Triple
T12836606
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eemsmond |
E306931
|
entity |
| Predicate | sharesBorderWith |
P224
|
FINISHED |
| Object | Loppersum |
—
|
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: Loppersum | Statement: [Eemsmond, sharesBorderWith, Loppersum]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Loppersum Context triple: [Eemsmond, sharesBorderWith, Loppersum]
-
A.
Loppersum
chosen
Loppersum is a village and former municipality in the province of Groningen in the Netherlands, known for its historic churches and its location in an area affected by gas-extraction-induced earthquakes.
-
B.
Brunssum
Brunssum is a town in the Dutch province of Limburg known for hosting a major NATO headquarters and having a history rooted in coal mining.
-
C.
Veldhoven
Veldhoven is a town and municipality in the southern Netherlands, located near Eindhoven in the province of North Brabant.
-
D.
Kloosterburen
Kloosterburen is a small village in the Dutch province of Groningen, known for its historic churches and rural character.
-
E.
Schoonhoven
Schoonhoven is a historic Dutch town in South Holland, renowned for its silver craftsmanship and picturesque riverside setting.
- 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_69d7bdf52b94819096d6f0ba4ab50a98 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96ff015f4819090070a01f3938acc |
completed | April 10, 2026, 9:47 p.m. |
Created at: April 9, 2026, 5:35 p.m.