Triple
T6743110
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wilhelm Meyer-Lübke |
E154137
|
entity |
| Predicate | birthPlace |
P1
|
FINISHED |
| Object | Diedenhofen |
E291734
|
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: Diedenhofen | Statement: [Wilhelm Meyer-Lübke, birthPlace, Diedenhofen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Diedenhofen Context triple: [Wilhelm Meyer-Lübke, birthPlace, Diedenhofen]
-
A.
Diedenhofen
chosen
Diedenhofen is the historical German name for the town of Thionville in northeastern France, near the border with Luxembourg and Germany.
-
B.
Gerolzhofen
Gerolzhofen is a small historic town in northern Bavaria, Germany, known for its medieval architecture and wine-growing surroundings.
-
C.
Hettstadt
Hettstadt is a small municipality in the Würzburg district of Bavaria, Germany, known for its rural character and proximity to the city of Würzburg.
-
D.
Hägendorf
Hägendorf is a municipality in the canton of Solothurn in northwestern Switzerland, known for its residential character and proximity to the Jura mountains.
-
E.
Dingolshausen
Dingolshausen is a small municipality in the Schweinfurt district of Lower Franconia in northern Bavaria, Germany, known for its rural character and surrounding wine-growing areas.
- 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_69c6880d84d8819095d19de2295f26ac |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d1b3b1448190a94b4b64f01af14a |
completed | March 27, 2026, 6:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7cbb542f08190bfea892ea90390b3 |
completed | March 28, 2026, 12:38 p.m. |
Created at: March 27, 2026, 2:10 p.m.