Triple
T6848272
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Müggelsee |
E157949
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object |
Müggelheim
Müggelheim is a village-like district in the southeastern part of Berlin, Germany, characterized by its forests, lakes, and tranquil, semi-rural atmosphere.
|
E659708
|
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: Müggelheim | Statement: [Müggelsee, locatedNear, Müggelheim]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Müggelheim Context triple: [Müggelsee, locatedNear, Müggelheim]
-
A.
Poppenhausen
Poppenhausen is a small German town located in the Schweinfurt administrative region of northern Bavaria.
-
B.
Möhringen
Möhringen is a district of Stuttgart in the German state of Baden-Württemberg, known as a residential area that also hosts U.S. military facilities.
-
C.
Memmingen
Memmingen is a historic town in the Bavarian region of Germany, known for its well-preserved medieval old town and role as a regional transport hub.
-
D.
Blaubeuren
Blaubeuren is a historic town in the Alb-Donau district of Baden-Württemberg, Germany, known for its medieval old town and the karst spring Blautopf.
-
E.
Rödelheim
Rödelheim is a district of Frankfurt am Main, Germany, known as a largely residential area with good transport links and local amenities.
- 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: Müggelheim Triple: [Müggelsee, locatedNear, Müggelheim]
Generated description
Müggelheim is a village-like district in the southeastern part of Berlin, Germany, characterized by its forests, lakes, and tranquil, semi-rural atmosphere.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Müggelheim Target entity description: Müggelheim is a village-like district in the southeastern part of Berlin, Germany, characterized by its forests, lakes, and tranquil, semi-rural atmosphere.
-
A.
Poppenhausen
Poppenhausen is a small German town located in the Schweinfurt administrative region of northern Bavaria.
-
B.
Möhringen
Möhringen is a district of Stuttgart in the German state of Baden-Württemberg, known as a residential area that also hosts U.S. military facilities.
-
C.
Memmingen
Memmingen is a historic town in the Bavarian region of Germany, known for its well-preserved medieval old town and role as a regional transport hub.
-
D.
Blaubeuren
Blaubeuren is a historic town in the Alb-Donau district of Baden-Württemberg, Germany, known for its medieval old town and the karst spring Blautopf.
-
E.
Rödelheim
Rödelheim is a district of Frankfurt am Main, Germany, known as a largely residential area with good transport links and local amenities.
- 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_69c6882ed4c081909dc465a7cf8838be |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d7ce3e7481908e0472b8faafa473 |
completed | March 27, 2026, 7:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8029da4688190ae479d3f791aa1c2 |
completed | March 28, 2026, 4:32 p.m. |
| NEDg | Description generation | batch_69c80362bcb88190ad8c42f520d7dd56 |
completed | March 28, 2026, 4:35 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c803da5e2c819098814b16d14cf837 |
completed | March 28, 2026, 4:37 p.m. |
Created at: March 27, 2026, 2:20 p.m.