Triple
T14830837
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grand-Rosière-Hottomont |
E348696
|
entity |
| Predicate | hasNameInLanguage |
P15
|
FINISHED |
| Object | Grand-Rosière-Hottomont (French) |
E348696
|
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: Grand-Rosière-Hottomont (French) | Statement: [Grand-Rosière-Hottomont, hasNameInLanguage, Grand-Rosière-Hottomont (French)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Grand-Rosière-Hottomont (French) Context triple: [Grand-Rosière-Hottomont, hasNameInLanguage, Grand-Rosière-Hottomont (French)]
-
A.
Grand-Rosière-Hottomont
chosen
Grand-Rosière-Hottomont is a village in the municipality of Ramillies in Walloon Brabant, Belgium.
-
B.
Rosières
Rosières is a village and district within the municipality of Rixensart in Walloon Brabant, Belgium.
-
C.
Rosières
Rosières is a locality within the municipality of Vaux-sur-Sûre in the Walloon region of Belgium.
-
D.
Montbéliarde
Montbéliarde is a French dairy cattle breed from the Franche-Comté region, valued for its high-quality milk used in traditional cheeses.
-
E.
Champex-Lac
Champex-Lac is a picturesque alpine village and lake resort in the Swiss canton of Valais, popular for hiking, skiing, and mountain tourism.
- 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_69d822ec69008190a9232caa68836872 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69ded0748eec8190a39c94024c3a3e3e |
completed | April 14, 2026, 11:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe38a16fd881909d246d8d1811a673 |
completed | May 8, 2026, 7:25 p.m. |
Created at: April 10, 2026, 1:52 a.m.