Triple

T1444177
Position Surface form Disambiguated ID Type / Status
Subject Michel Foucault E31139 entity
Predicate placeOfBirth P1 FINISHED
Object Poitiers, France E72193 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: Poitiers, France | Statement: [Michel Foucault, placeOfBirth, Poitiers, France]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Poitiers, France
Context triple: [Michel Foucault, placeOfBirth, Poitiers, France]
  • A. Poitiers chosen
    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.
  • B. Bourges, France
    Bourges, France is a historic city in central France known for its well-preserved medieval architecture and the UNESCO-listed Bourges Cathedral.
  • C. Amiens, France
    Amiens, France is a historic city in northern France known for its Gothic cathedral and as the birthplace of French President Emmanuel Macron.
  • D. Montlouis-sur-Loire, France
    Montlouis-sur-Loire is a commune in central France’s Loire Valley, known for its vineyards and historic châteaux along the Loire River.
  • E. Montpellier, France
    Montpellier, France is a historic and vibrant city in southern France near the Mediterranean coast, known for its medieval architecture, large student population, and role as a regional cultural and economic center.
  • 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_69a4991633388190a4d61b5a98aa407a completed March 1, 2026, 7:52 p.m.
NER Named-entity recognition batch_69a4c5353fdc819090481cbdd1162929 completed March 1, 2026, 11:01 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad0e70103081908b958acc6873d974 completed March 8, 2026, 5:51 a.m.
Created at: March 1, 2026, 8 p.m.