Triple
T3225689
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lünen |
E67615
|
entity |
| Predicate | hasRiver |
P165
|
FINISHED |
| Object | Lippe |
E148047
|
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: Lippe | Statement: [Lünen, hasRiver, Lippe]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lippe Context triple: [Lünen, hasRiver, Lippe]
-
A.
Lippe
chosen
The Lippe is a river in western Germany that flows through North Rhine-Westphalia and is a right-bank tributary of the Rhine.
-
B.
Lippe
Lippe is a historical region in northwestern Germany that once formed a small principality and later a Free State within the German Reich.
-
C.
Rheine
Rheine is a German city in the state of North Rhine-Westphalia, known for its historical town center and location along the River Ems.
-
D.
Wupper
The Wupper is a river in North Rhine-Westphalia, Germany, known for flowing through the industrial city of Wuppertal and its surrounding region.
-
E.
Erft River
The Erft River is a tributary of the Rhine in western Germany, flowing through North Rhine-Westphalia and known for passing historic towns and former mining areas before joining the Rhine near Neuss.
- 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_69ad858c61888190a31196310d9b30b5 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69adaeb4cd3481908af8a2c9b6c0742d |
completed | March 8, 2026, 5:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5c6da6620819099f537bd8aded2bf |
completed | March 14, 2026, 8:36 p.m. |
Created at: March 8, 2026, 3:08 p.m.