Triple
T8595165
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monnickendam |
E203525
|
entity |
| Predicate | nearbyCity |
P350
|
FINISHED |
| Object | Edam |
E137272
|
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: Edam | Statement: [Monnickendam, nearbyCity, Edam]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Edam Context triple: [Monnickendam, nearbyCity, Edam]
-
A.
Edam
chosen
Edam is a historic Dutch town in North Holland, internationally known for its namesake Edam cheese and traditional cheese markets.
-
B.
Edam cheese
Edam cheese is a semi-hard Dutch cheese, traditionally sold in red wax-coated spheres, known for its mild, slightly nutty flavor and good keeping qualities.
-
C.
Gouda
Gouda is a historic Dutch city renowned worldwide for its namesake cheese, traditional cheese market, and well-preserved medieval architecture.
-
D.
Gouda
Gouda is a small town in South Africa’s Western Cape province, known for its rural setting amid farmlands and nearby mountain wilderness areas.
-
E.
Maasdam cheese
Maasdam cheese is a Dutch semi-hard cow's milk cheese similar to Swiss Emmental, known for its large holes, sweet nutty flavor, and good melting properties.
- 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_69ca832a7f108190b4e4f5648abf4aa2 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cc46c76fd48190ae67b660c5b8e29f |
completed | March 31, 2026, 10:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cea8c9fcc48190ba20c85e226ef5f3 |
completed | April 2, 2026, 5:35 p.m. |
Created at: March 30, 2026, 6:23 p.m.