Triple
T15187714
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mayen-Koblenz |
E362920
|
entity |
| Predicate | containsTown |
P847
|
FINISHED |
| Object | Bendorf |
E1051019
|
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: Bendorf | Statement: [Mayen-Koblenz, containsTown, Bendorf]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bendorf Context triple: [Mayen-Koblenz, containsTown, Bendorf]
-
A.
Bendorf
chosen
Bendorf is a town on the Rhine River in Rhineland-Palatinate, Germany, known for its industrial heritage and proximity to Koblenz.
-
B.
Waltershof
Waltershof is an industrial and port district of Hamburg, Germany, located within the borough of Hamburg-Mitte.
-
C.
Frenkendorf
Frenkendorf is a municipality in the canton of Basel-Landschaft in northwestern Switzerland, located near the city of Basel.
-
D.
Antdorf
Antdorf is a small rural municipality in Upper Bavaria, Germany, known for its traditional Bavarian character and scenic Alpine foothill landscape.
-
E.
Obergoms
Obergoms is a municipality in the canton of Valais in southwestern Switzerland, known for its high Alpine landscapes and traditional mountain villages.
- 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_69d85a09a39c81908759f23268e2d408 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0067995fc8190b048f15086bd42f0 |
completed | April 15, 2026, 9:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fedd2a2eb48190a569847d2f583c61 |
completed | May 9, 2026, 7:07 a.m. |
Created at: April 10, 2026, 3:09 a.m.