Triple
T19278360
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Millstätter See |
E482116
|
entity |
| Predicate | locatedInMunicipality |
P40
|
FINISHED |
| Object | Seeboden |
—
|
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: Seeboden | Statement: [Millstätter See, locatedInMunicipality, Seeboden]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Seeboden Context triple: [Millstätter See, locatedInMunicipality, Seeboden]
-
A.
Seeboden
chosen
Seeboden is a lakeside market town and popular tourist resort on the shore of Lake Millstatt in Carinthia, Austria.
-
B.
Blausee
Blausee is a small, crystal-clear alpine lake in the Swiss Bernese Oberland, famed for its striking blue waters and tranquil forest surroundings.
-
C.
Unterer See
Unterer See is a small lake located in the town of Böblingen in the German state of Baden-Württemberg.
-
D.
Totes Meer
Totes Meer is a surreal 1940–41 wartime painting by British artist Paul Nash depicting a sea of wrecked German aircraft under a moonlit sky.
-
E.
Obersee
Obersee is a small, picturesque alpine lake in Bavaria, Germany, known for its clear emerald waters and dramatic mountain surroundings near the Königssee.
- 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_69d8e8ce54cc8190998418ff1f66ef28 |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e5fbbe6e9c8190a7fa2ef3aa598e6c |
completed | April 20, 2026, 10:11 a.m. |
Created at: April 10, 2026, 1:30 p.m.