Triple
T5301149
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ortenaukreis |
E119983
|
entity |
| Predicate | containsMunicipality |
P852
|
FINISHED |
| Object |
Hornberg
Hornberg is a small town in the Black Forest region of Baden-Württemberg, Germany, known for its scenic landscape and traditional cuckoo clock craftsmanship.
|
E509535
|
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: Hornberg | Statement: [Ortenaukreis, containsMunicipality, Hornberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hornberg Context triple: [Ortenaukreis, containsMunicipality, Hornberg]
-
A.
Hornsberg
Hornsberg is a waterfront residential and commercial district on the island of Kungsholmen in central Stockholm, Sweden.
-
B.
Hangelsberg
Hangelsberg is a village in the German state of Brandenburg, known as a district of the municipality Grünheide (Mark) in the Oder-Spree region.
-
C.
Schlossberg
Schlossberg is a historic hill in Graz, Austria, known for its fortress ruins, iconic clock tower, and panoramic views over the city.
-
D.
Nadelhorn
Nadelhorn is a prominent 4,000-meter-class peak in the Swiss Alps, known for its sharp, needle-like summit and popular alpine climbing routes.
-
E.
Hesselberg
Hesselberg is a prominent hill in Bavaria, Germany, known as the highest elevation of the Franconian Alb 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: Hornberg Triple: [Ortenaukreis, containsMunicipality, Hornberg]
Generated description
Hornberg is a small town in the Black Forest region of Baden-Württemberg, Germany, known for its scenic landscape and traditional cuckoo clock craftsmanship.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hornberg Target entity description: Hornberg is a small town in the Black Forest region of Baden-Württemberg, Germany, known for its scenic landscape and traditional cuckoo clock craftsmanship.
-
A.
Hornsberg
Hornsberg is a waterfront residential and commercial district on the island of Kungsholmen in central Stockholm, Sweden.
-
B.
Hangelsberg
Hangelsberg is a village in the German state of Brandenburg, known as a district of the municipality Grünheide (Mark) in the Oder-Spree region.
-
C.
Schlossberg
Schlossberg is a historic hill in Graz, Austria, known for its fortress ruins, iconic clock tower, and panoramic views over the city.
-
D.
Nadelhorn
Nadelhorn is a prominent 4,000-meter-class peak in the Swiss Alps, known for its sharp, needle-like summit and popular alpine climbing routes.
-
E.
Hesselberg
Hesselberg is a prominent hill in Bavaria, Germany, known as the highest elevation of the Franconian Alb 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_69bd44704be88190acdb2ac481b0ff55 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd8509f67c8190b2f82a8370301a59 |
completed | March 20, 2026, 5:34 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf10f040f48190b34a586d362264ee |
completed | March 21, 2026, 9:43 p.m. |
| NEDg | Description generation | batch_69bf11635b9c819092130c46caea9306 |
completed | March 21, 2026, 9:45 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69bf11e982d08190bc74312bf6ee7127 |
completed | March 21, 2026, 9:47 p.m. |
Created at: March 20, 2026, 1:53 p.m.