Triple
T3641707
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Liskamm |
E77201
|
entity |
| Predicate | region |
P40
|
FINISHED |
| Object | Valais |
E13342
|
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: Valais | Statement: [Liskamm, region, Valais]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Valais Context triple: [Liskamm, region, Valais]
-
A.
Valais
chosen
Valais is a mountainous canton in southwestern Switzerland known for its Alpine scenery, vineyards, and popular ski resorts such as Zermatt and Verbier.
-
B.
Vianen
Vianen is a historic Dutch town known for its medieval city center and location near major rivers in the western Netherlands.
-
C.
Ile-Rousse
Île-Rousse is a coastal town and popular seaside resort in the Balagne region of northern Corsica, known for its red granite islets and sandy beaches.
-
D.
Kutaisi
Kutaisi is one of Georgia’s major cities, historically significant and formerly a capital, located in the western part of the country.
-
E.
Seeland region
The Seeland region is an area in western Switzerland known for its lakes, fertile plains, and intensive agriculture, particularly vegetable farming.
- 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_69ad85de1b988190a45f8dbfebc806fc |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc359a91481908aef1c022f45e55c |
completed | March 8, 2026, 6:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b44f2e464881909d62dd2fad811a83 |
completed | March 13, 2026, 5:53 p.m. |
Created at: March 8, 2026, 3:24 p.m.