Triple
T18501372
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wankdorf Bahnhof stop |
E452074
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Wankdorf |
—
|
NE NERFINISHED |
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: Wankdorf | Statement: [Wankdorf Bahnhof stop, locatedIn, Wankdorf]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wankdorf Context triple: [Wankdorf Bahnhof stop, locatedIn, Wankdorf]
-
A.
Wankdorf
chosen
Wankdorf is a well-known football stadium complex in Bern, Switzerland, historically associated with BSC Young Boys and major international matches.
-
B.
Winnental
Winnental is a historical town that served as the capital of the former German territory of Württemberg-Winnental.
-
C.
Könnern
Könnern is a small town in the German state of Saxony-Anhalt, known for its rural character and location near the Saale River.
-
D.
Fricktal
Fricktal is a region in northwestern Switzerland known for its rural landscapes, vineyards, and location along the Rhine near the German border.
-
E.
Seebruck
Seebruck is a Bavarian lakeside village and popular holiday resort on the northern shore of Lake Chiemsee in southern Germany.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8d3855d50819097fc8561b0299dd9 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e532c43de48190b49b87c1bb591016 |
completed | April 19, 2026, 7:53 p.m. |
Created at: April 10, 2026, 11:36 a.m.