Triple
T13253724
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kitzingen |
E315601
|
entity |
| Predicate | hasTwinTown |
P919
|
FINISHED |
| Object | Prades |
E152411
|
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: Prades | Statement: [Kitzingen, hasTwinTown, Prades]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Prades Context triple: [Kitzingen, hasTwinTown, Prades]
-
A.
Prades
chosen
Prades is a small town in southern France known for its picturesque setting in the Pyrenees and its cultural and historical heritage.
-
B.
Calvé
Calvé is a well-known food brand, particularly recognized for its peanut butter and sauces, that forms part of Unilever’s global brand portfolio.
-
C.
Desnos
Desnos is the surname of Robert Desnos, a notable French surrealist poet and member of the Resistance during World War II.
-
D.
Bonnieux
Bonnieux is a picturesque hilltop village in southeastern France’s Provence region, known for its historic stone houses, terraced streets, and panoramic views over the Luberon valley.
-
E.
Lacanau
Lacanau is a coastal resort town in southwestern France known for its Atlantic beaches, surfing, and large freshwater lake.
- 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_69d806b1072881909e46bd212259c5f0 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98f7517048190b4eac4e44e81ff66 |
completed | April 11, 2026, 12:01 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f70a3d6b808190b4ae5225961de03f |
completed | May 3, 2026, 8:41 a.m. |
Created at: April 9, 2026, 9:24 p.m.