Triple
T4938360
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vaduz |
E110866
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object | Schaan |
E112963
|
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: Schaan | Statement: [Vaduz, borderedBy, Schaan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Schaan Context triple: [Vaduz, borderedBy, Schaan]
-
A.
Schaan
chosen
Schaan is a major municipality in Liechtenstein known as its principal urban and industrial center.
-
B.
Dudelange
Dudelange is a town in southern Luxembourg known as one of the country’s larger industrial and residential centers near the French border.
-
C.
Differdange
Differdange is a town in southwestern Luxembourg known as an industrial center with a strong steelmaking heritage.
-
D.
Schafhausen
Schafhausen is a village and district of the town Weil der Stadt in the German state of Baden-Württemberg.
-
E.
Sélestat
Sélestat is a historic town in the Alsace region of northeastern France, known for its well-preserved medieval architecture and cultural heritage.
- 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_69bd4415eee08190bdce70276e56a5b4 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd7088f6e48190bf09e58ab053a4d1 |
completed | March 20, 2026, 4:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be77bcb8d881908d393223bdea145a |
completed | March 21, 2026, 10:49 a.m. |
Created at: March 20, 2026, 1:31 p.m.