Triple
T8977712
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Muttenz |
E214435
|
entity |
| Predicate | borders |
P224
|
FINISHED |
| Object | Arlesheim |
E523826
|
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: Arlesheim | Statement: [Muttenz, borders, Arlesheim]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Arlesheim Context triple: [Muttenz, borders, Arlesheim]
-
A.
Arlesheim
chosen
Arlesheim is a municipality in the canton of Basel-Landschaft in northwestern Switzerland, known for its historic cathedral and picturesque setting near Basel.
-
B.
Adorp
Adorp is a small village in the municipality of Het Hogeland in the province of Groningen in the northern Netherlands.
-
C.
Adliswil
Adliswil is a municipality in the canton of Zurich, Switzerland, situated in the Sihl Valley just south of the city of Zurich.
-
D.
Schafhausen
Schafhausen is a village and district of the town Weil der Stadt in the German state of Baden-Württemberg.
-
E.
Bremgarten
Bremgarten is a historic Swiss town in the canton of Aargau, known for its well-preserved medieval old town and scenic riverside setting.
- 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_69ca839ea8b88190922c6a326ffcc0d3 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc67a33c8481909125acf4b7f0a919 |
completed | April 1, 2026, 12:32 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d02fb3a27081909bc8504c92d8942f |
completed | April 3, 2026, 9:22 p.m. |
Created at: March 30, 2026, 7:02 p.m.