Triple
T6416901
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Château de Chambéry |
E127850
|
entity |
| Predicate | hasView |
P854
|
FINISHED |
| Object | city of Chambéry |
E46643
|
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: city of Chambéry | Statement: [Château de Chambéry, hasView, city of Chambéry]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: city of Chambéry Context triple: [Château de Chambéry, hasView, city of Chambéry]
-
A.
Chambéry
chosen
Chambéry is a historic city in southeastern France that served as the political and cultural center of the former Duchy of Savoy.
-
B.
Grenoble
Grenoble is a major city in southeastern France, known for its Alpine setting, universities, and research centers.
-
C.
Villefranche-sur-Saône
Villefranche-sur-Saône is a commune in eastern France that serves as the principal town of the Beaujolais region and a key urban center north of Lyon.
-
D.
Briançon
Briançon is a fortified alpine town in southeastern France, known as one of the highest cities in Europe and a key historical stronghold near the Italian border.
-
E.
Embrun
Embrun is a historic town in southeastern France’s Hautes-Alpes department, known for its picturesque setting in the Alps and proximity to the Lac de Serre-Ponçon.
- 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_69c0083815208190a9b299b8e0640218 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c068ea06b08190901e0c0a18fd5170 |
completed | March 22, 2026, 10:10 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6d50423808190817cad8601490a77 |
completed | March 27, 2026, 7:05 p.m. |
Created at: March 22, 2026, 4:42 p.m.