Triple

T6729471
Position Surface form Disambiguated ID Type / Status
Subject Saintonge E153597 entity
Predicate containsCity P294 FINISHED
Object Saint-Jean-d’Angély
Saint-Jean-d’Angély is a historic market town in southwestern France, noted for its medieval architecture and former royal abbey on the pilgrimage route to Santiago de Compostela.
 E622910 NE FINISHED

How this triple was built (4 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: Saint-Jean-d’Angély | Statement: [Saintonge, containsCity, Saint-Jean-d’Angély]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Saint-Jean-d’Angély
Context triple: [Saintonge, containsCity, Saint-Jean-d’Angély]
  • A. Rochefort
    Rochefort is a town in the Walloon region of Belgium, known for its historic abbey and Trappist beer.
  • B. Rochefort
    Rochefort is a historic French port town on the Atlantic coast known for its naval heritage and maritime museum sites.
  • C. Rochefort
    Rochefort is a municipality in the canton of Neuchâtel in western Switzerland.
  • D. Niort
    Niort is a historic city in western France known as an administrative and economic center, particularly for its strong mutual insurance and financial services sector.
  • E. La Rochelle
    La Rochelle is a historic French Atlantic port city that became a major stronghold and refuge for Huguenots during the French Wars of Religion.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Saint-Jean-d’Angély
Triple: [Saintonge, containsCity, Saint-Jean-d’Angély]
Generated description
Saint-Jean-d’Angély is a historic market town in southwestern France, noted for its medieval architecture and former royal abbey on the pilgrimage route to Santiago de Compostela.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Saint-Jean-d’Angély
Target entity description: Saint-Jean-d’Angély is a historic market town in southwestern France, noted for its medieval architecture and former royal abbey on the pilgrimage route to Santiago de Compostela.
  • A. Rochefort
    Rochefort is a town in the Walloon region of Belgium, known for its historic abbey and Trappist beer.
  • B. Rochefort
    Rochefort is a historic French port town on the Atlantic coast known for its naval heritage and maritime museum sites.
  • C. Rochefort
    Rochefort is a municipality in the canton of Neuchâtel in western Switzerland.
  • D. Niort
    Niort is a historic city in western France known as an administrative and economic center, particularly for its strong mutual insurance and financial services sector.
  • E. La Rochelle
    La Rochelle is a historic French Atlantic port city that became a major stronghold and refuge for Huguenots during the French Wars of Religion.
  • F. None of above. chosen

Provenance (5 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_69c6880bdd68819097de8b6099992682 completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d15591c8819082620d194eba3e9d completed March 27, 2026, 6:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69c72f8255ac81909e7732947d2a5f53 completed March 28, 2026, 1:31 a.m.
NEDg Description generation batch_69c7303c58a08190a1a71850e3874be1 completed March 28, 2026, 1:34 a.m.
NED2 Entity disambiguation (via description) batch_69c7308117548190be91fac0a9dd2989 completed March 28, 2026, 1:36 a.m.
Created at: March 27, 2026, 2:08 p.m.