Triple

T14201879
Position Surface form Disambiguated ID Type / Status
Subject White Terror (1815) E351983 entity
Predicate location P40 FINISHED
Object Provence E269383 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: Provence | Statement: [White Terror (1815), location, Provence]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Provence
Context triple: [White Terror (1815), location, Provence]
  • A. Provence chosen
    Provence is a historic region in southeastern France known for its picturesque lavender fields, Mediterranean coastline, and rich cultural and culinary traditions.
  • B. Languedoc
    Languedoc is a historic region in southern France known for its Occitan culture, medieval towns, and long-standing wine-making tradition.
  • C. Provence-Alpes-Côte d’Azur
    Provence-Alpes-Côte d’Azur is a region in southeastern France known for its Mediterranean coastline, picturesque villages, and cultural hubs such as Marseille and Nice.
  • D. Southern France
    Southern France is a culturally rich and geographically diverse region known for its Mediterranean coastline, historic cities, and renowned cuisine and wine.
  • E. Occitanie
    Occitanie is a large administrative region in southern France known for its Mediterranean coastline, historic cities like Toulouse and Montpellier, and diverse landscapes ranging from coastal plains to the Pyrenees.
  • 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_69fd19395924819084d78815c3f12f43 completed May 7, 2026, 10:59 p.m.
Created at: April 10, 2026, 1:05 a.m.