Triple

T1117660
Position Surface form Disambiguated ID Type / Status
Subject Tarascon E11137 entity
Predicate locatedNear P294 FINISHED
Object Beaucaire E168753 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: Beaucaire | Statement: [Tarascon, locatedNear, Beaucaire]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Beaucaire
Context triple: [Tarascon, locatedNear, Beaucaire]
  • A. Beaucaire chosen
    Beaucaire is a historic town in southern France known for its medieval architecture and its location along the Rhône River.
  • B. Aurillac
    Aurillac is a historic town in south-central France, known as the capital of the Cantal department and for its traditional umbrella-making industry.
  • C. Ribérac
    Ribérac is a small historic town in southwestern France’s Dordogne department, known for its traditional markets and rural charm.
  • D. Figeac
    Figeac is a historic town in southwestern France known for its medieval architecture and as the birthplace of Jean-François Champollion, who deciphered Egyptian hieroglyphs.
  • E. 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.
  • 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_69a493252a648190ac48f8742474a5e8 completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a4bba425a8819099116e479552332e completed March 1, 2026, 10:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad36f2940c81908bea3f0242d2d734 completed March 8, 2026, 8:44 a.m.
Created at: March 1, 2026, 7:43 p.m.