Triple
T5122488
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rosemarie Trockel |
E115501
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object |
Schwerte
Schwerte is a town in North Rhine-Westphalia, Germany, known as a small industrial and commuter community near Dortmund.
|
E496588
|
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: Schwerte | Statement: [Rosemarie Trockel, placeOfBirth, Schwerte]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Schwerte Context triple: [Rosemarie Trockel, placeOfBirth, Schwerte]
-
A.
Siegburg
Siegburg is a historic town in North Rhine-Westphalia, Germany, known for its medieval abbey and location near Bonn and Cologne.
-
B.
Solingen
Solingen is a city in western Germany renowned for its centuries-old blade-making tradition and production of high-quality knives and swords.
-
C.
Siegen
Siegen is a city in western Germany known as the birthplace of the Baroque painter Peter Paul Rubens and for its historic mining and university traditions.
-
D.
Kleve
Kleve is a historic town in western Germany near the Dutch border, known for its medieval castle and role as the former capital of the Duchy of Cleves.
-
E.
Schelklingen
Schelklingen is a small historic town in the Alb-Donau district of Baden-Württemberg in southern Germany, known for its picturesque setting near the Swabian Jura.
- 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: Schwerte Triple: [Rosemarie Trockel, placeOfBirth, Schwerte]
Generated description
Schwerte is a town in North Rhine-Westphalia, Germany, known as a small industrial and commuter community near Dortmund.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Schwerte Target entity description: Schwerte is a town in North Rhine-Westphalia, Germany, known as a small industrial and commuter community near Dortmund.
-
A.
Siegburg
Siegburg is a historic town in North Rhine-Westphalia, Germany, known for its medieval abbey and location near Bonn and Cologne.
-
B.
Solingen
Solingen is a city in western Germany renowned for its centuries-old blade-making tradition and production of high-quality knives and swords.
-
C.
Siegen
Siegen is a city in western Germany known as the birthplace of the Baroque painter Peter Paul Rubens and for its historic mining and university traditions.
-
D.
Kleve
Kleve is a historic town in western Germany near the Dutch border, known for its medieval castle and role as the former capital of the Duchy of Cleves.
-
E.
Schelklingen
Schelklingen is a small historic town in the Alb-Donau district of Baden-Württemberg in southern Germany, known for its picturesque setting near the Swabian Jura.
- 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_69bd4442ade0819087b9461f892b206b |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd78045e448190961db0ca7692370e |
completed | March 20, 2026, 4:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bec4b401a481909abf6660401c47dc |
completed | March 21, 2026, 4:17 p.m. |
| NEDg | Description generation | batch_69bec6ff39a08190adb303fa2a6b5193 |
completed | March 21, 2026, 4:27 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69bec808062c8190b2c0ee234477af95 |
completed | March 21, 2026, 4:32 p.m. |
Created at: March 20, 2026, 1:42 p.m.