Triple
T16462872
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bugs’ White Water Rapids |
E399850
|
entity |
| Predicate | parkSection |
P5641
|
FINISHED |
| Object | Spassburg |
E399856
|
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: Spassburg | Statement: [Bugs’ White Water Rapids, parkSection, Spassburg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Spassburg Context triple: [Bugs’ White Water Rapids, parkSection, Spassburg]
-
A.
Spassburg
chosen
Spassburg is the German-themed area of the Six Flags Fiesta Texas amusement park, featuring rides, shops, and architecture inspired by traditional German towns.
-
B.
Meersburg
Meersburg is a historic town in southern Germany known for its medieval castle, picturesque old town, and scenic location on the shores of Lake Constance.
-
C.
Haunsheim
Haunsheim is a small municipality in the Bavarian region of southern Germany, known for its rural character and historic village setting.
-
D.
Biburg
Biburg is a small municipality in the Lower Bavarian region of Germany, known for its rural character and historic monastery.
-
E.
Siegburg
Siegburg is a historic town in North Rhine-Westphalia, Germany, known for its medieval abbey and location near Bonn and Cologne.
- 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_69d87f2dac988190b74d6e185fa88ba4 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e32d824cd881909b1f2fd40e14ee35 |
completed | April 18, 2026, 7:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a004f555f6081908b1f0d524b6fb9a7 |
completed | May 10, 2026, 9:26 a.m. |
Created at: April 10, 2026, 5:10 a.m.