Triple
T10950934
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hamburg S-Bahn line S1 |
E258722
|
entity |
| Predicate | connects |
P390
|
FINISHED |
| Object | Wedel |
E894696
|
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: Wedel | Statement: [Hamburg S-Bahn line S1, connects, Wedel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wedel Context triple: [Hamburg S-Bahn line S1, connects, Wedel]
-
A.
Wedel
chosen
Wedel is a town on the outskirts of Hamburg, Germany, known for being a suburban terminus of the city's S-Bahn network.
-
B.
Oudenburg
Oudenburg is a small historic town in the Belgian province of West Flanders, known for its Roman heritage and medieval abbey.
-
C.
Wolkenburg
Wolkenburg is a prominent hill in Germany’s Siebengebirge range, known for its volcanic origin and scenic views over the Rhine Valley.
-
D.
Stolberg
Stolberg is a historic German town in the Harz region, known for its well-preserved medieval architecture and role in early Reformation-era history.
-
E.
Badenburg
Badenburg is an ornate pavilion within Munich’s Nymphenburg Palace park, known for its richly decorated interiors and historical bathing hall.
- 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_69d6aa88500c819097d7032ca578e74f |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d770fc156c8190826e124c13ce7242 |
completed | April 9, 2026, 9:27 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e2d733f7d88190b45df5c155ff5a46 |
completed | April 18, 2026, 12:58 a.m. |
Created at: April 8, 2026, 9:23 p.m.