Triple
T583693
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rutte II cabinet |
E15110
|
entity |
| Predicate | numberOfStateSecretaries |
P15820
|
FINISHED |
| Object | 7 |
—
|
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: 7 | Statement: [Rutte II cabinet, numberOfStateSecretaries, 7]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfStateSecretaries Context triple: [Rutte II cabinet, numberOfStateSecretaries, 7]
-
A.
numberOfGovernors
Indicates the total count of governors associated with or governing a specified entity.
-
B.
succeededInOfficeAsSecretaryOfStateBy
Indicates that one individual was followed in the role of Secretary of State by another individual, who took over the office after them.
-
C.
numberOfStatesRepresented
Indicates how many distinct states are represented or covered in a given context or entity.
-
D.
hasLieutenantGovernor
Indicates that one entity serves as the lieutenant governor of another entity (typically a state, province, or territory).
-
E.
precededInOfficeAsSecretaryOfStateBy
Indicates that one person assumed the role of Secretary of State after another specific person, who held the office immediately before them.
- 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_69a4935783b8819082b77726ec10cc42 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a49b8745c88190af9672e5fe8396c3 |
completed | March 1, 2026, 8:03 p.m. |
| PD | Predicate disambiguation | batch_69a494c9315c8190a773e8e00737d8a0 |
completed | March 1, 2026, 7:34 p.m. |
| PDg | Predicate description generation | batch_69a4985a2d08819090947895d9439e06 |
completed | March 1, 2026, 7:49 p.m. |
Created at: March 1, 2026, 7:33 p.m.