Triple
T9751541
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dark |
E236452
|
entity |
| Predicate | setInFictionalLocation |
P18263
|
FINISHED |
| Object |
Winden
Winden is the eerie, time-tangled small town that serves as the central setting of the German science fiction thriller series "Dark."
|
E818078
|
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: Winden | Statement: [Dark, setInFictionalLocation, Winden]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Winden Context triple: [Dark, setInFictionalLocation, Winden]
-
A.
Waidberg
Waidberg is a wooded hill and recreational area on the outskirts of Zurich, Switzerland, known for its hiking trails, viewpoints, and proximity to the Hönggerberg.
-
B.
Rheydt
Rheydt is a district of the German city of Mönchengladbach in North Rhine-Westphalia, historically an independent town in the Rhineland.
-
C.
Wernborn
Wernborn is a village and district of the town of Usingen in the Hochtaunus region of Hesse, Germany.
-
D.
Radevormwald
Radevormwald is a small historic town in North Rhine-Westphalia, western Germany, known for its hilly Bergisches Land landscape and traditional textile and metalworking industries.
-
E.
Wippingen
Wippingen is a village and district (Ortsteil) of the municipality of Blaustein in the Alb-Donau district of Baden-Württemberg, Germany.
- 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: Winden Triple: [Dark, setInFictionalLocation, Winden]
Generated description
Winden is the eerie, time-tangled small town that serves as the central setting of the German science fiction thriller series "Dark."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Winden Target entity description: Winden is the eerie, time-tangled small town that serves as the central setting of the German science fiction thriller series "Dark."
-
A.
Waidberg
Waidberg is a wooded hill and recreational area on the outskirts of Zurich, Switzerland, known for its hiking trails, viewpoints, and proximity to the Hönggerberg.
-
B.
Rheydt
Rheydt is a district of the German city of Mönchengladbach in North Rhine-Westphalia, historically an independent town in the Rhineland.
-
C.
Wernborn
Wernborn is a village and district of the town of Usingen in the Hochtaunus region of Hesse, Germany.
-
D.
Radevormwald
Radevormwald is a small historic town in North Rhine-Westphalia, western Germany, known for its hilly Bergisches Land landscape and traditional textile and metalworking industries.
-
E.
Wippingen
Wippingen is a village and district (Ortsteil) of the municipality of Blaustein in the Alb-Donau district of Baden-Württemberg, Germany.
- 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_69ca84d4eddc8190996fec1417d2bae8 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9facd5b881909f0569b23f308815 |
completed | April 1, 2026, 10:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1b020829481908456e7977c5f9adb |
completed | April 5, 2026, 12:43 a.m. |
| NEDg | Description generation | batch_69d1b0dde93881908fcec28de9cfa99d |
completed | April 5, 2026, 12:46 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d1b1bbe6108190af17b75f79c0f465 |
completed | April 5, 2026, 12:50 a.m. |
Created at: March 30, 2026, 8:24 p.m.