Triple
T9935486
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wilmès II Government |
E192748
|
entity |
| Predicate | externalParliamentarySupportFrom |
P50318
|
FINISHED |
| Object | Groen |
E37248
|
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: Groen | Statement: [Wilmès II Government, externalParliamentarySupportFrom, Groen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Groen Context triple: [Wilmès II Government, externalParliamentarySupportFrom, Groen]
-
A.
Groen
chosen
Groen is a Flemish green political party in Belgium known for its progressive stance on environmental and social issues.
-
B.
Geel
Geel is a city in the Flemish region of Belgium, noted for its long-standing tradition of community-based psychiatric care.
-
C.
Green
Green is a common English surname of Anglo-Saxon origin, typically derived from a descriptive nickname related to the color green or someone who lived near a village green.
-
D.
Green
Green is a color commonly used in transit systems to designate specific routes or lines, such as the Green Line E branch streetcar.
-
E.
Rood
Rood is a surname most notably associated with Ogden Rood, an American physicist and color theorist known for his influential work on color science.
- 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_69ca82dd978c8190947124ab0d3315ac |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cdb5e3bfa88190a88f20f2687a2583 |
completed | April 2, 2026, 12:18 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d228ddecfc8190850a098bf4074cc3 |
completed | April 5, 2026, 9:18 a.m. |
Created at: March 30, 2026, 8:44 p.m.