis of France is an ecclesiastical jurisdiction of the Ecumenical Patriarchate of Constantinople overseeing Greek Orthodox parishes and institutions throughout France.
  • 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_69bd46466c7081909d07f36be2d08804 completed March 20, 2026, 1:06 p.m.
NER Named-entity recognition batch_69bd58c5afa48190bb8505e2cc16e89f completed March 20, 2026, 2:25 p.m.
NED1 Entity disambiguation (via context triple) batch_69bdd3cf5e10819099b2927c6f571673 completed March 20, 2026, 11:10 p.m.
Created at: March 20, 2026, 1:10 p.m.