Triple
T20168945
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Triesen |
E491902
|
entity |
| Predicate | borders |
P224
|
FINISHED |
| Object | Triesenberg |
—
|
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: Triesenberg | Statement: [Triesen, borders, Triesenberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Triesenberg Context triple: [Triesen, borders, Triesenberg]
-
A.
Triesenberg
chosen
Triesenberg is a mountainous municipality in Liechtenstein known for its traditional Walser culture and scenic alpine landscapes.
-
B.
Reinsberg
Reinsberg is a municipality in the German state of Saxony, located within the administrative district of Mittelsachsen.
-
C.
Geiersthal
Geiersthal is a small municipality in the Bavarian Forest region of southeastern Germany.
-
D.
Landensberg
Landensberg is a small municipality in the Bavarian region of southern Germany.
-
E.
Wettenberg
Wettenberg is a municipality in the German state of Hesse, located near the city of Gießen and known for its mix of rural character and proximity to urban centers.
- 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_69da6266c6888190bc1a3ecf24814d34 |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e66846f4ec81908b0dc6a6e0ec27dd |
completed | April 20, 2026, 5:54 p.m. |
Created at: April 11, 2026, 11:35 p.m.