Triple
T21858598
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Haspengouw region |
E539697
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Alken |
—
|
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: Alken | Statement: [Haspengouw region, contains, Alken]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Alken Context triple: [Haspengouw region, contains, Alken]
-
A.
Alken
Alken is a small winegrowing village on the Moselle River in Rhineland-Palatinate, Germany, known for its medieval architecture and the nearby Thurant Castle.
-
B.
Alken
chosen
Alken is a municipality in the Belgian province of Limburg, known for its rural character and local brewing tradition.
-
C.
Alen
Alen is a given name and surname used in various cultures, often considered a variant of the name Allen or Alan.
-
D.
Alkett
Alkett was a German World War II–era armaments manufacturer best known for producing assault guns and other armored fighting vehicles for the Wehrmacht.
-
E.
Alaskey
Alaskey is the surname of Joe Alaskey, an American voice actor best known for portraying iconic Looney Tunes characters such as Bugs Bunny and Daffy Duck.
- 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_69e0c47829648190bbe2d1d7033768ec |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f0d63944d88190b6bd5e6ba4cc8ec1 |
completed | April 28, 2026, 3:46 p.m. |
Created at: April 16, 2026, 6:56 p.m.