Triple
T17023470
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Siegen-Wittgenstein |
E413003
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Siegerland |
E742000
|
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: Siegerland | Statement: [Siegen-Wittgenstein, locatedIn, Siegerland]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Siegerland Context triple: [Siegen-Wittgenstein, locatedIn, Siegerland]
-
A.
Siegerland
chosen
Siegerland is a hilly, forested region in western Germany known for its historic iron ore mining and metalworking industry.
-
B.
Rhineland
The Rhineland is a historically significant region in western Germany along the Rhine River, long contested as a strategic and economic heartland in European conflicts.
-
C.
Westfalen
Westfalen is a historical region in northwestern Germany, now largely part of the state of North Rhine-Westphalia, known for its distinct cultural identity and medieval heritage.
-
D.
Roerstreek
Roerstreek is a historical and geographical region in the southeastern Netherlands, named after the river Roer and known for its rural landscapes and small towns.
-
E.
Rübeland
Rübeland is a village in the Harz Mountains of central Germany, known for its show caves and scenic natural surroundings.
- 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_69d886cc4170819093deddc7b8b4b6a7 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d5d2abbc81908943becf5f539fc6 |
completed | April 18, 2026, 7:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a012334c3b48190b125ab926450c45b |
completed | May 11, 2026, 12:30 a.m. |
Created at: April 10, 2026, 5:33 a.m.