Triple

T4014567
Position Surface form Disambiguated ID Type / Status
Subject Gard E90724 entity
Predicate containsCity P294 FINISHED
Object Uzès E107689 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: Uzès | Statement: [Gard, containsCity, Uzès]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Uzès
Context triple: [Gard, containsCity, Uzès]
  • A. Uzès chosen
    Uzès is a historic town in southern France’s Occitanie region, known for its well-preserved medieval architecture and proximity to the Pont du Gard.
  • B. Ussel
    Ussel is a small commune in central France known as a local administrative and service center in the Corrèze department of the Nouvelle-Aquitaine region.
  • C. Truyère
    Truyère is a river in south-central France that flows through the Massif Central, known for its deep gorges and hydroelectric dams before joining the Lot River.
  • D. Gaillac
    Gaillac is a historic town in southern France renowned for its long-established wine production and picturesque setting along the Tarn River.
  • E. Vézac
    Vézac is a small French commune, best known for its picturesque Dordogne Valley setting and historic gardens such as the Jardins de Marqueyssac.
  • 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_69aed95e44088190aff7d90a151b1b20 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aefa8ad6348190b71feaf8c18c90c2 completed March 9, 2026, 4:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69b589ccd0a48190b98dbe7268df678f completed March 14, 2026, 4:16 p.m.
Created at: March 9, 2026, 3:35 p.m.