Triple
T1246873
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daniël Stalpaert |
E26786
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Daniël Stalpaert |
E26786
|
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: Daniël Stalpaert | Statement: [Daniël Stalpaert, name, Daniël Stalpaert]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Daniël Stalpaert Context triple: [Daniël Stalpaert, name, Daniël Stalpaert]
-
A.
Daniël Stalpaert
chosen
Daniël Stalpaert was a 17th-century Dutch architect and city planner known for his influential role in shaping Amsterdam’s urban landscape.
-
B.
Bart De Wever
Bart De Wever is a prominent Belgian politician known as a leading figure of Flemish nationalism and a key power broker in contemporary Belgian politics.
-
C.
Ben Weyts
Ben Weyts is a Belgian politician from Flanders who has served in prominent roles within the Flemish government, particularly in areas such as education and mobility.
-
D.
Rogier Stoffers
Rogier Stoffers is a Dutch cinematographer known for his work on a range of international films and television productions.
-
E.
Leo Geurts
Leo Geurts was a Dutch computer scientist known for co-developing the ABC programming language, an influential precursor to Python.
- 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_69a4948689d08190b3a4a3f388c02148 |
completed | March 1, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69a4bf6750a48190b86e9248ed54d90a |
completed | March 1, 2026, 10:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad1c93bbe8819092dab6a3ed616998 |
completed | March 8, 2026, 6:52 a.m. |
Created at: March 1, 2026, 7:47 p.m.