Triple
T22459272
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tommy Wieringa |
E555188
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Tommy Wieringa |
—
|
NE NERFINISHED |
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: Tommy Wieringa | Statement: [Tommy Wieringa, name, Tommy Wieringa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tommy Wieringa Context triple: [Tommy Wieringa, name, Tommy Wieringa]
-
A.
Tommy Wieringa
chosen
Tommy Wieringa is a Dutch novelist known for his critically acclaimed and internationally translated works, including the novel "Joe Speedboot."
-
B.
Tim Kruithoff
Tim Kruithoff is a German local politician who serves as the mayor of the city of Emden in Lower Saxony.
-
C.
Tom Heskes
Tom Heskes is a machine learning researcher and professor known for his work in probabilistic modeling, Bayesian methods, and neural networks.
-
D.
Greg de Vries
Greg de Vries is a retired Canadian professional ice hockey defenceman who played over 800 NHL games and won the Stanley Cup with the Colorado Avalanche in 2001.
-
E.
Christian Huitema
Christian Huitema is a French computer scientist and Internet pioneer known for his influential work on networking protocols and IPv6 transition technologies.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e11e51fdec8190adfdf9f8a6362221 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15b7e01fc8190825c3dc024484440 |
completed | April 29, 2026, 1:14 a.m. |
Created at: April 16, 2026, 8:48 p.m.