Triple

T23008589
Position Surface form Disambiguated ID Type / Status
Subject Chinonais E572845 entity
Predicate namedAfter P63 FINISHED
Object Chinon NE NERFINISHED

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: Chinon | Statement: [Chinonais, namedAfter, Chinon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Chinon
Context triple: [Chinonais, namedAfter, Chinon]
  • A. Chinon chosen
    Chinon is a renowned Loire Valley wine appellation in France, best known for its elegant, medium-bodied red wines primarily made from Cabernet Franc.
  • B. 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.
  • C. Blois
    Blois is a historic city in central France known for its Renaissance château, picturesque setting on the Loire River, and rich royal heritage.
  • D. Peize
    Peize is a village in the Dutch province of Drenthe, known for its rural character and location within the municipality of Noordenveld.
  • E. Lusignan, Vienne
    Lusignan, Vienne is a commune in western France historically notable as the ancestral seat of the medieval Lusignan noble dynasty.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e245b764cc8190a51be76f1d9611e1 completed April 17, 2026, 2:37 p.m.
NER Named-entity recognition batch_69f1835919b08190ba78e182b87358d4 completed April 29, 2026, 4:04 a.m.
Created at: April 17, 2026, 3:51 p.m.