Triple
T3753299
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nieuwkoop |
E81382
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object | Nieuwkoop (town) |
E81382
|
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: Nieuwkoop (town) | Statement: [Nieuwkoop, hasSettlement, Nieuwkoop (town)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nieuwkoop (town) Context triple: [Nieuwkoop, hasSettlement, Nieuwkoop (town)]
-
A.
Nieuwkoop
chosen
Nieuwkoop is a rural municipality and town in South Holland, Netherlands, known for its lakes, peatlands, and nature reserves.
-
B.
Nieuwenhoorn
Nieuwenhoorn is a village in the western Netherlands that forms part of the province of South Holland.
-
C.
Nieuwolda
Nieuwolda is a small village in the municipality of Oldambt in the province of Groningen in the northeastern Netherlands.
-
D.
Boskoop
Boskoop is a Dutch town historically renowned as a major center of tree and nursery cultivation.
-
E.
Nieuwendijk
Nieuwendijk is one of Amsterdam’s oldest and busiest shopping streets, running through the historic city center near Dam Square.
- 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_69ad8b19b7b08190a6188804e99c53e9 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adcb9340e0819083215989718b4598 |
completed | March 8, 2026, 7:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4f02fb680819092ea86040b4b5bcf |
completed | March 14, 2026, 5:20 a.m. |
Created at: March 8, 2026, 3:35 p.m.