Triple
T21597275
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United States Senate seat from Pennsylvania |
E532933
|
entity |
| Predicate | numberOfSeatsForState |
P59176
|
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: [United States Senate seat from Pennsylvania, numberOfSeatsForState, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfSeatsForState Context triple: [United States Senate seat from Pennsylvania, numberOfSeatsForState, 2]
-
A.
maximumSeatsPerState
Indicates the upper limit on the number of seats that any single state is allowed to have.
-
B.
minimumSeatsPerState
Indicates the smallest number of seats that must be allocated to each state in a representative body or legislative apportionment.
-
C.
electoralRegionSeatCount
chosen
Indicates the number of seats allocated to a given electoral region within a representative body or legislature.
-
D.
numberOfSeatsInSenate
Indicates the total count of seats allocated in a given senate.
-
E.
numberOfStatesRepresented
Indicates how many distinct states are represented or covered in a given context or entity.
- 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_69e0c46364608190a337dc8720dc2a35 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69eefae20c8881909c5354313d06183a |
completed | April 27, 2026, 5:57 a.m. |
| PD | Predicate disambiguation | batch_69e632109d048190b4ac3f14fe48d1a0 |
completed | April 20, 2026, 2:02 p.m. |
Created at: April 16, 2026, 6:32 p.m.