Triple

T3270120
Position Surface form Disambiguated ID Type / Status
Subject Loir E68625 entity
Predicate flowsThroughTown P42402 FINISHED
Object La Flèche E242466 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: La Flèche | Statement: [Loir, flowsThroughTown, La Flèche]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: La Flèche
Context triple: [Loir, flowsThroughTown, La Flèche]
  • A. La Flèche chosen
    La Flèche is a historic town in western France known for its royal heritage, educational institutions, and the renowned Zoo de La Flèche.
  • B. Fort-de-France
    Fort-de-France is the largest city and administrative, economic, and cultural center of the French Caribbean island of Martinique.
  • C. Saumur
    Saumur is a historic town in western France renowned for its château, wine production, and cavalry school on the banks of the Loire River.
  • D. Blois
    Blois is a historic city in central France known for its Renaissance château, picturesque setting on the Loire River, and rich royal heritage.
  • E. Amboise
    Amboise is a historic town in central France on the Loire River, known for its royal château and as the place where Leonardo da Vinci spent his final years.
  • 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_69ad859b54f881909bf530d549caf2fd completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adaff349148190beae8c0994b7ad83 completed March 8, 2026, 5:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69b2f3c4323c819087a7c6e6c8a7b030 completed March 12, 2026, 5:11 p.m.
Created at: March 8, 2026, 3:09 p.m.