Triple
T15634692
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Agger |
E375909
|
entity |
| Predicate | flowsThrough |
P225
|
FINISHED |
| Object | Engelskirchen |
E1002260
|
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: Engelskirchen | Statement: [Agger, flowsThrough, Engelskirchen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Engelskirchen Context triple: [Agger, flowsThrough, Engelskirchen]
-
A.
Engelskirchen
chosen
Engelskirchen is a municipality in western Germany’s North Rhine-Westphalia, known for its picturesque setting in the hilly Bergisches Land region and its traditional industrial and Christmas-related heritage.
-
B.
Lüdenscheid
Lüdenscheid is a town in western Germany’s Sauerland region, historically noted for its role in World War II and known today for its metal and plastics industries.
-
C.
Neuenkirchen
Neuenkirchen is a municipality in the German state of North Rhine-Westphalia, known for its rural character and location within the Münsterland region.
-
D.
Bergisch Neukirchen
Bergisch Neukirchen is a district of the German city of Leverkusen, located in North Rhine-Westphalia.
-
E.
Rüttenscheid
Rüttenscheid is a lively, upscale district of Essen, Germany, known for its bustling shopping streets, restaurants, and cultural venues.
- 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_69ff5f472b648190b7cd532a1b16373e |
completed | May 9, 2026, 4:22 p.m. |
Created at: April 10, 2026, 4:14 a.m.