Triple
T4533113
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grand Vizier Sinan Pasha |
E106343
|
entity |
| Predicate | termCountAsGrandVizier |
P57308
|
FINISHED |
| Object | 5 |
—
|
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: 5 | Statement: [Grand Vizier Sinan Pasha, termCountAsGrandVizier, 5]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: termCountAsGrandVizier Context triple: [Grand Vizier Sinan Pasha, termCountAsGrandVizier, 5]
-
A.
successorAsGrandVizier
Indicates that one entity became the next Grand Vizier following another entity in that office.
-
B.
numberOfConsulships
Indicates the total count of times an entity has held the office or role of consul.
-
C.
numberOfCaliphs
Indicates the quantity of caliphs associated with a given entity or context.
-
D.
numberOfConsuls
Indicates the number of consuls associated with or holding office in relation to a given entity or context.
-
E.
hasNumberOfMinisters
Indicates the specific count of ministers associated with an entity, such as a government, cabinet, or organization.
- 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_69bd43f3d6e08190a91824f833d51bbe |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd57a08ec4819091b84d53d7b564a7 |
completed | March 20, 2026, 2:20 p.m. |
| PD | Predicate disambiguation | batch_69bd521edd00819099dfccaa65dddd61 |
completed | March 20, 2026, 1:56 p.m. |
| PDg | Predicate description generation | batch_69bd56b3e4c88190a7ade3d0ed0ab606 |
completed | March 20, 2026, 2:16 p.m. |
Created at: March 20, 2026, 1:04 p.m.