Triple
T746450
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Black Forest |
E15351
|
entity |
| Predicate | hasTown |
P847
|
FINISHED |
| Object |
Titisee-Neustadt
Titisee-Neustadt is a popular resort town in Germany’s Black Forest region, known for its scenic lake Titisee, winter sports facilities, and tourism.
|
E102973
|
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: Titisee-Neustadt | Statement: [Black Forest, hasTown, Titisee-Neustadt]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Titisee-Neustadt Context triple: [Black Forest, hasTown, Titisee-Neustadt]
-
A.
Satara
Satara is a historic city in the Indian state of Maharashtra, known for its role as a key political and cultural center of the Maratha dynasty.
-
B.
Woodfin
Woodfin is the surname of Randall Woodfin, an American politician and mayor of Birmingham, Alabama.
-
C.
Fort Payne
Fort Payne is a small city in northeastern Alabama known for its scenic Appalachian setting and historic role in the Cherokee Nation and the Trail of Tears.
-
D.
LaGrange
LaGrange is a town in Dutchess County, New York, known as a suburban community in the Hudson Valley region.
-
E.
LaGrange
LaGrange is a small city in western Georgia known for its historic downtown, proximity to West Point Lake, and role as an economic and cultural center for the surrounding region.
- 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: Titisee-Neustadt Triple: [Black Forest, hasTown, Titisee-Neustadt]
Generated description
Titisee-Neustadt is a popular resort town in Germany’s Black Forest region, known for its scenic lake Titisee, winter sports facilities, and tourism.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Titisee-Neustadt Target entity description: Titisee-Neustadt is a popular resort town in Germany’s Black Forest region, known for its scenic lake Titisee, winter sports facilities, and tourism.
-
A.
Satara
Satara is a historic city in the Indian state of Maharashtra, known for its role as a key political and cultural center of the Maratha dynasty.
-
B.
Woodfin
Woodfin is the surname of Randall Woodfin, an American politician and mayor of Birmingham, Alabama.
-
C.
Fort Payne
Fort Payne is a small city in northeastern Alabama known for its scenic Appalachian setting and historic role in the Cherokee Nation and the Trail of Tears.
-
D.
LaGrange
LaGrange is a town in Dutchess County, New York, known as a suburban community in the Hudson Valley region.
-
E.
LaGrange
LaGrange is a small city in western Georgia known for its historic downtown, proximity to West Point Lake, and role as an economic and cultural center for the surrounding region.
- 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_69a49358aa308190adbc9b5a0a2adcf9 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a62ca1d081908e3191411f86498d |
completed | March 1, 2026, 8:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a7b83948b48190af0349dd73ec3951 |
completed | March 4, 2026, 4:42 a.m. |
| NEDg | Description generation | batch_69a7b99c19fc819090e0f9a042a8c12c |
completed | March 4, 2026, 4:48 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a7ba44b79c8190b0ce8a430fe928e5 |
completed | March 4, 2026, 4:51 a.m. |
Created at: March 1, 2026, 7:37 p.m.