Triple

T13677244
Position Surface form Disambiguated ID Type / Status
Subject Diocese of Langres E327908 entity
Predicate department P1467 FINISHED
Object Haute-Marne E131592 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: Haute-Marne | Statement: [Diocese of Langres, department, Haute-Marne]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Haute-Marne
Context triple: [Diocese of Langres, department, Haute-Marne]
  • A. Haute-Marne chosen
    Haute-Marne is a rural department in northeastern France known for its forests, rivers, and historic towns such as Chaumont and Langres.
  • B. Seine-et-Marne
    Seine-et-Marne is a largely rural department in north-central France east of Paris, known for its historic towns, agricultural landscapes, and attractions such as the Château de Fontainebleau and Disneyland Paris.
  • C. Haute-Saône
    Haute-Saône is a rural department in the Bourgogne-Franche-Comté region of eastern France, known for its forests, rivers, and historic villages.
  • D. Meurthe-et-Moselle
    Meurthe-et-Moselle is a department in northeastern France known for its capital Nancy, rich industrial history, and Art Nouveau architectural heritage.
  • E. Seine-et-Oise
    Seine-et-Oise was a former department of France surrounding Paris, abolished in 1968 and divided into several new departments including Yvelines.
  • 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_69d8076f1fa8819094664a59b55010df completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbc65d8dc081909664e69bb38610ba completed April 12, 2026, 4:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69f794405a38819085f38170c56564f2 completed May 3, 2026, 6:30 p.m.
Created at: April 9, 2026, 9:53 p.m.