Triple
T2463894
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Montgenèvre |
E55197
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object |
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.
|
E309330
|
NE FINISHED |
How this triple was built (4 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: Briançon | Statement: [Montgenèvre, near, Briançon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Briançon Context triple: [Montgenèvre, near, Briançon]
-
A.
Grenoble
Grenoble is a major city in southeastern France, known for its Alpine setting, universities, and research centers.
-
B.
Chambéry
Chambéry is a historic city in southeastern France that served as the political and cultural center of the former Duchy of Savoy.
-
C.
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.
-
D.
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.
-
E.
Nyons
Nyons is a small town in southeastern France renowned for its olive production and picturesque setting in the Drôme Provençale region.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Briançon Triple: [Montgenèvre, near, Briançon]
Generated description
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.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Briançon Target entity description: 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.
-
A.
Grenoble
Grenoble is a major city in southeastern France, known for its Alpine setting, universities, and research centers.
-
B.
Chambéry
Chambéry is a historic city in southeastern France that served as the political and cultural center of the former Duchy of Savoy.
-
C.
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.
-
D.
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.
-
E.
Nyons
Nyons is a small town in southeastern France renowned for its olive production and picturesque setting in the Drôme Provençale region.
- F. None of above. chosen
Provenance (5 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_69ab49e3622c8190ad22afa2c4fbb807 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd12059788190a6493f64bb725aed |
completed | March 7, 2026, 7:17 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b055a71200819095c2a5481c61deb5 |
completed | March 10, 2026, 5:32 p.m. |
| NEDg | Description generation | batch_69b05f4ec26c8190bf1edd143353c715 |
completed | March 10, 2026, 6:13 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b061c642588190a6402ce34c430f53 |
completed | March 10, 2026, 6:24 p.m. |
Created at: March 6, 2026, 9:44 p.m.