Triple
T5370051
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gare de Nantes |
E108824
|
entity |
| Predicate | connectsTo |
P845
|
FINISHED |
| Object | La Rochelle |
E56822
|
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 Rochelle | Statement: [Gare de Nantes, connectsTo, La Rochelle]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: La Rochelle Context triple: [Gare de Nantes, connectsTo, La Rochelle]
-
A.
La Rochelle
chosen
La Rochelle is a historic French Atlantic port city that became a major stronghold and refuge for Huguenots during the French Wars of Religion.
-
B.
Toulon
Toulon is a major port city on France’s Mediterranean coast that serves as the principal base of the French Navy.
-
C.
Nantes
Nantes is a historic port city in western France on the Loire River, known for its maritime heritage, cultural institutions, and vibrant arts scene.
-
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.
Rennes
Rennes is the capital city of France’s Brittany region, known for its historic medieval center, vibrant student population, and role as a major cultural and economic hub in western France.
- 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_69bd440c77948190aad2a5f39b7b80f5 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd86873e0c8190bf5ecede2cc2bd8b |
completed | March 20, 2026, 5:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c04c916df08190a95e3320ab364b52 |
completed | March 22, 2026, 8:09 p.m. |
Created at: March 20, 2026, 2:02 p.m.