Triple
T15842186
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Spa 24 Hours |
E384125
|
entity |
| Predicate | hasCorner |
P42380
|
FINISHED |
| Object |
Blanchimont
Blanchimont is a famously fast, sweeping left-hand corner at Belgium’s Circuit de Spa-Francorchamps, known for its high-speed challenge and minimal runoff.
|
E1180573
|
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: Blanchimont | Statement: [Spa 24 Hours, hasCorner, Blanchimont]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Blanchimont Context triple: [Spa 24 Hours, hasCorner, Blanchimont]
-
A.
Fernelmont
Fernelmont is a rural municipality in the province of Namur in Wallonia, Belgium, known for its agricultural landscape and small villages.
-
B.
d’Oultremont
d’Oultremont is a noble Belgian family name historically associated with aristocratic lineages in Belgium.
-
C.
Stoumont
Stoumont is a rural municipality in the province of Liège in eastern Belgium, known for its Ardennes landscapes and World War II Battle of the Bulge history.
-
D.
Deûlémont
Deûlémont is a commune in northern France’s Nord department, situated near the Belgian border in the Hauts-de-France region.
-
E.
Corgémont
Corgémont is a municipality in the French-speaking Jura Bernois region of the canton of Bern in Switzerland.
- 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: Blanchimont Triple: [Spa 24 Hours, hasCorner, Blanchimont]
Generated description
Blanchimont is a famously fast, sweeping left-hand corner at Belgium’s Circuit de Spa-Francorchamps, known for its high-speed challenge and minimal runoff.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Blanchimont Target entity description: Blanchimont is a famously fast, sweeping left-hand corner at Belgium’s Circuit de Spa-Francorchamps, known for its high-speed challenge and minimal runoff.
-
A.
Fernelmont
Fernelmont is a rural municipality in the province of Namur in Wallonia, Belgium, known for its agricultural landscape and small villages.
-
B.
d’Oultremont
d’Oultremont is a noble Belgian family name historically associated with aristocratic lineages in Belgium.
-
C.
Stoumont
Stoumont is a rural municipality in the province of Liège in eastern Belgium, known for its Ardennes landscapes and World War II Battle of the Bulge history.
-
D.
Deûlémont
Deûlémont is a commune in northern France’s Nord department, situated near the Belgian border in the Hauts-de-France region.
-
E.
Corgémont
Corgémont is a municipality in the French-speaking Jura Bernois region of the canton of Bern in Switzerland.
- 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_69d86da34c888190976e06c4019d415a |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e142e88ff08190a1035269e8fdaa6a |
completed | April 16, 2026, 8:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffa13eab4881908794104508ba53af |
completed | May 9, 2026, 9:03 p.m. |
| NEDg | Description generation | batch_69ffa527af048190b1f87d85e50bf254 |
completed | May 9, 2026, 9:20 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ffa5df00e481909e203e78940395ed |
completed | May 9, 2026, 9:23 p.m. |
Created at: April 10, 2026, 4:50 a.m.