Triple
T11926158
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paris–Nantes railway |
E283786
|
entity |
| Predicate | passesThrough |
P225
|
FINISHED |
| Object | Sarthe |
E229255
|
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: Sarthe | Statement: [Paris–Nantes railway, passesThrough, Sarthe]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sarthe Context triple: [Paris–Nantes railway, passesThrough, Sarthe]
-
A.
Sarthe
chosen
Sarthe is a department in western France known for its capital Le Mans and the famous 24 Hours of Le Mans endurance race.
-
B.
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.
-
C.
Essonne
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.
-
D.
Mayenne
Mayenne 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.
Mayenne
Mayenne is a department in northwestern France known for its rural landscapes, historic towns, and location within the former province of Maine.
- 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_69d6ab2ce9c48190b5d39511b524f666 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8e8e3ff308190851ce656286bc67e |
completed | April 10, 2026, 12:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f440525e9881909b21df139d97d986 |
completed | May 1, 2026, 5:55 a.m. |
Created at: April 8, 2026, 9:45 p.m.