Triple
T5370060
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gare de Nantes |
E108824
|
entity |
| Predicate | connectsTo |
P845
|
FINISHED |
| Object | Le Mans |
E43233
|
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: Le Mans | Statement: [Gare de Nantes, connectsTo, Le Mans]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Le Mans Context triple: [Gare de Nantes, connectsTo, Le Mans]
-
A.
Le Mans
chosen
Le Mans is a historic city in northwestern France best known for its annual 24 Hours of Le Mans endurance sports car race.
-
B.
Montlhéry
Montlhéry is a commune in northern France best known for its historic motor racing circuit, the Autodrome de Linas-Montlhéry.
-
C.
Deauzya
Deauzya is the given first name of American professional basketball player DiDi Richards.
-
D.
Arques
Arques is a river in northern France that flows through the Normandy region and reaches the English Channel at the port city of Dieppe.
-
E.
Le Mans metropolitan area
The Le Mans metropolitan area is an urban and economic hub in western France centered on the city of Le Mans, renowned for its automotive industry and the 24 Hours of Le Mans endurance race.
- 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_69bf29307cc481908fa4d8b52711bbd4 |
completed | March 21, 2026, 11:26 p.m. |
Created at: March 20, 2026, 2:02 p.m.