Triple
T16805079
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Groesbeek municipality |
E408459
|
entity |
| Predicate | sharesBorderWith |
P224
|
FINISHED |
| Object | Kleve (Germany) |
—
|
NE NERFINISHED |
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: Kleve (Germany) | Statement: [Groesbeek municipality, sharesBorderWith, Kleve (Germany)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kleve (Germany) Context triple: [Groesbeek municipality, sharesBorderWith, Kleve (Germany)]
-
A.
Kleve
chosen
Kleve is a historic town in western Germany near the Dutch border, known for its medieval castle and role as the former capital of the Duchy of Cleves.
-
B.
Krefeld, Germany
Krefeld, Germany is an industrial city in North Rhine-Westphalia known historically for its textile and silk production.
-
C.
Lübbecke, Germany
Lübbecke is a small town in North Rhine-Westphalia, Germany, known for its location at the foot of the Wiehen Hills and its traditional brewing industry.
-
D.
Markkleeberg
Markkleeberg is a town in the German state of Saxony known for its proximity to Leipzig and its recreational lakes and green spaces.
-
E.
Krefeld
Krefeld is a city in western Germany near the Rhine River, known historically for its textile and silk industry.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d88393905081908d00a86b99996ac8 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3b2cb68508190a05749bad68f7b43 |
completed | April 18, 2026, 4:35 p.m. |
Created at: April 10, 2026, 5:22 a.m.