Triple
T23081420
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gäu industrial zone |
E575483
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Oensingen |
—
|
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: Oensingen | Statement: [Gäu industrial zone, locatedIn, Oensingen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Oensingen Context triple: [Gäu industrial zone, locatedIn, Oensingen]
-
A.
Oensingen
chosen
Oensingen is a Swiss municipality located in the canton of Solothurn, known as a regional transport hub near the Jura mountains.
-
B.
Hinsingen
Hinsingen is a small commune in northeastern France, situated in the Moselle department within the historical region of Lorraine.
-
C.
Unterensingen
Unterensingen is a small municipality in the German state of Baden-Württemberg, situated near the Neckar River and known for its residential character within the Stuttgart metropolitan region.
-
D.
Weiningen
Weiningen is a small Swiss municipality in the canton of Zurich, located in the Limmat Valley near the city of Zurich.
-
E.
Orsingen-Nenzingen
Orsingen-Nenzingen is a small municipality in the state of Baden-Württemberg in southern Germany.
- 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_69e245be28d48190ad1348d5a73db37d |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f18c67e06881908d24d6267bb49553 |
completed | April 29, 2026, 4:43 a.m. |
Created at: April 17, 2026, 3:56 p.m.