Triple
T2247118
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A86 autoroute |
E49528
|
entity |
| Predicate | passesThrough |
P225
|
FINISHED |
| Object | Essonne |
E45084
|
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: Essonne | Statement: [A86 autoroute, passesThrough, Essonne]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Essonne Context triple: [A86 autoroute, passesThrough, Essonne]
-
A.
Essonne
chosen
Essonne is a department in northern France that forms part of the Paris metropolitan region and includes a mix of suburban communities, research centers, and rural areas.
-
B.
Seine-et-Oise
Seine-et-Oise was a former department of France surrounding Paris, abolished in 1968 and divided into several new departments including Yvelines.
-
C.
Loir-et-Cher
Loir-et-Cher is a department in central France known for its historic châteaux, including parts of the Loire Valley UNESCO World Heritage site.
-
D.
Eure-et-Loir
Eure-et-Loir is a department in north-central France, located in the Centre-Val de Loire region and known for including the historic city of Chartres.
-
E.
Seine-et-Marne
Seine-et-Marne is a largely rural department in north-central France east of Paris, known for its historic towns, agricultural landscapes, and attractions such as the Château de Fontainebleau and Disneyland Paris.
- 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_69a88aa979788190ad6500f1d8eee2fc |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc0ec46b08190ae12de8b255eb71d |
completed | March 7, 2026, 6:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af17695e6481908bb7e4848a5ffc09 |
completed | March 9, 2026, 6:54 p.m. |
Created at: March 4, 2026, 7:47 p.m.