Triple
T23338297
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Langenberg |
E591659
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object |
town of Willingen
The town of Willingen is a German resort town in the Sauerland region of Hesse, known for its winter sports facilities, ski areas, and annual ski jumping World Cup events.
|
E1581282
|
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: town of Willingen | Statement: [Langenberg, locatedNear, town of Willingen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: town of Willingen Context triple: [Langenberg, locatedNear, town of Willingen]
-
A.
Willingshausen
Willingshausen is a small municipality in central Germany known for its historic artists’ colony and rural cultural heritage.
-
B.
Weiningen
Weiningen is a small Swiss municipality in the canton of Zurich, located in the Limmat Valley near the city of Zurich.
-
C.
Town of Wadern
The Town of Wadern is a small municipality in the Saarland region of western Germany, known for its rural character and surrounding natural landscapes.
-
D.
Woringen
Woringen is a small municipality in the Unterallgäu district of Bavaria in southern Germany.
-
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: town of Willingen Triple: [Langenberg, locatedNear, town of Willingen]
Generated description
The town of Willingen is a German resort town in the Sauerland region of Hesse, known for its winter sports facilities, ski areas, and annual ski jumping World Cup events.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: town of Willingen Target entity description: The town of Willingen is a German resort town in the Sauerland region of Hesse, known for its winter sports facilities, ski areas, and annual ski jumping World Cup events.
-
A.
Willingshausen
Willingshausen is a small municipality in central Germany known for its historic artists’ colony and rural cultural heritage.
-
B.
Weiningen
Weiningen is a small Swiss municipality in the canton of Zurich, located in the Limmat Valley near the city of Zurich.
-
C.
Town of Wadern
The Town of Wadern is a small municipality in the Saarland region of western Germany, known for its rural character and surrounding natural landscapes.
-
D.
Woringen
Woringen is a small municipality in the Unterallgäu district of Bavaria in southern Germany.
-
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_69e25d20156c81908c5c53195bd9c738 |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f1983099188190a2e05cf81d62a641 |
completed | April 29, 2026, 5:33 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0c4cb44540819093c6d899dd17347d |
completed | May 19, 2026, 11:42 a.m. |
| NEDg | Description generation | batch_6a0c4ef9dcc48190b0c0ca52555f5490 |
completed | May 19, 2026, 11:52 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0c506fc3448190bbd36ba192f30e58 |
completed | May 19, 2026, 11:58 a.m. |
Created at: April 17, 2026, 5:17 p.m.