Triple
T6957652
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nördlingen |
E161285
|
entity |
| Predicate | twinTown |
P1072
|
FINISHED |
| Object | Riom |
E174959
|
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: Riom | Statement: [Nördlingen, twinTown, Riom]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Riom Context triple: [Nördlingen, twinTown, Riom]
-
A.
Riom
chosen
Riom is a historic town in central France known for its preserved medieval architecture and role as a former capital of the Auvergne region.
-
B.
Marseille
Marseille is a historic Mediterranean port city in southern France known for its diverse culture, maritime heritage, and role as a major economic hub.
-
C.
Vichy
Vichy is a spa town in central France renowned for its thermal springs, health resorts, and role as the seat of the World War II Vichy regime.
-
D.
Garches
Garches is a suburban commune in the western outskirts of Paris, France, known for its residential character and proximity to major Parisian business districts.
-
E.
Toulon
Toulon is a major port city on France’s Mediterranean coast that serves as the principal base of the French Navy.
- 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_69c68852a9a0819097797e31d492e273 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6dad0e52081908b524dc6a66bab01 |
completed | March 27, 2026, 7:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7588e2c5c8190a66a0205f3c2bf99 |
completed | March 28, 2026, 4:26 a.m. |
Created at: March 27, 2026, 2:29 p.m.