Triple
T10847036
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | XX Olympic Winter Games |
E256039
|
entity |
| Predicate | mascot |
P52
|
FINISHED |
| Object | Neve |
E44196
|
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: Neve | Statement: [XX Olympic Winter Games, mascot, Neve]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Neve Context triple: [XX Olympic Winter Games, mascot, Neve]
-
A.
Neve
Neve is a specialized driving mode in the Lamborghini Urus optimized for enhanced traction and stability on snow and low-grip surfaces.
-
B.
Neve
chosen
Neve is one of the official mascots of the 2006 Winter Olympics in Turin, depicted as a stylized snowball symbolizing winter sports and the spirit of the Games.
-
C.
Nieves
Nieves is a Spanish-language surname commonly found in Puerto Rico and other Spanish-speaking regions.
-
D.
Ice Mountain
Ice Mountain is a regional bottled water brand in the United States known for its spring water sourced from Midwestern aquifers.
-
E.
Tennenlohe
Tennenlohe is a district of Erlangen in Bavaria, Germany, known for its proximity to research institutions and the Tennenlohe Forest nature reserve.
- 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_69d6aa81a5d08190aa86689061d1ddd2 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d75113bc188190ac78df0c51d95de6 |
completed | April 9, 2026, 7:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69deb170e714819097babb2b850342d2 |
completed | April 14, 2026, 9:28 p.m. |
Created at: April 8, 2026, 9:20 p.m.