Triple
T17174238
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rheden |
E416816
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Spankeren |
E1247027
|
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: Spankeren | Statement: [Rheden, contains, Spankeren]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Spankeren Context triple: [Rheden, contains, Spankeren]
-
A.
Spankeren
chosen
Spankeren is a small village in the Dutch province of Gelderland, located near the town of Rheden.
-
B.
Krabbendijke
Krabbendijke is a village in the Dutch province of Zeeland, known for its agricultural surroundings and location on the former island of Zuid-Beveland.
-
C.
Spijkerboor
Spijkerboor is a small village in the Dutch province of North Holland, situated within the municipality of Wormerland.
-
D.
Spijkerboor
Spijkerboor is a small village in the Dutch province of South Holland, known for its location amid the lakes and waterways of the Kagerplassen area.
-
E.
De Baarsjes
De Baarsjes is a residential neighborhood in Amsterdam, Netherlands, known for its diverse population, early 20th-century architecture, and canalside urban character.
- 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_69d886d5f34c8190b24564dfaa63f3fb |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3fc0c329081909f118bd4b7be8653 |
completed | April 18, 2026, 9:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0148435f6081909bfc6cc1ef59d971 |
completed | May 11, 2026, 3:08 a.m. |
Created at: April 10, 2026, 5:37 a.m.