Triple
T17642876
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wüstegarten |
E429280
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Gilserberg |
—
|
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: Gilserberg | Statement: [Wüstegarten, locatedNear, Gilserberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gilserberg Context triple: [Wüstegarten, locatedNear, Gilserberg]
-
A.
Gilserberg
chosen
Gilserberg is a small municipality in the German state of Hesse, known for its rural character and location within the Schwalm-Eder district.
-
B.
Spiegelberg
Spiegelberg is a rebellious and scheming member of the robber band in Friedrich Schiller’s play "Die Räuber," known for his ruthless ambition and treacherous nature.
-
C.
Syrgenstein
Syrgenstein is a small municipality in the Heidenheim district of the German state of Baden-Württemberg.
-
D.
Syrgenstein
Syrgenstein is a small municipality in the Bavarian region of southern Germany.
-
E.
Hasliberg
Hasliberg is a Swiss alpine village and municipality in the canton of Bern, known for its mountain scenery and ski and hiking resort facilities.
- 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_69d889e2c2608190b762e76d9b2262f1 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e46de7055c819080d315bb3637882b |
completed | April 19, 2026, 5:53 a.m. |
Created at: April 10, 2026, 6:03 a.m.