Triple

T1695211
Position Surface form Disambiguated ID Type / Status
Subject Cher E36641 entity
Predicate contains P35 FINISHED
Object Bourges E206721 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: Bourges | Statement: [Cher, contains, Bourges]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bourges
Context triple: [Cher, contains, Bourges]
  • A. Bourges chosen
    Bourges is a historic city in central France known for its well-preserved medieval architecture and its UNESCO-listed Gothic cathedral, Saint-Étienne.
  • B. Blois
    Blois is a historic city in central France known for its Renaissance château, picturesque setting on the Loire River, and rich royal heritage.
  • C. Mâcon
    Mâcon is a historic town in eastern France’s Burgundy region, known for its wine production and picturesque setting along the Saône River.
  • D. Poitiers
    Poitiers is a historic city in western France known for its Romanesque architecture, medieval heritage, and role as a regional center in the Nouvelle-Aquitaine region.
  • E. Angoulême
    Angoulême is a historic city in southwestern France known for its hilltop old town, medieval ramparts, and status as a major center of the French comics industry.
  • 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_69a886163dec8190859c514232a37a05 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69aa62b3b8908190afc3f9e4a384684f completed March 6, 2026, 5:14 a.m.
NED1 Entity disambiguation (via context triple) batch_69aeb3a777a481909e8b350811ee29c0 completed March 9, 2026, 11:48 a.m.
Created at: March 4, 2026, 7:30 p.m.