Triple
T33963047
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Government of Alexandros Papanastasiou |
E870771
|
entity |
| Predicate | hasCabinetHead |
P90169
|
FINISHED |
| Object | Alexandros Papanastasiou |
—
|
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: Alexandros Papanastasiou | Statement: [Government of Alexandros Papanastasiou, hasCabinetHead, Alexandros Papanastasiou]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCabinetHead Context triple: [Government of Alexandros Papanastasiou, hasCabinetHead, Alexandros Papanastasiou]
-
A.
hasCabinet
Indicates that one entity possesses, includes, or is equipped with a cabinet associated with it.
-
B.
hasCabinetName
Indicates that an entity is associated with a specific cabinet by its name.
-
C.
hasCabinetRole
chosen
Indicates that an entity holds or is assigned a specific role or position within a cabinet (such as a governmental or executive cabinet).
-
D.
hasLeaderAndCabinetModel
Indicates that an entity uses a governance structure characterized by a leader (such as a president or prime minister) and an associated cabinet as the primary executive model.
-
E.
hasHeadboard
Indicates that one object (typically a bed) is equipped with or features a headboard as part of its structure.
- F. None of above.
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_69f3499ce8e88190b66e1d49ad8c7037 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69ff4de66ba481908e7184b3cf9d4d2d |
completed | May 9, 2026, 3:08 p.m. |
| PD | Predicate disambiguation | batch_69ff4c702a5881909c6684c74807e945 |
completed | May 9, 2026, 3:02 p.m. |
Created at: May 1, 2026, 1:50 a.m.