Triple
T9949149
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sombreffe |
E195283
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Fleurus |
E831482
|
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: Fleurus | Statement: [Sombreffe, locatedNear, Fleurus]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fleurus Context triple: [Sombreffe, locatedNear, Fleurus]
-
A.
Fleurus
chosen
Fleurus is a town in Wallonia, Belgium, historically notable as the site of several major European battles.
-
B.
Brecheret
Brecheret is the surname of Victor Brecheret, a prominent Italian-Brazilian modernist sculptor known for his monumental public works in Brazil.
-
C.
Greuze
Greuze is a French surname most famously associated with Jean-Baptiste Greuze, an 18th-century painter known for his sentimental and moralizing genre scenes.
-
D.
Cornalin
Cornalin is a traditional Swiss red wine grape variety from the Valais region, known for producing deeply colored, aromatic wines with vibrant acidity and notes of red fruit and spice.
-
E.
Manceau
Manceau is the French term used to refer to an inhabitant or native of the city of Le Mans.
- 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_69ca82e96a108190932bd1fc4acd73a0 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cdb65a4e6c8190968192a24aad1b7d |
completed | April 2, 2026, 12:20 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d23d5c6fdc81909b44d0b321201222 |
completed | April 5, 2026, 10:45 a.m. |
Created at: March 30, 2026, 8:45 p.m.