Triple
T334245
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Willem |
E6688
|
entity |
| Predicate | hasShortForm |
P43
|
FINISHED |
| Object | Wim |
E6688
|
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: Wim | Statement: [Willem, hasShortForm, Wim]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wim Context triple: [Willem, hasShortForm, Wim]
-
A.
Willem
chosen
Willem is a given name, primarily used in Dutch-speaking regions, that corresponds to the English name William.
-
B.
Max Deuring
Max Deuring was a German mathematician known for his influential work in algebraic number theory and the theory of algebraic function fields.
-
C.
Albert Dekker
Albert Dekker was an American character actor known for his prolific film, stage, and television career from the 1930s to the 1960s, often playing complex or villainous roles.
-
D.
Erwin
Erwin is a masculine given name of German origin, historically associated with figures such as the World War II field marshal Erwin Rommel.
-
E.
Royal Eijsbouts
Royal Eijsbouts is a renowned Dutch bell foundry and clockmaker known worldwide for casting large carillons and tower bells for churches, public buildings, and monuments.
- 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_69a2e79434908190a9d5afe415153ad9 |
completed | Feb. 28, 2026, 1:03 p.m. |
| NER | Named-entity recognition | batch_69a2eac641708190b85fa21368e5de8e |
completed | Feb. 28, 2026, 1:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a3d4e3dbc8819097e96187ba20dcb0 |
completed | March 1, 2026, 5:55 a.m. |
Created at: Feb. 28, 2026, 1:08 p.m.