Triple
T15063219
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | von Lossberg |
E379687
|
entity |
| Predicate | hasNameElement |
P3097
|
FINISHED |
| Object |
Lossberg
Lossberg is a German surname most notably associated with the military strategist Fritz von Lossberg of the Imperial German Army during World War I.
|
E1144711
|
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: Lossberg | Statement: [von Lossberg, hasNameElement, Lossberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lossberg Context triple: [von Lossberg, hasNameElement, Lossberg]
-
A.
Schöngarth
Schöngarth is a German surname most notably associated with Eberhard Schöngarth, a high-ranking Nazi SS officer and war criminal during World War II.
-
B.
Widdersberg
Widdersberg is a small village that forms one of the local subdivisions of the municipality of Münsing in Bavaria, Germany.
-
C.
Hesselberg
Hesselberg is a prominent hill in Bavaria, Germany, known as the highest elevation of the Franconian Alb region.
-
D.
Landensberg
Landensberg is a small municipality in the Bavarian region of southern Germany.
-
E.
Wildenberg
Wildenberg is a small municipality in the Kelheim district of Lower Bavaria, Germany, known for its rural character and agricultural surroundings.
- 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: Lossberg Triple: [von Lossberg, hasNameElement, Lossberg]
Generated description
Lossberg is a German surname most notably associated with the military strategist Fritz von Lossberg of the Imperial German Army during World War I.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lossberg Target entity description: Lossberg is a German surname most notably associated with the military strategist Fritz von Lossberg of the Imperial German Army during World War I.
-
A.
Schöngarth
Schöngarth is a German surname most notably associated with Eberhard Schöngarth, a high-ranking Nazi SS officer and war criminal during World War II.
-
B.
Widdersberg
Widdersberg is a small village that forms one of the local subdivisions of the municipality of Münsing in Bavaria, Germany.
-
C.
Hesselberg
Hesselberg is a prominent hill in Bavaria, Germany, known as the highest elevation of the Franconian Alb region.
-
D.
Landensberg
Landensberg is a small municipality in the Bavarian region of southern Germany.
-
E.
Wildenberg
Wildenberg is a small municipality in the Kelheim district of Lower Bavaria, Germany, known for its rural character and agricultural surroundings.
- 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_69d85cd7683881908d405c1b5d7b4f7f |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69dedee803ac81908bb7d66e49c2eb72 |
completed | April 15, 2026, 12:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fedd2268dc8190882e5a489e0c49c2 |
completed | May 9, 2026, 7:07 a.m. |
| NEDg | Description generation | batch_69fedea1fea88190b891485794acfa8d |
completed | May 9, 2026, 7:13 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fedf2b52348190bd8fc6999cb0abd7 |
completed | May 9, 2026, 7:15 a.m. |
Created at: April 10, 2026, 3:02 a.m.