Triple
T27349168
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kiev Voivodeship |
E684308
|
entity |
| Predicate | hadNumberOfDeputiesToSenate |
P80248
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [Kiev Voivodeship, hadNumberOfDeputiesToSenate, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadNumberOfDeputiesToSenate Context triple: [Kiev Voivodeship, hadNumberOfDeputiesToSenate, 2]
-
A.
hasNumberOfDeputies
chosen
Indicates the specific count of deputies associated with or assigned to an entity.
-
B.
representedInSenate
Indicates that an entity serves as a representative for another entity within a senate or upper legislative chamber.
-
C.
numberOfSenates
Indicates the total count of senate bodies associated with or present in a given context or entity.
-
D.
hasSenateSeatsSince
Indicates that an entity has held a specified number of seats in a senate starting from a particular point in time.
-
E.
numberAppointedBySenate
Indicates the number of individuals who were appointed to a position or role through the formal approval or decision of a senate.
- 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_69ef1480a76481908684256ddd5bfda3 |
completed | April 27, 2026, 7:47 a.m. |
| NER | Named-entity recognition | batch_69f62ba5a110819092fe773ade8fec2e |
completed | May 2, 2026, 4:51 p.m. |
| PD | Predicate disambiguation | batch_69f623a91b9c8190b2e2fdbc55cb89b6 |
completed | May 2, 2026, 4:17 p.m. |
Created at: April 27, 2026, 11:47 a.m.