Triple
T10798655
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of the Seelow Heights |
E254778
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object |
Seelow Heights
Seelow Heights is a strategically important ridge east of Berlin in Germany, known as the site of one of the final major battles on the Eastern Front during World War II.
|
E885704
|
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: Seelow Heights | Statement: [Battle of the Seelow Heights, locatedIn, Seelow Heights]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Seelow Heights Context triple: [Battle of the Seelow Heights, locatedIn, Seelow Heights]
-
A.
Oberkassel
Oberkassel is a district on the right bank of the Rhine in Bonn, Germany, known for its residential character and historic riverside setting.
-
B.
Oberkassel
Oberkassel is a riverside district of Düsseldorf in western Germany, known for its affluent residential areas and scenic location along the Rhine.
-
C.
Leopoldshöhe
Leopoldshöhe is a municipality in the district of Lippe in North Rhine-Westphalia, Germany, known as a residential community near the city of Bielefeld.
-
D.
Langendorf
Langendorf is a municipality in the canton of Solothurn in northwestern Switzerland.
-
E.
Langendorf
Langendorf is a municipality in the Weißenfels area of Saxony-Anhalt in eastern Germany.
- 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: Seelow Heights Triple: [Battle of the Seelow Heights, locatedIn, Seelow Heights]
Generated description
Seelow Heights is a strategically important ridge east of Berlin in Germany, known as the site of one of the final major battles on the Eastern Front during World War II.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Seelow Heights Target entity description: Seelow Heights is a strategically important ridge east of Berlin in Germany, known as the site of one of the final major battles on the Eastern Front during World War II.
-
A.
Oberkassel
Oberkassel is a district on the right bank of the Rhine in Bonn, Germany, known for its residential character and historic riverside setting.
-
B.
Oberkassel
Oberkassel is a riverside district of Düsseldorf in western Germany, known for its affluent residential areas and scenic location along the Rhine.
-
C.
Leopoldshöhe
Leopoldshöhe is a municipality in the district of Lippe in North Rhine-Westphalia, Germany, known as a residential community near the city of Bielefeld.
-
D.
Langendorf
Langendorf is a municipality in the canton of Solothurn in northwestern Switzerland.
-
E.
Langendorf
Langendorf is a municipality in the Weißenfels area of Saxony-Anhalt in eastern Germany.
- 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_69d6aa61c15c8190a1839550c56e75e1 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d73334feb08190aae967eaa37659f7 |
completed | April 9, 2026, 5:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69de566352608190ab15e3a4b690c9a5 |
completed | April 14, 2026, 2:59 p.m. |
| NEDg | Description generation | batch_69de5eae7ab88190a0c512cfe61e3458 |
completed | April 14, 2026, 3:35 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69de60907e1081908405b6d71adbd388 |
completed | April 14, 2026, 3:43 p.m. |
Created at: April 8, 2026, 9:17 p.m.