Triple
T1387142
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saône-et-Loire |
E29871
|
entity |
| Predicate | namedAfter |
P63
|
FINISHED |
| Object | Loire |
E9847
|
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: Loire | Statement: [Saône-et-Loire, namedAfter, Loire]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Loire Context triple: [Saône-et-Loire, namedAfter, Loire]
-
A.
Loire
chosen
The Loire is the longest river in France, renowned for its scenic valley dotted with historic châteaux and vineyards.
-
B.
Loiret
Loiret is a department in north-central France, named after the Loiret River and known for its historic towns and proximity to the Loire Valley.
-
C.
Loir
The Loir is a river in central France that flows through the regions of Pays de la Loire and Centre-Val de Loire before joining the Sarthe.
-
D.
Nièvre
Nièvre is a rural department in central France’s Bourgogne-Franche-Comté region, known for its rolling countryside, the Loire River, and its capital city Nevers.
-
E.
Rive Droite
Rive Droite is the northern, historically affluent bank of the Seine in Paris, known for its grand boulevards, major museums, and iconic landmarks.
- 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_69a498dc92f8819094a1108f8ac90f43 |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c33b6e108190b6b2bca4ddd97b6c |
completed | March 1, 2026, 10:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae5d762bb0819092685731269e57c4 |
completed | March 9, 2026, 5:41 a.m. |
Created at: March 1, 2026, 7:59 p.m.