Triple
T15634734
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sieg Railway |
E375911
|
entity |
| Predicate | followsValleyOf |
P3944
|
FINISHED |
| Object | River Sieg |
E1222044
|
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: River Sieg | Statement: [Sieg Railway, followsValleyOf, River Sieg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: River Sieg Context triple: [Sieg Railway, followsValleyOf, River Sieg]
-
A.
river Sieg
chosen
The river Sieg is a tributary of the Rhine in western Germany, flowing through North Rhine-Westphalia and Rhineland-Palatinate and giving its name to several nearby towns.
-
B.
Möhne River
The Möhne River is a waterway in North Rhine-Westphalia, Germany, known for the large reservoir and hydroelectric infrastructure associated with the Möhne Dam.
-
C.
Schwalm River
The Schwalm River is a waterway in the German state of Hesse that lends its name to the surrounding Schwalm-Eder region.
-
D.
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.
-
E.
Lippe
Lippe is a historical region in northwestern Germany that once formed a small principality and later a Free State within the German Reich.
- 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_69d85cd035a48190b73d5579ab73969a |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04eb8b4c48190b80fea6877483089 |
completed | April 16, 2026, 2:51 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00758380d08190bfe73d3e052c1f0a |
completed | May 10, 2026, 12:09 p.m. |
Created at: April 10, 2026, 4:14 a.m.