Triple
T1468743
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hollandsche IJssel |
E27084
|
entity |
| Predicate | flowsThrough |
P225
|
FINISHED |
| Object | Gouda |
E70496
|
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: Gouda | Statement: [Hollandsche IJssel, flowsThrough, Gouda]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gouda Context triple: [Hollandsche IJssel, flowsThrough, Gouda]
-
A.
Gouda
chosen
Gouda is a historic Dutch city renowned worldwide for its namesake cheese, traditional cheese market, and well-preserved medieval architecture.
-
B.
Edam
Edam is a historic Dutch town in North Holland, internationally known for its namesake Edam cheese and traditional cheese markets.
-
C.
Comté cheese
Comté cheese is a traditional French cow’s milk cheese from the Jura region, known for its firm texture, complex nutty flavor, and long aging process.
-
D.
Munster cheese
Munster cheese is a strong-smelling, soft cow’s milk cheese from eastern France, especially known for its washed rind and pungent, tangy flavor.
-
E.
Mont d'Or cheese
Mont d'Or cheese is a soft, rich, washed-rind cow’s milk cheese from the Jura region of France, traditionally sold in a spruce-wood box and eaten warm and spoonable.
- 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_69a496d25d6881909dbd84f86d763992 |
completed | March 1, 2026, 7:43 p.m. |
| NER | Named-entity recognition | batch_69a4c5d8bb68819095b7b413247ad657 |
completed | March 1, 2026, 11:03 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad0e805fa481908d6428a4e2fb4c4a |
completed | March 8, 2026, 5:52 a.m. |
Created at: March 1, 2026, 8:01 p.m.