Triple
T10220564
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Termunten |
E242564
|
entity |
| Predicate | hasWaterBody |
P165
|
FINISHED |
| Object | Ems |
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 | Statement: [Termunten, hasWaterBody, Ems]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ems Context triple: [Termunten, hasWaterBody, Ems]
-
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.
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.
-
D.
Rhens
Rhens is a historic town on the Rhine River in western Germany, known for its medieval role as a meeting place of the prince-electors of the Holy Roman Empire.
-
E.
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.
- 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_69d381ae26c48190985abd0e25ee5d04 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d3aa72b258819097d8d50a714e19dc |
completed | April 6, 2026, 12:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e215ac56248190a75ad5ceb8152d5a |
completed | April 17, 2026, 11:12 a.m. |
Created at: April 6, 2026, 11:09 a.m.