Triple
T362030
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Koop |
E7875
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | De Koop |
E7875
|
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: De Koop | Statement: [Koop, hasVariant, De Koop]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: De Koop Context triple: [Koop, hasVariant, De Koop]
-
A.
Koop
chosen
Koop is a surname most prominently associated with C. Everett Koop, the influential former Surgeon General of the United States.
-
B.
Achterhooks
Achterhooks is a regional Low Saxon dialect spoken in the Achterhoek area of the eastern Netherlands.
-
C.
Kaag en Braassem
Kaag en Braassem is a municipality in the Dutch province of South Holland known for its lakes, waterways, and water sports tourism.
-
D.
Bloemenmarkt
Bloemenmarkt is Amsterdam’s famous floating flower market, known for its stalls selling tulips, bulbs, and other flowers along the Singel canal.
-
E.
De Pijp
De Pijp is a vibrant, bohemian neighborhood in Amsterdam known for its lively streets, diverse eateries, and the famous Albert Cuyp Market.
- 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_69a2e7e880008190a6ad7e06e5d03007 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ebcfb0f48190b9a9010c7837ac58 |
completed | Feb. 28, 2026, 1:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a3e86386e08190a21d34fea9faaef0 |
completed | March 1, 2026, 7:18 a.m. |
Created at: Feb. 28, 2026, 1:08 p.m.