Triple
T667060
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Annecy Round (1949) |
E12887
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Annecy |
E84150
|
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: Annecy | Statement: [Annecy Round (1949), locatedIn, Annecy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Annecy Context triple: [Annecy Round (1949), locatedIn, Annecy]
-
A.
Thonon-les-Bains
Thonon-les-Bains is a French spa and resort town in the Haute-Savoie region, known for its lakeside setting on Lake Geneva and views of the Alps.
-
B.
Évian-les-Bains
Évian-les-Bains is a French spa and resort town in the Alps renowned worldwide for its mineral water and scenic lakeside setting.
-
C.
Annecy, France
chosen
Annecy, France is a picturesque Alpine town in southeastern France known for its medieval old town, canals, and the clear blue waters of Lake Annecy.
-
D.
Chambéry
Chambéry is a historic city in southeastern France that served as the political and cultural center of the former Duchy of Savoy.
-
E.
Grenoble
Grenoble is a major city in southeastern France, known for its Alpine setting, universities, and research centers.
- 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_69a493355dec819098d4244b2fa34885 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49ff7ca788190bef58ce46849b9d0 |
completed | March 1, 2026, 8:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac996309fc8190a177034f23807b2e |
completed | March 7, 2026, 9:32 p.m. |
Created at: March 1, 2026, 7:36 p.m.