Triple
T2936731
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | South Beveland |
E79285
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Walcheren |
E73296
|
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: Walcheren | Statement: [South Beveland, near, Walcheren]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Walcheren Context triple: [South Beveland, near, Walcheren]
-
A.
Walcheren
chosen
Walcheren is a peninsula and former island in the Dutch province of Zeeland, known for its coastal towns, beaches, and strategic location at the mouth of the Western Scheldt.
-
B.
Hoendiep
Hoendiep is a canal in the Dutch province of Groningen that serves as an important regional waterway and transport route.
-
C.
Warburg
Warburg is a prominent German-Jewish banking and philanthropic family historically influential in international finance and economic policy.
-
D.
Lonsee
Lonsee is a small municipality in the Alb-Donau district of Baden-Württemberg, Germany, situated on the Swabian Jura near the city of Ulm.
-
E.
Bertioga
Bertioga is a coastal municipality in the state of São Paulo, Brazil, known for its beaches and Atlantic Forest landscapes.
- 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_69ad8b0fbab081908f6a61567c045d8d |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad983df5e08190939cd8acf8ad5b55 |
completed | March 8, 2026, 3:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b0fc6c2cc08190ab34973c3f33a34d |
completed | March 11, 2026, 5:23 a.m. |
Created at: March 8, 2026, 2:56 p.m.