Triple
T7799516
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hermann Roesler |
E180391
|
entity |
| Predicate | workLocation |
P7
|
FINISHED |
| Object | Jena |
E60682
|
NE FINISHED |
How this triple was built (2 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: Jena | Statement: [Hermann Roesler, workLocation, Jena]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jena Context triple: [Hermann Roesler, workLocation, Jena]
-
A.
Jena
chosen
Jena is a historic university city in the German state of Thuringia, known for its role in optics, philosophy, and science.
-
B.
Gotha
Gotha is a historic German city in Thuringia known for its former ducal court, cultural heritage, and role as a residence of various German noble houses.
-
C.
Neustrelitz
Neustrelitz is a town in northeastern Germany known for hosting a key research center of the German Aerospace Center (DLR), particularly focused on satellite data and space-related technologies.
-
D.
Heilbronn
Heilbronn is a city in the German state of Baden-Württemberg known for its industrial base, wine production, and role as a regional economic and educational hub.
-
E.
Oschersleben
Oschersleben is a town in the German state of Saxony-Anhalt, known for its motorsport race track Motorsport Arena Oschersleben.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69ca827e50cc8190a92a733577184938 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cae985d8f08190b38d9d6848a7dc83 |
completed | March 30, 2026, 9:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cbdec158788190aae5038ea72f2a99 |
completed | March 31, 2026, 2:48 p.m. |
Created at: March 30, 2026, 4:32 p.m.