Triple
T21645658
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Biel-Benken |
E534205
|
entity |
| Predicate | region |
P40
|
FINISHED |
| Object | Leimental |
—
|
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: Leimental | Statement: [Biel-Benken, region, Leimental]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Leimental Context triple: [Biel-Benken, region, Leimental]
-
A.
Leimental
chosen
Leimental is a region in the canton of Basel-Landschaft in Switzerland, known for its suburban communities and scenic landscapes near the city of Basel.
-
B.
Gardein
Gardein is a plant-based food brand known for its wide range of meatless products such as chicken, beef, and fish alternatives made from soy, wheat, and pea proteins.
-
C.
Witellikon
Witellikon is a small settlement within the municipality of Küsnacht in the canton of Zürich, Switzerland, situated along the shores of Lake Zurich.
-
D.
Schindellegi
Schindellegi is a village in the municipality of Feusisberg in the canton of Schwyz, Switzerland, known as a residential community in the greater Zurich area.
-
E.
Schaumainkai
Schaumainkai is a prominent riverside street along the south bank of the Main River in Frankfurt, Germany, known for its concentration of major museums and cultural institutions.
- 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_69e0c466aec88190ba39c7543dbc8ba2 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ef5394c570819081dbbe7e0f98f7d3 |
completed | April 27, 2026, 12:16 p.m. |
Created at: April 16, 2026, 6:35 p.m.