Triple
T3943280
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Second Balkenende cabinet |
E92084
|
entity |
| Predicate | cabinetNumberInNetherlands |
P52155
|
FINISHED |
| Object | 27 |
—
|
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: 27 | Statement: [Second Balkenende cabinet, cabinetNumberInNetherlands, 27]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: cabinetNumberInNetherlands Context triple: [Second Balkenende cabinet, cabinetNumberInNetherlands, 27]
-
A.
cabinetNumberInHistory
Indicates the specific cabinet number assigned to an entity within a historical record or context.
-
B.
officeNameInDutch
Indicates the Dutch-language name or title used for a particular office, position, or role.
-
C.
cabinet
Indicates that one entity serves as a cabinet (a storage or enclosure unit) for another entity.
-
D.
hasCabinet
Indicates that one entity possesses, includes, or is equipped with a cabinet associated with it.
-
E.
currencyDamageEstimateNetherlands
Indicates an assessment of monetary damage or loss specifically calculated for the Netherlands.
- 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_69aed965502c8190904ebad1203a4ae8 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aef1515c688190a38332aedeed8a76 |
completed | March 9, 2026, 4:12 p.m. |
| PD | Predicate disambiguation | batch_69aee764235081909309b3c982f322a9 |
completed | March 9, 2026, 3:29 p.m. |
| PDg | Predicate description generation | batch_69aef14f9bb4819098e64b527b546d74 |
completed | March 9, 2026, 4:11 p.m. |
Created at: March 9, 2026, 3:24 p.m.