Triple
T32233900
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Barend Biesheuvel |
E823412
|
entity |
| Predicate | reasonForCabinetFall |
P192506
|
FINISHED |
| Object | internal coalition conflicts |
—
|
LITERAL 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: internal coalition conflicts | Statement: [Barend Biesheuvel, reasonForCabinetFall, internal coalition conflicts]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: reasonForCabinetFall Context triple: [Barend Biesheuvel, reasonForCabinetFall, internal coalition conflicts]
-
A.
cabinet
Indicates that one entity serves as a cabinet (a storage or enclosure unit) for another entity.
-
B.
cabinetType
Indicates the specific kind or category of cabinet associated with an entity.
-
C.
cabinetRequires
Indicates that a particular cabinet depends on or needs another specified item, condition, or component to be present or satisfied.
-
D.
hasCabinet
Indicates that one entity possesses, includes, or is equipped with a cabinet associated with it.
-
E.
numberOfCabinet
Indicates the quantity of cabinets associated with a given entity.
- F. None of above. chosen
Provenance (4 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_69f3490c140481908ed53b98b561eaa1 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69fd0d0ba5c48190bddb3f0e6637544c |
completed | May 7, 2026, 10:07 p.m. |
| PD | Predicate disambiguation | batch_69fd0c4324a8819086c90adf46216e0e |
completed | May 7, 2026, 10:03 p.m. |
| PDg | Predicate description generation | batch_69fd0d0aebac8190868a7714ddb4f1fd |
completed | May 7, 2026, 10:07 p.m. |
Created at: May 1, 2026, 12:39 a.m.