Triple

T14201649
Position Surface form Disambiguated ID Type / Status
Subject Canut revolts E351977 entity
Predicate cityQuarter P12103 FINISHED
Object Croix-Rousse E79036 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: Croix-Rousse | Statement: [Canut revolts, cityQuarter, Croix-Rousse]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Croix-Rousse
Context triple: [Canut revolts, cityQuarter, Croix-Rousse]
  • A. La Croix-Rousse chosen
    La Croix-Rousse is a historic hilltop district in Lyon, France, known for its silk-weaving heritage, steep slopes, and distinctive village-like atmosphere.
  • B. Le Panier district
    Le Panier district is Marseille’s oldest neighborhood, known for its narrow streets, colorful facades, and vibrant mix of historic charm, street art, and local cafés.
  • C. Montmartre
    Montmartre is a historic hilltop district in Paris known for its artistic heritage, bohemian atmosphere, and panoramic views over the city.
  • D. Saint-Lazare district
    The Saint-Lazare district is a busy commercial and transport hub in central Paris, centered around the Gare Saint-Lazare railway station and its surrounding shopping streets and offices.
  • E. Quartier Part-Dieu
    Quartier Part-Dieu is Lyon’s main business district, known for its high-rise offices, major shopping center, and one of France’s busiest railway stations.
  • 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_69d827894ac0819097803e57f3227b23 completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de61f589a08190b71ad4e69d92ffd0 completed April 14, 2026, 3:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd3d0a172c819096874f1bdd290cbb completed May 8, 2026, 1:31 a.m.
Created at: April 10, 2026, 1:04 a.m.