Triple
T20102833
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Schwerte |
E496588
|
entity |
| Predicate | hasSubdivision |
P747
|
FINISHED |
| Object |
Mitte
Mitte is a central district of the German town of Schwerte, typically encompassing its historic core and main administrative and commercial areas.
|
E1412131
|
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: Mitte | Statement: [Schwerte, hasSubdivision, Mitte]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mitte Context triple: [Schwerte, hasSubdivision, Mitte]
-
A.
Mitte
Mitte is the central district of Berlin, Germany, known as the historic core of the city and home to many major landmarks and government institutions.
-
B.
Mitte
Mitte is the central urban district of Ludwigshafen am Rhein, Germany, encompassing the city’s core commercial and administrative areas.
-
C.
Mitte
Mitte is the central urban district of Saarbrücken, Germany, encompassing much of the city’s administrative, commercial, and cultural core.
-
D.
Mitte
Mitte is a central urban district of the German city of Koblenz, encompassing key administrative, commercial, and historic areas.
-
E.
Mitten
Mitten is the NATO reporting name for the Yakovlev Yak-130, a Russian advanced jet trainer and light attack aircraft.
- 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: Mitte Triple: [Schwerte, hasSubdivision, Mitte]
Generated description
Mitte is a central district of the German town of Schwerte, typically encompassing its historic core and main administrative and commercial areas.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mitte Target entity description: Mitte is a central district of the German town of Schwerte, typically encompassing its historic core and main administrative and commercial areas.
-
A.
Mitte
Mitte is the central district of Berlin, Germany, known as the historic core of the city and home to many major landmarks and government institutions.
-
B.
Mitte
Mitte is the central urban district of Ludwigshafen am Rhein, Germany, encompassing the city’s core commercial and administrative areas.
-
C.
Mitte
Mitte is a central urban district of the German city of Koblenz, encompassing key administrative, commercial, and historic areas.
-
D.
Mitte
Mitte is the central urban district of Saarbrücken, Germany, encompassing much of the city’s administrative, commercial, and cultural core.
-
E.
Mitten
Mitten is the NATO reporting name for the Yakovlev Yak-130, a Russian advanced jet trainer and light attack aircraft.
- 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_69da626eee3881909f3454986d4a6511 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e6667170a4819085d07a4188ded541 |
completed | April 20, 2026, 5:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a082712b04c81908e0413dee9a04ea6 |
completed | May 16, 2026, 8:13 a.m. |
| NEDg | Description generation | batch_6a082864abdc819080a9822114eb8c25 |
completed | May 16, 2026, 8:18 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a082946da0c81909db45f0989020a17 |
completed | May 16, 2026, 8:22 a.m. |
Created at: April 11, 2026, 11:27 p.m.