Triple
T10141522
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Papenburg |
E231594
|
entity |
| Predicate | locatedOn |
P40
|
FINISHED |
| Object | Ems River |
E34843
|
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: Ems River | Statement: [Papenburg, locatedOn, Ems River]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ems River Context triple: [Papenburg, locatedOn, Ems River]
-
A.
Ems
Ems is a historic spa town in present-day Germany, renowned for its mineral springs and 19th-century status as a fashionable European resort.
-
B.
Ems
chosen
The Ems is a river in northwestern Germany that flows through several states before emptying into the North Sea.
-
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.
Rhine
The Rhine is one of Europe's most important rivers, historically serving as a vital trade route and cultural boundary from the Alps through Germany to the North Sea.
-
E.
Emsdetten
Emsdetten is a town in the district of Steinfurt in North Rhine-Westphalia, Germany, known for its textile industry heritage and location along the Ems River.
- 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_69ca848364f881908a24366a6feec1db |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cdeb2599e0819090184631e481310c |
completed | April 2, 2026, 4:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69de21d85ee08190adfab9926fea1709 |
completed | April 14, 2026, 11:15 a.m. |
Created at: March 30, 2026, 9:07 p.m.