Triple
T16463299
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Green Belt of Frankfurt am Main |
E399864
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Lohrberg
Lohrberg is a hill and popular recreational area in Frankfurt am Main, known for its vineyards, panoramic city views, and green spaces.
|
E1215259
|
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: Lohrberg | Statement: [Green Belt of Frankfurt am Main, contains, Lohrberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lohrberg Context triple: [Green Belt of Frankfurt am Main, contains, Lohrberg]
-
A.
Lohrberg
Lohrberg is a hill in Germany’s Siebengebirge range, known for its forested slopes and scenic hiking paths overlooking the Rhine valley.
-
B.
Lohberg
Lohberg is a small Bavarian village in the Bavarian Forest region of Germany, known as a gateway to outdoor activities around the Großer Arber mountain.
-
C.
Mahlberg
Mahlberg is a small town and municipality in the Ortenau district of Baden-Württemberg in southwestern Germany.
-
D.
Löhr
Löhr is a German-language surname borne by various notable individuals, including figures in military, arts, and public life.
-
E.
Eickeloh
Eickeloh is a small municipality in Lower Saxony, Germany, situated in the Heidekreis district.
- 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: Lohrberg Triple: [Green Belt of Frankfurt am Main, contains, Lohrberg]
Generated description
Lohrberg is a hill and popular recreational area in Frankfurt am Main, known for its vineyards, panoramic city views, and green spaces.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lohrberg Target entity description: Lohrberg is a hill and popular recreational area in Frankfurt am Main, known for its vineyards, panoramic city views, and green spaces.
-
A.
Lohrberg
Lohrberg is a hill in Germany’s Siebengebirge range, known for its forested slopes and scenic hiking paths overlooking the Rhine valley.
-
B.
Lohberg
Lohberg is a small Bavarian village in the Bavarian Forest region of Germany, known as a gateway to outdoor activities around the Großer Arber mountain.
-
C.
Mahlberg
Mahlberg is a small town and municipality in the Ortenau district of Baden-Württemberg in southwestern Germany.
-
D.
Löhr
Löhr is a German-language surname borne by various notable individuals, including figures in military, arts, and public life.
-
E.
Eickeloh
Eickeloh is a small municipality in Lower Saxony, Germany, situated in the Heidekreis district.
- 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_69d87f2dac988190b74d6e185fa88ba4 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e32d824cd881909b1f2fd40e14ee35 |
completed | April 18, 2026, 7:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a004f555f6081908b1f0d524b6fb9a7 |
completed | May 10, 2026, 9:26 a.m. |
| NEDg | Description generation | batch_6a0050c5d4548190a674c1c19f08a9fd |
completed | May 10, 2026, 9:32 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0051a7ae208190b33d42cc8d4bb21f |
completed | May 10, 2026, 9:36 a.m. |
Created at: April 10, 2026, 5:10 a.m.