Triple
T3498141
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alpes-Maritimes |
E73900
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object |
Valbonne
Valbonne is a picturesque village in southeastern France known for its preserved medieval old town and proximity to the technology hub of Sophia Antipolis.
|
E399970
|
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: Valbonne | Statement: [Alpes-Maritimes, containsCity, Valbonne]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Valbonne Context triple: [Alpes-Maritimes, containsCity, Valbonne]
-
A.
Ambert
Ambert is a small commune in central France known for its traditional paper mills and as a center of production for Fourme d'Ambert blue cheese.
-
B.
Bilhères
Bilhères is a small mountain village in southwestern France, situated in the Ossau Valley of the Pyrenees.
-
C.
Chiroubles
Chiroubles is a French appellation in the Beaujolais region known for producing light, aromatic red wines primarily from the Gamay grape.
-
D.
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.
-
E.
Voiron
Voiron is a commune in southeastern France known for its historical town center and proximity to the Chartreuse Mountains.
- 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: Valbonne Triple: [Alpes-Maritimes, containsCity, Valbonne]
Generated description
Valbonne is a picturesque village in southeastern France known for its preserved medieval old town and proximity to the technology hub of Sophia Antipolis.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Valbonne Target entity description: Valbonne is a picturesque village in southeastern France known for its preserved medieval old town and proximity to the technology hub of Sophia Antipolis.
-
A.
Ambert
Ambert is a small commune in central France known for its traditional paper mills and as a center of production for Fourme d'Ambert blue cheese.
-
B.
Bilhères
Bilhères is a small mountain village in southwestern France, situated in the Ossau Valley of the Pyrenees.
-
C.
Chiroubles
Chiroubles is a French appellation in the Beaujolais region known for producing light, aromatic red wines primarily from the Gamay grape.
-
D.
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.
-
E.
Voiron
Voiron is a commune in southeastern France known for its historical town center and proximity to the Chartreuse Mountains.
- 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_69ad85cdb6e48190a335d412b9194ed8 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbbd299ec8190b76b165b2fd70537 |
completed | March 8, 2026, 6:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5281d59c881909a23b5d7e7eff307 |
completed | March 14, 2026, 9:19 a.m. |
| NEDg | Description generation | batch_69b52bd80bc08190884807c9993e41c5 |
completed | March 14, 2026, 9:35 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b52c2b2bd48190b027af337823918a |
completed | March 14, 2026, 9:36 a.m. |
Created at: March 8, 2026, 3:18 p.m.