Triple
T32927427
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Washington Class 3 Senate seat |
E842308
|
entity |
| Predicate | totalSenateSeatsPerState |
P48166
|
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: [Washington Class 3 Senate seat, totalSenateSeatsPerState, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: totalSenateSeatsPerState Context triple: [Washington Class 3 Senate seat, totalSenateSeatsPerState, 2]
-
A.
numberOfSenateDistricts
Indicates the total count of senate districts associated with a given entity or jurisdiction.
-
B.
numberOfSeatsInSenate
chosen
Indicates the total count of seats allocated in a given senate.
-
C.
maximumSeatsPerState
Indicates the upper limit on the number of seats that any single state is allowed to have.
-
D.
numberOfSenates
Indicates the total count of senate bodies associated with or present in a given context or entity.
-
E.
minimumSeatsPerState
Indicates the smallest number of seats that must be allocated to each state in a representative body or legislative apportionment.
- 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_69f34948adfc8190a937f1f622783c0b |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f78fd5a6388190bfda4bbb2e222e5b |
completed | May 3, 2026, 6:11 p.m. |
| PD | Predicate disambiguation | batch_69f78e2ac3fc819081a45c6841375c8d |
completed | May 3, 2026, 6:04 p.m. |
Created at: May 1, 2026, 1:20 a.m.