Triple
T1695012
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Krefeld |
E36636
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object |
Mönchengladbach
Mönchengladbach is a city in western Germany known for its textile industry heritage and its football club Borussia Mönchengladbach.
|
E382016
|
NE FINISHED |
How this triple was built (4 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: Mönchengladbach | Statement: [Krefeld, locatedNear, Mönchengladbach]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mönchengladbach Context triple: [Krefeld, locatedNear, Mönchengladbach]
-
A.
Dortmund
Dortmund is a major city in western Germany known for its rich football culture, industrial heritage, and home club Borussia Dortmund.
-
B.
Gelsenkirchen
Gelsenkirchen is a city in western Germany known for its strong football culture and modern stadium, Veltins-Arena, home to FC Schalke 04.
-
C.
Bochum
Bochum is a major city in Germany’s Ruhr region known for its industrial heritage, cultural institutions, and large university.
-
D.
Wolfsburg
Wolfsburg is a German city best known as the headquarters and main production site of the Volkswagen automobile company.
-
E.
Mülheim an der Ruhr
Mülheim an der Ruhr is a city in western Germany’s Ruhr area, known for its industrial heritage, riverside setting on the Ruhr River, and role as a regional economic and cultural center.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Mönchengladbach Triple: [Krefeld, locatedNear, Mönchengladbach]
Generated description
Mönchengladbach is a city in western Germany known for its textile industry heritage and its football club Borussia Mönchengladbach.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mönchengladbach Target entity description: Mönchengladbach is a city in western Germany known for its textile industry heritage and its football club Borussia Mönchengladbach.
-
A.
Dortmund
Dortmund is a major city in western Germany known for its rich football culture, industrial heritage, and home club Borussia Dortmund.
-
B.
Gelsenkirchen
Gelsenkirchen is a city in western Germany known for its strong football culture and modern stadium, Veltins-Arena, home to FC Schalke 04.
-
C.
Bochum
Bochum is a major city in Germany’s Ruhr region known for its industrial heritage, cultural institutions, and large university.
-
D.
Wolfsburg
Wolfsburg is a German city best known as the headquarters and main production site of the Volkswagen automobile company.
-
E.
Mülheim an der Ruhr
Mülheim an der Ruhr is a city in western Germany’s Ruhr area, known for its industrial heritage, riverside setting on the Ruhr River, and role as a regional economic and cultural center.
- F. None of above. chosen
Provenance (5 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_69a886163dec8190859c514232a37a05 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aa62b3b8908190afc3f9e4a384684f |
completed | March 6, 2026, 5:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4cdaf3ea08190b2663866c984b203 |
completed | March 14, 2026, 2:53 a.m. |
| NEDg | Description generation | batch_69b4d182c75081909413b17597c10c0e |
completed | March 14, 2026, 3:09 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b4d20a7fa8819093e8e66ba9272f31 |
completed | March 14, 2026, 3:12 a.m. |
Created at: March 4, 2026, 7:30 p.m.