Triple
T725987
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aéroport Charles de Gaulle 2 TGV station |
E14726
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Val-d'Oise |
E45085
|
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: Val-d'Oise | Statement: [Aéroport Charles de Gaulle 2 TGV station, locatedIn, Val-d'Oise]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Val-d'Oise Context triple: [Aéroport Charles de Gaulle 2 TGV station, locatedIn, Val-d'Oise]
-
A.
Val-d'Oise
chosen
Val-d'Oise is a department in northern France that forms part of the Paris metropolitan region and includes both suburban areas and rural landscapes.
-
B.
Aisne
Aisne is a department in northern France known for its historic towns, World War I battlefields, and rural landscapes.
-
C.
Aube
Aube is a department in northeastern France known for its historic towns, Champagne vineyards, and rural landscapes.
-
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.
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.
- 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_69a4934c753c81909b309027e48b9b3a |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a5a7fb7c819096db848fe2ba246a |
completed | March 1, 2026, 8:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a7cf4b62b08190bc8d5978595ce60b |
completed | March 4, 2026, 6:20 a.m. |
Created at: March 1, 2026, 7:37 p.m.