Triple
T14679877
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lüdenscheid |
E344748
|
entity |
| Predicate | hasSportsClub |
P346
|
FINISHED |
| Object |
Rot-Weiß Lüdenscheid
Rot-Weiß Lüdenscheid is a German sports club based in the town of Lüdenscheid, best known for its football team competing in regional leagues.
|
E1113629
|
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: Rot-Weiß Lüdenscheid | Statement: [Lüdenscheid, hasSportsClub, Rot-Weiß Lüdenscheid]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rot-Weiß Lüdenscheid Context triple: [Lüdenscheid, hasSportsClub, Rot-Weiß Lüdenscheid]
-
A.
Lüdenscheid
Lüdenscheid is a town in western Germany’s Sauerland region, historically noted for its role in World War II and known today for its metal and plastics industries.
-
B.
Rheinhausen
Rheinhausen is a district of the German city of Duisburg, located on the western bank of the Rhine in North Rhine-Westphalia.
-
C.
Wülscheid
Wülscheid is a small locality within the Aegidienberg district of Bad Honnef in North Rhine-Westphalia, Germany.
-
D.
Ritterhude
Ritterhude is a small town in northern Germany’s Lower Saxony, situated just northwest of Bremen.
-
E.
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.
- 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: Rot-Weiß Lüdenscheid Triple: [Lüdenscheid, hasSportsClub, Rot-Weiß Lüdenscheid]
Generated description
Rot-Weiß Lüdenscheid is a German sports club based in the town of Lüdenscheid, best known for its football team competing in regional leagues.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Rot-Weiß Lüdenscheid Target entity description: Rot-Weiß Lüdenscheid is a German sports club based in the town of Lüdenscheid, best known for its football team competing in regional leagues.
-
A.
Lüdenscheid
Lüdenscheid is a town in western Germany’s Sauerland region, historically noted for its role in World War II and known today for its metal and plastics industries.
-
B.
Rheinhausen
Rheinhausen is a district of the German city of Duisburg, located on the western bank of the Rhine in North Rhine-Westphalia.
-
C.
Wülscheid
Wülscheid is a small locality within the Aegidienberg district of Bad Honnef in North Rhine-Westphalia, Germany.
-
D.
Ritterhude
Ritterhude is a small town in northern Germany’s Lower Saxony, situated just northwest of Bremen.
-
E.
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.
- 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_69d822e34b348190ada4d1cdb6c7c226 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb5692284819090f775be8e478522 |
completed | April 14, 2026, 9:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fde180ff0c8190a8b7c7804e36c3f8 |
completed | May 8, 2026, 1:13 p.m. |
| NEDg | Description generation | batch_69fde447189881909b4b0dc654a05e0d |
completed | May 8, 2026, 1:25 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fde53290a48190b3701472bb4e3d63 |
completed | May 8, 2026, 1:29 p.m. |
Created at: April 10, 2026, 1:27 a.m.