Triple
T650471
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Guernica |
E11334
|
entity |
| Predicate | twinnedWith |
P1072
|
FINISHED |
| Object |
Bochum
Bochum is a major city in Germany’s Ruhr region known for its industrial heritage, cultural institutions, and large university.
|
E248839
|
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: Bochum | Statement: [Guernica, twinnedWith, Bochum]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bochum Context triple: [Guernica, twinnedWith, Bochum]
-
A.
Gelsenkirchen
Gelsenkirchen is a city in western Germany known for its strong football culture and modern stadium, Veltins-Arena, home to FC Schalke 04.
-
B.
Duisburg
Duisburg is a major industrial and port city in western Germany’s Ruhr region, known for its steel production and one of the world’s largest inland harbors.
-
C.
Düsseldorf
Düsseldorf is a major German city on the Rhine River known for its fashion and art scenes, modern architecture, and status as an important economic and financial center.
-
D.
Osnabrück
Osnabrück is a historic city in Lower Saxony, Germany, known for its medieval architecture and role in the Peace of Westphalia.
-
E.
Dortmund
Dortmund is a major city in western Germany known for its rich football culture, industrial heritage, and home club Borussia Dortmund.
- 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: Bochum Triple: [Guernica, twinnedWith, Bochum]
Generated description
Bochum is a major city in Germany’s Ruhr region known for its industrial heritage, cultural institutions, and large university.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bochum Target entity description: Bochum is a major city in Germany’s Ruhr region known for its industrial heritage, cultural institutions, and large university.
-
A.
Gelsenkirchen
Gelsenkirchen is a city in western Germany known for its strong football culture and modern stadium, Veltins-Arena, home to FC Schalke 04.
-
B.
Duisburg
Duisburg is a major industrial and port city in western Germany’s Ruhr region, known for its steel production and one of the world’s largest inland harbors.
-
C.
Düsseldorf
Düsseldorf is a major German city on the Rhine River known for its fashion and art scenes, modern architecture, and status as an important economic and financial center.
-
D.
Osnabrück
Osnabrück is a historic city in Lower Saxony, Germany, known for its medieval architecture and role in the Peace of Westphalia.
-
E.
Dortmund
Dortmund is a major city in western Germany known for its rich football culture, industrial heritage, and home club Borussia Dortmund.
- 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_69a493266a2881909daf4c40f719dee8 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49f33b6d881908b6662b73d6fe833 |
completed | March 1, 2026, 8:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae6ad5cfe4819083cb536c5d521d5d |
completed | March 9, 2026, 6:38 a.m. |
| NEDg | Description generation | batch_69ae6b9da51c819085beb79a14f5d8b5 |
completed | March 9, 2026, 6:41 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae6c2a465c8190a9fe2a465e9ac3f0 |
completed | March 9, 2026, 6:43 a.m. |
Created at: March 1, 2026, 7:36 p.m.