Triple
T11231833
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Charlottenburg-Wilmersdorf |
E265840
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Schmargendorf
Schmargendorf is a residential locality in southwestern Berlin known for its quiet streets, historic buildings, and proximity to the Grunewald forest.
|
E1009405
|
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: Schmargendorf | Statement: [Charlottenburg-Wilmersdorf, contains, Schmargendorf]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Schmargendorf Context triple: [Charlottenburg-Wilmersdorf, contains, Schmargendorf]
-
A.
Hubersdorf
Hubersdorf is a small municipality located in the canton of Solothurn in northwestern Switzerland.
-
B.
Wermsdorf
Wermsdorf is a municipality in Saxony, Germany, best known as the site of the large Baroque hunting lodge and former royal residence Hubertusburg Palace.
-
C.
Malgersdorf
Malgersdorf is a small municipality in the Rottal-Inn district of Lower Bavaria, Germany.
-
D.
Aulendorf
Aulendorf is a small town in the Upper Swabia region of southern Germany, known for its historic castle and spa facilities.
-
E.
Siegsdorf
Siegsdorf is a Bavarian town in southeastern Germany known for its scenic Alpine surroundings and proximity to the Chiemsee lake.
- 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: Schmargendorf Triple: [Charlottenburg-Wilmersdorf, contains, Schmargendorf]
Generated description
Schmargendorf is a residential locality in southwestern Berlin known for its quiet streets, historic buildings, and proximity to the Grunewald forest.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Schmargendorf Target entity description: Schmargendorf is a residential locality in southwestern Berlin known for its quiet streets, historic buildings, and proximity to the Grunewald forest.
-
A.
Hubersdorf
Hubersdorf is a small municipality located in the canton of Solothurn in northwestern Switzerland.
-
B.
Wermsdorf
Wermsdorf is a municipality in Saxony, Germany, best known as the site of the large Baroque hunting lodge and former royal residence Hubertusburg Palace.
-
C.
Malgersdorf
Malgersdorf is a small municipality in the Rottal-Inn district of Lower Bavaria, Germany.
-
D.
Aulendorf
Aulendorf is a small town in the Upper Swabia region of southern Germany, known for its historic castle and spa facilities.
-
E.
Siegsdorf
Siegsdorf is a Bavarian town in southeastern Germany known for its scenic Alpine surroundings and proximity to the Chiemsee lake.
- 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_69d6aac656d48190b275efaa7d6074ee |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e9026e1c81909456ac946bbba972 |
completed | April 9, 2026, 5:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6a52daf1c81909c586e470a998073 |
completed | May 3, 2026, 1:30 a.m. |
| NEDg | Description generation | batch_69f6a616f6e4819096c9850434882548 |
completed | May 3, 2026, 1:34 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f6a716bb2c81909dccc5ddbf3c92b5 |
completed | May 3, 2026, 1:38 a.m. |
Created at: April 8, 2026, 9:30 p.m.