Triple
T8883130
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alpsee |
E211458
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Schwangau |
E211457
|
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: Schwangau | Statement: [Alpsee, locatedIn, Schwangau]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Schwangau Context triple: [Alpsee, locatedIn, Schwangau]
-
A.
Schwangau
chosen
Schwangau is a Bavarian village in southern Germany best known as the home of the fairy-tale Neuschwanstein Castle and other nearby royal palaces amid the Alpine foothills.
-
B.
Schwetzingen
Schwetzingen is a town in southwestern Germany renowned for its Baroque palace and extensive formal gardens.
-
C.
Schongau
Schongau is a historic Bavarian town in southern Germany known for its well-preserved medieval old town and location along the Romantic Road.
-
D.
Geiselgasteig
Geiselgasteig is a district in the southern part of Munich, Germany, best known as a major center of film and television production.
-
E.
Herrsching am Ammersee
Herrsching am Ammersee is a lakeside municipality in Bavaria, Germany, known for its scenic location on the shores of Lake Ammersee and its popularity as a recreational and tourist destination.
- 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_69ca838f9e20819096ab1f236a70381a |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc616b2d988190b923ef1e33aab787 |
completed | April 1, 2026, 12:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfc92c2f088190b953de2bfcbb5bb5 |
completed | April 3, 2026, 2:05 p.m. |
Created at: March 30, 2026, 6:53 p.m.