Triple
T1736050
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Transilien |
E37920
|
entity |
| Predicate | serviceAreaIncludes |
P82
|
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: [Transilien, serviceAreaIncludes, Essonne]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Essonne Context triple: [Transilien, serviceAreaIncludes, 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.
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.
-
C.
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.
-
D.
Sarthe
Sarthe is a river in western France that flows through the regions of Normandy and Pays de la Loire before joining other waterways to form the Loire basin.
-
E.
Yvelines
Yvelines is a department in north-central France, west of Paris, known for the Palace of Versailles and its mix of historic towns, forests, and affluent suburbs.
- 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_69a8861cc6ac8190ac0b2e31ccf62851 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69aa63a369048190bae352573f5082f1 |
completed | March 6, 2026, 5:18 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adb5c065088190b831c65ae55eed14 |
completed | March 8, 2026, 5:45 p.m. |
Created at: March 4, 2026, 7:30 p.m.