Triple
T10216842
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | La Flèche |
E242466
|
entity |
| Predicate | hasTwinTown |
P919
|
FINISHED |
| Object | Bückeburg |
E380040
|
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: Bückeburg | Statement: [La Flèche, hasTwinTown, Bückeburg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bückeburg Context triple: [La Flèche, hasTwinTown, Bückeburg]
-
A.
Bückeburg
chosen
Bückeburg is a historic town in Lower Saxony, Germany, known for its former role as the residence of the Counts and Princes of Schaumburg-Lippe and its well-preserved Renaissance castle.
-
B.
Nordhausen
Nordhausen is a historic town in central Germany known for its medieval architecture, former role as a key trading center, and association with the nearby Mittelbau-Dora concentration camp site.
-
C.
Hildesheim
Hildesheim is a historic city in northern Germany renowned for its medieval architecture and UNESCO-listed Romanesque churches.
-
D.
Braunschweig
Braunschweig is a historic city in northern Germany known for its medieval architecture, cultural institutions, and role as an important economic and scientific center.
-
E.
Staßfurt
Staßfurt is a town in Saxony-Anhalt, Germany, historically known for its salt mining and chemical industry.
- 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_69d381ae26c48190985abd0e25ee5d04 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d3aa6e544c8190961cdd7f1fbe24e6 |
completed | April 6, 2026, 12:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69dbd93b506c8190bbff63903770355a |
completed | April 12, 2026, 5:41 p.m. |
Created at: April 6, 2026, 11:06 a.m.