Triple
T11200807
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grande Chartreuse |
E265033
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Grenoble |
E91863
|
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: Grenoble | Statement: [Grande Chartreuse, locatedNear, Grenoble]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Grenoble Context triple: [Grande Chartreuse, locatedNear, Grenoble]
-
A.
Grenoble
chosen
Grenoble is a major city in southeastern France, known for its Alpine setting, universities, and research centers.
-
B.
Aix-les-Bains
Aix-les-Bains is a French spa and resort town in the Savoie department, renowned for its thermal baths and lakeside setting on the edge of the Alps.
-
C.
Chambéry
Chambéry is a historic city in southeastern France that served as the political and cultural center of the former Duchy of Savoy.
-
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.
Clermont-Ferrand
Clermont-Ferrand is a central French city known for its historic cathedral built of black volcanic stone and as the longtime headquarters of the tire company Michelin.
- 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_69d6aa9eb9248190b20211772621b4bc |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e8c36c188190bfa4d5f8e6cbbbea |
completed | April 9, 2026, 5:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e58ac418f08190b2936e8dbf9fb27d |
completed | April 20, 2026, 2:09 a.m. |
Created at: April 8, 2026, 9:29 p.m.